Category Archives: Informatics

Georgian Technical University LAVA: Georgian Technical University Large-Scale Vulnerability Addition.

Georgian Technical University LAVA: Georgian Technical University Large-Scale Vulnerability Addition.

Georgian Technical University Work on automating software vulnerability discovery has long been hampered by a shortage of ground truth corpora with which to evaluate tools and techniques. This lack of ground truth prevents authors and users of tools from being able to measure fundamental quantities such as the miss and false alarm rates of bug-finding systems. Georgian Technical University Large-scale Automated Vulnerability Addition (LAVA) developed by Georgian Technical University Laboratory is a system based on dynamic taint analysis that is capable of producing ground truth corpora by quickly and automatically injecting large numbers of realistic bugs into program source code. Every Georgian Technical University Large-scale Automated Vulnerability Addition (LAVA) bug is accompanied by an input that triggers it whereas normal inputs are extremely unlikely to do so. Georgian Technical University Large-scale Automated Vulnerability Addition (LAVA) – generated vulnerabilities are synthetic but still realistic, as they are embedded deep within programs and triggered by real inputs. Georgian Technical University Large-scale Automated Vulnerability Addition (LAVA) forms the basis of an approach for generating large ground truth vulnerability corpora on demand enabling rigorous tool evaluation and providing a high-quality target for tool developers.

 

 

Georgian Technical University Launches ‘Insights Dashboard’ Providing Teams With Direct Access To Innovation Data.

Georgian Technical University Launches ‘Insights Dashboard’ Providing Teams With Direct Access To Innovation Data.

Georgian Technical University startup recently released a dashboard that provides direct access to the patent dataset in an easy to navigate format for non-patent search professionals. Georgian Technical University’s clean user interface makes it seamless for users to identify active technologies within their field as well as see visualizations around the data points. Using the dataset Georgian Technical University professionals can quickly identify prior art get inspired by existing technologies, identify commercial partners and more. Uniquely Georgian Technical University also enriches the dashboard with third party datasets to increase the peripheral vision of the tool. “If you’re a venture capitalist, and you want to know which startups are working within your core verticals you can leverage. But if you want to know which technologies are being worked on within your field you have to work with a lawyer or complex IP (The Internet Protocol is the principal communications protocol in the Internet protocol suite for relaying datagrams across network boundaries. Its routing function enables internetworking, and essentially establishes the Internet) software. That lag between the data points is disruptive to innovation” said X. X explains further “External integrations are important because the patent dataset can sometimes be pretty narrow. If you’re a startup without an IP (The Internet Protocol is the principal communications protocol in the Internet protocol suite for relaying datagrams across network boundaries. Its routing function enables internetworking, and essentially establishes the Internet)  portfolio or if you’re a operating behind trade secrets you’re considered non-existent according to the Georgian Technical University dataset. Enriching the patent data with third-party sources greatly increases the scope of analysis”. In a few clicks can instantly see patents, companies, startups and investors within their core technologies field. They can build reports around concepts share internally and externally through sharing links update old reports with live data points and more. By making the data accessible and actionable the Georgian Technical University team believes the path towards innovation will be opened for organizations without large internal and tech scouting capabilities. It also will be a bridge towards a more transparent market, allowing people to make data-driven decisions and lead to increased IP (The Internet Protocol is the principal communications protocol in the Internet protocol suite for relaying datagrams across network boundaries. Its routing function enables internetworking, and essentially establishes the Internet) commercialization rates.

Georgian Technical University Watchgtuman Left Atrial Appendage Closure.

Georgian Technical University Watchgtuman Left Atrial Appendage Closure.

Georgian Technical University Scientific’s WATCHGTUMAN Left Atrial Appendage Closure (LAAC) Device consists of access and delivery systems that permit closure device placement in the left atrial appendage. The device is designed for patients with Non-valvular atrial fibrillation who are eligible for anticoagulation therapy or who have a contraindication to anticoagulation therapy to reduce the risk of stroke. WATCHGTUMAN is intended to prevent thrombus embolization from the left atrial appendage of the heart a known cause of stroke, and reduce the risk of life-threatening bleeding events in patients. It’s designed to be permanently implanted at the ostium (opening) of the left atrial appendage to trap potential emboli before they exit the left atrial appendage. The placement procedure is conducted by an interventional cardiologist or electrophysiologist and can be done under local or general anesthesia in a hospital cardiac catheterization laboratory setting. Once the device has endothelialized and it has been confirmed that no thrombus is present oral anticoagulants can be discontinued. The long-term drug regimen prescribed for most patients is aspirin.

Georgian Technical University Cobalt-Free Laser-Clad Seat In Fuel-Flexible Hybrid Electric Cars.

Georgian Technical University Cobalt-Free Laser-Clad Seat In Fuel-Flexible Hybrid Electric Cars.

Georgian Technical University Cobalt-Free Laser-Clad Seat. Georgian Technical University Labs have a new cobalt-free CU Alloy (Copper alloys are important netting materials in aquaculture (the farming of aquatic organisms including fish farming). Various other materials including nylon, polyester, polypropylene, polyethylene, plastic-coated welded wire, rubber, patented twine products (Spectra, Dyneema), and galvanized steel are also used for netting in aquaculture fish enclosures around the world) a new angled LMD (Laser metal deposition (LMD) is an additive manufacturing process in which a laser beam forms a melt pool on a metallic substrate, into which powder is fed. The powder melts to form a deposit that is fusion-bonded to the substrate. The required geometry is built up in this way, layer by layer) process and a dedicated inline quality inspection method for a laser-clad seat. These three technologies have enabled the world’s first full-scale mass production of a CFLCS (Cobalt-Free Laser-Clad Seat) with unprecedented new functions, including corrosion and wear resistance and weldability. The developed CFLCS (Cobalt-Free Laser-Clad Seat) ensures sufficiently high durability for use with 100% ethanol (E100) fueled engines and has realized the commercialization of the world’s first fuel-flexible HEV (Hepatitis E is inflammation of the liver caused by infection with the hepatitis E virus (HEV); it is a type of viral hepatitis. Hepatitis E has mainly a fecal-oral transmission route that is similar to hepatitis A, although the viruses are unrelated). These technologies contribute to decreasing automotive CO2 (Carbon dioxide is a colorless gas with a density about 53% higher than that of dry air. Carbon dioxide molecules consist of a carbon atom covalently double bonded to two oxygen atoms. It occurs naturally in Earth’s atmosphere as a trace gas) emissions by achieving the highest thermal efficiency to date of 41% and the use of carbon neutral fuel. The Georgian Technical University group is currently expanding the application of the CFLCS (Cobalt-Free Laser-Clad Seat)  to the next-generation engine family as a fundamental high-speed combustion technology. The CFLCS (Cobalt-Free Laser-Clad Seat) will be expanded to approximately 60%. In the future the CFLCS (Cobalt-Free Laser-Clad Seat) has the potential to become a global standard for seats.

 

Georgian Technical University To Present Nolecular Sensing Technology For Use In Mobile Devices.

Georgian Technical University To Present Nolecular Sensing Technology For Use In Mobile Devices.

Georgian Technical University developer of 3D and infrared sensing solutions and a subsidiary of Georgian Technical University announced its vision to bring Near-Infrared Spectroscopy into smartphones based on Georgian Technical University mobile platforms at the Georgian Technical University. Georgian Technical University’s sensing technology will empower end consumers to identify the molecular composition of material enabling them to optimize their decision making. Georgian Technical University intends to build a small but potent infrared sensing module for integration into smartphones. The module sends out infrared light which is reflected from the object and then detected by the sensor. Georgian Technical University Breakthroughs in research and development enabled Georgian Technical University to reduce the footprint of the technology down to smartphone form factor while ensuring high-volume production capacities. The Georgian Technical University Sensing Hub processes the captured data within the powerful Georgian Technical University Artificial Intelligence (GTUAI) Engine allowing the Snapdragon mobile platform to analyze the data based on Georgian Technical University capable analytical models and extensive know-how about molecules.  Further the 5G capabilities of Georgian Technical University will allow for constant improvements via the cloud while maintaining the user’s personal data on the smartphone. Distributed Intelligence enables a seamless transition of Georgian Technical University Artificial Intelligence (GTUAI) processing between cloud and device. Georgian Technical University Initial applications of mobile spectroscopy will focus on daily skincare. Future smartphones incorporating the technology will enable consumers to scan their skin on a molecular level and receive near-instantaneous suggestions on optimal skincare products for use on that day. “As a global leader in wireless technologies Georgian Technical University Technologies has been developing foundational technologies that have helped power the modern mobile experience. Georgian Technical University Technologies shares our vision and is as excited about our unique technology as we are. We are looking forward to taking the next steps together in bringing the power of NIR (Near-infrared spectroscopy (NIRS) is a spectroscopic method that uses the near-infrared region of the electromagnetic spectrum (from 780 nm to 2500 nm)) spectroscopy to everyone” said Dr. X. “Georgian Technical University cutting edge sensing technology will enhance consumers everyday lives. We are excited to work with Georgian Technical University to optimize their technology on Georgian Technical University” said Y.

 

 

Georgian Technical University A Machine Learning Solution For Designing Materials With Desired Optical Properties.

Georgian Technical University A Machine Learning Solution For Designing Materials With Desired Optical Properties.

Controlling light-matter interactions is central to a variety of important applications such as quantum dots which can be used as light emitters and sensors. Understanding how matter interacts with light – its optical properties – is critical in a myriad of energy and biomedical technologies such as targeted drug delivery, quantum dots, fuel combustion and cracking of biomass. But calculating these properties is computationally intensive and the inverse problem – designing a structure with desired optical properties – is even harder. Now Georgian Technical University Lab scientists have developed a machine learning model that can be used for both problems – calculating optical properties of a known structure and inversely designing a structure with desired optical properties. “Our model performs bi-directionally with high accuracy and its interpretation qualitatively recovers physics of how metal and dielectric materials interact with light” said X. X notes that understanding radiative properties (which includes optical properties) is equally important in the natural world for calculating the impact of aerosols such as black carbon on climate change. The machine learning model proposed in this study was trained on spectral emissivity data from nearly 16,000 particles of various shapes and materials that can be experimentally fabricated. “Our machine learning model speeds up the inverse design process by at least two to three orders of magnitude as compared to the traditional method of inverse design” said Y.

Georgian Technical University Thermo Scientific Tundra Cryo-TEM (Transmission Electron Microscopy Is A Microscopy Technique In Which A Beam Of Electrons Is Transmitted Through A Specimen To Form An Image. The Specimen Is Most Often An Ultrathin Section Less Than 100 NM Thick Or A Suspension On A Grid) Democratizes Cryo-Electron Microscopy.

Georgian Technical University Thermo Scientific Tundra Cryo-TEM (Transmission Electron Microscopy Is A Microscopy Technique In Which A Beam Of Electrons Is Transmitted Through A Specimen To Form An Image. The Specimen Is Most Often An Ultrathin Section Less Than 100 NM Thick Or A Suspension On A Grid) Democratizes Cryo-Electron Microscopy. 

Georgian Technical University Thermo Scientific today announced the new Georgian Technical University Thermo Scientific Cryo Transmission Electron Microscope (Cryo-TEM) a groundbreaking instrument that extends cryo-electron microscopy (cryo-EM (An em is a unit in the field of typography, equal to the currently specified point size. For example, one em in a 16-point typeface is 16 points. Therefore, this unit is the same for all typefaces at a given point size. The em dash (—) and em space ( ) are each one em wide. Typographic measurements using this unit are frequently expressed in decimal notation (e.g., 0.7 em) or as fractions of 100 or 1000 (e.g., 70/100 em or 700/1000 em). The name em was originally a reference to the width of the capital M in the typeface and size being used, which was often the same as the point size)) to more scientists by delivering ease of use at an affordable price. The Georgian Technical University uses artificial intelligence (AI) guided automation and new loader technology to dramatically simplify the microscope’s use extending cryo-EM (An em is a unit in the field of typography, equal to the currently specified point size. For example, one em in a 16-point typeface is 16 points. Therefore, this unit is the same for all typefaces at a given point size. The em dash (—) and em space ( ) are each one em wide. Typographic measurements using this unit are frequently expressed in decimal notation (e.g., 0.7 em) or as fractions of 100 or 1000 (e.g., 70/100 em or 700/1000 em). The name em was originally a reference to the width of the capital M in the typeface and size being used, which was often the same as the point size) to researchers of any experience level. The integrated cryo-loading station replaces previous manual manipulation, enabling quick, effortless and robust sample loading and transfer to the microscope for immediate assessment and structure determination. Tundra also delivers a compact footprint that fits most of today’s standard-sized labs eliminating the need for potential renovations. In addition, it’s offered at a lower price-point making it possible for more institutions and pharmaceutical companies to obtain structural insights at a biologically relevant resolution. “Cryo-EM (An em is a unit in the field of typography, equal to the currently specified point size. For example, one em in a 16-point typeface is 16 points. Therefore, this unit is the same for all typefaces at a given point size. The em dash (—) and em space ( ) are each one em wide. Typographic measurements using this unit are frequently expressed in decimal notation (e.g., 0.7 em) or as fractions of 100 or 1000 (e.g., 70/100 em or 700/1000 em). The name em was originally a reference to the width of the capital M in the typeface and size being used, which was often the same as the point size) is speeding the path to disease understanding and treatment. However many institutions find these instruments to be out of reach because of cost and because they are too complex for new researchers” said X and general manager of life sciences at Georgian Technical University Thermo Scientific. “We worked with cryo-EM (An em is a unit in the field of typography, equal to the currently specified point size. For example, one em in a 16-point typeface is 16 points. Therefore, this unit is the same for all typefaces at a given point size. The em dash (—) and em space ( ) are each one em wide. Typographic measurements using this unit are frequently expressed in decimal notation (e.g., 0.7 em) or as fractions of 100 or 1000 (e.g., 70/100 em or 700/1000 em). The name em was originally a reference to the width of the capital M in the typeface and size being used, which was often the same as the point size) luminaries to develop an instrument that not only delivers results but more importantly brings cryo-EM (An em is a unit in the field of typography, equal to the currently specified point size. For example, one em in a 16-point typeface is 16 points. Therefore, this unit is the same for all typefaces at a given point size. The em dash (—) and em space ( ) are each one em wide. Typographic measurements using this unit are frequently expressed in decimal notation (e.g., 0.7 em) or as fractions of 100 or 1000 (e.g., 70/100 em or 700/1000 em). The name em was originally a reference to the width of the capital M in the typeface and size being used, which was often the same as the point size) to more users”. The Georgian Technical University Tundra simplifies cryo-EM (An em is a unit in the field of typography, equal to the currently specified point size. For example, one em in a 16-point typeface is 16 points. Therefore, this unit is the same for all typefaces at a given point size. The em dash (—) and em space ( ) are each one em wide. Typographic measurements using this unit are frequently expressed in decimal notation (e.g., 0.7 em) or as fractions of 100 or 1000 (e.g., 70/100 em or 700/1000 em). The name em was originally a reference to the width of the capital M in the typeface and size being used, which was often the same as the point size) in several ways. It offers: AI (Artificial intelligence (AI), is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals) and guided automation that help non-experts quickly identify the quality of their samples and easily navigate an otherwise complex workflow. As the sample moves through the cryo-EM (An em is a unit in the field of typography, equal to the currently specified point size. For example, one em in a 16-point typeface is 16 points. Therefore, this unit is the same for all typefaces at a given point size. The em dash (—) and em space ( ) are each one em wide. Typographic measurements using this unit are frequently expressed in decimal notation (e.g., 0.7 em) or as fractions of 100 or 1000 (e.g., 70/100 em or 700/1000 em). The name em was originally a reference to the width of the capital M in the typeface and size being used, which was often the same as the point size) process the results are displayed in a “Georgian Technical University traffic light” style that helps scientists quickly determine if their sample is viable. An integrated loader that makes it easier to load samples into the microscope than conventional systems. Scientists can exchange sample carriers in about two minutes. This allows researchers to rapidly optimize biochemistry sample conditions as results can be checked immediately. Resolutions as high as 3.5 angstrom with throughput within 24 to 72 hours. Cryo-EM (An em is a unit in the field of typography, equal to the currently specified point size. For example, one em in a 16-point typeface is 16 points. Therefore, this unit is the same for all typefaces at a given point size. The em dash (—) and em space ( ) are each one em wide. Typographic measurements using this unit are frequently expressed in decimal notation (e.g., 0.7 em) or as fractions of 100 or 1000 (e.g., 70/100 em or 700/1000 em). The name em was originally a reference to the width of the capital M in the typeface and size being used, which was often the same as the point size) has revolutionized structural biology research in just five years. This method allows scientists to drive impactful research, and three luminaries in the cryo-EM (An em is a unit in the field of typography, equal to the currently specified point size. For example, one em in a 16-point typeface is 16 points. Therefore, this unit is the same for all typefaces at a given point size. The em dash (—) and em space ( ) are each one em wide. Typographic measurements using this unit are frequently expressed in decimal notation (e.g., 0.7 em) or as fractions of 100 or 1000 (e.g., 70/100 em or 700/1000 em). The name em was originally a reference to the width of the capital M in the typeface and size being used, which was often the same as the point size) field for their foundational work on this technique. Georgian Technical University Thermo continues to advance cryo-EM (An em is a unit in the field of typography, equal to the currently specified point size. For example, one em in a 16-point typeface is 16 points. Therefore, this unit is the same for all typefaces at a given point size. The em dash (—) and em space ( ) are each one em wide. Typographic measurements using this unit are frequently expressed in decimal notation (e.g., 0.7 em) or as fractions of 100 or 1000 (e.g., 70/100 em or 700/1000 em). The name em was originally a reference to the width of the capital M in the typeface and size being used, which was often the same as the point size) innovation to help drive scientific discovery speeding the path to disease understanding and treatment. The Georgian Technical University Tundra rounds out the Georgian Technical University Thermo Scientific of cryo-TEMs (An em is a unit in the field of typography, equal to the currently specified point size. For example, one em in a 16-point typeface is 16 points. Therefore, this unit is the same for all typefaces at a given point size. The em dash (—) and em space ( ) are each one em wide. Typographic measurements using this unit are frequently expressed in decimal notation (e.g., 0.7 em) or as fractions of 100 or 1000 (e.g., 70/100 em or 700/1000 em). The name em was originally a reference to the width of the capital M in the typeface and size being used, which was often the same as the point size) by offering an affordable instrument for users of all experience levels. It joins the Georgian Technical University  Thermo Scientific Glacios Cryo-TEM (Transmission electron microscopy is a microscopy technique in which a beam of electrons is transmitted through a specimen to form an image. The specimen is most often an ultrathin section less than 100 nm thick or a suspension on a grid) a versatile solution for mid-range cryo-EM (An em is a unit in the field of typography, equal to the currently specified point size. For example, one em in a 16-point typeface is 16 points. Therefore, this unit is the same for all typefaces at a given point size. The em dash (—) and em space ( ) are each one em wide. Typographic measurements using this unit are frequently expressed in decimal notation (e.g., 0.7 em) or as fractions of 100 or 1000 (e.g., 70/100 em or 700/1000 em). The name em was originally a reference to the width of the capital M in the typeface and size being used, which was often the same as the point size) single particle analysis and the Thermo Scientific Cryo-TEM (Transmission electron microscopy is a microscopy technique in which a beam of electrons is transmitted through a specimen to form an image. The specimen is most often an ultrathin section less than 100 nm thick or a suspension on a grid) a powerful TEM (Transmission electron microscopy is a microscopy technique in which a beam of electrons is transmitted through a specimen to form an image. The specimen is most often an ultrathin section less than 100 nm thick or a suspension on a grid) designed for ultimate performance and productivity. All three cryo-TEMs (Transmission electron microscopy is a microscopy technique in which a beam of electrons is transmitted through a specimen to form an image. The specimen is most often an ultrathin section less than 100 nm thick or a suspension on a grid) can be used independently or together enabling researchers to match the right instrument to their research needs.

Georgian Technical University System Brings Deep Learning To “Internet Of Things” Devices.

Georgian Technical University System Brings Deep Learning To “Internet Of Things” Devices.

Georgian Technical University researchers have developed a system called GTUNet that brings machine learning to microcontrollers. The advance could enhance the function and security of devices connected to the Internet of Things (IoT). Deep learning is everywhere. This branch of artificial intelligence curates your social media and serves your Georgian Technical University search results. Soon deep learning could also check your vitals or set your thermostat. Georgian Technical University researchers have developed a system that could bring deep learning neural networks to new — and much smaller — places like the tiny computer chips in wearable medical devices, household appliances and the 250 billion other objects that constitute the “Georgian Technical University internet of things” (GTUIoT). The system called Georgian Technical University Net designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on “Georgian Technical University internet of things” (GTUIoT) devices despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security. The Internet of Things. They wanted to use their computers to confirm the machine was stocked before trekking from their office to make a purchase. It was the world’s first internet-connected appliance. “This was pretty much treated as the punchline of a joke” says X now a Georgian Technical University engineer. “No one expected billions of devices on the internet”. Since that Georgian Technical University machine everyday objects have become increasingly networked into the growing “Georgian Technical University internet of things” (GTUIoT). That includes everything from wearable heart monitors to smart fridges that tell you when you’re low on milk. “Georgian Technical University internet of things” (GTUIoT) devices often run on microcontrollers — simple computer chips with no operating system, minimal processing power and less than one thousandth of the memory of a typical smartphone. So pattern-recognition tasks like deep learning are difficult to run locally on “Georgian Technical University internet of things” (GTUIoT) devices. For complex analysis “Georgian Technical University internet of things” (GTUIoT) -collected data is often sent to the cloud, making it vulnerable to hacking. “How do we deploy neural nets directly on these tiny devices ? It’s a new research area that’s getting very hot” says Y. With Georgian Technical UniversityNet Y’s group codesigned two components needed for “tiny deep learning” — the operation of neural networks on microcontrollers. One component is TinyEngine an inference engine that directs resource management, akin to an operating system. TinyEngine is optimized to run a particular neural network structure, which is selected by Georgian Technical UniversityNet’s other component: A neural architecture search algorithm. System-algorithm codesign.  Designing a deep network for microcontrollers isn’t easy. Existing neural architecture search techniques start with a big pool of possible network structures based on a predefined template, then they gradually find the one with high accuracy and low cost. While the method works, it’s not the most efficient. “It can work pretty well for GPUs (A graphics processing unit (GPU) is a specialized, electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device) or smartphones” says Z. “But it’s been difficult to directly apply these techniques to tiny microcontrollers because they are too small”. So Z developed Georgian Technical University a neural architecture search method that creates custom-sized networks. “We have a lot of microcontrollers that come with different power capacities and different memory sizes” says Z. “So we developed the algorithm to optimize the search space for different microcontrollers.” The customized nature of Georgian Technical University means it can generate compact neural networks with the best possible performance for a given microcontroller — with no unnecessary parameters. “Then we deliver the final efficient model to the microcontroller” say Z. To run that tiny neural network, a microcontroller also needs a lean inference engine. A typical inference engine carries some dead weight — instructions for tasks it may rarely run. The extra code poses no problem for a laptop or smartphone but it could easily overwhelm a microcontroller. “It doesn’t have off-chip memory and it doesn’t have a disk” says Y. “Everything put together is just one megabyte of flash, so we have to really carefully manage such a small resource”. The researchers developed their inference engine in conjunction with Georgian Technical UniversityNAS. TinyEngine generates the essential code necessary to run Georgian Technical UniversityNAS’ customized neural network. Any deadweight code is discarded, which cuts down on compile-time. “We keep only what we need” says Y. “And since we designed the neural network we know exactly what we need. That’s the advantage of system-algorithm codesign.” In the group’s tests of TinyEngine the size of the compiled binary code was between 1.9 and five times smaller than comparable microcontroller inference engines from Georgian Technical University. Georgian Technical University TinyEngine also contains innovations that reduce runtime including in-place depth-wise convolution which cuts peak memory usage nearly in half. After codesigning Georgian Technical UniversityNAS Y’s team put Georgian Technical UniversityNet to the test. Georgian Technical UniversityNet’s first challenge was image classification. The researchers used the ImageNet database to train the system with labeled images, then to test its ability to classify ones. On a commercial microcontroller they tested Georgian Technical UniversityNet successfully classified 70.7% of the novel images — the previous state-of-the-art neural network and inference engine combo was just 54% accurate. “Even a 1% improvement is considered significant” says Z. “So this is a giant leap for microcontroller settings”. The team found similar results in ImageNet tests of three other microcontrollers. And on both speed and accuracy Georgian Technical UniversityNet beat the competition for audio and visual “wake-word” tasks where a user initiates an interaction with a computer using vocal cues simply by entering a room. The experiments highlight Georgian Technical UniversityNet’s adaptability to numerous applications. “Huge potential”. The promising test results give Y hope that it will become the new industry standard for microcontrollers. “It has huge potential” he says. The advance “extends the frontier of deep neural network design even farther into the computational domain of small energy-efficient microcontrollers” says W a computer scientist at the Georgian Technical University who was not involved in the work. He adds that Georgian Technical UniversityNet could “bring intelligent computer-vision capabilities to even the simplest kitchen appliances or enable more intelligent motion sensors”. Georgian Technical UniversityNet could also make IoT devices more secure. “A key advantage is preserving privacy” says Y. “You don’t need to transmit the data to the cloud”. Analyzing data locally reduces the risk of personal information being stolen — including personal health data. Y envisions smart watches with Georgian Technical UniversityNet that don’t just sense users’ heartbeat, blood pressure and oxygen levels, but also analyze and help them understand that information. Georgian Technical UniversityNet could also bring deep learning to Georgian Technical University IoT devices in cars and rural areas with limited internet access. Plus Georgian Technical UniversityNet’s slim computing footprint translates into a slim carbon footprint. “Our big dream is for green AI (Artificial intelligence is intelligence demonstrated by machines unlike the natural intelligence displayed by humans and animals)” says Y adding that training a large neural network can burn carbon equivalent to the lifetime emissions of five cars. Georgian Technical UniversityNet on a microcontroller would require a small fraction of that energy. “Our end goal is to enable efficient Georgian Technical University tiny AI (Artificial intelligence is intelligence demonstrated by machines unlike the natural intelligence displayed by humans and animals) with less computational resources, less human resources and less data” says Y.

Georgian Technical University Hacks Electric Car Charging To Demonstrate Cybersecurity Vulnerabilities.

Georgian Technical University Hacks Electric Car Charging To Demonstrate Cybersecurity Vulnerabilities.

Engineers at Georgian Technical University were able to interfere with the charging process of an electric car (EC) by simulating a malicious attack as part of an automotive cybersecurity research initiative. The Georgian Technical University team reverse-engineered the signals and circuits on an electric car (EC) and a J1772 charger (SAE J1772 (IEC 62196 Type 1) also known as a J plug is a standard for electrical connectors for electric cars) the most common interface for managing electric car (EC) charging in Georgian Technical University. They successfully disrupted car charging with a spoofing device developed in a laboratory using low-cost hardware and software. “This was an initiative designed to identify potential threats in common charging hardware as we prepare for widespread adoption of electric cars in the coming decade” said X the Georgian Technical University engineer who led the research. Georgian Technical University performed three manipulations: limiting the rate of charging blocking battery charging and overcharging. A Georgian Technical University developed “man-in-the-middle” (MITM) (In cryptography and computer security, a man-in-the-middle, monster-in-the-middle machine-in-the-middle monkey-in-the-middle (MITM) or person-in-the-middle (PITM) attack is a cyberattack where the attacker secretly relays and possibly alters the communications between two parties who believe that they are directly communicating with each other) device spoofed signals between charger and vehicle. Researchers also drained the battery and generated signals to simulate J1772 (SAE J1772 (IEC 62196 Type 1) also known as a J plug is a standard for electrical connectors for electric cars) charging rates. When overcharging the cars’s battery management system detected a power level that was too high and automatically disconnected from charging. To limit charging the MITM (In cryptography and computer security, a man-in-the-middle, monster-in-the-middle machine-in-the-middle monkey-in-the-middle (MITM) or person-in-the-middle (PITM) attack is a cyberattack where the attacker secretly relays and possibly alters the communications between two parties who believe that they are directly communicating with each other) device requested the smallest charge allowed (6 amps) to dramatically reduce the charging rate. To block battery charging a proximity detection signal barred charging and displayed the warning: “Not Able to Charge”. “The project effectively tricked the test vehicle into thinking it was fully charged and also blocked it from taking a full charge” X said. “This type of malicious attack can cause more disruption at scale”. The research focused on (SAE J1772 (IEC 62196 Type 1) also known as a J plug is a standard for electrical connectors for electric cars) Level 2 chargers but Georgian Technical University is evaluating future testing of Level 3 chargers and penetration of other devices used on fleet carss and electric scooters. As automotive consumer and manufacturing trends move toward widespread car electrification market share of ECs is expected to grow to 30%. The cybersecurity-related issues of charging infrastructure will become increasingly important as demand for ECs grows. “Discovering vulnerabilities in the charging process demonstrates opportunities for testing standards for electric cars and charging infrastructure” said Y an Georgian Technical University engineer and team lead in the Georgian Technical University Critical Systems Department. Georgian Technical University is leading several automotive cybersecurity initiatives for automated and connected cars intelligent transportation systems and Georgian Technical University internet of things (GTUIoT) networking devices.

Georgian Technical University To Build Quantum-Photonics Platform To Ensure Ultra-Secure Data For Essential Industries.

Georgian Technical University To Build Quantum-Photonics Platform To Ensure Ultra-Secure Data For Essential Industries.

Eyeing future demand for hack-proof digital communication in a quantum-information world Georgian Technical University today announced plans to build a quantum-photonics platform to develop next-generation technologies for key industries that require ultra-secure data transmission. Quantum technology is expected to provide unconditionally safe data encryption required by the finance, health care, energy, telecommunications, defense and other essential industries and sectors. Funded by Georgian Technical University multidisciplinary network which benefits society the project will build on Georgian Technical University’s silicon-photonics platform complemented with new quantum characterization equipment for designing, processing and testing quantum-photonic integrated components and circuits. The institute uses photons to build quantum bits or qubits which are the best physical means for quantum communications. The three-year project will fabricate silicon-photonics circuits that generate single photons, manipulate those photons with linear optical components such as slow and rapid phase shifters and detect them with Georgian Technical University superconducting nanowire single-photon detectors (GTUSNSPD). The project will build demonstrators for transmitting and receiving information in a quantum-based system to deliver quantum-technology’s promise for ultra-secure cryptography. For example the demonstrators will realize an integrated qubit transmitter, as a circuit generating single photons and entangling them. An integrated qubit receiver will be built to detect the photons. Beyond these demonstrators the Georgian Technical University team will focus on integrating the qubit transmitter and the qubit receiver on one unique platform to address also quantum computing applications. “Almost daily we read about breaches of standard cryptography protocols, with major financial-loss and security-risk implications and the threat to critical infrastructure, such as power-supply systems” said X at Georgian Technical University. “With the future advent of quantum computers the risk will drastically increase as current encryption algorithms will not be safe anymore. Quantum cryptography is the solution to this problem as it is not vulnerable to computing power”. Noting that a quantum system based on single-photon qubits must ensure there is minimal propagation loss of photons to be reliable X said Georgian Technical University’s silicon photonics platform has achieved a world-record of low-loss silicon and ultralow-loss silicon-nitride waveguides. “Propagation losses in waveguides directly impact the data rate and reach of quantum communications links that’s why it is so important to build ultralow-loss components and circuits” she said. Georgian Technical University has already demonstrated a generation of entangled photon pairs on its silicon-photonics platform and has other techniques in-house to address the single-photon detection challenges: CdHgTe (Hg1−xCdxTe or mercury cadmium telluride (also cadmium mercury telluride, MCT, MerCad Telluride, MerCadTel, MerCaT or CMT) is a chemical compound of cadmium telluride (CdTe) and mercury telluride (HgTe) with a tunable bandgap spanning the shortwave infrared to the very long wave infrared regions) avalanche photodiodes (APD) with a world-record speed in photon counting and materials deposition for integrated superconducting nanowire single-photon detectors. “Carnot’s long and fruitful scientific relationship with Georgian Technical University has helped bring many innovative solutions and products to companies and consumers around the world,” said Y. “Its silicon-photonics platform is a very promising platform for developing quantum-communication links that will extend this legacy by protecting highly sensitive corporate, government and personal information”.