Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognition of Sign Language Using Leap Motion Controller Data

Sign language is a main communication tool for hearing impaired people. The sign language recognition systems are designed to convert it into text or speech, breaking the interaction barrier for deaf person. The paper reviews the state of the art in sign language recognition. The study focuses on data acquisition methods, because they put the major restrictions on a further recognition process. We perform the sign language recognition using data collected by the Leap Motion Controller. The device detects user’s hands and transmits it into a three-dimensional model, providing hands and fingers location and posture. This paper presents a system that is capable of recognition and classification of the sign language using deep-convolutional neural network. We highlight the main features of the Leap Motion Controller that improves and simplifies feature extraction. Also, the paper discusses the problems we faced during the sign language recognition.
Effects of pairwise, self-associating functional side groups on polymer solubility, solution viscosity, and mist control

Solution properties are reported for homologous series of narrowly distributed polymers with systematically varied content of self-associating groups. Anionically polymerized polybutadienes of two lengths (510 and 1250 kg/mol) serve as prepolymers that are modified by incorporation of carboxylic acid side groups using thiol−ene coupling to pendant vinyl groups. Carboxylic acid groups strongly reduce polymer solubility in hydrocarbon solvents, restricting the extent of functionalization that can be examined in single-phase solutions (e.g., in chlorododecane, functionalization must be kept <1.8 mol % even for the shorter of the two backbones). In the single-phase regime, addition of hydrogen bond “stickers” weakly affects solution viscosity. Even at concentrations that produce overlap at the scale of strand length between stickers, viscosity increases are less than 1 order of magnitude. These controlled studies (using functionalized and unmodified polymer homologues of matched, well-defined length) challenge the pre-existing understanding of the rheology of self-associating polymers. The results indicate that effects of intrachain pairing are important beyond the dilute regimebehavior unaccounted for in earlier experimental and theoretical studies. The implications for mist control of aviation fuel are that self-associating polymers of acceptable solubility in the fuel are not superior to nonassociating polymers even at concentrations several times above overlap.
Distributed combined acoustic echo cancellation and noise reduction using GEVD-based distributed adaptive node specific signal estimation with prior knowledge

Distributed combined acoustic echo cancellation (AEC) and noise reduction (NR) in a wireless acoustic sensor network (WASN) is tackled by using a specific version of the PK-GEVD-DANSE algorithm (cfr. [1]). Although this algorithm was initially developed for distributed NR with partial prior knowledge of the desired speech steering vector, it is shown that it can also be used for AEC combined with NR. Simulations have been carried out using centralized and distributed batch-mode implementations to verify the performance of the algorithm in terms of AEC quantified with the echo return loss enhancement (ERLE), as well as in terms of the NR quantified with the signal- to-noise ratio (SNR).
A control system for managing the flexibility in BPMN models of cloud service workflows

In this paper, we present a real time interactive system for running flexible workflow applications. This system facilitates the execution of these applications in terms of time execution complexity as it is based on flexibility mechanism. These workflows are built on an abstract level, through BPMN models using flexibility patterns. We define two flexibility patterns based on BPMN (Business Process Model Notation) that deals with changes of resource requirements for workflows. The provided models specify the functional view of workflows, whereas, their behavioral view is described using state-chart diagrams. A state-chart diagram represents the model specifying our real time system which controls the execution of workflow applications by making decisions on the use of flexibility actions. This execution is supported by the BPEL4WS engine that we have amended to execute flexible workflows and to be controlled by our real time system. Finally, we present some results of evaluations of our system.
Modelling Wealth Inequality: A Structural Vector Autoregression Approach

In recent years, an increasing number of studies has been undertaken to estimate inequality and to find out its determinants. Most of the works have been executed on income inequality data because wealth is notably harder to estimate. In this study the impact of economic growth and financial development on wealth concentration is examined. There were collected yearly data on Russian economic system covering the period 1995-2014. A set of stochastic difference equations (structural vector autoregression, SVAR) was implemented to represent the dynamics of economic and financial system and their impact on wealth concentration were studied. Our findings suggest that in the short and long run, even if at the very beginning the development of financial system increases the inequality, both dynamics decrease the inequality, while in the middle the two tend to exacerbate the concentration of wealth.
Single Molecular Observation of Hop Diffusion in a Lipid Bilayer at Metallic Nanogates

Single molecular tracking was carried out for a lipid molecule (TR-DHPE) in a self-spreading lipid bilayer (egg-PC) on a glass substrate with and without Ag nanoarchitectures. Without Ag architectures, the mean square displacement (MSD) analyses demonstrated that lipid molecules diffuse randomly within the self-spreading lipid bilayer with a diffusion coefficient of 3.1 μm2/s, which is comparable to the value for an artificial solid supported lipid bilayer. However, in the presence of the Ag architectures, TR-DHPE molecules undergo hop diffusion between two neighboring compartments surrounded by the Ag architectures. Smaller diffusion coefficients observed for the substrates with the Ag architectures are attributable to the suppressed diffusion at the gap between the Ag architectures. The escape probability from the initial compartment to the neighboring compartment estimated from the MSD analysis agreed well with the values estimated from the compartment configuration, which proves that the Ag nanoarchit.
Developing More Efficient Wind Turbines: A Survey of Control Challenges and Opportunities

Wind energy has received considerable attention during the past two decades, owing to its vital role in battling both the energy crisis and global climate change. Wind energy has grown to become the most significant nonconventional renewable energy source. Today, modern wind turbines (WTs) are controlled to offer several functionalities that improve their flexibility at a level similar to conventional power sources. However, until now, there have been few reports in the literature summarizing and listing WT control concepts to encourage industry and academia to pursue improvements in developing more efficient WTs and bridge the gap between businesses and universities. Hence, this article focuses on a thoughtful investigation into developing better and more efficient WTs, detailing different issues involved in wind energy generation, such as WT components, control systems, operational modes, and, especially, new research and development trends.
SURE Based Truncated Tensor Nuclear Norm Regularization for Low Rank Tensor Completion

Low rank tensor completion aims to recover the underlying low rank tensor obtained from its partial observations, this has a wide range of applications in Signal Processing and Machine Learning. A number of recent low rank tensor methods have successfully utilised the tensor singular value decomposition method with tensor nuclear norm minimisation via tensor singular value thresholding. This approach while proving to be effective has the potential issue that it may over or under shrink the singular values which will effect the overall performance. A truncated nuclear norm based method has been introduced which explicitly exploits the low rank assumption within the optimization in combination with tensor singular value thresholding. In this work the truncated nuclear norm approach is extended to incorporate a data driven approach based on Stein’s unbiased risk estimation method which efficiently thresholds the singular values. Experimental results in a colour image denoising problem demonstrate the efficiency and accuracy of the method.
Adaptive Measurement Matrix Design in Compressed Sensing Based Direction of Arrival Estimation

Design of measurement matrices is an important aspect of compressed sensing (CS) based direction of arrival (DoA) applications that enables reduction in the analog channels to be processed in sparse target environments. Here, a novel measurement matrix design methodology for CS based DoA estimation is proposed and its superior performance over alternative measurement matrix design methodologies is demonstrated. The proposed method uses prior probability distribution of the targets to improve performance. Compared to the state-of-the-art techniques, it is quantitatively demonstrated that the proposed measurement matrix design approach enables significant reduction in the number of analog channels to be processed and adapts to a priori information on the target scene.
An End-to-end Multitask Learning Model to Improve Speech Emotion Recognition

In this paper, we propose an attention-based CNN-BLSTM model with the end-to-end (E2E) learning method. We first extract Mel-spectrogram from wav file instead of using handcrafted features. Then we adopt two types of attention mechanisms to let the model focuses on salient periods of speech emotions over the temporal dimension. Considering that there are many individual differences among people in expressing emotions, we incorporate speaker recognition as an auxiliary task. Moreover, since the training data set has a small sample size, we include data from another language as data augmentation. We evaluated the proposed method on SAVEE dataset by training it with single task, multitask, and cross-language. The evaluation shows that our proposed model achieves 73.62% for weighted accuracy and 71.11% for un-weighted accuracy in the task of speech emotion recognition, which outperforms the baseline with 11.13 points.
Robust Hypersphere-based Weight Imprinting for Few-Shot Learning

Performing fast few-shot learning is increasingly important in a number of embedded applications. Among them, a form of gradient-descent free learning known as Weight Imprinting was recently established as an efficient way to perform few-shot learning on Deep Learning (DL) accelerators that do no support back-propagation, such as Edge Tensor Processing Units (Edge TPUs). Despite its efficiency, WI comes with a number of critical limitations. For example, WI cannot effectively handle multimodal novel categories, while it is especially prone to overfitting that can have devastating effects on the accuracy of the models on novel categorizes. To overcome these limitations, in this paper we propose a robust hypersphere-based WI approach that allows for regularizing the training process in an imprinting-aware way. At the same time, the proposed formulation provides a natural way to handle multimodal novel categories. Indeed, as demonstrated through the conducted experiments, the proposed method leads to significant improvements over the baseline WI approach.
Machine Learning Algorithms Based on Hidden Markov Models in Low-Speed Speech Codecs for Assessing Speech Quality

a machine learning algorithm for a low-speed primary coding algorithm based on automatic recognition of speech signal fragments (SS) using hidden Markov models (HMM) has been developed. When assessing the quality of SS at transmitter output, spectral dynamics algorithm is used, as well as complex algorithms for assessing speech quality. Reduction of redundancy in SS is provided by using speech recognition and synthesis algorithms. Training with HMM to solve speech recognition problem included the definition of such parameters as: matrix of probabilities of transitions between different states of the model. To solve these problems two iterative algorithms were used: forward-backward algorithm and Baum-Welsh algorithm. As an experiment, using Hidden Markov Model Toolkit (HTK) software model, an independent monophonic speech model of Russian language was built. The model was based on the recognition from a list of phonemes consisting of 52 phonetic units and Russian language dictionary including 500000 words. Well-known algorithms to assess speech quality at low-speed codec output are investigated in the interests of machine learning. It is shown that the use of these algorithms does not provide necessary correlation of objective and subjective algorithms to assess speech quality under the action of acoustic interference. Algorithms for objective quality assessment that ensure the correlation of objective and subjective assessments of speech quality with the accuracy of 0.5 points in accordance with GOST R 50840-95 are proposed. The research has shown that the use of these algorithms in the encoder allows increasing speech quality at the output of low-speed codec with its machine learning by 0.5 … 1 point in accordance with GOST R 50840-95.
Deep neural architecture for geospatial trajectory completion over occupancy gridmap

GPS data is widely used in many real world applications. The quality of GPS data is critically important to produce high quality results. In real world applications, certain GPS trajectories are sparse and incomplete; which causes challenges to GPS trajectory based applications. Few of existing studies have tried to address this problem using complicated algorithms that are based on conventional heuristics; this requires extensive domain knowledge of underlying applications. Deep learning in recent era has achieved the great success to solve many sequence to sequence prediction problems. In this paper, a deep learning based bidirectional convolutional recurrent encoder decoder architecture using attention mechanism is proposed that predicts the missing data points; resulting in a complete GPS trajectory. The proposed method shows the significant improvement over state-of-the-art benchmark methods.
Pothole Detection and Dimension Estimation System using Deep Learning (YOLO) and Image Processing

The world is advancing towards an autonomous environment at a great pace and it has become a need of an hour, especially during the current pandemic situation. The pandemic has hindered the functioning of many sectors, one of them being Road development and maintenance. Creating a safe working environment for workers is a major concern of road maintenance during such difficult times. This can be achieved to some extent with the help of an autonomous system that will aim at reducing human dependency. In this paper, one of such systems, a pothole detection and dimension estimation, is proposed. The proposed system uses a Deep Learning based algorithm YOLO (You Only Look Once) for pothole detection. Further, an image processing based triangular similarity measure is used for pothole dimension estimation. The proposed system provides reasonably accurate results of both pothole detection and dimension estimation. The proposed system also helps in reducing the time required for road maintenance. The system uses a custom made dataset consisting of images of water-logged and dry potholes of various shapes and sizes.
Tool wear prediction based on Canonical Correlation Analysis and Neural Network fitting method

Tool wear is an essential factor affecting the machining process of a machine tool. In order to improve the use efficiency of machinery and accurately predict the wear of the equipment in use, a method based on Canonical correlation analysis and Neural Network fitting is proposed in this paper. Using the time domain signal to extract multidimensional features, then build a initial feature set. Combined with Canonical correlation analysis, constructed the training feature set, finally, build a prediction model. Results show that the proposed method is able to predict tool wear with high accuracy.
GPX-ADMM-Net: ADMM-based Neural Network with Generalized Proximal Operator

In this paper, we propose a highly efficient and well interpretable deep learning solver, called Generalized ProXimal ADMM-Net (GPX-ADMM-Net), for the linear inverse problem, which is conventionally solved with intensive computations.GPX-ADMM-Net is characterized by the generalized proximal operator, convolutional dictionary, and modified loss function. Without loss of interpretability, GPX-ADMM-Net only needs a small number of parameters in a learning model to retain elegant reconstruction quality.Different from traditional optimization methods, all the parameters of GPX-ADMM-Net need no more hand-crafted but determined by learning strategies. Furthermore, unlike other deep learning-based methods, GPX-ADMM-Net is able to adapt to various measurement rates with only one single set of training parameters. Extensive experimental results further demonstrate the advantages of our proposed method.
Recognition of Actions and Subjects from Inertial and FSR Sensors Attached to Objects

This paper describes automatic systems for human activity recognition and identification of subjects based on sensorised objects. The action recognition system is based on a deep neural network – hidden Markov model (DNN–HMM) with augmentation of feature vectors by the i-vector of a given recording to deal with the subject variability. The subject identification system is based on i-vectors. The sensors, comprising an accelerometer, gyroscope, magnetometer and force-sensitive resistors (FSRs), are packaged in a coaster attached to the base of an object, here a jug. Evaluations are performed using nearly 11 hours of data recordings from 26 subjects, containing actions involved in manipulating a jug to make cups of tea. We demonstrate the performance of the DNN–HMM action recognition system in a subject-dependent and subject-independent case and in controlled and natural scenarios. While the subject-dependent system achieved error rate of 0.15% in controlled scenario, this increased up to 15.33% for subject-independent system in natural scenario. The proposed i-vector augmentation provided 26% relative error rate reduction. The subject identification system based on i-vectors and cosine similarity calculation achieved over 68% recognition accuracy in natural scenario.
Aqueous-phase reforming of glycerol using Ni–Cu catalysts prepared from hydrotalcite-like precursors

Ni–Cu catalysts derived from hydrotalcite-like compounds were prepared with 20 wt% of NiO and 0, 5 and 10 wt% of CuO, and evaluated in the aqueous-phase reforming of glycerol. The catalysts were characterized by chemical composition, textural analysis, crystalline structure, reducibility and acidity. The reaction was performed in a continuous flow reactor with a solution of 10 vol% glycerol, at 250 °C/35 atm and 270 °C/50 atm. The maximum glycerol conversion at 250 °C/35 atm was 70% and total conversion of glycerol was achieved at 270 °C/50 atm with Cu-containing catalysts. In the gas phase, the H2 selectivity was around 40% and CO selectivity was very low at 250 °C/35 atm. The addition of Cu decreased the CH4 formation. The main products formed in the liquid phase were acetol and lactic acid and a small quantity of propanoic acid. The Cu-containing catalysts showed higher formation of acetol, which was correlated with their higher acidity. Characterization of the spent catalysts revealed a sintering of the metal particles, although no deactivation was observed during 6 h of reaction.
Efficient Vehicle Counting by Eliminating Identical Vehicles in UAV aerial videos

Traffic surveillance using Unmanned Aerial Vehicles (UAV‘s) has gained a lot of attraction in civilian applications and remote sensing tasks. Thanks to its high mobility and large field of view and ability to cover regions at different altitudes UAVs have made a mark in recent years for surveillance. The primary purpose of UAV in traffic surveillance is to monitor the daily activities in the busy traffic areas and report the abnormal activities which may take place. In recent years, many gated campuses such as educational institutions, organizations, shopping malls, etc. have taken steps to keep a track of vehicles trespassing within its vicinity. Vehicle counting is one of the monitoring tasks performed in surveillance to estimate the density of vehicles in an event or areas where traffic congestion is common. In this paper, a vehicle counting framework is proposed to eliminate the problem of redundant vehicle information count when a vehicle has appeared in successive frames of UAV videos. This work demonstrates that the comparison of concatenated three features vectors (Histogram of Oriented Gradients, Local Binary Pattern, and mean RGB value) can be used to recognize identical vehicles in UAV aerial videos.
A Robust RGB-D SLAM with Loosely-Coupled Semi-Direct Method

In last few years, there has been growing interest in RGB-D Simultaneous localization and mapping (RGB-D SLAM) due to its ability of 3D dense reconstruction. Current main feature-based RGB-D SLAM methods are prone to fail in low-texture scenes due to insufficient feature matches. Previous studies of improving the robustness of RGB-D SLAM have focused on adding the line and plane features. However, these methods affect the real-time performance of the system. This paper aims to propose a real-time RGB-D SLAM method that has a robust performance in low-texture scene. We combined the strength of both direct methods and feature-based methods, which is the robust performance of direct methods in low-texture scenes and global optimization ability of feature-based method. The experimental results demonstrated that our method performs better than currently main RGB-D SLAM methods in terms of robustness and computational efficiency. Our method provides a prospective solution for RGB-D SLAM in low-texture scenes.
A Grant-Free Method for Massive Machine-Type Communication With Backward Activity Level Estimation

Massive machine type communications (mMTC) is one of the three major scenarios of the fifth generation (5G) communication system, and raises new challenges for the development of new radio access technology. Unlike human type communications (HTC), mMTC is typically characterised by a massive number of devices, small-sized packets, low or no mobility, low energy consumption and sporadic transmission, which requires novel solutions. In this paper, we propose the 2-step random access with early data transmission (2-step-EDT) framework. To solve the optimization problem proposed in the framework, we introduce an algorithm, namely, Backward Sparsity Adaptive Matching Pursuit with Checking and Projecting (BSAMP-CP), which jointly conducts the sparsity level estimation, active device detection, channel estimation and data recovery in two phases. Specifically, in the first phase, BSAMP-CP conducts the sparsity level estimation in a backward manner exploiting the data length diversity information. In the second phase, BSAMP-CP jointly conducts activity detection, channel estimation and data recovery, taking the joint sparsity information of pilot and data symbols, the error checking information and the modulation constellation information into account. Furthermore, we provide a theoretical analysis on the convergence of the proposed BSAMP-CP in the noiseless case and the rationale for the improvement yielded by exploiting data length diversity. Simulation results demonstrate the superiority of the proposed solution in comparison to other existing methods.