Spectrum AI

spectrumai

AI technology is firmly carving out a niche in defense technology. News reports refer to partnership agreements between large defense technology corporations such as Northrop Grumman, Lockheed Martin, Raytheon, and startups in Artificial Intelligence/Machine Learning (AI/ML), Image Intelligence (IMINT) Recognition, Battlefield Awareness, Multi‐Domain Battle Management Team (MBMT) Mobility Platform, Tactical Augmented Reality (TAR), Radio Frequency and Wireless Surveillance (RFWS) Systems. At the same time, defense startups like Palantir and Anduril, which have relied on the use of AI since their inception, have now emerged as major game-changing market players. Undoubtedly, this trend will continue in the future and we will soon see significant changes in the direction of enhanced national security and deployed defense technologies. As proof, Project Maven, announced in 2017 according to various sources, is being tested in combat use in military conflicts in Ukraine and Israel. A wearable mobile platform with a high-performance computing module within the Soldier-as-Sensor concept is expected to complement the Battlefield Management System (BMS) and Joint All-Domain Command and Control (JADC2) ecosystems. At least a few defense agencies periodically publish innovation requests for these and related research and technology areas in the prototype stage. Today's highly mobile battlefield threats and higher demands on the cognitive capabilities of operators are driving the need to act simultaneously on multiple fronts. Everyone is required to have accurate Tactical Situational Awareness, Friend-or-Foe Identification, Synthetic Day/Night Vision, BLOS Communications, Sensors, Signal Intelligence, RF Wideband Digital Beamforming, and be an element for sharing this information in peer-to-peer networks. Until recently, some technological limitations have prevented the creation of a convenient mobile platform with dimensions that would allow a person to wear an electronic device for extended periods. For several years, Microsoft tried to adapt its HoloLense 2 device for the IVAS defense project. However, immersive technologies (AR/MR) designed for entertainment, education and training are not well-suited for the military. At Furtherium, my team and I initially created the concept of AI-powered Tactical Situational Awareness Mobility Platform primarily for the military, security forces, rescuers, and firefighters. The key advantages of this technology are its compact design, fast performance, integration of all important functions, and affordable price. And a special feature is the application of several AI technologies on a local device. I founded Furtherium in 2021 and started developing an integrated product ARHUDFM (Augmented Reality Head-Up Display Fullface Mask) - Hardware modules, SaaS and Healthcare modules. Several detection and recognition functions cover visual information in the visible and infrared spectrum, sound and radio frequency waves over a very wide range, passive radar signatures and an EOD scanner for explosive detection. The core of the technology is not only pre-trained Machine Learning models, but also neural network generative AI models that are trained together with each user. For this purpose, advanced CPU and NPU technologies are used in each device. You could say this is the next step in using generative AI in mobile platforms, focused not only on defense applications, with huge dual-use potential. Fine-tuning a local model on a user's device still looks unfamiliar right now, but that's the future. In addition to the key goals of detection and recognition, other basic functions are Omnichannel Messenger, Media Capture and Exchange, Ballistic Fire Assistant and Spotter, Multi-Domain Operations, RC Universal Controller for drones and robots. You don't have to look into the future to understand the trends, just open YouTube and see for yourself the training programs at the U.S. Marine Corps and Army. I'd like to share the new YouTube video we created that demonstrates the capabilities of my startup's mobile platform, controlled primarily by a voice assistant that adapts to each user. https://www.youtube.com/watch?v=kIUt2jbYHMs&embedable=true It seems natural and logical now, but we didn't come to this immediately, it took several years of research into behavioral capabilities, cognitive constraints, tactical documentation and the capabilities of different electronic component architectures. The technical requirements of the military are very different from civilian applications. This applies not only to Hardware, but also to Software, including how generative AI models should be used, what criteria they are required to meet. Many features must be considered when designing for military applications: Night Vision and Augmented Reality without outward illumination, protection against ionizing radiation, main and backup power system, battery life, active cooling, shock wave and fragments exposure, encryption and hopping frequencies for data exchange, reduced traffic while maintaining data quality. It would be wrong to think about the protection of the device and not provide for the protection of the user. For this purpose, several options are created simultaneously for hearing protection and protection against concussion, eye and face protection, respiratory protection, built-in drinking system, automatic tourniquets to stop acute bleeding. Sensors in the user's arms, legs, back, and chest allow health monitoring during exercises, training, and tactical missions. SAM (Serviceable Addressable Market) for this technology estimates exceed $31B and include markets in over 32 NATO countries and its allies. Amid the Research, Development, Test & Evaluation budget of the U.S. DoD alone of $148B this looks promising. Military innovation budgets are on the rise, that's another trend today. My startup's project involves integrating products from companies such as Palantir, Anduril, Teledyne FLIR, Shield AI, Epirus, Elbit Systems, Raytheon, Northrop Grumman, and Thales Group. Combining non-competing systems creates unique synergies. I am also pleased to announce that we are close to raising funds. Investors are expected to include companies with expertise in the Defense and Security area. More Details Furtherium, Inc. | Website | Wiki | Community | Pitch Deck AI technology is firmly carving out a niche in defense technology. News reports refer to partnership agreements between large defense technology corporations such as Northrop Grumman, Lockheed Martin, Raytheon, and startups in Artificial Intelligence/Machine Learning (AI/ML), Image Intelligence (IMINT) Recognition, Battlefield Awareness, Multi‐Domain Battle Management Team (MBMT) Mobility Platform, Tactical Augmented Reality (TAR), Radio Frequency and Wireless Surveillance (RFWS) Systems. At the same time, defense startups like Palantir and Anduril, which have relied on the use of AI since their inception, have now emerged as major game-changing market players. Undoubtedly, this trend will continue in the future and we will soon see significant changes in the direction of enhanced national security and deployed defense technologies. As proof, Project Maven, announced in 2017 according to various sources, is being tested in combat use in military conflicts in Ukraine and Israel. A wearable mobile platform with a high-performance computing module within the Soldier-as-Sensor concept is expected to complement the Battlefield Management System (BMS) and Joint All-Domain Command and Control (JADC2) ecosystems. At least a few defense agencies periodically publish innovation requests for these and related research and technology areas in the prototype stage. Today's highly mobile battlefield threats and higher demands on the cognitive capabilities of operators are driving the need to act simultaneously on multiple fronts. Everyone is required to have accurate Tactical Situational Awareness, Friend-or-Foe Identification, Synthetic Day/Night Vision, BLOS Communications, Sensors, Signal Intelligence, RF Wideband Digital Beamforming, and be an element for sharing this information in peer-to-peer networks. Until recently, some technological limitations have prevented the creation of a convenient mobile platform with dimensions that would allow a person to wear an electronic device for extended periods. For several years, Microsoft tried to adapt its HoloLense 2 device for the IVAS defense project. However, immersive technologies (AR/MR) designed for entertainment, education and training are not well-suited for the military. At Furtherium, my team and I initially created the concept of AI-powered Tactical Situational Awareness Mobility Platform primarily for the military, security forces, rescuers, and firefighters. The key advantages of this technology are its compact design, fast performance, integration of all important functions, and affordable price. And a special feature is the application of several AI technologies on a local device. I founded Furtherium in 2021 and started developing an integrated product ARHUDFM (Augmented Reality Head-Up Display Fullface Mask) - Hardware modules, SaaS and Healthcare modules. Several detection and recognition functions cover visual information in the visible and infrared spectrum, sound and radio frequency waves over a very wide range, passive radar signatures and an EOD scanner for explosive detection. The core of the technology is not only pre-trained Machine Learning models, but also neural network generative AI models that are trained together with each user. For this purpose, advanced CPU and NPU technologies are used in each device. You could say this is the next step in using generative AI in mobile platforms, focused not only on defense applications, with huge dual-use potential. Fine-tuning a local model on a user's device still looks unfamiliar right now, but that's the future. In addition to the key goals of detection and recognition, other basic functions are Omnichannel Messenger, Media Capture and Exchange, Ballistic Fire Assistant and Spotter, Multi-Domain Operations, RC Universal Controller for drones and robots. You don't have to look into the future to understand the trends, just open YouTube and see for yourself the training programs at the U.S. Marine Corps and Army. I'd like to share the new YouTube video we created that demonstrates the capabilities of my startup's mobile platform, controlled primarily by a voice assistant that adapts to each user. the new YouTube video https://www.youtube.com/watch?v=kIUt2jbYHMs&embedable=true https://www.youtube.com/watch?v=kIUt2jbYHMs&embedable=true It seems natural and logical now, but we didn't come to this immediately, it took several years of research into behavioral capabilities, cognitive constraints, tactical documentation and the capabilities of different electronic component architectures. The technical requirements of the military are very different from civilian applications. This applies not only to Hardware, but also to Software, including how generative AI models should be used, what criteria they are required to meet. Many features must be considered when designing for military applications: Night Vision and Augmented Reality without outward illumination, protection against ionizing radiation, main and backup power system, battery life, active cooling, shock wave and fragments exposure, encryption and hopping frequencies for data exchange, reduced traffic while maintaining data quality. It would be wrong to think about the protection of the device and not provide for the protection of the user. For this purpose, several options are created simultaneously for hearing protection and protection against concussion, eye and face protection, respiratory protection, built-in drinking system, automatic tourniquets to stop acute bleeding. Sensors in the user's arms, legs, back, and chest allow health monitoring during exercises, training, and tactical missions. SAM (Serviceable Addressable Market) for this technology estimates exceed $31B and include markets in over 32 NATO countries and its allies. Amid the Research, Development, Test & Evaluation budget of the U.S. DoD alone of $148B this looks promising. Military innovation budgets are on the rise, that's another trend today. My startup's project involves integrating products from companies such as Palantir, Anduril, Teledyne FLIR, Shield AI, Epirus, Elbit Systems, Raytheon, Northrop Grumman, and Thales Group. Combining non-competing systems creates unique synergies. I am also pleased to announce that we are close to raising funds. Investors are expected to include companies with expertise in the Defense and Security area. More Details Furtherium, Inc. | Website | Wiki | Community | Pitch Deck Website Wiki Community Pitch Deck

AI's Influence is Going to Go Deeper Than Just the Corporate Sector

AI এর প্রভাব শুধু কর্পোরেট সেক্টরের চেয়ে গভীরে যেতে চলেছে৷

Der Einfluss der KI wird über den Unternehmenssektor hinausgehen

La influencia de la IA va a llegar más allá del sector corporativo

L’influence de l’IA va s’étendre au-delà du secteur privé

एआई का प्रभाव सिर्फ कॉर्पोरेट क्षेत्र तक ही सीमित नहीं रहेगा

AIの影響は企業部門だけにとどまらない

AI의 영향력은 기업 부문에만 국한되지 않고 더욱 깊어질 것입니다.

A influência da IA vai além do setor corporativo

Влияние ИИ выйдет за рамки корпоративного сектора

Yapay Zekanın Etkisi Sadece Kurumsal Sektörle Sınırlı Kalmayacak

Ảnh hưởng của AI sẽ không chỉ giới hạn trong lĩnh vực doanh nghiệp

人工智能的影响将不仅仅局限于企业领域

The current decade will always remain etched in the annals of tech history for the unprecedented advent of artificial intelligence. It wouldn’t be farfetched to say that AI is making an indelible mark on almost every aspect of human life. Tech pundits and gurus are already predicting its far-reaching transformative effects and the occupations it will render obsolete. A potent question is whether the cybersecurity landscape is slated to incur ramifications from this onslaught. Is AI going to edge out humans, or is it likely to augment human roles? What do the industry trends tell us about it? The Ever-evolving World of Cyber Threats According to Forbes, the share impact of AI is likely to affect 15% of the global workforce. However, the Cybersecurity realm will remain immune to it because of a few key points. First is the ever-evolving nature of cyber threats. What was once considered a bane for cyber security specialists has become a blessing in disguise. AI models are adept at identifying threats and risks, but they depend on pattern feed, allowing them to pinpoint deviations. AI lacks the intuition and critical thinking to cope with anomalies falling outside the jurisdiction of the feed. In this particular scenario, AI would be incapable of formulating timely countermeasures, resulting in the compromise of organizational infrastructure. A human brain, on the other hand, is capable of rational thinking and contextual understanding, making it more adaptable, superior, and capable of interpreting previously unencountered threats and security lapses. It can also devise on-the-spot inventive measures to safeguard digital assets. After such an instance, the AI feed pattern will likely be updated with the latest information to improve and enhance its threat detection by a human engineer! Ethical and Legal Considerations Traversing ethical and legal considerations is still not the forte of artificial intelligence. When it comes to cybersecurity, these ethics and considerations become very convoluted with human behavior added to the mix. For instance, how can privacy violations of an individual’s data be curbed in a way that doesn’t infringe any rights? Regulation of surveillance and monitoring in a non-pervasive manner and, lastly, drawing a boundary between user responsibility and organizational security protocols. In broad-spectrum, AI can be taught to discern these factors but it can't substitute for human judgment and decisiveness to reach decisions that are majorly aligned with implemented standards and practices. Incident Response and Management Incident response and management capability of AI and ML algorithms have been a much-debated topic. Some proponents deem it indispensable, and some opponents are quick to point out various proverbial chinks in the armor. Though the autonomous response of cybersecurity-based AI systems operates on containment, remediation, and recovery, but again they rely on historical security and threat analysis data points. The delegation of tasks according to the criticality of threat level and undertaking adaptive strategies are better deployed when relying on the collective cognitive prowess of a human-led response team. Who is Protecting the Protector? Lastly, the AI system, which an organization might trust to shield itself, is prone to attacks. In the absence of human oversight and supervision, miscreants find inventive ways to manipulate the AI and achieve their nefarious objectives. This includes intentional and subtle data manipulation to hoodwink the AI. The technical moniker of this is “adversarial attacks”. Adversarial attacks dupe the AI into misclassifying or failing to detect a threat. Hackers might also keenly and zealously observe the AI model and record its behavior when given certain inputs. This allows them to deduce the model’s behavior, response time, and preventive measures and then develop ways to bypass it. Conclusion In conclusion, it is safe to assume that in the near future, AI will play a pivotal role in cybersecurity, but it still hasn’t attained the level of maturity, refinement, and dependability to circumvent the human element. Cybersecurity specialists will remain crucial for nurturing, training, and refining these systems for peak performance. The current decade will always remain etched in the annals of tech history for the unprecedented advent of artificial intelligence. It wouldn’t be farfetched to say that AI is making an indelible mark on almost every aspect of human life. Tech pundits and gurus are already predicting its far-reaching transformative effects and the occupations it will render obsolete. A potent question is whether the cybersecurity landscape is slated to incur ramifications from this onslaught. Is AI going to edge out humans, or is it likely to augment human roles? What do the industry trend s tell us about it? industry trend The Ever-evolving World of Cyber Threats According to Forbes, the share impact of AI is likely to affect 15% of the global workforce. However, the Cybersecurity realm will remain immune to it because of a few key points. First is the ever-evolving nature of cyber threats. What was once considered a bane for cyber security specialists has become a blessing in disguise. AI models are adept at identifying threats and risks, but they depend on pattern feed, allowing them to pinpoint deviations. Forbes , Forbes AI lacks the intuition and critical thinking to cope with anomalies falling outside the jurisdiction of the feed. In this particular scenario, AI would be incapable of formulating timely countermeasures, resulting in the compromise of organizational infrastructure. A human brain, on the other hand, is capable of rational thinking and contextual understanding, making it more adaptable, superior, and capable of interpreting previously unencountered threats and security lapses. It can also devise on-the-spot inventive measures to safeguard digital assets. After such an instance, the AI feed pattern will likely be updated with the latest information to improve and enhance its threat detection by a human engineer! Ethical and Legal Considerations Traversing ethical and legal considerations is still not the forte of artificial intelligence. When it comes to cybersecurity, these ethics and considerations become very convoluted with human behavior added to the mix. For instance, how can privacy violations of an individual’s data be curbed in a way that doesn’t infringe any rights? Regulation of surveillance and monitoring in a non-pervasive manner and, lastly, drawing a boundary between user responsibility and organizational security protocols. In broad-spectrum, AI can be taught to discern these factors but it can't substitute for human judgment and decisiveness to reach decisions that are majorly aligned with implemented standards and practices. Incident Response and Management Incident response and management capability of AI and ML algorithms have been a much-debated topic. Some proponents deem it indispensable, and some opponents are quick to point out various proverbial chinks in the armor. Though the autonomous response of cybersecurity-based AI systems operates on containment, remediation, and recovery, but again they rely on historical security and threat analysis data points. The delegation of tasks according to the criticality of threat level and undertaking adaptive strategies are better deployed when relying on the collective cognitive prowess of a human-led response team. cybersecurity-based AI systems cybersecurity-based AI systems Who is Protecting the Protector? Lastly, the AI system , which an organization might trust to shield itself, is prone to attacks. In the absence of human oversight and supervision, miscreants find inventive ways to manipulate the AI and achieve their nefarious objectives. This includes intentional and subtle data manipulation to hoodwink the AI. The technical moniker of this is “adversarial attacks”. the AI system the AI system Adversarial attacks dupe the AI into misclassifying or failing to detect a threat. Hackers might also keenly and zealously observe the AI model and record its behavior when given certain inputs. This allows them to deduce the model’s behavior, response time, and preventive measures and then develop ways to bypass it. Adversarial attacks Adversarial attacks Conclusion In conclusion, it is safe to assume that in the near future, AI will play a pivotal role in cybersecurity, but it still hasn’t attained the level of maturity, refinement, and dependability to circumvent the human element. Cybersecurity specialists will remain crucial for nurturing, training, and refining these systems for peak performance.

The Persistent Human Element in the Future of Cybersecurity

Web3 is about to disrupt a bunch of industries that mostly have no clue what’s about to happen… but it’s no guarantee that it will be as decentralized as advertised. So far blockchain technology has had a mixed record of decentralization. All old-school hodlers can look at the ability of besieged Ukrainians to raise funds from around the world and rightfully pat themselves on the back. And the Russian freelance coders who've been dodging taxes by getting paid in crypto for the past five years now seem suddenly prescient as the Ruble collapses in value overnight. But the quiet and rapid adoption of blockchain to decrease currency exchange & money transfer costs and streamline supply chains has largely been implemented by banks and mainstream companies, who have not passed the savings on to customers. As any company with a fiduciary responsibility to shareholders would do, they’ve used the technology to reduce their expenses while billing customers the same amounts as they did before. You can expect more of this as__ __ and banks experiment further with crypto. If you have any doubt about where this is going, just look at the management of Goldman Sachs-backed Circle and it’s clear that centralized traditional finance is already fully on board with crypto. clearinghouses team and board members But what about DeFi? That’s decentralized… Certainly, Web3 optimists can look at the boom in DeFi over the past few years and point to the obvious increase in the  adoption of peer-to-peer lending and borrowing outside of traditional banks as a key achievement. This is from an awareness and adoption standpoint – and even from a technological and UX advancement standpoint; but are these staked crypto loans being used by people in your neighborhood to open a new pizza joint? Or pay for a kid’s college education? No. They are mainly used by crypto daytraders to increase their leverage on speculative trades with other crypto daytraders. The infrastructure has been built for wider, decentralized adoption, but the actual users of the system are the same crypto insiders, technophiles, decentralization idealists and speculators that were there before. If your mom still doesn’t get it, it’s not mainstream and any crypto idealist needs to be realistic about this. We’ve been here before That is not to say that Web3 can’t or won’t be decentralized. It’s merely a modest warning that it will require the purposeful adoption of some technologies over others to guide the eventual adoption of blockchain technology by mainstream audiences. For those old enough to remember, Web1 and Web2 were both envisioned to be utopian knowledge-sharing revolutions that would free people from the constraints of large corporations, media and governments as well. Craigslist and Wikipedia are the surviving species from these early idealist eras… and sites like Github are probably their Web2 descendants. But we all know what happened. In a space with no regulation, the deep-seated caveman instincts of humans were freed of constraining Enlightenment ideas such as – and even ancient religious constraints such as Monopolies thrived, throttling smaller companies. Bullies shouted down disagreeing viewpoints, stalking and threatening dissenters. “human equality” “treat others how you want to be treated.” Racists and pedophiles created forums to amplify their obsessions. The desperate and the dishonest were empowered to extort any person or company they could outwit with almost no repercussion and a low likelihood of being caught or stopped.  And a sociopath who founded his company by stealing it from the people who hired him to do the coding became the owner of the most powerful publishing and communications platform that has ever existed… with predictable results. What’s at stake All I’m saying is this. Facebook has changed its name to Meta – which, aside from being a lame rebranding tactic after years of bad press, is also a clear indication of Zuckerberg’s intent to own the metaverse like a real-world James Halliday. And how does one monetize the metaverse? Digital products and NFTs are already growing exponentially within gaming. If NFTs are the future medium of exchange within immersive or virtual worlds, it is very clear that Big Tech has no intention of sitting on the sidelines while decentralized versions of marketplaces, virtual worlds and content licensing platforms form organically. Even now, the biggest NFT marketplace in the world, OpenSea is a private company, not a DAO. The NFT creators who have driven its exponential growth – widely marketed as proof of the coming Web3 wave – have as much power over its governance as the musicians on Spotify have. And if there were any doubt left, according to Uphold’s Unboxed newsletter, “Meta's total fee for sales made through the Meta Quest Store… [is a] whopping 47.5% all-in, after including hardware platform fees and partner Horizon Worlds' sizable taste.” Compare that to OpenSea’s 2.5% take – or even Apple’s App Store 30% cut and you can see Zuck is not playing around here. And why should anyone expect Spotify or YouTube or Getty Images or any other content licensing & monetization platform to do nothing while a decentralized version of its marketplace forms and drains away their customers and artists? Just as video game makers are realizing that they can make their own interoperable NFT products that can be ported between games without needing ImmutableX or anyone else, we should expect that other platforms ripe for NFT disruption will likewise discover that they can get all the benefits of the NFT economy without decentralizing anything. like Sega Consciously building decentralization into Web3 Okay, enough of the dark side. Believe it or not, I’m not at all pessimistic about decentralization, but the time to impact how this ecosystem develops is now. For a short time, the technology is still too insular to have been adopted yet, which means there is time for the Web3 idealists, visionaries, technologists and yes, even the hustler-speculators to shape how it develops. There are some key nascent technologies in the space that deserve your attention… not just for their potential speculative value, but for the type of Web3 that could exist if we decide we want it to. Projects are built to be decentralized and to truly shift platform ownership, profit-sharing and governance to the users and value-creators of the platform. This is not at all anywhere close to a comprehensive list. I have a day job and kids so I can’t spend all day researching this stuff, but you should – and add anything you think is worth checking out as well. The point is that non-crypto dollars and people will flock to existing marketplaces and products with followings. So it’s important to support projects that are decentralized before the gates fly open and everyone just ends up in Facebook3 because that’s where everyone else seemed to be. Octopus Network Yes, I am submitting this article to the Octopus Network writing contest – which is why I first heard about this project (point for you Octopus marketing team!). BUT, it’s a great project – very much like Polkadot or ImmutableX in that it aims for making interoperability a normal part of the crypto sphere since I think we can all agree non-crypto people are not that interested in what token does what and just want applications that are simple to use. What makes it particularly interesting is that it is built to be something like an incubator for Web3 startups. Everything they’ve built is about reducing the startup capital required to bootstrap a new company – making it possible to rent instead of build all of the expensive proof-of-stake/work security that makes the blockchain valuable and scale it as needed. Contrast this to the current method of spending millions of dollars to build, launch and gain enough adoption of a new token for it to have any real security or liquidity and you can see that this throws the doors wide open to a LOT more scrappy entrepreneurs with crazy and/or visionary ideas. My one tip is to skip the website, which I found baffling and jargon-packed (I’m not an engineer, just a crypto marketer & UX person), and . It spells out in plain English what the vision is and why they built it. (Sorry - point taken back from the Octopus marketing team). read the white paper (built in the Octopus Network - Near protocol) DEIP The NFT space is awash in hyperbole about the creator economy… but so far most of what I’ve seen are really expensive animated gifs. Clearly, I’m not the target audience. This is why I find DEIP so interesting. It is aimed squarely at disrupting the IP, licensing, and rights management industry by creating a drag-and-drop marketplace for copyrights, trademarks as well as other non-tangible assets with real-world value (ie: actual copyrights, not an animated gif of a cat). This could not only disrupt the massive rent-seeking industry of IP attorneys, licensing agents, talent managers, and creative artist agents as well as Web2 content marketplaces but make the barrier to entry for unknown talents extremely low as well. This could drive an explosive creative marketplace of talented nobodies getting royalties and licensing deals. You could imagine whole product licensing, stock video, image and sound marketplaces and even decentralized streaming networks being built on top of something like this (someday). has a good explainer. This medium post (on Ethereum) Immutable X A competitor to Octopus that has built a Layer 2 onto Ethereum to address the high gas fees and other technical issues that are barriers to entry for new businesses. To be clear, while there are some voting rights with their token, this is not the fully decentralized web3 idealist project that Octopus, Solana or Cardano are. However, its technology and service offerings, much like Octopus’ are built around helping Web3 companies launch. The main upside here is that it’s built on Ethereum which has a much larger audience built-in than other protocols…. I guess the whole point of interoperable systems is that they can exchange tokens easily so it’s unclear if this advantage will turn out to matter that much to future mainstream users. If you flip a light switch in the US or one in France you don’t really care whether it’s running on AC or DC power; you just care that somehow it works. The opportunities for digital product makers could be huge within the gaming space with this platform as part of its technology makes adding royalties to NFTs simple and it’s already got its own well-known NFT games. – and Audius Solana Audius, the Web3 competitor to Apple Music, Spotify, and other Web2 music streaming services is growing rapidly. It’s also truly decentralized, which has made it bump up against technical scaling issues within Ethereum, to the ultra-fast and very inexpensive Solana system. so it’s announced a move This is a great case study of how nascent Web3 companies that start within accelerator-like platforms can scale to compete with Web2 user experience as they grow. Solana may turn out to be the enterprise-grade version of Web3 infrastructure needed to compete at scale with the no-lag UX that users have come to expect from their apps. Avalanche One of the most exciting – and hyped – players in the space, AVA Labs, just raised to keep the Avalanche ecosystem growing rapidly. Another Ethereum competitor, Avalanche has attracted attention in the crypto developer space because it claims to be the fastest smart contract platform, that’s both eco-friendly and adaptable to the transaction volumes and speed common in current securities trading. a lot of money Its token model is fully non-custodial and is built to avoid winner-take-all fees as Bitcoin has and gas-per-transaction costs like Ethereum have. Although it currently doesn’t seem to have the supportive incubator-like system that both Octopus and Immutable have, for sophisticated and funded teams of pros this platform could truly replace incumbent financial systems if it lives up to the hype. and Cardano SingularityNET If you want to freak out about how fast things could get out of control, (and actually pretty much anything from Singularity) about their move to Cardano is a great place to start. Cardano positions itself as the most academically-accredited Ethereum competitor with verifiable security and environmental cred. They don’t talk about NFTs or games. They talk about the future of humanity… and how important it is to marry blockchain and AI before AI, which is currently controlled by a handful of people, runs the world (or at least the entire financial system). this post by SingularityNET Critical to this will be AI’s inevitable role in content moderation in the future. It’s clear what’s happening now is not working, and also clear that the stakes of getting content moderation wrong can foment , and . In this context, I think we’d all prefer decentralized AI content moderation governed by users to the centralized ones we have now. insurrections wars ethnic cleansing This enterprise-grade focus on decentralized technology is a critical part of any realistic plan to maintain anything more than a cosmetic version of democracy for the future and deserves support. Spectrum.AI While not a crypto company, independent AI companies like Spectrum Labs can act as a critical stepping stone to decentralized content moderation (see the bullet above for why this matters if you skimmed it). No one really trusts the current Web2 social media platforms to moderate content properly when their financial model is still built around driving engagement for ad views. They can hire as many ombudsmen and make as many Ted Talk speeches as they want, but until their revenue models are no longer built on keeping people scrolling as long as possible, you can count on algorithms that continue to promote content that makes people angry, self-righteous and outraged because it keeps them on the page longer than cute kitten posts do. By using tools, community platforms can begin to separate themselves from the conflict of interest inherent in policing their own platforms until a fully decentralized system emerges. Given the importance of this, and its immediate impact on daily headlines, there’s no time to wait for a perfect solution and companies like this can buy some time. science-driven third-party AI content moderation Rarify I just learned about this company that has just closed a to build a system of APIs to make it simple for non-crypto businesses to easily integrate NFTs (and potentially other blockchain products) into their existing businesses. If it works as advertised, this throws open the doors for Web2 companies to add NFT digital products into their existing eCommerce stores, games, marketplaces, etc without having to hire blockchain engineers. round of funding from Pantera An advancement like this could lead to the very rapid adoption of NFTs since Rarify figures out all of the crypto mechanics in the background so end users can just use their normal fiat-based credit cards as they’ve always done and not have to figure out how to buy crypto or research what a wallet is and how to set it up separately. There are (probably) thousands more great companies doing interesting things that, if adopted broadly, could help the transition to Web3 live up to its potential. These are just a few I’ve read about recently, but the point is not to start day trading these specific tokens but to build on, invest in or grow applications that reflect the values you want to see in the world 5 or 10 years from now. The current crypto community is, for better or worse, the early-adopter base whose attention and investment will help define which platforms get outside capital and mainstream user adoption in the next few years. If you don’t want to live in the Zuckerverse, please consider carefully which companies you invest your money in, provide your talent and expertise to and trade your sweat equity to build. We’ll all have to live with it until Web4 comes along. Disclaimer - The only token I own mentioned in this story is ETH at the time of writing. This story is part of the Web3 Writing Contest hosted by The Octopus Network in collaboration with HackerNoon. Use this template to share your story today! You can use the lead image of your choice.

Now Is the Time to Make Sure Web3 Doesn’t Just Become Web2 with NFTS

Dreams to Reality - The AI Evolution Story

ህልሞች ወደ እውነታ - የ AI የዝግመተ ለውጥ ታሪክ

Snovi u stvarnost - priča o evoluciji umjetne inteligencije

De los sueños a la realidad: la historia de la evolución de la IA

رویاها به واقعیت - داستان تکامل هوش مصنوعی

Dreams to Reality - AI Evolution Story

सपनों से हकीकत तक - एआई विकास की कहानी

夢を現実に - AI の進化の物語

Армандардан шындыққа - AI эволюциясының тарихы

Svajonės į realybę – AI evoliucijos istorija

යථාර්ථයට සිහින - AI පරිණාමය කතාව

Amaphupha kwiNyaniso-Ibali le-AI Evolution

Authors:
(1) Benzar Glen S. Grepon, Northern Bukidnon State College, Philippines ben.it2c@gmail.com (corresponding author);
(2) JC P. Margallo, Northern Bukidnon State College, Philippines;
(3) Jonathan B. Maserin, Northern Bukidnon State College, Philippines;
(4) Rio Al-Di A. Dompol, Northern Bukidnon State College, Philippines. Table of Links Abstract and Intro Literature Review Methodology Results Discussions Conclusions Recommendations Research Implications Practical Implications Declarations References and Author's Biography CONCLUSIONS The developed web application meets the objectives to provide the users. The web application is well-recommended and acceptable by the users. The researchers provided cheaper premium web hosting every year for its renewal. The researchers will incorporate ads for additional revenue for the web application and a registration form for the users to get an acknowledgment of their contribution to the post and to improve the web application. Agencies such as DPWH, RTA, and the like should be able to contribute accurate reports using the web application to ensure reliable and well-grounded information is delivered to the community. RECOMMENDATIONS It is recommended that the future development expand the coverage of the road updates, including the entire Philippine archipelago, for long-drive commuters and drivers to be more updated regarding road updates. Also, include using mobile applications for more user-friendly design and interactions. RESEARCH IMPLICATIONS The study allows viewing and reporting road updates with attachments as proof, informing commuters about what to expect when they travel. The system has been implemented in the municipality of Bukidnon and has been updated now and then for continuous quality improvement. PRACTICAL IMPLICATIONS The main benefit that the Road Updates Information System can do is to inform people who are constantly traveling to be updated on the current situation of a specific road by accessing the system and checking the map on areas to travel and see for  themselves the photos and description of a place where road construction, accidents and other causes that delay the traveling time of passengers and travelers. Aside from being informed of the road updates, citizens can also participate by uploading photos and descriptions of what is still going on the roads to the system and be viewed by people who try to access the web portal. DECLARATIONS Conflict of Interest No author has disclosed any conflicts of interest. Informed Consent The study did not involve humans as participants and only used scene text datasets that are already available online hence this is not applicable. Ethics Approval The conducted research uses only scene text datasets, which are already available online, and did not include humans as participants; hence this is not applicable. REFERENCES Altvater, A. (2023) What is agile methodology? tools, best practices & more, Stackify. Retrieved from https://stackify.com/agile-methodology Amado, T. and Dela Cruz,J.C. (2022).Localized Intelligent Traffic Monitoring System through Ontological Modelling. 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Boracay Island, Philippines, pp. 1-5, doi: 10.1109/HNICEM57413.2022.10109419. Balahadia, F. F., Fabito, B. S., & Fernando, M. C. G. (2015). E-Participation: Incident mapping portal for local government units. In 2015 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1-6). IEEE. Dadvar, S., Lee, Y. J., Shin, H. S., & Khodaparasti, H. (2020). Integrated Highway Safety Information System (HSIS) datasets by means of a Roadway Safety Data Integrator (RSDI) tool. Data in Brief, 32, 106154. https://doi.org/10.1016/j.dib.2020.106154. ISO 25010. (n.d.). ISO 25000 PORTAL. https://iso25000.com/index.php/en/iso-25000- standards/iso-25010 Khan, M. E., Shadab, S. G. M., & Khan, F. (2020). Empirical study of software development life cycle and its various models. International Journal of Software Engineering, 8, 16-26. Khan, M., Shadab, S., & Khan, F. (2020). Empirical Study of Software Development LifeCycle and its Various Models. International Journal of Software Engineering (IJSE),8(2), 16-26. Khoo, H.L., Ong, G.P. (2013). Evaluating perceived quality of traffic information system using structural equation modeling. KSCE Journal of Civil Engineering, 17(4), 837-849. ScholarBank@NUS Repository. https://doi.org/10.1007/s12205-013-0248-6 Li, H. & Zhijian, L. (2010). The study and implementation of mobile GPS navigation system based on Google Maps. 2010 International Conference on Computer and Information Application, Tianjin, China, pp. 87-90, doi: 10.1109/ICCIA.2010.6141544. Luthfi, A. M., Karna, N., & Mayasari, R. (2019). Google maps API implementation on IOT platform for tracking an object using GPS. In 2019 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob) (pp. 126-131). IEEE. Macfarlane, J. (2023). Your navigation app is making traffic unmanageable. IEEE Spectrum. Retrieved April 17, 2023, from https://spectrum.ieee.org/your-navigation-app-ismaking-traffic-unmanageable McCall, M. (2021). Participatory Mapping and PGIS: Secerning Facts and Values, Representation and Representativity. International Journal of E-Planning Research (IJEPR), 10(3), 105-123. http://doi.org/10.4018/IJEPR.20210701.oa7 Markus Löchtefeld. (2019). DetourNavigator - Using Google Location History to Generate Unfamiliar Personal Routes. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA '19). Association for Computing Machinery, New York, NY, USA, Paper LBW1117, 1–6. https://doi.org/10.1145/3290607.3313047 Novikov, A., Borovskoy, A., Gorbun, Y., Terentyev A. and Pletney,M. (2021). Geographic Information Systems to Improve Road Safety. Systems of Signals Generating and Processing in the Field of on Board Communications, Moscow, Russia, 2021, pp. 1-6, doi: 10.1109/IEEECONF51389.2021.9416096. Slamet, C., Rahman, A., Sutedi, A., Darmalaksana, W., Ramdhani, M. A., & Maylawati, D. S. A. (2018). Social media-based identifier for natural disaster. In IOP conference series: materials science and engineering (Vol. 288, No. 1, p. 012039). IOP Publishing. Srivastava, A., Bhardwaj, S., & Saraswat, S. (2017). SCRUM model for agile methodology. In P. N. Astya, A.Swaroop, V. Sharma, M. Singh, & K. Gupta (Eds.), Proceedings of the International Conference on Computing, Communication, and Automation (ICCCA). Paper presented at the 2017 International Conference on Computing, Communication, and Automation, India (pp. 864-869). India: IEEE. Thalluri, L., Veeravalli, H., Rekapalli, D., Katragadda, S., Teja, T., & Sundar, C., (2022). Smart Vehicle Tracking System with Collision Avoidance. 7th International Conference on Communication and Electronics Systems (ICCES) (pp. 462-467). IEEE. Wörndl, W., Herzog, D. (2020). Mobile Applications for e-Tourism. In Xiang, Z., Fuchs, M., Gretzel, U., Höpken, W. (eds) Handbook of e-Tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-05324-6_17-1 Xu, R., Zhang, S., Xiong, P., Lin, A. and Ma, J. (2022). Towards Better Driver Safety: Empowering Personal Navigation Technologies with Road Safety Awareness. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, pp. 3004-3009, doi: 10.1109/ITSC55140.2022.9922545. Yang, S. Y., & Hsu, C. L. (2016). A location-based services and Google maps-based information master system for tour guiding. Computers & Electrical Engineering, 54, 87-105. Yamsaengsung, S., & Papasratorn, B. (2018). Towards Improving User Interaction with Navigation Apps: an Information Quality Perspective. KnE Social Sciences, 3(1), 119- 131.https://doi.org/10.18502/kss.v3i1.1401 Authors’ Biography Benzar Glen S. Grepon is the Director of the Information Management Office and the previous IT Program Head of Northern Bukidnon State College, specializing in research, Programming, Project Management, Management Information Systems, Network Management, and Administration JC P. Margallo is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College, specializing in Web development and programming. Jonathan B. Maserin is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College, specializing in Web development and programming. Rio Al-Di A. Dompol is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College and a computer programmer in the Information Management Office of NBSC, specializing in Web development, programming, Database Management and Mobile Design and Development. This paper is available on arxiv under CC 4.0 license. Authors: (1) Benzar Glen S. Grepon, Northern Bukidnon State College, Philippines ben.it2c@gmail.com (corresponding author); (2) JC P. Margallo, Northern Bukidnon State College, Philippines; (3) Jonathan B. Maserin, Northern Bukidnon State College, Philippines; (4) Rio Al-Di A. Dompol, Northern Bukidnon State College, Philippines. Authors: Authors: (1) Benzar Glen S. Grepon, Northern Bukidnon State College, Philippines ben.it2c@gmail.com (corresponding author); (2) JC P. Margallo, Northern Bukidnon State College, Philippines; (3) Jonathan B. Maserin, Northern Bukidnon State College, Philippines; (4) Rio Al-Di A. Dompol, Northern Bukidnon State College, Philippines. Table of Links Abstract and Intro Abstract and Intro Literature Review Literature Review Methodology Methodology Results Results Discussions Discussions Conclusions Conclusions Recommendations Recommendations Research Implications Research Implications Practical Implications Practical Implications Declarations Declarations References and Author's Biography References and Author's Biography CONCLUSIONS The developed web application meets the objectives to provide the users. The web application is well-recommended and acceptable by the users. The researchers provided cheaper premium web hosting every year for its renewal. The researchers will incorporate ads for additional revenue for the web application and a registration form for the users to get an acknowledgment of their contribution to the post and to improve the web application. Agencies such as DPWH, RTA, and the like should be able to contribute accurate reports using the web application to ensure reliable and well-grounded information is delivered to the community. RECOMMENDATIONS It is recommended that the future development expand the coverage of the road updates, including the entire Philippine archipelago, for long-drive commuters and drivers to be more updated regarding road updates. Also, include using mobile applications for more user-friendly design and interactions. RESEARCH IMPLICATIONS The study allows viewing and reporting road updates with attachments as proof, informing commuters about what to expect when they travel. The system has been implemented in the municipality of Bukidnon and has been updated now and then for continuous quality improvement. PRACTICAL IMPLICATIONS The main benefit that the Road Updates Information System can do is to inform people who are constantly traveling to be updated on the current situation of a specific road by accessing the system and checking the map on areas to travel and see for  themselves the photos and description of a place where road construction, accidents and other causes that delay the traveling time of passengers and travelers. Aside from being informed of the road updates, citizens can also participate by uploading photos and descriptions of what is still going on the roads to the system and be viewed by people who try to access the web portal. DECLARATIONS Conflict of Interest Conflict of Interest Conflict of Interest No author has disclosed any conflicts of interest. Informed Consent Informed Consent Informed Consent The study did not involve humans as participants and only used scene text datasets that are already available online hence this is not applicable. Ethics Approval Ethics Approval Ethics Approval The conducted research uses only scene text datasets, which are already available online, and did not include humans as participants; hence this is not applicable. REFERENCES Altvater, A. (2023) What is agile methodology? tools, best practices & more, Stackify. Retrieved from https://stackify.com/agile-methodology Amado, T. and Dela Cruz,J.C. (2022).Localized Intelligent Traffic Monitoring System through Ontological Modelling. 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Boracay Island, Philippines, pp. 1-5, doi: 10.1109/HNICEM57413.2022.10109419. Balahadia, F. F., Fabito, B. S., & Fernando, M. C. G. (2015). E-Participation: Incident mapping portal for local government units. In 2015 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1-6). IEEE. Dadvar, S., Lee, Y. J., Shin, H. S., & Khodaparasti, H. (2020). Integrated Highway Safety Information System (HSIS) datasets by means of a Roadway Safety Data Integrator (RSDI) tool. Data in Brief, 32, 106154. https://doi.org/10.1016/j.dib.2020.106154. ISO 25010. (n.d.). ISO 25000 PORTAL. https://iso25000.com/index.php/en/iso-25000- standards/iso-25010 Khan, M. E., Shadab, S. G. M., & Khan, F. (2020). Empirical study of software development life cycle and its various models. International Journal of Software Engineering, 8, 16-26. Khan, M., Shadab, S., & Khan, F. (2020). Empirical Study of Software Development LifeCycle and its Various Models. International Journal of Software Engineering (IJSE),8(2), 16-26. Khoo, H.L., Ong, G.P. (2013). Evaluating perceived quality of traffic information system using structural equation modeling. KSCE Journal of Civil Engineering, 17(4), 837-849. ScholarBank@NUS Repository. https://doi.org/10.1007/s12205-013-0248-6 Li, H. & Zhijian, L. (2010). The study and implementation of mobile GPS navigation system based on Google Maps. 2010 International Conference on Computer and Information Application, Tianjin, China, pp. 87-90, doi: 10.1109/ICCIA.2010.6141544. Luthfi, A. M., Karna, N., & Mayasari, R. (2019). Google maps API implementation on IOT platform for tracking an object using GPS. In 2019 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob) (pp. 126-131). IEEE. Macfarlane, J. (2023). Your navigation app is making traffic unmanageable. IEEE Spectrum. Retrieved April 17, 2023, from https://spectrum.ieee.org/your-navigation-app-ismaking-traffic-unmanageable McCall, M. (2021). Participatory Mapping and PGIS: Secerning Facts and Values, Representation and Representativity. International Journal of E-Planning Research (IJEPR), 10(3), 105-123. http://doi.org/10.4018/IJEPR.20210701.oa7 Markus Löchtefeld. (2019). DetourNavigator - Using Google Location History to Generate Unfamiliar Personal Routes. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA '19). Association for Computing Machinery, New York, NY, USA, Paper LBW1117, 1–6. https://doi.org/10.1145/3290607.3313047 Novikov, A., Borovskoy, A., Gorbun, Y., Terentyev A. and Pletney,M. (2021). Geographic Information Systems to Improve Road Safety. Systems of Signals Generating and Processing in the Field of on Board Communications, Moscow, Russia, 2021, pp. 1-6, doi: 10.1109/IEEECONF51389.2021.9416096. Slamet, C., Rahman, A., Sutedi, A., Darmalaksana, W., Ramdhani, M. A., & Maylawati, D. S. A. (2018). Social media-based identifier for natural disaster. In IOP conference series: materials science and engineering (Vol. 288, No. 1, p. 012039). IOP Publishing. Srivastava, A., Bhardwaj, S., & Saraswat, S. (2017). SCRUM model for agile methodology. In P. N. Astya, A.Swaroop, V. Sharma, M. Singh, & K. Gupta (Eds.), Proceedings of the International Conference on Computing, Communication, and Automation (ICCCA). Paper presented at the 2017 International Conference on Computing, Communication, and Automation, India (pp. 864-869). India: IEEE. Thalluri, L., Veeravalli, H., Rekapalli, D., Katragadda, S., Teja, T., & Sundar, C., (2022). Smart Vehicle Tracking System with Collision Avoidance. 7th International Conference on Communication and Electronics Systems (ICCES) (pp. 462-467). IEEE. Wörndl, W., Herzog, D. (2020). Mobile Applications for e-Tourism. In Xiang, Z., Fuchs, M., Gretzel, U., Höpken, W. (eds) Handbook of e-Tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-05324-6_17-1 Xu, R., Zhang, S., Xiong, P., Lin, A. and Ma, J. (2022). Towards Better Driver Safety: Empowering Personal Navigation Technologies with Road Safety Awareness. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, pp. 3004-3009, doi: 10.1109/ITSC55140.2022.9922545. Yang, S. Y., & Hsu, C. L. (2016). A location-based services and Google maps-based information master system for tour guiding. Computers & Electrical Engineering, 54, 87-105. Yamsaengsung, S., & Papasratorn, B. (2018). Towards Improving User Interaction with Navigation Apps: an Information Quality Perspective. KnE Social Sciences, 3(1), 119- 131.https://doi.org/10.18502/kss.v3i1.1401 Authors’ Biography Benzar Glen S. Grepon is the Director of the Information Management Office and the previous IT Program Head of Northern Bukidnon State College, specializing in research, Programming, Project Management, Management Information Systems, Network Management, and Administration Benzar Glen S. Grepon JC P. Margallo is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College, specializing in Web development and programming. JC P. Margallo Jonathan B. Maserin is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College, specializing in Web development and programming. Jonathan B. Maserin Rio Al-Di A. Dompol is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College and a computer programmer in the Information Management Office of NBSC, specializing in Web development, programming, Database Management and Mobile Design and Development. Rio Al-Di A. Dompol 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

Impactful Road Update Systems: Practical and Research Implications for Commuters and Agencies

প্রভাবশালী রাস্তা আপডেট সিস্টেম: যাত্রী এবং সংস্থাগুলির জন্য ব্যবহারিক এবং গবেষণার প্রভাব

Wirkungsvolle Straßenaktualisierungssysteme: Praktische und forschungsbezogene Auswirkungen für Pendler und Behörden

Sistemas impactantes de actualización de carreteras: implicaciones prácticas y de investigación para viajeros y agencias

Systèmes de mise à jour des routes percutants : implications pratiques et de recherche pour les navetteurs et les agences

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영향력 있는 도로 업데이트 시스템: 통근자와 기관을 위한 실제적 및 연구적 시사점

Sistemas impactantes de atualização rodoviária: implicações práticas e de pesquisa para passageiros e agências

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Could a Hard Drive Supply Chain Crisis Push AI and Digital Ads to the Breaking Point?

একটি হার্ড ড্রাইভ সাপ্লাই চেইন ক্রাইসিস কি এআই এবং ডিজিটাল বিজ্ঞাপনকে ব্রেকিং পয়েন্টে ঠেলে দিতে পারে?

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Introduction In the karaoke scene, it is often necessary to pull multiple streams and display the user who is speaking. We need to identify whether the user is speaking, the volume of the speech (sound wave), and display it on the UI. The SDK provides the following two methods to achieve this function： refers to the volume of a stream. The ZEGO Express SDK provides the ability to capture the sound level of a stream in real time and delivers the captured sound level data to the app client through related callbacks.  A typical use case of this feature is that you can visualize the sound level data on your app UI to indicate the current active speaker and their volume. The following picture shows an example of such use cases. Sound level refers to a series of values, each of which represents an audio signal's energy level at a point in a frequency range.  The ZEGO Express SDK provides the ability to capture the audio spectrum data of a stream in real time and delivers the captured audio spectrum data to the app client through related callbacks.  A typical use case of this feature is that you can use it to create and display an audio spectrum visualizer on your app UI, especially for those online karaoke apps. The following picture shows an example of audio spectrum visualizers. Audio spectrum Prerequisites Before you begin to use sound level and audio spectrum in your project, make sure you complete the following steps: Create a project in , and get the AppID of your project. ZEGOCLOUD Admin Console The ZEGO Express SDK has been integrated into the project. For details, see and . Integration Implementation Enable the Sound Level Callbacks To enable sound level callbacks,  call the method. startSoundLevelMonitor /// Enable sound level callbacks.
///
- (void)startSoundLevelMonitor;

/// Enable the sound level monitor, supports enabling advanced features.
///
/// @note After enabled the monitor, the SDK sends out notification through the callback [onCapturedSoundLevelUpdate] to deliver the sound level data of locally captured audio, and through the callback [onRemoteSoundLevelUpdate] to deliver sound level data of remote streams.
/// @note You can call the [startPreview] method before entering a room, and together with the callback [onCapturedSoundLevelUpdate] to check whether the audio device is working properly. 
/// @note The notification interval of the [onCapturedSoundLevelUpdate] and [onRemoteSoundLevelUpdate] callbacks is the value of the parameter.
///
/// @param config The configuration to enable sound level monitor. 
- (void)startSoundLevelMonitorWithConfig:(ZegoSoundLevelConfig *)config; After the above step is completed: When you start previewing the local video (by calling the method or publishing a local stream by calling the method, the SDK will trigger the callback at an interval of 100ms to deliver the sound level data of the locally captured audio. startPreview startPublishingStream onCapturedSoundLevelUpdate /// The callback to deliver the sound level data of locally captured audio.
///
/// @param soundLevel: The sound level value of the locally captured audio, which is in the range [0.0, 100.0].
- (void)onCapturedSoundLevelUpdate:(NSNumber *)soundLevel;

/// The callback to deliver the sound level data of locally captured audio.
///
/// @note To trigger this callback, you must call the [startSoundLevelMonitor] method to enable the sound level monitor.
/// @note The callback notification interval is the value set when [startSoundLevelMonitor] is called.
///
/// @param soundLevelInfo The sound level value of the locally captured audio, which is in the range [0.0, 100.0].
- (void)onCapturedSoundLevelInfoUpdate:(ZegoSoundLevelInfo *)soundLevelInfo; When you start playing remote streams by calling the method,  the SDK will trigger the callback at an interval of 100ms to deliver the sound level data of the remote streams. startPlayingStream onRemoteSoundLevelUpdate /// The callback to deliver the sound level data of remote streams.
///
/// @param soundLevels: The sound level data (key-value pairs) of the remote streams, of which the key is a stream ID, and the value is the sound level value of the corresponding stream. Each sound level value is in the range [0.0, 100.0].
- (void)onRemoteSoundLevelUpdate:(NSDictionary<NSString *, NSNumber *> *)soundLevels;

/// The callback to deliver the sound level data of remote streams when stream playing.
///
/// @note To trigger this callback, you must call the [startSoundLevelMonitor] method to enable the sound level monitor when playing streams. 
/// @note The callback notification interval is the value set when [startSoundLevelMonitor] is called.
///
/// @param soundLevelInfos The sound level data (key-value pairs) of the remote streams, of which the key is a stream ID, and the value is the sound level value of the corresponding stream. Each sound level value is in the range [0.0, 100.0].
- (void)onRemoteSoundLevelInfoUpdate:(NSDictionary<NSString *, ZegoSoundLevelInfo *> *)soundLevelInfos; Enable the Audio Spectrum Callbacks To enable audio spectrum callbacks, call the method. startAudioSpectrumMonitor /// Enable audio spectrum callbacks
///
- (void)startAudioSpectrumMonitor; After the above step is completed: When you start previewing the local video by calling the method or publishing a local stream by calling the method, the SDK will trigger the callback at an interval of 100ms to deliver the audio spectrum data of locally captured audio. startPreview startPublishingStream onCapturedAudioSpectrumUpdate /// The callback to deliver the audio spectrum data of locally captured audio.
///
/// @param audioSpectrum: An array of audio spectrum values of the locally captured audio. Each audio spectrum value is in the range [0, 2^30].
- (void)onCapturedAudioSpectrumUpdate:(NSArray<NSNumber *> *)audioSpectrum; When you start playing remote streams by calling the method,  the SDK will trigger the callback at an interval of 100ms to deliver audio spectrum data of the remote streams. startPlayingStream onRemoteAudioSpectrumUpdate /// The callback to deliver the audio spectrum data of remote streams
///
/// @param audioSpectrums: The audio spectrum data (key-value pairs) of remote streams, of which the key is a stream ID, and the value is an array of audio spectrum values of the corresponding stream. Each audio spectrum value is in the range [0, 2^30].
- (void)onRemoteAudioSpectrumUpdate:(NSDictionary<NSString *, NSArray<NSNumber *> *> *)audioSpectrums; Obtain Data from the Callbacks The SDK delivers the sound level data and audio spectrum data of the remote streams as key-value pairs in an . In each key-value pair, the key is the stream ID of a remote stream published by another user in the same room, and the value is the sound level value or an array of audio spectrum values that stream. NSDictionary You can first obtain and save the list of remote streams published by the other users in the current room through the callback, and then use each stream ID in the list as an index to get the sound level value or the audio spectrum data of that stream from the . onRoomStreamUpdate NSDictionary The following examples show how to obtain the sound level and audio spectrum data from the callbacks. // The callback to deliver the sound level data of locally captured audio.
- (void)onCapturedSoundLevelUpdate:(NSNumber *)soundLevel {
    ZGSoundLevelTableViewCell *cell = [self.tableView cellForRowAtIndexPath:[NSIndexPath indexPathForRow:0 inSection:0]];
    cell.soundLevel = soundLevel;
}

// The callback to deliver the sound level data (key-value pairs) of remote streams. In each key-value pair, the key is the stream ID of a remote stream, and the value is the sound level data of that stream.
- (void)onRemoteSoundLevelUpdate:(NSDictionary<NSString *,NSNumber *> *)soundLevels {
    NSInteger rowCount = [self.tableView numberOfRowsInSection:1];
    for (NSInteger row = 0; row < rowCount; row++) {
        ZGSoundLevelTableViewCell *cell = [self.tableView cellForRowAtIndexPath:[NSIndexPath indexPathForRow:row inSection:1]];
        if ([soundLevels objectForKey:cell.streamID]) {
            cell.soundLevel = soundLevels[cell.streamID];
        }
    }
}

// The callback to deliver the audio spectrum data of locally captured audio.
- (void)onCapturedAudioSpectrumUpdate:(NSArray<NSNumber *> *)audioSpectrum {
    ZGSoundLevelTableViewCell *cell = [self.tableView cellForRowAtIndexPath:[NSIndexPath indexPathForRow:0 inSection:0]];
    cell.spectrumList = audioSpectrum;
}

// The callback to deliver the audio spectrum data (key-value pairs) of the remote streams. Audio spectrum callback for remote streams. In each key-value pair, the key is the stream ID of a remote stream, and the value is the audio spectrum data of that stream.
- (void)onRemoteAudioSpectrumUpdate:(NSDictionary<NSString *,NSArray<NSNumber *> *> *)audioSpectrums {
    NSInteger rowCount = [self.tableView numberOfRowsInSection:1];
    for (NSInteger row = 0; row < rowCount; row++) {
        ZGSoundLevelTableViewCell *cell = [self.tableView cellForRowAtIndexPath:[NSIndexPath indexPathForRow:row inSection:1]];
        if ([audioSpectrums objectForKey:cell.streamID]) {
            cell.spectrumList = audioSpectrums[cell.streamID];
        }
    }
} Disable the Sound Level Callbacks To disable the sound level callbacks, call the method. stopSoundLevelMonitor /// Disable sound level callbacks
- (void)stopSoundLevelMonitor; After the above step is completed, the SDK will stop triggering the callbacks and . onCapturedSoundLevelUpdate onRemoteSoundLevelUpdate Disable the Audio Spectrum Callbacks To disable audio spectrum callbacks, call the method. stopAudioSpectrumMonitor /// Stop listening for audio spectrum callbacks
///
- (void)stopAudioSpectrumMonitor; After the above step is completed, the SDK will stop triggering the callbacks and . onCapturedAudioSpectrumUpdate onRemoteAudioSpectrumUpdate

How to Implement Sound Waves in iOS by Using ZEGOCLOUD SDK

The Cost of Building 5G Infrastructure Have you ever wondered how much it will cost to build the 5G infrastructure? According to GSMA Intelligence’s CAPEX forecast, 5G will cost almost US $1 trillion to deploy over the next half-decade. With 5G, it will require more network densification, which means a lot of the capex investment will go into the RAN* (Radio Access Network.) In fact, the share of RAN in total capex will grow from 62 per cent in 2018 to 86 per cent in 2025 (please ). see figure 1 *In simple terms, a Radio Access Network (RAN) is part of a telecommunications system that connects individual devices such as our cell phones to other parts of a network through radio connections. The network operators are paying for the cost of building the 5G infrastructure to provide service to their customers and it has a huge impact to their revenue and market capitalization. The network operators have no choice but to find ways to lower their capex. Why Open RAN (O-RAN)? Since the share of RAN in capex is over 60% and rising, the most natural question to ask is how to make it cheaper and cost less to operate. And at the same time, there is increased pressure for the operators to complete their 5G rollouts. To deal with the challenge associated with upgrading and rolling out networks, several operators (AT&T, China Mobile, Deutsche Telekom, NTT DOCOMO and Orange) created the in February 2018 to drive the development of standards and technologies that would lead to Open RAN implementation. What they need is faster innovation, easier deployment, simplified operations, and a more competitive vendor ecosystem. O-RAN Alliance Open RAN will meet these needs by providing greater choice, flexibility and agility for network operators racing to deploy 5G. Open RAN also helps operators to speed service introduction and innovation, improve supply chain security, and reduce Opex and Capex. The O-RAN Alliance now has over 300 mobile operators, vendors, and research & academic institutions operating in the Radio Access Network (RAN) industry worldwide. Please see figures 2 & 3 above. You’re Crazy If You Don’t Embrace the Open RAN Ecosystem Recently, Dell’Oro Group raised its Open RAN revenue forecast by 50% as the strong momentum behind the technology has continued to grow. It now believes the cumulative open RAN revenue from 2020 to 2025 could soar to as high as $15 billion, with open RAN revenues accounting for more than 10% of the overall RAN market by 2025. The market is moving at a faster pace than expected, especially in the APAC region. With the US government providing more than $1.5 billion funding to support Open RAN and the Japanese government offering financial incentives and tax benefits for companies that develop, supply, and deploy Open RAN equipment, there is a growing concern of the future viability of a European ecosystem able to compete with other regions in the world. Feeling that Europe as a whole is lagging behind other regions such as the U.S. and Japan in developing Open RAN, Deutsche Telekom, Orange, Telecom Italia, Telefónica, and Vodafone are calling upon European Union member states, policymakers, and industry to embrace the Open RAN ecosystem with the new report — “ .” Building an Open RAN Ecosystem For Europe To understand why embracing Open RAN should be an easy decision, we need to have a better understanding of what is Open RAN and what are the benefits of Open RAN? What is Open RAN (O-RAN)? Open RAN is a set of open specifications defining interfaces between radio and base station RAN. The disaggregation of different RAN elements would provide new ways to mix and match network components by “opening up” the interfaces between them. It will drive down the costs of network deployments and allow new players to enter the RAN market. Please see figure 4 below. Future Radio Access Networks will be based on a wide range of solutions deployments, one size will no longer fit all**.** Continued 4G & 5G traffic growth also requires a significant increase in RAN capacity and network operators to maximize existing investments. Open RAN enables all types of RAN deployment models: distributed, centralized, virtualized, macro & small cell. And it is built on general-purpose computing hardware with carrier-grade reliability. Along with resource pooling, load balancing, and spectral efficiency, Open RAN enables network operators to maximize resource utilization and deliver new network edge services at a lower cost. Many Paths to Success According to Dell’Oro Group, the mix between incumbent RAN players and new challengers with smaller shares is “relatively healthy at this juncture, with smaller RAN suppliers including Airspan, Altiostar, Fujitsu, Mavenir, NEC, and Parallel Wireless accounting for the majority of the Open RAN revenues in 1H21.” The beauty of Open RAN is that network operators can mix and match hardware with different suppliers. For example, the O-RAN DU reference designs from Supermicro can support RAN software from either Mavenir or Parallel Wireless. And thanks to Moore’s Law, each generation of processors is getting increasingly powerful, and the ever-falling cost of processing power is making Open RAN even more appealing economically. Please see Figure 5 above. Conclusion Open RAN marks a profound change in how the network operators is building the wireless infrastructure. This change is driven by an incredibly powerful value proposition, driving efficiencies both in operations and economics. The Open RAN specifications also help cultivate innovation as resources are freed up to allow the network operators to focus on new products and growth. While it’s still ramping, Open RAN will help the network operators to cut billions of dollars in capital spending and speed up the rollout of the 5G network. Wouldn’t it be crazy if we don’t embrace the Open RAN ecosystem? References Intelligence Brief: How much will 5G cost? The year is 2019 and 5G is finally here. Or is it? At MWC19 Barcelona, there were so many 5G announcements that it… www.mobileworldlive.com Dell'Oro bumps revenue forecast 50% as open RAN gains steam Dell'Oro Group raised its open RAN revenue forecast by 50%, citing strong momentum behind the technology in the first… www.fiercewireless.com European Telcos Sound Alarm Over Flagging Open RAN Progress European telecom companies recently sounded an alarm that they may be falling behind the rest of the world in their… spectrum.ieee.org 5G Testing: What Is O-RAN? - Part 1 Open radio access network (O-RAN) is an operator-led alliance that defines the next-generation radio access network… blogs.keysight.com

Edge Computing is So Fun Part 6: Why You Need to Embrace The Open RAN Ecosystem

This will be my first review, where I will cover a single game released for multiple computer platforms and compare each version accordingly - in this case, I will be taking a look at the 1988 budget game for the main trinity of 8-bit home computers in the West: the (which is the lead platform), the and the . This game is a childhood favourite of mine, as I remember playing it on my ZX Spectrum and trying to get a high enough score to enter my name. multiplatform Codemasters Super Stuntman ZX Spectrum Amstrad CPC Commodore 64 Premise The premise of this game, according to the cassette/disk inlay, is that the player assumes the role of a stuntman in a major motion picture, requiring their talents in order to accomplish a series of increasingly elaborate and dangerous vehicular acrobatics. However, as with many games of this era, the plot is merely a means to an end. On all versions of the game, the player must traverse through seven (7) scenes which need to be completed within a strict time limit, and you are given three (3) lives or for each scene. The scenes in question are: VERY “takes” Desert Speedboat Chase/Boat Race Forest Jump Over the Grand Canyon (Press the fire button to activate a turbo booster at the right time) City Car Chase White Water/Rapids Boat Chase The Grand Finale Main Menu Comparison All three versions showcase the eponymous daredevil standing outside his car, which appears to be a Ferrari/Porsche hybrid. The main menu of each version offers a jaunty little theme tune by David Whittaker using each computer’s respective sound chip (the “beeper” speaker for the Spectrum since this is a 48K only game with no 128K version, the AY chip on the CPC and the SID chip on the C64). The Spectrum version also offers a choice of keyboard controls, the option to redefine them, or a joystick interface of the player’s choice (if you read my review, then you will get a brief description of the Spectrum’s various joystick interfaces and how they work). For this review, I will be playing with the Kempston joystick interface, which is arguably the most popular interface for the Spectrum, using my custom-made 2-button arcade stick. Zythum The Spectrum and C64 only recognize a single fire button due to them using the antiquated standard, while the CPC can support two (2) separate fire buttons using an joystick, although not all games were coded with this ability in mind. Atari 2600 MSX The Game This is what we get when the game starts up - a time limit, score, and a visual damage indicator, which is mainly for show and has no actual bearing on the player vehicle’s functionality (except for the C64 version, which excludes the vehicular mugshot). The goal of each scene is to get to the finishing line at the end before the countdown timer reaches zero (0). On all scenes except for Scene 4 (The Grand Canyon), the player’s vehicle (a car in all but Scenes 2 and 6, where the player controls a speedboat) is equipped with what appears to be a front-facing cannon, since pressing the fire button on the joystick fires a cannonball, which can be used to destroy computer-controlled enemy vehicles (which are controlled with a very simplistic - they just seem to charge forwards indiscriminately and occasionally block the player’s path while shooting at anything in their way). The player will also have to avoid rocks (which block your path) and fireballs (which will cost you a life - on the water-based scenes, the player must avoid crashing into the land, which will yield the same result). AI The sole exception to this rudimentary gunplay mechanic is, as mentioned, on Scene 4, where the player’s cannon is replaced with a turbo booster, which be activated at the correct time in order to successfully jump across the Grand Canyon and reach the finish line at the other end. Engage it too early or late and the player will fall to their death. MUST Oddly enough and possibly unique to this game, the player is awarded points for losing lives or “takes”, gaining 1,000 points in the form of what the game calls , presumably as a sort of reward for showcasing their inner daredevil. You can do this up to twice per scene (your takes are reset at the beginning of a new scene) - if you crash a third time, then the game ends. “Amazing Action” Cheat Mode All 3 versions of this game have a built-in cheat mode, which gives the player infinite time and lives, and given the rather clumsy controls and haphazard level design (we’ll get to that later on, when I’m comparing the 3 versions against each other), it would be wise to enable it as soon as possible. To do this, the player must first get a high enough score to enter their name on the high score table, entering a specific word as their name and confirming it (see the table below): Game Version Name to Enter ZX Spectrum BIG SCORE Amstrad CPC LIVEWIRE Commodore 64 CHEAT (Press key to skip to the next scene during gameplay) Tab Super Stuntman Review and Comparison Presentation The ZX Spectrum version is the original version of this game, and as such, has a decent amount of content and presentation, along with a cool-looking image on the main menu. This look and feel is carried over to the Amstrad CPC port, which improves the visual and auditory aspect, as is to be expected for a multipurpose computer like the CPC. The C64 version is sadly lacking in these elements, and has a much more basic menu. The game’s content is barebones but sufficient to keep the player interested, if only for a limited time, given it is a budget game, and the C64 version actually uses its custom hardware to try to make the in-game world look more lively. Overall Winner: Tie between ZX Spectrum & Amstrad CPC Graphics (Detail & Colour) Graphically, the game looks adequate on all 3 platforms, although each version has some features unique to that particular release. For example, the original Spectrum version has a higher resolution than the CPC and C64, so it wins out in the detail department - you can see what each in-game sprite is supposed to be, although the red fireballs on the Spectrum and C64 are recoloured to blue on the CPC, presumably to prevent them from blending in with the environment (thanks to its low-resolution, 16-colour mode, this is no Spectrum port, something which plagued many CPC games back in the day). My personal favourite colour palette is on the Amstrad CPC, with its vibrant -esque colours and sprite design, although the muted colours on the C64 also work, giving the game a bit more atmosphere. I also like how the water in Scenes 2 and 6 is animated on the C64, while the Spectrum and CPC simply have white spots on a blue background. Sega Overall Winner: ZX Spectrum (Detail), Tie between Amstrad CPC & Commodore 64 (Colour) Sound (SFX & Music) The Spectrum version has what sounds like digitised flatulence due to its single-channel “beeper” speaker, and the vehicular engine noises do not sound at all like a car or a speedboat’s motor bursting into action, although the player does get to hear a noise when the countdown timer goes below 10 seconds, warning them to hurry up (they lose a take if they run out of time). CPC version probably has the best balance of sound effects and music (the main title theme would later reappear in the CPC-exclusive , which I also reviewed on this site recycling music from their previous games seemed to be a Codemasters trademark). C64 version has dire sound effects, worse than the Spectrum version, although the engine sound can be increased in pitch depending on how fast the player goes, which can provide some amusement, and less-than-perfect music. tick-tock Turbo Chopper Simulator - Overall Winner: Tie between ZX Spectrum & Amstrad CPC (SFX), Amstrad CPC (Music) Controls The controls of any videogame can make or break the experience, and in this game, it is certainly a very polarising affair. The game’s controls are very strange in the sense that the player’s vehicle is limited to 45-degree turns in either direction, as if the car or speedboat is standing in the middle of a 180-degree (a device used for measuring angles) and can only rotate within its range. The C64 version also suffers from a case of sudden loss of momentum, meaning if the player’s vehicle hits any kind of obstacle, then they will stop moving immediately, requiring the player to back up (which costs time) and reaccelerate all over again (costing more time), while the Spectrum and CPC versions keep the vehicle at full speed even when hitting a rock or some other obstacle. Firing the cannon is also a bit of a pain, since only a single cannonball can be fired at any time. The C64 version also suffers from slippery controls, while the Spectrum and CPC version’s controls are too stiff. protractor Overall Winner: Draw Super Stuntman Gameplay Review The most important element of this game is also the weakest, sadly - all 3 versions have the exact same game mechanics and there is no exclusive feature for any of them, meaning that if you’ve played one version, then you’ve essentially played them all. I will find, however, that the Spectrum and CPC versions are the most forgiving in terms of difficulty and have a greater margin for error than the C64 port, which appears to be a rushed conversion evidenced by the fact that it has no ending sequence upon completing the last scene of the game (Scene 7), and the game repositions the player just behind the final finishing line, to repeat the process until they run out of time or lives (the Spectrum and CPC versions at least have the courtesy to congratulate the player). The Spectrum and CPC versions have the exact same level layouts and stage designs, while the C64 port tries to redesign the scenes and make them more linear but only ends up cluttering the already chaotic scenery, and the time limit ticks down faster in that version compared to the Spectrum and CPC. Overall Winner: Tie between ZX Spectrum & Amstrad CPC Overall is far from a super game. Codemasters were synonymous with quality games back in this era, although this release from 1988 seems to be a chink in the armour, released during a time when the 8-bit computer era was coming to an end and the 16-bit computers in the form of the , and Japan-exclusive were beginning to hit their stride. A sequel of sorts, entitled , would hit stores 3 years later in 1991 for both 8-bit and 16-bit machines and improve upon this rather problematic first attempt. If I must recommend which version to play, then I suggest either the original ZX Spectrum version or the surprisingly decent Amstrad CPC conversion, which uses that computer’s expanded capabilities with a lower resolution but with the added benefit of additional colours and faster (but less smooth) controls and gameplay. Avoid the C64 port. A somewhat disappointing game, but for a budget title, it could have been worse. Super Stuntman Atari ST Commodore Amiga Sharp X68000 Sky High Stuntman Final Score: 2 out of 5 (ZX Spectrum), 2 1/2 out of 5 (Amstrad CPC), 1 out of 5 (Commodore 64).

Super Stuntman Review (ZX Spectrum/Amstrad CPC/Commodore 64)

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 5 CONCLUSION A convenient mathematical expression to solve for the spectrum of irregularly sampled time-domain data has been demonstrated in a way that satisfies the conditions for an inverse non-uniform Fourier transform. This methodology has been derived from cost function proposed in the original study, and via its idealised quadratic form strongly suggests that its minimum is unique and convex. Using a randomly sample time series from data with few missing values, the observed relative error is on the same order of magnitude as the most current solution to this problem [10]. Upon cross-validation with contiguous blocks, we report a relative error that is much higher, and suggest this is a more critical, but informative lens through which to view interpolation problems. he proposed formula will find use in a variety of signal processing applications, where the use of an FFT is impractical given its assumption of a consistent time-domain density. The software package used in this work is available online: https://github.com/CoDaSLab/intrp infft 1d 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 5 CONCLUSION A convenient mathematical expression to solve for the spectrum of irregularly sampled time-domain data has been demonstrated in a way that satisfies the conditions for an inverse non-uniform Fourier transform. This methodology has been derived from cost function proposed in the original study, and via its idealised quadratic form strongly suggests that its minimum is unique and convex. Using a randomly sample time series from data with few missing values, the observed relative error is on the same order of magnitude as the most current solution to this problem [10]. Upon cross-validation with contiguous blocks, we report a relative error that is much higher, and suggest this is a more critical, but informative lens through which to view interpolation problems. he proposed formula will find use in a variety of signal processing applications, where the use of an FFT is impractical given its assumption of a consistent time-domain density. The software package used in this work is available online: https://github.com/CoDaSLab/intrp infft 1d 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

Novel Mathematical Expression for Spectrum Determination of Irregularly Sampled Data

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