Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical construction of sensing matrices for a greedy sparse recovery algorithm over finite fields

Compressed sensing aims to retrieve sparse signals from very few samples. It relies on dedicated reconstruction algorithms and well-chosen measurement matrices. In combination with network coding, which operates traditionally over finite fields, it leverages the benefits of both techniques. However, compressed sensing has been primarily investigated over the real field. F2OMP is one of the few recovery algorithms to reconstruct signals over finite fields. However, its use in practical cases is limited since its performance depends mainly on binary matrices for signal recovery. This paper reports results of extensive simulations enhancing the features of well-performing measurement matrices for F2OMP as well as methods to build them. Moreover, a modified version of the algorithm, F2OMP-loop, is proposed. It offers a compromise between performance, stability, and processing time. This allows to design a joint compressed sensing and network coding framework over finite fields.
Quantum pump effect in a four-terminal mesoscopic structure

Quantum pump effect in a four-terminal mesoscopic structure constructed from a homogeneous two-dimensional electron gas is investigated. Oscillating electric potentials are applied to the two opposite terminals of the four-terminal mesoscopic structure. In both the remaining two opposite terminals and in the central region there are constant potentials that do not change with time. The oscillating potentials change slowly in comparison with all of the internal time scales of the structure and the amplitude of the oscillating potentials is small in comparison with the Fermi energy. The current of each lead and the transmission coefficients from one lead to another are calculated by using the non-equilibrium Green's function approach under the adiabatic approximation. In the remaining two opposite terminals of the four-terminal structure, the quantum pump effect can produce an electric current whose magnitude and direction depends on the Fermi energy. The pumped currents are ascribed to the asymmetry of transmission coefficients with respect to the Fermi energy.
Automatic Design of CAE Architecture Using Genetic Algorithms for Diagnosis of Crop Diseases

Crop diseases are one of the factors behind the decrease in crop sales. Since the kind of crop diseases is large and there are diseases that damage other crops, early detection is required. However it is difficult for new farmers to distinguish them; it is necessary to automate the early detection of diseased strains. This paper proposes a disease strain detection method using CAE (convolutional autoencoder) for onion strains. Since CAE learns only from strain images that are not disease, it is effective when there are few disease strain images. When determining the structure of a CAE, manual adjustment of hyperparameters such as the type and number of layers requires a lot of trial and error. Therefore, an automatic CAE structure determination method is required. In this study, a genetic algorithm that can find promising structures with a small amount of computational resources are used. The results of the experiment showed that the discrimination rate increased with generation. In the last generation, the maximum discrimination rate of 88.7% was confirmed.
Hand gesture decoding using ultra-high-density EEG

The exploration of high EEG electrode densities is of great interest in current BCI research. This paper investigates the neural representation of high-level hand gestures using ultra-high-density EEG electrodes. The EEG of two subjects was recorded using a total of 352 and 256 electrodes placed over the sensorimotor cortex, respectively. Subject S1 performed motor execution, and subject S2 performed motor imagery of the hand gestures. Band power features for the mu (8-12 Hz) and beta (13–30 Hz) frequency bands were exploited for pairwise classifications for the respective gestures using a linear SVM. Topography plots were generated using the reconstructed head models from T1-weighted MRI scans of both subjects. The spatiotemporal dynamics from the topographies allow a detailed understanding of the associated brain patterns. Subjects S1 and S2 achieved a mean classification accuracy of 72.7% and 71.3% across all classification pairs, respectively. The classification of the gestures “rock” vs. “scissors” resulted in the highest classification accuracy for both subjects, with 78.9% for subject S1 and 73.9% for subject S2 on average. Classification models based on electrode subsets reflecting the 10–10 and the extended 10–10 systems performed worse than the ultra-high-density EEG system resulting in a decrease of about 10 and 8% (on median). The implementation of feature reduction algorithms, as well as real-time BCI feedback, are expected to enhance performance in future applications.
The diurnal and seasonal cycles of wind-borne dust over Africa North of the Equator

Abstract This article presents a study of the diurnal and seasonal cycles of dust over North Africa, using surface visibility as an indicator of dust. The diurnal cycle shows a reduction of visibility during the daytime hours in the areas where dust is generated, a consequence of the elimination of the nocturnal inversion. The annual cycle reveals that, at latitudes from 5° to 16°N, there is a latitudinal increase in the duration of the presence of aerosols over the course of the year. The presence of aerosols dimishes in the latitudes from 20° to 35°N, indicating that the aerosol content of the Saharan air is lower than that over the semiarid sub-Saharan zones, such as the Sahel. A comparison of three periods, 1957–61, 1970–74, and 1983–87, shows a continually increasing presence of dust, particularly in the western Sahel. The interannual variability of the dust and its annual cycles in these three periods throughout North Africa bear a strong relationship to rainfall fluctuations in the Sahel. Overall, .
Networks of Injectable Microdevices Powered and Digitally Linked by Volume Conduction for Neuroprosthetics: a Proof-of-Concept

Wireless power transfer (WPT) methods such as inductive coupling and ultrasounds are used as an alternative to electrochemical batteries to energize active implantable medical devices. However, existing WPT methods require the use of bulky components (e.g., coils) that hinder miniaturization. To avoid this, we proposed the use of volume conduction of high frequency (HF) current bursts to power and bidirectionally communicate with threadlike implants that can be used for electrical stimulation and sensing (e.g., electromyography acquisition). Here, we in vitro demonstrate that several wireless devices can be powered and digitally linked by volume conduction. Eleven devices were randomly placed in a saline medium and were enclosed by two electrodes connected to an external system. The system interrogated the devices, and the waveforms obtained by the external system's demodulator were digitally processed to decode the information sent by the devices through the same HF current bursts. Data was successfully decoded from the devices, independently of the power that each single device obtained. The WPT efficiency of volume conduction boosts when a massive number of devices are powered with a single external system, creating a network of microdevices for neuroprosthetics. This will allow to accomplish closed-loop control for neural interphases using highly miniaturized injectable devices.
Identifying Mild Traumatic Brain Injury via Vision Transformer and Bag of Visual Features

Due to lack of established criteria and reliable biomarkers, timely diagnosis of mild traumatic brain injury (mTBI) has remained a challenging problem. Widefield optical imaging of cortical activity in animals provides a unique opportunity to study injury-induced alterations of brain function. Motivated by the results of medical-imaging studies that employ patch-level-based approaches, this paper proposes to use two patch-based deep learning techniques for classifying brain images of mTBI and healthy Thyl-GCaMP6s transgenic mice. The first approach uses a Bag of Visual Word (BoVW) technique to represent each image as a histogram of local features derived from patches from all training data. The local features are extracted using an unsupervised convolutional autoencoder (CAE). The second approach employs a pre-trained vision transformer (ViT) model. The average accuracy for classifying mTBI and healthy brains for the CAE-BoVW and the ViT are 96.8% and 97.78%, respectively, outperforming results of a convolutional neural network (CNN) model. This work suggests that attention-based models can be utilized for the problem of classifying mTBI and healthy brain images.
Phase Transfer Entropy to Assess Nonlinear Functional Corticokinematic Coupling

To understand the mechanism of complex human sensorimotor control system, it is necessary to investigate the functional relationship between the cortex and peripheral movement. Typical cortico-peripheral interaction measures include corticomuscular coupling and corticokinematic coupling (CKC). Functional CKC reflects movement-related proprioceptive inputs, it could be through kinematics and physiological signals. The process consists of both linear and nonlinear properties of the whole neuromotor control system. Previous studies mainly applied linear techniques to assess the functional coupling between the cortex and periphery, but the nonlinear coupling properties were rarely discussed. In this preliminary study, 128 channels of electroencephalography (EEG) and 3-axis acceleration (ACC) signals were acquired for both dominant and non-dominant hand in four healthy participants during passive finger movement stimulus. Corticokinematic coherence and partial directed coherence analysis was performed to investigate linear coupling and directionality between EEG and ACC during movement, special concerns were focused at stimulus frequency (3Hz) and its harmonics. Further, we utilized phase transfer entropy to characterize top-down and bottom-up nonlinear cortico-peripheral information flow, and for the first time, we found typical nonlinear coupling patterns between the cortex and periphery during passive movement tasks. Such results might facilitate our understanding of human sensorimotor control system and imply potential clinical applications.
Millimeter-Wave Silicon Photonics Circuits with Automatic Calibration for Wireless Communications

This paper discusses the limitations of current electronic solutions for integrated mm-wave transceivers that are suitable for 5G mobile communications and introduces silicon photonics (SiP) as an alternate solution to address these limitations. Also the issue regarding distorted initial responses of SiP circuits, such as tunable bandpass filters and true-time-delay units, due to process variation is discussed and automatic calibration techniques algorithms are proposed to alleviate this problem.
Global classification of intentional movement across upper limb myoelectric pattern recognition-controlled prosthesis users

One idealized vision of advanced upper limb prosthetic control is a plug-and-play design that any new user can don and instantly control attached devices as intended. Traditional body-powered prostheses are likely the closest available option but are limited in their range of motion and can have negative long-term impacts. Basic dual-site myoelectric-controlled prostheses require only minor adjustments prior to users being able to control their prosthetic device, but still requires training to learn despite only having a limited number of motions available. This contrasts with state-of-the-art myoelectric pattern recognition-controlled prostheses where a machine learning algorithm also learns the individual user; specifically, their unique patterns of muscle activity corresponding to prosthesis motions. However, the wide variation in muscle activity patterns both within and between users, mainly due to physiological differences, has been the primary reason why it is difficult to develop a true off-the-shelf prosthesis component for myoelectric pattern recognition control. In this paper, we take a small step towards this vision by investigating statistical and machine learning methods that classify prosthesis motion for any pattern recognition user. Specifically, we use a large dataset of EMG training data collected from 191 users over a six-month period to develop, as a first step, a binary classifier built to simply identify intended motion or no motion for all users. Our results could have an immediate impact on prosthesis performance for current users and justify further development of a potential global classification model which can be used by any persons with upper limb difference who wish to use a myoelectric-controlled prosthesis.
Improved combined tangential formulation for electromagnetic analysis of penetrable bodies

The surface integral equation (SIE) method is one of the most popular numerical methods for the electromagnetic analysis of any kind of homogeneous and piecewise homogeneous dielectric, metallic, or composite objects. This work focuses on the tangential formulations (usually more accurate than the normal ones), paying special attention to their capability of modeling the plasmonic behavior of metals at optical and quasi-optical frequencies, in which the localized surface plasmon resonances (LSPRs) bring out significant accuracy problems. The authors present a new tangential SIE, the improved combined tangential formulation (ICTF), in which the fast convergence behavior of the original CTF is combined with a high accuracy, because of the first-kind nature of the new ICTF. Some numerical results are shown to illustrate the behavior and accuracy of the proposed formulation, even when dealing with highly resonant plasmonic problems.
Lightweight Light-Diffusing Fiber Transmitter Equipped Unmanned-Aerial-Vehicle (UAV) for Large Field-of-View (FOV) Optical Wireless Communication

We propose a light-diffusing-fiber transmitter (LDF-Tx) equipped unmanned-aerial-vehicle (UAV) for optical-wireless-communication (OWC). Long-short-term-memory-neural-network (LSTMNN) provides efficient rolling-shutter-pattern decoding at 360° around LDF-Tx circumference.
An Automated Tactile Stimulator Apparatus for Neuromorphic Tactile Sensing

Tactile sensing is an active area of research in robotics and neural engineering, particularly in relation to sensory feedback for neural prostheses. Sensory feedback relies on neuromorphic models for touch, which must be characterized and validated through tactile sensing experiments. Currently, no standardized, automated method exists for performing these experiments. Thus, there exists a need for new methods/workflows for providing tactile stimulation in neuromorphic tactile sensing. In this work, we describe a rotary-drum tactile stimulator that provides complete user control over force and velocity setpoints and applied textures using PID tuning and an interchangeable, snap-in 3D-printed texture plate system. We achieve high accuracy and precision closed-loop force control (3.4% average deviation in force between first and last ten trials with 4.2% standard deviation) and open-loop velocity control (4.6% average deviation from velocity setpoint with 2.6% standard deviation). Additionally, the apparatus features an automated data pipeline, which records analog tactile sensor readings at each experimental condition, automatically segments them into individual palpation trials, and transforms them into neuromorphic spiking activity. Though designed to develop neuromorphic models of touch for prostheses, the apparatus is generalizable to a wide array of neural engineering experiments, including characterizing tactile sensors and generating tactile sensing databases.
Supersymmetric quantum matrix cosmology

We exhibit a relation between the SU(2) invariant matrix model arising from the quantization of the 11-dimensional supermembrane in the light cone gauge and supersymmetric quantum cosmology. We use a Dirac-like gamma matrix representation for the fermionic degrees of freedom and find explicit solutions to reduced lower dimensional SU(2) matrix models for the zero energy states. One of the solutions we encounter in the matrix model resembles exactly a wave function found independently in the context of SUSY quantum cosmology; this allows us to give these regularized models a physical meaning and shows a direct relation between the SU(2) matrix model and SUSY quantum cosmology.
Constellation Independent Look-up Table Enabled Digital Predistortion for Digital-Analog Radio-over-Fiber System

A constellation independent look-up table (LUT) method for transceiver nonlinearity predistortion in digital-analog radio-over-fiber system is proposed and experimentally demonstrated, achieving a SNR gain of 1.04dB. The table size can be reduced to 0.3% of the conventional LUT.
Entrainment effects upon synaptically connected microcircuit in transcranial alternating current stimulation

Transcranial alternating current stimulation (tACS) has garnered attention as a popular neuromodulation method for clinical applications. However, neurons' responses to tACS are uncertain and controversy remains whether tACS can entrain neural activity or not. To resolve this controversy, we constructed a realistic cortical microcircuit from validated experimental data to investigate tACS's effects on neural entrainment. We analyzed the firing rate and neural synchronization in response to 10Hz tACS for models with/without synaptic connections. Consequently, we observed that our results were consistent with in vivo experimental data and that synaptic connections influence neural entrainment's effects. Clinical Relevance-This finding may help us to have a better understanding of the tACS mechanism with respect to neural entrainment. Moreover the realistic microcircuit model may help predict the neural responses to tACS in clinical applications
A Locally Linear Least Squares Method for Simultaneously Smoothing DWI Data and Estimating Diffusion Tensors

Magnetic resonance diffusion-weighted imaging (MR-DWI) data usually contain a great deal of noise and a significant number of outlier data points that can undermine the accurate estimation of diffusion tensors (DTs). Raw MR-DWI data therefore usually must undergo substantial preprocessing prior to tensor estimation. This study proposes an approach for the reconstruction of DT fields from MR-DWI data that combines into a single step the regularization of raw MR-DWI data and the optimized estimation of DT fields. The approach uses locally weighted linear least squares (LWLLS) estimation to correlate information within the local neighborhood of each voxel. It incorporates into the linear least squares (LLS) framework a bilateral filter which assigns different weights to neighbor voxels according to their intensities and relative distance. This method efficiently smoothes the MR-DWI data and estimates optimal tensors simultaneously. The performance of the proposed method was compared to that of traditional LLS estimation of tensors using both simulated and real-world human MR-DWI data. Both the simulated and real-world datasets demonstrated that the proposed method greatly outperforms the conventional LLS method and that the simultaneous smoothing of MR-DWI data and tensor estimation performs as well as the separate and sequential execution of these procedures.
Video Transformer based Video Quality Assessment with Spatiotemporally adaptive Token Selection and Assembly

Video quality assessment (VQA) for user generated content (UGC) videos plays important role in video compression and processing. Convolutional neural network (CNN) based quality assessment for UGC is the research focus with inspiring model accuracy increment in the past three years. However, regularly temporal-sampling with temporal feature loss, as well as fixed token selection strategy video transformer (ViT) with insufficient representational capacity of tokens, jointly degrade the accuracy of conventional ViT based quality assessment. Facing these two challenges, this article proposes an adaptive token-selection ViT (ATSViT) structure for UGCVQA. Accounting for the uneven distribution of spatiotemporal distortion-related features, this work proposes a timing block sampling (TBS) module to adaptively select video blocks and assemble them into content compacted subsequence for further processing. In addition, inspired by the mental filter theory in terms of visual information, we propose a stage-wise adaptive screening network (SSNet) in which (noise” features of tokens in the sense of perception are progressively detected and processed by imitating the behavior of perception process in the eye-brain system. Experimental results verify that the proposed VQA model achieves state-of-the-art (SOTA) accuracy, with the highest correlation with mean opinion scores (MOS).
Refine EEG Spectrogram Synthesized by Generative Adversarial Network for Improving The Prediction of Epileptic Seizures*

Epilepsy is one of the most common neurological disorders and affects 1% of the world's population. Early prediction of the onset time of the seizure can enable responsive treatments and greatly improve the quality of life of epileptic patients. Although seizure detection has been extensively studied, reliable prediction of the onset of seizures remains a challenge. Despite recent advances in deep learning, most existing methods require a large amount of data to train the models, which are often not available for privacy, proprietary, or cost reasons. In this work, we show that the performance of seizure prediction can be improved by using data augmentation from only a limited amount of EEG recordings. We developed a generative adversarial network (GAN) and a refiner based on a publicly available CHB-MIT dataset. We trained a classifier using only synthesized data, but tested the classifier using real data from all 23 patients in the dataset. With a seizure prediction horizon (SPH) of 5 min and a seizure occurrence period (SOP) of 30 min, the classifier achieved an average AUC score of 61%. Improvement in the use of the refiner is 2.1% on average in all patients. The proposed method holds great promise for alleviating the problem of data scarcity and imbalance in seizure prediction and many other applications.
Optical Phantoms for Calibrating a Novel Neuroimaging System Targeting Central Nervous System Fluid Flow Dynamics

Fluid flow dynamics in the brain's ventricles, interstitial spaces, and perivascular spaces, known as the “glymphatic system,” are hypothesized to play an important role in brain waste clearance. Healthy function of this complex fluid transporter is most active during sleep, may be critical for maintaining neurological health, and is hypothesized to be important for recovery after acute and chronic injury (e.g. concussion). At present, all sensors for monitoring brain fluid dynamics require invasive contrast agents (e.g. fluorescent dyes injected into cerebrospinal fluid, CSF) and/or are not portable or amenable to long-term repeated monitoring (e.g., magnetic resonance imaging (MRI) methods). We aim to adapt near infrared spectroscopy technologies, which traditionally track hemodynamic activity, to target fluid flow in the glymphatic system and to monitor the temporal dynamics of this water-dominated signal, with an eye toward future applications in continuous portable monitoring. Our goal is to extend frequency domain functional near infrared spectroscopy sensors (FD-fNIRS) to track these CSF-dominated fluid dynamics. In support of this aim, we developed two novel phantoms that mimic key elements of glymphatic system function to demonstrate application of novel FD-fNIRS sensors to human brains in a portable, noninvasive form factor amenable to repeated, continuous testing in a sleep lab-type environment.