Miracle-dz/UniFAR-FTUs huggingface dataset:
A fast matrix-vector multiplier in discrete vortex method

A general scheine of a fast matrix-vector multiplier for a matrix generated by a smooth enough function of two vector variables is proposed. Fast algorithms for 2D and 3D discrete vortex method problems are obtained as the special cases of the general scheme. The results of numerical experiments which demonstrate the actual efficiency of the 2D fast algorithm, are given. Any numerical implementation of the discrete vortex method can be reduced to performing two principal operations, namely: (1) the solution of a linear algebraic system; (2) the multiplication of a matrix by a vector. Computing time is essentially determined by the two operations. At present, iterative processes requiring usually only few iterates have been constructed for problem (1). Therefore, problem (1) can be reduced to a few operations of problem (2), and the latter turns out to be the only bottleneck in implementing the discrete vortex method. A rather efficient accelerated matrix-vector multiplier has been developed for the 2D discrete vortex method in [8], The asymptotic complexity of similar 3D problems is under study in [6]. In this work we suggest an algorithm generalizing the approaches in the above works. In fact, we present a general scheme that is not confined to the discrete vortex method solely. An important point is that a matrix being multiplied by a vector is generated by a rather smooth function. The 2D and 3D discrete vortex method algorithms are then derived as particular cases of the general scheme. We study the following problem. Let f(x,y) be a function of two variables χ and y, where χ = (xl9x2> · · · >n)> y ~ (3Ί>^2> · · · »3/j) are real-valued vectors, χ e Ωχ c R, yeQ cR. Suppose that we have a set of Ν vectors *', Μ vectors y*, and, in addition, Μ real numbers g. The problem is to calculate Ν quantities of the form:
Inertia Estimation for Interconnected Networks and its Application to the Uruguayan Power System

This paper describes a technique for estimating the inertia of a power system based on frequency and active power measurements following a power imbalance. The technique was specially designed to estimate the inertia of a power system connected to another one. Classical techniques for inertia estimation are not applicable for this case since the power imbalance will be far to be constant. The possible error sources are examined in detail with the help of the New England benchmark. The technique is applied to the Uruguayan power system and some of its subsystems with the help of field synchrophasor data.
Comparative Analysis of Testing Black Hole Attack and Rushing Attack on VANET (Vehicular Ad-Hoc Network) with AOMDV Routing Protocol

Increased transportation and communication allow communication between vehicles to occur. Wireless technology such as VANET (Vehicular Ad-Hoc Network) is a network that can communicate vehicles to vehicles (V2V) technology and make safety and comfort when driving. VANET networks are still vulnerable to malicious nodes which can damage data communication on the VANET network. Offensive attacks against VANET networks are rushing attacks and black hole attacks. The routing protocol used in this study is AOMDV. This study uses a simulation method and using supporting applications such as OpenStreet Map, SUMO, NS2, NAM, and AWK to do simulations. This study uses the Quality of Service (QoS) parameters such as throughput, packet loss, packet delivery ratio, and end-to-end delay. The number of simulation nodes is as many as 30 nodes, with the condition of 10 black hole nodes and 10 rushing nodes in a simulation it is different from the attacker node that appears at the beginning of the appearance of the node. In comparison testing of a black hole and rushing attacks, the results show that the throughput value decreases, packet loss increases, the packet delivery ratio decreases and the end-to-end delay results increase.
Dynamic Fog Intelligence with Flow Control for Green Internet of Things

With the growing number of green Internet of Things (IoT) applications, the underlining network and decision services need more Machine Learning (ML) models at the same time while reducing latency and utilized energy. In this paper, we propose HI-SDN, a Heterogeneous fog Intelligence enabled architecture for complex Software-Defined Network (SDN)-based IoT network running applications such as a smart city and healthcare. HI-SDN proposes a fog node and ML algorithm selection mechanism with dynamic traffic forwarding for green IoT to reduce delay and energy consumption. It adopts a reconfigurable approach to the ML method to dynamically change the required prediction policy and flow rules to be executed in a selected edge/fog node for critical computation. The results from the performance analysis show that HI-SDN has a substantial reduction in delay by 32% and consumed energy by 25%, in comparison to the existing state-of-the-art, besides having an increase in packet delivery ratio.
Deep neural networks for gas concentration estimation and the effect of hyperparameter optimization on the estimation performance

Many studies propose gas concentration estimators using machine learning algorithms owing to their high performance. Recently, estimation models using deep neural network have been studied due to their higher performance than conventional machine learning algorithms. The performance of deep neural network can be increased by hyperparameter optimization. In this paper, we propose two deep neural networks for gas concentration estimation and analyze how hyperparameter optimization affects the performance of the proposed deep neural networks. We optimize the hyperparameters of the proposed neural networks and compare the performance with conventional machine learning models. We train the proposed neural networks and evaluate the performance of the models with an open dataset. We confirm that the optimized neural network models show the high performance in gas concentration estimation, and that models using unoptimized parameters may show worse performance than conventional machine learning model.
Exact and Scalable Network Reliability Evaluation for Probabilistic Correlated Failures

Network reliability, that is, the probability of con-necting specified nodes under link failures, is a key metric of network infrastructure. Because network reliability evaluation is a computationally heavy task, past research has relied on unre-alistically simple failure models such as the independent failure model, wherein each link fails stochastically and independently, ignoring large-scale failures such as disasters, or the deterministic-correlated failure model, wherein all links within a disaster area always fail. However, actual networks follow the probabilistic-correlated (PC) failure model, wherein links in a disaster area fail stochastically with respect to each disaster. This paper proposes an efficient method to accurately compute network reliability under the PC model. Following a conventional method for the independent model, the proposed method uses binary decision diagrams (BDDs) to efficiently handle an exponential number of failure states. Additionally, it employs a probabilistic inference technique to support probabilistic correlation, which is represented as another BDD for integration with the conventional method. The computational complexity was theoretically analyzed, and its performance was experimentally verified; it can compute the network reliability within 1 h for a large network with nearly 200 links and 100 potential disasters.
A Control of Magnetic Resonance Wireless Power Transmission to Multiple Devices Using the SP-PS Coupling Circuit

We have proposed the use of an SP-PS coupling circuit in the magnetic resonance wireless power transmission systems. The efficiency of power transfer can be controlled by tuning capacitance ratio of the coupling circuit. In this paper, we extend the range of utilization of the SP-PS coupling circuit to power supply to multiple objects. After explaining the characteristics of the SP-PS circuit, we will propose and verify the control of power supply to multiple devices using the SP-PS circuit by the simulation and experimental results.
Microphone System Based Path Following Control of Quadrotor Unmanned Vehicle Autonomous Flight

Currently, interest in autonomous driving of small quadrotors in various ways is increasing. In most cases, quad-rotor autonomous driving is mainly performed with vision data acquired using cameras. However, this method is challenging to implement in an environment where using a camera is difficult, such as in a space with insufficient light. In this work, we designed a low-cost autonomous-driving system for a drone using a microphone sensor instead of vision data using a camera. The microphones are connected to a data acquisition system (DAQ) to read data, and the laptop connected to the DAQ controls the operation of the drone using the data values in real time. We used a microphone sensor to measure the sound of the quadrotor to estimate its position and set up several flight paths to test the autonomous flight of quadrotor. Unlike drones that use vision data, our research has presented a new method in the field of quadrotor autonomous driving by implementing autonomous driving of drones using a microphone sensor.
Cluster-based Federated Learning Framework for Intrusion Detection

With the rapid development of Industrial Internet, the network intrusion detection has become particularly important. In the Industrial Internet, large-scale data is distributed in the edge nodes caused the joint analysis of network intrusion detection at each edge node has become necessary. Federated learning structure can avoid data out of local nodes to protect user privacy data. However, the data distribution is different for each edge nodes, which limits the effectiveness of federated learning models. We focus on the non-IID data features and propose a new cluster-based federated learning framework for network intrusion detection. In this method, we cluster clients into different communities by data labels, which the clients contain the similar proportion of data labels in the same community. Based on the clustering results, we decompose federated learning model aggregation into cluster aggregation and global aggregation by leveraging similarities both within and between clusters. We conduct extensive experiments based on UNSW_NB15 dataset. The results show that our method has better performance than FedAvg and FedProx. It can work well in scenarios with different distributions of data samples while ensuring data security and privacy protection.
Extraction Algorithm for the Characteristics of the Microwave Connector with Correction Algorithm

This paper introduces an algorithm that accurately extracts the characteristics of the microwave connector. The characteristics of the microwave connector can be extracted by measuring the thru 1 and thru 2 structure. The thru 1 structure is one in which two connector structures face each other. The thru 2 structure is one that has the known line which assumes that the characteristics are obtained by calculation. The characteristics of the connector structure were accurately extracted by applying T model of the thru 1 and thru 2 structures to the proposed algorithm in the 0.1-18 GHz range.
On Generalized Homogeneous Leader-Following Consensus

The Multi-agent System (MAS) with agents being linear single input plants is considered. The problem of a design of a leader-following (generalized homogeneous) consensus control protocol is studied under the assumption that a control of the leader is unknown but possibly bounded by a known constant. It is shown that the required control protocol can be obtained as an "upgrade" of the existing linear consensus control protocol.
Detection of Driver Gene Mutations from Thoracic CT Images Based on LightGBM with Radiomics Features

Lung cancer is one of the most common cancers worldwide and has become a general medical problem. To lessen the risk of death, early detection and treatment is particularly required. The patients can use molecularly targeted drugs when the driver gene mutations of the cancer are detected, but invasive biopsies are required. So, development of new methods to detect it noninvasively and in a short time are expected. we propose a new machine learning method for identifying the presence or absence of driver gene mutations of lung cancer on Thoracic CT Images that is a non-invasive, in a short time, and low-cost CAD (Computer Aided Diagnosis) system. In the proposed method, radiomics features are given as explanatory variables in addition to Thoracic CT Images, and supervised learning using LightGBM is performed to conduct binary classification with/without driver gene mutations.
Compensation Approach for Miniaturized Wilkinson Power Divider Design

Although coupled microstrip line structure are widely used in various microwave components for miniaturization, because of its phase velocity difference in Wilkinson power divider design, port reflections (S11, $S$22 and $S$33) cannot match with port isolation (S32) at the same center frequency. In this paper, several compensation approaches are summarized for compact Wilkinson power dividers with single-band application. By adding extra lumped elements, capacitor or inductor can be shunted into the coupled microstrip line structures. Considering less parasitic effects around lumped elements, extra 50 Ω transmission lines and open-circuited stubs are preferred for high frequency applications. Finally, several selected examples are fabricated and measured, all the simulated results are matched with measured results very well.
Fuzzy Reinforcement Learning Based Trajectory-tracking Control of an Autonomous Mobile Robot

The aim of this study is to achieve trajectory-tracking of an Autonomous Mobile Robot (AMR). The proposed Fuzzy Reinforcement Learning Control (FRLC) takes fuzzified sliding surfaces consisting of tracking errors and local velocities as inputs and generates actuator voltages as outputs. The relationship between inputs and outputs is reasoned by using the trained knowledge of a reinforcement learning agent. Simulations are conducted to investigate the performance of the proposed FRLC. It is shown that the proposed FRLC has excellent control performance for trajectory-tracking without reasoning human designed.
Beam Switching with Low Control Overhead for Millimeter-wave Communication

Mm-wave communication using massive Multiple-Input Multiple Output (MIMO) is highly directional in nature, hence making beam scanning and alignment a crucial aspect. In high mobility scenarios, frequent beam scanning may incur a performance degradation due to high control overhead. In this paper, we have adopted a supervised learning approach to determine the precoders and combiners for a hybrid beamforming architecture. To avoid frequent beam scanning and switching, we propose an overhead-aware strategy that addresses a trade-off between the spectral efficiency (SE) and the overhead signalling. Simulations are performed using channel data generated using the open source channel simulator NYUSIM.
Autonomous Research Platform for Cleaning Operations in Mixed Indoor & Outdoor Environments

In recent years, following the advances in autonomous driving technology, low-speed autonomous service vehicles such as delivery, patrolling, and road-cleaning vehicles have started to emerge. As a promising future cleaning solution, autonomous cleaning vehicles are expected to address the workforce shortage many countries face in the near future. This paper describes the detailed design of an autonomous research platform for cleaning operations in mixed indoor & outdoor environments. An electric manual vacuum sweeper is retrofitted into an autonomous sweeper equipped with the Drive-by-Wire (DBW) system, computer, sensors, and actuators essential for autonomous driving. A complete autonomous driving software stack is also developed upon this hardware setup to enable the vehicle to navigate itself safely in various challenging operating environments. The system has been extensively tested in different environments on the National University of Singapore (NUS) campus, including private roads, car parks, warehouses, and public plaza areas.
Piecewise Weighted Smoothing Regularization in Tight Framelet Domain for Hyperspectral Image Restoration

Hyperspectral images captured by remote-sensing satellites are easily corrupted by various types of noise. Generally, hyperspectral signatures appear to be scattered in spatial-spectral domain, as well as noise. In transform domain, however, the principal components of a image are often centralized in the low-frequency band, while noise and some details are mainly contained in high-frequency components. The traditional transformation domain based smoothing methods take no account of the respective information distribution carried by different frequency bands. Hyperspectral image restoration aims to remove mixed noise for clean data, which usually amounts to an ill-posed inverse problem. Moreover, the matrix-decomposition-based model needs to reshape hyperspectral datacube into matrix form, which will lead to certain loss of spatial structure information. In order to address these issues, this paper incorporates piecewise weighted smoothing regularization in tight framelet domain to reformulate a novel convex model for hyperspectal image restoration, which maintains image signature to a great extent. Based on the noise-perturbed degradation model, the smoothing regularizer in tight framelet domain is imposed on abundance signature, in which the transformation matrix, as well as the weighted coefficient, is well designed for different frequency bands. As a surrogate of sparsity, the $l_{q}$ -norm is employed to denoise for the enhancement of hyperspectral signatures. To the end, an efficient solver is carefully designed to derive the closed-form solutions by proximal alternating optimization. The experimental results on several synthetic and real datasets, not only demonstrate that the performance of proposed method is better than that of the current state-of-the-art approaches, but also verify the validation of regularization terms for hyperspectral image restoration.
Delay Optimization in LoRaWAN by Employing Adaptive Scheduling Algorithm With Unsupervised Learning

Low Power Wide Area Network (LPWAN) technologies have been exponentially growing because of the tremendous growth of the Internet of Things (IoT) devices across the globe. Several LPWAN technologies have been utilized by the researchers to address certain issues like increased number of collisions, retransmissions, delay, and energy consumption. However, Long Range Wide Area Network (LoRaWAN) is the most suitable and attractive technology in terms of delay optimization, low cost and efficient energy consumption. The main issue which arises in LoRaWAN is because of its high packet drop rate due to collision. The reason behind this packet drop rate is the MAC scheme known as Pure Aloha used by LoRaWAN for the transmission of the frames. Long Range (LoRa) End Devices (EDs) initiate communication with Pure Aloha that leads to a high number of retransmissions. These retransmissions further enhance the delay in LoRa networks. This paper aims to optimize the delay in LoRaWAN by using an Adaptive Scheduling Algorithm (ASA) with an unsupervised probabilistic approach called Gaussian Mixture Model (GMM). By using ASA with GMM, the retransmissions are reduced which optimizes the delay in LoRaWAN. The results show that in our approach, Packet Collision Rate (PCR) is reduced by 39% as compared to conventional LoRaWAN. In addition, the Packet Success Ratio (PSR) is also increased by 39% as compared to the conventional LoRaWAN and Dynamic Priority Scheduling Technique (PST). Further, the delay is optimized by 91% and 79%. This research could be effective for the environments where the critical data of patients need to be sent with optimised retransmissions and minimum delay towards gateways.
The MPU RX-4 Project: Design, Electronics, and Software Development of a Geofence Protection System for a Fixed-Wing VTOL UAV

Unmanned aerial vehicles (UAVs) are at the forefront of this century’s technological shift, becoming ubiquitous in research and market areas. However, as a UAV navigates autonomously, there are unanticipated occasions, e.g., collisions with dynamic obstacles or loss of data provided by the global navigation satellite system (GNSS), where the aircraft has to change its mission plans. In particular, to be protected from possible accidents, the platform’s geofence protection system should adjust the trajectory appropriately when obstacles are detected and select the proper ground surface when emergency landing is prompted. As these processes require fast reaction times, utilizing low latency sensors and algorithms is necessary. This article proposes a complete and low-complexity geofence protection system for recognizing moving objects and assessing the ground surface’s suitability using onboard sensing and processing modules. The proposed system is implemented on a novel fixed-wing UAV, designated as MPU RX-4, which features an unconventional flying wing layout and vertical take-off and landing (VTOL) capabilities. Our system is based on a forward-facing laser imaging, detection, and ranging (LIDAR) sensor and three downward-facing laser rangefinders. We take advantage of the high-precision distance measurements and operational speed to identify moving obstacles using the LIDAR module, while the ground’s slope and the existence of any obstacle therein are computed through the rangefinders. First, the article describes the UAV design procedure and its aerodynamic performance characteristics, which allowed us to evaluate our approach on a testbed aircraft. Then, the evaluation protocol shows that our system can perform robustly and under real-time constraints reaching an overall latency of only 165.5 ms, sufficient for reliable detection and avoidance of moving obstacles.
A Model-free False Data Injection Attack Strategy in Networked Control Systems

The data-driven attack strategies recently have received much attention when the full knowledge of the system model is unknown or difficult to be obtained for the adversary. Note that despite the critical parameters of the system model being unavailable for the adversary, the existing data-driven attack methods still depend on the linearity of the unknown system model. In this paper, we design a completely model-free attack strategy where the adversary with limited capability aims to compromise state variables such that the output value follows the expected trajectory. Specifically, we first construct a zeroth-order feedback optimization framework and uninterruptedly use probing signals for real-time measurements. Then, we iteratively update the attack signals along the composite direction of the gradient estimates of the objective function evaluations and the projected gradients. These objective function evaluations can be obtained only by real-time measurements. Furthermore, we characterize the optimality of these solutions via the optimality gap, which is affected by the dimensions of the attack signal, the iterations of solutions, and the convergence rate of the system. Extensive simulations are conducted to show the effectiveness of the proposed attack strategy.