The uniaxial tensile mechanical stress (MS) is induced up to 1.4 GPa on the junctionless channel of the twin junctionless nanowire (JL-NW) gate-all-around (GAA) field-effect transistors (FETs) using a four-point bending technique. The variation of the electrical parameters is measured before and during induced MS to analyze the performance. The ON-state current, carrier mobility, threshold voltage, and subthreshold swing are directly proportional to the induced MS due to the reduced energy band gap and intervalley scattering effect. The reduced subthreshold swing shows low power consumption and better switching ability in advanced CMOS technologies. In addition, the change of drain current shows highly piezoresistive sensing ability in nanoelectromechanical sensor applications. Thus, this study demonstrates the importance of mechanical stress engineering for performance improvement in CMOS technology, piezoresistive sensing applications, and device reliability.

Nowadays, electro-thermal transport behavior analyzing is important in emerging nanoscale devices because thermal management is a critical issue in improving the device's performance and cooling strategies. In this article, a comparative study for electro-thermal performance analysis of junctionless and an inversion mode nanowire gate-all-around (GAA) field-effect transistors (FETs) is presented in advanced technology nodes by considering nonlocal effects. The junctionless device showed ~15.2% better thermal reliability and ~26.7%/37.6% better HCI/BTI lifetime compared to the inversion mode device. The transient behavior of electro-thermal reliability is also investigated for both devices; where the devices turn on for a short time within a duty cycle, the devices showed better thermal reliability and ~52.8%/68.2% HCI/BTI lifetime improvement. This study also provides strategies for thermal management in advanced node devices.

In this paper, a tunable piezoresistive pressure sensor is designed and developed using junctionless nanowire (JL-NW) gate-all-around (GAA) field-effect transistors (FETs) integrated into a circular diaphragm. The measured results show that the piezoresistive sensitivity is improved by ~4 times in the subthreshold (SS) regime (depletion regime) compared to the ON-state (ION) condition (partial depletion regime). This improvement is achieved by reducing the channel conductivity with a low gate bias below the threshold voltage (VTH). Piezoresistivity variability is observed to be ~20% in the SS regime and ~10% in the ION conditions due to a diameter variation of ~±4 nm in the nanowires during a single fabrication run. The symmetric thin diameter of nanowires also shows a higher gauge factor compared to the asymmetric thickness of nanowires. This variability estimation suggests that controlling fabrication process variations is crucial for developing stable sensing elements for nanoelectromechanical (NEMS) sensors in advanced technology nodes. Additionally, low-frequency noise (LFN) is measured to estimate the minimum detectable strain (MDS) and signal-to-noise ratio (SNR). These measurements indicate that the JL-NW GAA FET offers higher resolution and better performance as a sensing element compared to the inversion mode NW GAA FET in advanced technology nodes. In addition to offering better reliability and sensing resolution, it is also much easier to integrate with advanced CMOS technologies.

AC grid-forming (GFM) resources rely on power synchronization, i.e., transient transfer of real power in response to changes in frequency, to reach frequency equilibrium with other GFM resources in the associated grid. While operating at the limit of hardware constraints, such as converter current or power limits, and DC source constraints such as real power limit, inverter based resources (IBRs) are forced to suspend power-synchronization as no transient power exchange is feasible beyond the capacity limit. Despite the large class of research efforts for augmenting synchronization stability under currentlimiting operation, an overall ride-through strategy is missing under a comprehensive set of hardware and source constraints. In this work, we contemplate a broader fundamental question: What constitutes GFM response when an IBR reaches one or more such capacity limits? We propose a general framework for defining and optimizing GFM response under capacity constraints. The proposed control framework leverages constrained optimization to minimize the deviation in IBR response from that of an ideal GFM source. The resulting estimated deviation is utilized to dynamically adapt a droop model which enables continued power-synchronization while operating at capacity limit. An exemplary architecture of the proposed framework is presented with experimental validation utilizing a laboratory inverter prototype.

Parameter estimation for MIMO channel sounding data aims at accurately describing channel measurements with physically realistic and interpretable parameters. The performance of model based approaches, e.g. maximum likelihood, is determined by the accuracy of the imposed signal model. For channel sounding data it has turned out to be beneficial to use two distinct concepts for the description of the propagation process. The specular components account for the dominant propagation paths of plane waves, whereas diffuse components model the weaker but more diverse propagation processes by means of a colored noise process. In order to improve the accuracy of the model for the diffuse components we propose a simple but still flexible parametric covariance model that allows to account for a smooth power angle profile that describes the correlation in the spatial domain. Moreover, the model for the deterministic part of the signal is usually contaminated by calibration errors, which in turn deteriorate the reliability of the specular path estimates. This is most prominently visible by the estimation of so-called ghost paths. To mitigate this we introduce a new model order selection scheme based on the so-called misspecified Cramèr Rao bound which accounts for the unavoidable modeling errors. Additionally, to avoid the fitting of ghost paths caused by the faulty modeling of strong specular components we locally decrease the estimated SNR in time domain around already estimated ones. Further, as these changes to the signal model require more computational resources compared to existing algorithms, we also showcase how necessary quantities like likelihoods, score functions and fisher information matrices can still be computed efficiently. We implement our proposed extensions within the RIMAX framework. We also showcase that they improve the reliability of the produced estimates compared to plain vanilla RIMAX on real measurement data.

We propose two attention functions that capture cross-channel statistical scaling regularities: Monofractal and Multifractal recalibration. We build an experimental framework centered around the U-Net [1] and show that these approaches, especially Multifractal recalibration, lead to substantial improvements over a baseline augmented with other attention functions that may also describe each channel in terms of higher-order statistics [2]-[4]. Our experiments cover three public datasets from diverse modalities: ISIC18 (dermoscopy) [5], Kvasir-SEG (endoscopy) [6], and BUSI (ultrasound) [7]. Additionally, we also study the dynamics of the Squeezeand-Excite attention layer [8] and our findings suggest that (a) excitation response does not get increasingly specialized with encoder depth in the U-Net due to its skip connections, and that its effectiveness may be linked to global statistics of their instance-variability. To the best of our knowledge, we present the first instance of end-to-end multifractal analysis for semantic segmentation.

The ongoing evolution of cyber threats has demanded increasingly sophisticated detection methods to counteract ransomware, which continues to impose severe risks on global systems. The Behavioral Entropy Detection Protocol (BEDP) introduces a real-time ransomware detection framework that leverages entropy-based metrics and behavioral analysis to identify ransomware activities before significant harm occurs. BEDP's design incorporates a dual-layered architecture, combining entropy thresholding with machine learning classifications to detect ransomware-linked file manipulations and encryption patterns, enabling it to effectively handle both known and unknown ransomware variants without reliance on pre-existing signatures. Extensive testing revealed BEDP's high accuracy and low false positive rate across various datasets, achieving significant reductions in computational resource usage while ensuring scalable performance in high-frequency file environments. The protocol's hierarchical resource allocation further optimizes entropy calculations, demonstrating low latency and reliability in real-time applications. Findings indicate BEDP's robust adaptability within enterprise-level cybersecurity infrastructures, where rapid detection of ransomware is essential, showing its suitability for deployment in both decentralized and large-scale networks. BEDP's innovative use of entropy metrics presents an adaptable, efficient approach to ransomware defense, establishing a foundation for the enhancement of proactive security measures.

Fall-related injuries pose a significant health risk, particularly among the elderly population, necessitating advancements in fall detection technologies. While traditional sensor-based methods offer some solutions, they are limited by issues, like user compliance and unreliability in high-stakes situations. Computer vision (CV) and artificial intelligence (AI)-based alternatives, notably thermal imaging, emerge as promising, privacy-preserving tools. However, the lack of standardized, generalizable benchmark datasets hampers the validation of these technologies' effectiveness. The thermal fall 66 (TF-66) dataset introduced in this work addresses this crucial gap by providing an extensive, diverse collection of fall scenarios, recorded in various environments and featuring a wide range of participants. This dataset introduces sample subsets for tailored model training and a comprehensive data generator for customized access, setting a new benchmark in fall detection research. As TF-66 continues to evolve, it aims to include even more diverse environments, such as bathrooms and staircases, further enhancing its applicability and serving as a robust resource for the research community. This work contributes significantly to the field by offering a dataset that not only improves the reliability of fall detection systems (FDS) but also facilitates a standardized approach to evaluating and advancing these technologies.

In this work, we present a novel thermomolecular communications gateway allowing communication through the human skin into the human body connecting the internet of things (IoT) with the internet of bio-nano things (IoBNT). We developed an experimental setup as a proof of concept and provide a detailed description of the testbed, its assembly and fabrication. Mathematical models for all components are derived and verified experimentally. Finally we analyze the communications performance of the system and elaborate on future works.

The development of increasingly sophisticated language models has led to substantial progress in generating coherent and contextually relevant text for a wide range of applications. Despite these advancements, challenges persist in ensuring that models can integrate newly acquired knowledge without undergoing resource-intensive retraining, which severely limits their adaptability and scalability in dynamic environments. Dynamic Knowledge Synchronization (DKS) emerges as a novel framework addressing this limitation through the introduction of selective, modular updates that allow models to retain their core knowledge while incrementally adapting to new information. By compartmentalizing the knowledge architecture and integrating reinforcement-based feedback mechanisms, DKS offers an efficient method to incorporate new data without compromising the stability and coherence of previously learned knowledge. The study presented here demonstrates the efficacy of DKS in overcoming the rigidity of traditional large-scale training processes through modular parameter adjustments and selective synchronization procedures. Experimental results showed significant improvements in model adaptability, knowledge retention, computational efficiency, and response consistency when compared to conventional fine-tuning or retraining methods. The novel architecture introduced through DKS not only provides a scalable approach to continual learning but also enhances the model's ability to operate effectively across diverse and evolving domains, thereby extending its applicability to real-world scenarios requiring frequent and incremental knowledge updates. Furthermore, the incorporation of attention-based filters and targeted parameter adjustments in DKS contributes to reducing computational overhead, enabling more efficient deployment in resource-constrained environments. This innovative approach redefines the framework for updating and managing knowledge within language models, offering a more sustainable solution for long-term learning while maintaining high responsiveness and consistency in performance.

Modern academic research encompasses a broad spectrum of subjects, reflecting the intricate and evolving challenges and opportunities that societies encounter globally. This study delves into the role of literature in shaping cultural identities and the profound impact of technology on traditional structures. Current scholarly contributions offer deep insights into the blend of humanistic concerns and technological advancements, underscoring their combined relevance in tackling contemporary societal issues.

Biophotonics includes a wide range of applications that use light-based technologies to investigate and manipulate biological systems. Traditionally, bioluminescence has been extensively used as a reporting agent in various biophotonics applications. However, its potential as a light source has not been explored. In this study, we propose the use of wireless Aequorinbased illumination as a bioinspired light-emitting source within biological tissue. This approach can have applications in a range of technologies; from optogenetics to bio-optical communications to human-brain interfaces. Drawing inspiration from the natural bioluminescent properties found in marine organisms, we designed a wireless Aequorin-based bioluminescence unit and developed an equivalent circuit model to describe the biological processes generating illumination. Our model predicts the behavior of the bioluminescent units under various physical conditions, offering a framework for understanding how variations in physical parameters influence luminescence characteristics. In the absence of experimental studies focusing on Aequorinbased bioluminescence as a light source, our findings provide valuable guidance for researchers. These insights can help in understanding the system's behavior, designing more complex bioluminescence systems composed of multiple illumination units, and selecting parameters for future experimental research.

We present the training and validation dataset to be utilized in the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. This challenge is being virtually organized by the Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria, and the Medical Imaging and Signal Analysis Hub (MISAHUB) in collaboration with the 9th International Conference on Computer Vision Image Processing (CVIP 2024) organized by the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India.

Health is a top priority, especially given that cardiovascular diseases (CVDs) remain the leading cause of death in the Philippines, affecting one in six Filipinos and accounting for 20% of all deaths. To combat this, healthcare professionals are implementing community-based programs in rural areas, though patient profiling is still manual. ”Pitik,” a Cebuano-Binisaya intent-based chatbot, was developed to gather information, assess cardiovascular risks, and profile individuals. Guided by Gricean Maxims, Pitik detects communication violations, ensuring effective interaction. This study used Action Research, fostering collaboration between researchers, experts, and end-users. Through three software development iterations, the Diag-Ex framework with Pre-Intent and Post-Intent Matching algorithms significantly improved Pitik’s ability to respond smoothly to user prompts.

Big Data has revolutionized how organizations across industries handle vast quantities of information, enabling valuable insights that can drive business growth. However, with these advancements come numerous challenges, including data management complexities, security concerns, and the integration of various technologies. This paper discusses the overarching challenges in Big Data and provides a strategic framework to resolve them. By understanding key strategic points, methodology, use cases, and implementation steps, organizations can effectively navigate these challenges, ensuring the successful activation of Big Data initiatives. The paper also highlights tools and technologies essential to Big Data operations, potential risks, and mitigation strategies.

In the modern business landscape, the ability to couple and derive value from Big Data has become essential for organizational success. This paper explores the strategic importance of Big Data Value Realization, focusing on methodologies, challenges, and case studies that illustrate effective practices. By aligning data initiatives with business goals and leveraging advanced analytics, organizations can transform vast datasets into actionable insights. This article aims to provide a comprehensive guide to understanding and implementing Big Data strategies that drive innovation and enhance decision-making.

Dynamic Temporal Signature Analysis (DTSA) offers a novel, fully automated approach to ransomware detection through the real-time monitoring of entropy changes within a system. Designed to detect ransomware activities independently of predefined signatures or human intervention, DTSA provides a solution that dynamically adjusts to the entropy deviations indicative of ransomware behaviors, capturing a wide range of patterns that elude traditional static or behavior-based detection techniques. The DTSA framework operates through a layered modular design, integrating real-time data collection, entropy monitoring, ransomware identification, and response mechanisms that collectively ensure high detection accuracy and resource efficiency across diverse ransomware profiles. Testing results demonstrate that DTSA achieves significant advances in detection precision, minimizing both false positive and false negative rates while maintaining low latency. Additionally, the adaptive thresholding mechanism enables DTSA to recalibrate autonomously, handling entropy variations across different network conditions, data packet sizes, and ransomware types, without burdening system resources. The entropy-focused approach underscores DTSA's capacity to bridge gaps in current detection methodologies by eliminating human-in-the-loop dependencies, thereby establishing an adaptable, scalable framework suitable for highdemand environments. Through validating the efficacy of entropy monitoring as a principal ransomware indicator, DTSA contributes a transformative perspective to automated cybersecurity defenses, reinforcing the viability of self-sufficient ransomware detection systems for modern digital infrastructures.

In artificial intelligence, the challenge of maintaining knowledge stability across sequentially evolving tasks and contexts has become increasingly apparent, as language models are deployed in applications requiring prolonged semantic coherence. While traditional training paradigms emphasize rapid adaptation and generalization, they often overlook the essential need for sustained knowledge retention, resulting in semantic drift and inconsistencies over time. Addressing this gap, the novel Residual Knowledge Stability (RKS) framework presented in this study introduces a robust approach for quantifying longterm knowledge retention, offering a systematic evaluation of the model's internal consistency in handling complex and varied task sequences. The framework's design emphasizes autonomous task sequences, employing metrics that detect semantic drift and measure retention accuracy, without requiring human intervention, thereby allowing for scalable, unbiased assessment. Results indicate that RKS reveals key retention patterns, specifically in scenarios with increased task complexity, high domain diversity, and frequent task assignments, with implications for advancing model design strategies to support stable retention across extensive temporal applications. By prioritizing long-term retention in model evaluation, the RKS framework offers valuable insights for enhancing language model resilience, aligning architectural development more closely with the demands of real-world, continuously evolving information ecosystems.