In order to remedy the underestimation of an advanced convex penalty defined via minimization, this paper proposes its nonconvex enhancement while preserving convexity of the associated regularized least-squares model. We first design a generalized Moreau enhanced minimization induced (GME-MI) penalty function by subtracting from the MI penalty its generalized Moreau envelope. Then, we derive an overall convexity condition for the GME-MI regularized least-squares model. Finally, under the overall convexity condition, characterizing the solution set of the GME-MI model with a carefully designed averaged nonexpansive operator, we develop a proximal splitting algorithm which is guaranteed to converge to a globally optimal solution. Numerical examples demonstrate the effectiveness of the proposed approach.

Neuromusculoskeletal (NMS) models enable non-invasive estimation of clinically important internal biomechanics. A critical part of NMS modelling involves estimating musculotendon kinematics, which comprise musculotendon unit lengths, moment arms, and lines of action. Musculotendon kinematics, which are partially dependent on joint motions, define the non-linear mapping of muscle forces to joint moments and contact forces. Currently, real-time computation of musculotendon kinematics requires creation of a per-individual surrogate model. The computational speed and accuracy of these surrogates degrade with increasing number of coordinates. We developed a feed-forward neural network that completely encodes musculotendon kinematics of a target model across a wide anthropometric range, enabling accurate real-time estimates of musculotendon kinematics without need for a priori per-individual surrogate model. Compared to reference, the neural network had median normalized errors ~0.1% for musculotendon lengths, <0.4% for moment arms, and <0.10° for line of action orientations. The neural network was employed within an electromyography-informed NMS model to calculate hip contact forces, demonstrating little difference (normalized root mean square error 1.23±0.15%) compared to using reference musculotendon kinematics. Finally, execution time was <0.04 ms per frame and constant for increasing number of model coordinates. Our approach to musculoskeletal kinematics may facilitate deployment of complex real-time NMS modelling in computer vision or wearable sensors applications to realize biomechanics monitoring, rehabilitation, and disease management outside the research laboratory.

Identity systems increasingly use biometrics to register and uniquely identify individuals. Governments use them to identify and authenticate citizens for voter-enrollment, socialwelfare, border-control, KYC, and healthcare. It is therefore essential to ensure people are not registered multiple times and duplicates are discovered promptly to avoid frauds. This paper proposes a framework for building a scalable deduplication system using facial biometrics and open-source tools. It examines the use of the open-source ArcFace algorithm to create embeddings of representative facial images and the Milvus database to quickly search through millions of images. Such systems will help ensure that duplicate identities are not registered in an identity enrollment system. Based on many experiments and combinations of different parameters, the authors achieve 99.79% accuracy, an F1-score of 89.44%, a false positive identification rate (FPIR) of 0.1%, and a false negative identification rate (FNIR) of 0.1%. This work aims to provide the potential configurations, architecture, parameters, and their effect on accuracy and speed for implementing a highly scalable deduplication system. They elaborate on the impact of each parameter on accuracy and performance. Readers can use this analysis to make an informed decision on the best architecture and combination of parameters for their use case.

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.

Large Language Models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, transparency, explainability, trust and accountability. We conclude this paper by summarizing potential research scopes with direction. Overall, the purpose of this position paper is to contribute to the ongoing discussion of LLM potentiality and awareness from the perspective of trustworthiness and responsibility in AI.

This study explores the potential enhancement of radio signal propagation in intricate environments through the deployment of basic resonant dipole scatterers. We employ the MATLAB ray tracer algorithm to examine the impact of dipolar scatterers on the power received at the user's location. The ray tracing model is modified for a dipolar scatterer model based on its bi-static scattering cross-section. Our analytical and simulation results indicate that the deployment of extra scattering components is nearly ineffective when a direct link is present. Conversely, in scenarios where there is no line-ofsight, the proposed approach can considerably boost the power delivered. This cost-effective and straightforward method could complement other channel optimization strategies, such as the application of reconfigurable intelligent surfaces.

The quality assessment of biomaterials in pathological anatomy is crucial for the optimal diagnosis and treatment of conditions like cancer. This is exemplified in the immunohistochemistry profiling of the human epidermal growth factor receptor 2 (HER2) in breast cancer. Therefore, it is relevant to understand how preanalytical processes, such as post-surgery handling and fixation quality, impact biomaterial quality and diagnostic accuracy. This study investigates first the influence of fixation steps on the performance of HER2 diagnosis. Then a quantitative and automated approach is proposed to correct these biases. This approach is derived from a previous supervised Machine Learning model. The method, which employs a high-performance logistic model, has been further enhanced with a compensation strategy based on tissue quality. This enhancement utilizes a correction derived from a Tissue Quality Index (TQI) to fine-tune the input parameters of the classification model (referred to as TQI-enhancer). Results, obtained from 60 quality control samples with Vimentin and 75 HER2 classification samples, first demonstrate that cold ischemia and fixation times lead to significant changes in immunoreactivity within a short period. Second, adjusting specific parameters quantified in HER2 samples through automated image analysis based on the TQI-Enhancer equation exhibits an improved correlation with the reference diagnosis. This adjustment significantly enhances the classification performance of the logistic classifier in ML-based diagnosis compared to uncompensated data with improved AUC values from 0.84 to 0.93. We anticipate that implementing similar strategies will enhance the performance of digital pathology techniques, ultimately leading to the development of robust diagnostic classifiers for cases of aggressive breast cancer. By analyzing the association between biomarkers like HER2 with patients' clinical outcomes, these classifiers are expected to provide invaluable insights.  

As biological wide-field visual neurons in locusts, lobula giant motion detectors (LGMDs) can effectively predict collisions and trigger avoidance before the collision occurs. This capability has extensive potential applications in the field of autonomous driving, unmanned aerial vehicles, and more. Currently, describing the LGMD characteristics is divided into two viewpoints, one emphasizing the presynaptic visual pathway and the other emphasizing the postsynaptic LGMDs neuron. Indeed, both have their research support leading to the emergence of two computational models, but both lack a biophysical description of the behavior in the individual LGMD neuron. This paper aims to mimic and explain LGMD's individual behavior based on fractional spiking neurons and construct a biomimetic visual model for the LGMD compatible with these two characteristics. Methods: We implement the visual model in the form of spikes by choosing an event camera rather than a conventional CMOS camera to simulate the photoreceptors and follow the topology of the ON/OFF visual pathway, enabling it to incorporate the lateral inhibition to mimic the LGMD's system from the bottom up. Second, most computational models of motion perception use only the dendrites within the LGMD neurons as the ideal pathway for linear summation, ignoring dendritic effects inducing neuronal properties. Thus, we introduced fractional spiking neuron (FSN) circuits into the model by altering dendritic morphological parameters to simulate multiscale spike frequency adaptation (SFA) observed in LGMDs. In addition, we have attempted to add one more circuit of dendritic trees into fractional spiking neurons to be compatible with the postsynaptic FFI in LGMDs and provide a novel explanatory approach and a predictive model for studying LGMD neurons. Results: Finally, we test that the event-driven biomimetic visual model can achieve collision detection and looming selection in different complex scenes, especially fast-moving objects.

Electronic parts in space inevitably subject to radiation effects leading to the degradation of electronic performance or even failure, so radiation performance of an electronic part must be assessed to ensure it work normally in space. At present, to assess the ion radiation effects on a semiconductor device is directly through irradiation tests. However, due to the scarcity of cyclotron resources, the test time is difficult to appoint and the cost is huge. Due to schedule and budget constraints, it is also impossible to conduct irradiation tests on all semiconductor devices in actual space missions. Therefore, assessment of the radiation effects on semiconductor devices through irradiation tests has caused difficulties. Radiation susceptibility of semiconductor device is determined by the design topology and fabrication technology, and the irradiation test data shows that similar semiconductor devices has similar radiation susceptibility, so a method to assess the radiation effects on semiconductor devices base on similarity theory is proposed at first time in this paper. This assessing method does not require irradiation testing and does not require separate sampling. It has the virtues of easy implementation, quick response and low cost, providing an efficient method of assessing radiation effects on semiconductor devices.

In the field of physiological signals monitoring and its applications, non-contact technology is often proposed as a possible alternative to traditional contact devices. The ability to extract information about a patient’s health status in an unobtrusive way, without stressing the subject and without the need of qualified personnel, fuels research in this growing field. Among the various methodologies, RADAR-based non-contact technology is gaining great interest. This scoping review aims to summarize the main research lines concerning RADAR-based physiological sensing and machine learning applications reporting recent trends, issues and gaps with the scientific literature, best methodological practices, employed standards to be followed, challenges, and future directions. After a systematic search and screening, one hundred and ninety two papers were collected following the guidelines of PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). The included records covered two macro-areas being regression of physiological signals or physiological features (n = 68 papers) and the other a cluster of papers regarding the processing of RADAR-based physiological signals and features applied to four fields of interest, being RADAR-based diagnosis (n = 73), RADAR-based human behaviour monitoring (n = 21), RADAR-based biometrics authentication (n = 18) and RADAR-based affective computing (n = 9). Papers collected under the diagnosis category were further divided, on the basis of their aims: in breath pattern classification (n = 39), infection detection (n = 10), sleep stage classification (n = 9), heart disease detection (n = 8) and quality detection (n = 7). Papers collected under the human behaviour monitoring were further divided based on their aims: fatigue detection (n = 8), human detection (n = 7), human localisation (n = 4), human orientation (n = 2), and activities classification (n = 3).

The generation of videos from textual input poses a significant computational challenge within computer science. Nonetheless, recent advancements in text-to-video artificial intelligence (AI) technologies have showcased notable progress within this domain. Foreseen advancements in realistic video generation and data-driven physics simulations are poised to further propel the field forward. The emergence of text-to-video AI holds transformative potential across a plethora of creative domains, including filmmaking, advertising, graphic design, and game development, as well as within sectors such as social media, influencer marketing, and educational technology. This research study seeks to comprehensively review generative AI methodologies in text-to-video synthesis, with an emphasis on large language models and AI architectures. Multiple methods such as literature review,  technical evaluation and solution proposal were applied for this purpose. Prominent models such as OpenAI Sora, Stable Diffusion, and Lumiere are evaluated for their efficacy and architectural intricacies. However, the pursuit of Artificial General Intelligence (AGI) is accompanied by a myriad of challenges. These encompass the imperative to safeguard human rights, prevent potential misuse, and protect intellectual property rights. Ensuring the accuracy and integrity of the generated content is paramount. The computationally intensive nature of transformer models results in substantial electricity and water consumption, necessitating the formulation of strategies to mitigate environmental and computational costs to ensure long-term sustainability. This research endeavors to explore potential avenues for addressing these challenges and proposes solutions to advance environmental and computational efficiency within the context of text-to-video AI.

The Industrial Internet of Things (IIoT) has become a critical technology to accelerate the process of digital and intelligent transformation of industries. As the cooperative relationship between smart devices in IIoT becomes more complex, getting deterministic responses of IIoT periodic time-critical computing tasks becomes a crucial and nontrivial problem. However, few current works in cloud/edge/fog computing focus on this problem. This paper is a pioneer to explore the deterministic scheduling and network structural optimization problems for IIoT periodic time-critical computing tasks. We first formulate the two problems and derive theorems to help quickly identify computation and network resource sharing conflicts. Based on this, we propose a deterministic scheduling algorithm, IIoTBroker, which realizes deterministic response for each IIoT task by optimizing the fine-grained computation and network resources allocations, and a network optimization algorithm, IIoTDeployer, providing a cost-effective structural upgrade solution for existing IIoT networks. Our methods are illustrated to be cost-friendly, scalable, and deterministic response guaranteed with low computation cost from our simulation results.

Switched-capacitor converters (SCCs) are subset of switched-mode converters which can be designed to buck/boost the input voltage. However, synthesizing SCC has been a challenging task due to the large number of possible circuit realizations. This work proposes a synthesis method for constructing an efficient reconfigurable SCC that adheres to the Fibonacci canonical structure. The optimization is achieved by operating the converter with the minimum number of capacitors required to achieve a certain voltage conversion ratio (VCR). Indeed, decreasing the number of flying capacitors minimizes the equivalent output resistance of the converter which improves the converter performance, like the settling time and the power conversion efficiency (PCE). Moreover, by analyzing the differences between each VCR terminal, the number of switches required to create a unique VCR is also decreased. The performance of the proposed synthesis tool is verified using SPICE simulations for 4-stage reconfigurable SCC. The simulated converter efficiency ranges from 85% to 95% for a 50mA load tested at 1 MHz switching frequency, compared to 43.5% to 86% using the conventional method. The performance of the converter is compared with recent reconfigruable SCCs in the literature.

We report herein the observed null or negative results in measuring any known finite light speed in air, of the one way near-field signal and information velocity, of the field displacement current between the poles of a total 1.5m separated poles spherical air capacitor, caused by its impulse spark discharge.

Earth Observation spacecraft play a pivotal role in various critical applications impacting life on Earth. Historically, these systems have adhered to conventional operational paradigms, namely the "mow-the-lawn" and "bent pipe" approaches. In these paradigms, operational schedules are formulated on the ground and subsequently uploaded to the spacecraft for execution. Execution involves either systematically acquiring vast amounts of data (mow-the-lawn) or targeting specific areas of interest as defined by end users or operators. We aim to depart from these traditional methodologies by integrating onboard Artificial Intelligence, real-time communication, and new observing strategies in one system called CogniSAT-6. These innovations will amplify the amount, speed, and quality of the information yielded by such a system by up to an order of magnitude. This paper provides an overview of the current state of the art in autonomous Earth Observation spacecraft and the application of onboard processing in Earth Observation spacecraft. An overview is given of the CogniSAT-6 mission, its concept of operations, system architecture, and data processing design. In addition, the first results of our in orbit functional tests are presented. Since we believe that the technology presented here will have a significant impact on society, an ethical framework for such systems is presented. Finally, the benefits of the technology and implications for Earth Observation systems going forward are discussed.

This paper introduces a new spatial domain-based self-interference cancellation (SIC) precoding method named constrained minimum mean square error (C-MMSE) for an asymmetric massive multiple-input multiple-output (mMIMO) full-duplex (FD) system. The main idea is to translate the commonly used singular value decomposition (SVD)-based null-space projection approach, which is unfeasible in our considered system model, into an optimization problem under MMSE criterion, where additional constraints are implemented to perform SIC. Theoretical derivation of the C-MMSE precoder is presented, followed by performance comparison with conventional MMSE precoding, where no constraints are added for SIC. We theoretically show that the C-MMSE scheme outperforms the conventional one in terms of SIC, and allows the FD system to work under an almost interference-free environment. Additionally, we also assess the performance of the proposed method under imperfect channel state information (CSI), to further evaluate the robustness of our spatial precoder in more realistic conditions. We show that the C-MMSE precoder outperforms MMSE in terms of interference suppression ratio (ISR), even in CSI imperfection. Additionally, the C-MMSE achieves the same spectral efficiency (SE) as an hypothetical perfect SIC in a wide SNR range, whereas the MMSE is upper bounded in large SNR range.

This work emerges from the intensifying need to understand and address security issues in rapidly advancing technologies such as 5G and beyond, including open radio access network (O-RAN). The current paper provides an in-depth examination of the security aspects of the E2 interface within the O-RAN context. The research underscores the diverse roles that the E2 interface assumes in enabling communication between the RAN Intelligent Controller (RIC) and the E2 node. It critically examines the various vulnerabilities and potential security threats of this interface. This work subsequently reviews the security mechanisms and methodologies proposed by the O-RAN Alliance to secure the E2 interface. This work aims to highlight the crucial role that the E2 interface undertakes in the network's overall communication and the indispensable security questions, given the stakes of these networks. The findings from this work could serve as a valuable addition to existing resources and provide insightful perspectives for future research in this field. The paper concludes with a discussion of potential directions for future work on the security of the E2 interface.

A document by Paola Arrubarrena. Click on the document to view its contents.