Miracle-dz/UniFAR-FTUs huggingface dataset:
Slip-Based Nonlinear Recursive Backstepping Path Following Controller for Autonomous Ground Vehicles

Path following accuracy and error convergence with graceful motion in vehicle steering control is challenging due to the competing nature of these requirements, especially across a range of operating speeds. This work is founded upon slip-based kinematic and dynamic models, which allow derivation of controllers considering error due to sideslip and the mapping between steering commands and graceful lateral motion. A novel recursive backstepping steering controller is proposed that better couples yaw-rate based path following commands to steering angle and rate. Observer based sideslip estimates are combined with heading error in the kinematic controller to provide feedforward slip compensation. Path following error is compensated by a Variable Structure Controller (VSC) to balance graceful motion, path following error, and robustness. Yaw rate commands are used by a backstepping dynamic controller to generate robust steering commands. A High Gain Observer (HGO) estimates sideslip and yaw rate for output feedback control. Stability analysis is provided and peaking is addressed. Field experimental results evaluate the work and provide comparisons to MPC.
Electric Transportation in Pakistan Under CPEC Project: Technical Framework and Policy Implications

Transportation sector in Pakistan has been rapidly growing, leading to an increase in diesel and petroleum consumption, heightened energy import bill, air pollution, and traffic congestion. The China–Pakistan economic corridor (CPEC) is a cluster of different multi-billion-dollar projects with a significant emphasis on enhancing regional connectivity, and the transportation sector is considered its backbone. Electric road transportation appears to be a game-changer to tackle energy, economy, and environmental issues of a country. This research paper is focused on a scheme and valuation of deploying electric transportation (e-trans) for mass transit under the CPEC umbrella in Pakistan. This paper identifies barriers and challenges and explores various technological options for adopting suitable electric vehicles (EVs) for mass transportation. The paper investigates technical infrastructural requirements, financial, and policy implications for the successful deployment of the proposed electric transportation. In particular, it proposes an EV charging infrastructure based on solar power generation to avoid any electricity burden to recharge EVs from the national power grid. In order to be comprehensive, it presents a case study to develop an EV charging station network with an estimation of the cost for accommodating charging requirements of one thousand electric trucks and hundred mobile charging trucks. Obtained results demonstrate that merely 0.29% of the CPEC’s energy sector approved budget will be incurred for deploying the proposed charging station network to support e-trans over the studies road segment, which appears to be a feasible initiative. Important factors which may influence the proposed scheme for implementation of e-trans under the CPEC is analyzed using SWOT analysis. The recommended set of policies for deploying electric transportation and its quantitative financial analysis will help the concerned stakeholders to discover and execute the necessary steps to ensure sustainable electric transportation under the CPEC in Pakistan.
Intrusion Detection Using Extremely Limited Data Based on SDN

In Western Europe, the number of Internet connected devices is expected to increase from the 2.3 billion devices in 2017, to 4 billion in 2022. Dealing with this growth is an increasing problem for administrators attempting to ensure that Quality of Service levels are maintained. Software Defined Networking (SDN) has been proposed as one of the solutions to some of the problems caused by this increasing volume of data, such as the time it takes to manually reconfigure switches in response to changing network conditions. SDN moves the distributed networking paradigm to a centralised solution, which is easier to manage, but comes with other issues for security focused administrators. SDN can lead to a reduction in the amount of information available for Intrusion Detection Systems (IDSs). This is because IDSs still rely on direct packet sampling techniques, which can provide more information than the aggregated view of networks SDN flow tables provide. As deep learning and other artificial intelligence techniques look likely to become more commonplace in IDSs, this reduction in information becomes an increasing problem. Many of these methods require large training sets with many features. In this paper, we propose a method to correct this imbalance through the creation of a novel framework, which will allow upwards of 90% precision on the state of the art UNSW-NB15 dataset while only using a small fraction of the features available, matching those available within a SDN environment.
Recognition of Wheat Pests

This article introduces a model of an intelligent system for the recognition of wheat crops pests. The recognition allows us to define the species of weeds, their density, the various diseases and vermin of beneficial plants, as well as the exact location of objects in the field. The recognition technology is based on a convolutional neural network and on the independent work of a drone.
Design and Implementation of an End-to-End Amplify and Forward Full-Duplex Relay Network

This paper presents the design and implementation of an In-Band Full-Duplex (IBFD) amplify and forward relay for wireless networks. First, we describe the system model of the full-duplex relay (FDR), where the relay extends the range of the source node to reach a destination node located outside the source’s radio coverage. We analyze the gain limitation of FDR under the stability and transmit power constraints. Further, the operation of tandem FDRs, and their limitation factors are discussed. We analyze the overall system performance as a function of relay location for constant gain and constant transmit power operating modes. The optimal FDR gain maximizing end-to-end network performance is computed. The full-duplex (FD) and half-duplex (HD) system performances are evaluated by simulation and experiments for both outdoor and indoor environments. Experimental results show up to 23dB signal to noise plus interference ratio (SINR) improvement in constant gain mode and up to 14dB SINR improvement in constant transmit power mode. The measured network throughput of FD network shows up to 1.8 times improvement compared to the HD counterpart. Finally, the optimal relay location is selected to maximize SINR at the destination.
Comparative Gait Analysis of Healthy Young Male and Female Adults using Kinect-Labview Setup

Gait analysis is an important criterion, nowadays, to diagnose various medical conditions of any individual. Moreover, the gender based disparities in a gait cycle can be witnessed in the social world. In this work, a Kinect-Labview based experimental setup is used to detect and estimate the gait kinematic parameters of ten healthy participants (5 male: 22.2 ± 2.14 years; 5 female: 21.8 ± 2.14 years) in sagittal plane. Primarily, hip, knee and ankle joint angles of right lower limb are evaluated for both gender groups. Thereafter, to draw a clear state of comparison between the gender groups, two sample t-test based statistical investigations are carried out for five prominent gait events at a significance level of 5%. From statistical results, significant gender based gait differences are found which might be useful in the early assessment of gait abnormalities.
Selfish Yet Optimal Routing by Adjusting Perceived Traffic Information of Road Networks

Traffic congestion in urban areas causes economic and time loss. Such traffic congestion is caused by selfish routing where users aim to minimize their own travel time. Even if a navigation system provides them with recommended routes, they may not follow the routes, due to dissatisfaction with the expected travel time. In this article, inspired by the concept of “Nudge,” we propose “selfish yet optimal routing,” where all users rationally aim to minimize their own travel time but social optimal routing emerges from such selfish routing by adjusting their perceived traffic information appropriately. We propose a scheme to derive nudging traffic information that fills the gap between selfish routing criterion and altruistic one by internalizing the marginal cost into the perceived traffic information. Users will conduct the selfish routing under their perceived traffic information, which unconsciously results in the optimal routing. Through numerical experiments using both the artificial road network and the real one, we show that the proposed scheme achieves almost the same performance compared with the optimal routing. In addition, the proposed scheme reduces the average travel time by 19.1% compared with notification of actual traffic information, in case of the central-area road network of Nagoya city, Japan.
Quantization Considerations of Dense Layers in Convolutional Neural Networks for Resistive Crossbar Implementation

The accuracy and power consumption of resistive crossbar circuits in use for neuromorphic computing is restricted by the process variation of the resistance-switching (memristive) device and the power overhead of the mixed-signal circuits, such as analog-digital converters (ADCs) and digitalanalog converters (DACs). Reducing the signal- and weightresolution can improve the robustness against process variation, relax requirements for mixed-signal devices, and simplify the implementation of crossbar circuits. This work aims to establish a methodology to achieve low-resolution dense layers for CNNs in terms of network architecture selection and quantization method. To this end, this work studies the impact of the dense layer configuration on the required resolution for its inputs and weights in a small convolutional neural network (CNN). This analysis shows that carefully selecting the network architecture for the dense layer can significantly reduce the required resolution for its input signals and weights. This work reviews criteria for appropriate architecture selection and the quantization method for the binary and ternary neural network (BNN and TNN) to reduce the weight resolution of CNN dense layers. Furthermore, this work presents a method to reduce the input resolution for the dense layer down to one bit by analyzing the distribution of the input values. A small CNN for inference with one-bit quantization for inputs signals and weights can be realized with only 0.68% accuracy degradation for MNIST Dataset.
Bio-inspired Tensegrity Fish Robot

This paper presents a method to create fish-like robots with tensegrity systems and describes a prototype modeled on the body shape of the rainbow trout with a length of 400 mm and a mass of 102 g that is driven by a waterproof servomotor. The structure of the tensegrity robot consists of rigid body segments and elastic cables that represent bone/tissue and muscles of fish, respectively. This structural configuration employing the tensegrity class 2 is much simpler than other tensegrity-based underwater robots. It also allows the tuning of the mechanical stiffness, which is often said to be an important factor in fish swimming. In our robot, the body stiffness can be tuned by changing the cross-section of the cables and their pre-stretch ratio. We characterize the robot in terms of body stiffness, swimming speed, and thrust force while varying the body stiffness i.e., the cross-section of the elastic cables. The results show that the body stiffness of the robot can be designed to approximate that of the real fish and modulate its performance characteristics. The measured swimming speed of the robot is 0.23 m/s (0.58 BL/s), which is comparable to other fish robots of the same type. Strouhal number of the robot 0.54 is close to that of the natural counterpart, suggesting that the presented method is an effective engineering approach to realize the swimming characteristics of real fish.
A study on the inheritance of resistance to whitefly in soybean.

【Objective】 Whitefly (Bemisia tabaci Gennadius) is a kind of agricultural pests with wide occurrence,severe harm,strong virulence in soybean around the world,so it is an effective way to breed resistant soybean varieties to control whitefly. This study was to reveal the inheritance of resistance to whitefly in soybean,and provide information for soybean breeding. 【Method】 Two crosses,Qihuang 26×Huapidou and Huapidou×Qihuang 26,were made with the highest resistant germplasm Huapidou and the highest susceptible germplasm Qihuang 26. From the crosses,4 F2 populations were derived in Jinan and Guanxian of Shandong. The means of whitefly per leaflet (MWPL) of all the populations were identified. Genetic analysis was performed under the major gene + polygene mixed inheritance model in these populations. 【Result】 Resistance of soybean to whitefly was controlled by two major genes and plus polygene,with the major genes inheritability of 86.41%,85.72%,95.90%,and 95.26%,respectively. 【Conclusion】 The inheritance of resistance to whitefly in soybean was controlled by major genes and polygene,and affected by the environment.
Energy Saving Visible Light Communication With Daylight

Visible Light Communication can provide high data rates with less security risk and is a promising technology for future indoor data access. This article proposes a transmitter structure which uses daylight as optical source. The proposed structure consists of a new optical intensity modulator operating at 520 nm, with 5 volt half wave switching voltage and 8 mm length.
When Agile Security Meets 5G

5G is a critical infrastructure that will connect the whole society and bridge other critical infrastructure systems. Thus, cybersecurity emerges as crucial tenet within the 5G pathway. In this article, we discuss the concept of agile security within a 5G infrastructure taking into account two of its major technologies: Mobile Edge Computing (MEC) and Network Functions Virtualization (NFV). In this sense, we first discuss the 5G-driven MEC deployment from a NFV perspective. Secondly, we present the concept of agile security and how it can be embedded in the daily activities of mobile network operators (MNOs). Thirdly, we discuss risk management as a key element of the agile security framework. To illustrate its application, we propose the design of an agile security risk-aware edge server mechanism for 5G driven MEC deployment, which uses multiple thresholds and a load-balancing security control to mitigate the risks of resource exhaustion and violation of the service level agreement (SLA) faced by the edge servers while taking advantage of multiple cloud layers to increase the degree of availability and dependability of the system. Numerical results show that the proposed mechanism is able to keep the risk at lower levels.
A Highly Reliable SIMO Converter Using Hybrid Starter and Overcharging Protector for Energy Harvesting Systems

This paper proposes a highly reliable single-inductor multi-output (SIMO) converter for energy harvesting systems, which is controlled by the power management controller (PMC) including the hybrid starter, overcharging protector (OCP), and switch controller (SWC). The proposed hybrid starter employs the power-on-reset to accurately control the activated and deactivated moments of the SWC, thus generating a system supply voltage (<inline-formula> <tex-math notation="LaTeX">$V_{SYS}$ </tex-math></inline-formula>) within the stable operating voltage range. The proposed OCP employs the diode-connection and Schmitt trigger schemes to monitor the voltage of a storage device (<inline-formula> <tex-math notation="LaTeX">$V_{STG}$ </tex-math></inline-formula>) in real time, thereby protecting the IC from being damaged without using either a capacitor or Zener diode, which causes the additional area or reverse leakage current, respectively. The proposed SIMO converter with the PMC was fabricated using 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. The measurement results show that <inline-formula> <tex-math notation="LaTeX">$V_{SYS}$ </tex-math></inline-formula> values at the activated and deactivated moments of the SWC are 1.798 V and 1.510 V, respectively, both of which are within the stable system voltage range for 1.8 V devices. In addition, the measured maximum <inline-formula> <tex-math notation="LaTeX">$V_{STG}$ </tex-math></inline-formula> is 5.892 V, which is much lower than an absolute maximum rated voltage of 9.2 V for 5 V devices. Furthermore, the measurement results reveal that the hybrid starter allows the proposed SIMO converter to be properly restarted immediately after <inline-formula> <tex-math notation="LaTeX">$V_{SYS}$ </tex-math></inline-formula> is shorted to ground, while the OCP safely protects the SIMO converter even when the storage device is removed from the IC while charging. Therefore, the proposed SIMO converter with the PMC is suitable for energy harvesting systems that require high reliability.
Performance Analysis of a Humidity Sensor Based on a Microresonator Functionalized With TiO2 Nanoparticles

This article proposes a novel humidity sensor based on a low-frequency micromachined electromechanical resonator functionalized with nanoparticles of TiO2 deposited on a preparation layer of Al2O3 nanoparticles. Using a low-frequency resonator allows for obtaining a better immunity to the nonidealities of the conditioning electronics components and minimizes the degradation of the resonator quality due to the deposition of the sensing material. In this article, a relationship is derived between the sensor construction parameters, the obtainable sensor resolution, and the requirements in terms of resolution and accuracy of the measurement system, showing the applicability of such sensors in the context of low-cost, low-power distributed sensing systems. An ad hoc characterization system allowed for characterizing the proposed sensor, proving that this solution is viable and requires a simple preparation route and that the sensor has satisfactory performance in a large humidity range. It was shown that the sensitivity can be increased simply by repeating the TiO2 nanoparticle deposition obtaining at least an increase of the relative sensitivity of 5 ppm/1% RH at each deposition step.
GPU-based Parallel Heuristics for Capacited Vehicle Routing Problem

This paper presents the novel GPU-based parallel strategy for the heuristic algorithms to solve the large-scale Capacited Vehicle Routing Problem (CVRP). A combination of five improvement heuristic approaches has been used to improve the constructed feasible solution. It is noticed that a large amount of CPU time is spent in the solution improvement phase while improving a feasible solution. We aim to discover an independent part of the improvement heuristic approaches and make it run over the GPU platform simultaneously. The proposed parallel version has been tested on large-scale instances of up to 20000 customers. The parallel version offers speedup up to 176.12 × compared to the corresponding sequential version.
Phantom Malware: Conceal Malicious Actions From Malware Detection Techniques by Imitating User Activity

State of the art malware detection techniques only consider the interaction of programs with the operating system’s API (system calls) for malware classification. This paper demonstrates that techniques like these are insufficient. A point that is overlooked by the currently existing techniques is presented in this paper: Malware is able to interact with windows providing the corresponding functionality in order to execute the desired action by mimicking user activity. In other words, harmful actions will be masked as simulated user actions. To start with, the article introduces User Imitating techniques for concealing malicious commands of the malware as impersonated user activity. Thereafter, the concept of Phantom Malware will be presented: This malware is constantly applying User Imitating to execute each of its malicious actions. A Phantom Ransomware (ransomware employs the User Imitating for every of its malicious actions) is implemented in C++ for testing anti-virus programs in Windows 10. Software of various manufacturers are applied for testing purposes. All of them failed without exception. This paper analyzes the reasons why these products failed and further, presents measures that have been developed against Phantom Malware based on the test results.
Integrated Li-Ion Battery and Super Capacitor based Hybrid Energy Storage System for Electric Vehicles

In this paper, system integration and hybrid energy storage management algorithms for a hybrid electric vehicle (HEV) having multiple electrical power sources composed of Lithium-Ion battery bank and super capacitor (SC) bank are presented. Hybrid energy storage system (HESS), combines an optimal control algorithm with dynamic rule based design using a Li-ion battery and based on the State Of Charge (SOC) of the super-capacitor. Battery bank offers higher energy density while Super Capacitors possess better power density to meet dynamic performance of the drive. The bidirectional two-quadrant front-end dc–dc converter (BDC) is the key component to control the energy flow between the battery and the super-capacitor. Through proper control and setting of key parameters, good acceleration/deceleration, reversible driving, and braking characteristics are obtained. To overcome the degradation of life-cycle in battery due to continuous acceleration and de-acceleration caused by high current due to regenerative braking mode, Super Capacitors is used in proposed HESS. Thus, the proposed energy storage model offers good dynamic performance, well regulated output voltage and maximum braking energy capture. The MATLAB/Simulink model developed shows the behaviour of HESS based electric vehicle under charging and constant speed using a 12/48 V BDC.
The OmniScape Dataset

Despite the utility and benefits of omnidirectional images in robotics and automotive applications, there are no datasets of omnidirectional images available with semantic segmentation, depth map, and dynamic properties. This is due to the time cost and human effort required to annotate ground truth images. This paper presents a framework for generating omnidirectional images using images that are acquired from a virtual environment. For this purpose, we demonstrate the relevance of the proposed framework on two well-known simulators: CARLA Simulator, which is an open-source simulator for autonomous driving research, and Grand Theft Auto V (GTA V), which is a very high quality video game. We explain in details the generated OmniScape dataset, which includes stereo fisheye and catadioptric images acquired from the two front sides of a motorcycle, including semantic segmentation, depth map, intrinsic parameters of the cameras and the dynamic parameters of the motorcycle. It is worth noting that the case of two-wheeled vehicles is more challenging than cars due to the specific dynamic of these vehicles.