Miracle-dz/UniFAR-FTUs huggingface dataset:
EMG-Based Gestures Classification Using a Mixed-Signal Neuromorphic Processing System

The rapid increase of wearable sensor devices poses new challenges for implementing continuous real-time processing of physiological data. Neuromorphic sensory-processing devices can enable both the measurement of bio-signals and their processing locally in compact embedded wearable systems. In particular, mixed-signal spiking neural networks implemented on neuromorphic processors can be integrated directly with the sensors to extract temporal data-streams in real-time with very low power consumption. In this work, we present a neuromorphic approach for classifying spatio-temporal data from electromyography (EMG) signals, which paves the way toward the realization of compact wearable solutions for neuroprosthetic control. Here we extend previously proposed delta-encoding methods to transform bio-signals into spike trains and use a spiking Recurrent Neural Network (SRNN) architecture to extract features from them. The SRNN was first simulated in software to find the optimal set of hyperparameters, and then validated on the neuromorphic hardware, with a difference in the performance of less than 2%. We describe how biologically plausible mechanisms such as Spike-Timing Dependent Plasticity (STDP) and soft Winner-Take-All (WTA) networks can be exploited to classify the EMG signals and show how their combined use in EMG data classification achieves competitive results with different datasets. Specifically, the classification performance for the Roshambo EMG dataset, which has three different classes, is above 85%, and for the basic finger movements dataset from the Ninapro database, which has eight different classes, reaches 55% accuracy.
Deep Neural Oracle With Support Identification in the Compressed Domain

We investigate the advantage of a two-step approach in the recovery of Compressed Sensing (CS) encoded signals in a realistic environment. First, the support of the signal is computed from the compressed measurements exploiting a Deep Neural Network (DNN). Once the support is known, the input signal can be easily recovered by a pseudoinverse operation. We consider a case study involving realistic biomedical signals and a processing architecture based on a limited precision fixed-point arithmetic unit for the implementation of the DNN and the pseudoinverse operation. In this setting, we show that the proposed approach results in a performance improvement of more than 5 dB in terms of average reconstructed signal to noise ratio (ARSNR) compared to CS state-of-the-art approach. This has been possible thanks to two main contributions reported in this paper. The first one is a theoretical investigation of the relationship between the definition of support and both the properties of the input signal and the adopted compression technique. The second one relies on replacing the pseudoinverse operation with a least mean square filter, whose small sensitivity to numerical errors grants advantages in architectures relying on limited precision fixed-point arithmetic units.
Quantitatively Analyzing Relay Networks in Bitcoin

Bitcoin’s throughput is far lower than those of centralized systems such as Visa and PayPal. To handle a comparable number of transactions, Bitcoin’s throughput must be improved, which will require a reduction in the block propagation time. Relay networks have attracted attention as a method to improve throughput. However, the nature and strength of the effects of relay networks are not yet clear. In particular, the effects on individual nodes have received little attention. Thus, this study aims to investigate the effects of relay networks on the Bitcoin network and its nodes under the current network state in approximately 2019. We simulate a relay network under the current network state because simulation is virtually the only way to obtain data for all nodes. A major contribution of this study is that it is the first to investigate the relay network effect on the 90th percentile of block propagation time. Moreover, this study shows that relay networks benefit not only the system but also the nodes utilizing them. The utilizing nodes’ blocks were more than six times more likely to be approved as valid blocks when nonutilizing and utilizing nodes generated blocks simultaneously. This finding can motivate the use of nodes in relay networks.
Study on How Catheter Ablation Affects Atrial Structures in Patients with Paroxysmal Atrial Fibrillation: The Case of the Coronary Sinus

It has been shown that substrate modification due to catheter ablation (CA), the first-line treatment of paroxysmal atrial fibrillation (PAF), provokes alterations in P-wave features of the surface electrocardiogram. The absence of knowledge on whether invasive recordings acquired during CA can track similar modifications, creates nevertheless a significant void in the thorough understanding of CA effect on PAF. By analyzing 5-minute bipolar coronary sinus (CS) recordings from 26 PAF patients in sinus rhythm (SR) undergoing CA, this study aims to shed light on whether CA affects local activation waves (LAWs) features recorded in the CS, which is a widely used reference point to assess AF substrate. Recordings were obtained before and after CA. The analysis consisted of LAWs detection and processing of features such as duration, amplitude, positive and negative area, morphology variability (MV), dispersion and number of deflections and inflections (NODI). These features were defined similarly as with previous studies made with surface P-waves. Statistical analysis contained median calculation and comparison with Mann-Whitney U-test. Although some variations in line with previously reported results on P-wave analysis have been observed, none of them was statistically significant $( p \gt 0.05)$. Duration, amplitude, positive and negative area marked a minor decrease (- 1.4% to - 14.3%). MV and dispersion showed a minor to moderate increase + 64.8%). NODI showed no alteration. CA in PAF does not lead to significant variations on LAWs features of bipolar CS recordings. The alterations reported by previous studies on the P-waves may be the product of substrate modification of other atrial regions or the superposition of less significant alterations occurring at each atrial site.
Furthering Innovation in Hyper Communication Era

Developments based on emerging digital technologies and supported by 5G are the cornerstones of strengthening innovation ecosystem and socio-economic welfare of any country. In fact, 5G communication technology is already touching variety of industries, from health care and advanced manufacturing to transportation, from entertainment and media to smart cities, clean energy exploration and agriculture. In this paper, we present an overview of technologies to enable next generation of communication, 5G requirements, potential use cases and global supply chain for 5G that will define communication over next decade and complement other digital technologies to unprecedented applications, products and services.
Assessment of stable light derived from DMSP/OLS night‐time imagery

A stable light image for south‐eastern Asia was extracted from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) night‐time imagery for 1999. The accuracy assessment of the stable light image has been completed using two methods: a reference data‐based comparison and a stratified random sampling method. The stable light image was compared with the conventional stable light image for 1999 and the Landsat ETM+ 2000 image for the Kanto region of Japan. The results show that the digital numbers of the new stable light image (almost <20) in the non‐urban area are much lower than those of the conventional stable light image (almost >60) because the new stable light image includes little noise. The stratified random sampling method could assess the accuracy for both the new stable light image and the conventional stable light image in Asia by classifying the images into stable and non‐stable light areas.
Lightweight Secure Message Delivery for E2E S2S Communication in the IoT-Cloud System

The continuous increase in the use of smart devices and the need for E2E smart2smart (S2S) services in IoT systems play effective and contemporary roles in the field of communication, and a large amount of resources is required. Thus, IoTs and cloud computing must be integrated. One of the results of this integration is the increase in the number of attacks and vulnerabilities in the E2E S2S message delivery service of such an IoT-cloud system. However, none of the traditional security solutions can be sufficiently regarded as a secure and lightweight mechanism for ensuring that the security requirements for E2E S2S message transmission in the IoT-cloud system are fulfilled. This work aims to provide an efficient and secure, lightweight E2E S2S message delivery function, which includes the E2E S2S secure key and biometric parameter exchange function, a bio-shared parameter and bio-key generation function, secure lightweight E2E S2S communication negotiation and secure E2E S2S lightweight message delivery. The secure, lightweight cryptographic communication procedure is negotiated between a pair of smart devices during each E2E session to minimize the power consumption required of limited-energy devices. Such a negotiation process prevents known attacks by providing responsive mutual authentication. Lightweight message delivery by the two smart devices can satisfy the basic security requirements of E2E communication and ensure that the computational cost required for a real-time system is as low as possible.
Cut-off Values for Biomarkers. A Review of Statistical Methods and an Application Study on the Association Between UTI and CRP in Febrile Seizure

Dichotomizing continuous biomarkers is a widely used technique in statistical and medical research (especially those regarding the relationship between continuous covariate and outcome). There are many methods in literature to determinate the optimal cut-off point value of a continuous biomarker. The present research describes methods based on (i) minimum p-value, (ii) AUC, sensitivity and specificity and (iii) decision tree. As application in this study, the results of Youden index, closest to (0,1), concordance probability and decision tree methods are used for estimating the optimal cut-off value of CRP for differentiating between without UTI and UTI in children with febrile seizure.
PETA: Privacy Enabled Task Allocation

Service organizations need to comply with numerous data regulations to protect and preserve their customers’ privacy. Any misuse of data and privacy breach can affect the organizations’ reputation and brand image. In service delivery scenarios, such as IT support help desk, agents need to access customer data to serve them effectively. This data often includes sensitive and personally identifiable information of the customer. While some amount of data exposure is needed to serve a customer, however, exposure to more data than required could be a threat to an individual’s privacy. Hence, organizations need to design methodologies to ensure customer privacy while achieving minimal cost of operations.In this paper, we propose the Privacy Enabled Task Allocation (PETA) model for assigning customer requests to agents so that the overall cost of operations and data exposure is minimal. Data exposure is minimized by restricting the amount of data exposure per agent and by regulating the assignment of tasks. The PETA problem is modelled as an integer linear program, which is NP-hard. To solve this combinatorial hard problem, we have designed an allocation algorithm based on the linear programming relaxation for finding a quick feasible solution.
Simplified Space-Vector Modulation Strategy for Indirect Matrix Converter With Common-Mode Voltage and Harmonic Distortion Reduction

This paper presents a simple space vector modulation (SVM) strategy for a three-phase indirect matrix converter (IMC) to reduce common-mode voltage (CMV) and output harmonic distortion. To suppress the CMV peak value to 57.7% of the input phase voltage, three active voltage vectors are used to generate the desired output voltage with arbitrary amplitude and frequency. However, the output waveform quality of the IMC deteriorates due to the absence of zero voltage vectors. To overcome this problem, this paper proposes to redesign control of the rectifier stage by utilizing three active current vectors instead of two, as is common. Consequently, a constant average DC-link voltage is achieved, which can improve the output performance in terms of output voltage and current harmonic distortion. Compared to the existing strategies, the proposed strategy can reduce the CMV and maintain output harmonic distortion without increasing the computation burden. Implementation of the proposed strategy is also straightforward and simple. Simulated and experimental results are provided to verify the effectiveness of the proposed strategy.
Graph Neural Network and Multi-view Learning Based Mobile Application Recommendation in Heterogeneous Graphs

With the popularity of smartphones, mobile applications (mobile apps) have become a necessity in people’s lives and work. Massive apps provide users with a variety of choices, but also bring about the information overload problem. In reality, the number of apps that users have used is very limited, resulting in a very sparse interaction matrix between users and apps. It is not accurate enough to use a sparse interaction matrix to predict numerous unknown ratings, so that the recommended results cannot satisfy users. This paper aims to exploit the user’s historical behavior data and the app’s side information to make app recommendation to solve the problem of information overload. Specifically, first of all, multiple semantic meta-graphs are designed by leveraging the user information, app information, user historical usage record information, and app’s side information. Then, similarity matrices between users and apps based on different semantic meta-graphs are obtained. The graph neural network with the attention mechanism is employed to learn the collaborative information between users and apps, and to selectively aggregate the feature information of the neighbors. Finally, the multi-view learning and attention mechanism are adopted to obtain users’ ratings for apps from different perspectives. Comprehensive experiments with different numbers of training samples show that the proposed method outperforms other comparison methods.
A Theoretical Persuasive Framework for Supporting and Evolving an Individual’s Sustainable Mobile Traveling Attitude

This paper proposes an innovative theoretical framework for the development of a persuasive prototype for supporting and evaluating an individual’s traveling attitude through the use of well-designed persuasive smartphone applications for sustainable urban mobility purposes. The newly introduced framework emphasizes the importance of engaging and committing users to improve their travel habits through continuous motivation and evaluation of both how their traveling habits affect the environment and how they contribute to better future mobility planning decisions. It consists of five stages; it takes into consideration various elements from existing frameworks and introduces a critical stage, that of the users’ rewarded assessment/feedback, based on the daily evaluation of the application effectiveness to persuade the user to move in an eco-friendlier way. For each stage of the proposed framework, a detailed description of the strategies that could be applied, and the related target group is provided.
Experimental Analysis of the Performance of a Hexapod Robot based on the Dynamic Parameters

In this paper, the experimental performance of a small hexapod robot driven by C-shaped legs has primarily been analyzed, based on its vertical and horizontal acceleration, for a chosen set of triangle gait phase angles. Firstly, a kinematic model was established for the hexapod robot designed by the Institute of Robotics and Intelligent Systems at Xi'an Jiaotong University. The triangle gait experiments were conducted on this robot, including straight line motion, turning motion, motion on a rugged terrain, climbing over an obstacle, and walking down a step. The acceleration during the course was plotted for each of the mentioned scenarios and the patterns were analyzed by relating to different experimental aspects. The robot achieved a maximum angular velocity of about 0.65 rad/s, crossed an obstacle of 4.5cm height and descended down a 13.5 cm high step. The analysis provides the ground towards the optimization of the triangular gait of the hexapod robot driven by C-shaped legs for robust movement.
Ultrahigh-frequency microplasma jet as a low-power, high-density, and localized ions/radicals source

An ultrahigh-frequency (450 MHz) microplasma jet was stably generated at a chamber pressure as low as 10−3 Pa. Electron and gas temperatures of this microplasma as estimated by optical-emission spectroscopy measurements were ∼7000 and 1000 K, respectively. The atomic oxygen flux at the torch exit was revealed to be of the order of 1019 atoms/cm2 s, which is 102–104 times higher than that obtained by other conventional plasma sources. This microplasma jet was also applied as an ion source for in situ neutralization of electron charging-up effects in a scanning-electron microscopy observation of insulating materials.
Emotion Recognition Results using Deep Learning Neural Networks for the Romanian and German Language

We used the SRoL database for the Romanian language and Emo-DB database for the German language. For the emotion recognition we applied a Deep Learning Neural Network with convolutional layers (DL-CNN). This study includes four emotions: joy, sadness, fury and neutral tone. A 3layer deep learning neural network is used, and the best accuracy rate for Romanian language is 84.5% with fine tunning and for German language 86.8%.
Development of Convolutional Neural Networkbased models for bone metastasis classification in nuclear medicine

Focusing on prostate cancer patients, this research paper addresses the problem of bone metastasis diagnosis, investigating the capabilities of convolutional neural networks (CNN) and transfer learning. Considering the wide applicability of CNNs in medical image classification, VGG16 and DenseNet, as being two efficient types of deep neural networks, are exploited for images recognition, being used to properly classify an image by extracting its insightful features. The purpose of this study is to explore the capabilities of transfer learning in VGG16 and DenseNet application process, which will be able to classify bone scintigraphy images in patients suffering from prostate cancer. Efficient VGG16 and DenseNet architectures were built based on a CNN exploration process for bone metastasis diagnosis and then were employed to identify the metastasis from the bone scintigraphy image data. The classification task is a three-class problem, which classifies images as normal, malignant, and healthy images with degenerative changes. The results revealed that both methods are sufficiently accurate to differentiate the metastatic bone from degenerative changes as well as from normal tissue.
Applying a Convolutional Neural Network in an IoT Robotic System for Plant Disease Diagnosis

Plant diseases are major threat to green product quality and agricultural productivity. Agronomists and farmers often encounter great difficulties in early detection of plant diseases and controlling their potential production damages. Thus, it is of great importance for stakeholders to diagnose plant diseases at very early stages of plant growing by exploiting state-of-the art technologies, consider appropriate actions and avoid further economic losses. Artificial Intelligence (AI) techniques, field sensors, data analytics and inference algorithms are some contemporary tools which could be helpful for early plant disease diagnosis. In this paper, we present a plant Disease Diagnosis Support System (DDSS) that utilizes an Internet of Things platform to control a lightweight robotic system. The DDSS applies a Convolution Neural Network learning algorithm to perform early plant disease diagnosis and classification. The system can help farmers to apply appropriate precision agriculture actions and better control their production. The proposed DDSS achieves around 98% success classification rate, according to our demonstration case study.
Off-Person ECG Biometrics Using Spatial Representations and Convolutional Neural Networks

Targeting off-person ECG-based biometrics, we report a comparative analysis of identification accuracy and verification Equal Error Rate (EER) performances of four distinct types of spatial representations of ECG signals applied as inputs to Convolutional Neural Networks. The actual algorithms used to transform the original time series into 2D/3D images are based on a modified version of the Continuous Wavelet Transform (the S-Transform), the Gramian Angular Field, the recurrence plot, and state-space representations. Extensive experiments have been conducted using UofT and CYBHI datasets including recordings acquired on fingers and hand palm under various activity scenarios. The wavelet-based approach yielded best results, while all analyzed solutions compare favorably with previously reported performances.
Interfacial phenomena in composite high voltage insulation

This paper deals with two different types of composite insulating materials for HV outdoor insulation technology. For outdoor applications fiberglass reinforced polymer (FRP) are used mainly as transmission line insulators (composite long rods),and as power apparatus housings (composite hollow core insulators). During the last decades mineral filled polymer (MFP) are found to be very suitable for outdoor insulation in the medium and HV ranges. To meet the outdoor demands, long-term stability and durability against environmental stresses are necessary, Composite insulation consists of more than one dielectric component, and linking at least two different kinds of materials leads to interface problems. These regions always appear as weak material structures that can be attacked by various aging mechanisms. Especially the long-term performance and aging resistance are determined by the interface quality. One can distinguish four principal kinds of interface: microscopic, e.g. those between fillers and matrix components; macroscopic, e.g. those between glass fiber rod and polymeric shielding material; internal, in the insulation bulk; and external, solid surface against a liquid or gaseous phase. Today the use of composite insulating materials in HV technology is state of the art. They offer a wide range of superior properties for indoor as well as for outdoor applications. Further improvements should focus on the hydrophobicity and on the long term resistance of the external interface and the stability of the internal interface.