Miracle-dz/UniFAR-FTUs huggingface dataset:
Log-Euclidean Metric-Based Signal Detector with Manifold Filter and Matrix Information Geometry

This paper proposes a Log-Euclidean metric (LEM)-based signal detector with a manifold filter in the framework of matrix information geometry to deal with the problem of detection of signals in the background of noise or clutter. In particular, the problem of signal detection is reformulated into the problem of discriminating two matrices on the manifold. At the design stage, we assume that the sample data is modelled as a Hermitian positive-definite (HPD) matrix. A geometric median detector is designed by resorting to the LEM on the HPD matrix manifold. The geometric median is very robust to outliers that are caused by the interference or the variation of clutter power. Additionally, we design a manifold-to-manifold projection by employing the manifold filter to derive a more discriminative manifold with the same dimension. Signal detection on this manifold can yield a significant performance improvement. Numerical results show that the proposed detector achieves a detection performance improvement over the conventional detectors and its state-of-the-art counterpart, especially in cases of limited data and interference environment.
Diagnosis of COVID-19 with a Deep Learning Approach on Chest CT Slices

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) first broke out in Wuhan, China and COVID-19 disease spread throughout the world by its highly contagious nature. High death numbers have caused a massive panic across the globe. Fast and early diagnosis is the key for preventing the virus from spreading. Besides PCR test, computed tomography (CT) of lungs is also used for diagnosis of COVID-19. Since the amount of testing kits for the diagnosis is insufficient and the conventional diagnosis methods are slow, developing AI-based fast diagnosis tools is not only an alternative way but also an urgent requirement for such alarming situations as those people faced with today. In this study, we employed three popular CNN models, VGG16, VGG19, and Xception, to classify CT scans of suspected patient cases as COVID-19 infected and non-COVID-19. VGG16 achieved 93% accuracy with the best parameters on the test set.
Accelerating All-SAT Computation with Short Blocking Clauses

The All-SAT (All-SATisfiable) problem focuses on finding all satisfiable assignments of a given propositional formula, whose applications include model checking, automata construction, and logic minimization. A typical ALL-SAT solver is normally based on iteratively computing satisfiable assignments of the given formula. In this work, we introduce BASOLVER, a backbone-based All-SAT solver for propositional formulas. Compared to the existing approaches, BASOLVER generates shorter blocking clauses by removing backbone variables from the partial assignments and the blocking clauses. We compare BASOLVER with 4 existing ALL-SAT solvers, namely MBLOCKING, BC, BDD, and NBC. Experimental results indicate that although finding all the backbone variables consumes additional computing time, BASOLVER is still more efficient than the existing solvers because of the shorter blocking clauses and the backbone variables used in it. With the 608 formulas, BASOLVER solves the largest amount of formulas (86), which is 22%, 36%, 68%, 86% more formulas than MBLOCKING, BC, NBC, and BDD respectively. For the formulas that are both solved by BASOLVER and the other solvers, BASOLVER uses 88.4% less computing time on average than the other solvers. For the 215 formulas which first 1000 satisfiable assignments are found by at least one of the solvers, BASOLVER uses 180% less computing time on average than the other solvers.
Research on programmed operation of power grid equipment based on Natural Language Processing

With the expanding of State Grid structure, operation instructions logics of power grid are increasingly complex, which makes it even more difficult to compile operation instructions based on the dispatchers’ experiences, consequently may lead to great risks in safe running of the power grid. This paper proposes an artificial intelligence method adopting NLP (Natural Language Processing) technology, this method can get the computers understand power grid operation tasks by themselves and automatically generate operation instructions with the deduction method designed by the authors. To reduce the dispatchers’ workload and to improve the accuracy and safety of operations, these instructions need to be checked for safety before and after execution.
Optimization of Super Capacitor Buffered Dynamic Wireless Power Transfer System

Wireless power transfer (WPT) systems operating in megahertz (MHz) have advantages of small size and high tolerance for coil misalignment. Existing MHz WPT systems mostly focus on static scenarios. But in real applications, many systems require dynamic charging and it is desirable to achieve longest charging distance with fewer number of transmitting coils. In this paper, super capacitor (SC) is added to the receiving side as an energy buffer. SC stores energy when the coupling is strong, and releases energy when the coupling is weak. In a certain range, a larger capacitance lead to a higher distance between transmitting coil which can significantly reduce the construction cost. However, excessive capacitance of the SC will increase the weight and volume of the system which will reduce the receiver power density. Therefore, this paper uses genetic algorithm (GA) to optimize the pole spacing between two adjacent transmitting coils and capacitance of SC. A set of optimized parameters was selected for simulation verification. Simulation results match well with the theoretical analysis, which implies that compared to the dynamic WPT system without SC, the SC buffered WPT system can effectively increase power density and decrease construction cost.
An investigation on digital signal processing for coherent optical communication system with advanced modulation format

In recent years, many new applications such as cloud computing, Internet of Things (IoT), artificial intelligence (AI) are developing rapidly, which put large capacity requirements on communication network. Coherent optical communication systems based on advanced modulation format have been extensively studied to improve transmission capacity. High-order modulation format requires a better signal-to-noise ratio (SNR) for signal receiving. However, the various damages in transmission links would decrease SNR. In this paper, digital signal processing (DSP) techniques are investigated to compensate signal damages, including Time-domain equalization (TDE) algorithm to compensate chromatic dispersion (CD), constant mode algorithm (CMA) and direct decision least mean square (DD-LMS) algorithm to compensate polarization mode dispersion (PMD), fourth power algorithm to correct frequency deviation, Viterbi & Viterbi and Blind Phase Search (BPS) algorithms to estimate phase noise. Moreover, constellation shaping technology is also discussed to make the transmission capacity closer to Shannon limit.
A DNN Framework for Secure Transmissions in UAV-Relaying Networks with a Jamming Receiver

Unmanned aerial vehicle (UAV) communication is one of the enabling technology in the 5G/B5G era, which has received extensive research attention recently. In this paper, we consider a UAV employing amplify-and-forward protocol to facilitate the secure transmissions for a legitimate user pair, where the direct link is not available. In order to protect the legitimate transmissions and prevent the eavesdropper from intercepting the source message, the legitimate receiver launches a jamming signal to confuse the eavesdropper. In this regard, the optimal jamming power and UAV placement are jointly investigated towards the highest secrecy rate. For this problem, we first propose an efficient power control scheme based on bi-section search with fixed UAV placement. Then, a deep neural network (DNN) is constructed and trained based on the data from exhaustive search in order to determine the best UAV placement. Through numerical simulations, we verify that the trained DNN effectively approximates the optimal UAV placement, also demonstrated is the evident superiority of our proposed scheme as compared to the baselines in terms of security performance.
Arrhythmia Classification on Electrocardiogram Signal Using Convolution Neural Network Based on Frequency Spectrum

Heart disease is the leading cause of death in the world. To find out heart disease early, it can be detected by examining the presence or absence of arrhythmias. Arrhythmia is an abnormal heart beat rhythm, can beat too fast, too slow, or beat with irregular patterns, so that the arrhythmia has many types. To diagnose arrhythmias, one method that can be used is by analyzing ECG (Electrocardiogram) signals. Currently, doctors and medical personnel analyze ECG signals manually. Because the number of cardiologist paramedics is far less than the number of patients, patients need hardware or software to analyze the heart independently. With the development of technology in this era, there is a technology called Deep Learning. Deep Learning is a development of Machine Learning. In this paper, we proposed one method of Deep Learning, namely Convolutional Neural Network (CNN), is used to classify 5 types of arrhythmias on ECG signals, that are: Normal Beat (NOR), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Premature Ventricular Contraction (PVC) and Fusion of Ventricular and Normal (FVN). Evaluating the performance of our CNN architecture, we tested it to classify the heart beat in the MIT BIH Arrhythmia database. The performance results of our proposed have highest accuracy is 98.6% with the following details: 713 Normal Beat samples can be detected correctly (100%), RBBB 689 samples (96.63%), LBBB 710 samples (99.58%), FVN 713 samples (100%), and PVC 690 samples (96.77%).
A Simple Phase Adaptation Algorithm for Compact Microwave Power Transmitter Array

In this paper, a simple phase adaptation algorithm that can reduce the system complexity of the microwave power transmitter (Tx) array is proposed. The proposed algorithm finds the optimal transmission phase through the variation of the received power according to the change in the power transmission phase. In order to compare the proposed algorithm to the conventional retroreflective method used in many recent microwave power transmission (MPT) systems, a massive Tx array operating in the 5.2 GHz band was implemented. Using the implemented system, power transfer experiments were conducted using both methods. No difference was found for the received power levels using both methods. Number of measurements, which is required to reach the optimum transmission phases for the maximum received power, is presented according to the number of array elements using the proposed algorithm.
Energy Efficient Resource Allocation for Wireless Power Transfer Enabled Massive MIMO Systems with Proportional Fairness

In this paper, an energy efficient resource allocation algorithm for massive multiple-input-multiple-output (MIMO) systems powered by wireless power transfer (WPT) with proportional fairness is investigated. In the system, multiple sensor nodes (SNs) harvest energy from the power beacon (PB) by WPT, and then the SNs communicate with the base station (BS) by exploiting the harvested energy. A proportional fair energy efficient resource allocation algorithm that combines time and power allocation is proposed to maximize the energy efficiency (EE). Compared with the minimum EE maximization algorithm and the total user’s energy efficiency maximization algorithm, simulation results verify the proposed algorithm can obtain a good tradeoff between the total EE of the system and minimum EE of the users.
A Novel Optimal Transmitting Source for Midfield Wireless Powering of Micro-implant

Midfield wireless power transfer has been proven as a solution for biomedical micro-implant that can operate at nearly any location in the body. Some previous experiments using an external patterned structure as optimal source demonstrated magnetic and current density focusing characteristic on multilayers tissue, therefore, significantly enhancing efficiency for wireless powering to miniature receivers than conventional inductively coupled coils. This paper proposed a novel compact and simple source transmitter while conserving convergence effect on human tissue.
Investigation on Probabilistic Shaping for Symbol-Level Uplink Non-orthogonal Multiple Access Visible Light Communication Systems

In this paper, we investigate the performance of probabilistic shaping for uplink non-orthogonal multiple access (NOMA) visible light communication (VLC) systems. The optimal decision threshold for hard decision decoding is analyzed to improve symbol error rate (SER) performance. Furthermore, we propose a constellation-adaptive approach to find the optimal tradeoff between altering the constellation scale and changing the probability distribution. Finally, we evaluate the achievable sum rate in two-user uplink NOMA VLC systems. The simulation results show that the proposed approach can both decrease the SER and increase the achievable sum rate.
When Directory Design Meets Data Explosion: Rethinking Query Performance for IoT

As IoT services scale up from single homes to smart cities, directories and mapping services are needed to manage potentially millions of devices. However, directory service providers will likely struggle to accommodate the increasing number of IoT devices, made more challenging by their heterogeneous metadata and the large volume of queries. One of the critical challenges, the high heterogeneity of IoT, is being addressed by a working standard of W3C, which formalizes a physical or virtual device as a formatted Thing Description (TD). We propose a local directory service architecture with a series of design requirements. With a focus on query performance, we build a proof-of-concept system to store metadata of IoT devices as TDs in terms of the working standard. A Raspberry Pi is configured to investigate the query performance of relational database and non-relational database as the classic choices for internal directories. Evaluation results demonstrate that compared with relational database, non-relational database can achieve 2.9 times higher resilience on property query and 2.35 times faster processing on spatial query, with mild loss on aggregation query.
Online and Unsupervised Lidar Calibration based on Grid Maps

Lidar sensors are an essential sensor for autonomous driving and advanced driver assistance systems. This paper proposes an accurate method for an online and unsupervised estimation of the yaw-mounting error of lidar sensors. A precise estimation is essential, when the extracted information of the lidar, like objects or obstacles, are fused with other sensors or interpreted in a road context, like lane assignment. The approach is based on a grid map, which is a discretized representation of the environment around the ego vehicle and enables an efficient processing of the large number of lidar detections. The proposed algorithm extracts features out of grid maps of two measurement cycles and reconstructs the ego movement. The comparison with the vehicle odometry enables the reconstruction of the lidar misalignment. By combining a large number of measurement cycles, a robust and precise estimation of the lidar misalignment is obtained. Features from the image domain could be directly applied, enabling an efficient and real-time calibration in a productive system. The system identifies itself valid driving sections and performs a continuous recalibration. Simulation and real experiments show a promising accuracy with an orientation error below 0.05° for roof-top lidars and 0.1° for low-cost lidars.
Wireless Power Transfer System for an Autonomous Electric Vehicle

KAIST (Korea Advanced Institute of Science and Technology) and KU (Khalifa University) in UAE have been progressing joint research of the semi-dynamic wireless power transfer system for autonomous electric vehicle from year 2018. The presented semi-dynamic wireless system consists of short length (1.5m) of power line for static charging and long length (5m) of power line for dynamic charging around vehicle stop. In this paper, several important system parameters are obtained through the magnetic simulation and the prototype system is implemented in the laboratory. Finally, the performance of the presented system is verified through test result, that is, the maximum power efficiency is 90.8% for static charging and is upper 85% for dynamic charging.
Machine Learning Based Plant Diseases Detection: A Review

Currently, applying machine learning technologies for management and monitoring of agricultural products are gaining significant interests. One of them is for plant diseases detection. Plant diseases are major cause of crop losses. The existence of automatic plant diseases detection is essential to predict the plant diseases as early as possible, and hence, reducing the crop losses. In this paper, we presents a review of advancement of machine learning technologies for plant diseases detection. Various approaches have been proposed in the field. In this review, we group them into two: works that focus on finding good features for shallow machine learning architectures such as SVM, those that focus on applying deep architectures of machine learning such as deep convolutional neural networks (CNN). For the later, we observe that the works either applied CNN as classifier or as feature learning. Our survey shows that while (CNN), have become the lead technologies in the field, replacing shallow architectures like SVM, many challenges still remain. First is the issue of robustness against environmental conditions. Second in on how to deal large variety of data and diseases with limited number of data.
Can Dynamic Social Events Impact Our Travel Patterns And Trends?

In recent years, travel route planning and recommendation systems have earned a great popularity. Although several works have focused on suggesting lists or sequences of Point of Interests (POIs), they do not address POIs with dynamic spatio-temporal happenings (i.e. live events) that may attract the tourist’s attention. In this paper, we present a system aiming to generate dynamic and personalized travel routes at a given destination based on tourist preferences and dynamic events that may attract large number of tourists. To achieve this, a rule-based classification model is developed to classify geotagged posts collected from Twitter into tourism related posts and non-related posts and further classify the tourism-related posts into types of events in terms of promoting the tourism industry such as festivals and sport tournament. Tourism-related events are detected from the classified posts after clustering all posts of each event type based on location, and then determine unusual temporal slices that may contain significant events and meet tourists’ interests and preferences. Our system finally generates a travel route of the top ranked attractions (events) by computing the similarity between the properties of each attraction and the tourist’s preferences and the attractions, the top ranked attractions are visualized on a dynamic map. Our system is applied on a real dataset collected from Twitter from the city of Dubai in United Arab Emirates.
Accurate Localization of Autonomous Vehicles Based on Pattern Matching and Graph-Based Optimization in Urban Environments

Accurate and reliable localization is a prerequisite for autonomous driving. Methods based on sparse landmarks, such as pole-like structures, have been widely studied because of their lower requirements for computing and storage. However, the number of landmarks of a single type is not always sufficient for reliable positioning. We propose a localization method using three different types of features in urban environments. The features we choose are poles, corners and walls which are persistent over time and can be reliably detected with LiDAR sensors. A pattern matching method for data association is introduced. Instead of using a filtering method, we adopt the graph-based optimization method to solve the pose estimation problem. Experiments conducted on two test roads show that the proposed method can provide accurate and reliable localization results in urban environments.
Efficiency analysis of Massive MIMO systems for 5G cellular networks under perfect CSI

The problem of finding theoretical sum-rate and spectral efficiency expressions for each mobile user that matches closely to realistic 5G models reduces to the application of these theoretical analysis to practical 3GPP spatial model with both the Line-Of-Sight (LOS) and Non-LOS (NLOS) propagation paths. To this end, we propose a solution to this problem by applying an uplink theoretical analysis of a massive MIMO system, with multiple cells, to the 3GPP model. In this paper, it is assumed that a BS has perfect knowledge of the channel estimates and two practical linear receivers namely the Maximum Ratio Combining (MRC) and Zero-Forcing (ZF) are considered. The Signal and Noise Interference Ratio (SINR) analysis for our proposed massive MIMO system is described and closed-form expressions are derived for the sum-rate, spectral and energy efficiencies for each mobile user with random displacement in the multi-cell network. Our analysis takes into account the effect of inter-cell interference or commonly known as pilot contamination. It is shown through simulations that when the BS antennas increase both the sum-rates and efficiencies significantly increase for the realistic 3GPP spatial channel model as compared to the analytical uncorrelated Rayleigh channel. It is observed that the spectral efficiency of mobile users for the 3GPP model is 60 bps/Hz higher than the Rayleigh counterpart when a ZF receiver is implemented at the BS with 300 antennas.
Robust vehicle pose estimation from vision and INS fusion

The vehicle relative pose, referenced to road boundaries or lanes, is fundamental for an autonomous driving vehicle to perform motion planning. The estimate of relative pose includes the difference between the car heading angle and road center-line direction (i.e., the relative heading angle), together with the lateral displacement of the vehicle from the road center-line. This information can be equally derived by an inertial navigation system equipped with GPS receivers or by a vision system based on cameras. However, both solutions present some disadvantages. GPS-based estimates of heading angle shows loss of accuracy during low-speed manoeuvres or within tunnels. On the other hand, a line-fitting based estimation typical of vision systems suffers from lines accuracy, and they are hard to be implemented in an urban environment or high-curvature track scenarios. This work presents an innovative integrated algorithm that fuses those two approaches, providing vehicle relative pose with respect to road lane combining data coming from an inertial navigation system and a line detection algorithm. This integrated solution is then able to always feed the panning algorithm with an accurate estimate of the vehicle pose in multiple challenging scenarios.