Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Motion Recognition by Three-view Kinect Sensors in Virtual Basketball Training

In recent years, human action recognition has received a considerable amount of research attention because of it’s potential in a variety of applications, such as video surveillance, human-computer interaction, and virtual reality (VR). However, many researches on human action recognition performed in single-camera or double-camera system, which achieve reduced performance due to vulnerability to partial occlusion and miss-recognition of back. Some works on human action recognition use multiple cameras but are too complex for practical application. In this paper, we propose a new human action recognition system using triple Kinect sensors for VR application. Particularly, we design a mark detection method to determine the front of user and fusion skeleton data in real time. Features are extracted from three-dimensional (3D) skeleton data sequences, and divided into five parts according to body parts. A classification model based on the part-aware long short-term memory networks is proposed to recognize human motion. Finally, we demonstrate the system with a virtual reality basketball application and the results of experiment validate the feasibility of the proposed system.
Fuzzy Power Control for Non-linear Distortion Suppression in MIMO-OFDM Systems

The hybridization of MIMO-OFDM systems became one of the most used wireless communication model for broadband, mobile, and multimedia applications because of its high bandwidth efficiency, bandwidth capacity, and robustness to fading. However, it suffers from the underlying disadvantage of OFDM system which is having a high peak-to-average-power ratio (PAPR) due to large envelope variations. These variations cause non-linear distortion when the OFDM signal is amplified for transmission. Hence, in order to eliminate the non-linear distortion effects of the high power amplifier in MIMO-OFDM systems, the input signal power must have an appropriate power level to satisfy an optimal input back off (IBO) value that also contributes to an amplifier’s maximum efficiency. A Fuzzy Logic Controller is used to control the IBO of the system as well as the signal power level. Results shows that using the proposed Single-Input Single-Output (SISO) Fuzzy Power Controller reduces the bit error rate (BER) significantly compared to the traditional scheme.
Modified Anti-windup Control for High Precision Motion System

This study aims to solve the windup problem caused by the actuator saturation of the high precision motion system after the acceleration process. We propose two anti-windup control methods by adding anticipatory saturation units to the traditional anti-windup controllers, hence the actuator saturation is suppressed when control inputs are approaching the actuators' limits. One of the two anti-windup controllers is based on modified back-calculation, and the other one modifies the variable structure. Simulation results show that the two modified controllers can both improve the scanning accuracy greatly, decease the settling time and overshoot evidently, and have a smooth and stable scanning speed. Moreover, the controller with modified variable structure performs even better than that with modified back-calculation.
Gender Recognition using in-built Inertial Sensors of Smartphone

The research work on user authentication using biometric parameters like face, iris, fingerprint, and gait, have been vastly carried out over the last few decades. Also, the researchers are putting great efforts in the field of gender recognition of a person. Predicting the gender of a person by considering the biometric parameters described above has been the topic of interest in many areas like security, forensic, e-commerce applications, investigation, and some smart automation systems. In this paper, we have presented an analytical approach for gender recognition based on the information of human gait obtained from the inertial sensors of a smartphone, and the comparative analysis of various machine learning classification and regression algorithms on the recognition performance. The overall aim of our research is to provide an insight and to improve the preciseness of the gender recognition system. Moreover, the proposed system can enhance the processing speed of the biometric recognition systems by determining the gender of the person at the initial stage to reduce the search area. We have achieved the state of the art performance and obtained an accuracy of 96.3% using the proposed method.
Taguchi-based Optimization of Process Parameters for Quality Weld of Tungsten Inert Gas Bead-on Plate Welding on Low Carbon Steel

It is of great significance to select appropriate welding process parameters and their limits for quality weld in any welding process. Optimization of any welding process variables is markedly difficult because welding is a multi-factors process, which is impacted by lots process unpredictability. In this study, a multi-response optimization process has been applied for Tungsten Inert Gas (TIG) bead-on plate welding on low carbon steel. Taguchi’s method has been introduced for the designing of experiments with the intension of reducing the number of experimental runs. Three welding process parameters—current (I) from 130-190A, traverse speed(S) 0.30-0.75 m/min and gas flow rate (Gf) 12-18 lit. /min with a constant voltage 25 V were considered in this analysis. The weld bead geometry viz. weld penetration, weld bead width or thickness and haz (heat affected zone) thickness were measured and also investigated the grain sizes and hardness of haz. The ANOVA (analysis of variance method) has been introduced to assess the significance of the parameters on overall quality of the weldment. It shows the workability of the Grey-based Taguchi method in manufacturing world for ongoing development in product quality. It has been noticed that the process variables were seriously impacted on weld bead as well as hardness and grains size of the haz.
Anisotropic Diffusion with Deep Learning

We propose a deep learning framework for anisotropic diffusion which is based on a complex algorithm for a single image. Our network can be applied not only to a single image but also to multiple images. Also by blurring the image, the noise in the image is reduced. But the important features of objects remain. To apply anisotropic diffusion to deep learning, we use total variation for our loss function. Also, total variation is used in image denoising pre-process. [1] With this loss, our network makes successful anisotropic diffusion images. In these images, the whole parts are blurred, but edge and important features remain. The effectiveness of the anisotropic diffusion image is shown with the classification task.
A Distributed In-Situ CNN Inference System for IoT Applications

CNN is a popular deep learning structure able to provide intelligent processing in IoT applications. Instead of deploying the resource-hungry CNN inference workloads on the cloud, it would be promising to utilize local IoT devices for the in-situ processing. Since a single IoT device has only limited resources available, distributing over multiple local devices becomes a potential solution, especially for high-accuracy and time-sensitive tasks. However, it is non-trivial to distribute the inference of existing CNN models efficiently as they are inherently tightly-coupled structure. In this paper, we propose a distributed in-situ CNN inference system with the loosely-coupled CNN structure (LCS), the synchronization-oriented partitioning (SOP), and the decentralized asynchronous communication (DAC) for IoT applications. LCS is based on two novel design ideas, the homogeneous group and the intermittent shuffle. Experiments on ImageNet classification illustrate that LCS has the leading accuracy compared with other structures, under a given computation budget. SOP and DAC target on converting the loosely-coupled feature of LCS into practical performance improvement. SOP tries to partition LCS with fewer synchronization points and DAC reduces the communication overhead by overlapping communications. When the number of IoT devices increases from 1 to 4, our system accelerates by up to 3.85 ×, and reduces the memory footprint in each device by 70%, outperforming other approaches.
High-Level Modular Autopilot Solution for Fast Prototyping of Unmanned Aerial Systems

A redundant fast prototyping autopilot solution for unmanned aerial systems has been developed and successfully tested outdoors. While its low-level backbone is executed in a Raspberry PiⓇ 3 + NAVIO2Ⓡ with a backup autopilot, the computational power of an IntelⓇ NUC mini-computer is employed to implement complex functionalities directly in SimulinkⓇ, thus including in-flight debugging, tuning and monitoring. Altogether, the presented tool provides a flexible and user-friendly high-level environment with enhanced computational capabilities, which drastically reduces the prototyping timespans of complex algorithms –between 50% and 75%, according to our long and proven experience in aerial robotics–, while preventing incidents thanks to its redundant design with a human-in-the-loop pilot on the reliable PX4. Three typical outdoor cases are carried out for validation in real-life scenarios, all mounted in a DJI© F550 platform. Full integration results and telemetry for more than 50 hours of outdoor flight tests are provided.
A framework for prioritising present and potentially invasive mammal species for a national list

The European Union (EU) has recently adopted a regulation on invasive alien species that foresees the possibility of developing lists of species of National Concern. We developed a prioritisation process for alien mammals already established in Italy, but not yet included in the EU list (n = 6 species) and a systematic horizon-scanning procedure to obtain ranked lists for those species that are already introduced worldwide or traded in Italy (n = 213). Experts were asked to score these species, by evaluating their likeliCopyright Sandro Bertolino et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. NeoBiota 62: 31–54 (2020) doi: 10.3897/neobiota.62. 52934 http://neobiota.pensoft. net ReseARCh ARtiCle Advancing research on alien species and biological invasions A peer-reviewed open-access journal
Tumor Budding Detection in H&E-Stained Images Using Deep Semantic Learning

Tumor buddings (TB), a special formation of cancerous cells that bud from the tumor front, are fast becoming the key indicator in modern clinical applications where they play a significant role in prognostic and evaluation of colorectal cancers in histopathological images. Recently, computational methods have been rapidly evolving in the domain of digital pathology, yet the literature lacks computerized approaches to automate the localization and segmentation of TBs in hematoxylin and eosin (H&E)-stained images. This research addresses this very challenging task of tumor budding detection in H&E images by presenting different deep learning architectures designed for semantic segmentation. The proposed design for a new Convolutional Neural Network (CNN) incorporates convolution filters with different factors of dilations. Multiple experiments based on a newly constructed colorectal cancer histopathological image collection provided promising performances. The best average intersection over union (IOU) for TB of 0.11, IOU for non-TB of 0.86, mean IOU of 0.49 and weighted IOU of 0.83 were observed.
An Enhanced Model-Free Reinforcement Learning Algorithm to Solve Nash Equilibrium for Multi-Agent Cooperative Game Systems

Solving the Nash equilibrium is important for multi-agent game systems, and the speed of reaching Nash equilibrium is critical for the agent to quickly make real-time decisions. A typical scheme is the model-free reinforcement learning algorithm based on policy iteration, which is slow because each iteration will be calculated from the start state to the end state. In this paper, we propose a faster scheme based on value iteration, using Q-function in an online manner to solve the Nash equilibrium of the system. Since the calculation is based on the value from the last iteration, the convergence speed of the proposed scheme is much faster than the policy iteration. The rationality and convergence of this scheme are analyzed and proved theoretically. An actor-critic network structure is used to implement this scheme through simulation. The simulation results show that the convergence speed of our proposed scheme is about 10 times faster than that of the policy iteration algorithm.
Underwater Visible Light Communication Using Phase-Shift Keying and Rolling-shutter Effect

We propose an underwater visible light communication (VLC) system for use in drones. The transmitter modulates a message using phase-shift keying (PSK) and emits the signal as LED fast-blinking light. The receiver captures this pattern with a CMOS image sensor using a rolling-shutter effect, and demodulates PSK. In existing VLC systems, rolling-shutter effect is used to transmit baseband signals; this system transmits a passband signal tolerant of low-frequency optical interference noise. System performance was evaluated in test-tank experiments. Findings suggested that the VLC system with PSK outperformed that using on-off keying when noise frequency did not exceed carrier frequency.
New Developments in Soft Lithography

Abstract The burgeoning area of soft lithography is reviewed with special emphasis on developments within the past three years. Applications in electronics have driven such developments, but more recently, other kinds of device structures and 3D prototyping have also found application, in part, through soft lithography. Microcontact printing (μCP), “lift off” μCP nano transfer printing (nTP), micromolding in capillaries (MIMIC), solvent assisted micromolding (SAMIM), replica molding (REM), and microtransfer molding are the main soft lithography schemes discussed.
Assessment of Customer Credit Risk using an Adaptive Neuro-Fuzzy System

Given the financial crises in the world, one of the most important issues of banking industry is the assessment of customers' credit to distinguish bad credit customers from good credit customers. The problem of customer credit risk assessment is a binary classification problem, which suffers from the lack of data and sophisticated features as main challenges. In this paper, an adaptive neuro-fuzzy inference system is exploited to tackle the customer credit risk assessment problem regarding the mentioned challenges. First of all, a SOMTE-based algorithm is introduced to overcome the data imbalancing problem. Then, several efficient features are identified using a MEMETIC meta-heuristic algorithm, and finally an adaptive neuro-fuzzy system is exploited for distinguishing bad credit customers from good ones. To evaluate and compare the performance of the proposed system, the standard German credit data dataset and the well-known classification algorithms are utilized. The results indicate the superiority of the proposed system compared to some well-known algorithms in terms of precision, accuracy, and Type II errors.
Multiscale Data Analysis Using Binning, Tensor Decompositions, and Backtracking

Large data sets can contain patterns at multiple scales (spatial, temporal, etc.). In practice, it is useful for data exploration techniques to detect patterns at each relevant scale. In this paper, we develop an approach to detect activities at multiple scales using tensor decomposition, an unsupervised high-dimensional data analysis technique that finds correlations between different features in the data. This method typically requires that the feature values are discretized during the construction of the tensor in a process called “binning. ” We develop a method of constructing and decomposing tensors with different binning schemes of various features in order to uncover patterns across a set of user-defined scales. While binning is necessary to obtain interpretable results from tensor decompositions, it also decreases the specificity of the data. Thus, we develop backtracking methods that enable the recovery of original source data corresponding to patterns found in the decomposition. These technique are discussed in the context of spatiotemporal and network traffic data, and in particular on Automatic Identification System (AIS) data.
HPPRM: Hybrid Potential Based Probabilistic Roadmap Algorithm for Improved Dynamic Path Planning of Mobile Robots

Path planning and navigation is a very important problem in robotics, especially for mobile robots operating in complex environments. Sampling based planners such as the probabilistic roadmaps (PRM) have been widely used for different robot applications. However, due to the random sampling of nodes in PRM, it suffers from narrow passage problem that generates unconnected graph. The problem is addressed by increasing the number of nodes but at higher computation cost affecting real-time performance. To address this issue, in this paper, we propose an improved sampling-based path planning method for mobile robot navigation. The proposed method uses a layered hybrid Probabilistic Roadmap (PRM) and the Artificial Potential Field (APF) method for global planning. We used a decomposition method for node distribution that uses map segmentation to produce regions of high and low potential, and propose a method of reducing the dispersion of sample set during the roadmap construction. Our method produces better goal planning queries with a smaller graph and is computationally efficient than the traditional PRM. The proposed planner called the Hybrid Potential based Probabilistic Roadmap (HPPRM) is an improved sampling method with respect to success rate and calculation cost. Furthermore, we present a method for reactive local motion planning in the presence of static and dynamic obstacles in the environment. The advantage of the proposed method is that it can avoid local minima and successfully generate plans in complex maps such as narrow passages and bug trap scenarios that are otherwise difficult for the traditional sample-based methods. We show the validity of our method with experiments in simulation and real environments for both local and global planning. The results indicate that the proposed HPPRM is effective for autonomous mobile robot navigation in complex environments. The success rate of the proposed method is higher than 95% both for local and global planning.
Data Augmentation Using User Attention for Educational Content Recommendation Based on FFM

This paper proposes a method for augmenting evaluation data using each user’s attention (view counts for each attribute of educational contents) for Field-aware Factorization Machines (FFM). In this study, we assume that the target user has no evaluation data. In the proposed method, by using the target user’s attention, we retrieve a similar user from active users. Then, the evaluation data of the active user is duplicated for the target user. By training FFM using the duplicated evaluation data, personalized recommendation for the target user becomes feasible. Experimental results using educational contents on Forestanet show the effectiveness of the proposed method.
10-to-112-Gb/s DSP-DAC-Based Transmitter in 7-nm FinFET With Flex Clocking Architecture

This article presents a multiprotocol DSP-DAC-based SerDes architecture. The lookup table (LUT)-based DSP provides flexible number of taps for equalization, and soft switching driver allows 1.2-Vpp transmit swing to achieve higher SNR. The architecture employs cascaded phase-locked loop (PLL)-based flexible clocking to support a wide range of data rates from 10 to 112 Gb/s. The $LC$ PLL generates 10.25–14.5 GHz but distributes a divided version of the clock between 2.25 and 3.625 GHz with less than 140-fs integrated jitter. The local ring PLL multiplies the clock to 28 GHz but keeps the jitter less than 180 fs thanks to wide loop bandwidth. The transmitter is implemented in 7-nm FinFET consuming 175 mW with 1.56-pJ/bit efficiency.
Line-Independent Plug-and-Play Voltage Stabilization and ℒ₂ Gain Performance of DC Microgrids

The plug-and-play nature of distributed generation (DG) units in converter-interfaced microgrids imposes significant challenges from the control point of view, mainly caused by the time-varying microgrid structure. In this letter, we propose a systematic plug-and-play decentralized voltage control solution for DC microgrids. The proposed control approach guarantees the stable operation and satisfactory performance of microgrids under the arbitrary interconnection of DG units. Based on the Lyapunov method, concise stability and $\mathscr {L}_{2}$ gain voltage tracking performance certificates for DC microgrids are derived. The main feature of the proposed control approach is the decentralized design of local voltage controllers in DC microgrods. The proposed voltage control framework is applied to a case study of a multiple-DG DC microgrid in MATLAB/Simscape Electrical environment.
A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

Designing reliable sequences of DNA (Deoxyribonucleic Acid) is a critical task in the fields of DNA computing, and nanotechnology. The quality and reliability of the DNA sequence can directly affect the accuracy of the processing of information stored in sequences. This problem of designing reliable sequences belongs to the NP-hard class of problems. It has many incompatible design criteria, which cannot be optimized at the same time. Many objective evolutionary algorithms can balance conflicting design criteria by using a diverse population of solutions. This paper proposes an opposition-based Memetic Generalized Differential Evolution (MGDE3) to handle four conflicting design criteria for reliable DNA sequence design. Opposition-based learning and local search strategies are suggested to strengthen the explorative and exploitative properties of the proposed MGDE3. The proposed algorithm is bench-marked with small, medium, and large data sets against 7 highly-cited many-objective and multi-objective algorithms. Experimental results and statistical analysis reveal that MGDE3 significantly outperforms the compared algorithms. The proposed method generates reliable real-life sequences of DNA that are substantially better than the DNA sequences generated by other considered algorithms.
Improving the Performance of DSRC using OFDM-IM, Dynamic Equalization and Multiple Receive Antennas

V2X communications is required to enable future connected and automated vehicles. Dedicated Short Range Communications (DSRC) is one of the two technologies used in vehicular communications. This paper considers combining OFDM with index modulation (IM), Spectral Temporal Averaging and multiple receive antennas to improve the error performance. Simulation results show that a gain of approximately 4 dB can be achieved by replacing the OFDM scheme in the IEEE 802.11p standard by this one, in addition to a significant power saving of 25%, which can be used to increase the gain or extend the battery life.
Optimization of Permanent Magnet Machine for Electric Vehicles Considering the Control Strategy and Inverter

The operation of the motor in electric vehicle (EV) applications depends on the vehicle driving cycle, which is different from the rated conditon. The paper describes an optimization technique of permanent magnet synchronous motor for EV, which optimizes the overall efficiency and torque ripple of the motor in a given driving cycle considering the control strategy and inverter, and the technique also improves the vehicle range and passenger comfort in driving cycle. The effectiveness of the optimization technique is verified by comparing the optimization results with the finite element analysis. Compared with the motor optimized at the rated conditon, the technique can effectively reduce the loss and torque ripple of the motor in a given driving cycle.