Miracle-dz/UniFAR-FTUs huggingface dataset:
Trait empathy modulates brain response to empathy for social rejection: Evidence from electrophysiology

Empathy which can understand and respond to the unique affective experiences of others plays an essential role in social interaction. Although many neuroimaging studies have investigated the neural mechanisms underlying empathy for social pain, how its mechanisms are modulated by trait empathy remains unknown. The present event-related potential (ERP) study used Chatroom Interact Task to examine how trait empathy modulates brain response to empathy for social rejection. The behavior results showed that participants were less pleasant when observing rejection compared to observing acceptance in both high- and low-levels empathy groups. The ERP results revealed more negative-going N2 for social acceptance compared to rejection in both groups, but there was no difference in N2 between high- and low- empathy group. However, the late components, i.e., the P3b, N400 and LPP, revealed significant difference between social acceptance and rejection in high empathic participants rather than low empathic participants. These findings suggested that individuals with high empathic traits could devote more attention and mental resources to process observing ostracism.
Using Low-Power, Low-Cost IoT Processors in Clinical Biosignal Research: an In-depth Feasibility Check

Research on biosignal (ExG) analysis is usually performed with expensive systems requiring connection with external computers for data processing. Consumer-grade low-cost wearable systems for bio-potential monitoring and embedded processing have been presented recently, but are not considered suitable for medical-grade analyses. This work presents a detailed quantitative comparative analysis of a recently presented fully-wearable low-power and low-cost platform (BioWolf) for ExG acquisition and embedded processing with two researchgrade acquisition systems, namely, ANTNeuro (EEG) and the Noraxon DTS (EMG). Our preliminary results demonstrate that BioWolf offers competitive performance in terms of electrical properties and classification accuracy. This paper also highlights distinctive features of BioWolf, such as real-time embedded processing, improved wearability, and energy-efficiency, which allows devising new types of experiments and usage scenarios for medical-grade biosignal processing in research and future clinical studies.
Spectral Tuning of Gold Nanoparticles Embedded in Dielectric by Changing Anisotropy and Interparticle Interaction: A Mathematical Correlation between the Shape and Interaction

The theoretical studies of optical absorption (OA) properties of anisotropic non-interacting Au nanoparticles (NPs) and the spherical interacting Au NPs embedded in dielectric matrix have been carried out separately. For the studies we have considered a modified Garcia et al. model depend on Maxwell-Garnett (MG) theory where anisotropy of the particles are represented by a parameter β known as shape parameter and interaction is represented by the parameter K. For calculation of OA spectra of anisotropic particles, the values of β is varied from 0.05 to 1.0, considering K = 0 and keeping particle radius fixed at R = 2 nm. The OA spectra of anisotropic Au NPs in the above range of β exhibit a large redshift of surface plasmon resonance (SPR) from 500 - 832 nm in contrary to what is observed for spherical non-interacting particle of same size at an around 520 nm. An exponential type decay of SPR peak position with increase of β has also been observed. The almost similar nature of OA spectra and the decay behavior of the SPR peak with decrease of K have also been observed for interacting spherical Au NPs by varying the interaction parameter K from 20 to 80 of same particle size. The observation gives a correlation between the parameter β and K which was not established. This correlation is essential and helpful for different applications of anisotropic Au nanoparticles in plasmonic, surface enhanced Raman scattering (SERC), photonics, optoelectronics, and others.
Music Genre Classification Using Transfer Learning

We proposed a transfer learning approach for audio-based classification of 11 western music genres, including Rock, Pop, Rap, Country, Folk, Metal, Jazz, Blues, R&B, Electronic Music and Classical Music. Multiple models were investigated. The best one can achieve 0.9799 ROC-AUC and 0.8938 PR-AUC on a private dataset of 1100 songs.
Dynamic Multi-channel Access in Wireless System with Deep Reinforcement Learning

Dynamic spectrum access (DSA) has been considered a promising technology to address the scarcity of spectrum. In this paper, We investigate the use of deep reinforcement learning (DRL) to solve the DSA problem by maximizing the expected long-term normalized reward. Specifically, we propose a deep Q-network (DQN) based algorithm to solve dynamic multichannel access problem where there are multiple orthogonal channels in the wireless system. The problem is formulated as a Partially Observable Markov Decision Process (POMDP) with partially unknown system dynamics. We train the agent by interacting with the built system environment to find the optimal access policy of the single user and combine deep neural network to reduce the computational complexity caused by large action space. Finally, we compare our DRL approach with two conventional algorithms, and the simulation results show it can achieve near-optimal performance in a dynamic system, and we also compare our DQN algorithm with Double DQN (DDQN) algorithm which can achieve better performance and address overestimating action values.
Prediction of Stress and Mental Workload during Police Academy Training Using Ultra-Short-Term Heart Rate Variability and Breathing Analysis

Heart rate variability (HRV) has been studied in the context of human behavior analysis and many features have been extracted from the inter-beat interval (RR) time series and tested as correlates of constructs such as mental workload, stress and anxiety. Such constructs are crucial in assessing quality-of-life of individuals, as well as their overall performance when doing critical tasks. Most studies, however, have been conducted in controlled laboratory environments with artificially-induced psychological responses. While this assures that high quality data are collected, the amount of data is limited and the transferability of the findings to more ecologically-appropriate settings remains unknown. Additionally, it is desirable for such mental state monitoring systems to have high temporal resolution, thus allowing for quick feedback and adaptive decision making. In this article, we explore the use of features computed from time windows much shorter than typically reported in the literature. More specifically, we evaluate the potential of HRV and breathing features computed over so-called ultra-short-term segments (i.e., < 5 minutes) for stress and mental workload prediction. Experiments with 27 police academy trainees show that short time windows as low as 60 seconds can provide useful insights, in particular for mental workload assessment. Moreover, the fusion of HRV and breathing features showed to be an important aspect for reliable behavioural assessment in highly ecological settings.
Spike Detection Technique Based on Spike Augmentation with Low Computational and Hardware Complexity

In this paper, a method for the detection and subsequently extraction of neural spikes in an intra-cortically recorded neural signal is proposed. This method distinguishes spikes from the background noise based on the natural difference between their time-domain amplitude variation patterns. According to this difference, a spike mask is generated, which takes on large values over the course of spikes, and much smaller values for the background noise. The "high" part of this mask is designed to be wide enough to contain a complete spike. By multiplying the input neural signal with the spike mask, spikes are amplified with a large factor while the background noise is not. The result is a spike-augmented signal with significantly larger signal-to-noise ratio, on which spike detection is performed much more easily and accurately. According to this detection mechanism, spikes of the original neural signal are extracted.Clinical Relevance—This paper presents an automatic spike detection technique, dedicated to brain-implantable neural recording devices. Such devices are developed for clinical applications such as the treatment of epilepsy, neuro-prostheses, and brain-machine interfacing for therapeutic purposes.
A Digital Capacitive MEMS Microphone for Speech Recognition With Fast Wake-Up Feature Using a Sound Activity Detector

This brief presents a digital capacitive MEMS microphone for speech recognition with fast wake-up feature using a sound activity detector. The proposed microphone wakes up only when an acoustic event within the voice-band occurs using the sound activity detector, which can significantly reduce the average current consumption. All wake-up processes are performed on-chip, which enables the fast wake-up feature to prevent missing keywords. In addition, the proposed deglitching technique is applied to prevent the sound activity detector from responding to non-acoustic signals such as glitch signals. An auxiliary low-dropout regulator is used to further reduce the wake-up time. Our microphone consumes only 16 <inline-formula> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula> in deep-sleep mode. In active mode, a high-performance readout circuit converts the voice signal into a digital signal. The proposed microphone achieves an A-weighted signal-to-noise ratio (SNR) of 62.8 dBA and an acoustic overload point of 119.4 dB sound pressure level. The readout circuit itself of our microphone, fabricated in a 0.18 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process, has an A-weighted SNR of 65.8 dBA.
VibroSleeve: A wearable vibro-tactile feedback device for arm guidance

Vibro-tactile feedback offers a complementary augmentation cue for motor guidance and training. Motivated by the needs of prosthetic vision rehabilitation, we explore the use of the cylindrical forearm surface to deliver arm guidance cues via vibro-tactile stimulation. We present ‘VibroSleeve', a novel wearable arm motion guidance aid made up of 4x4 arrangement of vibration coin motors embedded within an elastic sleeve for delivering vibro-tactile patterns to the forearm. In this paper, we present the concept and design of VibroSleeve, along with results of preliminary evaluation. We outline key insights gained into the perceptual aspects of calibration and usable intensity bandwidth critical for reliable interpretation of encoded information. Our results demonstrate feasibility of the approach, and provide foundations for future work on developing the sleeve as a rehabilitation aid for guiding the arm towards visually perceived targets.
Set-Invariance Based Fuzzy Output Tracking Control for Vehicle Autonomous Driving under Uncertain Lateral Forces and Steering Constraints

This paper presents a new control method for path tracking of autonomous vehicles. Takagi-Sugeno fuzzy control is used to handle the time-varying vehicle speed and the uncertain tire-road forces involved in the nonlinear vehicle dynamics. To avoid using costly vehicle sensors while keeping a simple control structure, a new fuzzy static output feedback (SOF) scheme is proposed. Moreover, robust set-invariance is exploited to take into account the physical limitations on the steering input and the vehicle state in the control design for safety and comfort improvement. Based on Lyapunov stability arguments, a non-parallel distributed compensation SOF controller is designed for autonomous driving with reduced conservatism. The control design is reformulated as an optimization problem under linear matrix inequalities, easily solved with available numerical solvers. The path tracking performance of the proposed fuzzy controller is evaluated via dynamic driving tests conducted with high-fidelity CarSim/Simulink co-simulations.
Toward Optimality in Both Repair and Update via Generic MDS Code Transformation

An (n, k) maximum distance separable (MDS) code encodes kα data symbols into nα symbols that are stored in n nodes with α symbols each, such that the kα data symbols can be reconstructed from any k out of n nodes. MDS codes achieve optimal repair access if we can repair the lost symbols of any single node by accessing $\frac{\alpha }{{d - k + 1}}$ symbols from each of d other surviving nodes, where k + 1 ≤ d ≤ n - 1. In this paper, we propose a generic transformation for any MDS code to achieve optimal repair access for a single-node repair among d - k + 1 nodes, while the transformed MDS codes maintain the same update bandwidth (i.e., the total amount of symbols transferred for updating the symbols of affected nodes when some data symbols are updated) as that of the underlying MDS codes. By recursively applying our transformation for existing MDS codes with the minimum update bandwidth, we can obtain multi-layer transformed MDS codes that achieve both optimal repair access for any single-node repair among all n nodes and minimum update bandwidth.
Towards Contactless Estimation of Electrodermal Activity Correlates

This paper presents a proof-of-concept for contactless and nonintrusive estimation of electrodermal activity (EDA) correlates using a camera. RGB video of the palm under three different lighting conditions showed that for a suitably chosen illumination strategy the data from the camera is sufficient to estimate EDA correlates which agree with the measurements done using laboratory grade physiological sensors. The effects we see in the recorded video can be attributed to sweat gland activity, which inturn is known to be correlated with EDA. These effects are so pronounced that simple pixel statistics can be used to quantify them. Such a method benefits from advances in computer vision and graphics research and has the potential to be used in affective computing and psychophysiology research where contact based sensors may not be suitable.
Generating photorealistic images of people's eyes with strabismus using Deep Convolutional Generative Adversarial Networks

Strabismus is a medical condition that affects about 4% of the world population. In severe cases, strabismus can lead to blindness. Besides affecting the vision, the aesthetic appeal of strabismus can generate sufficient public embarrassment to influence the patient's entire academic, personal and professional development. In most cases the condition can be treated. Studies relating computing and strabismus are still few, we understand that the main reason for this scarcity is the difficulty in acquiring dataset on strabismus. Motivated by this aspect, we propose in this research a generative model for the computational creation of synthetic images of the eye region of people with strabismus using Deep Convolutional Generative Adversarial Networks (DCGAN).
Quantized Corrupted Sensing With Random Dithering

Corrupted sensing concerns the problem of recovering a high-dimensional structured signal from a collection of measurements that are contaminated by unknown structured corruption and unstructured noise. In the case of linear measurements, the recovery performance of different convex programming procedures (e.g., generalized Lasso and its variants) is well established in the literature. However, in practical applications of digital signal processing, the quantization process is inevitable, which often leads to non-linear measurements. This paper is devoted to studying corrupted sensing under quantized measurements. Specifically, we demonstrate that, with the aid of uniform dithering, both constrained and unconstrained Lassos can stably recover signal and corruption from the quantized samples when the measurement matrix is sub-Gaussian. Our theoretical results reveal the role of quantization resolution in the recovery performance of Lassos. Numerical experiments are provided to confirm our theoretical results.
When does Partial Noisy Feedback Enlarge the Capacity of a Gaussian Broadcast Channel?

Feedback is known to enlarge the capacity region of a Gaussian Broadcast Channel (GBC) with independent noise realizations at the receivers, and an average power constraint at the transmitter. The capacity enlargement may occur even when there is noisy feedback from only one of the two receivers. However, recent results show the existence of a feedback noise threshold, beyond which one-sided feedback from only the stronger receiver is futile in enlarging the capacity region. The current paper presents a tight characterization of the feedback noise threshold, which separates the regimes where feedback from only the stronger receiver enlarges the capacity or leaves it unchanged. The scheme used to prove this result also leads to some interesting observations on noisy feedback from only the weak receiver.
Low Power Optimisations for IoT Wearable Sensors Based on Evaluation of Nine QRS Detection Algorithms

This paper aims to reduce the power consumption of electrocardiography based wearable healthcare devices, by introducing power reduction approaches and considerations at system level design, where we have the highest potential to influence power. It focuses, in particular, on algorithm design and implementation, data acquisition, and transmission under constrained resources. A thorough investigation of the suitability of nine existing algorithms for on-sensor QRS feature detection is conducted, with respect to metrics such as sensitivity, positive predictivity, power consumption, parameter choice and time delay. Optimisation of data acquisition on CPU-based IoT systems is performed, and the current consumption is reduced by a factor of 3 using a combination of direct memory access (DMA) list approach and low-level register manipulations for task delegation. The acquisition data rate, sampling rate, buffer and batch size are also optimised. To reduce the power consumption by data transmission, the effect of on-sensor versus off-sensor processing is investigated. While focusing on CPU-based systems with experiments performed on a generic low-power wearable platform, the design optimisation and considerations proposed in this work could be extended to custom designs and allow further investigation into QRS detection algorithm optimisation for wearable devices.
Optimizing OpenCL Code for Performance on FPGA: k-Means Case Study With Integer Data Sets

High Level Synthesis (HLS) tools targeting Field Programmable Gate Arrays (FPGAs) aim to provide a method for programming these devices via high-level abstractions. Initially, HLS support for FPGAs focused on compiling C/C++ to hardware circuits. This raised the issue of determining the programming practices which resulted in the best performing circuits. Recently, to further increase the applicability of HLS approaches, renewed effort was placed on support for HLS of OpenCL code for FPGA, raising the same issues of coding practices and performance portability. This paper explores the performance of OpenCL code compiled for FPGAs for different coding techniques. We evaluate the use of task-kernels versus NDRange kernels, data vectorization, the use of on-chip local memories, and data transfer optimizations by exploiting burst access inference. We present this exploration via a case study of the k-means algorithm, and produce a total of 10 OpenCL implementations of the kernel. To determine the effects of different data set characteristics, and to determine the gains from specialization based on number of attributes, we generated a total of 12 integer data sets. The data sets vary regarding the number of instances, number of attributes (i.e., features), and number of clusters. We also vary the number of processing cores, and present the resulting required resources and operating frequencies. Finally, we execute the same OpenCL code on a 4 GHz Intel i7-6700K CPU, showing that the FPGA achieves speedups up to $1.54 {\times } $ for four cases, and energy savings up to 80% in all cases.
Emotion assessment using Machine Learning and low-cost wearable devices

The advancement in bioelectrical measurement technologies and the push towards a higher impact of the Brain Computer Interfaces and Affective Computing in the daily life have made non-invasive and low-priced devices available to the large population to record physiological states. The aim of this study is the assessment of the abilities of the MUSE headband, together with the Shimmer GSR+ device, to assess the emotional state of people during stimuli exposure. Twenty-four pictures from the IAPS database were showed to 54 subjects and were evaluated in their emotional values by means of the Self-Assessment Manikin (SAM). Using a Machine Learning approach, fifty-two scalar features were extracted from the signals and used to train 6 binary classifiers to predict the valence and arousal elicited by each stimulus. In all classifiers we obtained accuracies ranging from 53.6% to 69.9%, confirming that these devices are able to give information about the emotional state.
Rotor Stress and Dynamics Analysis of a High-Speed Permanent Magnet Machine for a Micro Gas Turbine Considering Multiphysics Factors

For high-speed permanent magnet machines (HSPMMs), the precise analysis of rotor stress and rotor dynamics is essential to ensure the stable operation of rotor under high-speed conditions. This work conducts an in-depth research on the rotor of an HSPMM for a micro gas turbine with predetermined geometrical sizes. Firstly, the mechanical and electromagnetic losses are investigated by both, empirical methods and finite element method (FEM) respectively, and then the 3D steady-state temperature distribution of the HSPMM is evaluated on this basis. Then, considering the non-isothermal temperature field of the rotor, the 3D thermal-structural coupling method is adopted to analyze the influence of the carbon fiber wrapping direction and the epoxy resin adhesive between permanent magnets (PMs) and rotor core on the rotor thermal stress. Furthermore, in the steady-state thermal field, considering the change in elastic modulus and the generation of thermal stress caused by temperature rise, the results of the modal analysis are compared with those of room temperature to analyze the effect of temperature on the natural frequency of the rotor. Finally, two prototypes of 80 kW, 60000 rpm HSPMMs are fabricated to test the capability of the HSPMM. The experimental results of back-to-back drag test and modal test verify the accuracy of the temperature characteristics and modal analysis, respectively.
Analysis of Harmonic Levels from PWM Rectifiers due to Background Voltage Distortion

PWM rectifiers receive significant attention due to their flexible control and perceived low harmonic currents, and as a consequence are replacing controlled and uncontrolled rectifiers as the preferred technology in industrial applications. However, low harmonic currents from PWM rectifiers are generally limited to applications where supply voltages have very low background distortion. An investigation using derived mathematical equations, simulation and laboratory measurement is presented to assess the influence of higher levels of background voltage distortion on the harmonic current distortion of PWM rectifiers. The mathematical expression for the harmonic current is derived using the Double Fourier Integral method. A simulation model is developed to quantity the effect of increased supply voltage distortion on harmonic currents from PWM rectifiers. Simulation results and laboratory measurements are presented for the simple case of a PWM rectifier to validate the methodology, which will ultimately be used for future work to quantity the influence of supply voltage distortion. As expected the increase in harmonic current of a PWM rectifier is heavily impacted by the severity of the voltage distortion appearing at the PWM rectifier terminals.