(Bild: Stannol GmbH & Co. KG) Der No-Clean-Lötprozess auf Direct-Copper-Bonding-Substraten stellt hohe Anforderungen an Sauberkeit und Bauteilfixierung. Siemens suchte daher eine Alternative zu Graphitrahmen und aktivatorfreien Lotpasten. Stannol half bei der Neuentwicklung eines Tacky-Gels.
#SavetheDate: 2nd Power Electronics for Energy Transition SymposiumSeptember 29–30, 2026 | Vienna, Austria#Powerelectronics is one of the key enabling technologies for the energy transition. Following the successful first symposium last year, AIT Austrian Institute of Technology and Silicon Austria Labs (SAL) are jointly organizing the 2nd Power Electronics for Energy Transition #Symposium in Vienna, aiming to further establish this event as a central meeting point for industry and academia in Central Europe.The 2026 symposium will focus on the latest developments and future trends in medium-voltage power electronics, grid-forming systems, electrification of industrial processes, and e-mobility – from automotive to aerospace.#CallforAbstractsResearchers and industry experts are invited to actively contribute to the symposium with an oral or poster presentation.Submission deadline for short abstracts: May 4, 2026#Topics· Medium-voltage power electronics and charging· Grid-forming systems (AC and DC)· Electrification of industrial processes· E-mobility – from automotive to aerospace 👉 Learn more: #Sponsorship & Exhibition Opportunities The symposium offers three attractive packages for organizations to present themselves to the power electronics community before, during, and after the event: Sponsor, Exhibitor, and Gold Sponsor.#PowerElectronics #EnergyTransition #EnergySystems #MediumVoltage#GridForming #PowerSystems #EMobility #IndustrialElectrification
The Film Capacitor Market size is expected to reach US 1 99 Bn by 2030 at a CAGR of 2 55 during the forecast period Film Capacitor Market Overview The Film Capacitor Market is shaped by its wide usage across ...
Evertiq: Your Source for Electronics Manufacturing News & Industry Expos. Stay updated with breaking news and discover upcoming expos.
The DC wallbox charger market is positioned for remarkable growth as electric vehicle adoption accelerates worldwide With advances in charging technology and increasing investments in EV infrastructure this sector is set to transform how EVs are powered across residential commercial ...
The axial lead capacitors market is set for significant expansion in the coming years driven by technological advancements and increasing demand across various sectors This report explores the market s projected growth influential players key trends and the main segments ...
The new USB 2.0 peripheral controller is a flexible solution suitable for biometric, medical, robotics, and industrial applications.
Fortschritte beim Elektroantrieb sowie zusätzliche Serien- und Sonderausstattungen sollen die Attraktivität von BMW-Stromern 2026 steigern.
LONDON, Jan. 27, 2026 /PRNewswire/ -- DC Market Insights announces updated insights into the Global Data Center Power Market. The market shows strong and sustained growth across regions and
The European Commission has unveiled a new cybersecurity package aimed at reinforcing the European Union’s (EU) resilience against growing digital threats.
Calgary, Alberta--(Newsfile Corp. - January 22, 2026) - SuperQ Quantum Computing Inc. (CSE: QBTQ) (OTCQB: QBTQF) (FSE: 25X) ("SuperQ Quantum", "SuperQ", or...
Securitas AB transformiert sich von einem traditionellen Sicherheitsdienstleister zu einem technologiegetriebenen Anbieter mit KI-gestützter Videoanalyse.
The imminent threat of large-scale quantum computers to modern public-key cryptographic devices has led to extensive research into post-quantum cryptography (PQC). Lattice-based schemes have proven to be the top candidate among existing PQC schemes due to their strong security guarantees, versatility, and relatively efficient operations. However, the computational cost of lattice-based algorithms—including various arithmetic operations such as Number Theoretic Transform (NTT), polynomial multiplication, and sampling—poses considerable performance challenges in practice. This survey offers a comprehensive review of hardware acceleration for lattice-based cryptographic schemes—specifically both the architectural and implementation details of the standardized algorithms in the category CRYSTALS-Kyber, CRYSTALS-Dilithium, and FALCON (Fast Fourier Lattice-Based Compact Signatures over NTRU). It examines optimization measures at various levels, such as algorithmic optimization, arithmetic unit design, memory hierarchy management, and system integration. The paper compares the various performance measures (throughput, latency, area, and power) of Field-Programmable Gate Array (FPGA) and Application-Specific Integrated Circuit (ASIC) implementations. We also address major issues related to implementation, side-channel resistance, resource constraints within IoT (Internet of Things) devices, and the trade-offs between performance and security. Finally, we point out new research opportunities and existing challenges, with implications for hardware accelerator design in the post-quantum cryptographic environment.
A dangerous Android malware campaign has emerged, targeting users through mobile games and pirated streaming app modifications. The threat, known as Android.Phantom, employs machine learning technology to perform automated ad-click fraud on infected smartphones. Over 155,000 downloads of compromised games have been recorded, with additional infections spreading through modified
Künstliche Intelligenz, Quantencomputing und neue regulatorische Vorgaben wie NIS2 und DORA verändern Zero-Trust-Strategien ab 2026 in IT-, Cloud- und OT-Umgebungen umfassend. Der erste Teil dieses Zweiteilers hat gezeigt, weshalb Zero Trust heute betrieblich notwendig ist und wo klassische Ansätze an ihre Grenzen stossen. Im zweiten Teil geht es um KI in Angriff und Abwehr, Zero Trust als künftige Pflicht und drei konkrete Praxistipps für die strategische Ausrichtung de
Microsoft Teams führt ein Frühwarnsystem ein, das externe Cyberangriffe erkennt und Phishing-Angriffe abwehrt. Teil einer umfassenden Sicherheitsstrategie.
BleepingComputer reports that the newly emergent sophisticated Linux malware framework VoidLink, which sets sights on cloud environments, has been primarily developed using AI.
EU-Kommission will Huawei und ZTE aus Netzen verbannen
(Bild: Vogel IT-Medien / VMRay / Schonschek) Künstliche Intelligenz (KI) durchdringt und verändert die Cybersicherheit. Doch ersetzt die Erkennung von Angriffen durch Künstliche Intelligenz (KI) andere Verfahren wie Sandboxes oder werden Sandboxes in Zeiten von KI-basierter Cybersicherheit sogar noch mehr benötigt? Der aktuelle Videocast von Insider Research mit Dr. Carsten Willems von VMRay liefert Antworten.
The new DC link capacitors are optimized for SiC-based inverters requiring low inductances (ESL of 8 nH) and high-frequency operation.
ABB has dispatched its first wind power converter made in India from its Bengaluru facility, strengthening its wind and renewable energy portfolio. Sectors
Chinas Versorgeraktien erleben einen steilen Kursanstieg. Warum du jetzt auf diese Aktie setzen solltest – das steckt hinter dem Boom im Jahr 2026!
20.01.2026 - EQS-News: ROHM Co., Ltd. / Key word(s): Product Launch ROHM Adds Wide SOA MOSFETs for AI Servers with Compact 5 x 6mm Package 20.01.2026 / 07:05 CET/CEST The issuer is solely responsible for the content of this announcement. KYOTO, Japan, Jan. 20, ...
Market Overview QY Research Inc A global market research and consulting firm has announced the release of its latest 2025 report titled GaN Power Devices Global Market Share and Ranking Overall Sales and Demand Forecast 2026 2032 The report provides ...
APA ots news: Cybersicherheit gestärkt: FMA und OeNB ziehen positive Bilanz nach einem Jahr DORA EUweiter Aufsichtsrahmen macht Risiken digitaler Abhängigkeiten erstmals umfassend sichtbar Wien (APA-ots)
Laut dem "IoT & OT Cybersecurity Report 2025" von ONEKEY führt nur ein knappes Drittel der Unternehmen mindestens einmal im Jahr eine Schulung zum Cyber Resilience Act durch.
New KI- und Cybersecurity-Förderungen machen Kärntens Unternehmen fit für die digital Zukunft
The way people work, communicate, and innovate has changed with the rise of large language models (LLMs). This advanced form of artificial intelligence (AI), whether known as ChatGPT, Gemini, or by any other name, has enormous transformative power, enabling faster workflows, […]
STMicroelectronics’ SRK1004 synchronous-rectifier controllers save space and increase efficiency in the secondary side of active-clamp, resonant, and
A fantastic day of exchange on AI-based modelling for reliability and fault prediction in power electronics 🤖⚡ SAL co-organized this workshop together with Delft University of Technology and Fraunhofer-Gesellschaft within the R-PODID (Chips JU) project.We had an inspiring day, aligning AI-related research across R-PODID WP1 and brainstorming future research directions. SAL was represented by V S Bharath Kurukuru, Monika Stipsitz, Roberto Petrella and Ulrich Gaier, sharing insights on system level fault and degradation monitoring and physics grounded digital twin perspectives. A big thank you to Justin Dauwels (Delft University of Technology) and Marco Crescentini (Università di Bologna) for the excellent organization and support, and to Carsten Rolfes and Martin Schellenberger (Fraunhofer-Gesellschaft) for their great presentations. #SAL #RPODID #ChipsJU #powerelectronics #AI #reliability #researchcollaboration
Novee’s team includes veterans of Israel’s Unit 8200 and other national cyber defense units.
The transition to post-quantum cryptography (PQC) marks a pivotal shift in ensuring digital security, prompted by the potential of quantum computers to compromise classical systems such as Rivest-Shamir-Adlema and elliptic-curve cryptography. In response, NIST has standardized three foundational PQC algorithms: Module-lattice-based Key-encapsulation Mechanism for key establishment; Module-lattice-based Digital Signature, and Stateless Hash-based Digital Signature algorithms for digital signatures. Meanwhile, FALCON and Hamming Quasi-cyclic (HQC) schemes, both selected as finalists, are expected to join the standards soon. This paper presents a comprehensive survey of these NIST-selected PQC standards, with a dual focus on software implementations and hardware architecture designs. We analyze their mathematical frameworks, distinctive features, and optimization strategies related to performance, security, and resource efficiency. The software review examines algorithmic complexity, memory usage, and programming considerations, while the hardware review discusses FPGA and ASIC implementations, emphasizing modular arithmetic, polynomial operations, and resource efficiency challenges. A comparative analysis highlights the strengths and trade-offs of each algorithm, offering insights into their applicability across various platforms-from resource-constrained internet of things devices to high-performance computing environments. This study provides a foundational understanding of NIST’s selected PQC standards and their practical deployment in securing the post-quantum era.
Wie das BSI aktuell meldet, hat die IT-Sicherheitswarnung, welche eine vorliegende Schwachstelle für Siemens Industrial Edge Devices betrifft, ein Update erhalten. Eine Beschreibung der Sicherheitslücke inklusive der neuesten Updates sowie Infos zu betroffenen Betriebssystemen und Produkten lesen Sie hier.
Für OpenSSH wurde ein Update zur IT-Sicherheitswarnung einer bekannten Schwachstelle veröffentlicht. Eine Beschreibung der Sicherheitslücken inklusive der neuesten Updates sowie Infos zu betroffenen Betriebssystemen und Produkten lesen Sie hier.
The increasing cyber threats targeting Industrial Control Systems (ICS) and the Internet of Things (IoT) pose significant risks, especially in critical infra...
WISeKey teams with KSTPL to manufacture post-quantum secure WISeSat satellites in India, add India as a launch hub alongside the US, and prep a 2026 mission.
Ninety percent of companies are not prepared for modern cyber threats powered by artificial intelligence, according to the State of Cybersecurity Resilience 2025 study. AI
AI in Cybersecurity Market The AI in Cybersecurity Market reached US 26 29 billion in 2024 and is projected to reach US 109 33 billion by 2032 growing at a CAGR of 19 50 during 2025 2032 The market growth ...
Summary Exploring the intersection of tiny machine learning (TinyML) and cybersecurity, this chapter focuses on the application of lightweight cryptographic algorithms to enhance data security in microcontroller‐based systems. It begins with an overview of the unique challenges posed by the resource constraints of Internet of Things (IoT) devices in implementing standard cryptographic protocols. The discussion then moves to state‐of‐the‐art lightweight algorithms that are suitable for TinyML environments, including block ciphers, hash functions, and stream ciphers designed to operate efficiently on limited hardware. Comparative analysis of these algorithms provides insights into their security levels, computational demands, and energy consumption, supported by simulation results and practical implementations. The chapter further addresses the integration of these cryptographic tools into existing TinyML frameworks, exemplifying their real‐world applicability and effectiveness in securing TinyML applications against increasingly sophisticated cyberthreats. Code samples are provided to demonstrate the usage of lightweight cryptographic algorithms in TinyML contexts.
Seyond -- Nio's LiDAR supplier -- will deploy its detection technology in Sweden's transportation infrastructure.
PFAS sind Ewigkeitschemikalien, die sich in der Umwelt kaum abbauen. Sie gelangen in Böden und Gewässer, reichern sich in Pflanzen, Tieren und Menschen an u
Researchers have created a fully integrated and scalable chip device that combines silicon photonics with atomic vapour, achieving precise control of atomic density and paving the way for advanced applications in quantum technologies and atomic spectroscopy.
Purchase Now Up to 80 Discount on This Premium Report https www coherentmarketinsights com promo buynow 145406 The latest study by Coherent Market Insights titled Wind Power Converter System Market Size Share Trends Forecast 2025 2032 offers an in depth ...
classical liberalism, common sense, science and technology, history, international, open mind,
FMCW lidar offers mobile robots better precision for navigation at scale in complex environments, says Voyant Photonics' CEO.
Infineon is leveraging its technological strengths to expand into cutting-edge applications in AI data centers and robotics. The company is currently collaborating with Nvidia to develop power systems for next-generation AI racks, featuring direct current (DC) voltages up to 800V.
Researchers at Penn State developed an electrospun fiber material that could enable clothing-based health monitoring through advanced piezoelectric properties.
Objective: This study aimed to design and evaluate an explainable machine learning (ML) framework that integrates sensor-based motor assessments with demographic and clinical data to identify early indicators of mild cognitive impairment (MCI), fall risk, and frailty in older adults. Methods: Eighty-three community-dwelling older adults (60 years or older) completed multimodal motor assessments using the Mizzou Point-of-Care Assessment System (MPASS) to capture synchronized gait, balance, and sit-to-stand performance. Sensor-derived motor features were combined with demographic and clinical variables to develop predictive models for MCI, frailty, and fall risk using XGBoost and Decision Tree algorithms. A unified multilabel framework was also developed using XGBoost, Decision Tree, and AdaBoost to predict all three outcomes. Model interpretability was evaluated using SHapley Additive exPlanations (SHAP). Results: The ML model for MCI achieved the highest performance (94% accuracy, AUC = 0.88, F1 = 0.94), followed by fall risk (94% accuracy, AUC = 0.90) and frailty (82% accuracy, AUC = 0.77). Unified multilabel models showed moderate performance (67-73% accuracy), with XGBoost achieving the highest accuracy (73%), sensitivity, and F1 score, while the Decision Tree showed higher discrimination (AUC = 0.72). SHAP analyses identified stride length and time, center-of-pressure-based balance measures, and knee angular velocity during sit-to-stand as dominant predictors. Conclusions: This work introduces a novel ML framework using multimodal sensor-based motor assessments to predict MCI, fall risk, and frailty individually and within a unified model. By combining explainable ML with objective motor-function data, the framework supports transparent early screening of multidomain cognitive and physical decline in aging. Keywords: Explainable machine learning, Sensor-based motor assessment, Mild cognitive impairment, Fall risk, and Frailty.
🩺Spectral sensors: Key technology for smart diagnosticsThe requirements for modern medical technology are clear: precise, portable, and as non-invasive as possible. This is exactly where spectral sensors come into play. They detect visible and near-infrared light and provide detailed optical signatures — the basis for analyses ranging from skin to microfluidics and even pharmaceutical control.Our colleague Florian Lex has written an exciting article on this topic together with Manuel Gauß from RUTRONIK Electronics Worldwide in the current issue of RUTRONIKER 2025. Read more about how ams OSRAM is making this technology compact, energy-efficient, and ideal for portable systems with the TCS3448 Read the article (pp. 26-27): Landing page-Link )Learn more about our solutions for digital diagnostic devices: #RUTRONIKER2025 Markt&Technik
Parallel light detection and ranging (LiDAR) is widely adopted for its low computational burden and rapid three-dimensional (3D) reconstruction. Yet, it remains constrained by inter-channel crosstalk and limited long-distance performance. Here, we introduce a spectrally encoded parallel LiDAR based on super-bunching light, exhibiting negligible cross-correlation between wavelengths, enabling quasi-orthogonal channel division without crosstalk. This approach supports robust parallel ranging, rapid and accurate 3D reconstruction, and effective target classification. Our scheme achieves high-precision ranging with errors as low as 4 mm and can detect targets moving at velocities as low as 5 mm/s. It further enables reliable ranging and 3D imaging beyond 40 m, with exceptional anti-interference performance, even when noise exceeds the echo signal by three orders of magnitude. Combining high precision, sensitivity, long-range detection, dynamic target acquisition, precise 3D reconstruction, and robust anti-interference, our LiDAR offers significant potential for enhancing environmental perception technologies.
The IoT Sensor Market is witnessing robust growth as industries increasingly adopt smart technologies to enhance efficiency, automation, and real-time decision-making. IoT sensors play a pivotal role in collecting, transmitting, and analyzing data ac
Global Info Research announces the release of the report Global UWB Proximity Sensor Market 2025 by Manufacturers Regions Type and Application Forecast to 2031 The report is a detailed and comprehensive analysis presented by region and country type and application ...
Global Info Research announces the release of the report Global UWB Proximity Sensor Market 2025 by Manufacturers Regions Type and Application Forecast to 2031 The report is a detailed and comprehensive analysis presented by region and country type and application ...
Imaging technology has transformed how we observe the universe—from mapping distant galaxies with radio telescope arrays to unlocking microscopic details inside living cells. Yet despite decades of innovation, a fundamental barrier has persisted: capturing high-resolution, wide-field images at optical wavelengths without cumbersome lenses or strict alignment constraints.
ApsTron Science releases its next-generation physiological sensors, aimed at addressing growing global demand for continuous, data-driven health monitoring
Scientists at the U.S. Department of Energy's (DOE) Brookhaven National Laboratory and collaborators have developed a new type of lidar—a laser-based remote-sensing…
New York December 23 2025 The biomedical sensors market stands at the forefront of healthcare innovation empowering real time patient monitoring and personalized medicine like never before These compact devices integral to wearables implants and diagnostic tools detect vital signs ...
The agreement marks a deep integration of advanced sensing technology with low-altitude logistics applications.
CTI Connect, a leading provider of wireless broadband and connectivity solutions, delivers robust wireless connectivity for smart factory IoT sensors through
CTI Connect, a leading provider of wireless broadband and connectivity solutions, delivers robust wireless connectivity for smart factory IoT sensors through
Voyant Photonics introduces 4D FMCW lidar in a fully solid-state design, enabling high-performance depth sensing and velocity measurement.
KnowMade hat seinen aktuellen Patentlandschaftsbericht »LiDAR for Automotive – Patent Landscape Analysis 2025« veröffentlicht. Die Studie zeigt, wie globale Innovations- und IP-Strategien die Zukunft von LiDAR-Technologien für Fahrerassistenzsysteme (ADAS) und autonome Fahrzeuge prägen.
Siemens Energy India Ltd Stock Slides on Dec 22, 2025: Why ENRIN Fell, HVDC Order Miss, and What Analysts Forecast Next - TechStock²
As lithography resolution improves, the performance requirements for illumination pupils in immersion lithography machines have become increasingly stringent, particularly regarding energy balance and polarization properties. These characteristics are primarily achieved by adjusting the angular distribution of the micromirror array (MMA). For technology nodes below 40 nm, the impact of light polarization on imaging must be considered. This paper proposes a micro-mirror selection and angle setting algorithm to maintain key characteristics of the freeform pupil, ensuring energy balance in both unpolarized and polarized states. Energy balance in the unpolarized state is influenced by the eccentricity of light, while energy balance in the polarized state depends on both the eccentricity and polarization properties of light. To validate the accuracy of the algorithm, simulations were conducted using freeform pupil illumination optical models for both unpolarized and polarized states. The results demonstrated a significant improvement in energy balance, with a reduction to 0.08% in both states. For engineering applications, the computational speed of the algorithm was enhanced, reducing the calculation time from 600 to 0.1 s.
The LiDAR (Light Detection and Ranging) Market is witnessing remarkable growth as industries increasingly adopt advanced sensing technologies for precision mapping, autonomous navigation, and environmental monitoring. With applications spanning autom
CTI Connect, a leading provider of wireless broadband and connectivity solutions, delivers robust wireless connectivity for smart factory IoT sensors through
CTI Connect, a leading provider of wireless broadband and connectivity solutions, delivers robust wireless connectivity for smart factory IoT sensors through
In addition to the usual design techniques for improving sensor performance such as (1) calibration to correct errors including multi-point calibration for higher accuracy, and regular recalibration to correct for drift over time; (2) signal conditioning to correct for errors like offset and linearity, amplified signals for improved signal-to-noise ratio (SNR); and (3) optimizing data acquisition to minimize delays in data acquisition and transmission for time synchronization, there are specific sensor fusion improvement design considerations.
18. Dezember 2025. Das Fraunhofer Institut für Photonische Mikrosysteme IPMS arbeitet gemeinsam mit dem Max-Planck-Institut für Chemische Physik fester Stoffe CPfS an einem innovativen Projekt namens "OptoQuant". Dieses Projekt wird im Rahmen des Fraunhofer-Max-Planck-Kooperationsprogramms gefördert und zielt auf die Entwicklung von CMOS-integrierter, mikro-optoelektronischer Quantensensorik zur hochempfindlichen Magnetfeldabbildung bei Raumtemperatur.
Researchers from Empa, EPFL and CSEM have developed a green smart sensing tag that measures temperature and humidity in real time – and can also detect whether a temperature threshold has been exceeded. In future, this could be used to monitor sensitive shipments such as medicines or food. The sensor…
Monitoring von Naturgefahren und Bewegungsanalysen im Sport mithilfe innovativer Sensorsysteme: Forscher*innen am Lehrstuhl für Automation und Messtechnik an der Montanuniversität Leoben arbeiten an verteilten und vernetzten Sensorsystemen: Mit dieser Technologie können beispielsweise erste Anzeichen für Rutschungen und Murenabgängen in erosionsgefährdeten, meist ländlichen Bereichen überwacht werden. Durch die Integration von Sensoreinheiten in Schutzbauten wie Steinschlagnetzen lässt sich deren Zustand kontinuierlich überwachen, wodurch frühzeitig vor vermehrten Steinschlagereignissen und Murenabgängen gewarnt werden kann. Die neuartigen Sensor-Systeme können im Sinne des Gefahrenschutzes aber auch in Staumauern, an Brücken oder Gebäuden zur Anwendung kommen. Eine andere Anwendung der entwickelten Technologie ist der Sport: Bewegungssensoren können hier Performance-Daten von Sportlern etwa während des Schwimmens aufzeichnen, analysieren und auswerten.
The SMD capacitors are offered with rated voltages ranging from 25 V to 80 V and capacitance values between 56 µF and 1100 µF.
Infineon has announced an extension to its CoolSiC MOSFET 750V G2 family with new package options.
The global power electronics market is undergoing significant transformation fueled by advances in electrification, renewable energy, and data...
The SiC Power Semiconductor Market is witnessing rapid growth driven by increasing adoption of energy efficient and high performance power devices across automotive industrial and renewable energy sectors Valued at USD 0 3 Billion in 2024 the market is projected ...
A new generation of 1200 V silicon carbide power modules raises the bar on current density and thermal performance, targeting high-power EV charging, industrial drives, and energy conversion systems.
The event – jointly organized by MCL and Montanuniversität Leoben, Chair of Automation and Measurement on November 27, 2025 brought together a dedicated audience from academia and industry to exchange ideas and showcase innovations for wireless sensor nodes. We discussed emerging technologies in integrated sensors, edge computing, low power design, IoT system integration as well as application requirements. The inspiring presentations and demonstrations highlighted how research and collaboration can drive the next generation of smart, connected systems.A big thank you to all presenters, partners, and participants for sharing their insights and helping strengthen Austria’s position as a hub for wireless sensor nodes. Today’s exchange of ideas will shape tomorrow’s solutions. ***Die Veranstaltung vom 27. November 2025 brachte führende Köpfe aus Wissenschaft und Industrie zusammen, um Ideen auszutauschen und Innovationen im Bereich autonomer Sensorsysteme zu präsentieren. Das MCL ist stolz darauf, das Symposium gemeinsam mit der Montanuniversität Leoben organisiert zu haben. Im Fokus standen neue Entwicklungen bei integrierten Sensoren, Edge Computing, energieeffizientem Design und der Integration von IoT-Systemen – mit eindrucksvollen Beiträgen, die zeigen, wie Forschung und Zusammenarbeit die nächste Generation smarter, vernetzter Systeme vorantreiben.Ein großes Dankeschön an alle Vortragenden, Partner und Teilnehmer:innen für ihre Beiträge und den offenen Austausch. Unser gemeinsames Ziel war es auch die Rolle Österreichs als Hotspot für fortschrittliche Sensor- und IoT-Technologien zu stärken – die diskutierten Ideen werden die Lösungen von morgen gestalten.
RFID Smart Cabinets Market reached US 935 53 Million in 2023 and is expected to reach US 2 081 23 Million by 2033 growing at a CAGR of 10 2 during the forecast period 2024 2033 Initial investment for RFID ...
The growing volume of data generated by Internet of Things (IoT) devices requires real-time processing architectures that can overcome the latency and bandwidth limitations of centralized cloud infrastructures. Fog computing provides a viable alternative by bringing computational resources closer to end users. This paper presents a fog-based architecture tailored to real-time recommendation systems in a smart shopping mall scenario. The system leverages container-based virtualization using Docker, orchestrated by Kubernetes, and deployed on lightweight fog nodes such as Raspberry Pi boards. We detail the configuration of the infrastructure, its hierarchical deployment, and evaluate its feasibility using performance indicators such as data transfer time and dynamic container management. Results show that the proposed infrastructure achieves low-latency responses and supports concurrent mobile user interactions, validating its effectiveness for real-world fog-based applications.
The Ultra Wideband UWB Market is entering a high growth phase as demand surges for precise indoor positioning low power communication and secure device to device connectivity With rapid expansion across smartphones automotive systems asset tracking and IoT devices UWB ...
Summary Cloud computing has enabled the accumulation, processing, and review of large volumes of Internet of Things (IoT) data in a more efficient manner. Cloud offerings such as Software as a Service, Infrastructure as a Service, and Platform as a Service are accessible across private, hybrid, public, and community cloud environments. This paper presents a detailed study of cloud computing on IoT with emphasis on privacy concerns for both technologies. The paper examines how cloud computing and IoT have common features and explores the benefits of their union. Furthermore, paper highlights the offering of cloud being used for computing purposes in IoT and how it has the potential to be used to improve its function. The cloud‐based IoT architecture is composed of perception, network, middleware, and application layers that collect, process, and manage data gathered from IoT devices. This data is then used to add value to end‐users. Message Queuing Telemetry Transport, Constrained Application Protocol, Advance Message Queueing Protocol, and Hypertext Transfer Protocol are some of the various cloud‐based IoT standards and protocols. This research paper also explores the various potential use cases of this technology in health care, transportation, smart homes, and agriculture, as well as the challenges involved in integrating IoT with the cloud. The research paper presents two case studies that illustrate the application of cloud architecture that is IoT based on intelligent environment home automation and industrial IoT optimization. The paper concludes by outlining the potential of cloud computing applications in IoT and highlighting future scope in this emerging technology.
In this paper, we explore and validate the feasibility of using electroencephalography (EEG) based brain-computer interfaces (BCIs) to issue basic control commands to unmanned aerial vehicles (UAVs). We focus on integrating human cognitive motor commands with Internet of Things (IoT) devices, enabling hands-free UAV control. In our approach, neural signals captured during motor imagery of a right-hand upward movement and a left-hand downward movement are translated into discrete UAV instructions (conceptually analogous to "hover" and "land" commands). EEG data were acquired from a 14-channel Emotiv Epoc X headset worn by 10 participants, and features such as band power in key frequency bands were extracted. A lightweight decision tree classifier was trained and evaluated in a leave-one-participant-out (LOPO) cross-validation scheme to assess how well the model generalizes across individuals. The results indicate that certain participants can achieve classification accuracies above 65% for the two mental commands, although average accuracy across all subjects was modest (~55%). These findings highlight both the promise and the challenges of EEG-based hands-free drone control. They demonstrate the potential of neural interfaces as a bridge between human thought and machine action in IoT contexts, while also underscoring the need for improved signal processing and personalization to handle inter-subject variability. This work lays important groundwork for more advanced BCI-driven UAV control frameworks, aiming toward intuitive human-IoT interactions in high-impact domains.
The rapid growth of consumer IoT devices has introduced unprecedented challenges in trustworthy anomaly detection against AI-enabled cyber threats, requiring real-time, privacy-preserving, and scalable defense mechanisms. Traditional centralized strategies face critical limitations, including communication bottlenecks, single points of failure, and privacy vulnerabilities when processing distributed consumer data. We propose SwarmSense-DNN, a novel decentralized neural framework employing swarm intelligence for secure, cooperative anomaly detection across distributed IoT environments. The framework integrates autonomous agents with deep neural networks to form a self-organizing defense system that detects evolving anomalies without centralized coordination. It utilizes hierarchical federated learning with graph neural networks and attention mechanisms to capture local and global anomaly behaviors while ensuring data privacy. Extensive experiments demonstrate SwarmSense-DNN’s superior performance: it achieves 95.44% average detection accuracy across five benchmark datasets while reducing communication overhead by 67%. The framework maintains robust resilience against adversarial threats through differential privacy safeguards and demonstrates strong fault tolerance under node failures and AI-enabled attacks.
This paper describes a real-time Extended Reality (XR) Heart Twin based on ECG IoT measurements from wearable devices. Recent research has highlighted the importance of non-Euclidean features, such as wavelet graphs and heart meshes, in conveying information about a user's condition and enabling effective visualization in XR interfaces. We propose an end-to-end architecture to generate an XR Heart Twin from signals acquired by wearable devices. The XR Heart Twin architecture includes IoT communication protocols and middleware for data processing, extending to a web-based XR visualization tool that presents up-to-date features characterizing the user's condition. The proposed architecture enables a scalable monitoring solution suitable for cloud deployment. Examples of outputs presented via the XR interface are shown using ECG signals from publicly available datasets, processed in real time to extract XRrelevant heart features. This approach enables continuous user monitoring through a responsive XR dashboard. Experimental results demonstrate how an XR Heart Twin based on IoT measurements can be developed for healthcare systems using automated deployment capabilities on modern cloud platforms.
Chronic stress significantly affects cardiovascular, psychological, and immune health, and contributes to conditions such as hypertension and depression. Recent advances in wearable technology, such as electrocardiogram (ECG) sensors, have enabled continuous, non-invasive monitoring of physiological stress responses. This paper introduces CardioMind, a lightweight deep learning model designed for real-time stress detection using ECG data. It processes short ECG segments to extract heart rate variability (HRV) features across the time, frequency, and non-linear domains. To improve generalization across individuals, we propose subject-specific baseline normalization based on resting states. The model was trained and evaluated on the WESAD dataset, a widely used benchmark for wearable stress detection. CardioMind achieved strong performance across subjects, reaching an average accuracy of 90.8% using leave-one-subject-out validation. Its low resource requirements and real-time processing make it a strong candidate for integration into Internet of Things (IoT)-enabled wearable devices for early stress detection and mental health support. To the best of our knowledge, CardioMind is among the first models to integrate robust HRV-based features with subject-specific normalization in a lightweight architecture suitable for real-world, wearable deployment.
Efficient and evenly distributed irrigation is a crucial factor in greenhouse seedling cultivation. However, conventional sprayers utilization is often associated with water wastage and uneven water distribution. In this study, an IoTbased automatic plant-watering robot was designed and developed, which controllable via Arduino Cloud and equipped with an automatic push button to enhance operational flexibility. The robot development was performed using prototype-based design approach. The robot was built using an ESP32 as the main microcontroller, a 12 V water pump, an L298N motor driver, and a push button used as a motion limit sensor. The results demonstrated that water usage was more efficient with the watering robot, achieving savings of $\mathbf{8 1. 6 7 \%}$ compared to the conventional sprayer method. The watering process performed by the robot was proven to be more controlled and evenly distributed, although slightly higher moisture levels were observed in the center area compared to the left and right sides. In the watering time evaluation, 2 minutes were required by the robot to match the volume of water dispensed by the sprayer in 1 minute, due to the intermittent watering pattern employed by the robot. These findings highlight potential application of the proposed technology in greenhouse seedling cultivation, thus promoting better trade-off between sustainability and economic benefits in the agriculture sector.
In a vulnerable environment, an IoT device with limited resources poses a significant security threat. As IoT networks become more complex, efficiency, scalability, and adaptability become more important. A lightweight IDS, driven by machine learning, is proposed for IoT environments in this paper. A proposed approach utilizes a combination of models such as Decision Trees, K-Nearest Neighbors, Support Vector Machines, and Multi-Layer Perceptrons, as well as feature selection and traffic modelling techniques, to detect intrusions accurately and efficiently. Benchmark datasets (KDD-99, NSL-KDD, and UNSW-NB15) are used to validate the system, demonstrating its competitive performance across a variety of attack categories.
Wireless sensor networks (WSNs) are critical for industrial IoT, healthcare, and environmental monitoring, yet limited energy resources constrain their reliability and longevity. This paper presents a hybrid reinforcement learning framework to optimize simultaneous wireless information and power transfer (SWIPT) in MIMO-enabled WSNs, enabling energy-autonomous and sustainable sensing. By integrating Sequential Parametric Convex Approximation (SPCA) with SARSA (State–Action–Reward–State–Action) and Qlearning, the framework employs power-splitting and time-switching techniques to enhance routing efficiency and energy harvesting in dynamic sensor fields. A nonlinear energy harvesting model captures practical circuit constraints, such as diode sensitivity and leakage currents, improving prediction accuracy. Simulations in a 1000×1000 m² area with distributed sensor nodes show up to 20% improvement in energy efficiency and 15% increase in data throughput over baseline methods. These advancements position the framework as a transformative solution for energy-constrained WSNs in 5G/6G-enabled IoT and smart sensor applications, paving the way for sustainable large-scale deployments.
New Jersey US State The global Ultra Wideband UWB Module market in the Information Technology and Telecom category is projected to reach USD 5 50 billion by 2031 growing at a CAGR of 18 5 from 2025 to 2031 With ...
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Use code ONLINE30 to get 30 off on global market reports and stay ahead of tariff changes macro trends and global economic shifts How Large Will the Smart Lamps Market Size By 2025 In recent times the smart lamps market ...
The latest research publication on the Global Veterinary IoT Solutions Market 2025 2032 offers a comprehensive and data driven analysis of the industry landscape The report delivers actionable insights into the market s evolving dynamics backed by in depth quantitative ...
Renewable energy has emerged as one of the most reliable and widely accepted approaches to address the rising global energy requirements. Among these,…
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