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4D Sensor

4dsensor

Authors:
(1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work;
(2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work;
(3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University;
(4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix D The Case of the Gradient of a Scalar Abstract: (Ultra)light spin-1 particles—dark photons—can constitute all of dark matter (DM) and have beyond Standard Model couplings. This can lead to a coherent, oscillatory signature in terrestrial detectors that depends on the coupling strength. We provide a signal analysis and statistical framework for inferring the properties of such DM by taking into account (i) the stochastic and (ii) the vector nature of the underlying field, along with (iii) the effects due to the Earth’s rotation. On time scales shorter than the coherence time, the DM field vector typically traces out a fixed ellipse. Taking this ellipse and the rotation of the Earth into account, we highlight a distinctive three-peak signal in Fourier space that can be used to constrain DM coupling strengths. Accounting for all three peaks, we derive latitude-independent constraints on such DM couplings, unlike those stemming from single-peak studies. We apply our framework to the search for ultralight B − L DM using optomechanical sensors, demonstrating the ability to delve into previously unprobed arXiv:2403.02381v1 [hep-ph] 4 Mar 2024 regions of this DM candidate’s parameter space. 1 Introduction A widely pursued DM candidate is the light/ultralight vector dark matter (VDM) particle. Several early-universe production mechanisms exist for such dark matter [20–30] and, recently, numerical simulations of structure formation of light, vector dark matter in the nonlinear regime have been carried out (e.g. [31–33]). Our focus here is on the detection prospects of this kind of dark matter. Many dedicated studies have been conducted on the detection or exclusion of vectors [34–42] as well as scalars [35, 39, 42–49]. However, for the vector case, the analyses focusing on the Fourier space signal did not fully account for the vector nature and stochastic aspects of the field in their statistical treatment, and nor did they model the effect of the rotation of the Earth [36, 38]. In this work, we take these effects into account when studying the sensitivity of mono-directional accelerometers to ultralight vector dark matter. Given the recent and substantial research and development efforts in quantum technologies, we are specifically interested in timely studies aimed at understanding the potential of quantum optomechanical sensors in the direct detection of dark matter. Mechanical detectors have a rich history in tests of gravity, including LIGO, and in recent years there has been a surge in efforts to explore their potential in quantum sensing for fundamental physics investigations (see reviews [50–52]). We are only beginning to understand the new opportunities for dark matter searches [53–58] in light of significant advances in quantum readout and control of mechanical sensing devices using optical or microwave light [59– 61]. Accurately modeling the dark matter signal and the associated statistics is crucial for drawing representative inferences, guiding these quantum research and development efforts and aiding in experimental design. This is particularly relevant for large-scale accelerometer projects, such as the one proposed by the Windchime collaboration [62]. Such sensors have demonstrated potential as powerful probes for the wavelike signature produced by ultralight dark matter [63]. In this paper, we devise the analysis strategy for ultralight vector dark matter in the coherent regime to draw more representative exclusion inferences in the future. We begin by laying the theoretical groundwork for this DM paradigm in Section 2, where we derive the DM signal in both the time and frequency domains, taking into account both its stochastic and polarization properties, as well as accounting for the rotation of the Earth. We then perform statistical analyses of the DM signal in the frequency domain in Section 3. We derive a limit on a generalised parameter that is independent of the vector dark matter model and experimental parameters, which can be recast to concrete choices of them. Unlike other studies that focus solely on a single peak, our findings reveal that the signal power is distributed across three distinct peaks. Accounting for this distribution ensures the retention of constraining power, regardless of the experiment’s location on Earth. Finally, in Section 4, we apply our framework to a concrete dark matter model and sensor: B − L dark matter and the canonical optomechanical light cavity. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Authors: Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.1 The Dark Photon Field 2.2 The Detector Signal 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 5 Future Directions 6 Conclusions, Acknowledgments, and References 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix C Covariance Matrix D The Case of the Gradient of a Scalar D The Case of the Gradient of a Scalar Abstract: (Ultra)light spin-1 particles—dark photons—can constitute all of dark matter (DM) and have beyond Standard Model couplings. This can lead to a coherent, oscillatory signature in terrestrial detectors that depends on the coupling strength. We provide a signal analysis and statistical framework for inferring the properties of such DM by taking into account (i) the stochastic and (ii) the vector nature of the underlying field, along with (iii) the effects due to the Earth’s rotation. On time scales shorter than the coherence time, the DM field vector typically traces out a fixed ellipse. Taking this ellipse and the rotation of the Earth into account, we highlight a distinctive three-peak signal in Fourier space that can be used to constrain DM coupling strengths. Accounting for all three peaks, we derive latitude-independent constraints on such DM couplings, unlike those stemming from single-peak studies. We apply our framework to the search for ultralight B − L DM using optomechanical sensors, demonstrating the ability to delve into previously unprobed arXiv:2403.02381v1 [hep-ph] 4 Mar 2024 regions of this DM candidate’s parameter space. 1 Introduction A widely pursued DM candidate is the light/ultralight vector dark matter (VDM) particle. Several early-universe production mechanisms exist for such dark matter [20–30] and, recently, numerical simulations of structure formation of light, vector dark matter in the nonlinear regime have been carried out (e.g. [31–33]). Our focus here is on the detection prospects of this kind of dark matter. Many dedicated studies have been conducted on the detection or exclusion of vectors [34–42] as well as scalars [35, 39, 42–49]. However, for the vector case, the analyses focusing on the Fourier space signal did not fully account for the vector nature and stochastic aspects of the field in their statistical treatment, and nor did they model the effect of the rotation of the Earth [36, 38]. In this work, we take these effects into account when studying the sensitivity of mono-directional accelerometers to ultralight vector dark matter. Given the recent and substantial research and development efforts in quantum technologies, we are specifically interested in timely studies aimed at understanding the potential of quantum optomechanical sensors in the direct detection of dark matter. Mechanical detectors have a rich history in tests of gravity, including LIGO, and in recent years there has been a surge in efforts to explore their potential in quantum sensing for fundamental physics investigations (see reviews [50–52]). We are only beginning to understand the new opportunities for dark matter searches [53–58] in light of significant advances in quantum readout and control of mechanical sensing devices using optical or microwave light [59– 61]. Accurately modeling the dark matter signal and the associated statistics is crucial for drawing representative inferences, guiding these quantum research and development efforts and aiding in experimental design. This is particularly relevant for large-scale accelerometer projects, such as the one proposed by the Windchime collaboration [62]. Such sensors have demonstrated potential as powerful probes for the wavelike signature produced by ultralight dark matter [63]. In this paper, we devise the analysis strategy for ultralight vector dark matter in the coherent regime to draw more representative exclusion inferences in the future. We begin by laying the theoretical groundwork for this DM paradigm in Section 2, where we derive the DM signal in both the time and frequency domains, taking into account both its stochastic and polarization properties, as well as accounting for the rotation of the Earth. We then perform statistical analyses of the DM signal in the frequency domain in Section 3. We derive a limit on a generalised parameter that is independent of the vector dark matter model and experimental parameters, which can be recast to concrete choices of them. Unlike other studies that focus solely on a single peak, our findings reveal that the signal power is distributed across three distinct peaks. Accounting for this distribution ensures the retention of constraining power, regardless of the experiment’s location on Earth. Finally, in Section 4, we apply our framework to a concrete dark matter model and sensor: B − L dark matter and the canonical optomechanical light cavity. This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv

Vector Wave Dark Matter and Terrestrial Quantum Sensors

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 3 MATERIALS AND METHODS 3.0.1 Remote Sensor Data Soil temperature data were collected in the Sierra Nevada National Park in Andaluc´ıa, Spain, as part of the Smart EcoMountains program through the Lifewatch ERIC Program, from solar-powered remote multi-parametric micro-stations located within different environments (shrub, rock, and bare-earth) and altitudes (from 1500m to 2300m) from 29- 07-2020 until 04-05-2023. Measurements were continuously collected at a nominal 10 minute interval with micro-sensors (ibuttons, thermochron-type; Maxim integrated) connected to a network that transmits the collected information when connectivity is available. All the collected data were visually inspected for extreme values outside the normal ranges of the variable in the time series due to sensor failures, identifying a single sensor reporting erroneous values that were reported as NaNs. 3.0.2 Computational information All calculations were run with a B550 AORUS ELITE AX V2 motherboard with 128 GiB DDR4 RAM, an AMD Ryzen 9 5950x 32 core processor with an NVIDIA GeForce RTX 3080 graphics processor, and a 2 TB NVMe Samsung SSD 980. Software was installed on Ubuntu 22.04.3 LTS 64-bit operating system “Jammy Jellyfish”. Graphical inspections and data cleaning were conducted with the tidyverse 2.0.0 package [13] implemented in the R 4.3.1 software environment [14]. Calculations were performed using Python 3.10. Data was parsed and cleaned from the original .csv values using Pandas 2.0.3. Various numerical operations were performed using the vectorized functions available in NumPy 1.25 [15], with additional statistical functionality from SciPy 1.11 [16]. Graphics were created using Matplotlib 3.7.1 [17]. 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 3 MATERIALS AND METHODS 3.0.1 Remote Sensor Data Soil temperature data were collected in the Sierra Nevada National Park in Andaluc´ıa, Spain, as part of the Smart EcoMountains program through the Lifewatch ERIC Program, from solar-powered remote multi-parametric micro-stations located within different environments (shrub, rock, and bare-earth) and altitudes (from 1500m to 2300m) from 29- 07-2020 until 04-05-2023. Measurements were continuously collected at a nominal 10 minute interval with micro-sensors (ibuttons, thermochron-type; Maxim integrated) connected to a network that transmits the collected information when connectivity is available. All the collected data were visually inspected for extreme values outside the normal ranges of the variable in the time series due to sensor failures, identifying a single sensor reporting erroneous values that were reported as NaNs. 3.0.2 Computational information All calculations were run with a B550 AORUS ELITE AX V2 motherboard with 128 GiB DDR4 RAM, an AMD Ryzen 9 5950x 32 core processor with an NVIDIA GeForce RTX 3080 graphics processor, and a 2 TB NVMe Samsung SSD 980. Software was installed on Ubuntu 22.04.3 LTS 64-bit operating system “Jammy Jellyfish”. Graphical inspections and data cleaning were conducted with the tidyverse 2.0.0 package [13] implemented in the R 4.3.1 software environment [14]. Calculations were performed using Python 3.10. Data was parsed and cleaned from the original .csv values using Pandas 2.0.3. Various numerical operations were performed using the vectorized functions available in NumPy 1.25 [15], with additional statistical functionality from SciPy 1.11 [16]. Graphics were created using Matplotlib 3.7.1 [17]. 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

Remote Sensor Data Collection: Insights from Sierra Nevada National Park

Hello Hackers! Today we have a really interesting topic. Searching the internet, I didn't find any simple solution on "How to read data from car parking sensors," so I decided to write a short and easy-to-understand article to help robotics engineers reverse engineer the cheapest waterproof ultrasonic sensors on the market. What we need Cheapest Chinese sensors that you can find on the market (Alibaba, , Taobao, , etc.) Aliexpress Amazon 12-volt power supply (Picture 2); Arduino board with 5-volt logic (skip ARM versions with 3.3 volts); Any oscilloscope or logic analyzer; Computer with Arduino IDE. Basics First, we need to disassemble the sensor case ( Picture 5). We need to connect the black wire (pink dot) to “-” and the red wire (blue dot) to “+” of our 12-volt power supply. The green dot (GND) is connected to the GND of the Arduino board, and the red dot (DA) to any PWM-supported PIN of the Arduino board—PIN 3 in my case.  As you can see, we have 4 connectors to our sensors. We will not connect them because we need to find each sensor's address; it will be easy to detect them in that case (Picture 6). Before we download the Arduino sketch, we need to gather some information about the PWM signal used to send data to the parking display: The type of PWM signal (LOW/HIGH); The packet length; The number of packets sent (logically, it is 4, each packet corresponds to each sensor output in our case, there are 4 sensors); The duration between packets; The duration of the start bit that separates each packet from each other; The duration of the LOW and HIGH bits of each packet; To get all this necessary information, we will use an oscilloscope. Connect it to the "GND" (green dot) and "DA" (red dot) pins, and don't forget to power on the sensor's board and Arduino. Finally, we received 4 different . Don't panic if you don't understand, I will describe the first in detail with an explanation. packets packet From this picture, we see that this is a HIGH logic PWM signal because the default value is 0 volts in our pin, and then it rises to 5 volts (Pink arrow). Next, we see the start bit - marked as green ST (the longest signal). We also see the body of the . I have marked only the high length of each signal with red and blue colors. Red represents a long signal (logic 1), while blue represents a short signal (logic 0). packet The duration of the long, short, and start signals we will measure later. From this screen, we only need to extract the real binary data from each and write it down. packet We got this data: packet 1. 1000 1010 1111 1111 0 packet 2. 1000 1001 1111 1111 0 packet 3. 1000 1000 1111 1111 0 packet 4. 1000 1011 1111 1111 0 Next, we need to measure the duration of short, long, start bit, and time duration between the packets. As you can see from the picture, we see green lines, that measure the duration. In our case T = 220uS. It is the duration of the long bit. Finally, we got interesting data: The type of PWM signal is HIGH; The packet length is 2 bytes or 16 bits; The number of packets that sends is 4; The time between packets is 40mS; The  duration of the start bit is 1.02mS; The duration of LOW is 80nS and the HIGH bit is 220nS; Now we are ready to code a sketch. Arduino Sketch // our pin that supports PWM
int inputPin = 3;

// our packet, where we will store our 16bits information about sensor.
unsigned int packet = 0;

// our bit duration, for short = 80nS, long = 220nS and start bit = 1.02mS
unsigned long pulseDuration = 0;

void setup() {
  pinMode(inputPin, INPUT);
  Serial.begin(2000000);
}

void loop() {

  // pulseIn is a build-in function, that waits for HIGH signal. 
  // We have defined that our logic is PWM-HIGH, that's why we are using HIGH. 
  // 20.000 is a timeout of waiting for signal.
  pulseDuration = pulseIn(inputPin, HIGH, 20000);

  // We detect the start packet pulse. In our case the start bit is 1.02mS,
  // so it is between these values:
  if (pulseDuration > 900 && pulseDuration < 1040) {
    // We have successfully detected start bit, now we need to read the body of the packet.
    packet = 0;
    // Only read 16 bits of data.
    for (int i = 15; i >= 0; i--) {
      // Read packet (only HIGH bits, zeros are ignored, because we already have it)
      pulseDuration = pulseIn(inputPin, HIGH, 3000);

      // we detect only LONG bits with 220nS duration (logical 1);
      if (pulseDuration > 210 && pulseDuration < 250) {
        bitSet(packet, i);
      }
    
    }
    // Print the packet;
    Serial.println(packet, BIN);
  }  
} We got this output from Arduino IDE. Now, we can compare it with our investigated values from our oscilloscope: 1000101111111111
1000100111111111
1000101011111111
1000100011111111 Great! We have verified that the code is working fine and we got the same values from the oscilloscope. Our packet is divided into 2 parts. First 8 bits is the address of the sensor. The last 8 bits is the measured distance. 10001000 11111111
10001001 11111111
10001010 11111111
10001000 11111111 As we remember, we didn’t connect the sensors to board, that’s why we are receiving  “11111111”. We can change a code and split the address and values using this logic: // We delete last 8 bits of our packet to get the address value; 
byte sensorAddressCheck = (byte)(packet >> 8);
// Using logical operation AND we remove first 8 bits;
byte sensorValueCheck = (byte)(packet & B11111111); Now we have this code: int inputPin = 3;
unsigned int packet = 0;

byte sensorValue = 0;
byte sensorAddress = 0;

unsigned long pulseDuration = 0;

void setup() {
  pinMode(inputPin, INPUT);
  Serial.begin(2000000);
}

void loop() {
  pulseDuration = pulseIn(inputPin, HIGH, 20000);

  if (pulseDuration > 900 && pulseDuration < 1040) {

    sensorAddress = 0;
    sensorValue = 0;
    packet = 0;

    for (int i = 15; i >= 0; i--) {
      pulseDuration = pulseIn(inputPin, HIGH, 3000);

      if (pulseDuration > 210 && pulseDuration < 250) {
        bitSet(packet, i);
      }
    
    }
    byte sensorAddressCheck = (byte)(packet >> 8);
    byte sensorValueCheck = (byte)(packet & B11111111);

    Serial.print("Sensor addr: ");
    Serial.print(sensorAddressCheck, BIN);
    Serial.print(" value: ");
    Serial.println(sensorValueCheck);

  }  
} Arduino output: Sensor addr: 10001011 value: 255
Sensor addr: 10000001 value: 80
Sensor addr: 10000010 value: 82
Sensor addr: 10001000 value: 255 Great Job! Congrats! You have successfully reversed the PWM signal and I hope that it will help you in your robotics beginnings! If you want to know the distance in centimeters, additional work should be done. The general process is simple: You can put the roulette on the floor and take a box. A little creative work and you will get a correlation table between 0-255 values from the sensor with your roulette. Thanks for your time! PS. This work is part of a big project (Picture 7). If you want to learn more, subscribe to my channel! Thanks!

How To Hack Cheap Car Parking Sensors With Arduino and an Oscilloscope

Sensors and Edge-Sensing Devices: The Ways They Can Improve Our Lives

Would you leave a Google Staff Research Engineer role just because you want your TV to automatically pause when you get up to get a cup of tea? Actually, how is that even relevant, you might ask. Let’s see what Pete Warden, former Google Staff Research Engineer and now CEO and Founder of Useful Sensors, has to say about that. From Jetpac to Google and TinyML, from Google to AI in a Box Pete Warden wrote the world’s only mustache detection image processing algorithm. He also was the founder and CTO of startup Jetpac. He raised a Series A from Khosla Ventures, built a technical team, and created a unique data product that analyzed the pixel data of over 140 million photos from Instagram and turned them into in-depth guides for more than 5,000 cities around the world. Jetpac was acquired by Google in 2014, and Warden has been a Google Staff Research Engineer from then till March 2022. That’s when he founded , which he sees as the evolution of the work he’s been doing at Google. Warden was the Technical Lead of the TensorFlow Mobile team, responsible for deep learning on mobile and embedded devices. Useful Sensors . Naturally, much of what he did was based on things others were already working on: “A lot of my contribution has been helping publicize and document a bunch of these engineering practices that have emerged,” Warden said. Either way, is getting big, and Warden is a big part of it. Warden is sometimes credited as having kickstarted the TinyML subdomain of machine learning TinyML Tiny machine learning (TinyML) is broadly defined as a fast-growing field of machine learning technologies and applications, including hardware, algorithms, and software capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of always-on use-cases and targeting battery operated devices. Useful Sensors just launched a product called , which it dubs an “offline, private, open source LLM for conversations and more.” Even though it’s not the first product Useful Sensors has created, it’s the first one that’s officially launched. That was a good opportunity to catch up with Warden and talk about what Useful Sensors is working on. AI in a Box Click to listen to the full conversation on the . here Orchestrate all the Things podcast Simplicity and the creepiness factor While it is true that Warden cited wanting his TV to automatically pause when he gets up to get a cup of tea as part of the reason why he started Useful Sensors, some context is definitely needed. Part of Warden’s motivation for his work on Tensorflow for embedded devices was to see it used in everyday objects. As Warden related, when he went to talk to companies that made light switches or TVs to tell them “all about this wonderful open source code that they could get for free, and all the conferences and documentation and examples and books,” they would hear him out. But then, in the end, they’d usually say something like: “That’s great. But we barely have a software engineering team, let alone a machine learning team. So can you just give us something that gives us a voice interface or tells us when somebody sits down in front of the TV”? That’s quite telling, and producing self-contained hardware is a valid reason to set out on a new venture. However, that’s not something Google itself could not achieve. Google Pixel, for example, already provides automatic captions running on-device for content that plays on the phone. But there’s something more: privacy and data sovereignty, aka “the creepiness factor”. AI-enhanced In a video hosted on the Useful Sensors home page, Warden mentions how, during his tenure at Google, he would often get questions about whether Google was spying on people. Those questions are valid ones, triggered by a widely observed phenomenon: when mentioning the topic XYZ in the vicinity of your phone, you will often get bombarded with ads about XYZ for days on end. Warden, on his part, swears that the code he was working on does not do that. But, he goes on to add, he has no way of proving that because the code is proprietary. Plus, we may add, there’s nothing anyone can say about other parts of Google’s codebase, or other apps for that matter. It’s hard to light-heartedly dismiss such a widely shared experience. Useful Sensors That brings us to the core of it all – what Useful Sensors does and how it’s different. The vision, as Warden put it, is to be able to run machine learning locally and to do it in a private and checkable way. Everything should run locally with no internet connection, so conversations and data are completely secure. No account, setup, or subscription needed. Warden shared that Useful Sensors has already launched the person sensor, a small board that provides and indication of whether there’s a person nearby, as well as a tiny QR code. Both run entirely locally and retail at $10 and $7, respectively, Warden said. But these products have something else in common too: they are aimed at makers, i.e., hobbyists with enough motivation and technical skills to tinker with them, but also at electronics vendors. As Warden shared, Useful Sensors is currently in talks with a number of electronics vendors. Useful Sensors products are being evaluated, and Warden is hopeful that it won’t be too long before they end up being included in devices sold in the market. In fact, that is the audience that holds the greatest promise for Useful Sensors. Its backers also see the potential, apparently, as the company has received $5 million in seed funding already. Warden co-founded Useful Sensors with CTO Manjunath Kudlur, formerly of Cerebras. Kudlur was the compiler team lead at Cerebras as well as one of the founders of Google’s TensorFlow and Nvidia’s CUDA. Warden said Kudlur contributes greatly to things such as accelerating transformer models for Useful Sensors. The team lists a total of 8 people at this point, but if their plans come to fruition, another funding round and growth are well within sight, as per Warden. AI in a Box, the product that Useful Sensors just launched, seems like it was designed to do a number of things. First, it can grow awareness for Useful Sensors, as it’s aimed at makers. As Warden said, people can tinker with the code, but it already does some useful things out of the box. It can provide live captions, as well as receive voice commands and translate between multiple major languages on the fly. AI in a Box can also help raise some cash for Useful Sensors. But perhaps more importantly, it positions Useful Sensors as an ecosystem provider. This seems like part of the vision for the company, and Warden shared that he’s hoping people will get creative with AI in the Box. In fact, he added, some examples of things people have built with Useful Sensors products already exist on . Hackster Under the hood AI in a Box features a RockChip 3588S SoC with an . The NPU is a unit specifically designed to accelerate neural networks, and the team was able to leverage it to enable a Large Language Model to run locally. AI in a Box is built on a foundation of open-source models like and . In the same vein, the company is releasing all the code to accelerate and control the system under an open-source license. NPU Whisper Llama2 Useful Sensors is also available. The idea is that transparency should help with security and privacy auditing, with Warden noting he’d be happy to have regulators audit Useful Sensors products. Releasing open-source code will also enable developers to use the system as a base to build their own real-time voice input applications in Python. library for optimized transformer inference on the RockChip NPU Warden said that once they were able to get the real-time speech-to-text working, there was lots of choices around which LLMs they could run locally. The team is also looking into doing some of its own fine-tuning, but they’ve been able to get a long way by just providing prompt contexts for interactions. As Warden noted, anybody who’s familiar with LLMs would probably easily recognize what Useful Sensors did. Real-time speech-to-text enables AI in a Box in a box to function as a keyboard too, among other things. By having an LLM in the mix, too, that opens up a range of possibilities. For example, LLMs are known to be able to interface with APIs. Warden mentioned Raspberry Pi as an example that could enable people to control a number of devices using voice commands. AI, innovation, empowerment An example of what people are already working on, Warden said, is an actor who is using the company’s person sensor to automate spotlight operation for solo performances. Rather than having to pay someone to operate a spotlight, the actor is hoping it should be possible to automate this. In a way, that’s a perfect metaphor for the double-edged sword that innovation and AI truly are. That may sound like a good idea for the actor, but what about the operator? “That’s that’s a really big question with anything around innovation. If we’re, quote unquote, making things more efficient, what are the societal impacts of that? A big part of what I’m trying to do is get these technologies into people’s hands so that it’s not just a bunch of engineers who are making these decisions about what we should do. People can try these models for themselves, and see, for example, how useful, but also how flawed the current generation of LLMs are. I don’t want us technocrats to be the ones making these decisions. I want a well informed public who are actually able to say – hey, this is what we want”, Warden said. That certainly sounds like a noble aspiration. How compatible it really is with VC backing, a cut-throat competitive landscape dominated by the Googles of the world, the public’s ability to elaborate its own take on things, and administrations’ willingness to take the public into account remains to be seen. Also published . here

Useful Sensors: Pioneering AI with AI in a Box, A Different Paradigm for Edge Computing

Sensorium is thrilled to announce our first-ever NFT drop - the SENSO DAPP Avatars collection. We're launching an exclusive collection of 10,000 AI-powered avatars that will be made available to our community as part of a multi-stage event. The best part? If you're a SENSO holder with at least 1,500 tokens, you can mint your avatar for free! How Does the Drop Work? Each avatar in the collection will be minted on the Polygon blockchain using SENSO tokens. To get involved, you'll need to transfer a minimum of 1,500 SENSO from any exchange (KuCoin, Gate, etc.) to a cold wallet (Metamask) on the Ethereum blockchain before July 11th. Please note, there may be fees involved in this transfer. On July 18th, you'll need to switch your cold wallet network to Polygon and mint your avatar using the same address, all without needing any MATIC tokens. All minting fees are on us! Benefits of Minting a SENSO DAPP Avatar Minting a SENSO DAPP Avatar takes the SENSO DAPP experience to the next level. Each NFT avatar features next-generation traits and capabilities, which can be both randomly generated and AI-based. Here are some key benefits: Utility: These NFTs are designed for enhanced functionality and versatility. Use them to play the SENSO DAPP card game or as a unique skin in the VR metaverse. Rarity: Avatars are grouped into Casual, Exclusive, and Premium categories based on the rarity of their attributes. Transform your avatar into a virtual artwork that stands out in the SENSO community. Cross-Environment: Your avatar can help you explore the growing worlds of Sensorium, including Sensorium Arc and our VR environments. Exclusive Perks: Owning a SENSO DAPP avatar unlocks discounts and benefits across our entire ecosystem. Timeline and Conditions Remember, minting an avatar in the first stage requires holding at least 1,500 SENSO tokens in a cold wallet (on-chain). The number of avatars being initially released and pricing will be announced closer to the minting date. Don't miss out on this incredible event. Join us for the SENSO DAPP Avatar Collection drop. Make sure to check out all the details about our drop on the . SENSO DAPP website

Sensorium's First-Ever NFT Drop: Avatars Collection

La primera entrega de NFT de Sensorium: colección de avatares

Le tout premier drop NFT de Sensorium : collection d'avatars

सेंसोरियम का पहला एनएफटी ड्रॉप: अवतार संग्रह

Sensorium初のNFTドロップ：アバターコレクション

O primeiro lançamento de NFT da Sensorium: coleção de avatares

NFT Drop đầu tiên của Sensorium: Bộ sưu tập Avatars

Sensorium 的首个 NFT Drop：Avatars Collection

Studies in the Psychology of Sex, Volume 4 by Havelock Ellis is part of the HackerNoon Books Series. You can jump to any chapter in this book here . VISION III VISION III Beauty not the Sole Element in the Sexual Appeal of Vision—Movement—The Mirror—Narcissism—Pygmalionism—Mixoscopy—The Indifference of Women to Male Beauty—The Significance of Woman's Admiration of Strength—The Spectacle of Strength is a Tactile Quality made Visible. Our discussion of the sensory element of vision in human sexual selection has been mainly an attempt to disentangle the chief elements of beauty in so far as beauty is a stimulus to the sexual instinct. Beauty by no means comprehends the whole of the influences which make for sexual allurement through vision, but it is the point at which all the most powerful and subtle of these are focussed; it represents a fairly definite complexus, appealing at once to the sexual and to the æsthetic impulses, to which no other sense can furnish anything in any degree analogous. It is because this conception of beauty has arisen upon it that vision properly occupies the supreme position in man from the point of view which we here occupy. Beauty is thus the chief, but it is not the sole, element in the sexual appeal of vision. In all parts of the world this has always been well understood, and in courtship, in the effort to arouse tumescence, the appeals to vision have been multiplied and at the same time aided by appeals to the other senses. Movement, especially in the form of dancing, is the most important of the secondary appeals to vision. This is so well recognized that it is scarcely necessary to insist upon it here; it may suffice to refer to a single typical example. The most decent of Polynesian dances, according to William Ellis, was the hura, which was danced by the daughters of chiefs in the presence of young men of rank with the hope of gaining a future husband. "The daughters of the chiefs, who were the dancers on these occasions, at times amounted to five or six, though occasionally only one exhibited her symmetry of figure and gracefulness of action. Their dress was singular, but elegant. The head was ornamented with a fine and beautiful braid of human hair, wound round the head in the form of a turban. A triple wreath of scarlet, white, and yellow flowers adorned the head-dress. A loose vest of spotted cloth covered the lower part of the bosom. The tihi, of fine white stiffened cloth frequently edged with a scarlet border, gathered like a large frill, passed under the arms and reached below the waist; while a handsome fine cloth, fastened round the waist with a band or sash, covered the feet. The breasts were ornamented with rainbow-colored mother-of-pearl shells, and a covering of curiously wrought network and feathers. The music of the hura was the large and small drum and occasionally the flute. The movements were generally slow, but always easy and natural, and no exertion on the part of the performers was wanting to render them graceful and attractive." We see here, in this very typical example, how the extraneous visual aids of movement, color, and brilliancy are invoked in conjunction with music to make the appeal of beauty more convincing in the process of sexual selection. [168] It may be in place here to mention, in passing, the considerable place which vision occupies in normal and abnormal methods of heightening tumescence under circumstances which exclude definite selection by beauty. The action of mirrors belongs to this group of phenomena. Mirrors are present in profusion in high-class brothels—on the walls and also above the beds. Innocent youths and girls are also often impelled to contemplate themselves in mirrors and sometimes thus, produce the first traces of sexual excitement. I have referred to the developed forms of this kind of self-contemplation in the Study of Auto-erotism, and in this connection have alluded to the fable of Narcissus, whence Näcke has since devised the term Narcissism for this group of phenomena. It is only necessary to mention the enormous production of photographs, representing normal and abnormal sexual actions, specially prepared for the purpose of exciting or of gratifying sexual appetites, and the frequency with which even normal photographs of the nude appeal to the same lust of the eyes. Pygmalionism, or falling in love with statues, is a rare form of erotomania founded on the sense of vision and closely related to the allurement of beauty. (I here use "pygmalionism" as a general term for the sexual love of statues; it is sometimes restricted to cases in which a man requires of a prostitute that she shall assume the part of a statue which gradually comes to life, and finds sexual gratification in this performance alone; Eulenburg quotes examples, Sexuale Neuropathie, p. 107.) An emotional interest in statues is by no means uncommon among young men during adolescence. Heine, in Florentine Nights, records the experiences of a boy who conceived a sentimental love for a statue, and, as this book appears to be largely autobiographical, the incident may have been founded on fact. Youths have sometimes masturbated before statues, and even before the image of the Virgin; such cases are known to priests and mentioned in manuals for confessors. Pygmalionism appears to have been not uncommon among the ancient Greeks, and this has been ascribed to their æsthetic sense; but the manifestation is due rather to the absence than to the presence of æsthetic feeling, and we may observe among ourselves that it is the ignorant and uncultured who feel the indecency of statues and thus betray their sense of the sexual appeal of such objects. We have to remember that in Greece statues played a very prominent part in life, and also that they were tinted, and thus more lifelike than with us. Lucian, Athenæus, Ælian, and others refer to cases of men who fell in love with statues. Tarnowsky (Sexual Instinct, English edition, p. 85) mentions the case of a young man who was arrested in St. Petersburg for paying moonlight visits to the statue of a nymph on the terrace of a country house, and Krafft-Ebing quotes from a French newspaper the case which occurred in Paris during the spring of 1877 of a gardener who fell in love with a Venus in one of the parks. (I. Bloch, Beiträge zur Ætiologie der Psychopathia Sexualis, Teil II, pp. 297-305, brings together various facts bearing on this group of manifestations.) Necrophily, or a sexual attraction for corpses, is sometimes regarded as related to pygmalionism. It is, however, a more profoundly morbid manifestation, and may perhaps he regarded as a kind of perverted sadism. Founded on the sense of vision also we find a phenomenon, bordering on the abnormal, which is by Moll termed mixoscopy. This means the sexual pleasure derived from the spectacle of other persons engaged in natural or perverse sexual actions. (Moll, Konträre Sexualempfindung, third edition, p. 308. Moll considers that in some cases mixoscopy is related to masochism. There is, however, no necessary connection between the two phenomena.) Brothels are prepared to accommodate visitors who merely desire to look on, and for their convenience carefully contrived peepholes are provided; such visitors are in Paris termed "voyeurs." It is said by Coffignon that persons hide at night in the bushes in the Champs Elysées in the hope of witnessing such scenes between servant girls and their lovers. In England during a country walk I have come across an elderly man carefully ensconced behind a bush and intently watching through his field-glass a couple of lovers reclining on a bank, though the actions of the latter were not apparently marked by any excess of indecorum. Such impulses are only slightly abnormal, whatever may be said of them from the point of view of good taste. They are not very far removed from the legitimate curiosity of the young woman who, believing herself unobserved, turns her glass on to a group of young men bathing naked. They only become truly perverse when the gratification thus derived is sought in preference to natural sexual gratification. They are also not normal when they involve, for instance, a man desiring to witness his wife in the act of coitus with another man. I have been told of the case of a scientific man who encouraged his wife to promote the advances of a young friend of his own, in his own drawing-room, he himself remaining present and apparently taking no notice; the younger man was astonished, but accepted the situation. In such a case, when the motives that led up to the episode are obscure, we must not too hastily assume that masochism or even mixoscopy is involved. For information on some of the points mentioned above see, e.g., I. Bloch, Beiträge zur Ætiologie der Psychopathia Sexualis, Teil I, pp. 200 et seq.; Teil II, pp. 195 et seq. Wide, however, as is the appeal of beauty in sexual selection, it cannot be said to cover by any means the whole of the visual field in its sexual relationship. Beauty in the human species is, above all, a feminine attribute, making its appeal to men. Even for women, as has already been noted, beauty is still a feminine quality, which they usually admire, and in cases of inversion worship with an ardor which equals, if it does not surpass, that experienced by normal men. But the normal woman experiences no corresponding cult for the beauty of man. The perfection of the body of man is not behind that of woman in beauty, but the study of it only appeals to the artist or the æsthetician; it arouses sexual enthusiasm almost exclusively in the male sexual invert. Whatever may be the case among animals or even among savages, in civilization the man is most successful with women is not the most handsome man, and may be the reverse of handsome. The maiden, according to the old saying, who has to choose between Adonis and Hercules, will turn to Hercules. [169] A correspondent writes: "Men are generally attracted in the first instance by a woman's beauty, either of face or figure. Frequently this is the highest form of love they are capable of. Personally, my own love is always prompted by this. In the case of my wife there was certainly a leaven of friendship and moral sympathies but these alone would never have been translated into love had she not been young and good-looking. Moreover, I have felt intense passion for other women, in my relations with whom the elements of moral or mental sympathy have not entered. And always, as youth and beauty went, I believe I should transfer my love to some one else. "Now, in woman I fancy this element of beauty and youth does not enter so much. I have questioned a large number of women—some married, some unmarried, young and old ladies, shopgirls, servants, prostitutes, women whom I have known only as friends, others with whom I have had sexual relations—and I cannot recollect one instance when a woman said she had fallen in love with a man for his looks. The nearest approach to any sign of this was in the instance of one, who noticed a handsome man sitting near us in a hotel, and said to me: 'I should like him to kiss me.' "I have also noticed that women do not like looking at my body, when naked, as I like looking at theirs. My wife has, on a few occasions, put her hand over my body, and expressed pleasure at the feeling of my skin. (I have very fair, soft skin.) But I have never seen women exhibit the excitement that is caused in me by the sight of their bodies, which I love to look at, to stroke, to kiss all over." It is interesting to point out, in this connection, that the admiration of strength is not confined to the human female. It is by the spectacle of his force that the male among many of the lower animals sexually affects the female. Darwin duly allows for this fact, while some evolutionists, and notably Wallace, consider that it covers the whole field of sexual selection. When choice exists, Wallace states, "all the facts appear to be consistent with the choice depending on a variety of male characteristics, with some of which color is often correlated. Thus, it is the opinion of some of the best observers that vigor and liveliness are most attractive, and these are, no doubt, usually associated with some intensity of color, ... There is reason to believe that it is his [the male bird's] persistency and energy rather than his beauty which wins the day." (A. R. Wallace, Tropical Nature, 1898, p. 199.) In his later book, Darwinism (p. 295), Wallace reaffirms his position that sexual selection means that in the rivalry of males for the female the most vigorous secures the advantage; "ornament," he adds, "is the natural product and direct outcome of superabundant health and vigor." As regards woman's love of strength, see Westermarck, History of Marriage, p. 255. Women admire a man's strength rather than his beauty. This statement is commonly made, and with truth, but, so far as I am aware, its meaning is never analyzed. When we look into it, I think, we shall find that it leads us into a special division of the visual sphere of sexual allurement. The spectacle of force, while it remains strictly within the field of vision, really brings to us, although unconsciously, impressions that are correlated with another sense—that of touch. We instinctively and unconsciously translate visible energy into energy of pressure. In admiring strength we are really admiring a tactile quality which has been made visible. It may therefore be said that, while through vision men are sexually affected mainly by the more purely visual quality of beauty, women are more strongly affected by visual impressions which express qualities belonging to the more fundamentally sexual sense of touch. The distinction between the man's view and the woman's view, here pointed out, is not, it must be added, absolute. Even for a man, beauty, with all these components which we have already analyzed in it, is not the sole sexual allurement of vision. A woman is not necessarily sexually attractive in the ratio of her beauty, and with even a high degree of beauty may have a low degree of attraction. The addition of vivacity or the addition of languor may each furnish a sexual allurement, and each of these is a translated tactile quality which possesses an obscure potency from vague sexual implications. But while in the man the demand for these translated pressure qualities in the visible attractiveness of a woman are not usually quite clearly realized, in a woman the corresponding craving for the visual expression of pressure energy is much more pronounced and predominant. It is not difficult to see why this should be so, even without falling back on the usual explanation that natural selection implies that the female shall choose the male who will be the most likely father of strong children and the best protector of his family. The more energetic part in physical love belongs to the man, the more passive part to the woman; so that, while energy in a woman is no index to effectiveness in love, energy in a man furnishes a seeming index to the existence of the primary quality of sexual energy which a woman demands of a man in the sexual embrace. It may be a fallacious index, for muscular strength is not necessarily correlated with sexual vigor, and in its extreme degrees appears to be more correlated with its absence. But it furnishes, in Stendhal's phrase, a probability of passion, and in any case it still remains a symbol which cannot be without its effect. We must not, of course, suppose that these considerations are always or often present to the consciousness of the maiden who "blushingly turns from Adonis to Hercules," but the emotional attitude is rooted in more or less unerring instincts. In this way it happens that even in the field of visual attraction sexual selection influences women on the underlying basis of the more primitive sense of touch, the fundamentally sexual sense. [170] Women are very sensitive to the quality of a man's touch, and appear to seek and enjoy contact and pressure to a greater extent than do men, although in early adolescence this impulse seems to be marked in both sexes. "There is something strangely winning to most women," remarks George Eliot, in The Mill on the Floss, "in that offer of the firm arm; the help is not wanted physically at that moment, but the sense of help—the presence of strength that is outside them and yet theirs—meets a continual want of the imagination." Women are often very critical concerning a man's touch and his method of shaking hands. Stanley Hall (Adolescence, vol. ii, p. 8) quotes a gifted lady as remarking: "I used to say that, however much I liked a man, I could never marry him if I did not like the touch of his hand, and I feel so yet." Among the elements of sexual attractiveness which make a special appeal to women, extreme personal cleanliness would appear to take higher rank than it takes in the eyes of a man, some men, indeed, seeming to make surprisingly small demands of a woman in this respect. If this is so we may connect it with the fact that beauty in a woman's eye is to a much greater extent than in a man's a picture of energy, in other words, a translation of pressure contracts, with which the question of physical purity is necessarily more intimately associated than it is with the picture of purely visual beauty. It is noteworthy that Ovid (Ars Amandi, lib. I) urges men who desire to please women to leave the arts of adornment and effeminacy to those whose loves are homosexual, and to practice a scrupulous attention to extreme neatness and cleanliness of body and garments in every detail, a sun-browned skin, and the absence of all odor. Some two thousand years later Brummell in an age when extravagance and effeminacy often marked the fashions of men, introduced a new ideal of unobtrusive simplicity, extreme cleanliness (with avoidance of perfumes), and exquisite good taste; he abhorred all eccentricity, and may be said to have constituted a tradition which Englishmen have ever since sought, more or less successfully to follow; he was idolized by women. It may be added that the attentiveness of women to tactile contacts is indicated by the frequency with which in them it takes on morbid forms, as the délire du contact, the horror of contamination, the exaggerated fear of touching dirt. (See, e.g., Raymond and Janet, Les Obsessions et la Psychasthénie.) About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Havelock Ellis (2004). Studies in the Psychology of Sex, Volume 4. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/13613/pg13613-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org, located at https://www.gutenberg.org/policy/license.html.

The sensory element of vision in human sexual selection

Survey on Acoustic Sensors in Self-Driving Cars

Software no-code/low-code platforms made it possible to create applications, writing only a few lines of code, and in some cases, ¡no code at all!. This reduces development effort and deployment time. This project combines two ideas: no-code/low-code software platform and a quick, robust prototyping system that requires no soldering. In this spirit, it is possible to move from an idea, to a materialised device working in a real environment in a few hours. Key component: ESP 32 D1 MINI Quick, robust hardware-software prototyping The following application will be built as an example: a WiFi thermometer based on ESP32, DS18B20 temperature sensor, and local I2C indicator. All this enclosed in a waterproof, wall-mountable box and powered by 5V. This will be accomplished using the following projects: HARDWARE: Quick, Open Source, robust prototyping system: MISISTEMITA SOFTWARE: Tested and also . TASMOTA ESPHome Bill of materials Component Datasheet - Buy link. - ESP32 D1 MINI buy it OLED 0.96 I2C Display DS18B20 waterproof temperature sensor 1/4W 1% TH Resistor Generic waterproof “Sonoff” enclosure 100x68x50mm Components needed to build the required misistemita modules. Component Datasheet - Buy link. Reverse locking nylon spacer M2.6 B-type self-tapping screw 3.5mm kf350 screw terminal (2,3 pins) for PCB 2.54mm female header connector Printed circuit boards needed to build the required misistemita modules PRINTED CIRCUIT BOARD SOURCE FILES REPOSITORY  -  BUY LINK A06 - Backplate for generic waterproof 100 x 68 x 52 mm enclosure B02 - 3.5mm screw terminal 2x7 connection board C12 - 3.5mm screw terminal breakout for ESP32 D1 MINI C10 - 3.5mm screw terminal breakout for I2C display SOFTWARE REPOSITORY  -  DOWNLOAD LINK ESPHome - download TASMOTA - download Hardware assembly Soldering will not be required* if the modules to be used have been built or acquired beforehand. The first step is to locate the boards in the backplate, as good practice, the should be placed somewhere on the edge of the backplate and as close as possible to the cable entry point. screw terminal wire connection boards The second step is to wire the different modules depending on the originally proposed project. Both solid copper cable and multi-stranded copper cable can be used. Downloading a minimum test firmware is recommended to test the connectivity of the components. The third step is to remove the external connections, locating the backplate in the enclosure and securing it with self-tapping screws. Pass the power cables through the cable glands and reconnect them to the board. The final step consists of closing the cover, adjusting the cable glands, and installing on the wall. Firmware setup The following points are not intended to be a comprehensive installation or configuration guide. For more information, refer to the documentation of each platform used, ( and ). Briefly, some hints about how the sensor was created on each of them will be presented. Tasmota ESPHome Tasmota The philosophy of Tasmota consists of a basic pre-compiled firmware that is downloaded to the device and once downloaded it is customized using templates. The Tasmota installer is based on a web browser, so no additional software is required. The following parameters were used: Base Firmware: Display Template setup: I2C port pins, DS18B20 sensor pin Display mode: 0 Display type: SSD1306 Temperature visualization rule: rule 1 ON DS18B20#Temperature DO Displaytext[zs2y20] %value% C ENDON ESPHome The philosophy of ESPHome consists of compiling a custom firmware using a YAML configuration file. This means that Home Assistant needs to be installed, and once it’s up and running, ESPHome should be installed as an add-on. After these two steps, it’s already possible to create sensors. A part of the configuration file is shown here in the most significant sections. # GPIO setup
dallas:
  - pin: 26
i2c:
  sda: 21
  scl: 22

# Sensor setup
sensor:
  - platform: dallas
    address: 0x8c01131b44162184
    id: outside_temperature
    name: "External temperature"
font:  
  # gfonts://family[@weight]
  - file: "gfonts://Roboto"
    id: roboto
    size: 20
display:
  - platform: ssd1306_i2c
    model: "SSD1306 128x64"
    address: 0x3c
    lambda: |-
      it.printf(90, 35, id(roboto), TextAlign::BASELINE_RIGHT , "%.1f °C", id(outside_temperature).state); End result hardware: The assembly of the hardware, starting from pre-built modules, took approximately one hour. firmware: Sensor firmware setup, using Tasmota, took approximately 10 minutes. Making a change to configuration like an I/O pin or visualization rule takes approximately 1 minute. The same task, using ESPHome, took approximately 2 hours the first time, because Home Assistant needs to be installed (On a Raspberry Pi or other computer). Once ESPHome is installed Making a change in configuration takes around 5 to 10 minutes depending of the speed of the Rpi for code compilation. On both platforms, the first setup is wired (ESP32 connected to a PC). After that all updates are done wirelessly (OTA). The only “code” that was typed on both platforms was the minimum necessary to visualize temperature on the I2C display. Each platform has its own way to do that. In both cases, has been just a single line. Also published . here

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