Miracle-dz/UniFAR-FTUs huggingface dataset:
Virtual Reality for Evaluating Prosthetic Hand Control Strategies: A Preliminary Report

Improving prosthetic hand functionality is critical in reducing abandonment rates and improving the amputee’s quality of life. Techniques such as joint force estimation and gesture recognition using myoelectric signals could enable more realistic control of the prosthetic hand. To accelerate the translation of these advanced control strategies from lab to clinic, We created a virtual prosthetic control environment that enables rich user interactions and dexterity evaluation. The virtual environment is made of two parts, namely the Unity scene for rendering and user interaction, and a Python back-end to support accurate physics simulation and communication with control algorithms. By utilizing the built-in tracking capabilities of a virtual reality headset, the user can visualize and manipulate a virtual hand without additional motion tracking setups. In the virtual environment, we demonstrate actuation of the prosthetic hand through decoded EMG signal streaming, hand tracking, and the use of a VR controller. By providing a flexible platform to investigate different control modalities, we believe that our virtual environment will allow for faster experimentation and further progress in clinical translation.
Selective myelinated nerve fiber stimulation via temporal interfering electric fields

We have investigated selective electrical stimulation of myelinated nerve fibers using a computational model of temporal interfering (TI) fields. The model consists of two groups of electrodes placed on the outer bundle surface, each group stimulated at a different frequency. We manipulated the stimulus waveform, magnitude and frequency of short-duration stimuli (70ms), and investigated fiber-specific stimulus-elicited compound action potentials. Results show that under 100Hz & 200Hz TI stimulation with 0.6mA total current shared by the electrodes, continuous action potentials were generated in deeper nerve fibers, and that the firing region was steerable by changing individual electrode currents. This study provides a promising platform for non-invasive nerve bundle stimulation by TI fields.
Alterations in Multi-channel EEG Dynamics During a Stressful Shooting Task in Virtual Reality Systems

This paper explores power spectrum-based features extracted from the 64-channel electroencephalogram (EEG) signals to analyze brain activity alterations during a virtual reality (VR)-based stressful shooting task, with low and high difficulty levels, from an initial resting baseline. This paper also investigates the variations in EEG across several experimental sessions performed over multiple days. Results indicate that patterns of changes in different power bands of the EEG are consistent with high mental stress levels during the shooting task compared to baseline. Although there is one inconsistency, overall, the brain patterns indicate higher stress levels during high difficulty tasks than low difficulty tasks and in the first session compared to the last session.
The Resonant DC Link Converter--A New Concept in Static Power Conversion

This paper presents a new approach to realizing efficient high performance power converters. The concept of a resonant dc link inverter has been proposed and realized with the addition of only one small inductor and capacitor to a conventional voltage source inverter circuit. The new topology is capable of switching almost an order of magnitude faster than state-of-the-art voltage source inverters at significantly improved efficiencies using the same family of devices. The topology is especially suitable for high power applications using GTOs or other gate turn-off devices.
Investigating the Relationship between Cough Detection and Sampling Frequency for Wearable Devices

Cough detection can provide an important marker to monitor chronic respiratory conditions. However, manual techniques which require human expertise to count coughs are both expensive and time-consuming. Recent Automatic Cough Detection Algorithms (ACDAs) have shown promise to meet clinical monitoring requirements, but only in recent years they have made their way to non-clinical settings due to the required portability of sensing technologies and the extended duration of data recording. More precisely, these ACDAs operate at high sampling frequencies, which leads to high power consumption and computing requirements, making these difficult to implement on a wearable device. Additionally, reproducibility of their performance is essential. Unfortunately, as the majority of ACDAs were developed using private clinical data, it is difficult to reproduce their results. We, hereby, present an ACDA that meets clinical monitoring requirements and reliably operates at a low sampling frequency. This ACDA is implemented using a convolutional neural network (CNN), and publicly available data. It achieves a sensitivity of 92.7%, a specificity of 92.3%, and an accuracy of 92.5% using a sampling frequency of just 750 Hz. We also show that a low sampling frequency allows us to preserve patients’ privacy by obfuscating their speech, and we analyze the trade-off between speech obfuscation for privacy and cough detection accuracy.Clinical relevance—This paper presents a new cough detection technique and preliminary analysis on the trade-off between detection accuracy and obfuscation of speech for privacy. These findings indicate that, using a publicly available dataset, we can sample signals at 750 Hz while still maintaining a sensitivity above 90%, suggested to be sufficient for clinical monitoring [1].
Ensemble Learning Approach for Subject-Independent P300 Speller

P300 speller is a brain-computer interface (BCI) speller system, used for enabling human with different paralyzing disorders, such as amyotrophic lateral sclerosis (ALS), to communicate with the outer world by processing electroencephalography (EEG) signals. Different people have different latency and amplitude of the P300 event-related potential (ERP) component, which is used as the main feature for detecting the target character. In order to achieve robust results for different subjects using generic training (GT), the ensemble learning classifiers are proposed based on linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbors (kNN), and convolutional neural network (CNN). The proposed models are trained using data from healthy subjects and tested on both healthy subjects and ALS patients. The results show that the fusion of LDA, kNN and SVM provides the most accurate results, achieving the accuracy of 99% for healthy subjects and about 85% for ALS patients.
Application-oriented experiment design for model predictive control

The model predictive control (MPC) technique has been widely applied in a large number of industrial plants. Optimal input design should guarantee acceptable model parameter estimates while still providing for low experimental effort. The goal of this work is to investigate an application-oriented identification experiment that satisfies the performance objectives of the implementation of the model. Aand D-optimal input signal design methods for a non-linear liquid two-tank model are presented in this paper. The excitation signal is obtained using a finite impulse response filter (FIR) with respect to the accepted application degradation and the input power constraint. The MPC controller is then used to control the liquid levels of the double tank system subject to the reference trajectory. The MPC scheme is built based on the linearized and discretized model of the system to predict the system’s succeeding outputs with reference to the future input signal. The novelty of this model-based method consists in including the experiment cost in input design through the objective function. The proposed framework is illustrated by means of numerical examples, and simulation results are discussed.
The Mediating Role of Cognitive Emotion Regulation for Maladaptive Schemas and Negative Affects Among Youth

Citation:. Mizani S, Rezaei A, Khayyer M, Sohrabi Shegefti N. The mediating role of cognitive emotion regulation for maladaptive schemas and negative affects among youth. Psychological Methods and Models. 2021; 12 (43): 113. Abstract Introduction: Emotions are one aspect of human behavior. Positive and negative emotions are central to many theories of developmental and personality psychology as well as psychological pathology. This study aimed to explain the mediating role of cognitive emotion regulation for maladaptive schemas and negative affects . Materials and Methods: The present study was descriptive and correlational. The statistical population included all students of Marvdasht Azad University who were studying . Participants in the study were 299 students of different faculties of Marvdasht Azad University who were selected by multi-stage cluster sampling. Data were analyzed using the Cognitive Emotion Regulation Questionnaire by Garnowski et al., Watson et al. and Young's maladaptive schemas, which confirmed the validity and reliability of the instrument. Data analysis was performed using spss-24 software at both descriptive and inferential levels. To investigate the studied hypotheses, Baron and Kenny method and multiple regression in inter manner and hierachical regression were used to study hypothesis. Findings: The findings of the research showed that positive and negative cognitive emotion regulation has a mediating role in relationship between maladaptive schemas and negative affects. Conclusion: By teaching adaptive emotion regulation strategies in this group, the negative impact of maladaptive schemas on their negative affects, can be reduced and the level of negative affects, in these people can be reduced.
Proton induced single event upset cross section prediction for 0.15 μm six-transistor (6T) silicon-on-insulator static random access memories

In this paper, an efficient physics-based method to estimate the saturated proton upset cross section for six-transistor (6T) silicon-on-insulator (SOI) static random access memory (SRAM) cells using layout and technology parameters is proposed. This method calculates the effects of radiation based on device physics. The simple method handles the problem with ease by SPICE simulations, which can be divided into two stages. At first, it uses a standard SPICE program to predict the cross section for recoiling heavy ions with linear energy transfer (LET) of 14 MeV-cm2/mg. Then, the predicted cross section for recoiling heavy ions with LET of 14 MeV-cm2/mg is used to estimate the saturated proton upset cross section for 6T SOI SRAM cells with a simple model. The calculated proton induced upset cross section based on this method is in good agreement with the test results of 6T SOI SRAM cells processed using 0.15 μm technology.
Suppressing common-mode conducted EMI generated by PWM drive systems using a dual-bridge inverter

This paper presents a dual-bridge inverter approach to reducing the conducted electromagnetic interference (EMI) generated by pulse-width modulated (PWM) inverters in adjustable speed drive (ASD) motor systems. The novel dual-bridge inverter (DBI) is controlled to generate balanced excitation of induction motors under PWM ASD operation. Thus, the inherently generated common-mode voltage is eliminated and the resulting common-mode conducted EMI is significantly reduced. Theoretical analysis and experimental results are presented to verify this concept.
A Survey On Model-Free Deep Reinforcement Learning

—Deep Reinforcement Learning is a branch of machine learning techniques that is used to ﬁnd out the best possible path given a situation. It is an interesting domain of algorithms ranging from basic multi-arm bandit problems to playing complex games like Dota 2. This paper surveys the research work on model-free approaches to deep reinforcement learning like Deep Q Learning, Policy Gradients, Actor-Critic methods and other recent advancements.
Improving Transfer Performance of Deep Learning with Adaptive Batch Normalization for Brain-computer Interfaces*

Recently, transfer learning and deep learning have been introduced to solve intra- and inter-subject variability problems in Brain-Computer Interfaces. However, the generalization ability of these BCIs is still to be further verified in a cross-dataset scenario. This study compared the transfer performance of manifold embedded knowledge transfer and pre-trained EEGNet with three preprocessing strategies. This study also introduced AdaBN for target domain adaptation. The results showed that EEGNet with Riemannian alignment and AdaBN could achieve the best transfer accuracy about 65.6% on the target dataset. This study may provide new insights into the design of transfer neural networks for BCIs by separating source and target batch normalization layers in the domain adaptation process.
Management practices of Australian manufacturing firms: why are some firms more innovative?

Whilst many studies have focused on the adoption of individual or sets of innovative management practices (e.g. lean production), fewer studies have evaluated a diverse set of management practices and firm contextual factors which may limit (or enable) the accumulation of groups of innovations in organisations. The Australian manufacturing sector is a novel setting to investigate such issues due to, among other reasons, a protracted decline of the competitive position of the sector. In this paper, we use a data-set from the Australian government funded management practices benchmarking project which was part of the World Management Survey and empirically evaluate why some companies have more innovative management practices than others. The conceptual model developed draws mainly on innovation diffusion theory and prior empirical findings. We find that (1) firms which adopt clusters of better management practices have greater performance; and (2) several firm characteristics explain the adoption of better management practices, such as education level of employees and managers, firm size, ownership by a multinational firm, and diffused ownership structure. The study has practical implications for policy-makers and stakeholders who are interested in supporting the adoption of better management practices by firms to enhance productivity in the manufacturing sector.
Least square filters for approximating perfect reconstruction filter banks

This paper presents a least squares (LS) design methodology for approximating perfect reconstruction filter banks. A filter bank can be represented as a multi-input multi-output (MIMO) LTI system whose transfer function is described by the filter bank polyphase matrix. Rising this MIMO system representation, we frame a general least squares filter design problem. Then given an arbitrary set of rational analysis filters we find the causal synthesis filters for a filter bank which achieves the best causal LS approximation to perfect reconstruction. <<ETX>>
Automated Quadruped Robot Simulation using Internet of Things and MATLAB

In this paper, a MATLAB Simulink model of a Quadruped Robot is presented alongside its remote, control and monitor user interface that has been developed by using the fundamentals of Internet of Things on a Node-Red Flow and the FRED-Cloud Server. Robotics and Automation over the recent years have developed exponentially and hence have been a key factor in the rise of Industry 4.0 which has usurped manual supervision and operation in industrial and manufacturing processes around the globe. The design and creation of technologically advanced robots integrated with computer-based software for their automation has not only successfully made the tasks facile to manage within short spans of time, but also has increased the efficiency notably. The stability and mobility of quadruped robots is considered to be ideal on differing terrains with minimal subtle changes, thereby making it an asset. Internet of Things on the other hand, has paved its way over the control of robots as well, with its unparalleled benefits. This paper is focused on the design and execution of the Quadiuped model which includes the observation of the various significant graphs achieved post simulation with respect to electrical values such as power and current consumption, and a visual animation of the robot running in the workspace. Furthermore, a single platform is developed and displayed that allows a user to log in for security puiposes and thereby, operate and monitor the functions and conditions of the bot easily, ranging from remote visual support, directional integrity, damage control and more, without the need of multiple platforms to carry out varying tasks with respect to control.
Can Deep Synthesis of EMG Overcome the Geometric Growth of Training Data Required to Recognize Multiarticulate Motions?*

By being predicated on supervised machine learning, pattern recognition approaches to myoelectric prosthesis control require electromyography (EMG) training data collected concurrently with every detectable motion. Within this framework, calibration protocols for simultaneous control of multifunctional prosthetic hands rapidly become prohibitively long—the number of unique motions grows geometrically with the number of controllable degrees of freedom (DoFs). This paper proposes a technique intended to circumvent this combinatorial explosion. Using EMG windows from 1-DoF motions as input and EMG windows from 2-DoF motions as targets, we train generative deep learning models to synthesize EMG windows appertaining to multi-DoF motions. Once trained, such models can be used to complete datasets consisting of only 1-DoF motions, enabling simple calibration protocols with durations that scale linearly with the number of DoFs. We evaluated synthetic EMG produced in this way via a classification task using a database of forearm surface EMG collected during 1-DoF and 2-DoF motions. Multi-output classifiers were trained on either (I) real data from 1-DoF and 2-DoF motions, (II) real data from only 1-DoF motions, or (III) real data from 1-DoF motions appended with synthetic EMG from 2-DoF motions. When tested on data containing all possible motions, classifiers trained on synthetic-appended data (III) significantly outperformed classifiers trained on 1-DoF real data (II), although significantly underperformed classifiers trained on both 1- and 2-DoF real data (I) (I < 0.05). These findings suggest that it is feasible to model EMG concurrent with multiarticulate motions as nonlinear combinations of EMG from constituent 1-DoF motions, and that such modelling can be harnessed to synthesize realistic training data.
Using the Intact Human Hand to Benchmark Real-Time Myoelectric Control Performance for Robotic Interfaces

The objective of our study was to demonstrate how the intact human hand can be used as a benchmark for electromyogram (EMG)-based myoelectric control of robotic interfaces (e.g., myoelectric prostheses). Using the intact human hand as a gold standard for control algorithms is attractive because able-bodied participants are widely available, have stereotypical movements, and possess highly refined motor control. We compared within-subjects performance of a real-time virtual posture-matching task between a musculoskeletal model-based EMG controller (model trials) and the human hand (goniometer trials). Goniometer trials had lower (i.e., better) normalized path length (2.0±1.6) and task duration (3.3±3.4 sec) than model trials (4.1±4.3 and 12.3±10.7 sec, respectively; p<0.0001). Though, qualitatively, actual (measured by goniometers) and virtual joint angles assumed similar relative postures during model trials, there was a constant offset between them. Additionally, joint angles were more variable during model trials than goniometer trials. The results quantified the extent to which task performance and movement characteristics were not as good with the EMG controller (in this case, the musculoskeletal model-based controller) as with the gold-standard intact human hand. How EMG controllers compare with intact human hand control can drive and inform controller advancements.Clinical Relevance— The gold-standard intact human hand provides an objective way to decide which EMG control algorithms to translate to clinical robotic interfaces.
The Relationship Between Depressive Symptomology, Motivational Deficits and Striatal Activity in Traumatic Brain Injury (TBI)

Individuals with traumatic brain injury (TBI) have been shown to have high prevalence rates of depression. Reward processing and motivational deficits have been shown to be associated with depression, since both constructs rely on the ffonto-striatal network. In this study, we examined depressive symptomology, motivational tendencies and striatal activation during wins and losses in individuals with TBI. Participants (TBI and healthy controls [HC]) completed a gambling task composed of wins and losses in an MRI scanner. Depressive symptomology was assessed with the Chicago Multiscale Depression Inventory (CMDI), while motivational tendencies were assessed with the Behavioral Inhibition/Behavioral Activation (BIS/BAS) scale. Significant between-group differences were observed in BAS (p=.01), BASDrive (p = 0.003), BAS-Reward Responsiveness (p = 0.04) and in all CMDI subscales (Mood: p = .01; Evaluative: p = 0.02; Vegetative: p = .001), with significant correlations seen between both questionnaires. Using VBM analysis and the segmentation of subcortical structures, a significant GM decrease in both the right ACC and bilateral NAcc was seen in the TBI group. A positive correlation was found in the TBI group between the CMDI-Mood subscale and activation of the left NAcc during loss trials (r = -.858, p < 0.05), while a strong negative correlation in the TBI group was found between BIS and activation of the right NAcc during win trials (r = -.852, p < 0.05). When split between high and low BIS scores, the TBI group scoring higher on BIS showed significantly lower levels of NAcc activation during win trials (p < 0.05). No demographic between-group differences were detected. The current study replicates previous evidence in non-TBI individuals with depression, as well as provides pioneering evidence on the existence of the association between striatal engagement in depressive symptomology and motivation in individuals with TBI. Running head: DEPRESSION, MOTIVATION & THE STRIATUM IN TBI 1 THE RELATIONSHIP BETWEEN DEPRESSIVE SYMPTOMOLOGY, MOTIVATIONAL DEFICITS AND STRIATAL ACTIVITY IN TRAUMATIC BRAIN INJURY (TBI)