pwc-archive/papers-with-abstracts huggingface dataset:
ResEMGNet: A Lightweight Residual Deep Learning Architecture for Neuromuscular Disorder Detection from Raw EMG Signals
Authors: Minhajur Rahman, Md Toufiqur Rahman, Md Tanvir Raihan, Celia Shahnaz
Date: 2023-09-19 00:00:00
Tasks: Diagnostic, Electromyography (EMG)

Amyotrophic Lateral Sclerosis (ALS) and Myopathy are debilitating neuromuscular disorders that demand accurate and efficient diagnostic approaches. In this study, we harness the power of deep learning techniques to detect ALS and Myopathy. Convolutional Neural Networks (CNNs) have emerged as powerful tools in this context. We present ResEMGNet, designed to identify ALS and Myopathy directly from raw electromyography (EMG) signals. Unlike traditional methods that require intricate handcrafted feature extraction, ResEMGNet takes raw EMG data as input, reducing computational complexity and enhancing practicality. Our approach was rigorously evaluated using various metrics in comparison to existing methods. ResEMGNet exhibited exceptional subject-independent performance, achieving an impressive overall three-class accuracy of 94.43\%.
BeamSec: A Practical mmWave Physical Layer Security Scheme Against Strong Adversaries
Authors: Afifa Ishtiaq, Arash Asadi, Ladan Khaloopour, Waqar Ahmed, Vahid Jamali, Matthias Hollick
Date: 2023-09-19 00:00:00

The high directionality of millimeter-wave (mmWave) communication systems has proven effective in reducing the attack surface against eavesdropping, thus improving the physical layer security. However, even with highly directional beams, the system is still exposed to eavesdropping against adversaries located within the main lobe. In this paper, we propose \acrshort{BSec}, a solution to protect the users even from adversaries located in the main lobe. The key feature of BeamSec are: (i) Operating without the knowledge of eavesdropper's location/channel; (ii) Robustness against colluding eavesdropping attack and (iii) Standard compatibility, which we prove using experiments via our IEEE 802.11ad/ay-compatible 60 GHz phased-array testbed. Methodologically, BeamSec first identifies uncorrelated and diverse beam-pairs between the transmitter and receiver by analyzing signal characteristics available through standard-compliant procedures. Next, it encodes the information jointly over all selected beam-pairs to minimize information leakage. We study two methods for allocating transmission time among different beams, namely uniform allocation (no knowledge of the wireless channel) and optimal allocation for maximization of the secrecy rate (with partial knowledge of the wireless channel). Our experiments show that \acrshort{BSec} outperforms the benchmark schemes against single and colluding eavesdroppers and enhances the secrecy rate by 79.8% over a random paths selection benchmark.
A Multi Constrained Transformer-BiLSTM Guided Network for Automated Sleep Stage Classification from Single-Channel EEG
Authors: Farhan Sadik, Md Tanvir Raihan, Rifat Bin Rashid, Minhjaur Rahman, Sabit Md Abdal, Shahed Ahmed, Talha Ibn Mahmud
Date: 2023-09-19 00:00:00
Tasks: Decision Making, EEG, Electroencephalogram (EEG)

Sleep stage classification from electroencephalogram (EEG) is significant for the rapid evaluation of sleeping patterns and quality. A novel deep learning architecture, ``DenseRTSleep-II'', is proposed for automatic sleep scoring from single-channel EEG signals. The architecture utilizes the advantages of Convolutional Neural Network (CNN), transformer network, and Bidirectional Long Short Term Memory (BiLSTM) for effective sleep scoring. Moreover, with the addition of a weighted multi-loss scheme, this model is trained more implicitly for vigorous decision-making tasks. Thus, the model generates the most efficient result in the SleepEDFx dataset and outperforms different state-of-the-art (IIT-Net, DeepSleepNet) techniques by a large margin in terms of accuracy, precision, and F1-score.
EMG Signal Classification for Neuromuscular Disorders with Attention-Enhanced CNN
Authors: Md. Toufiqur Rahman, Minhajur Rahman, Celia Shahnaz
Date: 2023-09-19 00:00:00
Tasks: Diagnostic, Electromyography (EMG), Multi-class Classification

Amyotrophic Lateral Sclerosis (ALS) and Myopathy present considerable challenges in the realm of neuromuscular disorder diagnostics. In this study, we employ advanced deep-learning techniques to address the detection of ALS and Myopathy, two debilitating conditions. Our methodology begins with the extraction of informative features from raw electromyography (EMG) signals, leveraging the Log-spectrum, and Delta Log spectrum, which capture the frequency contents, and spectral and temporal characteristics of the signals. Subsequently, we applied a deep-learning model, SpectroEMG-Net, combined with Convolutional Neural Networks (CNNs) and Attention for the classification of three classes. The robustness of our approach is rigorously evaluated, demonstrating its remarkable performance in distinguishing among the classes: Myopathy, Normal, and ALS, with an outstanding overall accuracy of 92\%. This study marks a contribution to addressing the diagnostic challenges posed by neuromuscular disorders through a data-driven, multi-class classification approach, providing valuable insights into the potential for early and accurate detection.
Coverage Analysis of Dynamic Coordinated Beamforming for LEO Satellite Downlink Networks
Authors: Daeun Kim, Jeonghun Park, Namyoon Lee
Date: 2023-09-19 00:00:00
Tasks: Point Processes

In this paper, we investigate the coverage performance of downlink satellite networks employing dynamic coordinated beamforming. Our approach involves modeling the spatial arrangement of satellites and users using Poisson point processes situated on concentric spheres. We derive analytical expressions for the coverage probability, which take into account the in-cluster geometry of the coordinated satellite set. These expressions are formulated in terms of various parameters, including the number of antennas per satellite, satellite density, fading characteristics, and path-loss exponent. To offer a more intuitive understanding, we also develop an approximation for the coverage probability. Furthermore, by considering the distribution of normalized distances, we derive the spatially averaged coverage probability, thereby validating the advantages of coordinated beamforming from a spatial average perspective. Our primary finding is that dynamic coordinated beamforming significantly improves coverage compared to the absence of satellite coordination, in direct proportion to the number of antennas on each satellite. Moreover, we observe that the optimal cluster size, which maximizes the ergodic spectral efficiency, increases with higher satellite density, provided that the number of antennas on the satellites is sufficiently large. Our findings are corroborated by simulation results, confirming the accuracy of the derived expressions.
Enhancing Congestion Control to Improve User Experience in IoT Using LSTM Network
Authors: Atta Ur Rahman, Bibi Saqia, Wali Ullah Khan, Khaled Rabie, Mahmood Alam, Khairullah khan
Date: 2023-09-19 00:00:00
Tasks: Management

This study suggests a new strategy for improving congestion control by deploying Long Short-Term Memory (LSTM) networks. LSTMs are recurrent neural networks (RNN), that excel at capturing temporal relationships and patterns in data. IoT-specific data such as network traffic patterns, device interactions, and congestion occurrences are gathered and analyzed. The gathered data is used to create and train an LSTM network architecture specific to the IoT environment. Then, the LSTM model's predictive skills are incorporated into the congestion control methods. This work intends to optimize congestion management methods using LSTM networks, which results in increased user satisfaction and dependable IoT connectivity. Utilizing metrics like throughput, latency, packet loss, and user satisfaction, the success of the suggested strategy is evaluated. Evaluation of performance includes rigorous testing and comparison to conventional congestion control methods.
A Generalized Approach for Recovering Time Encoded Signals with Finite Rate of Innovation
Authors: Dorian Florescu
Date: 2023-09-19 00:00:00

In this paper, we consider the problem of recovering a sum of filtered Diracs, representing an input with finite rate of innovation (FRI), from its corresponding time encoding machine (TEM) measurements. So far, the recovery was guaranteed for cases where the filter is selected from a number of particular mathematical functions. Here, we introduce a new generalized method for recovering FRI signals from the TEM output. On the theoretical front, we significantly increase the class of filters for which reconstruction is guaranteed, and provide a condition for perfect input recovery depending on the first two local derivatives of the filter. We extend this result with reconstruction guarantees in the case of noise corrupted FRI signals. On the practical front, in cases where the filter has an unknown mathematical function, the proposed method streamlines the recovery process by bypassing the filter modelling stage. We validate the proposed method via numerical simulations with filters previously used in the literature, as well as filters that are not compatible with the existing results. Additionally, we validate the results using a TEM hardware implementation.
PrNet: A Neural Network for Correcting Pseudoranges to Improve Positioning with Android Raw GNSS Measurements
Authors: Xu Weng, Keck Voon Ling, Haochen Liu
Date: 2023-09-16 00:00:00

We present a neural network for mitigating biased errors in pseudoranges to improve localization performance with data collected from mobile phones. A satellite-wise Multilayer Perceptron (MLP) is designed to regress the pseudorange bias correction from six satellite, receiver, context-related features derived from Android raw Global Navigation Satellite System (GNSS) measurements. To train the MLP, we carefully calculate the target values of pseudorange bias using location ground truth and smoothing techniques and optimize a loss function involving the estimation residuals of smartphone clock bias. The corrected pseudoranges are then used by a model-based localization engine to compute locations. The Google Smartphone Decimeter Challenge (GSDC) dataset, which contains Android smartphone data collected from both rural and urban areas, is utilized for evaluation. Both fingerprinting and cross-trace localization results demonstrate that our proposed method outperforms model-based and state-of-the-art data-driven approaches.
Electroencephalogram Sensor Data Compression Using An Asymmetrical Sparse Autoencoder With A Discrete Cosine Transform Layer
Authors: Xin Zhu, Hongyi Pan, Shuaiang Rong, Ahmet Enis Cetin
Date: 2023-09-15 00:00:00
Tasks: Data Compression, Decoder, EEG, Electroencephalogram (EEG)

Electroencephalogram (EEG) data compression is necessary for wireless recording applications to reduce the amount of data that needs to be transmitted. In this paper, an asymmetrical sparse autoencoder with a discrete cosine transform (DCT) layer is proposed to compress EEG signals. The encoder module of the autoencoder has a combination of a fully connected linear layer and the DCT layer to reduce redundant data using hard-thresholding nonlinearity. Furthermore, the DCT layer includes trainable hard-thresholding parameters and scaling layers to give emphasis or de-emphasis on individual DCT coefficients. Finally, the one-by-one convolutional layer generates the latent space. The sparsity penalty-based cost function is employed to keep the feature map as sparse as possible in the latent space. The latent space data is transmitted to the receiver. The decoder module of the autoencoder is designed using the inverse DCT and two fully connected linear layers to improve the accuracy of data reconstruction. In comparison to other state-of-the-art methods, the proposed method significantly improves the average quality score in various data compression experiments.
Model-based Deep Learning for High-Dimensional Periodic Structures
Authors: Lucas Polo-López, Luc Le Magoarou, Romain Contreres, María García-Vigueras
Date: 2023-09-15 00:00:00
Tasks: Deep Learning

This work presents a deep learning surrogate model for the fast simulation of high-dimensional frequency selective surfaces. We consider unit-cells which are built as multiple concatenated stacks of screens and their design requires the control over many geometrical degrees of freedom. Thanks to the introduction of physical insight into the model, it can produce accurate predictions of the S-parameters of a certain structure after training with a reduced dataset.The proposed model is highly versatile and it can be used with any kind of frequency selective surface, based on either perforations or patches of any arbitrary geometry. Numeric examples are presented here for the case of frequency selective surfaces composed of screens with rectangular perforations, showing an excellent agreement between the predicted performance and such obtained with a full-wave simulator.
Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023
Authors: Mahboobeh Jafari, Delaram Sadeghi, Afshin Shoeibi, Hamid Alinejad-Rokny, Amin Beheshti, David López García, Zhaolin Chen, U. Rajendra Acharya, Juan M. Gorriz
Date: 2023-09-14 00:00:00
Tasks: Diagnostic, EEG

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings.
Singular Control in a Cash Management Model with Ambiguity
Authors: Arnon Archankul, Giorgio Ferrari, Tobias Hellmann, Jacco J. J. Thijssen
Date: 2023-09-21 00:00:00
Tasks: Management

We consider a singular control model of cash reserve management, driven by a diffusion under ambiguity. The manager is assumed to have maxmin preferences over a set of priors characterized by $\kappa$-ignorance. A verification theorem is established to determine the firm's cost function and the optimal cash policy; the latter taking the form of a control barrier policy. In a model driven by arithmetic Brownian motion, we numerically show that an increase in ambiguity leads to higher expected costs under the worst-case prior and a narrower inaction region. The latter effect can be used to provide an ambiguity-driven explanation for observed cash management behavior.
Biological insights from RIL-seq in bacteria
Authors: Aviezer Silverman, Sahar Melamed
Date: 2023-09-20 00:00:00

Bacteria reside in constantly changing environments and require rapid and precise adjustments of gene expression to ensure survival. Small regulatory RNAs (sRNAs) are a crucial element that bacteria utilize to achieve this. sRNAs are short RNA molecules that modulate gene expression usually through base-pairing interactions with target RNAs, primarily mRNAs. These interactions can lead to either negative outcomes such as mRNA degradation or translational repression or positive outcomes such as mRNA stabilization or translation enhancement. In recent years, high-throughput approaches such as RIL-seq (RNA interaction by ligation and sequencing) revolutionized the sRNA field by enabling the identification of sRNA targets on a global scale, unveiling intricate sRNA-RNA networks. In this review, we discuss the insights gained from investigating sRNA-RNA networks in well-studied bacterial species as well as in under-studied bacterial species. Having a complete understanding of sRNA-mediated regulation is critical for the development of new strategies for controlling bacterial growth and combating bacterial infections.
Intentional binding enhances hybrid BCI control
Authors: Tristan Venot, Arthur Desbois, Marie-Constance Corsi, Laurent Hugueville, Ludovic Saint-Bauzel, Fabrizio De Vico Fallani
Date: 2023-09-21 00:00:00
Tasks: EEG, Motor Imagery

Mental imagery-based brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system. Making BCIs efficient and accurate is paramount to improve the reliability of real-life and clinical applications, from open-loop device control to closed-loop neurorehabilitation. By promoting sense of agency and embodiment, realistic setups including multimodal channels of communication, such as eye-gaze, and robotic prostheses aim to improve BCI performance. However, how the mental imagery command should be integrated in those hybrid systems so as to ensure the best interaction is still poorly understood. To address this question, we performed a hybrid EEG-based BCI experiment involving healthy volunteers enrolled in a reach-and-grasp action operated by a robotic arm. Main results showed that the hand grasping motor imagery timing significantly affects the BCI accuracy as well as the spatiotemporal brain dynamics. Higher control accuracy was obtained when motor imagery is performed just after the robot reaching, as compared to before or during the movement. The proximity with the subsequent robot grasping favored intentional binding, led to stronger motor-related brain activity, and primed the ability of sensorimotor areas to integrate information from regions implicated in higher-order cognitive functions. Taken together, these findings provided fresh evidence about the effects of intentional binding on human behavior and cortical network dynamics that can be exploited to design a new generation of efficient brain-machine interfaces.
Rapid Changes in Synchronizability in Conductance-based Neuronal Networks with Conductance-based Coupling
Authors: Wilten Nicola
Date: 2023-09-20 00:00:00

Real neurons connect to each other non-randomly. How the connectivity of networks of conductance-based neuron models like the classical Hodgkin-Huxley model, or the Morris-Lecar model, impacts synchronizability remains unknown. One powerful tool to resolve the synchronizability of these networks is the Master Stability Function (MSF). Here, we apply and extend the MSF approach to networks of Morris-Lecar neurons with conductance-based coupling to determine under which parameters and graphs synchronous solutions are stable. We consider connectivity graphs with a constant row-sum, where the MSF approach can be readily extended to conductance-based synapses rather than the more well-studied diffusive connectivity case, which primarily applies to gap junction connectivity. In this formulation, the synchronous solution is a single, self-coupled or 'autaptic' neuron. We find that the primary determining parameter for the stability of the synchronous solution is, unsurprisingly, the reversal potential, as it largely dictates the excitatory/inhibitory potential of a synaptic connection. However, the change between "excitatory" and "inhibitory'' synapses is rapid, with only a few millivolts separating stability and instability of the synchronous state for most graphs. We also find that for specific coupling strengths (as measured by the global synaptic conductance), islands of synchronizability in the MSF can emerge for inhibitory connectivity. We verified the stability of these islands by direct simulation of pairs of neurons coupled with eigenvalues in the matching spectrum. These results were robust for different transitions to spiking (Hodgkin Class I vs Class II), which displayed very similar synchronizability characteristics.
From sound to meaning in the auditory cortex: A neuronal representation and classification analysis
Authors: Kumar Neelabh, Vishnu Sreekumar
Date: 2023-09-19 00:00:00

The neural mechanisms underlying the comprehension of meaningful sounds are yet to be fully understood. While previous research has shown that the auditory cortex can classify auditory stimuli into distinct semantic categories, the specific contributions of the primary (A1) and the secondary auditory cortex (A2) to this process are not well understood. We used songbirds as a model species, and analyzed their neural responses as they listened to their entire vocal repertoire (\(\sim \)10 types of vocalizations). We first demonstrate that the distances between the call types in the neural representation spaces of A1 and A2 are correlated with their respective distances in the acoustic feature space. Then, we show that while the neural activity in both A1 and A2 is equally informative of the acoustic category of the vocalizations, A2 is significantly more informative of the semantic category of those vocalizations. Additionally, we show that the semantic categories are more separated in A2. These findings suggest that as the incoming signal moves downstream within the auditory cortex, its acoustic information is preserved, whereas its semantic information is enhanced.
Markov Chain-Guided Graph Construction and Sampling Depth Optimization for EEG-Based Mental Disorder Detection
Authors: Yihan Wu, Tao Chang, Peng Xu, Yangsong Zhang
Date: 2023-09-18 00:00:00
Tasks: EEG, graph construction

Graph Neural Networks (GNNs) have received considerable attention since its introduction. It has been widely applied in various fields due to its ability to represent graph structured data. However, the application of GNNs is constrained by two main issues. Firstly, the "over-smoothing" problem restricts the use of deeper network structures. Secondly, GNNs' applicability is greatly limited when nodes and edges are not clearly defined and expressed, as is the case with EEG data.In this study, we proposed an innovative approach that harnesses the distinctive properties of the graph structure's Markov Chain to optimize the sampling depth of deep graph convolution networks. We introduced a tailored method for constructing graph structures specifically designed for analyzing EEG data, alongside the development of a vertex-level GNN classification model for precise detection of mental disorders. In order to verify the method's performance, we conduct experiments on two disease datasets using a subject-independent experiment scenario. For the Schizophrenia (SZ) data, our method achieves an average accuracy of 100% using only the first 300 seconds of data from each subject. Similarly, for Major Depressive Disorder (MDD) data, the method yields average accuracies of over 99%. These experiments demonstrate the method's ability to effectively distinguish between healthy control (HC) subjects and patients with mental disorders. We believe this method shows great promise for clinical diagnosis.