Miracle-dz/UniFAR-FTUs huggingface dataset:
Transform Learning Assisted Graph Signal Processing for Low Rate Electrical Load Disaggregation

Separating the individual sources with less differentiating features and random time overlaps in a superposition is a challenging problem that arises in many scenarios. Disaggregating low sampled smart meter data is one such important and interesting use case. In this paper, a novel joint optimization formulation using Transform Learning (TL) assisted with Graph Signal Processing (GSP) is presented to reconstruct the individual operational waveforms of the electrical loads. Data-driven transforms are utilized to learn the individual load characteristics. Treating the transform coefficients as load activations, graph signal smoothness is exploited to estimate the coefficients in the test phase using the coefficients learnt during the training phase. The requisite optimization formulation and the derivation of the necessary update steps are presented. The efficacy of the proposal is demonstrated by the load identification and consumption estimation results obtained for residential load disaggregation, considering both real and simulated data along with comparisons against some of the recent works in this domain.
Decision-Feedback Differential Detection with Optimum Detection Order Metric for Noncoherent Massive MIMO Systems

Noncoherent detection in massive MIMO systems is an attractive alternative to channel-estimation-based detection. So far, receivers employing ordered decision-feedback differential detection based on the phase quantization error of the candidate symbols of the correlation matrix have been proposed. This approach however ignores reliability information in certain cases. In this paper, we analytically derive the maximum-likelihood decision metric used to search for the optimum detection order. We compare the proposed metric to the phase-quantization-based one in terms symbol error rate performance.
Tucker-Regularized Tensor Bregman Co-clustering

Co-clustering of tensor data is an unsupervised learning task aiming to identify multidimensional structures hidden in a tensor. These structures are critical for understanding interdependencies across variables belonging to different tensor dimensions, often referred to as modes, which are frequently disregarded when tensor data are represented via one- or two-dimensional data structures. This work proposes a new tensor co-clustering algorithm that uses a class of Bregman divergences to measure the coherence of co-clusters on an individual mode basis, while ensuring that the interactions of their prototyping elements capture the tensor intra-modal structure. A co-clustering algorithm based on the alternating-direction method of multipliers is developed. The proposed algorithm decouples the co-clustering problem into an iterative two-step process whose steps are reminiscent of classical one-way clustering and Tucker decomposition problems. The performance of the proposed method is illustrated via numerical tests.
Neural Discrete Abstraction of High-Dimensional Spaces: A Case Study In Reinforcement Learning

We employ Neural Discrete Representation Learning to map a high-dimensional state space, made up from raw video frames of a Reinforcement Learning agent’s interactions with the environment, into a low-dimensional state space made up from learned discrete latent representations. We show experimentally that the discrete latents learned by the encoder of a Vector Quantized Auto-Encoder (VQ-AE) model trained to reconstruct the raw video frames making up the high-dimensional state space, can serve as meaningful abstractions of clusters of correlated frames. A low-dimensional state space can then be successfully constructed, where each individual state is a quantized vector encoding representing a cluster of correlated frames of the high-dimensional state space. Experimental results for a 3D navigation task in a maze environment constructed in Minecraft demonstrate that this discrete mapping can be used in addition to, or in place of, the high-dimensional space to improve the agent’s learning performance.
Compliance Control and External Force Estimation of 6-DOF Industrial Robots

Thanks to the advent of Industry 4.0, industrial robots have become more widely used, particularly for complex tasks such as human-robot cooperation. One such human-robot cooperation application is to teach a robot to execute a certain task by simply using the operator’s hand to drag the end-effector of the robot to move along a desired path or move to a desired goal position. It is a more intuitive and convenient solution compared with traditional teach pendant. To make the robot compliant to the applied force of the human operator, compliance control schemes such as impedance control and admittance control are often used. The external force information is essential when implementing compliance control. In this paper, both force sensors and observer-based approaches such as the generalized momentum method are used to estimate external torque/force. Finally, the compliance control-based teach pendant developed in this paper is applied to a 6-axis industrial robot. Experimental results indicate that the proposed approach exhibits satisfactory performance.
Evaluation of Zero Frequency Filtering based Method for Multi-pitch Streaming of Concurrent Speech Signals

Multiple pitch streaming from a mixture is a challenging problem for signal processing and especially for speech separation. In this paper, we use a Zero frequency filtering (ZFF) based new system to stream pitch of multiple concurrent speakers. We propose a workflow to estimate pitch values of all sources in each single frame then streaming them into trajectories, each corresponding to a distinct source. The method consists of detecting and localizing the involved speakers in a mixture, followed by a ZFF based approach where involved speakers’ pitches are iteratively streamed from the observed mixture. The robustness of the proposed system is tested over two, and three overlapping speech mixtures collected in reverberant environment. The results indicate that our proposal brings ZFF to a competitive level with another recently proposed streaming approach.
Joint Robust Linear Regression and Anomaly Detection in Poisson noise using Expectation-Propagation

In this paper, we propose a new Expectation-Propagation (EP) algorithm to address the problem of joint robust linear regression and sparse anomaly detection from data corrupted by Poisson noise. Adopting an approximate Bayesian approach, an EP method is derived to approximate the posterior distribution of interest. The method accounts not only for additive anomalies, but also for destructive anomalies, i.e., anomalies that can lead to observations with amplitudes lower than the expected signals. Experiments conducted with both synthetic and real data illustrate the potential benefits of the proposed EP method in joint spectral unmixing and anomaly detection in the photon-starved regime of a Lidar system.
Accelerated 3D Image Reconstruction for Resource Constrained Systems

We demonstrate an efficient and accelerated implementation of a parallel sparse depth reconstruction framework using compressed sensing (CS) techniques. Recent work suggests that CS can be split up into smaller sub problems. This allows us to efficiently pre-compute important components of the LU decomposition and subsequent linear algebra to solve a set of linear equations found in algorithms such as the alternating direction method of multipliers (ADMM). For comparison, a fully discrete least square reconstruction method is also presented.We also investigate how reduced precision is leveraged to reduce the number of logic units in field-programmable gate array (FPGA) implementations for such sparse imaging systems. We show that the amount of logic units, memory requirements and power consumption is reduced significantly by over 70% with minimal impact on the quality of reconstruction. This demonstrates the feasibility of novel high resolution, low power and high frame rate light detection and ranging (LiDAR) depth imagers based on sparse illumination.
Implementation of low-latency electrolaryngeal speech enhancement based on multi-task CLDNN

In this paper, we propose a low-latency speech enhancement technique for electrolaryngeal (EL) speech based on multi-task CLDNN. Although the EL speech can generate relatively intelligible speech, laryngectomees always suffer quality degradation of speech naturalness due to the mechanical excitation signals. To solve this problem, an EL speech enhancement technique based on CLDNN consisting of convolution, recurrent, and fully connected layers has been proposed. In this technique, an input feature vector of the EL speech is converted into several vocoder parameters such as excitation parameters and spectral parameters based on expert CLDNNs optimized for each feature. However, it is difficult to utilize speech communication because its bi-directional recurrent layers cause a large delay to wait for the end of the utterance. To address this issue, in this paper, we propose multi-task CLDNN with uni-directional recurrent layers for the low-latency EL speech enhancement. Moreover, to achieve comparable performance to the bi-directional CLDNN, we also propose the following techniques: 1) knowledge distillation, 2) data augmentation, and 3) phonetic regularization. The experimental results demonstrate that the proposed method makes it possible to achieve comparable objective results to the bi-directional CLDNN and outperform naturalness and speech intelligibility in the noisy condition.
Analysis of Quadratic Surface Fitting for Subpixel Motion Extraction from Video Images

Digital image correlation is a popular method for estimating object displacement in successive images. At the pixel level, displacement is estimated by maximizing the cross-correlation between two images. To achieve subpixel accuracy, displacement estimation can be refined in the vicinity of the cross-correlation peak. Among existing refinement methods, quadratic surface fitting provides a good trade-off between accuracy and computational burden. The purpose of this paper is to analyze the quadratic surface fitting method. It is shown that the quadratic surface fitted to the cross-correlation values in the vicinity of the cross-correlation peak does not always have a maximum. Then the conditions ensuring the existence of a maximum are analyzed. The reported results consolidate the theoretic basis of the quadratic surface fitting method for subpixel motion extraction.
U-Net based Multi-level Texture Suppression for Vessel Segmentation in Low Contrast Regions

Segmentation of retinal blood vessels is important for diagnosis of many retinal diseases. Precise segmentation of complete vessel-map is still a challenge in low contrast regions of fundus images. Vessel pixels belonging to these regions, such as, fine vessel-endings and boundaries of vessels, get merged in the neighboring vessel-like texture. This paper proposes a novel retinal vessel segmentation algorithm which handles the background vessel-like texture in a sophisticated manner without harming the vessel pixels. In this work, first we enhance all possible vessel-like features of fundus at different ‘levels’ using 2-D Gabor wavelet and Gaussian matched filtering. At each ‘level’, texture is suppressed using Local Laplacian filter while preserving the vessel edges. The resulting images are combined to produce a maximum response image with enhanced vessels of different thicknesses and suppressed texture. This handcrafted image is used to train the deep U-net model for further suppression of non-vessel pixels. Proposed segmentation method is tested on publicly available DRIVE and STARE databases. The algorithm has produced state-of-the-art results. It has performed outstandingly well in terms of sensitivity measure which is most affected with the correct segmentation of fine vessels and vessel-boundary pixels present in low-contrast regions.
Deep Learning Methods for Virus Identification from Digital Images

The use of deep learning methods for virus identification from digital images is a timely research topic. Given an electron microscopy image, virus recognition utilizing deep learning approaches is critical at present, because virus identification by human experts is relatively slow and time-consuming. In this project, our objective is to develop deep learning methods for automatic virus identification from digital images, there are four viral species taken into consideration, namely, SARS, MERS, HIV, and COVID-19. In this work, we firstly examine virus morphological characteristics and propose a novel loss function which aims at virus identification from the given electron micrographs. We take into account of attention mechanism for virus locating and classification from digital images. In order to generate the most reliable estimate of bounding boxes and classification for a virus as visual object, we train and test five deep learning models: R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and SSD, based on our dataset of virus electron microscopy. Additionally, we explicate the evaluation approaches. The conclusion reveals SSD and Faster R-CNN outperform in the virus identification.
Modeling of Two-Channel Management Mechanisms in Organizational Systems

The paper considers the problem of building a two-channel organizational management mechanism in a dynamic system. This system includes a control object with a possibly indefinite structure under unsteady disturbances, and a human operator making management decisions. A simulation of the management mechanism has been made for various human operator strategies in the system where there is the object described by a simple mathematical dynamic model.
Proposal of Estimation of Closed-loop Response Using Input and Output Data

In data-driven controller tuning methods, we some-times suffer from unexpected behavior of a designed closed-loop system since the response of the designed closed-loop system cannot be evaluated in advance. This paper proposes a method to estimate the response of the closed-loop system by using input and output data of the plant without any plant models. Finally, the effectiveness of the proposed method is confirmed by the simulation and the experiment.
Intelligent corrosion detection and rating based on faster region-based convolutional neural network

Surface of mechanical equipment often suffer from severe corrosion damage because it exposes to corrosive environments all year round. Various non-destructive monitoring methods were developed to detect the location and extent of corrosion so as to arrange maintenance. In this paper, a novel intelligent corrosion detection method is proposed by using faster region-based convolutional neural network and a new rating index is defined to evaluate surface condition of specimens, which might realize corresponding surface ratings. Finally, an experimental study on metals plates composed of several base metals and coatings commonly used in engineering application is implement to verify the effectiveness of the proposed method. The experimental results show that the proposed method can accurately detect corrosion damage on the specimens and give the correct appearance rating.
PONCHE: Personalized and Context-Aware Vehicle Rerouting Service

The use of contextual data to suggest distinct types of routes helps to understand new aspects of a city that may change the perception of drivers about routes. The impact of these aspects may differ from driver to driver requiring a way to change the suggestion according to the driver's point of view. Therefore, this paper presents an approach that identifies distinct situations in multiple types of contextual data and proposes a personalized and context-aware vehicle rerouting service called PONCHE. It considers common characteristics found in every dataset of spatiotemporal data to overcome the necessity of processing specific aspects of distinct data types. Regarding personalized service, each driver's profile is reflected into contextual data type weights considered by the system, i.e., the intensity he/she wants to avoid a contextual region. With that, a driver's profile may ignore a determined contextual data type. Performance evaluation results show that PONCHE identifies the best routes according to the weights given by a driver. It also improves the quality of contextual information obtained according to traffic, crime, and vehicle crashes. This study takes into consideration contextual data from Austin and Chicago in the USA, enabling comparison with two distinct cities.
Development of the Forecasting Model for the Complex Technical Systems' Failures Time During the Proactive Maintenance Using the Recurrent Neural Networks' Technology

The forecasting problem of the remaining useful life, which is regularly solved during the preventive maintenance of the complex technical systems, has been considered. The possibilities of effective solution of this problem using the neural networks, including the RNN, the LSTM and the GRU networks, have been investigated. A herewith, to develop the forecasting models based on the neural networks, the datasets based on the multidimensional time series, which are formed from the results of the readings of the corresponding sensors have been used. The approaches to improving the forecasting accuracy by engineering new features on the basis of the original dataset have been considered. The examples of forecasting the remaining useful life using the considered approaches in the case of solving the problem of detecting aircraft engine failures have been given.
State-of-Charge Estimation of Lithium-ion Batteries Based on Deep Neural Network

The State of Charge (SOC) of the battery is one of the core functions of the battery management system (BMS). Accurate estimation of SOC is essential for the reliability and safety of battery systems. The model-based SOC estimation method requires the establishment of an accurate battery model and the use of complex adaptive filtering algorithms, which is difficult to implement in engineering. Lithium-ion batteries generate a large amount of data such as voltage, current and temperature during the working process, which combines pure data-driven deep learning algorithms with lithium-ion batteries SOC estimation. This paper is based on the deep neural network to estimate the SOC of lithium-ion batteries, using public data sets and model simulation data sets to compare the effectiveness of Gated Recurrent Unit Neural Network (GRU), Long Short-Term Memory Network (LSTM) and Recurrent Neural Network (RNN) on SOC estimation. It verifies that GRU is superior to LSTM and RNN in terms of network performance and estimation accuracy.
Multicomponent Aerosol Dynamics of the Pb-O2 System in a Bench Scale Flame Incinerator

A study was carried out to understand the formation and growth of lead particles in a flame incinerator. A bench scale flame incinerator was used to perform controlled experiments with lead acetate as a test compound. A dilution probe in conjunction with real-time aerosol instruments was used to measure the evolution of the particle size distribution at different locations in the flame region. A multicomponent lognormal aerosol model is developed accounting for the chemistry of the lead-oxygen system, and various aerosol dynamic phenomena such as nucleation, coagulation, and condensation. Reasonable agreement is obtained between the predictions of the model using appropriate kinetic parameters and the experimental results.
The Million Dollar Handshake: Secure and Attested Communications in the Cloud

The number of applications and services that are hosted on cloud platforms is constantly increasing. Nowadays, more and more applications are hosted as services on cloud platforms, co-existing with other services in a mutually untrusted environment. Facilities such as virtual machines, containers and encrypted communication channels aim to offer isolation between the various applications and protect sensitive user data. However, such techniques are not always able to provide a secure execution environment for sensitive applications nor they offer guarantees that data are not monitored by an honest but curious provider once they reach the cloud infrastructure. The recent advancements of trusted execution environments within commodity processors, such as Intel SGX, provide a secure reverse sandbox, where code and data are isolated even from the underlying operating system. Moreover, Intel SGX provides a remote attestation mechanism, allowing the communicating parties to verify their identity as well as prove that code is executed on hardware-assisted software enclaves. Many approaches try to ensure code and data integrity, as well as enforce channel encryption schemes such as TLS, however, these techniques are not enough to achieve complete isolation and secure communications without hardware assistance or are not efficient in terms of performance. In this work, we design and implement a practical attestation system that allows the service provider to offer a seamless attestation service between the hosted applications and the end clients. Furthermore, we implement a novel caching system that is capable to eliminate the latencies introduced by the remote attestation process. Our approach allows the parties to attest one another before each communication attempt, with improved performance when compared to a standard TLS handshake.
Sliding Mode Control of Coupled Inductor Based Bidirectional Converter Topology for Electric Vehicle Application

This article presents the design and application of a coupled inductor based bidirectional DC-DC converter (BDC) with sliding mode controller on electric vehicle (EV) powertrain. The coupled inductor acts as an auxiliary circuit providing soft switching to minimize the switching losses. The resonant circuit is achieved with the help of mutual inductance where it further improves efficiency. The sliding mode control is applied for the closed-loop operation which provides better results compared to the standard PI controller. This design is verified with the help of MATLAB/Simulink model and the obtained results are in good settlement with the theoretical calculation. The efficiency of the converter with sliding mode control is found to be 98.3% in buck mode and 97.8% in boost mode. Also, the target of the research to maintain the output voltage at a constant level which is quite clear with the better response of the waveform.
Research on Palm Vein Recognition Algorithm Based on Improved Convolutional Neural Network

When collecting palm vein images, it is easy to be affected by external factors such as light source and placement angle, which result in poor recognition accuracy. In this paper, a new method, which involve a new method of region of interest segmentation and an improved palm recognition method of VGG16 deep convolutional neural network, was proposed to promote the recognition accuracy and be well adapted to the actual application scenarios. Firstly, the original palm vein image is obtained through profile original image, positioning key point of original image, and extract region of interest image. Afterwards, the adaptive histogram equalization technique and Gaussian Filters are utilized to improve image quality. Secondly, for palm vein image recognition application scenarios, the output of the convolutional layer of the VGG-16 convolutional neural network is standardized in batches, and the attention mechanism is introduced to optimize the VGG-16 neural network. The optimized network is used for feature extraction and recognition of palm vein images. Thirdly, data enhancement was performed on the public Polyu multispectral palm vein data set, and then a large number of experiments were carried out, and the best recognition rate was 99.57%.