Preprint of submitted article to TDEI. Compare partial discharge activity at different voltage shapes on aluminium nitride substrates embedded in silicone liquid and gel.Â

In analog computing, multimode fibers (MMFs) have been explored for their potential in performing computations by exploiting the inherent parallelism of light propagation through different transverse modes. However, traditional approaches using MMFs encounter challenges due to limitations in spatial-domain encoding, which limit computational speed and accuracy. Innovative research seeks to overcome these limitations, by leveraging novel techniques in temporal encoding and employing step-index few-mode fibers (FMFs), and enhance the computational capabilities of analog systems. In this work, we consider a 13 m FMF to implement up to 7-bit header classification at 28.5 Gb/s, when utilizing the information that is included in only one-bit duration. By activating a small number of spatial modes with proper dispersive optical characteristics, we are able to limit the complexity of the spatial transformation and solve such computing tasks efficiently. The selective activation of supported modes is performed by testing varying input displacement conditions between the optical beam encoding information and the FMF input tip. At the output of the fiber, we obtain multiple time series that correspond to different spatial sub-patterns of the output beam overall pattern. Finally, we train a logistic regression classifier that uses samples from these time series, and evaluate its performance in classification tasks. Notably, even in the presence of inter-header interference, our system achieves successful 6-bit header classification - a feat unattainable with conventional step-index MMFs.

Any endeavors to explain the network behavior, always falls in the network optimizer explanation trap and gets into the complication of algorithm math operation loop without reaching the final decision of how it really works. Weight divergence optimizer, which is a subcomponent of Synthetic Neural Network explains the inner works of neural network in a pure logical operation using only the dot product multiplication to illustrate this behavior and proposed a formula to calculate bias and weights. The new method uses the network divergence theory instead of trial-and-error method by calculating the maximum variation value between weight and its interior class patterns compared with the minimum variation value of the same weight with exterior patterns of other classes, the difference between the two values used in the formula. Synthetic Neural Network deals with many challenges starting from how Neural Network works and get its result, extending to reducing the size of training dataset, huge memory consumption, extensive processor calculation and creditability. The network output shows a promising result in testing and validation stage, it grants an accuracy of 90% to 75% with two and nine classes when using USPS dataset. Also, it produces accuracy of 95% to71% when using RMNIST expanded with the same classes. In audio patterns with MFCC feature extractions it gives accuracy of 73% with 10 classes. All the training operation performed between 0.5 to 15.5 seconds. The proposed method uses the lowest number of neurons that ever used with two levels of Neural Network, that helps to reduce the network size, discards redundant patterns, removes corrupted inputs and makes the network to converge in a few seconds with the lowest number of input dataset used

This paper utilizes a novel approach to analyzing remote monitoring systems by using the Age of Information (AoI) as a metric to measure the timeliness of status updates. The study focuses on a scenario with a single source and destination queueing system, two parallel heterogeneous servers, and no additional buffer, where the analysis of AoI or PAoI is difficult because of the out-of-order reception of packets. To analyze the performance of the system, we use stochastic hybrid systems (SHS) approach. We evaluate the AoI and Peak AoI (PAoI) of the system using different queueing disciplines, including Last Come First Serve (LCFS) and LCFS with probabilistic routing. We use Proactive Obsolete Packet Management (POPMAN) method to identify obsolete packets that can be discarded in advance, which saves server processing time and receive packets in order at the receiver. The study compares the performance of this system to a similar system with two homogeneous servers. The results show that by using the POPMAN method, there is an improvement in the AoI and PAoI for LCFS queueing system and the performance of LCFS with probabilistic routing using the POPMAN approach is same as the traditional scheme. Additionally, the study analyzes various parameters, such as the probability of preempted packets, the probability of obsolete packets, the probability that a packet is informative or successfully delivered, and optimal splitting probabilities for probabilistic routing.

This paper investigates the Diophantine equation p r + (p + 1) s = z 2 Where p > 3, s ≥ 3 , z is an even integer. The focus of the study is to establish rigorous results concerning the existence of solutions within this specific parameter space. The main result presented in this paper demonstrates the absence of solutions under the stated conditions. The proof employs mathematical techniques to systematically address the case when the prime p exceeds 3, and the exponent s is equal to or greater than 2, while requiring the solution to conform to the constraint of an even z. This work contributes to the understanding of the solvability of the given Diophantine equation and provides valuable insights into the interplay between prime powers and the resulting solutions.

Over the past decade, there has been growing interest in using human behavioral and physiological data to detect Social Anxiety Disorder (SAD). Machine learning and deep learning techniques that use multimodal sensing have emerged as promising tools for detecting SAD characteristics. Additionally, extensive research on technology-assisted psychological interventions for SAD aims to enhance treatment efficacy and address the shortcomings of existing treatments by exploring how these interventions can be tailored to individual anxiety levels, symptom severity, and personal preferences. This review provides an overview of approaches for generalised SAD, covering advancements in both sensing and interventions while highlighting the potential of affective computing. It synthesises key insights on current emerging trends, identifies research gaps, and outlines directions for future research.

This paper presents the design and development of a low-cost ventilator intended for emergency situations and resource-constrained environments. An overview of various research is done on how human lungs are used for respiration to understand the working principle of the ventilator. The ventilator is engineered to deliver breaths within a range of 5 to 30 per minute through a pulley and lever mechanism driven by an electromagnetic stepper motor, drawing power from a 12V supply. Offering flexibility, the device provides a tidal volume output ranging from 400ml to 1000ml and an adjustable inspiration to expiration ratio of 1:3, which can be tailored to meet individual patient requirements. To enhance usability, the ventilator is equipped with an LCD screen user interface that displays the set tidal volume, breaths per minute, and inspiration to expiration time ratio. Additionally, the system is equipped with two pressure sensors integrated into the patient connector, enabling precise measurements of flow rate and pressure changes in the oxygen delivery tube. Ensuring reliability, the device has been carefully calibrated to maintain a 10% accuracy rate in delivering the set output. This prototype showcases the potential of automated Ambu bag compression, offering a cost-effective and portable ventilator system that delivers essential ventilation features, rivaling existing technology at a significantly reduced cost.

An effective and important technique, Deep Learning has been found to be recurrently used in different fields, including computer vision, machine vision, and natural language processing. As a result of this technology, deepfakes generate visual media of a particular person that are nearly similar to the original material. Over the past few years, a variety of studies have examined how deepfakes operate, and numerous methods utilizing deep learning have come forward to detect both deepfakes videos and images. In this work, the dangers deepfakes present to corporate cybersecurity are investigated, underscoring the need to understand the technology related to deepfakes and the importance of constructing effective detection and mitigation methods. The article includes coverage of the merits and shortcomings of several methods for locating deepfakes, including deepfake detection algorithms along with human review and contextual analysis. A technique that unites automated detection with human judgment is critical for discovering deepfakes. The article brings to attention the necessity for businesses and individuals to strengthen their awareness and evasion capabilities against deepfake deception, decrease the risk that deepfakes can get, and take advantage of the potential opportunities deepfakes might create. Key takeaways reveal that facial landmark analysis, lip synchronization detection, and micro-expression analysis play key roles in deepfake detection and that a zero-trust and multi-factor authentication protocol is necessary to address the risks associated with deepfakes.

Many methods for signal analysis are based in the framework of time-frequency analysis, and some of them are supposed to be able to deal with non-stationary signals, as the Hilbert-Huang transform, which is based on the Hilbert Spectrum Analysis (HSA). HSA is a classical methodology which allows to assign an amplitude-phase representation, and hence an instantaneous energy and frequency, to a signal, which are supposed to be meaningful when applied to some specific signals. However, the resulting methods not always produce consistent or satisfactory frequential analyses. In this work, we propose a new method based on the HSA for time-frequency analysis, which is easy to implement, computationally cheap, and highly customizable to adapt the results to some specific requirements on the frequential behaviour of the signal. To do that, some previous theoretical analysis are performed, which are interesting by themselves. We investigate the time-frequency localization problem of the Fourier transform, defining the support of measurable functions, and characterizing the spaces of functions with support contained into a specific set. This leads us to consider the Hardy-type spaces, composed by square-summable functions such that the support of its Fourier transform is contained into a specific set. On the other hand, we approximate some time-frequency components of a signal, linked to specific time windows and frequency ranges, with generalized trigonometric polynomials, with frequencies into these specific ranges. This leads us to introduce the Tremolo-Vibrato spaces, intimately linked to the Hardy-type spaces, and to study the Hilbert spectrums of generalized trigonometric polynomials, which can be performed directly by using explicit general expressions. Finally, we propose a simple method for quantifying the time-varying relative importance of each component in the frequential decomposition. This method is applied to the analysis of medical signals, as EEG and ECG.

Data cooperatives with fiduciary obligations to members provide a useful source of truthful information regarding a given member whose personal data is managed by the cooperative. Since one of the main propositions the cooperative model is to protect the data privacy of members, we explore the notion of blinded attestations in which the identity of the subject is removed from the attestations issued by the cooperative regarding one of its members. This is performed at the request of the individual member. We propose the use of a legal entity to countersign the blinded attestation, one that has an attorney-client relationship with the cooperative, and which can henceforth become the legal point of contact for inquiries regarding the individual related to the attribute being attested. There are several use-cases for this feature, including the Funds Travel Rule in transactions in digital assets, and the protection of privacy in decentralized social networks.

The detection accuracy and speed of grasp detection models on benchmarks are the focal points of concern in the robotic grasping community. Especially in a collaborative robot setting, the safety of the model is an essential aspect that cannot be overlooked. In this paper, we explore how to enhance the safety of grasp detection models in autonomous vision-guided grasping. Specifically, we propose a simple yet practical Safety-optimized Strategy, which consists of two parts. The first part involves depth prioritization, optimizing the grasp sequence from top to bottom based on the order of depth values, which can mitigate the issue of grasp collisions that may arise when the depth value of the object with the highest grasp quality is significantly higher than that of other objects in high-clutter scenarios. The second part is false-positive protection, where we introduce the robust Aruco marker as the lowest grasp priority. The marker is fixed at certain positions within the camera's field of view, enabling the robot to halt its movement, thereby restraining the robot from grasping objects that should not be grasped. Once the marker disappears, the robot can resume its operations. We validate our method through real grasping experiments with a parallel-jaw gripper and an industrial robotic arm, demonstrating its effectiveness in high-clutter scenarios.

Cyber security for power systems is becoming a concern for many researchers. However, the impact of cyberattacks on actual power systems is unclear. This paper discusses analytically the significance of cyber security for power systems and evaluates whether cyber-attacks are a real threat, or are just a hypothetical concept. First, this paper categorizes the different types of power system attacks and then systematically reviews the cyber-attack modeling, detection, and mitigation approaches for bulk power systems. This paper also discusses prominent cyber and physical attacks on actual power systems and draws a comparison among them based on economical and societal impacts. Based on the facts uncovered in the paper, when compared to physical attacks, cyber-attacks on power systems have had negligible economic and societal impacts. In fact, there is only one documented case (the Ukrainian cyber-attack by Russia) that successfully caused customer disconnections.

6G networks intend to be highly dynamic, automated, and distributed while enabling novel user-centric services, which has raised the attention to trustworthiness aspects. In this context, trustworthiness has been pointed out as a key driver for the development of 6G technology. However, a common understanding of the definition and extension of trustworthiness is still missing in the community. Therefore, this paper aims at providing critical definitions for trustworthiness to steer the development of 6G by building upon 3 pillars: trustworthiness attributes, context, and assessment. First, we provide a comparative study of attributes for trustworthiness and identify the set of self-contained attributes that allow for a quantitative characterization of trustworthiness in 6G systems. Then, we recognize and discuss the main misconceptions regarding trustworthiness in the context of 6G and propose an assessment framework of two components for trustworthiness evaluation. This framework includes a numerical scaling of relevance of attributes, a scoring system, and a labeling approach to admit flexibility to identify a degree of trustworthiness evaluation for different capabilities in 6G.

This article targets first-year engineering studentsâ\euro™ self-efficacy beliefs and their relationship with performance attainments in the first half of the semester and the final grade of the programming course. Self-efficacy is the belief in oneâ\euro™s capabilities to reach a desired goal or outcome by setting and implementing the required courses of action. In education, self-efficacy is crucial to academic growth, for it helps students take charge of their own learning, develop their skills, set goals, and regulate their motivation in order to accomplish these goals. Considering the difficulty of the programming course, self-efficacy plays a vital role in the challenges the students face in the programming course, their magnitude, and the skills the students use to overcome them. Self-efficacy beliefs were measured through a survey administered twice to 118 engineering students in the first half of the semester and correlated with the first quiz of the semester and the midterm grade, then with the final grade. The analyses targeted all engineering students, then each engineering major separately: Computer, Electrical, Mechatronics, Mechanical, and Industrial engineering. The results showed that there is in fact a relationship between studentsâ\euro™ self-efficacy beliefs and performance in the programming course. However, the relationship was shown to be inverse in the first half of the semester but positive with the final grade. These findings highlight the importance of student efficacy beliefs especially in the first half of the semester while also presenting new empirical findings regarding each engineering major targeted.

This paper deals with the potential of affine frequency division multiplexing (AFDM) to operate within a multiuser multi-input multi-output (MU-MIMO) system, facilitated by spatial precoding preformed in frequency domain. To this end, we take advantage of the fact that AFDM based on discrete affine Fourier transform (DAFT) can be rewritten as a discrete Fourier transform (DFT)-based precoded orthogonal frequency division multiplexing (OFDM) waveform. By considering the wellknown zero-forcing (ZF) precoder and white noise, we show that MU-MIMO AFDM achieves the same performance as MU-MIMO OFDM in terms of both sum rate and bit error rate (BER).

The integration of AI-generated content into various applications has highlighted significant concerns regarding the potential for deceptive information, necessitating robust methods to ensure the accuracy and trustworthiness of outputs. Introducing a novel game theory-based framework for identifying deception in language models, this study addresses the critical need for reliable verification mechanisms. By simulating interactions between liar and verifier roles within the same model, the research provides a structured approach to evaluate and enhance the reliability of automated systems. Key findings demonstrate the effectiveness of iterative prompt refinement and strategic analysis in detecting deceptive behaviors, contributing to the development of more trustworthy AI applications. The methodology offers a comprehensive solution for improving the accuracy of AI-generated content, with broader implications for its deployment in sensitive domains such as healthcare and legal services. Future research directions include refining the proposed framework and expanding its application to encompass a wider range of deceptive behaviors, including multimedia content, thereby ensuring the robustness of AI systems in diverse real-world scenarios.

This paper explores the realm of intelligent systems through an analogy inspired by RLC circuits, delving into the interconnected dynamics of reasoning, learning, and control. Leveraging the simplicity and clarity of the analogy, we navigate the conceptual landscape by drawing parallels between electrical components and the cognitive functions of modern AI. The presented analogical framework is the conclusion of the personal experiences of the author in developing intelligent systems, sparked by conversations with fellow researchers and students and presentations of research outcomes. It is worth recognizing the limitations of this analogy, as its reductionist nature may simplify the complexities inherent in intelligent systems. Yet, this exploration provides a fresh perspective on the foundational components of intelligent systems through the lens of the well-established RLC circuit theory.

Elliptic curve scalar multiplication (ECSM) is a fundamental element of pre-quantum public key cryptography, which is the predominant choice for public key cryptography. ECSM implementations on deeply-embedded architectures and Internet-of-nano-Things have been vulnerable to both permanent and transient errors, as well as fault attacks. Consequently, error detection is crucial. In this work, we present a novel algorithm-level error detection scheme on Montgomery ladder often used for a number of elliptic curves featuring highly efficient point arithmetic, known as Montgomery curves. Our error detection simulations achieve high error coverage on loop abort and scalar bit flipping fault model utilizing binary tree data structure. Assuming n is the size of the private key, the overhead of our error detection scheme is O(n). Finally, we conduct a benchmark of our suggested error detection scheme on both ARMv8 and FPGA platforms to illustrate the implementation and resource utilization. Deployed on Cortex-A72 processors, our proposed error detection scheme maintains a clock cycle overhead of less than 3%. Additionally, integrating our error detection approach into FPGAs, including AMD/Xilinx Zynq Ultrascale+ and Kintex Ultrascale+, results in comparable throughput and less than 1% increase in area compared to the original hardware implementation. We note that we envision using the proposed architectures in the post-quantum cryptography (PQC) based on elliptic curves.