Crypto Quantique in London has added TRNG to the physical unclonable function (PUF) in its QDID IoT semiconductor hardware security IP block.
ABB India has announced that it has signed an agreement to acquire the power electronics business of Gamesa Electric in Spain from Siemens Gamesa.
The GaN industrial devices market is anticipated to witness robust growth during the forecast period fueled by factors such as the growing adoption of electric vehicles renewable energy systems and advanced manufacturing technologies GaN Industrial Devices market is projected to ...
Phishing attacks threaten individuals and organizations globally. While existing phishing detection tools predominantly focus on technical aspects, there exists a notable void in addressing the informational needs of users beyond numerical data. Additionally, there is a lack of studies focusing on users and few have explored feature selection techniques in machine learning-based phishing detection. To bridge this gap, we have developed a user-centric phishing URL detection tool powered by machine learning models. The objective is twofold: to effectively categorize phishing links and to provide users with contextual understanding and educational resources to enhance their online safety. This paper offers unique contributions by a comprehensive, user-focused approach to phishing detection combined with ML techniques. For the ML aspects, three feature selection approaches—Recursive Feature Elimination with Cross-Validation (RFECV), Particle Swarm Optimization (PSO), and Random Forest selection—were explored using three different machine learning algorithms: Random Forest, LightGBM, and SVC. The study found that the combination of LightGBM with RFECV provided the highest performance, achieving an accuracy of 95.07% after hyperparameter tuning. By focusing on user-centric design, this research not only enhances phishing detection capabilities but also empowers users with the knowledge to make informed decisions, thereby improving overall online safety.
Cybersecurity experts warn of rising threats, predicting a NotPetya-like incident due to AI, quantum advancements, and increased geopolitical tensions.
Keyfactor foresees a pivotal shift in data security practices by 2025, driven by quantum computing advances and the adoption of post-quantum cryptography.
Cyberattacks utilising generative artificial intelligence (GenAI) technology as a tool are expected to grow next year
(Bild: Aysel - stock.adobe.com / KI-generiert) Informieren Sie sich, wie Unternehmen ihre NIS2-Compliance bewerten und verbessern können mithilfe von Beurteilungstools von Sophos.
The world is on the cusp of a revolution in information security driven by the rapid advancement of quantum technologies. As quantum computers gain power, traditional cryptographic systems are facing unprecedented threats to their integrity. This article delves into the analysis of the impact of quantum technologies on cryptography, highlighting the need for new solutions to protect information in the face of quantum progress.
Spanish bank Banco Sabadell has taken a step toward preparing for the quantum computing era by collaborating with Accenture and QuSecure to plan how to incorporate post-quantum cryptography (PQC) technologies into its infrastructure.
The advent of quantum computing has raised significant concerns about the security of current cryptographic methods. To address this threat, organizations such as the European Telecommunications Standards Institute (ETSI) and the National Institute of Standards and Technology (NIST) have established initiatives to develop standards for post-quantum cryptography.
/CNW/ -- NEXCOM, a leading supplier of network solutions, has introduced a new generation of cybersecurity uCPEs powered by Intel® architecture CPUs. Designed...
The influence of Artificial Intelligence in our society is becoming important due to the possibility of carrying out analysis of the large amount of data that the increasing number of interconnected devices capture and send as well as making autonomous and instant decisions from the information that machines are now able to extract, saving time and efforts in some determined tasks, specially in the cyberspace. One of the key issues concerns security of this cyberspace that is controlled by machines, so the system can run properly. A particular situation, given the heterogeneous and special nature of the environment, is the case of IoT. The limited resources of some components in such a network and the distributed nature of the topology make these types of environments vulnerable to many different attacks and information leakages. The capability of Generative Artificial Intelligence to generate contents and to autonomously learn and predict situations can be very useful for making decisions automatically and instantly, significantly enhancing the security of IoT systems. Our aim in this work is to provide an overview of Generative Artificial Intelligence-based existing solutions for the very diverse set of security issues in IoT environments and to try to anticipate future research lines in the field to delve deeper.
A research team in the Netherlands investigated how copper planar air-core inductors can yield the required inductor properties to support sub-module power conversion in PV modules. The scientists claim that implementing MPPT at the sub-module level can increase the panel's tolerance to shade.
onsemi acquires Qorvo's SiC JFET business, enhancing its power portfolio for AI and EV markets with advanced, efficient power solutions.
No single material is ideal, but unique combinations are emerging.
The Hartree algorithm allows us to determine the quantum-mechanical state of electrons in a solid taking into account the mutual Coulomb interaction.
(Bild: Dall-E / KI-generiert) Google hat auf gefährliche Schwachstellen im Chrome-Browser mit einem Sicherheitsupdate reagiert. Erfahren Sie mehr über die Details und wie Sie Ihr System schützen können.
The Power MOSFET Metal Oxide Semiconductor Field Effect Transistor Market valued at US 7 41 billion in 2023 is expected to grow at a compound annual growth rate CAGR of 6 61 from 2023 to 2033 This growth is primarily ...
To combat the rising threat of AI-powered phishing, a comprehensive strategy is crucial.
Galliumoxid-Leistungskomponenten Marktprognose und Wachstumstreiber 2031
As artificial intelligence continues to evolve, a new generation of AI systems is emerging. The new systems go beyond answering questions to autonomously completing tasks and solving problems.
We recently compiled a list of the Top 10 AI Stocks on Latest News and Analyst Ratings. In this article, we are going to take a look at where ON Semiconductor Corporation (NASDAQ:ON) stands against the other AI stocks. Hugh Gimber, global market strategist at J.P. Morgan Asset Management, joined CNBC’s Squawk Box Europe to discuss […]
Ein Hacker und ein paar fehlerhafte Solarmodule genügen anscheinend, um die Sicherheit der europäischen Solarstrom-Produktion zu gefährden - und das Stromnetz
Two companies, Wiz and Cyera played key roles in this growth.
City-state's aerospace sector grows rapidly on strength in niche fields
Verschlüsselter Datenverkehr entwickelte sich zu einem wachsenden Einfallstor für immer raffiniertere Bedrohungen und dieser Trend wurde durch den Einsatz von Künstlicher Intelligenz (KI) auf Seiten der Malware-Akteure im letzten Jahr noch weiter verstärkt.
Perception Point is a Prevention-as-a-Service company, offering fast interception of any content-based attack across all collaboration channels
(Bild: Fraunhofer IEM) Der Cyber Resilience Act wurde verabschiedet, was bedeutet dies für Unternehmen? Das Fraunhofer IEM gibt Tipps zur Umsetzung und Einhaltung der neuen Vorschriften.
This is Valeo and ROHM's first step toward collaboration to optimize the next generation of power modules for electric motor inverters.The post Valeo and ROHM Partner on Next-gen Power Electronics appeared first on EDN Asia.
LG Electronics is strengthening the cybersecurity competitiveness of networked products to respond to the development of artificial intelligence (AI) technology and the popularization of the Internet of Things (IoT). LG Electronics recently...
Japan-based Rohm Semiconductor and Taiwan Semiconductor Manufacturing Company (TSMC) have announced a strategic partnership to advance the development and mass production of gallium nitride (GaN) power devices for electric vehicle (EV) applications. The collaboration combines Rohm's expertise in device development with TSMC's GaN-on-silicon process technology to address the growing demand for high-performance power solutions in the automotive sector.
Source: www.securityweek.com - Author: Kevin Townsend Forget the 10 septillion years needed for a classical computer to solve this problem, and focus ins
The integration of AI into cybersecurity is changing the very way organizations protect their digital infrastructure. AI presents novel solutions that help combat cyber threats. However, it also…
Hsinchu, Taiwan, Dec. 12, 2024 (GLOBE NEWSWIRE) -- Jmem Tek, a specialist in hardware security and post-quantum cryptography for IoT devices, announces a...
In this quickfire video with Patrick Donegan; the Founder & Principal Analyst at HardenStance and Stephane Quetglas; the Embedded Solutions Marketing
ROHM Co., Ltd. (ROHM) announced today that ROHM and TSMC have entered a strategic partnership on development and volume production
This is Valeo and ROHM's first step toward collaboration to optimize the next generation of power modules for electric motor inverters.The post Valeo and ROHM Partner on Next-gen Power Electronics appeared first on EE Times Asia.
US semiconductor supplier Onsemi has unveiled silicon carbide power-integrated modules for utility-scale PV systems. It says the new modules increase solar inverter power from 300 kW to 350 kW and weigh 245 grams.
ASMPT präsentiert mit der SilverSAM™ ein Highlight der modernen Leistungselektronik: eine innovative und vielseitige Maschine für das Silbersintern. Diese Technologie erfüllt die hohen Anforderungen an das Bonding, die insbesondere in der Elektromobilität entscheidend sind. Die
We discuss silicon photonics and SiC innovations with Christophe Maleville, CTO and SEVP of innovation at Soitec.
STMicroelectronics' STGAP3S advanced galvanically isolated gate drivers feature flexible protection for IGBTs and SiC MOSFETs.The post ST Advances Protection for IGBTs and SiC MOSFETs appeared first on EE Times India.
GaN Power Device Market Expected to Reach $12,849.3 Million by 2033
Navitas Semiconductor (Nasdaq: NVTS), the only pure-play, next-generation power semiconductor company and industry leader in gallium nitride (GaN) power ICs and silicon carbide (SiC) technology, has announced it will showcase several breakthroughs for AI data centers, EVs, and mobile technology at CES 2025(Tech West, Venetian suite 29-335, January7th-10th). Navitas was recently acknowledged as the Top …
Wechselrichter mit GaN-Komponenten erreicht auf Motorprüfstand bei 5 kHz einen Benchmark-Wirkungsgrad von 99,9 %.
The role of advanced power electronics technologies is strategic in the integration of renewable energy.The post PEN eBook December 2024: Revolutionizing Power Delivery, from Industrial Automation to EV Charging appeared first on Power Electronics News.
STMicroelectronics' STGAP3S advanced galvanically isolated gate drivers feature flexible protection for IGBTs and SiC MOSFETs.The post ST Advances Protection for IGBTs and SiC MOSFETs appeared first on EE Times Asia.
Vishay Intertechnology introduces a 40 V MOSFET, enhancing power density and efficiency for industri
ESSEX JUNCTION, Vt., Dec. 04, 2024 (GLOBE NEWSWIRE) -- GlobalFoundries (NASDAQ:GFS) (GF) has received an additional $9.5 million in federal funding from the U.S. government to advance the manufacturing of GF's
(Bild: Mercedes-Benz AG) Mercedes-Benz introduces micro-converters that regulate the voltage at the cell level. This enables a novel battery management system.
"Internet of Water" is marketed through AIoT4All, a Tec de Monterrey startup focused on developing IoT products with artificial intelligence.The interchangeable sensors can measure a wide range of parameters
Infrgy tech powering bulb and fan Demonstration at the University of Kashmir Radio powering 4 bulbs Live presentation: INFRGY’s technology converts radio frequencies into usable electricity The technology is scalable, efficient, and offers a way to power devices without the need for precise alignment or physical contact” — Parvez Rishi LOS ANGELES, CA, UNITED STATES, […]
High-precision indoor positioning is essential for various applications, such as the Internet of Things, robotics, and smart manufacturing, requiring accuracy better than 1 m. Conventional indoor positioning methods, like Wi-Fi or Bluetooth fingerprinting, typically provide low accuracy within a range of several meters, while techniques such as laser or visual odometry often require fusion with absolute positioning methods. Ultra-wideband (UWB) and Wi-Fi Round-Trip Time (RTT) are emerging radio positioning technologies supported by industry leaders like Apple and Google, respectively, both capable of achieving high-precision indoor positioning. This paper offers a comprehensive survey of UWB and Wi-Fi positioning, beginning with an overview of UWB and Wi-Fi RTT ranging, followed by an explanation of the fundamental principles of UWB and Wi-Fi RTT-based geometric positioning. Additionally, it compares the strengths and limitations of UWB and Wi-Fi RTT technologies and reviews advanced studies that address practical challenges in UWB and Wi-Fi RTT positioning, such as accuracy, reliability, continuity, and base station coordinate calibration issues. These challenges are primarily addressed through a multi-sensor fusion approach that integrates relative and absolute positioning. Finally, this paper highlights future directions for the development of UWB- and Wi-Fi RTT-based indoor positioning technologies.
Microwave wireless power transmission (MWPT) applications have attracted worldwide interest and attention in recent years. Rectennas are a crucial component of a MWPT system. The rectenna’s power capacity and output DC power have great significance with regard to the MWPT system’s performance. In this article, a compact 4 × 4 S-band rectangular patch rectenna array for MWPT is proposed and experimentally verified. Firstly, an S-band rectifier with better consistency and lower cost than a traditional output design using parallel capacitors as a filter is achieved. Then, a rectenna array based on the proposed rectifier and a novel design idea is proposed. The rectenna can achieve an output DC power of 117.6 mW/cm3 and an efficiency of 47.6%. Finally, a MWPT verification experiment is conducted. A 12-inch LCD screen powered by the rectenna with a rated power of 12 W successfully works without any other power supply. This article provides a new design of a rectenna for MWPT, and the proposed rectenna array demonstrates its good engineering significance and application prospects.
India RFID Market Expected to Reach $1,864.5 Million by 2030
This paper discusses how integrating renewable energy, AI, and IoT becomes important in promoting climate-smart agriculture. Due to the changing climate, rise in energy costs, and ensuring food security, agriculture faces unprecedented challenges; therefore, development toward innovative technologies is emerging for its sustainability and efficiency. This review synthesizes existing literature systematically to identify how AI and IoT could optimize resource management, increase productivity, and reduce greenhouse gas emissions within an agricultural context. Key findings pointed to the importance of managing resources sustainably, the scalability of technologies, and, finally, policy interventions to ensure technology adoption. The paper further outlines trends in the global adoption of renewable energy and smart agriculture solutions, indicating areas of commonality and difference and emphasizing the need for focused policies and capacity-building initiatives that will help, particularly in the developing world, the benefits of such innovations. Eventually, this research covers some gaps in understanding how AI, IoT, and renewable energy could jointly contribute to driving towards a greener and more resilient agriculture sector.
The evolution of 5G and Internet of Things (IoT) technologies is leading to ubiquitous connections among humans and their environment, such as autopilot transportation, mobile e-commerce, unmanned vehicles, and healthcare applications, bringing revolutionary changes to our daily lives [...]
In the cloud-assisted industrial Internet of Things (IIoT), since the cloud server is not always trusted, the leakage of data privacy becomes a critical problem. Dynamic symmetric searchable encryption (DSSE) allows for the secure retrieval of outsourced data stored on cloud servers while ensuring data privacy. Forward privacy and backward privacy are necessary security requirements for DSSE. However, most existing schemes either trade the server’s large storage overhead for forward privacy or trade efficiency/overhead for weak backward privacy. These schemes cannot fully meet the security requirements of cloud-assisted IIoT systems. We propose a fast and firmly secure SSE scheme called Veruna to address these limitations. To this end, we design a new state chain structure, which can not only ensure forward privacy with less storage overhead of the server but also achieve strong backward privacy with only a few cryptographic operations in the server. Security analysis proves that our scheme possesses forward privacy and Type-II backward privacy. Compared with many state-of-the-art schemes, our scheme has an advantage in search and update performance. The high efficiency and robust security make Veruna an ideal scheme for deployment in cloud-assisted IIoT systems.
Water pollution presents one of the biggest challenges in the world today, as the degradation of water quality of rivers in many instances is increasing so fast and poses a big danger to all forms of life, eventually causing many aquatic species and other species that depend on them to be endangered. Hence, with the development of Internet of Things (IoT) and Wireless Sensor Networks (WSNs), there arises a need to monitor river waters for a timely response in protecting the rivers, which is the aim of this paper. With respect to this project, we searched a little bit for some existing IoT technologies and other related work. In this paper, we propose a practical low-cost solution based on Long Range (LoRa) technology to obtain real-time observations of, with certain sensors, such water parameters as temperature, pH, conductivity and turbidity. Data gathered at a sensor node are transmitted via LoRa modulation to a gateway for processing and local storage on a Message Queuing Telemetry Transport (MQTT) server, visualization on a Node-RED interface, or transmission to the cloud. The prototype system created is employed in the actual field and demonstrates that the water quality monitoring in the river can be carried out effectively within a small scale of the area of roughly 20 km2 depending on the location of the study site.
According to a new report published by Allied Market Research titled Wireless Mesh Networks Market The Wireless Mess Networks Market Size was 5 2 billion in 2021 and is estimated to reach 12 8 billion by 2031 growing at a ...
Eseye, a leading provider of global IoT connectivity solutions, has announced its partnership with Sateliot, the leader in the IoT satellite connectivity
Here's a look at Infineon's platforms and solutions showcase for the contactless payment industry at the Singapore FinTech Festival 2024.The post Infineon Highlights Latest Contactless Payment Solutions at SFF 2024 appeared first on EE Times India.
Recently, the growing popularity of the Internet of Things (IoT) presents a promising opportunity not only for the expansion of various home automation systems but also for diverse industrial applications. By leveraging these benefits, automation is being implemented in industries, leading to the Industrial Internet of Things (IIoT). Although IoT simplifies daily activities that benefit human operations, it poses significant security challenges that warrant attention. Consequently, implementing an Intrusion Detection System (IDS) is a vital and effective solution. IDS aims to address the security and privacy challenges by detecting various IoT attacks. Various IDS methodologies, including those using Machine Learning (ML), Deep Learning (DL) and Large Language Models (LLMs), are employed to identify intrusions within the data; however, improvements to the detection systems are still needed. A literature survey on IDS in the IoT domain is provided, focusing primarily on the recent approaches used in the field. The survey aims to evaluate the literature, identify current trends, retest these approaches on recent data, and highlight open problems and future directions.
Currently, the process of tracking moving objects and determining their indoor location is considered to be one of the most attractive applications that have begun to see widespread use, especially after the adoption of this technology in some smartphone applications. The great developments in electronics and communications systems have provided the basis for tracking and location systems inside buildings, so-called indoor positioning systems (IPSs). The ultra-wideband (UWB) technology is one of the important emerging solutions for IPSs. This radio communications technology provides important characteristics that distinguish it from other solutions, such as secure and robust communications, wide bandwidth, high data rate, and low transmission power. In this paper, we review the implementation of the most important real-time indoor positioning and tracking systems that use ultra-wideband technology for tracking and localizing moving objects. This paper reviews the newest in-market UWB modules and solutions, discussing several types of algorithms that are used by the real-time UWB-based systems to determine the location with high accuracy, along with a detailed comparison that saves the reader a lot of time and effort in choosing the appropriate UWB-module/method/algorithm for real-time implementation.
Given the high risk of Internet of Things (IoT) device compromise, it is crucial to discuss the attack detection aspect. However, due to the physical limitations of IoT, such as battery life and sensing and processing power, the widely used detection techniques, such as signature-based or anomaly-based detection, are quite ineffective. This research extracted loop-based cases from the transmission session dataset of “CTU-IoT-Malware-Capture-7-1” (“Linux, Mirai”) and implemented a loop-based detection machine learning approach. The research employed nine machine learning models to illustrate how the loop patterns of the datasets can facilitate detection. The results of this study indicate that the XGBoost model achieves the best performance in terms of “Accuracy: 8.85%”, “Precision: 96.57% (Class)”, “Recall: 96.72% (Class 1)”, and “F1-Score: 6.24%”. The XGBoost model demonstrated exceptional performance across all metrics, indicating its capability in handling large IoT datasets effectively. It provides not only high accuracy but also strong generalization, which is crucial for detecting intricate and diverse patterns of malicious behavior in IoT networks. Its precision and recall performance further highlight its robustness in identifying both attack and normal activity, reducing the chances of false positives and negatives, making it a superior choice for real-time IoT threat detection.
The Transparent RFID Tag Wall (TRT-Wall) tracks the behavior of at-risk individuals in their homes, without having to wear RFID tags.
A new innovation lab based in central London is believed to be the UK’s first Cloud native facility dedicated to the manufacturing sector. Launched by Cloud engineering company Storm Reply following a six-figure investment, the industrial Internet of Things (IIoT) lab has already been successfully used by Kingspan’s Insulated Panels division and wire joiner manufacturer
Digital wallets are becoming essential tools for French consumers, especially as the country embraces the convenience of mobile payment solutions. In the
This paper presents a novel approach to robotic control by integrating nonlinear dynamics with machine learning (ML) in an Internet of Things (IoT) framework. This study addresses the increasing need for adaptable, real-time control systems capable of handling complex, nonlinear dynamic environments and the importance of machine learning. The proposed hybrid control system is designed for a 20 degrees of freedom (DOFs) robotic platform, combining traditional nonlinear control methods with machine learning models to predict and optimize robotic movements. The machine learning models, including neural networks, are trained using historical data and real-time sensor inputs to dynamically adjust the control parameters. Through simulations, the system demonstrated improved accuracy in trajectory tracking and adaptability, particularly in nonlinear and time-varying environments. The results show that combining traditional control strategies with machine learning significantly enhances the robot’s performance in real-world scenarios. This work offers a foundation for future research into intelligent control systems, with broader implications for industrial applications where precision and adaptability are critical.
Canadian startup Beacon has opened the waitlist for its purpose-built digital wallet for immigrants moving to the country.
The solution features a disposable Bluetooth Low Energy (BLE) sticker recently released by Kontakt.io, along with the a full software suite.
The Internet of Medical Things (IoMT), an extension of the Internet of Things (IoT), is still in its early stages of development. Challenges that are inherent to IoT, persist in IoMT as well. The major focus is on data transmission within the healthcare domain due to its profound impact on health and public well-being. Issues such as latency, bandwidth constraints, and concerns regarding security and privacy are critical in IoMT owing to the sensitive nature of patient data, including patient identity and health status. Numerous forms of cyber-attacks pose threats to IoMT networks, making the reliable and secure transmission of critical medical data a challenging task. Several other situations, such as natural disasters, war, construction works, etc., can cause IoMT networks to become unavailable and fail to transmit the data. The first step in these situations is to recover from failure as quickly as possible, resume the data transfer, and detect the cause of faults, failures, and errors. Several solutions exist in the literature to make the IoMT resilient to failure. However, no single approach proposed in the literature can simultaneously protect the IoMT networks from various attacks, failures, and faults. This paper begins with a detailed description of IoMT and its applications. It considers the underlying requirements of resilience for IoMT networks, such as monitoring, control, diagnosis, and recovery. This paper comprehensively analyzes existing research efforts to provide IoMT network resilience against diverse causes. After investigating several research proposals, we identify that the combination of software-defined networks (SDNs), machine learning (ML), and microservices architecture (MSA) has the capabilities to fulfill the requirements for achieving resilience in the IoMT networks. It mainly focuses on the analysis of technologies, such as SDN, ML, and MSA, separately, for meeting the resilience requirements in the IoMT networks. SDN can be used for monitoring and control, and ML can be used for anomaly detection and diagnosis, whereas MSA can be used for bringing distributed functionality and recovery into the IoMT networks. This paper provides a case study that describes the remote patient monitoring (RPM) of a heart patient in IoMT networks. It covers the different failure scenarios in IoMT infrastructure. Finally, we provide a proposed methodology that elaborates how distributed functionality can be achieved during these failures using machine learning, software-defined networks, and microservices technologies.
Agriculture faces unprecedented challenges as the global population races towards 9.1 billion by 2050. Feeding this burgeoning population amidst climate change, depleting resources, and soil degradation requires bold innovation and technology-driven …
Siemens is to retrofit 60 government buildings with IoT sensors and software to slash energy and water consumption by 27 percent per annum.
Bluetooth ranging relies on two-way multi-carrier phase difference (MCPD) channel frequency response (CFR) measurements to mitigate time and phase offsets. However, the challenge of doubled multi-path components under the two-way MCPD approach, especially with a low number of snapshots, further degrades the performance of the commonly utilized MUSIC algorithm. In this paper, we investigate a reduced complexity signal-subspace-based approach for wireless ranging using Bluetooth Low Energy (BLE) in high multi-path environments. We propose a novel signal subspace decomposition (SSD) algorithm where we utilize the span of individual signal subspace eigenvectors for range estimation. We formulate the integration of the Fourier transform and randomized low rank approximation (RLA) into the SSD algorithm to reduce the computational complexity for better utilization in embedded devices. We then make use of the output features from estimated psuedo-spectrum of the individual eigenvectors, obtained from enhanced SSD algorithm, as an input to the long-short-term-memory (LSTM) recurrent neural network (RNN) to obtain a data-driven SSD-LSTM wireless range estimator for BLE. We evaluate our proposed approach using our real-world BLE data for single- and multiple-antenna scenarios. Our results show an improved performance of our proposed approach by more than 37%, while still enjoying lowest computational complexity than existing MUSIC and Support Vector Regression approaches for BLE ranging.
There is an increasing industry demand for reliable Anomaly Detection (AD) solutions to detect erroneous measurements or alarm events in marine sensor…
Petroleum-derived polyimides (PIs) are indispensable in microelectronics. However, these polymers face significant challenges including limited chemic…
Edge computing on a smartphone has been used to analyze data collected by a multimodal flexible wearable sensor patch and detect arrhythmia, coughs and falls.
(Bild: Magna) Magna Electronics diskutiert die nächste Generation von ADAS-Systemen, Sensordatenfusion und wie die Cloud das autonome Fahren sicherer machen könnte.
ST develops a highly integrated biosensor that combines a high-accuracy biopotential input with its inertial sensing and AI core.
ACS Applied Materials & InterfacesDOI: 10.1021/acsami.4c12507
STMicroelectronics is aiming at healthcare wearables with an IC that senses bio-potentials and acceleration, and includes an AI processor and a finite
The global ultrasonic sensor market size was USD 6 09 Billion in 2022 and is expected to register a rapid revenue CAGR of 11 1 during the forecast period Increasing adoption of robotics in industrial sectors and use of ultrasonic ...
Gesture Recognition Market Size WILMINGTON, NEW CASTLE, DE, UNITED STATES, October 28, 2024 /EINPresswire.com/ — According to a new report published by Allied Market Research, titled, “Gesture Recognition Market,” The gesture recognition market was valued at $13.9 billion in 2021, and is estimated to reach $88.2 billion by 2031, growing at a CAGR of 20.6% […]
Photonic Sensors Market Size Photonic Sensors Market Expected to Reach $94.2 Billion by 2030 Increasing adoption of Biophotonics and laser technology in healthcare and industrial applications is expected to be the major trend in the world” — Allied Market Research WILMINGTON, DE, UNITED STATES, October 25, 2024 /EINPresswire.com/ — Allied Market Research, titled, “Photonic Sensors […]
A hybrid sensing configuration combining fiber Bragg grating (FBG) with a Fabry-Perot interferometer is proposed for highly sensitive detection of pre…
Borophene, a novel two-dimensional (2D) nanomaterials with high surface-to-volume ratio and abundant active sites, show great promise for humidity sen…
Researchers at Tampere University have developed the world's first soft touchpad that can sense the force, area and location of contact without electricity. The device utilizes pneumatic channels, enabling its use in environments such as MRI machines and other conditions that are unsuitable for electronic devices. Soft devices like soft robots and rehabilitation aids could also benefit from this new technology.
Dublin, Oct. 24, 2024 (GLOBE NEWSWIRE) -- The
Human Activity Recognition (HAR) is the task to automatically analyze and recognize human body gestures or actions. HAR using time-series multi-modal …
Lidwave, a developer of 4D lidar-on-chip sensors, has secured $10 million in seed round funding to further develop its optical chip, launch a software
The quest for effective cancer treatments has driven transformative strategies in drug discovery, and biosensors have emerged as indispensable tools, renowned for their precision, sensitivity, and real-time monitoring capabilities. The review begins with a brief overview of cancer drug discovery, underscoring the pivotal role of biosensors in advancing cancer research. Various types of biosensors employed in cancer drug discovery, including fluorescence- and bioluminescence-based biosensors, are then explored. Emphasis is placed on the integration of biosensors in high throughput screening (HTS) and validation for cancer drug compounds, elucidating their pivotal role in accelerating the identification of potential therapeutics. Real-time monitoring of cellular responses through biosensors yields invaluable insights into cancer cell signaling pathways, drug efficacy, and resistance mechanisms. The review illuminates how biosensors contribute to unraveling the dynamic nature of cancer cells, thereby facilitating the development of more targeted and personalized therapies. Challenges linked to biosensor applications in cancer drug discovery are addressed, alongside a discussion of emerging trends and future perspectives. In conclusion, this review offers a comprehensive analysis of biosensor applications in the dynamic field of cancer drug discovery. The integration of biosensor technologies holds the promise of revolutionizing the discipline, presenting novel avenues for the development of targeted and personalized cancer therapies.
Advanced Healthcare Materials, EarlyView.
Researchers used multimodal wearable sensors and machine learning to track real-time fatigue in manufacturing workers, revealing critical insights into individual fatigue patterns and potential ergonomic improvements.
HKU introduces a stretchable wearable biosensor, integrating on-device AI computing for real-time health monitoring, enhancing efficiency, accuracy, and privacy in digital healthcare solutions.
Infineon Technologies AG launches the new automotive-qualified fingerprint sensor ICs CYFP10020A00 and CYFP10020S00. The devices are optimized for
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