Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Discriminative Domain Adaptation and Transformers for EEG-based Cross-Subject Emotion Recognition

Decoding the human emotional states based on electroencephalography (EEG) in affective brain-computer interfaces (BCI) is a great challenge due to inter-subject variability. Existing methods mostly use large amounts of EEG data of each new subject to calibrate the algorithm, which could be time-consuming and not user-oriented. To address this issue, we propose a combination of using transformers (TF) and adversarial discriminative domain adaptation (ADDA) to perform the emotion recognition task in a cross-subject manner. TF principally relies on the attention mechanism. Our proposed approach performs scaledot product attention on the feature-channel aspect of EEG data to improve the spatial features. Then, the temporal transforming is applied to get the global discriminative representations from the time component. Moreover, ADDA aims to minimize the discrepancy of EEG data from various subjects. We evaluate the proposed ADDA-TF on the publicly available DEAP dataset and demonstrate the improvements it provides on low versus high valence and arousal classification.
A Framework for Developing Social Awareness of the Effect of Plastic Waste on the Environment

Plastic is cheap and incredibly versatile with properties that make it ideal for many applications. Meanwhile, plastic can take anywhere from 20 to 500 years to decompose, depending on the material and structure. It is hard to find any other materials that can replace plastic in short findings. The study proposes 4-steps of action for a loop of plastic waste collection by raising social awareness of the hazardous plastic waste to the environment. An experimental portal has been set up for collecting the images of the 4-steps of action labeled with a designed schema of metadata. Social awareness of environmental concerns is experimentally conducted through the 4-steps of action. The resulting labeled image bank is designed effectively retrieving to raise environmental consciousness regarding the use of plastic.
The analysis of electroneurographic and electromyographic activity recorded in the medial nerve of a transhumeral amputee during phantom finger movements

The control of bionic prostheses for upper-limb amputees is often conducted under electromyographic (EMG) control, where enacting individual finger movements presents a significant challenge. With the development of neuroprosthetical systems based on peripheral-nerve implants, electrical activity of the afferent and efferent nerve fibers can be employed to improve decoding and control. Notwithstanding these developments, it is still poorly understood how electroneurographic (ENG) recordings could be applied for neuroprosthetic motor control and what the relevant decoding algorithms would be. Here we show that mental efforts applied to perform movements of phantom fingers of a transhumeral amputee could be represented as ENG activity and assessed with time-frequency analysis. Participant who underwent implantation in peripheral nerves for the phantom limb pain treatment underwent an experiment performing 7 gestures including movements of each phantom finger independently. Our intrafascicular recording from the medial nerve showed that ENG power increased in the frequency range from 100 to 500 Hz for the movements of phantom index, middle and ring fingers. Using EMG recorded in the areas of electrode implantation in a residual limb as a control, we concluded that the ENG activity could not be always explained as an effect of overt movements but could be related to a more sophisticated mechanism instead.
Edge AI-Based Closed-Loop Peripheral Nerve Stimulation System for Gait Rehabilitation after Spinal Cord Injury

Recently, it has been revealed that Vsx2 neurons in the lumbar of the spinal cord are essential to restore locomotor function after spinal cord injury (SCI). However, Vsx2 neurons still need afferent feedback from peripheral nerves for locomotor rehabilitation. Also, movement-dependent electrical stimulation after SCI can solidify synaptic connections in a directed way. Based on these two facts, we hypothesized that providing movement-dependent electrical stimulation to hindlimb nerves may facilitate the rehabilitation process for restoring locomotion function after SCI while preventing aberrant remodeling of denervated spinal circuits. To evaluate our hypothesis, we developed an edge artificial intelligence (AI)-based closed-loop peripheral nerve stimulation system which can timely generate stimulation pulses for distal-tibial and peroneal nerves based on stance phase detection. The main parts of the system consist of a multi-site EMG electrode, neural amplifiers, an edge AI processing circuit, and neural stimulators. Medial gastrocnemius (MG) electromyography (EMG) was used as a input source of the AI model to detect the stance phase. The AI model was deployed on a dual-core 32-bit microprocessor. The whole system was evaluated with two SCI rats walking bipedally on a motorized treadmill. The accuracy of the presented AI model was calculated as 97.19%. In the animal experiments with SCI rats, stimulation pulses for the distal-tibial and peroneal nerves were timely generated for 200 ms and 100 ms, respectively based on the stance phase detected by the AI model. The experimental results suggest that the presented system can be a powerful neural interface tool to investigate the efficacy of edge AI-based closed-loop peripheral nerve stimulation on restoring locomotion function after SCI.
Design of an Iron Loss Tester for the Evaluation of Assembled Stator Cores of Electric Machines

The manufacturing processes of an electric machine can lead to a deviation between predicted and actual iron losses. This work presents an iron loss tester for measuring the iron losses produced by an assembled stator core including the effects of manufacturing and actual flux distribution. The tester is designed with accessible excitation and measurement windings with a toroidal configuration enabling a high degree of flexibility in emulating various flux distributions in AC electric machines. A prototype of the proposed tester is built and the experimental results of a manufactured stator are obtained under different pole configurations showing the magnetization characteristics in different regions of the tester/stator along with the total measured iron loss (IL). A noticeable deviation between experimental and FE simulation results is present at high frequencies. As the saturation level in steel laminations is reduced, the deviation in total IL is increased. Finally, the specific IL obtained through the tester is compared against standard Epstein frame results. Importing the experimental IL data obtained through the tester to the FE model led to a close match between simulation and experimental results.
Neural Circuit Model of Long-Term Potentiation from Intermittent Theta Burst Stimulation

Neural circuits have explained motor learning observed in animal populations, and plastic synaptic reorganization, but simulation of network modeling at larger scales lacks circuit organization. As a result, while large scale models capture general population behavior, there remains no canonical circuit, or simplest circuit-based functional subunit, that can be studied to determine the most discrete level of neuromodulation effect. The purpose of this study was to integrate a canonical neural circuit of the motor cortex with mathematical representation of membrane potentials across multiple neurons. The circuit model was evaluated by comparing changes in modeled resting outputter cell spike rate to changes in empirically recorded motor evoked potential amplitudes following intermittent theta burst stimulation (iTBS) delivered via transcranial magnetic stimulation. Effect sizes measured the magnitude of effect in both modeled and experimental data. The model successfully showed an increase in resting spike rate with relation to increased AMPA receptor permeability, scaled by simulated effects of iTBS to induce long-term potentiation (LTP), in an excitatory change similar to that seen in empirically recorded motor evoked potentials from human subjects. Thus, the model captured the mechanism of LTP in its simplest form. The representation is incomplete, however, as the model overestimated the responsivity based on the increased effect size relative to empirical data and noise. The overestimation is likely due to the lack of the disinhibition mechanism being fully represented in this iteration of the circuit, the simplicity of the cellular compartments modeled relative to the physiological complexity of neurons, and model parameter approximations of values controlling AMPA receptors. The circuit model is a building block for wider and more complex cortical networks that can be used to test mechanisms and applications of neuromodulation modalities.
Combining Density based and Linear Discriminant Approaches for Motor Imagery Classification*

For transferring brain-computer interfaces outside of the lab to clinical settings, it is necessary to have a high accuracy with models having a limited number of hyper-parameters. State of the art techniques include discriminant approaches using spatial filters, deep learning, and density based methods using Riemannian geometry. We propose a pattern recognition system for the multiclass classification of brain evoked responses corresponding to motor imagery that combines features obtained from the Riemannian geometry, with distances to the mean of each class, with a discriminant approach (Bayesian linear discriminant analysis) using 15 frequency bands from 8 to 24 Hz to cover the mu and beta bands. We investigate the impact of these different frequency bands, separated in four sets, on the accuracy and how frequency bands selection using backward elimination or forward addition can enhance the accuracy of the classification tasks. These approaches were evaluated on the publicly available dataset 2A (4 classes - 9 subjects) and (2 classes - 14 subjects). While there are differences between bands and across subjects, the best overall performance was obtained with all the bands. The kappa value for multiclass motor imagery detection is 0.60. The average binary classification across the six pairwise tasks is 80.83%.
Tube Sheet Annular Seam Measurement Technology Based on Laser Collaboration With Vision Sensor

The fast and accurate locating of the tube-sheet annular seam is the key factors to ensure the high efficiency and quality of automatic welding of pressure vessels. Considering the problems of welding sheet deformation and tubes deviation caused by machining process and welding thermal, this paper builds an automatic welding robot platform, which is equipped with a measurement system coordinated by multiple lasers and visual sensors, and put forward a real-time welding distance measurement method based on cross lasers and visual sensors, at the same time, the multi spot laser assisted vision sensor is used to quickly and accurately measure the center of the tube, so as to guide the adjustment of the welding robot and posture of the welding torch in real time. Then, through the measurements of system error, static and dynamic measurement error, it is known that the dynamic accuracy of tube sheet welding distance is not less than 0.22mm, and the positioning accuracy of welded tube center is not less than 0.219mm, which can meet the accuracy requirements of automatic tube sheet welding.
Automatic In-situ Optical Linearization of Silicon Photonic Ring-Assisted MZ Modulator for Integrated RF Photonic SoCs

We experimentally demonstrate automatic optical linearization of a Ring-Assisted MZ modulator with SFDR > 110dB.Hz2/3, fabricated in a silicon photonic foundry process. The linearization algorithm reconfigures the modulator to its optimum SFDR or other desired regimes regardless of random phase offsets and process/temperature variations. © 2022 The Author(s)
Investigation of functional integration of cortical organoids transplanted in vivo towards future neural prosthetics applications

Cortical organoids are rapidly emerging as models for brain development and dysfunction because they bridge the benefits of physiological manipulability of in vitro cultures with the complexity of in vivo brains. Organoids offer a host of benefits including the ability to perform patient-specific drug screening, study brain development, and investigate disease. Another promising application of organoids is as neural prosthetics, where if a brain region is damaged or degenerated, an organoid can be grown from the patient's stem cells and be implanted to restore lost function. Here, we implant hiPSC-derived cortical organoids in the retrosplenial cortices (RSC) of mice and to see if the organoids can form functional connections to the surrounding host cortices. Using transparent graphene microelectrode arrays (MEA) for electrophysiology and two-photon imaging of organoids, we observe a response to the external light stimuli, suggesting functional synaptic connections had formed between organoids and cortex. Bursts of cell population calcium activity are observed with two-photon imaging and suggest a mature internal network. Post-mortem histology and vasculature tracing suggests morphological integration of organoid cells into mouse cortex and mouse vessels and synapses into organoid. Overall, our multimodal platform offers a way to study organoids in vivo longitudinally and investigate their development and integration with host cortex and their potential use for cell replacement therapy.
Comparison of Thermal Characteristics in Various Aspect Ratios for Radial-Flux and Axial-Flux Permanent Magnet Machines

This paper proposes an axial-flux permanent magnet machine (AFPM) that uses a neodymium-bonded permanent magnet (Nd-bonded PM) and a coreless rotor structure and assesses how it performs in industrial applications requiring high efficiency and low cost. The AFPM is able to achieve high efficiency in high-speed regions because its Nd-bonded PM can restrain eddy current loss. Additionally, the AFPM can continuously function at the rated operating point without a cooling system, so the expenses for one can be eliminated. Hence, the AFPM can simultaneously achieve high efficiency and low-cost mass production. In this paper, a comprehensive comparison including the thermal characteristics of the proposed AFPM and a conventional radial-flux permanent magnet machine (RFPM) is shown. Furthermore, this paper also focuses on how the aspect ratio influences the thermal characteristics of both machines. Finally, the effectiveness of the proposed AFPM in industrial applications is assessed using 3D-FEA and experimental results.
Green Spectrum Sharing Framework in B5G Era by Exploiting Crowdsensing

With the recent development of wireless technologies, a massive number of Internet of Things (IoT) devices will access the Beyond 5G (B5G) networks for various advanced applications. How to support this huge amount of wireless data traffic over limited spectrum resource is a critical problem. To address this problem, dynamic spectrum sharing has been extensively investigated recently to improve the spectrum utilization efficiency for sub-6GHz frequency bands. The secondary users are allowed to access the spatio-temporal spectrum holes of the primary users with the help of a spectrum database. In this paper, we propose a green spectrum sharing framework in B5G era by exploiting crowdsensing. The goal is to construct an accurate crowdsourced spectrum database in a cost-efficient way. Specifically, a machine learning based classifier is used to select the spots that need to be interpolated by crowdsensing users, and both offline and online user incentive mechanisms are proposed to attract users to participate into the crowdsensing task. To validate the proposed framework, we conduct a vehicle-based measurement campaign over a 2km $\times $ 7km area, and evaluate the performance of the proposed framework by using the collected real data.
Full 3-inch Wafer Processed 1060 nm Single-mode Transverse Coupled-cavity VCSEL for Data Transmission in Standard 1300nm Single-mode Fiber

We demonstrate full-3inch-wafer processed 1060nm single-mode metal-aperture VCSEL, exhibiting 1300nm-standard single-mode fiber transmission. We obtained a bandwidth•distance product of 2.5Tbps･km. The modal-intensity noise in two-modes fiber link is suppressed with modefilters.
Nonlinear Tolerant Conjugated RoF System Secured by Physical Layer Encryption with Deliberate Signal Randomization

We propose and demonstrate conjugated RoF (C-RoF) secured by physical layer encryption with deliberate signal randomization, for high-RF-link-gain and high-security analog RoF transmission. 19.4-dBm high-power secured C-RoF-QPSK is experimentally achieved with improved fiber nonlinearity tolerance.
MV-Partitions and MV-Powers

In this chapter, the authors generalize the Boolean partition to semisimple MV-algebras. MV-partitions together with a notion of refinement is tantamount a construction of an MV-power, analogous to Boolean power construction (Mansfield, 1971). Using this new notion we introduce the corresponding theory of MV-powers.
Cellular Digital Post-Distortion: Signal Processing Methods and RF Measurements

In this paper, we study the feasibility of digital post-distortion (DPoD) based mitigation of transmitter nonlinear distortion in cellular networks. With specific emphasis on downlink, we describe a computationally efficient one-shot method to estimate and mitigate the cascaded multipath channel and transmitter nonlinear distortion effects at terminal receiver, building on demodulation reference symbols (DMRSs). We also describe a DMRS boosting approach to match the envelope characteristics of the DMRS and the actual data-bearing multicarrier symbols such that accurate mitigation is feasible. We provide RF measurement results with a state-of-the-art 28 GHz active antenna array and 256-QAM data modulation, demonstrating larger performance enhancements in received signal error vector magnitude (EVM) compared to existing computationally expensive iterative methods.
Identification of Switching Linear Dynamics in Distributed Neural Populations

Modern techniques have enabled recording large numbers of anatomically distributed neural populations at high spatial and temporal resolution. These populations interact in a complex, often nonlinear and nonstationary, way. Thus, there is a need for robust and versatile methods that can capture these complex dynamics while remaining adequately constrained by the observed data. Recently, there has been increasing interest in Switching linear dynamical systems for modeling nonlinear neural dynamics in a piecewise linear fashion, especially when abrupt transitions occur that may be related to distinct behaviors. We present a computational and experimental study characterizing switching linear dynamics for interacting neural populations. First, we motivate the use of the time-varying autoregression with low rank tensors (TVART) method by recovering the linearized dynamics about the stable attractors of a simple model of two interacting neural populations. Then, we identify the time-varying dynamics of the power feature time series derived from local field potentials recorded from a non-human primate animal subject. Robust variation in the dynamics where concurrent with the onset of the task behavior. Finally, we consider the variations of dynamics in the resting state and demonstrate that low rank time-varying dynamics can reduce the prediction error when compared to a full rank stationary model.
First Demonstration of Nociceptive and Non-Nociceptive Responses from Spinal Neurons in a Porcine Model

The spinal cord plays a key role in pain processing, but it remains unexplored in large animal models. We have developed a methodology to record from spinal neurons using three pigs. Here we aim to determine (1) at which rostro-caudal level ulnar nerve evoked responses can be recorded and (2) at which depth distinctly different responses can be recorded after noxious and non-noxious stimulation. Neural signals were evoked by ulnar nerve stimulation and recorded at different levels of the spinal cord in anesthetized pigs. Event-related potentials and peri-stimulus histograms showed that most activity was recorded at the C7 level, which diminished when the electrodes were moved towards C6 or C8. At 1 mm depth, spinal neurons responded primarily to noxious stimulation, which is typical for nociceptive specific neurons. While at 2 mm depth, neurons showed responses typical for wide dynamic range neurons by responding differently to noxious and non-noxious stimulation. Histological analysis showed that these signals may indeed have been recorded from lamina I/II and IV/V, respectively. This method opens new possibilities for studying pain and other spinal mechanisms in large animals and can be combined with peripheral and brain recordings to provide a more integrated picture of (chronic) pain mechanisms.
FORMATION OF FOREIGN LANGUAGE COMMUNICATIVE COMPETENCE OF THE FUTURE SPECIALISTS

Communicative competence is considered as future specialist’ key competence, since it helps to successfully integrate into the system of social relations. Foreign language communicative competence is an integral quality of the individual, which contributes to the adaptation and adequate functioning of the individual in society, includes certain methods of communication, values and creative potential of the individual. Foreign language communicative competence appears as a special type of professional competence, which is defined as the readiness and ability to master objective, scientific knowledge in professional communication
The Ecological Significance of the Theory of Dissipative Structure

Dissipative structure focuses on studying the open system which is far from the state of balance. "Its meaning is far more than the study of physics and chemistry"(Thoffler). It is of great social signficance.This paper lays stress on analyzing its ecological significance and intends to show the importance of ecological protection from a new angle.
Sur les seuils de reconnaissance des formes

Un enjeu important de la recherche de formes dans une base d'images est la definition de seuils non supervises permettant d'eviter une deferlante de fausses detections, ou, au contraire, des rejets de formes qui auraient du etre reconnues. En prenant comme exemple une methode de reconnaissance de forme proposee par Lisani [15, 16], nous montrons que l'on peut repondre a la question suivante : etant donnee une forme requete et une base d'images, a partir de quelle distance entre la forme requete et une forme detectee est-on surs que la forme est reconnue ? Cette assurance est quantifiee par le nombre de fausses alarmes associe a la paire requete - forme candidate. Cette methode ne considere pour l'instant que des morceaux de forme et permet pourtant deja d'aboutir a des detections sures basees sur un seul morceau de forme.
Listing all maximal cliques in sparse graphs in optimal time

A graph is k-degenerate if there exists an ordering of its vertices v1, . , vn such that for all i, |N(vi) ∩ Vi| ≤ k where N(vi) is the open neighbourhood of vi and Vi = {vi, . , vn}. In [D. Eppstein, M. Loffler and D, Strash, Listing All Maximal Cliques in Large Sparse Real-World Graphs, JEA (2013)], it was proved that the largest possible number of maximal cliques in an n-vertex k-degenerate graph is (n − k)3. Then the authors prove an algorithm listing all the maximal cliques of these graphs in optimal time O(k(n − k)3). In this paper we prove another