Miracle-dz/UniFAR-FTUs huggingface dataset:
Object recognition and robot grasping technology based on RGB-D data

In this paper, a robot grasping method with object recognition and autonomous grasping ability based on RGB-D camera is designed. For object recognition, foreground extraction and point cloud clustering are proposed to realize object segmentation based on point cloud data and a set of object dataset. A kind of multiple modal convolution neural network model with dual channel is designed based on VGG and tested with homemade training dataset. For grasping planning, a heuristic nonuniform random grasp sample algorithm is presented according to the local reference frame and the local mean curvature of point clouds. The grasp candidates are scaled up from sample grasp pose by grid searching. The internal points in close region of every grasp hypothesis are encoded to an image and then the image is inputted into a simple convolutional neural network to evaluate the grasp success rate to rank the candidate set. The experimental results show that the proposed robot grasping method can realize object recognition and grasp objects accurately.
An Aero-olfactory-Effect Elimination Algorithm for Rotor UAV based Gas Distribution Mapping

In view of the negative influence of rotor wake (i.e. the aero-olfactory effect, the short name is AO effect) on gas distribution, this paper proposed an AO-effect elimination algorithm for rotor UAV (Unmanned Aerial Vehicle) based three-dimensional (3D) gas distribution mapping (GDM). Firstly, two types of AO effects, i.e. the lateral effect and the vertical effect, were summarized based on experimental phenomena. Secondly, the wake induced gas-patch path was calculated based on the prescribed wake model, and the AO effect was eliminated by estimating the original position of the detected gas patch through backstepping along the induced path. Thirdly, considering the different diffusion speeds along different directions, an ellipsoidal Gaussian distribution rather than the traditional spherical Gaussian distribution mapping method was put forward, which was validated via an experiment conducted using four wireless sensor nodes. The proposed AO-effect elimination algorithm combined with the ellipsoidal kernel GDM has been tested using a robot active olfaction simulator, and the results show that it is more effective in characterizing the gas distribution.
Non-orthogonal multiple access in cell-free massive MIMO networks

In this paper, we investigate the downlink performance of cell-free massive multi-input multi-output non-orthogonal multiple access (CF-mMIMO-NOMA) system with conjugate beamforming precoder and compare against the orthogonal multiple access (OMA) counterpart. A novel achievable closed-form spectral efficiency (SE) expression is derived, which characterizes the effects of the channel estimation error, pilot contamination, imperfect successive interference cancellation (SIC) operation, and power optimization technique. Then, motivated by the closed-form result, a sum-SE maximization algorithm with the sequential convex approximation (SCA) is proposed, subject to each AP power constraint and SIC power constraint. Numerical experiments indicate that the proposed sum-SE maximization algorithms have a fast converge rate, within about five iterations. In addition, compared with the full power control (FPC) scheme, our algorithms can significantly improve the achievable sum-SE. Moreover, NOMA outperforms OMA in many respects in the presence of the proposed algorithms.
Detection of the Incipient Degradation for Aluminum Electrolytic Capacitors in Track Circuits

Aluminum electrolytic capacitors used in the tuning and matching units of track circuits are prone to degrade. The decrease of capacitance results in the drop of main track output voltage (MTOV). However, it is difficult to detect the incipient degradation by monitoring the MTOV because the impact of ambient factors such as variations of temperature and humidity is significant. This paper proposes a data-driven method to reduce the detection delay for the degradation of capacitors. As the fault data is difficult to be collected, this method depends only on a few days of historical data for capacitors undegraded. A model is trained by support vector regression based on the on-site data of MTOV in normal condition. Then, this model is used to generate a residual signal for on-line monitoring. In order to verify the effectiveness of the proposed method, it is assumed that the degradation rate is constant and the remaining useful life is 1000 hours when degradation starts. Then, voltage data for degraded capacitor can be synthesized by utilizing a simulated curve of MTOV vs. capacitance. Simulation results demonstrate that impacts of daily variations of temperature and humidity on the MTOV can be reduced considerably from the residual signal. By monitoring the residual with a small threshold, the detection delay for the degradation can be reduced by several thousand minutes compared to method by setting a threshold of 20% drop on the MTOV. Data preprocessing to eliminate the sudden voltage drop by train occupancy and threshold selection are also reported.
Optimized Precoded Spatio-Temporal Partial-Response Signaling Over Frequency-Selective MIMO Channels

Due to the continuous demand for higher bit rates, the management of the spatio-temporal intersymbol interference in frequency-selective multiple-input multiple-output (MIMO) channels becomes increasingly important. For single-input single-output channels, equalized precoded partial-response signaling is capable of handling a large amount of intersymbol interference, but, to date, no equalization scheme with general partial-response signaling has been presented for the frequency-selective MIMO channel. Not only does this contribution extend partial-response signaling to the MIMO channel by proposing a general spatio-temporal partial-response precoder, but it also develops a minimum mean-squared-error optimization framework in which the equalization coefficients and the spatio-temporal target response are jointly optimized. Three iterative optimization algorithms are discussed, which update (part of) a row of the target impulse response matrix in each iteration. In particular, the third algorithm reformulates this row optimization as a lattice decoding problem. Numerical simulations confirm that the general partial-response signaling clearly outperforms the traditional full-response signaling in terms of the mean squared error and the bit error rate. The third optimization algorithm has a better performance but a higher complexity, compared to the first and the second algorithm.
The Error Analysis and the Error Calibration of the Bionic Polarized Light Compass

The bionic polarized light compass was made based on the compound eyes’ structure of the desert ants. According to the theoretical output formula of the compass, the error analysis of the compass was carried out. The results show that the relative proportional coefficient, the error angle of the polarization direction between any two channels and the zero deviation value of the system will affect the calculation of the polarization azimuth angle. But the degree of polarization will not affect the theoretical calculation value of the polarization azimuth angle. The error calibration experiment was designed in order to calculate the compensation parameters of the related error sources. The inverse calculation result of the polarization azimuth angle proved the validity of the compensation parameters.
Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

People use Online Social Networks (OSNs) to express their opinions and feelings about many topics. Depending on the nature of an event and its dissemination rate in OSNs, and considering specific regions, the users’ behavior can drastically change over a specific period of time. In this context, this work aims to propose an event detection system at the early stages of an event based on changes in the users’ behavior in an OSN. This system can detect an event of any subject, and thus, it can be used for different purposes. The proposed event detection system is composed of the following main modules: (1) determination of the user’s location, (2) message extraction from an OSN, (3) topic identification using natural language processing (NLP) based on the Deep Belief Network (DBN), (4) the user behavior change analyzer in the OSN, and (5) affective analysis for emotion identification based on a tree-convolutional neural network (tree-CNN). In the case of public health, the early event detection is very relevant for the population and the authorities in order to be able take corrective actions. Hence, the new coronavirus disease (COVID-19) is used as a case study in this work. For performance validation, the modules related to the topic identification and affective analysis were compared with other similar solutions or implemented with other machine learning algorithms. In the performance assessment, the proposed event detection system achieved an accuracy higher than 0.90, while other similar methods reached accuracy values less than 0.74. Additionally, our proposed system was able to detect an event almost three days earlier than the other methods. Furthermore, the information provided by the system permits to understand the predominant characteristics of an event, such as keywords and emotion type of messages.
Offloading Decision in Edge Computing for Continuous Applications Under Uncertainty

Edge computing (EC) is an emerging paradigm to push sufficient computation resources towards the network edge, improving application performance significantly by offloading applications to the edge computing node. We investigate continuous application offloading decision in EC, for which it is uncertain how users operate continuous applications and how long continuous applications last before completion. That means some characteristics of continuous applications, e.g., the number of user operations, the uploading and downloading data size for offloading computation of each user operation, and the number of central processing unit (CPU) cycles required to execute computation of each user operation, are unknown when making offloading decision. In this scenario, an energy consumption constrained average response time minimization problem among multiple users for continuous applications under uncertainty is formulated. To tackle this problem, we propose the Response Time-Improved Offloading algorithm with Energy Constraint (RTIOEC) to make offloading decision with fewer characteristics of applications. The evaluation results show that the RTIOEC algorithm achieves comparatively short average response time of continuous applications while satisfying the energy consumption constraint with a predefined upper bound of violation probability. Our results demonstrate the practicality of the RTIOEC algorithm in offloading decision in EC for continuous applications under uncertainty.
Joint Annotator-and-Spectrum Allocation in Wireless Networks for Crowd Labeling

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transportation to healthcare. The implementation of ubiquitous AI, however, entails labelling of an enormous amount of data prior to the training of AI models via supervised learning. To tackle this challenge, we explore a new direction called wireless crowd labelling, which involves downloading data to many imperfect mobile annotators for repetition labelling with an aim of exploiting multicasting in wireless networks. In this cross-disciplinary area, the rate-distortion theory and the principle of repetition labelling for accuracy improvement together give rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Building on the tradeoff and aiming at maximizing the labelling throughput, this work focuses on the joint optimization of encoding rate, annotator clustering, and sub-channel allocation, which results in an NP-hard integer programming problem. To devise an efficient solution approach, we establish an optimal sequential annotator-clustering scheme based on the order of decreasing signal-to-noise ratios, thereby allowing the optimal solution to be found by an efficient tree search. This solution can be further simplified when the channels are symmetric. Alternatively, the optimization problem can be recognized as a knapsack problem, which can be efficiently solved in pseudo-polynomial time by means of dynamic programming. In addition, the optimal polices are derived for the annotator constrained and spectrum constrained cases. Last, simulation results are presented to demonstrate the significant throughput gains based on the optimal solution compared with decoupled allocation of the two types of resources.
Optimal Volt–Var Curve Setting of a Smart Inverter for Improving Its Performance in a Distribution System

We propose an algorithm to set the parameters of volt–var curves to improve the performance of distributed generation. Specifically, we optimally set the volt–var curves of the smart inverter for proper control of the distributed generation output. To improve the overall distribution system performance, we consider the minimization of voltage deviation, system loss, and peak of reactive power in an objective function, thereby providing optimal volt–var curve parameters. The proposed algorithm overcomes various limitations of existing studies. First, we use distribution system factors in a multiobjective optimization function to adjust volt–var curve parameters that deviate from the default settings. Second, we consider the service transformers that affect the system voltage according to the photovoltaic generation output in the test model during optimization. Third, we analyze the system improvement according to the number of parameters to be optimized. Fourth, we analyze the implementation of the optimized volt–var curve according to the practical periods when the smart inverter settings can be updated. Fifth, we evaluate different suitable volt–var curves for multiple photovoltaic generators grouped using clustering. Using the proposed algorithm, the distribution system operator can set the optimal volt–var curve according to the system conditions and requirements. We conducted simulations of the proposed method using OpenDSS, a quasistatic time-series simulation, on a test model reflecting the characteristics of a South Korean distribution system. The simulation results confirm that the overall system performance increases when the optimal volt–var curve settings obtained from the proposed algorithm are used.
Study on Single-phase Ground Fault Location of Distribution Network Based on MDS and DBSCAN Clustering

When a single-phase grounding fault occurs in the distribution network with DG (Distributed Generation), the fault location is mainly completed by the switch information at the node. This method cannot make full use of the fault information and cannot be applied to the intelligent distribution network under the background of massive data. To solve the above problems, a single-phase grounding fault location method based on MDS (Multidimensional Scaling) and DBSCAN (Density- Based Spatial Clustering of Applications with Noise) is proposed. The simulation results show that the proposed method is not affected by fault location, fault resistance, fault angle and line type, and the fault location results are accurate, which can effectively use the fault information.
Fault diagnosis of TE process based on incremental learning

With the continuous research of deep neural networks, the network structure becomes more and more flexible, and it has achieved good results in terms of recognition, classification and so on. Traditional deep neural networks use one-time batch learning and suffer catastrophic forgetting when learning new data. This paper proposes a new fault diagnosis algorithm based on incremental structure. Through the use of improved residual neural networks, enhanced training sets, and enhanced loss functions, new knowledge can be continuously learned from new data while retaining what has been learned, thereby slowing catastrophic forgetting. Experiment demonstrates the effectiveness of the algorithm for mitigating catastrophic forgetting by using the Tennessee-Eastman (TE) chemical process dataset.
Role of Demand Side Management in Automatic Load Frequency control

This paper assesses the role of demand side management (DSM) in automatic load frequency control (ALFC) in presence of distributed generations (DGs). DSM has been introduced in ALFC in terms of demand response (DR). DR is introduced in the ALFC model of two area power system consisting of wind energy conversion system (WECS). The frequency regulation due to load variation is studied and better results are obtained after introduction of DR.
Uniform Generic Representation for Single Sample Face Recognition

In this article, we propose a uniform generic representation (UGR) method to solve the single sample per person (SSPP) problem in face recognition, which aims to find consistency between the global and local generic representations. For the local generic representation, we require the probe patches of the same image to be constructed respectively by the corresponding patches of the same gallery image and the intra-class variation dictionaries. Therefore, the probe patches’ coefficients, corresponding to patch gallery dictionaries, should be similar to each other. For the global generic representation, the probe image’s coefficient, corresponding to the gallery dictionary, should be similar to those of its probe patches. In order to meet the two requirements, we combine local generic representation with global generic representation in soft form. We obtain the representation coefficients by solving a simple quadratic optimization problem. UGR has been evaluated on Extended Yale B, AR, CMU-PIE, and LFW databases. Experimental results show the robustness and effectiveness of our method to illumination, expression, occlusion, time variation, and pose.
Spectral Analysis of EEG Signals obtained during Mirror Therapy and Imagined Hand Movements

Mirror therapy and motor imagery training can assist in the recovery of motor function after stroke. The changes in the brain activity through mirror therapy and motor imagery training can be monitored using electroencephalogram (EEG). The effect of mirror therapy on brain activities has not been examined extensively using EEG. This study analyses the EEG signals to identify the brain status during mirror therapy and motor imagery training. The EEG signals were acquired from 32 channels. The signals were then filtered and divided into three frequency bands. Power Spectral Density was then computed and EEG signal strength was examined at every channel. Seven channels provide strong EEG signals and contain brain status information which are suitable for monitoring changes in the brain activity during mirror therapy and motor imagery training.
Trajectory Tracking Control of Underactuated Ship Based on Backstepping and Dynamic Surface Control Method

A nonlinear control method is presented for trajectory tracking control of underactuated ships with parametric uncertainties under bounded exogenous disturbances. Because of the model’s non-nolonomic constraint, surge and sway control system are designed. By employing backstepping method and dynamic surface control, the controllers of surge and yaw are prestented, which can guarantee the asymptotic stability of the closed-loop control system without uncertainty estimating. Numerical simulation results on a training ship verify the invariability and excellent robustness of the proposed controller, and it can effectively solve the lateral drift of ship.
Towards a Scalable and QoS-Aware Load Balancing Platform for Edge Computing Environments

Edge computing is a new computing paradigm that brings the cloud applications close to the Internet of Things (IoT) devices at the edge of the network. It improves the resources utilization efficiency by using the resources already available at the edge of the network [8]. As a result, it decreases the cloud workload, reduces the latency, and enables a new breed of latency-sensitive applications such as the connected vehicles. Horizontal scalability is another advantage of edge computing. Unlike the cloud and fog computing, the latter takes advantages of the growing number of connected devices, as this growth results in increasing the number of the available resources. Most researches in this field were only interested in finding the optimal tasks offloading destination by minimizing the latency, the resources utilization, and the energy consumption. Therefore, they ignore the effect of the synchronization between the devices, and the applications (i.e. containers) deployment delay. Motivated by the advantages of edge computing, in this paper, we introduce a load balancing platform for IoT-edge computing environments. As opposed to the current trend, we will first focus on the applications deployment and the synchronization between devices in order to provide better scalability, enable a self-manageable IoT network, and meet the quality of service (QoS). According to the simulation results, the proposed approach provides better scalability; it reduces the network utilization and the cloud workload. In addition, it provides better applications deployment delays and a lower latency.
Sub-Linear Scalability of MQTT Clusters in Topic-Based Publish-Subscribe Applications

Message Queuing Telemetry Transport (MQTT) is a widespread protocol for topic-based publish-subscribe architectures supporting IoT and social networks applications. MQTT brokers are logical entities that couple publishers and subscribers and play a critical role in such architectures. MQTT brokers can be implemented either as standalone servers or in a cluster configuration. Clusters of brokers increase reliability, availability and overall performance, since operations can be highly parallelized among the brokers that form the cluster. The load-balancing strategy in a cluster usually consists in connecting an incoming client to a randomly selected broker. This random-attach strategy, it is very simple, but generates a significant amount of inter-broker traffic, as we demonstrate through theoretical and experimental evaluations. Inter-broker traffic is an overhead for the system and it increases the CPU load of the brokers, compromising the scaling behavior of the whole cluster. Indeed, we found that a linear increase of the number of brokers forming a cluster does not necessarily provide an equivalent linear gain in performance, and such a scaling penalty can be surprisingly significant, in the order of 40%. To solve this issue and improve performance, we propose a novel load-balancing strategy that envisages the use of multiple MQTT sessions per client to reduce inter-broker traffic and that can be implemented by means of a greedy algorithm. We show feasibility and effectiveness of our strategy for IoT and social-network applications by means of simulations and real measurements. The resulting scaling penalty is reduced to 10%.