Electrobac

electrobac

Authors:
(1) Hamed Alimohammadzadeh, University of Southern California, Los Angeles, USA;
(2) Rohit Bernard, University of Southern California, Los Angeles, USA;
(3) Yang Chen, University of Southern California, Los Angeles, USA;
(4) Trung Phan, University of Southern California, Los Angeles, USA;
(5) Prashant Singh, University of Southern California, Los Angeles, USA;
(6) Shuqin Zhu, University of Southern California, Los Angeles, USA;
(7) Heather Culbertson, University of Southern California, Los Angeles, USA;
(8) Shahram Ghandeharizadeh, University of Southern California, Los Angeles, USA. Table of Links Abstract and Introduction Flying Light Specks User Interaction Multimedia Systems Challenges Related Work Conclusions and Current Efforts, Acknowledgments, and References ABSTRACT Today’s robotic laboratories for drones are housed in a large room. At times, they are the size of a warehouse. These spaces are typically equipped with permanent devices to localize the drones, e.g., Vicon Infrared cameras. Significant time is invested to fine-tune the localization apparatus to compute and control the position of the drones. One may use these laboratories to develop a 3D multimedia system with miniature sized drones configured with light sources. As an alternative, this brave new idea paper envisions shrinking these room-sized laboratories to the size of a cube or cuboid that sits on a desk and costs less than 10K dollars. The resulting Dronevision (DV) will be the size of a 1990s Television. In addition to light sources, its Flying Light Specks (FLSs) will be network-enabled drones with storage and processing capability to implement decentralized algorithms. The DV will include a localization technique to expedite development of 3D displays. It will act as a haptic interface for a user to interact with and manipulate the 3D virtual illuminations. It will empower an experimenter to design, implement, test, debug, and maintain software and hardware that realize novel algorithms in the comfort of their office without having to reserve a laboratory. In addition to enhancing productivity, it will improve safety of the experimenter by minimizing the likelihood of accidents. This paper introduces the concept of a DV, the research agenda one may pursue using this device, and our plans to realize one. 1 INTRODUCTION A Dronevision, DV, is a 3D display to design and implement next generation multimedia applications using Flying Light Specks, FLSs. An FLS is a miniature sized drone with one or more light sources with adjustable brightness [25–27]. A swarm of cooperating FLSs will render 3D shapes, point clouds, and animated sequences in a fixed volume, a 3D display. This Brave New Idea (BNI) envisions shrinking today’s room sized robotics laboratories to the size of a cuboid that fits on a user’s desk for multimedia content creation and rendering. Figure 1 shows a DV setup configured for visual display only. Four glass panes enhance the user’s safety by preventing rogue FLSs from coming to contact with a user and injuring them. Once the rendering of an illumination is verified to be safe, the user may remove the glass panes. The DV may illuminate the rose beyond the size of its base, see Figure 2. Moreover, a user may touch and interact with the illumination. For 3D media content, a DV is a low cost alternative to an expensive robotics laboratory shared by different research groups. It will provide an experimenter with a dedicated 3D testbed to debug and evaluate their hardware and software solutions. It will enable a content creator to design and implement diverse multimedia applications that range from healthcare to entertainment. With healthcare, a designer may develop a 3D FLS display to illuminate 3D MRI scans of a patient and their organs, empowering a physician to separate the different organs (illuminations) and examine them in real time. With entertainment, the designer may illuminate characters of multi-player games such as Minecraft and Fortnite to come alive and interact with one another on a table top. In addition to improving productivity, a DV enhances the safety of both the experimenter and the content creator. Figure 1 shows a DV displaying an illuminated rose with a falling petal; its original may be captured using an Intel RealSense depth camera[1]. The top panel of the DV consists of wireless charging stations. FLSs with a low remaining flight time fly through the square hole and land on a charging coil to charge their battery. Hundreds of FLSs may land on a charging coil and simultaneously charge their battery. The floor of the DV consists of a Hub, Tiles, Terminus, and a conveyor belt. Each rod on the side acts as an elevator that delivers fully-charged FLSs to the base of the DV, where they are stored on the black tiles for future deployment. The Hub is comparable to today’s servers and provides both wired and wireless connectivity to peripheral devices such as a depth camera and earbuds. Black and white tiles are modular pieces that can be plugged into other tiles to construct DVs of different lengths and depths. Black tiles serve both as a hangar and a dispatcher of FLSs, which are moved up and down using an elevator, and white tiles are rigid pieces that keep the floor together. Black tiles containing FLSs may be stacked below the white tiles, see Figure 1. When an empty black tile is pushed below a white tile, a black tile with FLSs is pushed onto the elevator to bring its FLSs to the floor of the DV; these FLSs are then dispatched to illuminate virtual objects. Failed FLSs will fall onto a conveyor belt, which is a garbage collector that deposits failed FLSs to a Terminus. The Terminus is an empty row on the DV floor, e.g., the row closest to the user (the back panel) in Figure 1 (2). The conveyor belt protects FLSs sitting on black tiles from falling FLSs that have failed. The Hub stops the conveyor belt before deploying one or more FLSs atop a black tile. A deployed FLS flies below the conveyor belt and past the Terminus to navigate to its coordinates in the DV’s display volume. Stopping the conveyor belt minimizes the likelihood of a failed FLS hitting and damaging a deployed FLS. Similarly, by placing the charging stations at the top of the DV, the likelihood of failed FLSs hitting and damaging a functional FLS that is in transit to a charging station is minimized. A DV will be both modular and configurable. Once an experimenter is satisfied with the safety of their setup in Figure 1, they may remove the glass panes and place the top as a back panel in front of the Terminus, enabling the DV to render illuminations that extend beyond the length and width of its base and allowing the user to physically interact with the illuminations (Figure 2). The haptic feedback provided by the illumination will include a combination of kinesthetic and tactile sensations. The kinesthetic (force) feedback will enable a user to experience the shape and hardness of an illuminated object. For example, a user may grasp a rose petal by its sides using their fingers. The user may press their fingers together to bend the rose petal to experience its stiffness. The tactile (skin-based) feedback will enable a user to experience the object’s surface properties. For example, FLSs with electrotactile actuators may provide the sharp sensation a user would expect when running their finger over a thorn on the virtual rose of Figure 2. The contributions of this paper include: • DV as an experimental 3D platform to design and implement next generation multimedia applications using FLSs (Sections 1 and 2). • Use of DV for haptic interactions (Section 3). • Two multimedia systems research challenges raised by a DV (Section 4). We present related work in Section 5 and brief conclusions and our current efforts to realize a DV in Section 6. This paper is available on arxiv under CC 4.0 license. [1] The use of a camera is for illustration purposes only. The shown rose is a mesh file consisting of 65K points created using Blender [3, 26]. Authors: (1) Hamed Alimohammadzadeh, University of Southern California, Los Angeles, USA; (2) Rohit Bernard, University of Southern California, Los Angeles, USA; (3) Yang Chen, University of Southern California, Los Angeles, USA; (4) Trung Phan, University of Southern California, Los Angeles, USA; (5) Prashant Singh, University of Southern California, Los Angeles, USA; (6) Shuqin Zhu, University of Southern California, Los Angeles, USA; (7) Heather Culbertson, University of Southern California, Los Angeles, USA; (8) Shahram Ghandeharizadeh, University of Southern California, Los Angeles, USA. Authors: Authors: (1) Hamed Alimohammadzadeh, University of Southern California, Los Angeles, USA; (2) Rohit Bernard, University of Southern California, Los Angeles, USA; (3) Yang Chen, University of Southern California, Los Angeles, USA; (4) Trung Phan, University of Southern California, Los Angeles, USA; (5) Prashant Singh, University of Southern California, Los Angeles, USA; (6) Shuqin Zhu, University of Southern California, Los Angeles, USA; (7) Heather Culbertson, University of Southern California, Los Angeles, USA; (8) Shahram Ghandeharizadeh, University of Southern California, Los Angeles, USA. Table of Links Abstract and Introduction Abstract and Introduction Flying Light Specks Flying Light Specks User Interaction User Interaction Multimedia Systems Challenges Multimedia Systems Challenges Related Work Related Work Conclusions and Current Efforts, Acknowledgments, and References Conclusions and Current Efforts, Acknowledgments, and References ABSTRACT Today’s robotic laboratories for drones are housed in a large room. At times, they are the size of a warehouse. These spaces are typically equipped with permanent devices to localize the drones, e.g., Vicon Infrared cameras. Significant time is invested to fine-tune the localization apparatus to compute and control the position of the drones. One may use these laboratories to develop a 3D multimedia system with miniature sized drones configured with light sources. As an alternative, this brave new idea paper envisions shrinking these room-sized laboratories to the size of a cube or cuboid that sits on a desk and costs less than 10K dollars. The resulting Dronevision (DV) will be the size of a 1990s Television. In addition to light sources, its Flying Light Specks (FLSs) will be network-enabled drones with storage and processing capability to implement decentralized algorithms. The DV will include a localization technique to expedite development of 3D displays. It will act as a haptic interface for a user to interact with and manipulate the 3D virtual illuminations. It will empower an experimenter to design, implement, test, debug, and maintain software and hardware that realize novel algorithms in the comfort of their office without having to reserve a laboratory. In addition to enhancing productivity, it will improve safety of the experimenter by minimizing the likelihood of accidents. This paper introduces the concept of a DV, the research agenda one may pursue using this device, and our plans to realize one. 1 INTRODUCTION A Dronevision, DV, is a 3D display to design and implement next generation multimedia applications using Flying Light Specks, FLSs. An FLS is a miniature sized drone with one or more light sources with adjustable brightness [25–27]. A swarm of cooperating FLSs will render 3D shapes, point clouds, and animated sequences in a fixed volume, a 3D display. This Brave New Idea (BNI) envisions shrinking today’s room sized robotics laboratories to the size of a cuboid that fits on a user’s desk for multimedia content creation and rendering. Figure 1 shows a DV setup configured for visual display only. Four glass panes enhance the user’s safety by preventing rogue FLSs from coming to contact with a user and injuring them. Once the rendering of an illumination is verified to be safe, the user may remove the glass panes. The DV may illuminate the rose beyond the size of its base, see Figure 2. Moreover, a user may touch and interact with the illumination. For 3D media content, a DV is a low cost alternative to an expensive robotics laboratory shared by different research groups. It will provide an experimenter with a dedicated 3D testbed to debug and evaluate their hardware and software solutions. It will enable a content creator to design and implement diverse multimedia applications that range from healthcare to entertainment. With healthcare, a designer may develop a 3D FLS display to illuminate 3D MRI scans of a patient and their organs, empowering a physician to separate the different organs (illuminations) and examine them in real time. With entertainment, the designer may illuminate characters of multi-player games such as Minecraft and Fortnite to come alive and interact with one another on a table top. In addition to improving productivity, a DV enhances the safety of both the experimenter and the content creator. Figure 1 shows a DV displaying an illuminated rose with a falling petal; its original may be captured using an Intel RealSense depth camera[1]. The top panel of the DV consists of wireless charging stations. FLSs with a low remaining flight time fly through the square hole and land on a charging coil to charge their battery. Hundreds of FLSs may land on a charging coil and simultaneously charge their battery. The floor of the DV consists of a Hub, Tiles, Terminus, and a conveyor belt. Each rod on the side acts as an elevator that delivers fully-charged FLSs to the base of the DV, where they are stored on the black tiles for future deployment. The Hub is comparable to today’s servers and provides both wired and wireless connectivity to peripheral devices such as a depth camera and earbuds. Black and white tiles are modular pieces that can be plugged into other tiles to construct DVs of different lengths and depths. Black tiles serve both as a hangar and a dispatcher of FLSs, which are moved up and down using an elevator, and white tiles are rigid pieces that keep the floor together. Black tiles containing FLSs may be stacked below the white tiles, see Figure 1. When an empty black tile is pushed below a white tile, a black tile with FLSs is pushed onto the elevator to bring its FLSs to the floor of the DV; these FLSs are then dispatched to illuminate virtual objects. Failed FLSs will fall onto a conveyor belt, which is a garbage collector that deposits failed FLSs to a Terminus. The Terminus is an empty row on the DV floor, e.g., the row closest to the user (the back panel) in Figure 1 (2). The conveyor belt protects FLSs sitting on black tiles from falling FLSs that have failed. The Hub stops the conveyor belt before deploying one or more FLSs atop a black tile. A deployed FLS flies below the conveyor belt and past the Terminus to navigate to its coordinates in the DV’s display volume. Stopping the conveyor belt minimizes the likelihood of a failed FLS hitting and damaging a deployed FLS. Similarly, by placing the charging stations at the top of the DV, the likelihood of failed FLSs hitting and damaging a functional FLS that is in transit to a charging station is minimized. A DV will be both modular and configurable. Once an experimenter is satisfied with the safety of their setup in Figure 1, they may remove the glass panes and place the top as a back panel in front of the Terminus, enabling the DV to render illuminations that extend beyond the length and width of its base and allowing the user to physically interact with the illuminations (Figure 2). The haptic feedback provided by the illumination will include a combination of kinesthetic and tactile sensations. The kinesthetic (force) feedback will enable a user to experience the shape and hardness of an illuminated object. For example, a user may grasp a rose petal by its sides using their fingers. The user may press their fingers together to bend the rose petal to experience its stiffness. The tactile (skin-based) feedback will enable a user to experience the object’s surface properties. For example, FLSs with electrotactile actuators may provide the sharp sensation a user would expect when running their finger over a thorn on the virtual rose of Figure 2. The contributions of this paper include: contributions • DV as an experimental 3D platform to design and implement next generation multimedia applications using FLSs (Sections 1 and 2). • Use of DV for haptic interactions (Section 3). • Two multimedia systems research challenges raised by a DV (Section 4). We present related work in Section 5 and brief conclusions and our current efforts to realize a DV in Section 6. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv [1] The use of a camera is for illustration purposes only. The shown rose is a mesh file consisting of 65K points created using Blender [3, 26].

Dronevision: An Experimental 3D Testbed for Flying Light Specks

Author:
(1) V V Obukhov, Institute of research and development, Tomsk State Pedagogical University, Kievskaya str. 60, Tomsk 634061, Russia & International laboratory of theoretical cosmology, Tomsk State University of Control Systems and Radioelectronics, Lenina pr. 40, Tomsk 634050, Russia (E-mail: obukhov@tspu.edu.ru);
(2) K E Osetrin, Center for Mathematical and Computer Physics, Tomsk State Pedagogical University, Kievskaya str. 60, Tomsk 634061, Russia & International laboratory of theoretical cosmology, Tomsk State University of Control Systems and Radioelectronics, Lenina pr. 40, Tomsk 634050, Russia;
(3) A V Shapovalov, Department of Theoretical Physics, National Research Tomsk State University, Lenina pr. 36, Tomsk 634050, Russia & International laboratory of theoretical cosmology, Tomsk State University of Control Systems and Radioelectronics, Lenina pr. 40, Tomsk 634050, Russia. Table of Links Abstract and Introduction Conclusion, Statements and Declarations, and Bibliography Conclusion Summarising, we believe that the statement made in the papers [1, 2] about new scientific results in the problem of complete separation of variables in the HamiltonJacobi equation for geodesics does not correspond to reality. We did not find any new results in comparison with those contained in the theorem on necessary and sufficient conditions of Vladimir N. Shapovalov. The results of the article in question, unlike the original ones, have omissions, and the article contains incorrect statements. Thus, the methods and results proposed in the works [1, 2] do not contain scientific novelty and represent a special case of the theory of complete separation of variables in the free Hamilton-Jacobi equation, finally constructed in the 70s of the last century. Statements and Declarations Data availability statement All necessary data and references to external sources are contained in the text of the manuscript. All information sources used in the work are publicly available and refer to open publications in scientific journals and textbooks. Compliance with Ethical Standards The authors declare no conflict of interest. Bibliography [1] Katanaev M O, Complete separation of variables in the geodesic HamiltonJacobi equation in four dimensions, 2023 Physica Scripta 98 104001 URL https://dx.doi.org/10.1088/1402-4896/acf251 [2] Katanaev M O 2023 Complete separation of variables in the geodesic Hamilton-Jacobi equation (Preprint 2305.02222) URL https://arxiv.org/abs/2305.02222 [3] Obukhov V, Hamilton–Jacobi equation for a charged test particle in the St¨ackel space of type (2.0), 2020 Symmetry 12 URL https://www.mdpi.com/2073-8994/12/8/1289 [4] Obukhov V, Separation of variables in Hamilton–Jacobi equation for a charged test particle in the St¨ackel spaces of type (2.1), 2020 International Journal of Geometric Methods in Modern Physics 17 2050186 URL https://doi.org/10.1142/S0219887820501868 [5] Obukhov V, Separation of variables in Hamilton–Jacobi and Klein–Gordon–Fock equations for a charged test particle in the St¨ackel spaces of type (1.1), 2021 International Journal of Geometric Methods in Modern Physics 18 2150036 URL https://doi.org/10.1142/S0219887821500365 [6] Obukhov V V 2006 St¨ackel spaces in gravity theory (Tomsk: Tomsk State Pedagogical University) ISBN 978-5-89428-242-8 [7] Bagrov V G, Meshkov A G, Shapovalov V N and Shapovalov A V, Separation of variables in the Klein-Gordon equations. I, 1973 Soviet Physics Journal 16 1533–1538 URL https://doi.org/10.1007/BF00889957 [8] Shapovalov V N, Symmetry and separation of variables in Hamilton-Jacobi equations. I, 1978 Soviet Physics Journal 21 1124–1129 URL https://doi.org/10.1007/BF00894559 [9] Shapovalov V N, St¨ackel spaces, 1979 Siberian Mathematical Journal 20 790–800 URL https://doi.org/10.1007/BF00971844 [10] Bagrov V G, Obukhov V V and Shapovalov A V, Special St¨ackel electrovac spacetimes, 1986 Pramana - J. Phys. 26 93–108 URL https://doi.org/10.1007/BF02847629 [11] Bagrov V G and Obukhov V V, Classes of exact solutions of the Einstein–Maxwell equations, 1983 Annalen der Physik 495 181–188 URL https://doi.org/10.1002/andp.19834950402 [12] Bagrov V G, Obukhov V V and Osetrin K E, Classification of null St¨ackel electrovac metrics with cosmological constant, 1988 General Relativity and Gravitation 20 1141–1154 URL https://doi.org/10.1007/BF00758935 This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Author: (1) V V Obukhov, Institute of research and development, Tomsk State Pedagogical University, Kievskaya str. 60, Tomsk 634061, Russia & International laboratory of theoretical cosmology, Tomsk State University of Control Systems and Radioelectronics, Lenina pr. 40, Tomsk 634050, Russia (E-mail: obukhov@tspu.edu.ru); (2) K E Osetrin, Center for Mathematical and Computer Physics, Tomsk State Pedagogical University, Kievskaya str. 60, Tomsk 634061, Russia & International laboratory of theoretical cosmology, Tomsk State University of Control Systems and Radioelectronics, Lenina pr. 40, Tomsk 634050, Russia; (3) A V Shapovalov, Department of Theoretical Physics, National Research Tomsk State University, Lenina pr. 36, Tomsk 634050, Russia & International laboratory of theoretical cosmology, Tomsk State University of Control Systems and Radioelectronics, Lenina pr. 40, Tomsk 634050, Russia. Author: Author: (1) V V Obukhov, Institute of research and development, Tomsk State Pedagogical University, Kievskaya str. 60, Tomsk 634061, Russia & International laboratory of theoretical cosmology, Tomsk State University of Control Systems and Radioelectronics, Lenina pr. 40, Tomsk 634050, Russia (E-mail: obukhov@tspu.edu.ru); (2) K E Osetrin, Center for Mathematical and Computer Physics, Tomsk State Pedagogical University, Kievskaya str. 60, Tomsk 634061, Russia & International laboratory of theoretical cosmology, Tomsk State University of Control Systems and Radioelectronics, Lenina pr. 40, Tomsk 634050, Russia; (3) A V Shapovalov, Department of Theoretical Physics, National Research Tomsk State University, Lenina pr. 36, Tomsk 634050, Russia & International laboratory of theoretical cosmology, Tomsk State University of Control Systems and Radioelectronics, Lenina pr. 40, Tomsk 634050, Russia. Table of Links Abstract and Introduction Abstract and Introduction Conclusion, Statements and Declarations, and Bibliography Conclusion, Statements and Declarations, and Bibliography Conclusion Summarising, we believe that the statement made in the papers [1, 2] about new scientific results in the problem of complete separation of variables in the HamiltonJacobi equation for geodesics does not correspond to reality. We did not find any new results in comparison with those contained in the theorem on necessary and sufficient conditions of Vladimir N. Shapovalov. The results of the article in question, unlike the original ones, have omissions, and the article contains incorrect statements. Thus, the methods and results proposed in the works [1, 2] do not contain scientific novelty and represent a special case of the theory of complete separation of variables in the free Hamilton-Jacobi equation, finally constructed in the 70s of the last century. Statements and Declarations Data availability statement Data availability statement All necessary data and references to external sources are contained in the text of the manuscript. All information sources used in the work are publicly available and refer to open publications in scientific journals and textbooks. Compliance with Ethical Standards Compliance with Ethical Standards The authors declare no conflict of interest. Bibliography [1] Katanaev M O, Complete separation of variables in the geodesic HamiltonJacobi equation in four dimensions, 2023 Physica Scripta 98 104001 URL https://dx.doi.org/10.1088/1402-4896/acf251 [2] Katanaev M O 2023 Complete separation of variables in the geodesic Hamilton-Jacobi equation (Preprint 2305.02222) URL https://arxiv.org/abs/2305.02222 [3] Obukhov V, Hamilton–Jacobi equation for a charged test particle in the St¨ackel space of type (2.0), 2020 Symmetry 12 URL https://www.mdpi.com/2073-8994/12/8/1289 [4] Obukhov V, Separation of variables in Hamilton–Jacobi equation for a charged test particle in the St¨ackel spaces of type (2.1), 2020 International Journal of Geometric Methods in Modern Physics 17 2050186 URL https://doi.org/10.1142/S0219887820501868 [5] Obukhov V, Separation of variables in Hamilton–Jacobi and Klein–Gordon–Fock equations for a charged test particle in the St¨ackel spaces of type (1.1), 2021 International Journal of Geometric Methods in Modern Physics 18 2150036 URL https://doi.org/10.1142/S0219887821500365 [6] Obukhov V V 2006 St¨ackel spaces in gravity theory (Tomsk: Tomsk State Pedagogical University) ISBN 978-5-89428-242-8 [7] Bagrov V G, Meshkov A G, Shapovalov V N and Shapovalov A V, Separation of variables in the Klein-Gordon equations. I, 1973 Soviet Physics Journal 16 1533–1538 URL https://doi.org/10.1007/BF00889957 [8] Shapovalov V N, Symmetry and separation of variables in Hamilton-Jacobi equations. I, 1978 Soviet Physics Journal 21 1124–1129 URL https://doi.org/10.1007/BF00894559 [9] Shapovalov V N, St¨ackel spaces, 1979 Siberian Mathematical Journal 20 790–800 URL https://doi.org/10.1007/BF00971844 [10] Bagrov V G, Obukhov V V and Shapovalov A V, Special St¨ackel electrovac spacetimes, 1986 Pramana - J. Phys. 26 93–108 URL https://doi.org/10.1007/BF02847629 [11] Bagrov V G and Obukhov V V, Classes of exact solutions of the Einstein–Maxwell equations, 1983 Annalen der Physik 495 181–188 URL https://doi.org/10.1002/andp.19834950402 [12] Bagrov V G, Obukhov V V and Osetrin K E, Classification of null St¨ackel electrovac metrics with cosmological constant, 1988 General Relativity and Gravitation 20 1141–1154 URL https://doi.org/10.1007/BF00758935 This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Revisiting Separation of Variables in Hamilton-Jacobi Equations: Conclusion, Statements and More

Authors:
(1) Michael Sorochan Armstrong, Computational Data Science (CoDaS) Lab in the Department of Signal Theory, Telematics, and Communications at the University of Granada;
(2) Jose Carlos P´erez-Gir´on, part of the Interuniversity Institute for Research on the Earth System in Andalucia through the University of Granada;
(3) Jos´e Camacho, Computational Data Science (CoDaS) Lab in the Department of Signal Theory, Telematics, and Communications at the University of Granada;
(4) Regino Zamora, part of the Interuniversity Institute for Research on the Earth System in Andalucia through the University of Granada. Table of Links Abstract & Introduction Optimization of the Optical Interpolation Materials and Methods Results and Discussion Conclusion Appendix A: Proof of Hermitian Self-Adjoint product identity for Equidistant Time-Domain Measurements Appendix B: AAH ̸= MIN I in the Non-Equidistant Case Acknowledgments & References ACKNOWLEDGMENTS The proof for the equidistant case was adapted from a proof by David J. Fleet and Allan D. Jepson from the University of Toronto [18]. REFERENCES [1] C. Haley, “Missing-data nonparametric coherency estimation,” IEEE Signal Processing Letters, vol. 28, pp. 1704–1708, 2021. [2] S. Dilmaghani, I. C. Henry, P. Soonthornnonda, E. R. Christensen, and R. C. Henry, “Harmonic analysis of environmental time series with missing data or irregular sample spacing,” Environmental science & technology, vol. 41, no. 20, pp. 7030–7038, 2007. [3] J. W. Cooley and J. W. Tukey, “An algorithm for the machine calculation of complex fourier series,” Mathematics of computation, vol. 19, no. 90, pp. 297–301, 1965. [4] F. Rasheed, P. Peng, R. Alhajj, and J. Rokne, “Fourier transform based spatial outlier mining,” in Intelligent Data Engineering and Automated Learning-IDEAL 2009: 10th International Conference, Burgos, Spain, September 23-26, 2009. Proceedings 10. Springer, 2009, pp. 317–324. [5] C. Van Loan, Computational frameworks for the fast Fourier transform. SIAM, 1992. [6] H. Carslaw, “A historical note on gibbs’ phenomenon in fourier’s series and integrals,” Bulletin of the American Mathematical Society, 1925. [7] J. Markel, “Fft pruning,” IEEE transactions on Audio and Electroacoustics, vol. 19, no. 4, pp. 305–311, 1971. [8] J. C. Bowman and Z. Ghoggali, “The partial fast fourier transform,” Journal of Scientific Computing, vol. 76, pp. 1578–1593, 2018. [9] M. Kircheis and D. Potts, “Direct inversion of the nonequispaced fast fourier transform,” Linear Algebra and its Applications, vol. 575, pp. 106–140, 2019. [10] S. Kunis and D. Potts, “Stability results for scattered data interpolation by trigonometric polynomials,” SIAM Journal on Scientific Computing, vol. 29, no. 4, pp. 1403–1419, 2007. [11] K. B. Petersen, M. S. Pedersen et al., “The matrix cookbook,” Technical University of Denmark, vol. 7, no. 15, p. 510, 2008. [12] C. M. Bishop, Neural networks for pattern recognition. Oxford university press, 1995. [13] H. Wickham, M. Averick, J. Bryan, W. Chang, L. D. McGowan, R. Franc¸ois, G. Grolemund, A. Hayes, L. Henry, J. Hester, M. Kuhn, T. L. Pedersen, E. Miller, S. M. Bache, K. Muller, J. Ooms, D. Robin- ¨ son, D. P. Seidel, V. Spinu, K. Takahashi, D. Vaughan, C. Wilke, K. Woo, and H. Yutani, “Welcome to the tidyverse,” Journal of Open Source Software, vol. 4, no. 43, p. 1686, 2019. [14] R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2023. [Online]. Available: https://www.R-project.org/ [15] C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del R´ıo, M. Wiebe, P. Peterson, P. Gerard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, ´ H. Abbasi, C. Gohlke, and T. E. Oliphant, “Array programming with NumPy,” Nature, vol. 585, no. 7825, pp. 357–362, Sep. 2020. [Online]. Available: https://doi.org/10.1038/s41586-020-2649-2 [16] P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, ˙I. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, and SciPy 1.0 Contributors, “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,” Nature Methods, vol. 17, pp. 261– 272, 2020. [17] J. D. Hunter, “Matplotlib: A 2d graphics environment,” Computing in Science & Engineering, vol. 9, no. 3, pp. 90–95, 2007. [18] “Notes on fourier analysis,” http://www.cs.toronto.edu/ ∼jepson/csc320/notes/fourier.pdf, accessed: 2023-09-01. This paper is available on arxiv under CC 4.0 license. Authors: (1) Michael Sorochan Armstrong, Computational Data Science (CoDaS) Lab in the Department of Signal Theory, Telematics, and Communications at the University of Granada; (2) Jose Carlos P´erez-Gir´on, part of the Interuniversity Institute for Research on the Earth System in Andalucia through the University of Granada; (3) Jos´e Camacho, Computational Data Science (CoDaS) Lab in the Department of Signal Theory, Telematics, and Communications at the University of Granada; (4) Regino Zamora, part of the Interuniversity Institute for Research on the Earth System in Andalucia through the University of Granada. Authors: Authors: (1) Michael Sorochan Armstrong, Computational Data Science (CoDaS) Lab in the Department of Signal Theory, Telematics, and Communications at the University of Granada; (2) Jose Carlos P´erez-Gir´on, part of the Interuniversity Institute for Research on the Earth System in Andalucia through the University of Granada; (3) Jos´e Camacho, Computational Data Science (CoDaS) Lab in the Department of Signal Theory, Telematics, and Communications at the University of Granada; (4) Regino Zamora, part of the Interuniversity Institute for Research on the Earth System in Andalucia through the University of Granada. Table of Links Abstract & Introduction Abstract & Introduction Optimization of the Optical Interpolation Optimization of the Optical Interpolation Materials and Methods Materials and Methods Results and Discussion Results and Discussion Conclusion Conclusion Appendix A: Proof of Hermitian Self-Adjoint product identity for Equidistant Time-Domain Measurements Appendix A: Proof of Hermitian Self-Adjoint product identity for Equidistant Time-Domain Measurements Appendix B: AAH ̸= MIN I in the Non-Equidistant Case Appendix B: AAH ̸= MIN I in the Non-Equidistant Case Acknowledgments & References Acknowledgments & References ACKNOWLEDGMENTS The proof for the equidistant case was adapted from a proof by David J. Fleet and Allan D. Jepson from the University of Toronto [18]. REFERENCES [1] C. Haley, “Missing-data nonparametric coherency estimation,” IEEE Signal Processing Letters, vol. 28, pp. 1704–1708, 2021. [2] S. Dilmaghani, I. C. Henry, P. Soonthornnonda, E. R. Christensen, and R. C. Henry, “Harmonic analysis of environmental time series with missing data or irregular sample spacing,” Environmental science & technology, vol. 41, no. 20, pp. 7030–7038, 2007. [3] J. W. Cooley and J. W. Tukey, “An algorithm for the machine calculation of complex fourier series,” Mathematics of computation, vol. 19, no. 90, pp. 297–301, 1965. [4] F. Rasheed, P. Peng, R. Alhajj, and J. Rokne, “Fourier transform based spatial outlier mining,” in Intelligent Data Engineering and Automated Learning-IDEAL 2009: 10th International Conference, Burgos, Spain, September 23-26, 2009. Proceedings 10. Springer, 2009, pp. 317–324. [5] C. Van Loan, Computational frameworks for the fast Fourier transform. SIAM, 1992. [6] H. Carslaw, “A historical note on gibbs’ phenomenon in fourier’s series and integrals,” Bulletin of the American Mathematical Society, 1925. [7] J. Markel, “Fft pruning,” IEEE transactions on Audio and Electroacoustics, vol. 19, no. 4, pp. 305–311, 1971. [8] J. C. Bowman and Z. Ghoggali, “The partial fast fourier transform,” Journal of Scientific Computing, vol. 76, pp. 1578–1593, 2018. [9] M. Kircheis and D. Potts, “Direct inversion of the nonequispaced fast fourier transform,” Linear Algebra and its Applications, vol. 575, pp. 106–140, 2019. [10] S. Kunis and D. Potts, “Stability results for scattered data interpolation by trigonometric polynomials,” SIAM Journal on Scientific Computing, vol. 29, no. 4, pp. 1403–1419, 2007. [11] K. B. Petersen, M. S. Pedersen et al., “The matrix cookbook,” Technical University of Denmark, vol. 7, no. 15, p. 510, 2008. [12] C. M. Bishop, Neural networks for pattern recognition. Oxford university press, 1995. [13] H. Wickham, M. Averick, J. Bryan, W. Chang, L. D. McGowan, R. Franc¸ois, G. Grolemund, A. Hayes, L. Henry, J. Hester, M. Kuhn, T. L. Pedersen, E. Miller, S. M. Bache, K. Muller, J. Ooms, D. Robin- ¨ son, D. P. Seidel, V. Spinu, K. Takahashi, D. Vaughan, C. Wilke, K. Woo, and H. Yutani, “Welcome to the tidyverse,” Journal of Open Source Software, vol. 4, no. 43, p. 1686, 2019. [14] R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2023. [Online]. Available: https://www.R-project.org/ [15] C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del R´ıo, M. Wiebe, P. Peterson, P. Gerard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, ´ H. Abbasi, C. Gohlke, and T. E. Oliphant, “Array programming with NumPy,” Nature, vol. 585, no. 7825, pp. 357–362, Sep. 2020. [Online]. Available: https://doi.org/10.1038/s41586-020-2649-2 [16] P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, ˙I. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, and SciPy 1.0 Contributors, “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,” Nature Methods, vol. 17, pp. 261– 272, 2020. [17] J. D. Hunter, “Matplotlib: A 2d graphics environment,” Computing in Science & Engineering, vol. 9, no. 3, pp. 90–95, 2007. [18] “Notes on fourier analysis,” http://www.cs.toronto.edu/ ∼jepson/csc320/notes/fourier.pdf, accessed: 2023-09-01. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Groundbreaking Research in Signal Theory

The Noonification: Educational Byte: Electronic Money vs. Cryptocurrency (9/20/2024)

Everything is digital these days, and that, of course, includes our money. It’s less and less typical worldwide to pay with physical cash or coins, instead preferring some kind of virtual transaction. It’s just more practical, even safer if we think about physical assaults. On the other hand, not all electronic money works the same or offers the same features. We need to have this in mind for the sake of our financial privacy and autonomy: electronic money isn’t the same as cryptocurrencies. Instead, we can say that cryptos are barely a type of electronic money, which is more of a category than a single thing. Electronic money or e-money refers to digital forms of currency used for online transactions and stored in electronic devices or systems, such as bank accounts, computers, or digital wallets. The category encompasses various forms, including cryptocurrencies, transfers with digital fiat (national) currencies, Central Bank Digital Currencies (CBDCs), and e-money services, each with different underlying technologies, issuers, regulations, and use cases. An important factor to distinguish among these types is the centralization (or not) of these systems. This way, the e-money can be centralized, like fiat currencies and CBDCs, where a central authority (e.g., a government or bank) controls issuance and regulation; or decentralized, like cryptocurrencies, where a distributed network manages transactions and governance without central oversight. A bit of history David Chaum, a renowned cryptographer and cypherpunk, is considered the inventor of electronic cash. In 1983, he published the paper "Blind Signatures for Untraceable Payments", which would later be used to build the first-ever e-money system available for public use: Ecash. The company Digicash handled this type of anonymous electronic money until its bankruptcy in 1998 —but that would only be the beginning. Other exponents would join over the years. From 1996 to 2009, e-gold, a gold-based digital currency, amassed millions of users before being shut down by the U.S. government. By 1997, Coca-Cola introduced mobile payments for vending machines, and PayPal began offering its USD-based service in 1998. All of the above were fully dependent on banks and governments, though. Money still came from the same ultimate source: a national central bank. The landscape changed significantly with the advent of Bitcoin in 2009, which introduced decentralized digital currencies. These cryptocurrencies are distinct in their resistance to government regulation due to their lack of a central controlling entity. In other words, there are no companies or closed groups behind their control, but only a global network of computers controlled by their owners. This setup ensures that no single entity has control over the currency, making it more resistant to censorship and manipulation. Benefits of decentralization Since traditional currencies are issued and regulated by a government, they play by the rules of that government at every step —no matter if they’re physical or digital. Systems like PayPal or Venmo, and of course, banks and digital wallets that work with a certain national currency (USD, EUR, etc.) are obligated to follow a whole set of rules and limitations, and are free to add their own. Real privacy and control for users are almost nonexistent this way. One major advantage of cryptocurrencies over traditional fiat e-money is, indeed, the level of privacy and control they offer to users. Transactions can be made pseudonymously, providing greater privacy compared to traditional banking systems, where personal information is often required and tracked. Additionally, users have greater (or full, depending on the underlying technology — see below) control over their funds, as they hold private keys to their digital wallets, unlike fiat money which is stored and controlled by corporations that can freeze it or restrict the user’s freedom to use their money. Cryptocurrencies also offer flexibility in terms of accessibility and usage. They can be sent and received globally without the need for intermediaries, often with lower fees and faster transaction times compared to traditional cross-border payments. This makes them particularly useful for people in regions with limited access to banking services, or for those who value independence from traditional financial systems.\ More autonomy, less censorship While cryptocurrencies are designed to be decentralized, they can still be susceptible to manipulation or censorship, especially in networks where there are middlemen before a final transaction approval. Miners and “validators”, for instance, could potentially collude to influence the network, such as blocking certain transactions or favoring others, thus undermining the system's integrity. Obyte addresses these concerns by using a minerless DAG (Directed Acyclic Graph) structure, which eliminates the need for traditional miners or “validators.” In this system, transactions are both created and added to the DAG by users, referencing other transactions in a web-like network, and preventing any single group from gaining enough power to censor transactions. This design enhances user autonomy and eliminates the risk of censorship, as there are no middlemen who can manipulate the transaction process. If you have your Obyte private keys, no one can limit, freeze, or manipulate your funds, and you won’t depend on the success or failure of any institution. The future is decentralized! And Obyte is already there. Featured Vector Image by Freepik Everything is digital these days, and that, of course, includes our money. It’s less and less typical worldwide to pay with physical cash or coins, instead preferring some kind of virtual transaction. It’s just more practical, even safer if we think about physical assaults. On the other hand, not all electronic money works the same or offers the same features. We need to have this in mind for the sake of our financial privacy and autonomy: electronic money isn’t the same as cryptocurrencies. Instead, we can say that cryptos are barely a type of electronic money, which is more of a category than a single thing. Electronic money or e-money refers to digital forms of currency used for online transactions and stored in electronic devices or systems, such as bank accounts, computers, or digital wallets. The category encompasses various forms, including cryptocurrencies, transfers with digital fiat (national) currencies, Central Bank Digital Currencies ( CBDCs ), and e-money services, each with different underlying technologies, issuers, regulations, and use cases. We need to have this in mind for the sake of our financial privacy and autonomy: electronic money isn’t the same as cryptocurrencies. CBDCs CBDCs An important factor to distinguish among these types is the centralization (or not) of these systems. This way, the e-money can be centralized, like fiat currencies and CBDCs, where a central authority (e.g., a government or bank) controls issuance and regulation; or decentralized, like cryptocurrencies, where a distributed network manages transactions and governance without central oversight. A bit of history David Chaum, a renowned cryptographer and cypherpunk , is considered the inventor of electronic cash. In 1983, he published the paper "Blind Signatures for Untraceable Payments", which would later be used to build the first-ever e-money system available for public use: Ecash. The company Digicash handled this type of anonymous electronic money until its bankruptcy in 1998 —but that would only be the beginning. a renowned cryptographer and cypherpunk a renowned cryptographer and cypherpunk Other exponents would join over the years. From 1996 to 2009, e-gold , a gold-based digital currency, amassed millions of users before being shut down by the U.S. government. By 1997, Coca-Cola introduced mobile payments for vending machines, and PayPal began offering its USD-based service in 1998. All of the above were fully dependent on banks and governments, though. Money still came from the same ultimate source: a national central bank. e-gold e-gold Coca-Cola introduced Coca-Cola introduced Money still came from the same ultimate source: a national central bank. The landscape changed significantly with the advent of Bitcoin in 2009, which introduced decentralized digital currencies. These cryptocurrencies are distinct in their resistance to government regulation due to their lack of a central controlling entity. In other words, there are no companies or closed groups behind their control, but only a global network of computers controlled by their owners. This setup ensures that no single entity has control over the currency, making it more resistant to censorship and manipulation. Benefits of decentralization Since traditional currencies are issued and regulated by a government, they play by the rules of that government at every step —no matter if they’re physical or digital. Systems like PayPal or Venmo, and of course, banks and digital wallets that work with a certain national currency (USD, EUR, etc.) are obligated to follow a whole set of rules and limitations, and are free to add their own. Real privacy and control for users are almost nonexistent this way. One major advantage of cryptocurrencies over traditional fiat e-money is, indeed, the level of privacy and control they offer to users. Transactions can be made pseudonymously, providing greater privacy compared to traditional banking systems, where personal information is often required and tracked. Additionally, users have greater (or full, depending on the underlying technology — see below) control over their funds, as they hold private keys to their digital wallets, unlike fiat money which is stored and controlled by corporations that can freeze it or restrict the user’s freedom to use their money. One major advantage of cryptocurrencies over traditional fiat e-money is, indeed, the level of privacy and control they offer to users. Cryptocurrencies also offer flexibility in terms of accessibility and usage. They can be sent and received globally without the need for intermediaries, often with lower fees and faster transaction times compared to traditional cross-border payments. This makes them particularly useful for people in regions with limited access to banking services, or for those who value independence from traditional financial systems.\ More autonomy, less censorship While cryptocurrencies are designed to be decentralized, they can still be susceptible to manipulation or censorship, especially in networks where there are middlemen before a final transaction approval. Miners and “validators”, for instance, could potentially collude to influence the network, such as blocking certain transactions or favoring others, thus undermining the system's integrity. Obyte addresses these concerns by using a minerless DAG (Directed Acyclic Graph) structure, which eliminates the need for traditional miners or “validators.” In this system, transactions are both created and added to the DAG by users, referencing other transactions in a web-like network, and preventing any single group from gaining enough power to censor transactions . This design enhances user autonomy and eliminates the risk of censorship, as there are no middlemen who can manipulate the transaction process. Obyte Obyte In this system, transactions are both created and added to the DAG by users, referencing other transactions in a web-like network, and preventing any single group from gaining enough power to censor transactions If you have your Obyte private keys , no one can limit, freeze, or manipulate your funds, and you won’t depend on the success or failure of any institution. The future is decentralized! And Obyte is already there. private keys private keys Featured Vector Image by Freepik Featured Vector Image by Freepik Freepik Freepik

Educational Byte: Electronic Money vs. Cryptocurrency

Vzdělávací bajt: Elektronické peníze vs. kryptoměna

Bildungsbyte: Elektronisches Geld vs. Kryptowährung

Byte Educativo: Dinero Electrónico vs. Criptomonedas

Koulutustavu: Elektroninen raha vs. kryptovaluutta

Obrazovni bajt: elektronički novac protiv kriptovalute

教育バイト: 電子マネー vs. 暗号通貨

Educational Byte: Vola elektronika vs. Cryptocurrency

Едукативен бајт: Електронски пари наспроти криптовалути

Byte Educativo: Qullqi Electrónico vs. Criptomoneda

Образовательный байт: Электронные деньги против криптовалюты

Eğitim Baytı: Elektronik Para ve Kripto Para

Educational Byte: Tiền điện tử so với tiền điện tử

Artificial Intelligence (AI) is transforming the electronics manufacturing sector through higher output, more robust quality control, and greater creativity. Machine learning techniques enhance production schedules while minimizing downtime and maximizing resource utilization. Artificial intelligence (AI)-powered predictive maintenance solutions anticipate equipment issues and address them before they worsen, protecting production cycles. With the use of modern computer vision, AI-powered quality control performs entire product inspections, significantly reducing operational costs and defect rates. Additionally, Generative AI makes adaptive manufacturing processes possible. In these processes, machines freely adapt their operations in response to actual time data on performance, enabling consumer demands for operational flexibility and flexibility. Let's analyze how AI impacts electronics in the future. Overview of AI in Electronics: Artificial intelligence (AI) in electronics is the imitation of human intelligence in machines, enabling them to perform tasks like language comprehension, learning, reasoning, and problem-solving. The core concepts of artificial intelligence (AI) include neural networks, which process information through networked nodes to identify patterns, machine learning, which uses algorithms to teach computers to learn from data and make decisions without the need for explicit programming, and data processing, which is necessary for creating AI models and involves obtaining, cleaning, and analyzing vast amounts of data to increase accuracy and efficiency. Specific Difficulties in the Manufacturing of Electronics The sector faces several distinct challenges. Accuracy must be preserved throughout the production process since even minor errors can have an important effect on the dependability and functionality of electronic products. Rapid advances in technology also require substantial costs for brand-new machinery and instruction, in addition to continuous process alterations in the manufacturing sector. Durability is a major concern as well because there is an increasing desire to embrace sustainable methods even in spite of the consumption of dangerous chemicals and waste. Furthermore, companies need to continuously innovate in the face of intense competition in order to remain economic and of use in the market. AI in the Background of Electronics Timeline Over the years, there have been several phases of transition in artificial intelligence (AI) development on electronics. Machine learning and neural networks are much newer ideas but can be traced back to early AI research, which was first written about in the 1950s-60s. In the 1980s, robotics and automated technology began to make their way into the industry. This is when AI first really started disrupting industries with mini-cars that ran through tubes taking parts from one end of a factory floor to another. Consumer electronics manufacturers integrated a form of artificial intelligence in their products such as smartphones and smart home appliances, starting in the 2000s. The 2010s saw a transformation in AI capabilities, propelled primarily by advancements in deep learning and big data analytics. AI and Digital Transformation Are Expanding Manufacturers in particular have had their regulations evolve because of the digitization across many industries. This move can mostly be attributed to the work of AI which is responsible for increasing efficiency, bringing in quality control levels, and predicting maintenance. This would improve productivity by decreasing wait times and maximizing the use of resources as production schedules and resources go through the Terms of Service. It does a thorough quality check of any particular product using cutting-edge computer vision, which reduces faults and scrap rates. Predictive maintenance uses artificial intelligence (AI) to quickly determine when equipment is likely to be more dependable before it actually fails, keeping your business running smoothly. An AI-driven manufacturing IoT business meets-loop is the key to cutting-edge innovation in a competitive market, allowing firms to stay ahead of the curve without falling into their own success trap. Artificial Intelligence (AI) explained Creating machines and software that can undertake tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and language understanding. AI also involves developing the technologies that will make this possible. It can range from simple rule-based systems to complex machine learning and deep learning algorithms. How Production Can Benefit From AI AI is utilized throughout a large number of the device-making process. Predictive maintenance is improved by using machine data to anticipate and prevent equipment issues before they occur. Artificial intelligence (AI)-based quality control systems precisely manage inspection procedures to ensure that products strictly fulfill quality requirements. Making Gadgets With Artificial Intelligence. AI is being used in a new way in the business of making gadgets. Systems that use artificial intelligence (AI) to find problems before they happen look at data from industry monitors from the past and the present. As a result, maintenance and downtime costs are decreased. When used for inspection jobs, AI's better visual recognition systems can find flaws more accurately than human inspectors. This reduces downsizing, improves the quality of products, satisfies customers, and more efficiency in the production of the goods. Additionally, AI adjusts production schedules by using data analytics to better manage supplies, modify schedules, and respond quickly to changes in demand. A Look at How AI Has Been Used in the Making of Electronics Putting AI to use in the real world shows how it can help make things better and faster. While individuals take longer to create things, AI-powered robots are significantly more useful than human labor since they can accomplish challenging jobs more rapidly and consistently. Machine learning systems employ production line data to identify issues and defects early on. This improves quality control. This ensures that buyers will only get products free of flaws. By utilizing AI's power and intelligence, organizations may pinpoint important issues, establish goals, and get ready for a reliable, scalable solution. AI applications require a robust infrastructure made up of data management systems, clever software, and long-lasting hardware. Training and development for the workforce is also very important because it gives workers the skills they need to use AI systems properly and get the information they need to make smart business choices. AI is a big deal in the business of making gadgets. It helps companies solve long-standing problems, become more efficient, and make better products that they sell. Artificial Intelligence (AI) is transforming the electronics manufacturing sector through higher output, more robust quality control, and greater creativity. Machine learning techniques enhance production schedules while minimizing downtime and maximizing resource utilization. Artificial intelligence (AI)-powered predictive maintenance solutions anticipate equipment issues and address them before they worsen, protecting production cycles. With the use of modern computer vision, AI-powered quality control performs entire product inspections, significantly reducing operational costs and defect rates. Additionally, Generative AI makes adaptive manufacturing processes possible. In these processes, machines freely adapt their operations in response to actual time data on performance, enabling consumer demands for operational flexibility and flexibility. Generative AI Generative AI Let's analyze how AI impacts electronics in the future. Overview of AI in Electronics: Artificial intelligence (AI) in electronics is the imitation of human intelligence in machines, enabling them to perform tasks like language comprehension, learning, reasoning, and problem-solving. The core concepts of artificial intelligence (AI) include neural networks , which process information through networked nodes to identify patterns, machine learning, which uses algorithms to teach computers to learn from data and make decisions without the need for explicit programming, and data processing, which is necessary for creating AI models and involves obtaining, cleaning, and analyzing vast amounts of data to increase accuracy and efficiency. Artificial intelligence (AI) in electronics Artificial intelligence (AI) in electronics artificial intelligence (AI) include neural networks artificial intelligence (AI) include neural networks Specific Difficulties in the Manufacturing of Electronics The sector faces several distinct challenges. Accuracy must be preserved throughout the production process since even minor errors can have an important effect on the dependability and functionality of electronic products. Rapid advances in technology also require substantial costs for brand-new machinery and instruction, in addition to continuous process alterations in the manufacturing sector. Durability is a major concern as well because there is an increasing desire to embrace sustainable methods even in spite of the consumption of dangerous chemicals and waste. Furthermore, companies need to continuously innovate in the face of intense competition in order to remain economic and of use in the market. AI in the Background of Electronics Timeline Over the years, there have been several phases of transition in artificial intelligence (AI) development on electronics. Machine learning and neural networks are much newer ideas but can be traced back to early AI research, which was first written about in the 1950s-60s. In the 1980s, robotics and automated technology began to make their way into the industry. neural networks neural networks This is when AI first really started disrupting industries with mini-cars that ran through tubes taking parts from one end of a factory floor to another. Consumer electronics manufacturers integrated a form of artificial intelligence in their products such as smartphones and smart home appliances, starting in the 2000s. The 2010s saw a transformation in AI capabilities, propelled primarily by advancements in deep learning and big data analytics. AI and Digital Transformation Are Expanding Manufacturers in particular have had their regulations evolve because of the digitization across many industries. This move can mostly be attributed to the work of AI which is responsible for increasing efficiency, bringing in quality control levels, and predicting maintenance. This would improve productivity by decreasing wait times and maximizing the use of resources as production schedules and resources go through the Terms of Service. It does a thorough quality check of any particular product using cutting-edge computer vision, which reduces faults and scrap rates. Predictive maintenance uses artificial intelligence (AI) to quickly determine when equipment is likely to be more dependable before it actually fails, keeping your business running smoothly. An AI-driven manufacturing IoT business meets-loop is the key to cutting-edge innovation in a competitive market, allowing firms to stay ahead of the curve without falling into their own success trap. Artificial Intelligence (AI) explained Creating machines and software that can undertake tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and language understanding. AI also involves developing the technologies that will make this possible. It can range from simple rule-based systems to complex machine learning and deep learning algorithms. How Production Can Benefit From AI AI is utilized throughout a large number of the device-making process. Predictive maintenance is improved by using machine data to anticipate and prevent equipment issues before they occur. Artificial intelligence (AI)-based quality control systems precisely manage inspection procedures to ensure that products strictly fulfill quality requirements. Making Gadgets With Artificial Intelligence. AI is being used in a new way in the business of making gadgets. Systems that use artificial intelligence (AI) to find problems before they happen look at data from industry monitors from the past and the present. As a result, maintenance and downtime costs are decreased. When used for inspection jobs, AI's better visual recognition systems can find flaws more accurately than human inspectors. This reduces downsizing, improves the quality of products, satisfies customers, and more efficiency in the production of the goods. Additionally, AI adjusts production schedules by using data analytics to better manage supplies, modify schedules, and respond quickly to changes in demand. A Look at How AI Has Been Used in the Making of Electronics Putting AI to use in the real world shows how it can help make things better and faster. While individuals take longer to create things, AI-powered robots are significantly more useful than human labor since they can accomplish challenging jobs more rapidly and consistently. Machine learning systems employ production line data to identify issues and defects early on. This improves quality control. This ensures that buyers will only get products free of flaws. AI-powered robots AI-powered robots By utilizing AI's power and intelligence, organizations may pinpoint important issues, establish goals, and get ready for a reliable, scalable solution. AI applications require a robust infrastructure made up of data management systems, clever software, and long-lasting hardware. Training and development for the workforce is also very important because it gives workers the skills they need to use AI systems properly and get the information they need to make smart business choices. AI is a big deal in the business of making gadgets. It helps companies solve long-standing problems, become more efficient, and make better products that they sell.

AI: Powering the Future of Electronics

এআই: ইলেকট্রনিক্সের ভবিষ্যতকে শক্তিশালী করে

KI: Antrieb für die Zukunft der Elektronik

IA: impulsando el futuro de la electrónica

IA : alimenter l’avenir de l’électronique

एआई: इलेक्ट्रॉनिक्स के भविष्य को सशक्त बनाना

AI: エレクトロニクスの未来を支える

AI: 전자제품의 미래를 주도하다

IA: impulsionando o futuro da eletrônica

Искусственный интеллект: будущее электроники

Yapay Zeka: Elektroniğin Geleceğine Güç Vermek

AI: Cung cấp năng lượng cho tương lai của điện tử

人工智能：驱动电子产品的未来

The famous cypherpunk mailing list from which Bitcoin (and other privacy tools) came was founded in late 1992 by the activists Eric Hughes, Tim May, and John Gilmore. However, this is far from being the only merit attributed to Gilmore. Indeed, while that specific mailing list isn’t active anymore, other important projects launched or supported by Gilmore are still around, always helping to keep our privacy and digital rights. Born in 1955 in Pennsylvania (US), . Gilmore's journey as a programmer began in 1982 at Sun Microsystems (now Oracle), where he was the fifth employee. John Gilmore is a pioneer in software development and cryptography, who made significant contributions that continue to shape the digital landscape He focused there on further developing Berkeley Software Distribution (BSD), an open-source operating system. In 1985, he also co-authored the Bootstrap Protocol, which evolved into DHCP, a fundamental mechanism for assigning IP addresses on networks. This essentially ensures that devices connected to a network can communicate with each other, forming the backbone of modern Internet connectivity. his efforts Likewise, Gilmore is a frequent collaborator in free software and has been involved in various GNU projects. They include maintaining the GNU Debugger and initiating GNU Radio, demonstrating his commitment to fostering open-source solutions. Besides, his entrepreneurial spirit led him to found Cygnus Solutions in 1989. This was a company dedicated to supporting free software and was sold to Red Hat in 1999. Electronic Frontier Foundation (EFF) In 1990, Gilmore took another step to fight for privacy and freedom online. Along with John Perry Barlow (author of the ), and Mitch Kapor (creator of Firefox), they founded the Electronic Frontier Foundation ( ). Declaration of Independence of Cyberspace EFF This is an NGO with a mission to safeguard online rights, whether it entails combatting mass surveillance or defending freedom of expression worldwide. Not only do they advocate for these causes, but they also equip people with the requisite tools and insights to fortify their online defenses. Through legal advocacy, the EFF takes on cases involving privacy violations, government surveillance, and free speech issues. They also work to shape policies and legislation that protect digital rights, promote Internet freedom, and develop technologies to enhance online privacy and security. One notable example is HTTPS Everywhere, a browser extension created in collaboration with the Tor Project. HTTPS Everywhere automatically switches websites from insecure HTTP to secure HTTPS, enhancing users' security and privacy while browsing the web. Other significant EFF software projects are Privacy Badger, a browser extension designed to block online trackers and advertisements; and Let's Encrypt, a free, automated, and open certificate authority that enables website owners to obtain and install SSL/TLS certificates for secure HTTPS connections. Cypherpunks and Activism Gilmore didn’t create any form of digital money himself, but he supported as a movement from the very beginning. The first physical meetings were held between Eric Hughes’s house and Cygnus Solutions headquarters in California, whereas the mailing list was originally hosted on toad.com, a domain owned and maintained by Gilmore. The mission of cypherpunks is protecting privacy and freedom by using cryptographic tools, something fully shared by : the cypherpunks Gilmore too “I want a guarantee—with physics and mathematics, not with laws—that we can give ourselves things like real privacy of personal communications. Encryption strong enough that even the NSA can’t break it.” Additionally, Gilmore has been a vocal advocate for digital rights and civil liberties. such as the creation of the FreeS/WAN project for IPsec encryption and sponsorship of the EFF's Deep Crack DES cracker. Both projects worked towards making digital communication more secure by either enhancing encryption standards or demonstrating weaknesses in existing ones. He’s also been involved in the development of numerous software projects aimed at enhancing digital security and privacy, Gilmore's commitment to promoting Internet freedom has also led him to support initiatives aimed at increasing transparency and accountability in government surveillance practices. He has been involved in legal battles challenging unconstitutional surveillance and censorship, demonstrating his dedication to defending civil liberties in the digital age. A remarkable case was , where he challenged the requirement to show identification for domestic air travel, arguing it violated his constitutional rights to travel. He also objected to the lack of transparency regarding the directive mandating ID checks, which is fair: although the courts reviewed the Security Directive privately, its contents remain undisclosed to the public. This is deemed an unconstitutional “ .” Sadly, despite Gilmore's claims, the court ruled against him for now. Gilmore v. Gonzales secret law Against Censorship Gilmore, as much as other cypherpunks, has a firm position against censorship. He’s still active in the fight for privacy by like Freedom Box, a small device that works as an independent server to avoid surveillance; and Freedom of Movement, the legal battle against secret laws. , censorship is a social weakness, and it doesn’t offer any advantages. supporting projects For him “Censorship is a counterproductive social policy and weakens the national security, by suppressing the flow of useful information among the honest citizenry. Widespread use of encryption also enhances the national security, by making private information more truly private, and by making systems and networks harder for dishonest people to penetrate.” In these days of censorship and surveillance, we need decentralized and privacy tools more than ever. Luckily, developers and activists like Gilmore keep working to deliver them free to use. One of them is the . Obyte crypto network is structured as a Directed Acyclic Graph (DAG) to achieve consensus and record transactions. This decentralized approach ensures greater transparency, security, and resilience against censorship and surveillance. Its architecture enables peer-to-peer transactions without the need for intermediaries. Moreover, its open and permissionless nature fosters innovation, allowing developers to build decentralized applications (Dapps) and smart contracts on the platform. Obyte Besides, relying on the , users have the option to verify their personal information, such as their real name, while ensuring it remains securely stored in their wallet and nowhere else. This verified information can then be shared selectively with trusted parties, allowing others to check its authenticity using the Obyte DAG. Self-Sovereign ID features Importantly, users retain full control over what information they share. By decentralizing control and empowering users with sovereignty over their data and assets, Obyte paves the way for a more inclusive and equitable digital economy. Just like John Gilmore and other cypherpunks have dreamed of. Read more from series: Cypherpunks Write Code Tim May & Crypto-anarchism Wei Dai & B-money Nick Szabo & Smart Contracts Adam Back & Hashcash Eric Hughes & Remailer St Jude & Community Memory Hal Finney & RPOW Featured Vector Image by Garry Killian / Freepik John Gilmore Photograph by Joi Ito / Flickr

Cypherpunks Write Code: John Gilmore and the Electronic Frontier Foundation

সাইফারপাঙ্কস কোড লিখুন: জন গিলমোর এবং ইলেকট্রনিক ফ্রন্টিয়ার ফাউন্ডেশন

Cypherpunks schreiben Code: John Gilmore und die Electronic Frontier Foundation

Los Cypherpunks escriben código: John Gilmore y la Electronic Frontier Foundation

Cypherpunks écrivent du code : John Gilmore et l'Electronic Frontier Foundation

साइफरपंक्स राइट कोड: जॉन गिलमोर और इलेक्ट्रॉनिक फ्रंटियर फाउंडेशन

サイファーパンクはコードを書く: ジョン・ギルモアと電子フロンティア財団

사이퍼펑크의 코드 작성: 존 길모어(John Gilmore)와 전자 프론티어 재단(Electronic Frontier Foundation)

Cypherpunks escrevem código: John Gilmore e a Electronic Frontier Foundation

Шифропанки пишут код: Джон Гилмор и Electronic Frontier Foundation

Şifrepunklar Kod Yazıyor: John Gilmore ve Electronic Frontier Foundation

Cypherpunks viết mã: John Gilmore và Electronic Frontier Foundation

密码朋克编写代码：约翰·吉尔摩和电子前沿基金会

TOPTAL, LLC v. DENIS GROSZ Court Filing, retrieved on February 26, 2024, is part of . You can jump to any part in this filing . This part is 7 of 7. HackerNoon’s Legal PDF Series here CERTIFICATE OF ELECTRONIC SERVICE I certify that I am an employee of the Second Judicial District Court of the State of Nevada, in and for the County of Washoe that on the October 2023, I electronically filed the foregoing with the Second Judicial District Court's electronic filing system which will send a notice of electronic filing to the following: JOHN DESMOND, ESQ. KENT ROBISON, ESQ PHILIP MANNELLY, ESQ. ADAM HOSMER-HENNER, ESQ. HANNAH WINSTON, ESQ. BRIAN IRVINE, ESQ. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on October 11, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. media.licdn.com

Certificate of Electronic Service for Upload of Toptal v. Denis Grosz Court Filing 

I wrote this series of posts primarily for myself, or at least someone who is trying to make something electronic to sell. If that’s you, then I hope this helps you on your journey! If you just want to see the end product then you can find it at . https://hortus.dev/products/social-battery This is a long post, so I have broken it up into chunks to make it a bit easier to digest. These are: (this article) Designing and Prototyping the Board Selling the Prototypes Online Going from Prototype to Production Introduction I wanted to experiment with JLC PCB’s assembly service - whilst I’ve designed and manufactured bare PCBs before placing and soldering components manually, I’ve got some future projects in mind that will be impractical to solder up by hand due to both the quantity and size of components. It’s kind of amazing how cheap this service is when you think about what’s involved, but it’s still expensive enough that it can be a bit daunting handing over your money, and then waiting to find out whether you’ve made some mistake that will ruin the end result. I figured I’d get started with something simple, something I could test the waters of not just PCB assembly but also e-commerce. My plan was to come up with a small item that I could realistically design and submit for manufacture in less than a day, then hopefully sell reasonably easily on a marketplace like Etsy. I was expecting the design and manufacturing side of things to be the hard part and listing the product on Etsy to be easy, but it was very much the other way around as you will see if you keep reading! The Product I had a quick scan of Etsy to look at the kinds of things people were successfully selling that I could make. I found a couple of examples of people making electronic pin badges - the perfect project! These mostly consisted of LEDs with either a random or pre-set flashing pattern on a novelty-shaped board. These looked alright to me, but I wanted to try something a bit more interactive and meaningful that I could sell for enough money to make a sensible margin on a small number of initial units. Looking at the regular pin badges being sold on Etsy, I saw a bunch of enamel ‘Social Battery’ pins with a sliding indicator. These immediately jumped out at me as something that a) I could identify with personally (people make endless jokes about my social battery…), b) would be really fun as an electronic version, and c) would be easy to design - just a few LEDs, a switch, and a microcontroller to tie it all together! Designing and Prototyping the Board Confident in my skills, I threw together a quick circuit diagram in Kicad. I decided to use an ATtiny13A - mainly because I had a few on hand from a previous project, and because I have a decent amount of experience with similar chips. If you’re not familiar with it, the ATtiny13A is a small 8-bit microprocessor with 6 IO pins and is part of the AVR family of MCUs. It’s similar to the ATmega chips that have historically been at the core of most Arduinos, except its capabilities are much more limited. The benefit is that the ATtiny range of chips are smaller and cheaper, so if you don’t need a lot of memory or peripherals, then they are great! (although possibly a bit dated now with the endless variety of ARM chips that are available). I laid out the PCB for my circuit in KiCad, then got it to spit out the gerbers and drill files (which are used to manufacture the PCB), and the bill of materials and placement files (which are used to assemble components onto PCBs). I submitted them to JLC PCB to see if they were able to process them correctly. The BoM and placement files needed a bit of tweaking from the default to get them in the right format (turns out I did this the hard way, and there’s a much easier plugin for KiCad that does everything perfectly in a single click). JLC PCB maintains a pretty large library of components that they keep in stock for assembly orders. However, in my case, the ATtiny13a was not available, so I had to order them in. This was pretty straightforward using their global sourcing service. I was able to find the supplier with the best price for the quantity I needed and then let JLC PCB order them to their warehouse on my behalf. Whilst I waited, I figured I might as well breadboard my design and start working on the code, and I’m glad I did because I immediately discovered an issue! My design used five of the IO pins available on the ATtiny13a to drive the LEDs directly (with the sixth being used to monitor the button). What I didn’t realize/remember from the last time I made this mistake (yes, It’s happened before), was that one of those IOs is also the reset pin. You can use it, but it’s not able to supply much current, and by tying it to the ground through an LED, I was keeping the chip in a permanent reset state. One way to get around this is to burn a fuse on the chip that permanently disables the reset functionality of the pin transforming it into a regular IO. The issue with this though is that you can only program the chip once (unless you own a high-voltage programmer, which I don’t), and given my propensity for learning things the hard way, that seemed potentially quite wasteful! The other option is to find a way to do more with fewer pins so that the reset pin can be left alone. This can be achieved using a technique called which allows you to address many more LEDs than the number of available pins. charlieplexing You can then scan through these LEDs, turning them on and off individually at a high rate to make it appear as if multiple are on at once through . persistence of vision In my case, I’m driving the four green LEDs from 3 pins, and the red LED with a dedicated pin. This isn’t the most efficient example of charlieplexing, as I could drive all the LEDs from the three pins, but keeping red on a dedicated pin allows for simpler code when it comes to programming. I revised my circuit diagram and PCB design, then re-exported the necessary files and sent them off to JLC PCB to manufacture and assemble an initial set of five prototypes. A little over a week later, I received my prototypes, and I was really pleased with them! There were a few things that I could see I needed to change: I hadn’t really planned how I was going to program the boards. I had just broken out the AVR programming pins and hoped for the best. This was fine for five prototypes as I could tack the programming wires on with the tiniest bit of solder, but this would get very tedious very quickly at scale. The spike for the fastener on the back was soldered onto a ground pad. This would have been fine except the thermal mass of the spike and the ground plane on the board made it difficult to make a good join. It also sticks out very close to the positive metal cage of the battery holder which meant that there was a good chance of accidental shorts occurring if people placed/attached the badge on/to conductive surfaces. The negative contact for the battery wasn’t prominent enough, so I had to add a dab of solder to it to make a good connection. Again - not the end of the world for a small number of prototypes, but a pain if I had to do this for loads. These were all simple fixes to implement. I added a proper programming header that could be used with a pogo pin jig for quick and repeatable programming. I disconnected the spike from the ground plane so that it was its own small disconnected island of copper that would heat up more easily and not cause shorts. And I expanded the negative contact for the battery so that it would have more surface area to make a solid connection. The code is pretty simple. I keep track of the mode the badge is in which is represented by an integer that gets decremented every time the button is pressed. I then loop and blink each LED on my way around as dictated by the mode. When mode 0 is reached, I reset it back to the original number and then put the ATtiny into sleep mode. Pressing the button again triggers the interrupt which wakes the chip and starts the whole process again. Overall, I was really pleased with the results, which meant it was time to see if I could sell them! You can read about my experience doing this in . the next post in this series

How I Manufactured and Sold Electronic Badges - Part 1: Designing and Prototyping the Board

আমি কীভাবে ইলেকট্রনিক ব্যাজ তৈরি ও বিক্রি করেছি - পার্ট 1: বোর্ডের ডিজাইনিং এবং প্রোটোটাইপিং

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Comment j'ai fabriqué et vendu des badges électroniques - Partie 1 : Conception et prototypage de la carte

मैंने इलेक्ट्रॉनिक बैज कैसे बनाए और बेचे - भाग 1: बोर्ड को डिज़ाइन करना और प्रोटोटाइप करना

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我如何制造和销售电子徽章 - 第 1 部分：电路板设计和原型制作

The financial landscape is currently undergoing a remarkable transformation, and at the heart of this revolution are Electronic Money Institutions (EMIs). EMIs are entities that issue and manage e-money, an electronic equivalent of physical cash that can be stored and transferred digitally. EMIs offer various products and services that facilitate e-money transactions, such as virtual IBAN accounts, merchant accounts, , e-wallets, cryptocurrency services, money transfers, foreign currency exchange services, and more. payment cards This article aims to offer a comprehensive understanding of the key distinctions between EMIs and traditional banks. In doing so, it sheds light on the transformative potential that EMIs bring to the rapidly evolving financial sector. The Surge in E-Money Transactions The growth in e-money transactions is nothing short of staggering. , the number of e-money users worldwide is expected to reach 4.4 billion by 2025, . The global e-money market size was valued at $653.8 billion in 2019 registering a CAGR of 10.89% from 2020 to 2027. In 2021, Europe witnessed an astounding 7.5 billion electronic money transactions, a significant increase from just over 4 billion in 2018. According to a report by Juniper Research up from 2.5 billion in 2020, representing a growth of 80% and is projected to reach $1332.02 billion by 2027, This astonishing growth reflects the well-established trend of e-money transactions replacing traditional cash, a trend that has been accelerated by the global COVID-19 pandemic. As the pandemic pushed consumers towards contactless payments, e-money transactions saw unprecedented growth. Simultaneously, the retail e-commerce sector in Western Europe experienced remarkable growth, more than doubling from £152.2 billion in 2015 to £328.91 billion in 2022. This seismic shift has transformed the payments sector, ushering in an era of innovative competition, including challenger banks, fintech firms, and retailers, all seeking to harness the advantages of an EMI License. Defining E-Money In its essence, e-money is an electronic equivalent of physical cash. Its convenience and versatility make it a preferred mode for financial transactions. E-money can be stored as digital records with banks, loaded onto prepaid cards, and securely housed in e-wallets. E-money can be used for numerous purposes, such as: Telecommunication Services: E-Money can facilitate the purchase or transfer of mobile credits and data plans. Fund Transfers: With E-Money, users can easily send or receive funds locally or across borders. Shopping and Services: Whether online or offline, E-Money can be exchanged for goods and services or redeemed for discount vouchers. Grocery Shopping: E-Money is accepted at a multitude of grocery outlets, making purchasing everyday items simple. Convenience and Department Stores: It is possible to use E-Money to cover costs at a variety of convenience or department stores. Specialty Stores: For specific purchases like books, music, or clothing, E-Money can be used at specialty stores. Discount Stores: E-Money is also applicable for purchasing household goods, electronics, or toys at discount stores. EMIs and the EMI License Electronic Money Institutions (EMIs) play a pivotal role in issuing and managing e-money. Authorized by regulatory bodies, there are currently over 550 EMIs in Europe, with the number of EMI licenses continually increasing. The EMI license empowers institutions to issue, distribute, and redeem e-money, facilitating various e-money transactions. The European Union has been at the forefront of regulating and promoting e-money, with the E-Money Directive (EMD) being the first legal framework for e-money in the world. The EMD was adopted in 2000 and revised in 2009 to harmonize the rules and conditions for issuing e-money across the EU. The EMD defines e-money as: a digital alternative to cash, which is stored on an electronic device or remotely at a server. It allows users to make cashless payments with money stored on a card or a phone, or over the internet. The EMD also sets out the requirements for obtaining an EMI license, such as: Having an initial capital of at least €350,000. Having adequate governance arrangements, risk management processes, and internal control mechanisms. Having sound and prudent business conduct, safeguarding customer funds, and ensuring adequate IT systems. Having a clear and transparent contractual relationship with customers, providing them with adequate information and protection. Traditional Banking License Unlike the more limited EMI licenses, traditional banks are governed by a comprehensive banking license. This enables them to offer an extensive array of banking services. By virtue of this license, these institutions can manage client deposits, extend credit, provide payment-related services, issue a variety of financial products, and are obliged to comply with rigorous regulatory mandates. There are several key distinctions between a traditional banking license and an EMI license: Capital Requirement: Traditional banking licenses necessitate a significantly higher initial capital, typically no less than €5 million. Lending Products: Such licenses permit banks to offer a range of lending products, including but not limited to loans, mortgages, overdrafts, and credit cards. Regulatory Oversight: Banks with a traditional license are subject to more stringent supervision by central banks and other regulatory authorities. Deposit Guarantee Schemes: A traditional banking license also allows banks to partake in deposit guarantee schemes, providing a safety net for customers' deposits up to a specified limit. Discerning EMIs from Banks The primary divergence between Electronic Money Institutions (EMIs) and traditional banks revolves around the ability to offer lending facilities. EMIs, by mandate, are prohibited from extending any form of lending and are required to separate client funds. In contrast, traditional banks generate income chiefly through the issuance of lending products, a critical element of their business model. Such a discrepancy carries significant implications for both the financial institutions and their clientele: For EMIs: The absence of lending services necessitates them to lean on alternative revenue streams, such as transaction fees, commissions, interest margins, or cross-selling strategies. For Traditional Banks: The provision of lending services entails handling more risks and costs associated with lending activities. For Customers: This distinction necessitates a careful evaluation of the advantages and disadvantages of both options, basing their choice on individual needs and preferences. Advantages of EMIs Electronic Money Institutions (EMIs) present several unique benefits, primarily attributed to their digital-first methodology and efficient operations: By operating primarily online, EMIs can maintain lower overhead costs. These savings can then be transferred to their customers through reduced fees or favorable exchange rates. Cost Efficiency: EMIs have the capability to rapidly adapt to evolving customer needs and market changes, launching new products or features at a pace often faster than traditional banks. Agility: Leveraging advanced technology and eliminating intermediaries, EMIs can process transactions faster than their traditional counterparts. Transaction Speed: As EMIs are prohibited from lending activities and abide by the rule of segregating customer funds, they can offer a higher level of security and trust compared to traditional banks. Safety Assurance: With offerings such as multi-currency accounts, e-wallets, and cryptocurrency services, EMIs provide a greater level of choice and convenience than traditional banks. Flexibility: The Role of EMI Licensing Acquiring an EMI license is a rigorous and time-consuming process, which may not align with the timeline and needs of all organizations. Many non-regulated businesses and fintechs choose to partner with EMI-licensed entities, utilizing BIN sponsorship and program management services. BIN Sponsorship and Program Management BIN sponsorship is a service that allows non-regulated entities to access payment networks (such as Visa or Mastercard) and offer payment services without holding their own EMI license. BIN sponsors, usually EMIs or banks, provide their BINs (bank identification numbers) to other institutions and support their connections to the payment networks. By doing so, BIN sponsors take responsibility for risk management, compliance, settlement, and reporting on behalf of their partners. Program management is a service that helps non-regulated entities launch and manage their payment products, such as cards or e-wallets, by providing them with technical, operational, and regulatory support. Program managers, usually issuer processors or fintechs, enable their partners to access and use payment accounts through payment card transactions. They also handle various aspects of the payment program, such as card design, production, distribution, activation, customer service, fraud prevention, and more. Many non-regulated businesses and fintechs choose to partner with EMI-licensed entities, utilizing BIN sponsorship and program management services. This offers them several benefits, such as: Faster time-to-market: By leveraging the existing infrastructure and expertise of their partners, they can launch their payment products in a matter of weeks or months, instead of years. Lower costs: By avoiding the high initial capital and ongoing operational expenses of obtaining and maintaining an EMI license, they can reduce their costs and increase their profitability. Greater flexibility: By having more control over their product features and pricing, they can tailor their offerings to their target market and customer segments. Reduced risk: By transferring the regulatory and financial risks to their partners, they can focus on their core competencies and business growth. Some examples of successful partnerships between non-regulated entities and EMI-licensed entities are: Zilch is a London-based fintech company that offers a buy now pay later (BNPL) digital card that allows customers to shop anywhere online and split their payments into four interest-free installments over six weeks. Zilch has partnered with Mastercard since its beta launch in 2019 and has recently expanded its partnership to launch its BNPL card across Europe. Zilch: Hyperjar is a fintech company that provides a smart spending app and a prepaid debit card that help customers budget and save money. Customers can create multiple jars for different spending categories and earn rewards from partner brands such as eBay, Shell, and Skyscanner. Hyperjar uses bank-grade encryption and Mastercard network to ensure security and convenience. Hyperjar: Kasssh is a digital cash platform that allows customers to pay for online purchases with cash at physical PayPoint stores across the UK. Kasssh aims to bridge the gap between cash and e-commerce and promote financial inclusion for unbanked and underserved consumers. Kasssh is free to use and download and has exited stealth mode after testing its service with selected merchants. Kasssh: Conclusion The financial industry's evolution is profoundly represented by the emergence of Electronic Money Institutions (EMIs), marking the onset of a new era characterized by innovation and customer-focused finance. Grasping the nuanced differences between EMIs and traditional banks is crucial for businesses, policymakers, and consumers as they steer through the continually transforming financial terrain. This in-depth analysis provides a thorough examination of each aspect, establishing a solid foundation for enlightened decisions that will sculpt the future of finance. We eagerly present this analysis to the leading fintech journal, confident that it paves the way to unlock the immense potential of EMIs in shaping our financial future. References EUR-Lex - 32009L0110 - EN - EUR-Lex (europa.eu) Online share of retail trade in EU countries 2022 | Statista Zilch and Mastercard Announce Expanded Partnership HyperJar review - is it the best prepaid debit card? - Money To The Masses Kasssh launches in the UK to enable consumers to pay for online purchases in cash - FinTech Futures: Global fintech news & intelligence

Navigating the Financial Frontier: A Comprehensive Analysis of Electronic Money Institutions (EMIs) 

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