Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Spiking and Artificial Neural Networks for Radar-Based Gesture Recognition

Artificial neural networks showed astonishing results in the last decades. However, they tend to consume large amounts of energy which is problematic on edge devices such as wearables where the energy budget is limited. We propose a hybrid neural network of convolutional layers and spiking neural networks that combines the feature extraction capabilities of CNNs with the energy efficiency of SNNs for low-power radar signal processing. This approach is applied to our own radar-based gesture recognition dataset and the publicly available Soli dataset. The hybrid neural network achieves competitive or better accuracies on both datasets in comparison to similarly sized spiking and traditional networks. Experiments on the SpiNNaker 2 system show that they are a little less efficient with noisy input data compared to standard SNNs. The proposed network architecture is a straight forward approach for low-power signal processing of radar data.
Convolving Emotions: A Compact CNN for EEG-Based Emotion Recognition

Emotion Recognition is a research area that has had a surge in interest, since areas such as mental health, psychological diagnosis, e-Learning and assistance for people who are not capable of communicating their feelings, depend on certain level, on the capacities of computer systems to reliably identify emotions. There are several approaches to this task, for instance, analyzing facial expressions, speech, and physiological signals (electrocardiogram, galvanic skin response, electroencephalogram, among others). Electroencephalogram is one of the preferred methods due, in part, to is great temporal resolution. Therefore, in this paper we used the EEG Brainwave Dataset as benchmark to test our model, which is a four layer, one dimensional convolutional neural network. After the preprocessing pipeline, consisting on considering certain features of the dataset as signals and processing them accordingly, by creating several channels by two decomposition methods, our model achieved accuracy values of 98.36% and 95.31 %, which is competitive with what is found on the state of the art, while being a significantly less complex model.
Transmitter IQ Mismatch Estimation by Number-Theoretic Net-Based Gaussian Particle Filter for Coherent Optical Communications

In this paper, we propose a novel approach for estimating in-phase/quadrature-phase (IQ) mismatch at the transmitter using a number-theoretic net (NT-net)-based Gaussian particle filter (NT-GPF). The proposed method generates particles on an ellipse of the given bivariate Gaussian distribution, which helps locate the correct region of the actual state parameters more efficiently. Simulation results demonstrate that the NT-GPF-based approach offers higher estimation accuracy, with the mean squared error (MSE) consistently lower than that of traditional GPF across different particle numbers. Moreover, the proposed NT-GPF-based algorithm significantly improves estimation efficiency and noise tolerance, requiring only 21 particles under low signal-to-noise ratio (SNR).
Ellagic Acid Vrs Covid -19 : Functional Food to Drug Discovery

Functional Foods (FF) are historical & cultural. Technologically the shortest, safest & most economic starting material for drug discovery. In-Silico studies are computational; multi-disciplinary versatile computational platforms to pre-view anti-body vrs anti-gen interactions and intra-compound compatibility, etc.; validates FFs.Ellagic acid (EA) comprises FF as in PunicaGranatum (peel-pulp-&-juice). Computation is used to evaluate EA as a possible RBD docking candidate of relevance vis-à-vis COV-2 spp. Indicates excellent docking.Good docking means antagonistic. EA is compared with anti-cancer Simeprevir (which is re-purposed use in Covid having docking affinity of – 8.52 Kcal/Mol). EA is observed to make very close call (-6.17 Kcal/Mol) with better indicators in all other parameters. Punica Granatum contains (posits) Attractive.Original, Ground breaking.Opportunity full.
Machine Learning Aided Spectrum Sensing

Spectrum sensing (SS) is a key concept that determines the effectiveness of cognitive radio (CR). Although multiple sensing practices have been proposed in the literature, out of them energy detector (ED) have been widely adopted. However, the performance of ED relies on pre-existing knowledge of noise distribution and threshold. Therefore, efficient spectrum sensing remains a challenge in wireless communication technologies. This paper aims to analyze the sensing performance of various machine learning (ML) models in different fading scenarios. We carefully model a spectrum sensing environment and generate a large-scale comprehensive dataset. We then train and test several well-established classical and ML techniques for spectrum sensing. The performance of classical spectrum sensing and ML-based spectrum sensing are compared under various parameters like the number of secondary user (SU), the distance between primary user (PU) and SU; and different fading environments such as Rayleigh, Rician, and Nakagami. The Simulation result shows that machine learning models outperform classical sensing techniques. Furthermore, it is found that support vector machine (SVM) outperformed other ML techniques in predicting channel availability.
Enhancing Vehicular Network Performance through Integrated RSU and UAV Deployment

The on growing demand for reliable vehicle commu-nication services creates issues in providing continuous connectiv-ity and effective data delivery. Traditional roadside units (RSUs) need help adapting to changing traffic circumstances and user density. Integrating unmanned aerial Vehicles (UAVs) as aerial base stations provides a solution by increasing the coverage, relia-bility, and line-of-sight communication, and allowing for dynamic deployment. This research work uses self-learning methodology to focus on efficient and effective RSUs, UAVs, and UAV-assistant (UAVa) deployment inside vehicular networks. The primary goals are to improve network performance, Quality of Service (QoS), and interference management. In this article, we have considered two different scenarios in vehicular networks in which we have integrated a UAVa to handle emergency situations on the roads which dynamically provides network coverage to the uncovered vehicles on the roadside. We strategically deploy UAVa using a hybrid method that combines heuristic algorithms and real-world traffic insights to balance existing vehicular network resources, minimize latency, and eliminate interference. The simulation findings show that the UAVa has a much higher throughput than RSUs, emphasizing the need for dynamic adaptation and strategic positioning. This study emphasizes the significance of proper location and intelligent support in maximizing throughput and ensuring the reliability of vehicle data exchange.
Efficient Multipath Service Function Chaining in Inter-Data Center Networks

This paper is complimentary research to our previous paper [1] on Service Function Chaining (SFC) symmetries effect on network function (NF) such as firewall (FW) application successfulness. This studies focus is on service function (SF) usability improvements in inter-data center networks. We introduce a method of network traffic simultaneous steering through multiple SFs in SFC domain stretching over two data centers (DCs), resulting in resource sharing among both DCs. We tested such methods efficiency via conducting a series of simulations in GNS3. Results indicate an overall increase in successful available bandwidth utilization (from 0.3 to 0.65) measuring AVG throughput.
Battery Health Prognosis Based on Sliding Window Sampling of Charging Curves and Independently Recurrent Neural Network

With the development of lithium-ion battery (LIB) technology and the increasing popularity of electric vehicles, the issue of battery safety has become increasingly urgent. The state of health (SOH), known as a critical parameter in the prognosis and health management of LIBs, has considerable attention from industry and academia. This article proposes a novel method for estimating the SOH of LIBs based on sliding window (SW) sampling of charging curves and independently recurrent neural network (IndRNN). Considering the number of battery cycles and practical applications, the SW sampling based on cycle number is utilized to determine the different partial voltages as the inputs to the SOH estimation model. To address the gradient disappearance and gradient explosion problems, in the proposed SOH estimation model, we suggest the IndRNN which introduces independent weights between inputs and outputs, trains the IndRNN with rectified linear units, and learns the long-term dependencies by stacking multiple layers of IndRNN to achieve long-term accurate aging tracking of batteries. Finally, experiments are validated on the most widely used Oxford University battery dataset, and the effectiveness of our method is also verified by comparing it against three methods on our laboratory data with different operating conditions.
Enhancing Scalability of Fast Electric Vehicle Charging Stations: Solutions for AC–DC Side Integration and Regulation

This article introduces a set of innovative solutions designed to enhance the scalability of ac–dc side operations in fast electric vehicle (EV) charging stations. The proposed solutions focus on streamlining and regulating the connection process for integrating a new active front-end rectifier (AFR) with existing AFRs at the high-voltage dc-link side, without requiring detailed knowledge of their specifications, such as control algorithms, switching frequencies, topologies, and modulation techniques. Neglecting these specifications can result in significant adverse effects, particularly on grid currents within the ac–dc side of the charging station. Conversely, these specifications have minimal impact on the dc–dc side. Therefore, this article primarily concentrates on addressing the scalability challenges associated with the ac–dc side of fast EV charging stations. Experimental analysis is conducted to assess the consequences of underestimating any specification, followed by the proposal of a general solution to compensate for or reduce each effect. The proposed methods effectively address the critical demand for scalability in fast EV chargers. Furthermore, these techniques are implemented using low-cost microcontrollers and validated experimentally through the utilization of three parallel-connected AFRs.
One Standard to Rule Them All? Assessing the Disruptive Potential of Jamming Attacks on Matter Networks

This study addresses the vulnerability of Matter networks against reactive jamming attacks in Internet of Things (IoT) solutions. Through an analysis of various scenarios, we provide empirical evidence demonstrating the substantial threat posed by these attacks, as indicated by their exceptionally high success rate of 91%. Additionally, we identify significant weaknesses in the jamming detection mechanism within OpenThread, a widely used implementation of the Thread protocol. To address this issue, we propose a novel passive jamming detection approach fot the transport and networking layer in Matter with an accuracy of 96%. Our findings emphasize the need for robust security measures in Matter networks, such as integrating passive detection nodes or directly incorporating detection mechanisms into the Matter standard. Future work involves expanding scenario coverage and exploring efficient integration options within the standard.
A retrospective analysis of post-treatment complications in patients with carcinoma vulva undergoing surgery as a primary treatment modality

Background: Vulvar carcinoma accounts for 5% of all malignant neoplasms in the female genital tract, with a higher incidence in developing countries and increased risk associated with human papillomavirus (HPV) infection. Aims and Objectives: The study aimed to retrospectively evaluate treatment complications in patients with carcinoma vulva who had undergone primary surgery alone or as part of multimodality therapy. It also aimed to evaluate HPV marker positivity in this group of patients and its relation to disease profile and prognosis. Materials and Methods: This is a retrospective study wherein data from the Regional Cancer Centre, Coimbatore, from 2017 to 2023, about 42 patients who underwent primary surgery for carcinoma vulva were analyzed. Patient information, including age, symptoms, imaging, biopsy, staging, and treatment options, were retrieved for analysis. Results: Regarding post-operative complications, ten patients developed seroma (23%), 9 had limb edema (21%), five patients had wound dehiscence (11%), and nine patients had post-radiotherapy/chemotherapy complications (21%). The mortality rate among patients in the study was 9% (4 patients). All four died because of nodal relapse. Six patients (14%) had disease recurrence in this study group, among whom 5 were HPV-negative (83%). Two patients had a recurrence in the primary site, and four had a nodal recurrence. HPV marker p16 was positive in 18 patients (43%) overall, and they were found to belong to the younger age group and had good treatment outcomes. Conclusion: Primary surgery for carcinoma vulva patients in our study is associated with limited but manageable complications amenable to rectification. HPV-associated carcinoma vulva is increasing in prevalence (43% in younger age group) with a positive prognosis.
Whole-body Stabilization of the REEM-C Humanoid on Actuated Balance Boards in Sagittal and Frontal Directions

This paper explores stabilizer design for humanoid balancing on actuated balance boards. We qualitatively study how humans balance on balance boards to motivate our humanoid controller design. The developed approach combines various strategies from the literature including ankle admittance, CoM admittance, base pitch admittance, foot force difference admittance, contact wrench distribution and angular momentum regulation in a new integration to achieve stabilization on an actuated balance board. We explore the frontal and sagittal plane balance board problems along with pushes at the upper body and show that our approach works in simulation and on real hardware. This brings us one step closer to balancing on unactuated balance boards, other dynamic environments such as moving platforms and transportation devices, skills that will make humanoids more versatile.
Direction of Arrival Estimation of Wideband Sources: Trend Analysis of Mean Square Error vs Regularization Parameters

The goal of this paper is to optimize the Direction of arrival (DoA) estimation of wideband sources based on sparse linear arrays by analyzing the variations of the mean square error of DoAs with respect to regularization parameters. The wideband source signals are partitioned into segments, a Discrete Fourier Transform (DFT) is performed, and then data is collected for each frequency bin. To combine the data which is collected from more than a single frequency bins, the Jacobi-Anger approximation is used, which converts the different co-array matrices into a single virtual uniform linear array. There are a number of estimation techniques available with different assumptions to be considered. The work in this paper is focused on a particular practical scenario where the power spectrum of all wideband sources is identical and known in advance. Thus, the DoA estimation approach used in this paper is a super-resolution off-the-grid approach. As the minimization problem is dependent on a factor called the regularization parameter, the dependency of the mean square error on the regularization parameter is analyzed. Simulation results show that the mean square error as a function of the regularization parameter possesses a minimum value only for a certain value of the regularization parameter. Therefore, the selection of the optimal value of the regularization parameter, which is very important, can be done by following the recommended range or value of the regularization parameter rather than using a hit-and-trial method.
Hard and Soft Sciences: How Both Fields Differ in Presenting Research Gaps

Studies on research gap presentation only focused on certain field of studies, whereas there are no studies that try to compare different field of studies. Therefore, this research aims to fill the gap and investigate how authors from different field of studies present research gap in their research articles. As the research gap presentation are mainly occurred in the beginning sections of an article, 100 article introductions from 10 journals have been analyzed. The journals were divided into two major fields which are Hard Science and Soft Science. Analysis was done to find out what types of research gap strategies that is used by the authors following the seven research gaps framework: evidence gap, knowledge gap, practical-knowledge gap, methodological gap, empirical gap, theoretical gap, and population gap. The results show that authors from both Hard Sciences and Soft Science used knowledge gap as the most frequently used strategy and theoretical gap as the least frequently used strategy. Other result is authors in Hard Sciences use more varied strategies than in Soft Science. Furthermore, both fields seem quite similar regarding the occurrence of research gaps while still has differences in some parts.
Effect of Concentration and Time on the Antimicrobial Activity of Human Saliva against Candida albicans

Mechanical cleaning is considered most effective in preventing oral mucosal diseases such as denture stomatitis and aspiration pneumonia; however, host saliva also have effective antimicrobial substances. Beta-defensin and histatin reportedly exert antimicrobial effects against Candida albicans; however, their optimal concentration in human saliva and optimum duration of activity are unclear. Therefore, this study aimed to investigate the effects of concentration and time on the antimicrobial activity of human saliva against C. albicans. Saliva was collected from 20 volunteers(10 men, 10 women; 25.7±1.95 years) twice daily for 1 week, and each antimicrobial substances was quantified. The viable count, ATP activity, and changes in the drug susceptibility test for C. albicans were assessed with respect to different concentrations and durations to analyse antimicrobial activity. Viable count tended to increase with an increase in beta-defensin 3 levels, but tended to decrease with an increase in histatin 5 levels. Yeast density and ATP activity level were significantly positively correlated with each antimicrobial substance(beta-defensin 3; 0.991, p<.000, histatin 5; 0.975, p<.000). Beta-defensin 3 and histatin 5 did not yield zones of inhibition with time; however, antifungal drugs reduced the viable count and ATP activity level regardless of time. The present results indicate that histatin 5 reduces the population of C. albicans and ATP activity levels and may be a potential marker for risk assessment of oral mucosal diseases and aspiration pneumonia.
Magnetic Resonance Spectroscopy Fitting through Spectrogram and Vision Transformers

This article presents a novel approach that employs Vision Transformer models for spectral fitting in Magnetic Resonance Spectroscopy (MRS). The method transforms MRS data into spectrograms, combining the time and frequency domains. These spectrograms are then used as input for the Vision Transformers, trained to predict fitting parameters with a custom loss function. The proposed method was trained and evaluated with synthetic Short-echo time PRESS data and the model’s performance was evaluated using the metrics Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R2), and Fit Quality Number (FQN). The distribution of the FQN for the best model exhibited low variance, with most values concentrated around the optimal value of 1.0. The model was compared to a recent deep learning approach and presented better results, particularly pronounced for metabolites susceptible to overlapping, which impose more difficulty on the fitting process.
The Clinical Analysis of Checkpoint Inhibitor Pneumonitis with Different Severities in Lung Cancer Patients: A Retrospective Study

Immune-related adverse events (irAEs) of immunotherapy would lead to the temporary or permanent discontinuation of immune checkpoint inhibitors (ICIs). Among them, checkpoint inhibitor pneumonitis (CIP) is a potentially life-threatening irAE. This study aimed to identify the differences between patients with low-grade CIPs (grades 1–2) and high-grade CIPs (grades 3–5) and to explore the prognostic factors. We retrospectively reviewed the medical records of 916 lung cancer patients who were treated with ICIs. Patients with CIPs were identified after multidisciplinary discussion, and their clinical, laboratory, radiological, and follow-up data were analyzed. Among the 74 enrolled CIP patients, there were 31 low-grade CIPs and 43 high-grade CIPs. Compared with low-grade CIP patients, patients with high-grade CIPs were older (65.8 years vs. 61.5 years) and had lower serum albumin (35.2 g/L vs. 37.9 g/L), higher D-dimer (5.1 mg/L vs. 1.7 mg/L), and more pulmonary infectious diseases (32.6% vs. 6.5%) during follow-up. In addition, complication with pulmonary infectious diseases, management with intravenous immunoglobulin, tocilizumab, and longer duration of large dosage corticosteroids might be associated with worse outcomes for patients with CIPs. This study highlights potential risk factors for high-grade CIP and poor prognosis among lung cancer patients who were treated with anti-cancer ICIs.
Towards the Detection of Microservice Patterns Based on Metrics

Microservices is a popular architectural approach for complex systems in companies, despite its nature of decentralization. There is a comprehensive set of microservices architectural patterns that guides implementations and helps developers to overcome issues. However, the community still scarcely adopts these patterns and only has a theoretical understanding of them. In this work, in order to increase awareness of such patterns and provide aid to developers to better understand an architecture based on microservices, we propose a detection approach based on metrics for microservices patterns. We focused on structural or architectural patterns, and implemented detection for five of them. We conducted two case studies with real-world applications and evaluated the accuracy and applicability of our approach with the developers of those applications.