Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach to Extract Nonlinear Muscle Synergies from sEMG through Multi-Model Learning

How does the Central Nervous System (CNS) controls a group of muscles is an important question in the field of motor control. A common conception is developed over the years that the CNS make use of predefined activation patterns, known as muscle synergies during task execution. These muscle synergies are extracted by applying any of the factorization algorithms such as Non-Negative Matrix Factorization (NNMF), Independent Component Analysis (ICA) or Principle Component Analysis (PCA) on a concatenated surface EMG data set recorded from the target muscles. However, the step to concatenate sEMG signals before they are given as input to these linear algorithm is crucial as the synergistic structure changes significantly based on the number and choice of muscles considered during concatenation step. To address this problem, we propose a new approach of extracting muscle synergies by treating sEMG signals from each muscle as an individual modality and then learning the synergistic structure among them if it exists using multi-view learning. In this study, we propose to use Manifold Relevance Determination (MRD) to find nonlinear synergies from sEMG by assuming the sEMG of a muscle as an individual modality. Results have shown that synergistic patterns extracted using our approach are consistent upon addition of sEMG signals from new muscles.
Optimal Trajectory Planning with Dynamic Constraints for Autonomous Vehicle

Trajectory planning with high accuracy is one of the key technologies of autonomous vehicles to travel safely on public roads. In this paper, we investigate the dependence of the path and velocity interactive planning based on cubic polynomial with vehicle dynamic as well as kinematic constraints including lateral acceleration and road curvature. The effect due to the vehicle dynamics of the host car by ignoring dependence states is studied through the comparison of longitudinal acceleration data in a simulation study and on-road test. Results show that our proposed methodology is more safety and comfort than the one without considering the dependency.
Design and Development of a Sitting Posture Recognition System

Sitting posture recognition can be used to evaluate the awareness of a person carrying out a task, such as working or driving, and can aid in avoiding accidents or other health risks, such as musculoskeletal disorders. In addition, sitting posture can reveal wellness or unhealthiness for the elderly and mobility disabled individuals. This paper focuses on body posture monitoring, by acquiring the pressure distribution of a sitting person with thirteen piezoresistive sensors placed on a seat. The measurements from the sensors passing through a microcontroller unit fed several machine learning techniques in order to discriminate among five sitting postures (upright, leaning left, leaning right, leaning forward and leaning backward). Experiments with body postures from twelve individuals (six men and six women) of different Body Mass Index (underweight, normal and overweight) were conducted. The developed classifiers achieved average discrimination accuracy over 98% among the aforementioned five body postures.
Using Discriminative Lasso to Detect a Graph Fourier Transform (GFT) Subspace for robust decoding in Motor Imagery BCI

A novel decoding scheme for motor imagery (MI) brain computer interfaces (BCI’s) is introduced based on the GFT concept. It considers the recorded EEG activity as a signal defined over (the graph of) the sensor array. A graph encapsulating the functional covariations emerging during the execution of a specific imagined movement is first defined, from a small training set of relevant trials. The ensemble of graphs signals corresponding to a multi-trial training dataset is then analyzed using a graph-guided decomposition and, based on discriminative Lasso (dLasso), an information-rich GFT subspace is defined. After training, only simple matrix operations are required for transforming the multichannel signal into features to be fed into a classifier that decides whether brain activity conforms with the graph structure associated with the targeted movement. The proposed decoding scheme is evaluated based on two different datasets and found to compare favorably against popular alternatives in the field.
International Conference on Intelligent Computing and its Emerging Applications ( ICEA ) Realizing Dynamic Network Slice Resource Management based on SDN networks

It is expected that the concept of Internet of everything will be realized in 2020 because of the coming of the 5G wireless communication technology. Internet of Things (IoT) services in various fields require different types of network service features, such as mobility, security, bandwidth, latency, reliability and control strategies. In order to solve the complex requirements and provide customized services, a new network architecture is needed. To change the traditional control mode used in the traditional network architecture, the Software Defined Network (SDN) is proposed. First, SDN divides the network into the Control Plane and Data Plane and then delegates the network management authority to the controller of the control layer. This allows centralized control of connections of a large number of devices. Second, SDN can help realizing the network slicing in the aspect of network layer. With the network slicing technology proposed by 5G, it can cut the 5G network out of multiple virtual networks and each virtual network is to support the needs of diverse users. In this work, we design and develop a network slicing framework. The contributions of this article are two folds. First, through SDN technology, we develop to provide the corresponding end-to-end (E2E) network slicing for IoT applications with different requirements. Second, we develop a dynamic network slice resource scheduling and management method based on SDN to meet the services’ requirements with time-varying characteristics. This is usually observed in streaming and services with bursty traffic. A prototyping system is completed. The effectiveness of the system is demonstrated by using an electronic fence application as a use case. Key Words-5G, Internet of Things (IoT), Network Slicing, Software Defined Networking (SDN), SDN monitoring, service quality
An Intrusion Detection System using Opposition based Particle Swarm Optimization Algorithm and PNN

Network security became a viral topic nowadays, Anomaly-based Intrusion Detection Systems [1] (IDSs) plays an indispensable role in identifying the attacks from networks and the detection rate and accuracy are said to be high. The proposed work explore this topic and solve this issue by the IDS model developed using Artificial Neural Network (ANN). This model uses Feed - Forward Neural Net algorithms and Probabilistic Neural Network and oppositional based on Particle Swarm optimization Algorithm for lessen the computational overhead and boost the performance level. The whole computing overhead produced in its execution and training are get minimized by the various optimization techniques used in these developed ANN-based IDS system. The experimental study on the developed system tested using the standard NSL-KDD dataset performs well, while compare with other intrusion detection models, built using NN, RB and OPSO algorithms.
Automatic Extraction of Dermatological Parameters from Nevi Using an Inexpensive Smartphone Microscope: A Proof of Concept

The evolution of smartphone technology has made their use more common in dermatological applications. Here we studied the feasibility of using an inexpensive smartphone microscope for the extraction of dermatological parameters and compared the results obtained with a portable dermoscope, commonly used in clinical practice. Forty-two skin lesions were imaged with both devices and visually analyzed by an expert dermatologist. The presence of a reticular pattern was observed in 22 dermoscopic images, but only in 10 smartphone images. The proposed paradigm segments the image and extracts texture features which are used to train and validate a neural network to classify the presence of a reticular pattern. Using 5-fold cross-validation, an accuracy of 100% and 95% was obtained with the dermoscopic and smartphone images, respectively. This approach can be useful for general practitioners and as a triage tool for skin lesion analysis.
How to merge opinions by using operations between sequences of orthopairs

Orthopairs are, analogously to rough sets, a mathematical tool for dealing with uncertainty. Sequences of orthopairs also take into account the possibility to deal with a refinement process of information and with missing information. After recalling the main definitions, we present different operations of conjunction between sequences of orthopairs. We further present an example in which, having available non complete information about applicants for a job, two examiners evaluate them in order to find the better candidates and then their opinions are merged into an individual result.
New Wearable Heart Rate Monitor for Contact Sports and Its Potential to Change Training Load Management

Athletes in all sports face injury or illness if they train too much. Therefore, it is crucial for them to manage their training load. Monitoring the heart rate is one way to estimate training load. However, there are limitations to a monitor’s measurement ability in contact sports like rugby. Another method to estimate training load in contact sports is the rating of perceived exertion of a player, which is based on a questionnaire. It however takes a long time to obtain answers to questionnaires in team sports. As a solution to this problem, we developed a wearable heart rate monitor for rugby players. The garment-type monitor with flank electrodes can measure the heart rate even in rugby training situations. We also propose a method to estimate the training load from the measured heart rate. The method can be used instead of a questionnaire administered to the players and can reduce the labor in the estimation of the training load.
Effect of Multiple Constraints on Multimedia Data Transmission in High Efficiency Video Coder (HEVC)

With lot of advancement in VLSI and signal processing, numerous digital video compression techniques and video coding standards is developed for multimedia communication and an integral part of our today’s life. More prominent requirement of today’s multimedia communication is based on high definition video transfer as these video contains fast amount of redundancy. One of the biggest problems of this high definition video is the requirement of large amount of space and bandwidth. To reduce the storage space for the high definition video stream, a number of high video compression techniques and standards are developed. High Efficiency Video Coding (HEVC) is one of the newest video coding standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group supporting encoding of the high definition video stream. One of the biggest achievements of HEVC is that it reduces bits up to 50% as compare to the previous state of art video coding standards such as H.264. In this paper, we have presented the concept of multi constraint shortest multiple in multi-constraints environment within the HEVC encoder. Bit allocation of multiple paths is changed with the variation in constraints condition imposed for finding optimal shortest path. Present work validated with simulation results by taking different value of constraints.
Research on Power Quality Control Method of V2G System of Electric Vehicle Based on APF

This paper studies the influence of electric vehicle V2G system on the power grid after it is connected to the power grid, analyzes the basic principle of harmonic pollution caused by the charging and discharging machine of V2G electric vehicle on the power grid, and puts forward a parallel active power filter with direct power control method for active control, so as to achieve the effect of eliminating harmonics and smoothing power. Simulation results show that this method can guarantee both charging effect and power quality.
IMU-based Assessment of Ankle Inversion Kinematics and Orthosis Migration

Ankle orthoses are used to prevent injuries during very fast inversion motions of the ankle. In order to provide proper protection, they must fit well and migrate as little as possible. Inertial measurement units (IMUs) have become a useful tool for accurate motion analysis and are frequently used for gait analysis. In the present paper, we examine orthosis migration and inversion kinematics of human ankles protected and unprotected by an orthosis. We present two test benches that were developed for these purposes, as well as a set of inertial sensor fusion methods that are used to determine kinematic parameters from the sensor readings. To avoid the common but restrictive assumption of a homogeneous magnetic field, we determine all motion parameters without the use of magnetometer readings. We conduct a measurement series to compare the proposed IMU-based method to alternative camera-based and goniometer-based methods. Using two different ankle orthosis prototypes, we demonstrate that the proposed IMU-based methods facilitate accurate assessment of orthosis migration and ankle inversion kinematics.
Evaluation of Facial Pulse Signals using Deep Neural Net Models

The reliable measurement of the pulse rate using remote photoplethysmography (PPG) is very important for many medical applications. In this paper we present how deep neural networks (DNNs) models can be used in the problem of PPG signal classification and pulse rate estimation. In particular, we show that the DNN-based classification results correspond to parameters describing the PPG signals (e.g. peak energy in the frequency domain, SNR, etc.). The results show that it is possible to identify regions of a face, for which reliable PPG signals can be extracted. The accuracy obtained for the classification task and the mean absolute error achieved for the regression task proved the usefulness of the DNN models.
MoneyBall - Data Mining on Cricket Dataset

Cricket is one of the most popular sports in the whole world, and also one of the most popular sports in India. Cricketing events such as the Indian Premier League (IPL) are thoroughly enjoyed by fans all across the country. Fans of the game love predicting the ongoing match results, and this is something that has ended up being a hobby for several people who follow the game. This is a sport with abundant amount of data and using this data, we can make an evaluation on whether a team can win an ongoing IPL match or not. This prediction is implemented by using machine learning algorithms such as Gaussian Naive Bayes, Support Vector Machine, K-Nearest Neighbor and Random Forest. The required dataset is obtained by collecting using a website and consolidated. As a result, the output is obtained which lists whether the home team has won the match or not. The accuracies obtained are 75%, 80%, 55%, 75%, 80%, 80%, 75% and 84% for the teams CSK, RR, DD, RCB, MI, SRH, KXIP and KKR respectively.
Comparison of Adaptive, Adaptable, and Hybrid Automation for Surveillance Task Completion in a Multi-Task Environment

Increasing applications of automation in system designs raise issues on how to achieve an ideal balance of automation with human interaction for optimal operator situation awareness and performance. This experiment examined four automation control schemes applied to surveillance tasks performed in a multi-task simulation environment. Participants completed trials with four level of automation (LOA) control schemes: fixed, performance-based adaptive, adaptable (participant controls LOA), and hybrid (adaptive and adaptable). Results showed that task accuracy was better when participants controlled LOA. Participants also preferred adaptable automation.
Hierarchical classification scheme for real-time recognition of physical activities and postural transitions using smartphone inertial sensors

This paper introduces a novel approach for real-time classification of human activities using data from inertial sensors embedded in a smartphone. We propose a hierarchical classification scheme to recognize seven classes of activities including postural transitions. Its structure has three internal nodes composed of three Support Vector Machines (SVMs) classifiers, each one is associated with a set of activities. Moreover, each SVMs is fed with a feature vector from an adapted and optimal frequency band. Experimental results conducted on a challenging publicly available dataset named SBHAR show that our method is effective and outperforms various state-of-the-art approaches. We also show the suitability of our method to recognize postural transitions.
EEG-based Classification of Microsleep by Means of Feature Selection: An Application in Aviation

This paper presents a method for classification of microsleep (MS) from baseline utilizing linear and non-linear features derived from electroencephalography (EEG), which is recorded from five brain regions: frontal, central, parietal, occipital, and temporal. The EEG is acquired from sixteen commercially-rated pilots during the window of circadian low (2:00 am-6:00 am). MS events are annotated using the Driver Monitoring System and further verified using electrooculogram (EOG). A total of 55 features are extracted from EEG. A subset of these features is then selected using a wrapper-based method. The selected features are fed into a linear or quadratic discriminant analysis (LDA or QDA) classifier to automatically differentiate baseline from MS states. The overall classification performance of the best-proposed algorithm is 87.11% in terms of F1 score. This preliminary result highlights the potential of the proposed method towards automatic drowsiness detection which could assist mitigating aviation accidents in the future, pending hardware development to record such EEG signals from the confines of the aviation headset.
Operational Optimization of a Remote Off-Grid Hybrid Renewable Energy System in Northern Canada

An optimal energy management strategy for a remote, off-grid Hybrid Renewable Energy System (HRES) in Northern Canada is presented. The proposed control strategy aims at optimizing the day-ahead operational plan to achieve minimized costs and emissions. Particle Swarm Optimization (PSO) is deployed to solve the underlying optimization problem. Special attention is given to enhance the operational efficacy of the oversized diesel generators. Two dispatching strategies are evaluated under the proposed operational optimization platform. Several case studies considering different seasons are presented. The results confirm the robust performance of the proposed method in achieving the intended objectives of day-ahead energy management.
Transfer Entropy in Artificial and Cardiovascular Environment in Stress

The paper examines the influence of acute and chronic stress on the relationship between systolic blood pressure (SBP) and pulse interval (PI) recorded from laboratory rats with different genetic predispositions for the development of the hypertensive disease. Transfer entropy (TE) was used to examine the direction of information flow between SBP and PI, spontaneous baroreflex sensitivity (BRS) was used to evaluate the ability of adaptation of PI time series to changes in SBP, and the cross-approximate entropy (XApEn) to quantify the SBP-PI synchronization. The effects of the time series length on TE estimation was also investigated in an artificial environment for the time series without a strong causal relation. The consistency of the TE estimation was achieved only for extremely long time series. The results showed that chronic stress influence on the increase in information transmission between SBP and PI (TE) while changes of (BRS) and XApEn were not noticed.
Delay optimization in Resource Allocation of f-OFDM Wireless Networks with Subcarrier Aggregation

This paper presents a resource allocation scheme in wireless networks, aiming at optimizing the user data delay. The performance of the resource allocation algorithm is verified and compared with others from the literature using computational simulations in terms of Quality of Service (QoS) parameters. In these simulations, this study also considers current and candidate techniques for next-generation networks such as carrier aggregation and filtered-Orthogonal Frequency Division Multiplexing (f-OFDM).
Combining Electrodermal Activity and Speech Analysis towards a more Accurate Emotion Recognition System

Current research in the emotion recognition field is exploring the possibility of merging the information from physiological signals, behavioural data, and speech. Electrodermal activity (EDA) is amongst the main psychophysiological arousal indicators. Nonetheless, it is quite difficult to be analyzed in ecological scenarios, like, for instance, when the subject is speaking. On the other hand, speech carries relevant information of subject emotional state and its potential in the field of affective computing is still to be fully exploited. In this work, we aim at exploring the possibility of merging the information from electrodermal activity (EDA) and speech to improve the recognition of human arousal level during the pronunciation of single affective words. Unlike the majority of studies in the literature, we focus on speakers’ arousal rather than the emotion conveyed by the spoken word. Specifically, a support vector machine with recursive feature elimination strategy (SVM-RFE) is trained and tested on three datasets, i.e using the two channels (i.e., speech and EDA) separately and then jointly. The results show that the merging of EDA and speech information significantly improves the marginal classifier (+11.64%). The six selected features by the RFE procedure will be used for the development of a future multivariate model of emotions.
Adaptive Memory and Storage Fusion on Non-Volatile One-Memory System

Non-volatile memory (NVM), such as phase change memory (PCM), can be a promising candidate to replace DRAM because of its lower leakage power and higher density. Since PCM is non-volatile, it can also be used as storage to support in-place execution and reduce loading time. However, as conventional operating systems have different strategies to satisfy various constraints on memory and storage subsystems, using PCM as both memory and storage in a system requires thorough consideration on the system’s inherent constraints, such as limited lifetime, retention time requirements, and possible overheads. Most existing work still divide NVM into separated memory and storage parts, but this strategy still incurs the overhead of loading data from storage to memory as in conventional systems. In our work, we rethink the data retention time requirements for PCM memory/storage and develop an adaptive memory-storage management strategy to dynamically reconfigure the One-Memory System, with considerations of the current average write-cycle and the number of retention-time qualified frames for storage, to reduce the extra data movement between memory and storage with a limited lifetime sacrifice. Experimental results show that our adaptive design improves the performance by reducing 86.1% of the extra writes of data movement, and only 3.4% of the system’s lifetime is sacrificed.