Miracle-dz/UniFAR-FTUs huggingface dataset:
A Flexible Consensus Protocol for Distributed Systems

This paper presents a new type of Consensus problem named the Consensus (<inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula>) with alternative plans, where <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> denotes the total number of processors in the network, <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> is the number of processors with an initial value, <inline-formula> <tex-math notation="LaTeX">${n} \ge 4$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$1 \le m\le n$ </tex-math></inline-formula>. Compared to the traditional Consensus problem, the Consensus (<inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula>) problem with alternative plans has two major features. First, each processor is no longer required to propose an initial value. It can flexibly choose to propose or not propose an initial value. This feature allows the Consensus problem to be flexibly applied in many new real-world applications of the distributed system. Second, the proposed protocol ensures that all correct processors always agree on a good plan from a correct processor and never on a bad plan. Compared to solutions of the traditional Consensus problem, which does not guarantee that all correct processors agree on a good plan, this feature ensures the rationality of the Consensus value. In other words, by solving the Consensus (<inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula>) problem with alternative plans, the fault tolerance and reliability of distributed systems can be improved.
An Energy-efficient Predictive Model for Object Tracking Sensor Networks

Among different applications of Wireless Sensor Networks (WSNs), Object Tracking Sensor Network (OTSN) is one of the most attractive applications. In WSN, power management plays a vital role in its overall performance. Hence, a reliable predicative method is proposed in this paper to reduce energy consumption of the nodes in OTSN. This strategy makes use of Machine Learning (ML) technique in order to predict future behaviours of a Mobile Object (MO) moving into a coverage range of WSN. Moreover, a simulator capable of reproducing the main features of a real MO in an OTSN is developed. Finally, several experiments have been carried out to evaluate the performance and reliability of the proposed methodology in terms of predictions accuracy and energy conservation.
Integrated Designs of Control and Compensation for Output Feedback Linear Quadratic Regulator across an Unreliable Communication Channel

This paper addresses the issue of discrete time-invariant optimal output feedback control across an unreliable communication channel in the context of networked control system where random packet loss of control signal occurs. By proposing a unified performance criterion, the work aims to integrate the designs of controller and compensator by connecting the latter and the performance index under the output feedback LQR setting. A new set of design equations for the controller and the compensator satisfying optimality conditions is derived which then is followed by a convergent algorithm that solves the design equations for the gains. Finally, a numerical example is given to compare performances and validate the proposed approach.
Low-Altitude Navigation for Multi-Rotor Drones in Urban Areas

Multi-rotor drones have witnessed a drastic usage increase in several smart city applications due to their 3D mobility, flexibility, and low cost. Collectively, they can be used to accomplish different short- and long-term missions that require low-altitude motion in urban areas. Therefore, it is important to efficiently manage the operation of the fleet to leverage its use and maximize its application performances. In this paper, we propose to investigate the path routing problem for the multiple drones in urban areas, where obstacles with different heights exist. The objective is to find the best trajectories in this 3D environment while ensuring collision-free navigation. The collision is prevented by three possible alternatives: forcing the drone to statically hover, so its peer can pass first, making it fly at a different altitude, or completely changing its path. Multiple charging stations are made available to allow the drones to recharge their batteries when needed. A mixed integer linear program is first developed to model the problem and achieve optimal navigation of the fleet. Afterward, two heuristic algorithms with different conceptual constructions are designed to solve the trajectory planning problem with faster convergence speed. The selected simulation results illustrate the performance of our framework in realistic 3D maps and show that the designed heuristic approaches provide close performances to the optimal ones.
Machine Vision Based Traffic Sign Detection Methods: Review, Analyses and Perspectives

Traffic signs recognition (TSR) is an important part of some advanced driver-assistance systems (ADASs) and auto driving systems (ADSs). As the first key step of TSR, traffic sign detection (TSD) is a challenging problem because of different types, small sizes, complex driving scenes, and occlusions. In recent years, there have been a large number of TSD algorithms based on machine vision and pattern recognition. In this paper, a comprehensive review of the literature on TSD is presented. We divide the reviewed detection methods into five main categories: color-based methods, shape-based methods, color- and shape-based methods, machine-learning-based methods, and LIDAR-based methods. The methods in each category are also classified into different subcategories for understanding and summarizing the mechanisms of different methods. For some reviewed methods that lack comparisons on public datasets, we reimplemented part of these methods for comparison. The experimental comparisons and analyses are presented on the reported performance and the performance of our reimplemented methods. Furthermore, future directions and recommendations of the TSD research are given to promote the development of the TSD.
Research on Radar Imaging Based on Complex Approximate Message Passing in the Terahertz Band

The large bandwidth signal of terahertz radar can achieve high resolution, meanwhile, it has higher requirements on the sampling rate and data processing capability of the system. A Complex Approximate Message Passing algorithm based on sparse recovery theory was proposed in this paper, which realized the reconstruction of echo signals at low sampling rate. The turntable model was used firstly to achieve two-dimensional imaging of the target. Then the Double Integration algorithm, the Complex Approximate Message Passing algorithm and the Fast Iteration Soft Threshold algorithm was used to reconstruct the target image. Comparison of the imaging results of the three algorithms verifies the superiority of the proposed algorithm.
A Secure, Open Source WSN-based Monitoring System using CM5000 Motes

In today's automated, interconnected world, remote monitoring and control are necessary procedures for the operation of several systems. The monitoring of environmental data is one of the most common telemetering activities, useful for many systems, from electricity microgrids, to smart buildings and remote facilities. Wireless Sensor Networks (WSNs) provide the technology for collecting, transmitting and processing information in a low-cost and integrated manner. They are functionally and energy-independent, easily deployed and scalable. In this paper, we present the design, implementation and testing of a secure monitoring system based on WSNs consisting of Advanticsys CM5000 motes. The sensor nodes collect a variety of environmental measurements and send them to a centralized data repository. Data transmissions inside the WSN are protected through encryption. At the repository, the measurements are presented to the end user in a graphical form using open source cloud technologies.
Convolution Theorems for Two-Dimensional LCT of Angularly Periodic Functions in Polar Coordinates

By focusing on a special type of functions that are angularly periodic with period <inline-formula><tex-math notation="LaTeX">$2\pi$</tex-math></inline-formula>, a relationship between two-dimensional (2-D) linear canonical transform (LCT) and linear canonical Hankel transform (LCHT) is disclosed, implying that the mapping from the <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>th Fourier coefficient of the original function to that of its 2-D LCT version is an <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>th-order LCHT. In view of this relation, three kinds of convolution theorems for the 2-D LCT in polar coordinates are proposed. The equivalence of the multiplicative filter models derived is also illustrated.
MCMC Based Selection Probability Estimation for Probabilistic Boolean Networks*

An estimation algorithm for selection probabilities of probabilistic Boolean networks is developed in this paper. A recursive estimation procedure is deduced from Bayes theorem and gives the expression of posterior probability distribution of the selection probabilities. To realize the distributions in the fashion of numerical computation, Markov chain Monte Carlo (MCMC) method with Metropolis-Hastings sampler is exploited and provides approximated probability densities. Some numerical examples are illustrated to demonstrate the effectiveness and computation complexity of the proposed framework.
Design, Theory, and Circuit Model of Wideband, Tunable and Polarization-Insensitive Terahertz Absorber Based on Graphene

We present a wideband tunable absorber which is insensitive to polarization in terahertz region. This absorber is composed of dielectric and graphene sheet layers with crossover elliptical dielectric cylinders on top of the structure. Graphene plasmon resonances are used to achieve wideband absorption with relative bandwidth of 68% for the absorption above 90% in THz regime. Thanks to the symmetric design, the structure is polarization-insensitive. Furthermore, the absorber has a good performance for wide angle of the incident waves. Also, the equivalent circuit model of the absorber is derived and the results are in good agreement with the simulation ones. The presented method for broadening the absorption spectrum and the technique of equivalent circuit model derivation can be used for related structures which have many applications in similar devices.
Enhancing MQTT-SN Performance via Fountain Codes in Extreme Conditions

Day after day, more Internet of Things (IoT) devices are joining the Internet. By 2025, it is expected to have more than 75 billion connected devices compared to only 15 billion in 2015 [1]. Despite their potential, IoT systems have many constraints such as limited capabilities and energy to mention a few. Power consumption is among the most constrained aspects of IoT devices which leads to many challenges. This becomes more challenging in extremely high erasure channel conditions where many packets may be lost due to interference and other impairments. The process of re-transmission creates a huge overhead in terms of power consumption, delay and processing. In this paper, we propose the use of LT codes at the application layer to enhance the performance of MQTT-SN protocol. Our simulation results show savings of up to 75% in power consumption, 90% in overhead, and reduction in delay by 89% for high erasure rates.
A novel architecture using iBeacons for localization and tracking of people within healthcare environment

Internet of Things (IoT) can help healthcare facilities to transform the experience for both the staff and patients. IoT can facilitate novel solutions for a classic problem, which is the real-time tracking of patients and staff. In this work, an architecture for real-time tracking using Bluetooth Low Energy (BLE) and iBeacons in hospitals is proposed. The proposed system is to locate not only humans but also other facilities such as medicines. It consists of four different frameworks: server communication, user interaction, cross-platform communication and indoor localization. A wearable BLE-enabled device or any smartphone can be used in our system. The first stage of the tracking system is setting up and calibrating the BLE iBeacons for initialization. Then the Received Signal Strength is collected from the BLE-enabled devices carried by users. These data are analyzed and calculated using our improved Least Square Estimation approach to estimate the actual location of users. Experimental results show that in a crowded environment, our tracking system achieves resolutions of the order of 12cm. This will be further integrated with other sensors such as accelerometer or heart rate monitor to improve the real-time healthcare monitoring experience.
A Survey on Recent Approaches in Intrusion Detection System in IoTs

Internet of Things (IoTs) are Internet-connected devices that integrate physical objects and internet in diverse areas of life like industries, home automation, hospitals and environment monitoring. Although IoTs ease daily activities benefiting human operations, they bring serious security challenges worth concerning. IoTs have become potentially vulnerable targets for cybercriminals, so companies are investing billions of dollars to find an appropriate mechanism to detect these kinds of malicious activities in IoT networks. Nowadays intelligent techniques using Machine Learning (ML) and Artificial Intelligence (AI) are being adopted to prevent or detect novel attacks with best accuracy. This survey classifies and categorizes the recent Intrusion Detection approaches for IoT networks, with more focus on hybrid and intelligent techniques. Moreover, it provides a comprehensive review on IoT layers, communication protocols and their security issues which confirm that IDS is required in both layered and protocol approaches. Finally, this survey discusses the limitations and advantages of each approach to identify future directions of potential IDS implementation.
Single Shot Anchor Refinement Network for Oriented Object Detection in Optical Remote Sensing Imagery

Object detection is a challenging task in the field of remote sensing applications due to the complex backgrounds and uncertain orientation of targets. Compared with the horizontal bounding box, the oriented bounding box can provide orientation information while retaining the true size. Most existing oriented object detection methods are based on Faster-RCNN and the other one-stage methods that can achieve real-time speed but have shortcomings in localization and detection accuracy. To further enhance the performance of one-stage methods, we propose an oriented object detection framework that is based on the single shot detector, namely, single shot anchor refinement network (S2ARN). The S2ARN obtains the accurate detection results by performing two consecutive regressions. More precisely, the multilevel features of the backbone are used to regress the coordinate offsets between the predefined rotated anchors and the ground-truth boxes to generate the refined anchors. The classification and regression subnetworks assigned to the output features are used to perform the second regression to determine the class labels and further adjust the location of the refined anchors. In addition, receptive field amplification modules (RFAMs) are inserted to enlarge the receptive field and extract more discriminative features. Furthermore, in the anchor matching step, angle-related Intersection over Union (ArIoU) is used to calculate the Intersection over Union (IoU) score instead of the traditional method. Benefiting from the multiple regressions and the insensitivity of the ArIoU score to the angle deviation, the angle sampling interval of the rotated anchor can be reduced. The experimental results for the two public datasets, HRSC2016 and UCAS-AOD, demonstrate the effectiveness of the proposed network.
A Neurologically Inspired Sequence Processing Model for Mobile Robot Place Recognition

Visual place recognition is the problem of camera-based localization of a robot given a database of images of known places, potentially under severe appearance changes (e.g., different weather or illumination). Typically, the current query images and the database images are sequences of consecutive images. In previous work, we proposed to adapt hierarchical temporal memory, a biologically plausible model of sequence processing in the human neocortex to address this task. The previous work described the algorithmic steps and showed synthetic experimental results from simulation. This letter extends the approach to application on real-world data based on a novel encoder for state-of-the-art image processing front ends. The neurologically inspired approach is compared with several state-of-the-art algorithms on a variety of datasets and shows preferable performance. Beyond the place recognition performance, the neurological roots of the algorithm result in appealing properties like potentially very energy efficient implementation due to the usage of sparse distributed representations and natural extendability like the integration of motion estimates similar to entorhinal grid cells. Finally, we underline its practical applicability by online, soft real-time application on a mobile robot.
Automatic Detection of Urban Features from Wheelchair Users’ Movements

Providing city navigation instructions to people with motion disabilities requires the knowledge of urban features like curb ramps, steps or other obstacles along the way. Since these urban features are not available from maps and change in time, crowdsourcing this information from end-users is a scalable and promising solution. Our preliminary study on wheelchair users shows that an automatic crowdsourcing mechanism is needed, avoiding users’ involvement.In this contribution we present a solution to crowdsource urban features from inertial sensors installed on a wheelchair. Activity recognition techniques based on decision trees are used to process the sensors data stream. Experimental results, conducted with data acquired from 10 real wheelchair users navigating in an outdoor environment show that our solution is effective in detecting urban features with precision around 0.9, while it is less reliable when classifying some fine-grained urban feature characteristics, like a step height. The experimental results also present our investigation aimed at identifying the best parameters for the given problem, which include number, position and type of inertial sensors, classifier type, segmentation parameters, etc.
HML-IDS: A Hybrid-Multilevel Anomaly Prediction Approach for Intrusion Detection in SCADA Systems

Critical infrastructures, e.g., electricity generation and dispersal networks, chemical processing plants, and gas distribution, are governed and monitored by supervisory control and data acquisition systems (SCADA). Detecting intrusion is a prevalent area of study for numerous years, and several intrusion detection systems have been suggested in the literature for cyber-physical systems and industrial control system (ICS). In recent years, the viruses seismic net, duqu, and flame against ICS attacks have caused tremendous damage to nuclear facilities and critical infrastructure in some countries. These intensified attacks have sounded the alarm for the security of the ICS in many countries. The challenge in constructing an intrusion detection framework is to deal with unbalanced intrusion datasets, i.e. when one class is signified by a lesser amount of instances (minority class). To this end, we outline an approach to deal with this issue and propose an anomaly detection method for the ICS. Our proposed approach uses a hybrid model that takes advantage of the anticipated and consistent nature of communication patterns that occur among ground devices in ICS setups. First, we applied some preprocessing techniques to standardize and scale the data. Second, the dimensionality reduction algorithms are applied to improve the process of anomaly detection. Third, we employed an edited nearest-neighbor rule algorithm to balance the dataset. Fourth, by using the Bloom filter, a signature database is created by noting the system for a specific period lacking the occurrence of abnormalities. Finally, to detect new attacks, we combined our package contents-level detection with another instance-based learner to make a hybrid method for anomaly detection. The experimental results with a real large-scale dataset generated from a gas pipeline SCADA system show that the proposed approach HML-IDS outperforms the benchmark models with an accuracy rate of 97%.
Detection and Recognition of Atomic Evasions Against Network Intrusion Detection/Prevention Systems

Network evasions can bypass network intrusion detection/prevention systems to deliver exploits, attacks, or malware to victims without being detected. This paper presents a novel method for the detection and recognition of atomic network evasions by the classification of a transmission control protocol (TCP) stream’s packet behavior. The syntax for the conversion of TCP streams to codeword streams is proposed to facilitate the extraction of statistical features while preserving the evasion behavior attributes of original network flows. We developed a feature extraction method of employing the normalized term frequencies of codewords to characterize intra and inter packet attribute patterns hidden in actual TCP streams. A TCP stream is then transformed to a fixed length numeric feature vector. Supervised multi-class classifiers are built on the extracted feature vectors to differentiate different types of evasions from normal streams. The quantitative evaluations on an evasion dataset consisting of normal network flows and eight types of atomic evasion flows demonstrated that the proposed approach achieved an encouraging performance with an accuracy of 98.95%.
The COG-LO Framework: IoT-based COGnitive Logistic Operations for next generation logistics

Objective of COG-LO is implement and assess a framework which exploits Internet of Things (IoT) concepts and technologies to improve effectiveness and efficiency of logistic operations. The COG-LO framework is based on the Cognitive Logistic Object (CLO) concept which represent any entity involved in logistic operations augmented with cognitive capabilities.CLOs are also enriched with social-like capabilities and therefore, COG-LO builds on the Social Internet of Things (SIoT) concept. Such capabilities allow the seamless ad-hoc interaction between logistic entities even if managed by different operators. To this new types of relationships will be integrated in the SIoT concept.
Harmonic Resonant Filters of Quick Battery Charging Station of Motor Vehicles

The article presents the results of computer tests of a resonant harmonic filter installed between the solar inverter and the rectifier in the battery charging stations for motor vehicles. The article shows that the reduction of harmonics in the alternating current enables the correct operation of a solar inverter equipped with differential voltage-mode filters (DM) without using a separating transformer behind the inverter. To limit the harmonic content in the alternating current line of the hybrid supply network, a resonant filter was used. The results showed the possibility of feeding the diode rectifier of battery charging converter directly from the solar power inverter without the need usage of separating transformer behind the solar inverter.