Miracle-dz/UniFAR-FTUs huggingface dataset:
EEG-SSVEP based Brain Machine Interface for Controlling of a Wheelchair and Home Appliances with Bluetooth Localization System

Performing motor activities including activities of daily living (ADL) on their own is challenging for individuals who have lost their motor functionalities due to various conditions such as spinal cord injuries, cerebral palsy, spinal muscle atrophy etc. Their mobility is also restricted depending on the severity of the disability. This paper proposes an Electroencephalography (EEG) signals based brain machine interface (BMI) for a wheelchair along with the facility to control various home appliances depending on the location of the user. The proposed BMI uses Steady State Visual Evoked Potentials (SSVEP) of EEG signals to identify the user intentions and recognized intentions are used to control the wheelchair (by wheelchair controller) or operate different home appliance connected to the system (by home appliance controller) based on the location information estimated by a Bluetooth localization beacon system. A set of experiments were carried out with healthy subjects to validate the proposed system and the results yielded that the proposed BMI system has the ability to effectively control the wheelchair as well as different home appliances connected to system using only less number of user intention signals.
Use of Big Data Analytics in WASH Sector

WASH (Water, Sanitation, Hygiene) plays vital role in the life style of human beings. It is an important public health issue to attain the sustainable development goals. To make WASH sector more effective, data from different sources like Water Point Mapping(WPM), Flow Monitoring and Leak Detection (FMLD), mWater, mWash, etc. should be collected and big data analytical tools like Apache Hadoop, Apache Spark should be implemented to store, manipulate and get results. Big data analytics process the huge amount of unstructured data in parallel and in distributed computing environment as a cluster of computers. After successful implementation of big data analytics in WASH sector, Monitoring and visualization of system, data for equity and sustainability, post implementation monitoring, Open data and accountability and Social Accountability can be executed for the betterment of this sector.
An Accelerated MMP With a Pruning Tree Strategy

Multipath matching pursuit (MMP) has been developed to solve the sparse signal recovery problems in compressed sensing, which can generate recovery error less than the traditional orthogonal matching pursuit type algorithms in terms of mean square error. However, the computational burden of MMP is seriously heavy, and limits itself in applications, finding the need to be improved urgently. To lighten the burden, in this paper, an accelerated version of the MMP is proposed based on pruning tree strategy for sparse signal recovery, that attempts to reduce some of the paths in subsequent searching. By this strategy, an accelerated MMP is established in the scenarios where the MMP works. It retains the desirable performance as MMP and can shorten the running time. We demonstrate the acceleration ability of the proposed algorithm with almost the same accuracy of the original MMP, for the reconstruction of both synthetic data and images.
Modeling Effect of Grain Orientation on Degradation in Tin-Based Solder: I. Current-Driven Diffusion

In this and subsequent papers, we consider the effects of the microstructure, in particular grain orientations, and the associated anisotropy of Sn grains on electromigration-induced degradation in lead-free solder bumps. This paper investigates the effect of the anisotropy of Sn grains on current-driven self-diffusion and the resulting atomic flux divergence that is associated with material depletion at the cathode side of the bump and ultimately failure. Finite-element thermoelectric simulations of a section of a chip scale package and a refined submodel are carried out assuming single-crystal and bi-crystal solder bumps. Anisotropic material properties of $\beta $ -Sn are used in the simulations. By varying crystal orientation of the solder bumps, the effect of grain orientation on the current density, temperature, and atomic flux divergence is studied and discussed. In the future papers, we will consider the coupled mechanical response including stress-driven diffusion.
Visual Attention-Aware Omnidirectional Video Streaming Using Optimal Tiles for Virtual Reality

Owing to its interactive look around nature and very large resolution requirement, providing immersive omnidirectional video (ODV) streaming experiences in virtual reality applications demands cost-effective solutions to meet both the content delivery network and device constraints. In this paper, we introduce an adaptive ODV streaming pipeline that optimizes dynamic adaptive streaming over HTTP representations of ODV content considering their visual attention (VA) maps. The main contribution of this paper is the use of VA maps: 1) to compute a novel objective quality metric that captures the fact that not all of the ODV is actually watched by users: the VA spherical weighted-based objective quality measurement; 2) to define optimal tile representations of the ODV frames, namely, tiling schemes, which are composed of variable-sized and non-overlapping tiles; and 3) to efficiently distribute a given bitrate budget among the set of tiles within a tiling scheme for an ODV. We evaluate the proposed system performance with varying bandwidth conditions and the tracked head orientations from user experiments. Results show that the proposed system significantly outperforms the existing non-tiled and tile-based streaming solutions.
Dual Redundancy Design of Brushless DC Motor for UAV Steering Gear

To improve the reliability of VAV steering gears to meet the requirements of modern VAV for all-weather operations, a redundancy control system, which takes the brushless DC motor as an example, is proposed in this paper. Several failures such as the power inverter circuit failure, hall position sensor failure, and stator armature winding failure, which usually occur in the traditional single steering gear circuit, are investigated respectively. The fault detection method of the above three failures is discussed in this paper. Then, based on the working principle of single-redundancy brushless DC motor, a control system has been designed, which uses the three-closed-loop control strategy of position, speed, and electric current. The experiments s that the control system can achieve redundancy management, and the performance of single and double redundancy control is basically no difference. Experiment shows the control system can respond to the signal quickly and effectively, and it can obviously improve the reliability of VAV steering system.
Entity Matching Using Different Level Similarity for Different Attributes

Entity matching (EM) recognizes records in one or different databases that represent the same entity in real-world. It is an essential part of data cleaning and data integration. Most existing studies determine whether two records match by calculating the similarity of the corresponding attribute values in the two records. Then series of similarity metrics are proposed. But according to our investigation, we found that no one has considered combining semantic level similarity with string level similarity. In some data tables, some attributes values are long text, such as product descriptions, and they are more semantically similar in different records that refer to a same entity. Other numeric or noun attributes, such as price and person name, are more suitable for string level similarity calculations. Therefore, we propose a model to calculate the similarity of the two types of attributes using modules of different similarity levels, and assign them different weights. Learning by labeled data, we get a model that can effectively solve the entity matching task.
Deduplication Triggered Compaction for LSM-Tree Based Key-Value Store

Key-value store is a type of NoSQL database providing much higher read and write throughput than traditional SQL databases. LSM-tree is a widely used data structure to improve the performance of small write in KV Stores. But its compaction induce to large write amplification. So tiered LSM-tree, which divides each level in LSM-tree implementation into multiple sub-levels to reduce the write amplification. Nowadays, the KV items are frequently updated in many applications, which induces too many stale copies of the same KV items in KV stores, i.e., space amplification. To achieve both low write amplification and low space amplification at the same time, we propose a novel compaction algorithm for tiered LSM-tree and we implemented it on FlameDB, a key-value store. Our experiments show that FlameDB achieves almost the same low space amplification with LevelDB and the comparably low write amplification with LSM-trie.
Automatic Classifier Based Reversible Image Transformation for Reversible Data Hiding in Encrypted Images

Reversible data hiding in encrypted image is one of the method to protect data in cloud network. This article proposes data hiding algorithm that use reversible data hiding in encrypted images (RDH-EI) and reversible image transformation. It is easy to understand that the encrypted image in cipher format contains some information. But in reversible image transformation, the content of original image is transformed to another transformed image that looks similar to target image. Usually a particular percentage of blocks are classified as two classes for image transformation. In proposed method a classifier is introduced that classifies the pixels into two classes using Otsu's threshold for image transformation. This transformed image acts as the encrypted image. This transformed image is uploaded to the cloud. In the cloud network, data can be embedded to the encrypted image (transformed image) using a reversible data hiding approach. Therefore the data embedded at the cloud network does not depend on the reversible image transformation at the encoder. Similarly the data extraction process does not depend on the inverse reversible image transformation at the decoder. The performance of the proposed reversible image transformation is measured by using metrics like MSE, PSNR and Correlation Coefficient. The performance and quality shows that proposed system is better than the conventional reversible data hiding method that uses reversible image transformation.
A Neuromorphic Approach to Tactile Texture Recognition

In the recent years, tactile sensing has attracted great interest of the robotics community, specially in high end applications such as prosthesis and exploration of unstructured environments. Tactile sensing in both humans and robots can help to estimate surface properties like temperature, texture and hardness. These properties are important to perceive the environment as well as for object recognition and robotic manipulation of objects. In this paper, we focus on the classification of textures using a neuromorphic tactile sensor. The sensor uses a piezoresistive fabric sandwiched between conductive traces and encodes only changes in pressure intensity in the form of spike events. Textures are classified based on their geometry and the spatial frequencies. To do so, high-contrast tactile images are created by combining event data obtained across a palpation sequence and rotation-invariant features are used for classification using support vector machines (SVM). Our best results show average classification accuracy of 98% across different textures and palpation done in multiple directions.
Design and Implementation of Adaptive Multi-Tap Analog Interference Canceller

Due to the severity of self-interferences, analog interference canceller is an essential part of a full duplex radio system. As the self-interference can potentially be even much stronger than the received signal, it must be meritoriously suppressed before analog-to-digital conversion. This paper is aimed to provide an optimal radio frequency domain multi-tap interference cancellation design. To this end, the following two key problems are addressed: the hardware design problem and the adaptive weight adjusting problem. For optimal hardware design, a trade-off between widening effective delay range and maximizing the minimum cancellation capability is formulated. The principles and guidelines for optimal solution and hardware parameter design are derived. We also formulate adaptive weight adjusting as a multi-dimensional optimization problem, which is proved to be non-convex while adjusting the attenuators and phase shifters independently. By using a coordinate transformation technique, it is then converted into a convex optimization problem, and a convergent iterative algorithm based on the gradient descent method is applied to obtain the global optimum. The hardware implementation and experimental results substantiated the effectiveness of the proposed principles and algorithms.
Efficient and Fast Copy Move Image Forgery Detection Technique

Nowadays, it is very easy to manipulate digital images in a skilful manner. Image processing systems are creating rapid pace and utilization of these strategies is likewise expanding day by day. Therefore, picture verification has turned out to be very difficult task due to the misuse of these systems. Copy-move forgery is the most widely recognized picture altering method in which some part of a picture is replicated and is stuck on other part of the same picture to hide or replicate some portion of image. Hence, police force and forensic expert require furthermore, productive method for recognizing duplicate move fabrication. In this paper a passive method is used to detect copy move forgery detection. Firstly forged image is divided into overlapping blocks followed by extraction of features using KPCA (kernel principal component analysis) then lexicographically sort these features. By indentifying the similar block in an image replicated image blocks are discovered. Experiment outcome exhibit that the planned strategy can precisely find the copied areas, even when images are crooked by translation including rotation by limited angels, shading diminishment, and changes of brightness. The proposed method gives good result by increasing precision, accuracy, and specificity and lesser degree of false detection and time required for detecting copy move forgery than original method which uses the histogram of gradient(HOG) technique for feature extraction.
Low Power and Low Noise Instrumentation Amplifier

The instrumentation amplifier (IA) is one of the most important block in ECG recording system. In this paper, an IA is presented that achieves low power dissipation and low noise. To reduce the effect of noise while stabilizing the output DC level, a chopper stabilized technique has been implemented. A low power operational transconductance amplifer (OTA) has been used in the designing of OTA. The proposed IA operates on 1.8 V supply. The proposed IA has been implemented in CADENCE 180 nm CMOS technology.
Low-Complexity State-Space-Based System Identification and Controller Auto-Tuning Method for Multi-Phase DC–DC Converters

The importance of online system identification (SI) in power electronics is ever increasing. It enables the tracking of system parameters, which in turn can be used for online controller tuning. Hence, SI is a key element for improving a converter's dynamic performance, stability, reliability. In this paper, a novel state-space-based SI approach utilizing the step-adaptive approximate least squares estimation algorithm with observation matrix randomization is proposed. The presented concept yields an accurate state-space model of the converter while simultaneously achieving a fast convergence rate and low computational complexity. Consequently, the estimated state-space model is utilized to automatically tune a full state feedback controller. This results in an improved converter performance in terms of overshoots, undershoots, and settling times. The proposed concept is verified by a prototype system comprising a two-phase buck converter and a field-programmable gate array. The provided measurement results highlight the effectiveness and benefits of the presented method over the state-of-the-art algorithms, as well as $z$-domain estimation. It is shown that the number of required estimation iterations is more than halved in comparison with the state-of-the-art parametric SI approaches, while accuracy is improved.
The Control System for Flexible Hip Assistive Exoskeleton

A novel wearable hip assistive exoskeleton is developed to provide comfortable and effective walking assistance. In this paper, we propose an alternate flexible joint control method, direct torque control, to reduce intrinsic mechanical impedance for fully compliant human-exoskeleton interaction. A new walking intention estimation approach based on gait synchronizer, which is just driven by hip angular velocity, is put forward. We utilize gait synchronizer to generate, recognize, synchronize and segment the actual walking gait, thus providing an indirect intention perceiving way. Besides, assistive hip torque trajectory is optimized based on the nominal biological hip moment. The mapping relationship between status and assistive torque trajectory is determined through lots of experiments. At last, the proposed control system has been implemented on the actual Flexible Hip Assistive Exoskeleton. And the results have confirmed the effectiveness of walking assistance, indicating up to 16.7% metabolic consumption reduction during walking.
Anti-Dust: A Method for Identifying and Preventing Blockchain’s Dust Attacks

As a global decentralized distributed ledger, Bitcoin’s POW mechanism is so complex that the transaction’s confirmation is very time-consuming. If the dust attacks occur in the blockchain, which sends large amounts of dust transactions to the blockchain, the time of transactions’ confirmation will be more longer, which will lead to more security problems, such as double-spending or blockchain forks. In order to identify and prevent dust attacks, this paper proposes an innovative Anti-Dust security model, which analyzes the characteristics of dust attacks and learns from the historical transactions big data, uses the Gaussian distribution to construct a dust recognition algorithm. At the same time, a dust transaction pool will be added to improve the blockchain architecture. In the experiment, a large amount of dust transactions will be send to the blockchain. Taking 50000 dust attacks and 500 normal transactions as an example, the conformation time of blockchain without Anti-Dust is about 20000 seconds, and Anti-Dust is about 210 seconds. The results show that Anti-Dust is an effective defense method to identify and prevent the dust attacks.
Epileptic Seizure Detection Based on Local Mean Decomposition and Dictionary Pair Learning

The automatic detection of epileptic Electroencephalogram (EEG) signals is of great significance in alleviating the pressure of neurologists and the clinical therapy of patients. In this paper, a novel method combining the local mean decomposition (LMD) and the projective dictionary pair learning (DPL) is proposed for seizure detection. The LMD is employed to decompose the EEG signals into a set of product functions, and the first three product functions are selected to extract three features to characterize the behavior of EEG recordings. After that, the DPL model can learn a synthesis dictionary and an analysis dictionary from the training samples, and then encode the test samples over the learned dictionary. At last, the test samples are categorized by checking whose class has the minimum reconstructed residual. The proposed model is evaluated on the Freiburg database, achieving the sensitivity of 95.89% with the specificity of 95.10%. The experimental results suggest that the proposed method can become a potential approach for detecting seizures in clinic application.
Design of Multi-DSP Digital Signal Processing System

This paper mainly aims at the realization of the target feature extraction algorithm based on DSP platform, introduces the structure features and advantages of TigerSHARC TS201, and uses DSP parallel structure to process and extract the data. The overall framework of the processing system is designed, and the functions of the processing system and the data flow of each chip are introduced.
A Pornographic Image and Video Filtering Application Using Optimized Nudity Recognition and Detection Algorithm

The combination of multimedia technology and Internet provides an amiss channel for pornographic contents accessible by certain sensitive groups of people. Furthermore, the same channel provides the easiest medium to distribute illicit images and videos without an autonomous content supervision process. In this study, an application was developed grounded from a pixel-based approach and a skin tone detection filter to identify images and videos with a large skin color count and considered as pornographic in nature. With nudity detection algorithm as the foundation of the system, all multimedia files were preprocessed, segmented, and filtered to analyze skin-colored pixels by processing in YCbCr space and then classifying it as skin or non-skin pixels. Afterwards, the percentage of skin pixels relative to the size of the frames is calculated to be part of the mean baseline for nudity and non-nudity materials. Lastly, the application classifies the files as nude or not, and then filter it. The application was evaluated by supplying a dataset of 1,239 multimedia files (Images = 986; Videos = 253) collected from the Web. On the final testing set, the application obtained a precision of 90.33% and accuracy of 80.23% using the supplied dataset.
A Light-Weight Wheel-Based Cable Inspection Climbing Robot: From Simulation to Reality

A new light-weight wheel-based cable inspection climbing robot (WCR) is proposed in this paper, which targets on climbing and inspecting cable surface of suspension bridges. Meanwhile, a virtual prototype designed with a simulation software V-REP is used to guide and test the design of the physical prototype. WCR's body is a ring consists of three similar modules. Each module includes an arc hoop connected to a damper by a spring. Every damper contains two wheels for the free movement of WCR on cables. Compared to heavy and complex climbing robots carrying various inspection equipment, WCR weighs only 849 grams and can move on cables with a theoretical maximum speed of 0.2 m/s under remote control with three cameras on three modules to obtain images of the whole cable's surface. In addition, its simple structure with easy assembly promotes mass production and widespread application. Lab experimental results show that the physical prototype designed based on the virtual prototype can move freely on different slant cables.
An Uncalibrated Robotic Weld Tracking System

To adapt to the operational conditions with a large workpiece scale and changeable working locations in the field of ship welding, a novel uncalibrated robotic weld seam tracking system is proposed in this paper. Unlike a traditional seam tracking system requiring calibration and extraction of the weld seam feature, we directly utilize the luminance of all pixels in the weld seam image as a visual feature, without a robot hand-eye calibration, image processing, or any learning steps. The experiment results prove that our system is suitable for application to not only for a lap weld seam, but also for different types of weld seams without any changes to the algorithm, except for the corresponding desired image of the weld seams.