Miracle-dz/UniFAR-FTUs huggingface dataset:
A Scalable and Adaptive Tasks Orchestration Platform for IoT

Edge computing is an emerging computing paradigm that aims to solve the cloud limitations by bringing its applications closer to the Internet of Things (IoT) devices. Thanks to its horizontal scalability, this paradigm leverages from the rapid growth of connected devices and makes it in its favor. As a result, it improves the scalability and reduces the latency. However, the adoption of this paradigm alone does not guarantee to meet the quality of service (QoS). Due to the heterogeneity of those devices and their requirements, the QoS is more influenced by the nature of the devices that are responsible for offloading the task, and by their location, which complicates the offloading process. To address this issue, in this paper, we present a tasks orchestration platform for IoT. It focuses on the role of edge computing in order to guarantee a high scalability and enable the self-capabilities of IoT. We also present a tasks orchestration algorithm that is based on Fuzzy Decision Tree. It leverages from reinforcement learning which enables it to adapt to the unpredictable environmental changes. As opposed to the existing solutions, the proposed architecture has provided more scalability and low delays regardless of the number of devices. On the other hand, the proposed algorithm has reduced the tasks completion delay by nearly 32%, the energy consumption by 52%, and the failure rate by 44%, as compared to the state of the art algorithm.
Machinery Early Fault Detection Based on Dirichlet Process Mixture Model

The most commonly used single feature-based anomaly detection method for the complex machinery, such as large wind power equipment, steam turbine generator sets, and reciprocating compressors, exhibits a defect of low-alarm accuracy due to the non-stationary characteristic of the vibration signals. In order to improve the accuracy of fault detection, a novel method based on the Dirichlet process mixture model (DPMM) is proposed. First, the features of the mechanical vibration signals are used to construct the feature space of the equipment. The DPMM modeling method is then applied to self-learn the probabilistic mixture model of the feature space. The normal working condition model is used as the benchmark model. The early fault detection is realized by using a precise difference measurement method based on Kullback–Leibler divergence to calculate the difference between the real-time model and the benchmark model accurately, and by comparing the calculation result with a self-learned alarm threshold. The effectiveness and the adaptability of this novel early fault detection method are verified by comparing it to the single feature-based anomaly detection method and the Gaussian mixture model (GMM)-based early fault detection method.
An access point load-balancing algorithm based on service attribute in wireless network

With the large deployment of WLANs in public hotspots, mobile terminals can access network services anytime and anywhere. In the conventional IEEE 802.11, the terminal is usually connected to the AP(Access Point) with highest signal strength, which is prone to the problem of unbalanced AP load and the terminal service cannot operate normally. This paper proposes an access point load-balancing strategy based on service attribute in wireless network. It determines whether the AP is load balanced according to whether the current network status of the STA meets the requirements of service operation. If the AP is overloaded, the STA with poor running status is switched to other suitable AP. The optimal AP is calculated by the state information broadcasted by the APs in the network. The experimental simulation shows that the AP load balancing strategy based on the service attribute can guarantee the quality of service operation and achieve AP load balancing as a whole.
A New Distributed Constrained Multi-Agent Optimization Protocol with Convergence Proof via Exactness of Penalized Objective Function

This paper is concerned with distributed multiagent optimization with inequality and equality constraints based on exact penalty methods. In the literature of exact penalty methods, constrained optimization problem is solved through optimization of penalized objective function and, under mild assumptions, the set of the optimal solutions of the penalized objective function coincides with that of the original constrained optimization problem. Exploiting the exactness of the penalized objective function, this paper proposes a new distributed multi-agent optimization protocol which simplifies the update law of previous protocols based on exact penalty methods with equality constraints. Also more concrete proof of the convergence of the protocol is provided under the assumption of the exactness of the penalized objective function.
Optimal placement of sectionalizing switches for radial distribution grids

The improvement of network reliability indices through the minimization of the number of customers affected by power outages is a major challenge for Distribution System Operators. To this purpose, automated procedures for fault location, isolation and service restoration have been recently proposed. To maximize the benefits provided by such automation schemes, the strategic placement of remotely controllable switches in the network is essential. This paper proposes an optimization algorithm to define the optimum location of sectionalizing switches in radial distribution grid for enhancing reliability indices such as System Average Interruption Duration Index or Expected Energy Not Supplied. The proposed method is based on an Integer Linear Programming approach and allows determining the optimal placement of a given number of switches. Tests performed on a sample grid highlight the performance of the proposed optimization algorithm and show the potential benefits deriving from a smart allocation of switches in the network.
IoT based Appliances Identification Techniques with Fog Computing for e-Health

To improve the living standard of urban communities and to render the healthcare services sustainable and efficient, e-health system is experiencing a paradigm shift. Patients with cognitive discrepancies can be monitored and observed through the analyses of power consumption of home appliances. This paper surveys recent trends in home-based e-health services using metered energy consumption data. It also analyses and summarizes the constant impedance, constant current and constant power (ZIP) approaches for load modelling. The analysis briefly recaptures both non-intrusive and intrusive techniques. The work reports an architecture using IoT technologies for the design of a smart-meter, and fog-computing paradigm for raw processing of energy dataset. Finally, the paper describes the implementation platform based on GirdLAB-D simulation to construct accurate models of household appliances and test the machine-learning algorithm for the detection of abnormal behaviour.
Dissipative Filter Design for Interval Type-2 Fuzzy Singular Systems with Stochastic Perturbation

This paper concerns with the dissipative filtering problem for nonlinear singular system with stochastic perturbation. By considering the uncertainties exist in membership functions, the nonlinear system is modeled by interval type-2 fuzzy (IT2F) stochastic singular system. Based on the Lyapunov stability theory, a sufficient condition is utilized to prove that IT2F filtering error system is mean-square admissible and strictly dissipative. And the corresponding solution of the dissipative filter model is obtained by solving a set of linear matrix inequalities. A numerical example is provided to prove the correctness of the obtained result and the designed method.
Range Sensor-based Occupancy Grid Mapping with Signatures

Recently autonomous navigation technologies have received huge attention in many fields, such as autonomous driving or mobile robotics. Simultaneous localization and mapping (SLAM) [1] is a key problem in this field and the current paper targets the mapping part of it. Mapping means to construct accurate static or even dynamic representation of the observable environment. There are of course solutions for range sensor-based mapping, but the current paper proposes a new approach, which involves signatures. Signature is a mathematical data representation, which handles well uncertainty in complex systems.
A Novel on WMSN using Single path communication for Distortion information leakage and content delivery

Traditional routing metrics designed for wireless networks are application-agnostic. In this project, we consider a wireless network where the application flows consist of video traffic. From a user perspective, reducing the level of video distortion is critical. We ask the question “Should the routing policies change if the end-to-end video distortion is to be minimized? ” Popular link-quality-based routing metrics (such as ETX) do not account for dependence (in terms of congestion) across the links of a path; as a result, they can cause video flows to converge onto a few paths and, thus, cause high video distortion. To account for the evolution of the video frame loss process, we construct an analytical framework to, first, understand and, second, assess the impact of the wireless network on video distortion. The framework allows us to formulate a routing policy for minimizing distortion, based on which we design a protocol for routing video traffic. We find via simulations and testbed experiments that our protocol is efficient in reducing video distortion and minimizing the user experience degradation. Keywords: Wireless Multimedia Sensor Networks; Single path communication; Distortion; information leakage and content delivery
DNALS: A Recommendation Algorithm Based on Chinese Vocabulary Emotion Analysis of Songs

The lyrics play important roles in emotion expression of songs, as well as the emotion words of the lyrics reveal the emotion theme of songs. By extracting the lyrics’ emotion information, this paper uses songs’ emotion themes as the recommended standard. We get all kinds of emotion information extraction coefficient combining emotion gene sequences of lyrics which named DNALS (DNA for Lyrics of Songs), and then put forward the DNALS recommendation algorithm based on emotion analysis. By analyzing the emotion information of user’s historical music data, we recommend to the user a list of songs with similar themes emotion, so as to help the users to find songs for mood.
Enhanced Frequency Response of Inverter Dominated Low Inertia Power Systems

This paper addresses the problem of frequency stability of power systems highly penetrated by distributed energy resources interfaced through power electronics inverters. The integration of asynchronously connected generation to the grid results in an overall system inertia reduction, which as a consequence, deteriorates the system frequency response by increasing the system's sensitivity to sudden changes in generation-load balance as is recorded by the RoCoF and frequency nadir characteristics. This study proposes a novel design of a Synthetic Inertia control scheme equipped on the wind turbine generators (WTGs) that dynamically alters the inertial response provision of the WTGs to the system, assisting this way into a fast system frequency stability recovery. The feasibility of the proposed control scheme is validated on the IEEE 68 bus power system model accommodating solar and wind generation installations. All simulation studies were performed within the DIgSILENT PowerFactory 2018 simulation environment.
A New Structure of High Voltage Gain SEPIC Converter for Renewable Energy Applications

The paper proposes a new structure of SEPIC with high voltage gain for renewable energy applications. The proposed circuit is designed by amalgamating the conventional SEPIC with a boosting module. Therefore, the converter benefits from various advantages that the SEPIC converter has, such as continuous input current. Also, high voltage gain and input current continuity make the presented converter suitable for renewable energy sources. The modified SEPIC converter (MSC) provides higher voltage gain compared to the conventional SEPIC and recently addressed converters with a single-controlled switch. The analysis of voltage gain in continuous current mode (CCM) and discontinuous current mode (DCM) is analyzed by considering the non-idealities of the semiconductor devices and passive components. The selection of the semiconductor devices depending on the voltage–current rating is presented along with the designing of reactive components. The numerical simulation and experimental work are carried out, and the obtained results prove the feasibility of the MSC concept and the theoretical analysis.
Image Reconstruction without an Inertial Navigation System using Backprojection Autofocus for Synthetic Aperture Radar

We propose an extension of the backprojection autofocus approach to reconstruct synthetic aperture radar images with high quality from extreme coarsely measured flight paths. Usually, expensive and heavy inertial navigation systems are used to measure flight paths with high precision. Small antenna position errors up to approximately one half of the range resolution can be corrected by state-of-the-art autofocus techniques. Our approach is able to correct position errors much larger than this upper bound. For this purpose, we use the idea of the backprojection autofocus by Ash and estimate pulse-by-pulse phase errors by maximizing image sharpness. In contrast to Ash, we use additionally the estimated phase error of each considered pulse to update the aperture positions of all subsequent pulses. The evaluation of our autofocus technique using real airborne X-band data acquired in spotmode shows that the proposed method can correct antenna position errors of more than 12 m. Thus, our algorithm allows the usage of much cheaper or even no inertial navigation systems.
A Lightweight IoT Security Solution

In this paper, a lightweight security solution is proposed for the Internet of Things (IoT) systems. In fact, this proposed solution is called TrioSec, and can ensure the basic security requirements such as Authentication/Authorization, Intrusion Detection System (IDS), and System Monitoring with minimum computation and resources overhead. The proposed solution uses optimized open source software agents that can be implemented in the Gateway node of the IoT network layer. In fact, several recent interesting security solutions were being presented in the literature to tackle these security requirements. However, there is not yet any complete security solution for IoT systems that integrates multiple software agents. The advantage of the proposed solution is its ability to combine the best existing and customized security tools to reach a maximum level of security with a minimum overhead level in terms of computation and resources.
Stability Bound of the Initial Mean-Square Deviation of High-Order Stochastic Gradient Adaptive Filtering Algorithms

The paper derives the stability bound of the initial mean-square deviation of an adaptive filtering algorithm based on minimizing the 2Lth moment of the estimation error, with L being an integer greater than 1. The analysis is done for a time-invariant plant with even input probability density function. Dependence of the stability bound on the algorithm step-size, type of the noise distribution, signal-to-noise ratio (SNR), and L is studied. It is shown that the stability bound is decreasing in the step-size. The stability bound decreases as the tail of the noise probability density function becomes lighter for the same steady-state misadjustment. This result is surprising since, for example, it is expected that the stability of the least mean 2Lth algorithm for binary noise distribution is better than that for uniform distribution. The stability bound is proportional to the reciprocal of the SNR, another surprising result. Finally, the stability bound is decreasing in L, which implies a limitation on the ability to improve the performance of the adaptive filter by increasing the order of the cost function. Theoretical results are supported by simulations.
A Reinforcement Learning-based Radio Resource Management Algorithm for D2D-based V2V Communication

Device-to-Device (D2D) communication is an emergent technology that provides many advantages for the LTE-A networks as higher spectral efficiency and wireless Peer-to-Peer services. It is considered as a promising technology used in many different fields like public safety, network traffic offloading, and social applications and services. However, the integration of D2D communications in cellular networks creates two main challenges. First, the interference caused by the D2D links to the cellular links could significantly affect the performance of the cellular devices. Second, the minimum QoS requirements of D2D communications need to be guaranteed. Thus, the synchronization between devices becomes a necessity while Radio Resource Management (RRM) always represents a challenge. In this paper, we study the RRM problem for Vehicle-to-Vehicle (V2V) communication. A dynamic neural Q-learning-based resource allocation and resource sharing algorithm is proposed for D2D-based V2V communication in the LTE-A cellular networks. Simulation results show that the proposed algorithm is able to offer the best-performing allocations to improve network performance.
Polarimetric Target Detection on SAR Images

Polarimetric radars sensors dealing with Polarimetric SAR (PolSAR) techniques are of high usefulness for environmental ground truth measurement and validation and change monitoring of the terrestrial covers. PolSAR remote sensing techniques offer efficient and reliable means of collecting information required to extract biophysical and geophysical parameters about the earth's surface. They have found successful applications in crop monitoring, damage assessment, forestry, land use assessment, and also surveillance monitoring (oil spill detection, marine surveillance, etc.). It is clear that radar polarimetric techniques, with the use of a combination of more than one channel, help to detect target hidden in clutter, or to detect highly pure coherent radar target, as man-made objects like cars, tanks, ships. Moreover, the same type of clutter behaves in different ways with respect to the different polarimetric transmission schemes, making the process of clutter filtering more efficient and, enhancing the Signalto-Noise ratio, improving target detection against various types of clutter.This paper is derived from a study to demonstrate the benefit of applying polarimetric technology in defence scenarios, with particular attention to target classification, target information extraction, and detection of man-made object in vegetated environment.
Profile for DCCP Congestion Control ID 4: the Small-Packet Variant of TFRC Congestion Control

This document contains the profile for Congestion Control Identifier 4, the Small-Packet variant of TCP-Friendly Rate Control (TFRC), in the Datagram Congestion Control Protocol (DCCP). CCID 4 is for experimental use, and uses TFRC-SP [TFRC-SP], a Small-Packet (SP) variant of TFRC designed for applications that send small packets. The goal for TFRC-SP is to achieve roughly the same bandwidth in bits per second (bps) as a TCP flow using packets of up to 1500 bytes but experiencing the same level of congestion. CCID 4 is for experimental use for senders that send small packets and would like a TCP-friendly sending rate, possibly with Explicit Congestion Notification (ECN), while minimizing abrupt rate changes.
Geometric Modeling and Attitude Stabilization of Quadcopters

The dynamics of an underactuated mechanical system, e.g. quadcopters, naturally evolve on a nonlinear manifold. Hence, differential geometric modeling concepts are better suited to arrive at the set of equations describing the dynamic behaviour of such systems. Resorting to a differential geometric approach, a generalized dynamical model for a quadcopter is proposed in this paper. The rotation matrices in forward kinematic map are represented using Euler angles. The modeling has been presented on a single chart. The Riemannian metric being a crucial ingredient for model development on a chart is derived from the total kinetic energy. A complete set of dynamic equations of motion neglecting external disturbances is derived for the quadcopter by representing the Levi-Civita affine connection in local coordinates. Further, based on the derived model, a proportional-derivative (PD) controller is designed for attitude stabilization. Simulation studies validate the proposed model and also demonstrate its robustness to wide variations in initial conditions.
Location Aided and Machine Learning-Based Beam Allocation for 3D Massive MIMO Systems

3D massive multiple-input multiple-output (MIMO) technology is considered as one of the key technologies of 5G. Compared with traditional 2D massive MIMO systems, the elevation angles of signal propagation are introduced in 3D massive MIMO systems, which make it fully utilize the advantages in horizontal and vertical dimensions at the same time, further increase the freedom of scheduling and resource allocation, and significantly improve the system capacity. To improve the system performance further, beamforming can form high-gain beams to reduce signal interference between user equipments (UEs). Traditionally, beam allocation is regarded as an optimization problem. Since most beam allocation problems are non-convex, it is difficult to obtain an optimal solution in real time. With the rise of artificial intelligence technology, machine learning has become one of the most promising tools. In order to optimize the beam allocation better in a short time, a location aided and machine learning based beam allocation algorithm (LMLBAA) is proposed in 3D massive MIMO systems. The algorithm absorbs the idea of k-NN or SVM to construct the multi-classifier, which uses collected location information of UEs as feature vectors and corresponding precoding codeword indexes as classes. When a new UE joins up, the base station (BS) will select a suitable codeword for the UE to perform precoding and form the corresponding serving beam according to the decision result of the multi-classifier. Simulation results show that the more location information of UEs is collected during the initialization process, the better the performance of the proposed algorithm will be. Meanwhile, as the transmit signal-to-noise ratio (SNR) increases, the average available sum rate will increase and approach the results of the exhaustive search method.
Trajectory Tracking Control of a Rolling Disk on a Smooth Manifold

This paper studies the trajectory tracking control problem for a vertical rolling disk on an arbitrary smooth surface in ${\mathbb{R}^3}$. The disk is assumed to roll without slipping about its axis and turn about the surface normal. A nonlinear controller is designed for the trajectory tracking of the disk based on a kinematic model that is globally defined on the manifold without singularities or ambiguities. The theoretical results are specialized for two different surfaces; a flat surface and a paraboloidal surface. Simulation results are included to illustrate the effectiveness of the tracking control law on the smooth surfaces.