Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting upper limb compensation during prehension tasks in tetraplegic spinal cord injured patients using a single wearable sensor

Upper limb (UL) compensation is a common strategy of patients with a high spinal cord injury (SCI), i.e., tetraplegic patients, to perform activities of daily living (ADLs) despite their sensorimotor deficits. Currently, an objective and sensitive tool to assess UL compensation, which is applicable in the clinical routine and in the daily life of patients, is missing. In this work, we propose a metric to quantify this compensation using a single inertial measurement unit (IMU). The spread of forearm pitch angles of an IMU attached to the wrist of 17 SCI patients and 18 healthy controls performing six prehension tasks of the graded redefined assessment of strength, sensibility and prehension (GRASSP) was extracted. Using the spread of the forearm pitch angles, a classification of UL compensation was possible with very good to excellent accuracies in all six different prehension tasks. Furthermore, the spread of forearm pitch angles correlated moderately to very strongly with qualitative and quantitative GRASSP prehension scores and the task duration. Therefore, we conclude that our proposed method has a high potential to classify compensation accurately and objectively and might be used to quantify the degree of UL compensation in ADLs. Thus, this method could be implemented in clinical trials investigating the effectiveness of interventions targeting UL functions.
Detection of Digital Image Forgery using Fast Fourier Transform and Local Features

Multimedia security is one of the key challenges in today’s world, as dependency on multimedia information is increasing day by day. Easily available image editing software have enabled every common user of a smart phone and computer, to hack into the information of the images and video and alter it to some extent. To authenticate the genuineness of images, detection of image tempering is need of the time. Various techniques have been proposed to use image features for detection of image forgery. The techniques of forgery detection work in two domains of image forgery; copy-move forgery detection (CMFD) and image splicing detection (ISD). This paper presents a comprehensive comparative analysis for the use of local texture descriptors i.e. local binary pattern (LBP) and local ternary pattern (LTP) for forgery detection in an image. The paper also presents a technique to integrate fast fourier transform (FFT) with local texture descriptors for image forgery detection using existing block-based methodology. Performance of the technique(s) and descriptor(s) is tested for benchmarked dataset CASIA v1.0. Results are evaluated by using standard detection metrics detection accuracy and recall. The paper also suggests a relatively better texture descriptor.
Multi-scale Convolution and Feature-weighting Network for Radar Target Recognition

Target recognition is one of the most significant applications of synthetic aperture radar (SAR). However, satisfactory results are impractical to achieve by human effort alone due to the continual and rapid growth of the quantity of radar data. In view of the great success of convolutional neural networks (CNNs) in optical image classification tasks, in this paper, we apply a modified CNN to improve the classification accuracy. Instead of simply stacking several convolutional, pooling and activation layers to build a structure, a module that groups three different forms of convolution is designed to improve the feature extraction ability. Considering the complexity of SAR image composition, targets are not adequately described with single-scale feature maps. In addition, to utilize the correlation of features, an extra module is designed to measure the weights of the features and preprocess the input of the next stage. Experiments are performed on a moving and stationary target acquisition and recognition dataset. The proposed method achieves an average accuracy of 98% and a maximum accuracy of 99.67%, which demonstrates its efficiency compared with existing methods.
Knowledge Management Technology, Knowledge Sharing and Learning - A Case Study

Modern day organizations focus on managing their intellectual capital as they have realized its potential in improving competitive advantage. Information technology (IT) companies, in particular, have implemented knowledge management systems in a bid to enhance productivity and foster innovation. This research work analyses the influence of knowledge management technology on the sharing of knowledge and learning. The study is carried out in the context of large Indian IT companies. A questionnaire survey was conducted among the top six Indian IT companies and data is analyzed using Partial Least Square - Structural Equation Modeling method. The results indicate that knowledge management technology positively impacts sharing and indirectly influences learning.
SNAP: A 1.67 — 21.55TOPS/W Sparse Neural Acceleration Processor for Unstructured Sparse Deep Neural Network Inference in 16nm CMOS

A Sparse Neural Acceleration Processor (SNAP) is designed to exploit unstructured sparsity in deep neural networks (DNNs). SNAP uses parallel associative search to discover input pairs to maintain an average 75% hardware utilization. SNAP’s two-level partial sum reduce eliminates access contention and cuts the writeback traffic by $22\times$. Through diagonal and row configurations of PE arrays, SNAP supports any CONV and FC layers. A 2.4mm2 16 nm SNAP test chip is measured to achieve a peak effectual efficiency of 21.55TOPS/W (16b) at 0.55V and 260MHz for CONV layers with 10% weight and activation density. Operating on pruned ResNet-50, SNAP achieves 90.98fps at 0.80V and 480MHz, dissipating 348mW.
Multi-class time series classification of EEG signals with Recurrent Neural Networks

Electroencephalogram (EEG) is one of the electrophysiological tests commonly used to record electrochemical reactions in neural network. In this process various electrodes are connected in 10-20 pattern in different points in brain, the acquisition of brain activities takes place with 16 channels or 32 channels, etc., Each channel records the information of electrode connection region as one dimensional (1D) signals. It is very important to interpret this 1D signals and classify different activities of brain for various diagnostic purpose. In this paper, different Deep learning algorithms for multiclass, time series classification of different electrical activities in brain are carried out. A comparative study between simple Recurrent Neural Network (simple RNNs), Long-Short Term Memory (LSTM) and Gated recurrent Units(GRUs) is tried out for EEG signals acquired from people having different pathological and physiological brain states .
A Novel Adaptive Focusing for Wireless Energy Transfer to Mobile Devices in Complex Environment

A novel adaptive focusing method for wireless power transfer (WPT) in complex multipath propagation environment of mobile electronic devices, based on the time-reversed electromagnetics and the principle of reciprocity, has been proposed in this paper to adaptively enhance the intensity of the field on the surface of receiving antenna. The simulation research on the electromagnetic induction WPT system, aiming at solving the difficulties of WPT to different types of mobile devices in public places and avoiding the electromagnetic pollution to the environment in the process of WPT, is based on the electromagnetic simulation software of HFSS. A seven-cell circular microstrip antenna array is designed as an external energy transmitting antenna and a high-isolation compact all-directional dual polarization slot antenna is designed as the receiving antenna. An adaptive focusing feed method based on time-reversed electromagnetic is used to automatically focus the electromagnetic wave energy at the position of the receiving antenna, which improves the energy transmission efficiency effectively.
Design of Ultra-miniaturised Frequency Selective Surfaces Based on 2.5-Dimensional Closed Loop

A novel approach for designing ultra-miniaturised frequency selective surfaces (UMFSSs) based on 2.5-dimensional closed loop (2.5DCL) is proposed. Segregated by a substrate and connected by metallic vias, two sets of parallel metallic lines are arranged compactly along two orthogonal directions. The proposed approach extends the total length of 2.5DCL efficiently, thus the unit cell can achieve a superior miniaturization. Single-band and dual-band FSSs are designed and simulated to verify this method. The single-band FSS operates at 1.98GHz with a unit cell size of merely 0. 029λ0×0. 029λ0, and element size of the dual-band FSS is about 0. 036λ0. Both the proposed FSSs provide stable frequency responses under various incident angles and polarisations.
A Novel High Efficiency Interleaved Flyback Inverter for Smart Home Applications

This paper presents a novel soft switching interleaved flyback inverter utilized in Photovoltaic (PV) AC module systems for smart home applications. The soft switching for turn-on and turn-off of the primary switches is provided by adding an auxiliary branch to the conventional interleaved flyback inverter. The auxiliary branch is simple and only one switch for both flyback converters is enough. In addition, the auxiliary switch is turned on and turned off at soft switching condition. Hence, the efficiency can be improved and high switching frequency and compact design can be achieved. Additionally, the resonant auxiliary cell will be only actuated at short period of time, which significantly reduces its conduction losses. The steady state operation of the proposed interleaved flyback inverter and its auxiliary branch are discussed. Finally, the performance of the introduced inverter is verified by simulation results.
Co-Ex: A Torque-Controllable Lower Body Exoskeleton for Dependable Human-Robot Co-existence

In this paper, we present our research study concerning the design and development of an exoskeleton that aims to provide 3D walking support with minimum number of actuators. Following a prior simulation study, the joint configuration was primarily determined. In order for the exoskeleton to possess advanced characteristics, the following design criteria were investigated: i) all the actuators (hip/knee/ankle) were deployed around the waist area to decrease leg weight and improve wearability, ii) custom-built series elastic actuators were used to power system for high fidelity torque-controllability, iii) 3D walking support is potentially enabled with reduced power requirements. As a result, we built the first actual prototype to experimentally verify the aforementioned design specifications. Furthermore, the preliminary torque control experiments indicated the viability of torque control.
Omnidirectional Dual-Band Dual Circularly Polarized Microstrip Antenna with Wide Axial-Ratio Beamwidth

A dual-band dual circularly polarized (CP) omnidirectional antenna is proposed in this paper. Antennas are based on square patches and curved branches. CP antenna is a simple single-layer structure, which generates horizontal and vertical polarization components through patch and shorted probes. Antennas produce left-hand circularly polarized (LHCP) and right-hand circularly polarized (RHCP) in low and high frequency bands, respectively. On the H-plane, the simulated gain of low frequency and high frequency is 1.63 dBi and 0.95 dBi, respectively. The antenna operates at 1.83 GHz and 3.5 GHz and exhibits the 3 dB axial ratio beamwidth of more than 120 degrees in the E-plane at both resonant bands.
Amalgamating the Service Quality Aspect in Supply Chain Management

World is progressing in all spheres of life and its developments be it technological Social or economic development. All development especially if we consider the economic development of our country it mainly relies on the growth of service sector, agriculture and manufacturing. The area of service sector is broadening very rapidly as in day by day hence it’s essential to maintain the economic developments of country. It is too simple to determine loss of poor sale value, but it emerges to be difficult to calculate this loss due to poor service aspect. The confidence of customers is developed more and more if the service of the firm is better. The main theme of the paper is to discuss the aspect of service of a product in firm and give a fundamental review. Three level stages of aspect of service in supply chain management has studied upon and dependency has entrenched between service aspect, supply chain management and aspect of service in SCM.
Sparse Identification Of Motor Learning Using Proxy Process Models*

Enhanced neurorehabilitation using robotic and virtual-reality technologies requires a computational framework that can readily assess the time course of motor learning in order to recommend optimal training conditions. Error-feedback plays an important role in the acquisition of motor skills for goal-directed movements by facilitating the learning of internal models. In this study, we investigated changes in movement errors during sparse and intermittent “catch” (no-vision) trials, which served as a “proxy” of the underlying process of internal model formations. We trained 15 healthy subjects to reach for visual targets under eight distinct visuomotor distortions, and we removed visual feedback (novision) intermittently. We tested their learning data from novision trials against our so-called proxy process models, which assumed linear, affine, and second-order model structures. In order to handle sparse (no-vision) observations, we allowed the proxy process models to either update trial-to-trial, predicting across voids of sparse samples or update sample-to-sample, disregarding the trial gaps. We exhaustively cross-validated our models across subjects and across learning tasks. The results revealed that the second-order model with trial-to-trial update best predicted the proxy process of visuomotor learning.
An Objective Functional Evaluation of Myoelectrically-Controlled Hand Prostheses: A Pilot Study Using the Virtual Peg Insertion Test

Assessing upper limb prostheses and their influence when performing goal-directed activities is essential to compare the quality of different devices and optimize their control settings. Currently available assessments are often subjective, insensitive, and cannot provide a detailed evaluation of prostheses and their usage. The goal of this pilot study was to explore the feasibility of using the Virtual Peg Insertion Test (VPIT) to provide an in-depth assessment of a prosthesis and its functional performance. One transradial amputee performed the goal-directed manipulation task of the VPIT with the sound body side and four different myoelectrically-controlled prostheses. The subject was able to complete the VPIT protocol successfully with technically advanced prosthesis (two out of four devices). The kinematic- and kinetic-based objective evaluation measures extracted from the VPIT were able to capture clear differences between the sound and amputated body side and were able to identify varying movement patterns for different prostheses. Additionally, the outcome measures were sensitive to changes in prosthesis control settings and showed clear trends across measures of subjectively perceived prosthesis quality assessed through a questionnaire. This work demonstrates the general feasibility of objectively evaluating functional prosthesis usage with the VPIT.
A 370-fJ/b, 0.0056 mm2/DQ, 4.8-Gb/s DQ Receiver for HBM3 with a Baud-Rate Self-Tracking Loop

This paper presents a data (DQ) receiver for HBM3 with a self-tracking loop that tracks a phase skew between DQ and data strobe (DQS) due to a voltage or thermal drift. The self-tracking loop achieves low power and small area by utilizing an analog-assisted baud-rate phase detector. The proposed pulse-to-charge (PC) phase detector (PD) converts the phase skew to a voltage difference and detects the phase skew from the voltage difference. An offset calibration scheme that can compensates for a mismatch of the PD is also proposed. The proposed calibration scheme operates without any additional sensing circuits by taking advantage of the write training of HBM. Fabricated in 65 nm CMOS, the DQ receiver shows a power efficiency of 370 fJ/b at 4.8 Gb/s and occupies 0.0056 mm2. The experimental results show that the DQ receiver operates without any performance degradation under ${a}\pm 10$% supply variation.
Development of a “transparent operation mode” for a lower-limb exoskeleton designed for children with cerebral palsy

Robot-assisted rehabilitation in children and young adults with Cerebral Palsy (CP) is expected to lead to neuroplasticity and reduce the burden of motor impairments. For a lower-limb exoskeleton to perform well in this context, it is essential that the robot be "transparent" to the user and produce torques only when voluntarily-generated motor outputs deviate significantly from the target trajectory. However, the development of transparent operation modes and assistance-as-need control schema are still open problems with several implementation challenges. This paper presents a theoretical approach and provides a discussion of the key issues pertinent to designing a transparent operation mode for a lower-limb exoskeleton suitable for children and young adults with CP. Based on the dynamics of exoskeletons as well as friction models and human-robot interaction models, we propose a control strategy aimed to minimize human-machine interaction forces when subjects generate motor outputs that match the target trajectory. The material is presented as a conceptual framework that can be generalized to other exoskeleton systems for overground walking.
Arousal Evaluation of VR Affective Scenes Based on HR and SAM

Various of methods have been proposed for emotional induction including affective pictures, audios, and video clips. However, significant limitations have been exhibited such as low emotion elicitation efficiency and high susceptibility to ambient interferences. Considering virtual reality (VR) provides a more immersive and authentic audio-visual sense, this paper puts forward an emotion elicitation system using VR named Affective Virtual Reality System (AVRS). Firstly, the emotional multi-features were extracted and classified from the existing standard affective systems. Then the VR scenes were designed by writing scripts, building 3D modeling and scenes using Unreal Engine 4.12. Through Self-Assessment Manikin (SAM) evaluation, it was verified that the emotions inducted by the VR scenes were evenly distributed in the three-dimensional space of emotion, which reached the standardization of affective system in field of psychology. Then we further validated the advantages of AVRS by putting the four VR scenes into the LCD and VR device respectively. We tested the arousal level with the heart rate data and the SAM scale. The result shows that the arousal of other scenes have no significant difference from the videos in the VR environment, except the fear scene. We found that there are some conditions for the VR scenes to achieve a good emotional effect. Enhancing the interactivity and plot design is an important aspect.
Novel Single Switch High Gain Non-isolated DC-DC Converter for Building Integrated Photovoltaic Systems

To meet the increase in electrical energy demand and to reduce CO2 emissions from conventional power plants, Building Integrated Photo-Voltaic (BIPV) systems are gaining more attention. High gain DC-DC converters are essential to utilize the power generated from renewable energy sources and play a significant role in the overall renewable generation system performance. In this paper, a novel single-switch non-isolated dc-dc converter with a high step-up voltage gain, reduced voltage stress, and low input current ripple is developed. These features make the proposed converter suitable for renewable energy sources. The principle of operation, theoretical analysis, elements design, simulation by PSIM, and techno-economic study are presented.
Stream Processing on Multi-cores with GPUs: Parallel Programming Models' Challenges

The stream processing paradigm is used in several scientific and enterprise applications in order to continuously compute results out of data items coming from data sources such as sensors. The full exploitation of the potential parallelism offered by current heterogeneous multi-cores equipped with one or more GPUs is still a challenge in the context of stream processing applications. In this work, our main goal is to present the parallel programming challenges that the programmer has to face when exploiting CPUs and GPUs' parallelism at the same time using traditional programming models. We highlight the parallelization methodology in two use-cases (the Mandelbrot Streaming benchmark and the PARSEC's Dedup application) to demonstrate the issues and benefits of using heterogeneous parallel hardware. The experiments conducted demonstrate how a high-level parallel programming model targeting stream processing like the one offered by SPar can be used to reduce the programming effort still offering a good level of performance if compared with state-of-the-art programming models.
A Linear Solver Framework for Flow and Geomechanics Reservoir Simulation

This paper describes a parallel solver framework focused on flow and geomechanics reservoir simulation applications. It has been designed to run efficiently on a wide range of target platforms, from desktop workstations to heterogeneous clusters of multicore nodes, with or without GPUs, using a framework for distributed matrices and vectors based on a two-tier hierarchical architecture. Results show good parallel scalability on clusters of multicore nodes. Comparisons with the PETSc library indicate it is competitive with the best available tools. Preliminary tests indicate good speedups and parallel scalability also on multiple GPUs.
Deep Learning in Reconfigurable Hardware: A Survey

Deep Learning has been applied successfully to solve complex problems that involves analysis of large data sets and these good results can be directly related to the size and complexity of the networks and training algorithms. However, these structures are considerably resource-consuming and demand extra effort to be used on embedded systems. Researchers have chosen different alternatives to execute deep learning algorithms as servers, clusters, GPUs and FPGAs. Specific hardware structures have been designed to solve these problems in different platforms such as FPGAs and even ASICs. Although there are surveys about this subject, they do not present a clear criteria to real implementations, detailed explanation of design techniques and applied metrics. Presenting a different approach, this work analyzes reconfigurable hardware based structures designed to optimize deep learning algorithms. This work includes results of hardware accelerators implemented and tested using commercially available development boards avoiding simulation-based results.Results of the proposed analysis showed that a lot of effort is directed to this subject but presented results are far from what is expected for the performance of a hardware structure for deep learning.
Efficient approach for Tendering by introducing Blockchain to maintain Security and Reliability

The problem with present tendering is its reach which is limited to number of people, though the internet is expanding and tendering is also not far from this, we have some online system for tendering but it is not secure as it should be because tendering has confidential data which is not supposed to be leaked and Blockchain solves that problem efficiently. The motive of this research is to find the better ways for tendering, as tendering is very essential part of businesses and development so improvement of this system leads to better development. Time efficiency, employment, fair system are some of the factors which can be improved by the proposed system of this research.