Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Hand Movements by Surface Myoelectric Signal Using Artificial-Spiking Neural Network Model

Real-time classification of the myoelectric signal has applications in the field of neuro-rehabilitation systems such as prosthesis. The classifier which is a human-computer-interaction (HCI) controller should be ideally fast and computationally less intensive. In this work, we have done a simulation-based study to estimate the performance of a deep artificial/spiking neural network (ANN) model for classification. The model parameters are tuned for a subject to get a 93.33 % and 89.39 % classification accuracy using the ANN and SNN classifiers respectively. A comparison between the two classifiers is studied in terms of computational complexity, external noise effect and trained parameters approximation.
An Extended Genetic Algorithm Based Gene Selection Framework for Cancer Diagnosis

Existing biomedical research shows that cancers can be diagnosed by effective feature selection and classification of DNA microarray in the molecular level. DNA microarrays are well-known as high-dimensional, possibly redundant, and small sample size data, which usually consist of many thousands of features. Therefore, feature selection methods are highly demanded to reduce the complexity for classification. Various approaches are proposed to select the most effective feature subsets for the classifiers to distinguish different cancer types. However, the optimal feature selection solutions are still difficult due to the trade-off between computational efficiency and classification accuracy. Our work shows that the genetic algorithm (GA) based hybrid feature selection methods balance the efficiency and accuracy. In this study, we propose a novel hybrid feature selection framework based on an extension of GA to obtain the optimal feature subset for gene expression data classification. Experimental results show that the proposed method is efficient, and extracts the minimal number of features with highest possible classification accuracy compared to existing methods.
Joint Optimal Power Flow Routing and Decentralized Scheduling with Vehicle-to-Grid Regulation Service

The collection of electric vehicles (EVs) can be regarded as a massive storage to the power grid so as to provide vehicle-to-grid (V2G) ancillary services, such as frequency regulation. In this paper, a novel hierarchical framework for joint optimal power flow routing and decentralized scheduling with V2G regulation services is proposed. First, the optimal power flow is formulated by incorporating with power flow routers (PFRs). The problem is solved through the semidefinite programming (SDP) relaxation to pursue the optimal solution. Second, the scheduling problem with V2G regulation service is proposed as a convex optimization problem. The related decentralized algorithm is then devised in order to find the schedules of EVs. Our simulation results show that voltage regulation is effectively achieved and PFRs can help reduce the apparent power loss of the system significantly. In addition, the decentralized scheduling algorithm with V2G regulation service can smooth out the power fluctuations at the buses attached with EVs.
Mining Patterns in Housing Characteristics of Migrants

Clustering deals with the investigation of information in a collection of unlabeled dataset. It is also one of the vital technologies in analyzing information for a country’s population and housing data. As aspect of demography, human migration is one of the most issues for country development and, if managed effectively, it brings benefit to the host and destination areas. Migrants’ demographic factors are crucial parts to be considered for country’s development. This study aims to investigate housing characteristics of migrant’s households. This paper discovers patterns of different migrants’ housing features by applying clustering techniques and evaluates these patterns using validity measures and cluster visualization. This study applies five clustering methods for analyzing and obtaining an appropriate method for dataset. In addition, the study implements a web application for visualization and evaluation of obtained patterns from clustering methods so it allows users to use and compare the clustering results with ease.
Trapezoidal Voltage Mode for Bidirectional Modular Multilevel DC-DC Converter

This paper proposes a trapezoidal voltage mode for bidirectional modular multilevel DC-DC converter (MMDC), which combines the benefits of modular multilevel converter (MMC), resonant modular multilevel converter (RMMC) and dual active bridge (DAB). It is feasible for the connection between Medium Voltage Direct Current (MVDC) transmission lines and Low Voltage Direct Current (LVDC) transmission lines. With proper modulation, its current is a trapezoidal waveform, which guarantees low switching losses and conduction losses. Due to the removing of shunt capacitor, the fault produced by short circuit can be handled. With proper modulation, inherent balancing of SM capacitor is achieved. In addition, the voltage waveform of transformer is trapezoidal, so the saturation issue is moderated. To illustrate its pros and cons, this converter is compared with dual active bridge (DAB) converter through a case study (30kV to 1 kV, 1MW).
Intelligent Control of a Quadrotor with Proximal Policy Optimization Reinforcement Learning

Aerial platforms, such as quadrotors, are inherently unstable systems. Generally, the task of stabilizing the flight of a quadrotor is approached by techniques based on classic and modern control algorithms. However, recent model-free reinforcement learning algorithms have been successfully used for controlling drones. In this work we show the feasibility of applying reinforcement learning methods to optimize a stochastic control policy (during training), in order to perform the position control of the "model-free" quadrotor. This process is achieved while maintaining a good sampling efficiency, allowing fast convergence even when using computationally expensive off-the-shelf simulators for robotics and without the necessity of any additional exploration strategy. We used the Proximal Policy Optimization (PPO) algorithm to make the agent learn a reliable control policy. The experiments for the resultant intelligent controller were performed using the V-REP simulator and the Vortex physics engine.
Wearable Wrist Joint Movement Detection Using Two Sensitivity-Enhanced Plastic Optical Fibers

We demonstrate two flexible, sensitivity-enhanced plastic optical fiber (PDF) strain sensors embedded in a polyamide wrist brace for human wrist joint movement monitoring. The two sensors are, respectively, featured with a D-shaped side-polished zone of ~40 µm depth and ~30 mm length and etched periodic gratings with a pitch of 3 mm, which correspondingly exhibit around 46.8 and 17.8 times in normalized bending sensitivity by far larger than conventional commercial POF due to more sensitive strain-induced evanescent field interaction with the side-machined fiber. In terms of different detection sensitivity to bending angles, the two modified sensors are separately arranged at the back and side of the wrist and then enable to decouple wrist joint behaviors induced by flexion-extension and abduction-adduction motions. The application therefore shows a great potential for the developed sensors in wearable exercise rehabilitation of human joints.
Security-Reliability Analysis of Power Beacon-Assisted Multi-hop Relaying Networks Exploiting Fountain Codes with Hardware Imperfection

In this paper, we consider a multi-hop relaying protocol using beacon-assisted energy harvesting. In the proposed protocol, a source encodes its data by Fountain codes, and sends encoded packets to a destination via intermediate relays. An eavesdropper who appears around the destination attempts to receive the encoded packets for the data recovery. For performance evaluation, we derive exact expressions of outage probability (OP) and intercept probability (IP) over Rayleigh fading channels under impact of hardware impairments. Computer simulations are then performed to verify our derivations.
Towards a Modular, Decentralized and Digital Industry 4.0 Learning Factory

The paper presents the concept and the current state of development of a modular, decentralized and digital learning factory. In the future, the learning factory shall enable students to create and evaluate different factory layouts. For researchers, the factory shall represent an environment to develop and evaluate new concepts and technologies related to Industry 4.0. Beside the presentation of the factory modules and their setup, we present a control and communication strategy allowing the creation and operation of any preferred factory layout. In addition, we briefly describe a discrete event simulation model and an emulation model for virtual commissioning.
On Characterization of Vibration Measurement Using Microwave Doppler Radar

Doppler radar is a compact, microwave sensor for detecting vibration of the target object, from a distance. In this paper, Doppler only measurements are used to characterize the vibrational properties namely vibration amplitude and the frequency. The focus of the study is to understand the potential effect of the measurement environment on measurement quality. The investigations lead us to propose a system consisting of two single channel radars (in lieu of one quadrature radar), spatially separated both along range and azimuth directions. The proposed system introduces diversity, ensures robustness in detection while keeping the system design cost-effective in large scale deployments.
Transmit Antenna Selection by Group Combination Precoding In Massive MIMO System

In this paper, we propose a new algorithm to select transmit antennas for precoding in a Massive MIMO system. At the Base Station (BS), the antennas are divided into subgroups with smaller dimensions. Then, they are selected based on the evaluation of the corresponding channel capacities. The groups, which give the best sum capacity, are selected for precoding process. To lower the complexity of antenna selection, we adopt the Singular Value Decomposition of channel matrices to compute the channel capacities. After the antennas are selected, the transmitted signal vector is precoded by the conventional Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) precoders before being transmitted to the users. Numerical and simulation results show that the proposed transmit antenna selection algorithm has remarkably lower complexity than the optimal one, yet at the cost of small degradation in the bit error rate (BER) performance of the system.
Exactly Periodic Spatial Filter for SSVEP Based BCIs

This study introduces a novel, high accuracy, calibration less spatial filter for reliable steady-state visual evoked potential (SSVEP) extraction from noisy electroencephalogram (EEG) data. The proposed method, exactly periodic subspace decomposition (EPSD), utilises the periodic properties of the SSVEP components to achieve a robust spatial filter for SSVEP extraction. It tries to extract the SSVEP components by projecting the EEG data onto a subspace where only the target signal components are retained. The performance of the method was tested on an SSVEP dataset obtained from ten subjects and compared with common SSVEP spatial filtering and detection techniques. The results obtained from the study shows that EPSD consistently provides a significant improvement in detection performance than other SSVEP spatial filters used in brain-computer interface (BCI) applications.
Physical Layer Security of Multi-user Full-Duplex One-Way Wireless Relaying Network

This paper investigates the physical layer security of the orthogonal frequency division multiplexing based wireless relaying network. The investigation considers a multi-user untrustworthy amplify-and-forward based one-way full-duplex wireless relaying network in the system model. According to the Monte-Carlo simulation results, the friendly jammers severely affect the system achievable secrecy outage probability performance and degrade the system performance curves in high signal-to-noise ratio regimes. Results also show that the system model that contains a large number of user-pairs achieves better secrecy performance in comparison to a small number of user-pairs.
Artificial Neural Networks to Predict Regulation between Smad7 and miRNA on Breast Cancer

Artificial neural networks (ANN) are presented to predict the regulation between Smad7 protein and micro ribonucleic acid (miRNA) expression. The protein regulation process is given by the miRNA union in ribonucleic acid messenger (mRNA) after the transcription process blocking protein translation or degrading the mRNA. Smad7 protein is an inhibitor of the transforming growth factor beta (TGF-β) signaling pathway. In search of an ANN that better fits that regulation relation, multiple ANN configurations were developed using Levenberg-Marquardt, Bayesian Regularization y Resilient Propagation algorithms for training purposes. The obtained ANN were able to fit biological information from real cancer databases, and their results show that miRNA expression regulates negatively in Smad7 protein expression.
Live Demonstration: Singing Photo Frame

Audio speakers have become an integral part of our life. They are essential components in mobile phones, music players, televisions, laptops and medical devices like hearing aids. Traditional speakers are voice coil based, and hence quite bulky. With advancements in MEMS (Micro-Electro-Mechanical-Systems) technology, though long after MEMS microphones, accelerometers and numerous types of other MEMS sensors and actuators, speakers have joined the race towards miniaturization.
Task-space Kinematic Control of a Quadruped Robot with a Floating Base

As a direct consequence of the use of more actuators than those needed to perform a specific task, a robot becomes a highly redundant system which complicates the solution of the inverse kinematics. This work considers a task-space kinematic control using a weighted quadratic optimization solver, which allows for whole-body motion control fully exploiting all the movements that a highly redundant robot can achieve. This represents a useful tool for legged robots in order to display more natural and stable movements. At the cost of higher computational power requirements, the proposed optimization problem is a fast, precise and stable solution to the robot’s kinematic redundancy. Compared to other methods such as the pseudo-inverse solution, the weighted inverse kinematics solver is capable of imposing hard constraints, guarantees the avoidance of kinematic singularities in most cases, and allows to solve multiple tasks at the same time. This work presents the simulation and implementation of the proposed model.
Random Node Recommend Algorithm for Influence Maximization in Social Network

When promotion, for advertisers, a key issue is how to choose a small subset of influential users which is called seed set in social network that can maximize the spread of influence. This problem is considered as “influence maximization”. In order to find out the seed set quickly and effectively at the lowest cost and make the influence maximization, a new concept named Moment of Influence (MoI) to estimate node influence and a hybrid heuristic algorithm called Random Node Recommend (RNR) to select seeds were proposed. We use SIR model to evaluate the performance of RNR in two real social networks. The experimental results show that MoI can better distinguish the spreading ability of nodes and RNR the proposed algorithm performance effectively.
Image Processing for Automated Welding Robot: Reducing Interference due to Fume in Camera Lenses

Welding is crucial for several industrial sectors. The problems associated with this laborious work and the worker's health are known for a long time. However, the welding is still highly dependent of a skilled human workforce. Researchers presented solutions to automate this process, many of them using robots equipped with cameras. Nevertheless, the fumes produced by the arc welding process adhere to the camera lens hinder the robot's perception. The fume is composed of by-products from melting metal becoming suspended in the air and adhering to surrounding surfaces. Thus, one of the main issues to be solved in the robotized welding with cameras is to deal with the fume adhered the camera lenses. In this paper, we propose a method to minimize the interference of the fumes present in the camera lens based on a fusion of image processing methods. The results shows the method is able to improve the groove detection allowing a better welding.
Design Philosophy for Optimizing Genetic Algorithms Through Embedded Intelligence

Traditionally Genetic algorithms are thought of as brute force approaches, aimed to arrive at solutions to problems which do not have a specific answer. In problems where the data is not structured for the general implementation of a specific idea, genetic algorithms are most useful. This paper proposes to mitigate the above problem of brute force approaches through elucidation of procedures ranging from exploratory analysis, followed by pattern analysis and classification. This novel conceptualization of the scheme and design will help in arriving at solutions through reduced iterations. Research conducted involves dropping of poorly performing hypotheses, controlled mutation, thereby adding a dimension of intelligence to evolutionary algorithms. The following paper describes the methodology used to solve the problem of addition of numbers using evolutionary algorithms of Neural Networks, whilst building intelligence into the system. The specific problem of addition has been dealt with in the following paper, however the same design philosophy can be extended for a paraphernalia of problems. The end goal is to obtain a generation of adroit and capable hypotheses to solve the problem in reduced number of iterations. The solution provided is generic and can be reused, it has been applied to a specific problem in the following paper.
Linear Bayesian Filter Based Low-Cost UWB Systems for Indoor Mobile Robot Localization

In this paper, we propose an improved UWB based indoor localization system using Bayesian filtering techniques. The system contains two key components: (1) miniaturized, high updating rate and highly reconfigurable UWB sensors with a linear regression model to calibrate range measurement errors; (2) a set of Bayesian filters which can improve the localization precision by utilizing the spatial correlation between the stationary UWB base stations and the mobile UWB station. Furthermore, a novel measurement transform is proposed to reduce the computational complexity. Experiments are performed in an indoor environment with the ground truth obtained by the motion capture system to validate and evaluate the proposed indoor localization system.
Malware Family Characterization with Recurrent Neural Network and GHSOM Using System Calls

Nowadays, a massive amount of sensitive data which are accessible and connected through personal computers and cloud services attract hackers to develop malicious software (malware) to steal them. Owing to the success of deep learning on image and language recognition, researchers direct security systems to analyze and identify malware with deep learning approaches. This paper addresses the problem of analyzing and identifying complex and unstructured malware behaviors by proposing a framework of combining unsupervised and supervised learning algorithms with a novel sequence-aware encoding method. Particularly, a hybrid GHSOM (the Growing Hierarchical Self-Organizing Map) algorithm is proposed to cluster and encode similar malware behavior sequences from system call sequences to clustering feature vectors. Then, a Recurrent Neural Network (RNN) is trained to detect malware and predict their corresponding malware families based on the sequence of the behavior vectors. Our experiments show that the accuracy rate can be up to 0.98 in malware detection and 0.719 in malware classification of an 18-category malware dataset.
Ceramic Evidence for Northern Intruders in Southern Greece at the Beginning of the Late Helladic IIIC Period

Three whole pots and sherds of thirteen other vases from early Late Helladic IIIC contexts at the site of Korakou are identified as non-Mycenaean on the grounds of fabric, shape, decoration, and surface treatment. It is argued that, although being alien to Mycenaean ceramics, these pieces were nevertheless manufactured locally. They are thus taken to be evidence of an intrusive population element whose ultimate origin is to be sought in Bulgaria or possibly Rumania, where the intrusive pottery has its closest published affinities in material of earlier date. Discoveries of comparable non-Mycenaean pottery in roughly contemporary contexts at the other southern Greek sites of Lefkandi, Perati, Athens, and Mycenae suggest that the presence of northern intruders at Korakou may be more than a local phenomenon. The close correspondence between certain classes of the intrusive Korakou pottery and particular wares found at Troy only in the VIIb 1 and 2 settlements points towards a parallel situation at Troy where one or more new population elements probably settled following the destruction of Troy VIIa. Connection of the intruders with disturbances in southern Greece at the end of LH IIIB and with the destruction of Troy VIIa is possible, but far from certain. No reliable estimate of the numbers of the intruders can be made on the basis of evidence at present available.
Sequence-Based Bit-Cycling in Double Layer Message Security

This paper proposes two different implementations of a double-layer message security scheme. In both schemes, the first layer is of a cryptographic nature, employing the AES-256, while the second layer is of a steganographic nature, employing a number of mathematical sequences that determine the color-plane cycling for least significant bit (LSB) embedding. Arithmetic and geometric sequences are employed for the first and the second schemes, respectively, to determine the location of embedding, whereas the specific color plane where the embedding occurs in the RGB pixel is based on a derived form of the Rudin-Shapiro sequence. Performance is measured in terms of the mean squared error (MSE), the peak signal to noise ratio (PSNR), histogram analysis and entropy values. Numerical results show that the proposed techniques are superior to some of the recurring approaches in the literature.
Sparker: Optimizing Spark for Heterogeneous Clusters

Spark is an in-memory big data analytics framework which has replaced Hadoop as the de facto standard for processing big data in cloud platforms. These frameworks run on cloud platforms where heterogeneity is a common scenario. Heterogeneity gets introduced due to the failure, addition or upgradation of nodes in the cloud platforms. It can arise from various factors such as variation in the number of CPU cores, amount of memory, disk read/write latencies across the nodes, etc. These factors have a significant impact on the performance of Spark jobs. Spark supports execution of a job on equal-sized executors which can result in under allocation of resources in a heterogeneous cluster. Insufficient resources can severely degrade the performance of CPU and memory intensive applications like machine learning, graph processing, etc. Existing techniques use equal-sized executors which can degrade the performance of jobs in heterogeneous environments. In this paper, we propose Sparker, an efficient resource-aware optimization strategy for Spark in heterogeneous clusters. It overcomes the limitation of heterogeneity in terms of CPU and memory resources by modifying the size of the executor. The executors are re-sized based on the available resources of the node. We have modified Spark source code to incorporate executor re-sizing strategy. Experimental evaluation on SparkBench benchmark shows that our approach achieves a reduction of up to 46% in execution time.