This paper presents a dataset of brain Electroencephalogram (EEG) signals created when Malayalam vowels and consonants are spoken. The dataset was created by capturing EEG signals utilizing the OpenBCI Cyton device while a volunteer spoke Malayalam vowels and consonants. It includes recordings obtained from both sub-vocal and vocal. The creation of this dataset aims to support individuals who speak Malayalam and suffer from neurodegenerative diseases. Moreover, this dataset is expected to advance brain-computer interface technology and has potential in developing effective communication solutions for individuals with limited verbal abilities.

Robustness to label noise within data is a significant challenge in federated learning (FL). From the data-centric perspective, the data quality of distributed datasets can not be guaranteed since annotations of different clients contain complicated label noise of varying degrees, which causes the performance degradation. There have been some early attempts to tackle noisy labels in FL. However, there exists a lack of benchmark studies on comprehensively evaluating their practical performance under unified experimental settings. To this end, we propose the first benchmark study FNBench to provide an experimental investigation. Meanwhile, we provide observations to understand why noisy labels impair FL. Our evaluation incorporates sixteen related state-of-the-art methods under diverse settings on five datasets. These datasets include three label noise types: (i) Manual-injected synthetic noisy datasets, (ii) Noisy datasets with human annotation error patterns, and (iii) One systematic large-scale noisy dataset crawled from well-noted online shopping websites. Through our empirical investigation, we draw diverse conclusions to guide future studies. We additionally exploit a representation-aware regularization method to enhance the robustness of existing methods against noisy labels based on our observations. Finally, we discuss the limitations of this work and propose threefold future directions. Our source code will be opensourced to facilitate related communities.

A document by Adham Sakhnini. Click on the document to view its contents.

A document by Jafar Shamsi. Click on the document to view its contents.

Due to electrification, magnetic sensors are increasingly deployed in magnetically polluted environments. To reject stray fields, differential sensing schemes are typically used. In this paper, we rely instead on a single-point gradiometric sensing scheme based on the force exerted on a magnet, which is directly related to the magnetic field gradient. Unlike the prior art, our concept uses a differential MEMS force-sensing mechanical transducer. The overall sensor rejects not only magnetic stray fields, but also mechanical disturbances such as vibration and gravity. It is the first single-point MEMS gradiometer that can operate unshielded in various orientations. Our prototype realization achieves a noise density of 4 nT/mm/√Hz in a ±300 μT/mm measurement range. We demonstrate its operational effectiveness in a bus-bar current sensing application. Finally, the paper discusses the remaining weaknesses and provides an outlook for MEMS-based magnetic sensors.

Multiuser gigantic-multiple-input multiple-output (MU-gMIMO) and nonorthogonal multiple access (NOMA) are jointly seen as important enabling technologies for sixth generation (6G) networks. They have many benefits, such as spatial multiplexing, spatial diversity, massive connectivity, and spectral efficiency (SE). However, gMIMO-NOMA suffers from many inherent challenges. In this paper, we propose an MU-gMIMO-hybrid multiple-access (HMA) heterogeneous network architecture to address the 'nearly same channel gain' issue. Then, an iterative minimal mean squared error (IMMSE) scheme is applied along with quadrature amplitude modulation (QAM) for maximal ratio transmission in the proposed transceiver design to address the 'residual error' caused by imperfect successive interference cancellation (ISIC). Finally, to assess the performance of the proposed architecture for the 'time offsets' issue, we investigate the asynchronous mode with an MMSE detector matrix following imperfect channel state information (ICSI) to provide a new analysis for HMA transmission and formulate an optimization problem for energy efficiency (EE).

In the burgeoning field of intelligent transportation systems, the integration of Generative Artificial Intelligence (AI) into vehicular networks presents a transformative potential for the automotive industry. This paper explores the innovative applications of generative AI in enhancing communication protocols, optimizing traffic management, and bolstering security frameworks within vehicular networks. By examining current technologies and recent advancements, we identify key challenges such as scalability, real-time data processing, and security vulnerabilities that come with AI integration. Additionally, we propose novel applications and methodologies that leverage generative AI to simulate complex network scenarios, generate adaptive communication schemes, and enhance predictive capabilities for traffic conditions. This study not only reviews the state of the art but also highlights significant opportunities where generative AI can lead to groundbreaking improvements in vehicular network efficiency and safety. Through this comprehensive exploration, our findings aim to guide future research directions and foster a deeper understanding of generative AI's role in the next generation of vehicular technologies.

This review explores the integration of conversational AI, particularly chatbots like ChatGPT, in problem-based teaching methodologies within diverse educational settings. The rapid advancement in AI technology offers unique opportunities for addressing the challenges faced by students from varied cultural and linguistic backgrounds. By focusing on problem-based learning (PBL) strategies enhanced by conversational AI, this paper examines how AI tools can facilitate personalized learning experiences, promote cultural understanding, and overcome language barriers. The review synthesizes current research findings, highlighting the effectiveness of AI in creating inclusive and adaptive learning environments. Special attention is given to the potential of these technologies in fostering critical thinking, collaboration, and problem-solving skills among students with diverse needs. The paper also addresses the ethical considerations and challenges in implementing AI in educational contexts.

Wideband millimeter wave and terahertz systems face severe nonlinearity and other practical impairments such as transmitter and receiver I/Q imbalances (IQIs), carrier frequency offset, and phase noise. Based on a simplified yet effective received signal model, this paper proposes a low-complexity digital post-cancellation (DPC) framework for transmitter IQI and overall system nonlinearity mitigation. The nonlinearity parameters with reduced nonlinearity order are firstly estimated with low-complexity using a novel transmission protocol incorporating both frame rotation and preamble power scaling. Through widely linear system equalization and interference cancellation, the signal distortion caused by frequency-dependent IQI and nonlinearity are then mitigated with significant performance improvement. The mean-squared-error measurement of the EMP-modelled signals also provides a practical means for the nonlinear system identification and characterization. Both simulation and experiment results obtained from a millimeter wave system with 2.5 GHz bandwidth and 73.5 GHz carrier frequency are presented to verify the theoretical analyses and demonstrate the effectiveness of the DPC technology.

Possible choices of flight paths on spherical surfaces - for example on hemispheres - for goniometric mapping with UAVs are compared and explicit parametrizations are given. The quantities measured during mapping should be easily integrable numerically in order to determine physical quantities like source strength, flux, etc.Some brief considerations regarding the UAVs energy budget are given also.

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).

A document by Chibuike Onuoha. Click on the document to view its contents.

Imaging photoplethysmography (iPPG) is a contactless approach for the extraction of the blood volume pulsation (BVP). Analyzing the small intensity changes resulting from fluctuations in light absorption in upper skin layers enables BVP extraction. Local and global dynamic effects, e.g. caused by shadows from inhomogeneous illumination or head movements, impede iPPG-based BVP extraction. To eliminate these effects, an important step is the accurate skin segmentation and weighting, which has received insufficient attention in state-of-the-art (SOFA) deep learning-based approaches in particular. Therefore, we propose DeepPerfusion, a two-branched deep learning architecture, that combines skin segmentation and BVP extraction into one model and thus is more capable of taking into account the aforementioned effects compared to SOFA approaches. We evaluated the mean absolute error (MAE) for heart rate extraction and the signal-to-noise ratio (SNR) on 156 subjects from three publicly available datasets and compared it with nine SOFA approaches that underwent the same training and evaluation pipeline. For the median over the subjects of each dataset, DeepPerfusion consistently achieved MAE below 1 beat per minute and thus significantly outperformed all SOFA approaches by up to 49 %. Furthermore, DeepPerfusion achieved high SNR with at least 5.81 dB which was about two to three times higher compared to the best SOFA approaches. In contrast to SOFA approaches, DeepPerfusion's performance was consistent, robust and highly precise. This demonstrates DeepPerfusion's ability to perform high-precision BVP extraction, which will open up new diagnostic applications for iPPG in the future. Note: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.  

The calculation of Tumor Stroma Ratio (TSR) is a challenging medical issue that could improve predictions of neoadjuvant chemotherapy benefits and patient prognoses. Although several studies on breast cancer and deep learning methods have achieved promising results, the drawbacks that pixel-level semantic segmentation processes could not extract core tumor regions containing both tumor pixels and stroma pixels make it difficult to accurately calculate TSR. In this paper, we propose a Vague-Segment Technique (VST) consisting of a designed SwinV2UNet module and a modified Suzuki algorithm. Specifically, the SwinV2UNet identifies tumor pixels and generate pixel-level classification results, based on which the modified Suzuki algorithm extracts the contour of core tumor regions in terms of cosine angle. Through this way, VST obtains vaguely segmentation results of core tumor regions containing both tumor pixels and stroma pixels, where the TSR could be calculated by the formula of Intersection over Union (IOU). For the training and evaluation, we utilize the well-known The Cancer Genome Atlas (TCGA) database to create an annotated dataset, while 150 images with TSR annotations from real cases are also collected. The experimental results illustrate that the proposed VST could generate better tumor identification results compared with state-of-the-art methods, where the extracted core tumor regions lead to more consistencies of calculated TSR with senior experts compared to junior pathologists. The experimental results demonstrate the superiority of our proposed pipeline, which has promise for future clinical application.

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.

This letter analyses the achievable performance of Low Earth Orbit (LEO) Position-Navigation-Timing (PNT) using the novel Orthogonal Time Frequency Space (OTFS) modulation. While originally designed for communication purposes, we show the advantages this modulation may bring to the PNT domain, in particular for doubly-selective channels exhibiting both delay and Doppler spreads. We show how the LEO channel fits this description due to its fast dynamics, and we present the improvements of using OTFS with respect to the standard Binary Phase Shift Keying (BPSK) modulation in terms of delay-Doppler estimation accuracy, in addition to a comparison of their respective Multipath Error Envelopes.

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked convolutional neural networks (CNNs). Our innovative technique leverages the distributed nature of federated learning to collaboratively train a highperformance model while preserving data privacy on local devices. We propose a stacked CNN architecture tailored for ECG data, effectively extracting discriminative features across different temporal scales. The evaluation confirms the strength of our approach, culminating in a final model accuracy of 98.6% after 100 communication rounds, significantly exceeding baseline performance. This promising result paves the way for accurate and privacy-preserving ECG classification in diverse healthcare settings, potentially leading to improved diagnosis and patient monitoring.

A load-independent constant current (CC)-constant voltage (CV) output is an important requirement of inductive power transfer (IPT) systems for electric vehicle charging applications. Zero phase angle (ZPA) is also a desirable feature, to ensure a lower power rating requirement for the switching converter. CC and CV output along with ZPA can be achieved by using a suitable compensation topology. Equation manipulation techniques can be used for designing the compensation topology. But, it can be mathematically intensive especially for higher order topologies. To overcome this problem, resonant-tank based approaches are adopted in several works to achieve CC and CV conditions. However, equation-based approaches are depended upon either wholly or partly for realizing ZPA. This approach can be tedious and lacks physical insight. The proposed method extends resonant tank approach to achieve ZPA also, besides CC and CV. The need for a separate method to achieve ZPA is eliminated. Further, it simplifies the process in arriving at the constraints that ensure ZPA. As a sample validation, the proposed method is applied to a S-SP compensation topology. The CC-ZPA and CV-ZPA constraints for the S-SP topology are shown to be in line with the ones arrived at using an existing equation-based impedance approach. The simplicity of the proposed method can be observed from the sample validation.