Miracle-dz/UniFAR-FTUs huggingface dataset:
Bluetooth based Sensor Monitoring in Industrial IoT Plants

Internet of Things adoption in traditional and slow changing industrial plants such as power, water, oil-gas and chemical has proven to be beneficial in providing business value by transforming the way data is utilized in decision-making and visualization. Typical industrial IoT use cases involve acquiring data from sensor devices in plant and communicating the same to internet for local or remote monitoring and control. The sensor data acquisition in an industrial plant thus becomes paramount as the same acquired data is used for bringing out the underlying knowledge of system. IoT typically requires a local, low power wireless communication to acquire data from sensor devices and local gateway that is connected to internet for local or remote monitoring and control. This paper describes how Bluetooth low energy (BLE) technology can be used to connect sensor nodes to Internet-based services and applications using gateway in an industrial plant. It also investigates the performance of BLE technology as a local communication for sensor device monitoring.
Coordinated Hybrid Precoding and QoS-Aware Power Allocation for Underlay Spectrum Sharing with Load-Controlled Antenna Arrays

Coordinated multi-point (CoMP) can be used as an enabler of underlay spectrum sharing instead of legacy technologies, such as multi-user multiple-input multiple-output, in order to further increase the overall spectral efficiency (SE) and provide quality-of-service (QoS) guarantees to the end users. This concept, though, has been largely overlooked in the literature. Moreover, while the performance of CoMP transmission techniques generally improves as the number of antennas on the base stations (BS) increases, cost and power consumption constraints (and even size limitations in the case of small-cell BSs and remote radio units) prohibit the deployment of a large number of antennas on these nodes. Load-controlled parasitic antenna arrays address this issue. However, the application of arbitrary precoding on such antenna systems is far from trivial. Furthermore, previous studies on the subject did not consider multi-active multi-passive (MAMP) antenna arrays and multiuser communication, let alone coordinated transmissions and spectrum sharing setups. Our goal in this work is to fill the aforementioned gaps in the literature. To this end: (i) We derive a low-complexity coordinated QoS-aware power allocation method for sum-SE maximization in an underlay spectrum sharing setup under given transmission and interference power constraints and per-user QoS requirements; (ii) we describe simple suboptimal alternatives of this power allocation policy; and (iii) we present coordinated hybrid precoding implementations of standard linear precoding schemes for load-controlled MAMP (LC-MAMP) antenna arrays. Numerical simulations demonstrate the feasibility of the proposed resource allocation strategies, illustrate their performance gains, and highlight the impact of various parameters on their efficiency.
Performance, Reliability and Scalability for IoT

So-called IoT, based on use of enabling technologies like 5G, Wi-Fi, BT, NFC, RFID, IPv6 as well as being widely applied for sensor networks, robots, Wearable and Cyber-PHY, invades rapidly to our every day.There are a lot of apps and software platforms to IoT support. However, a most important problem of QoS optimization, which lays in Performance, Reliability and Scalability for IoT, is not yet solved. The extended Internet of the future needs these solutions based on the cooperation between fog and clouds with delegating of the analytics blocks via agents, adaptive interfaces and protocols.The next problem is as follows: IoT can generate large arrays of unmanaged, weakly-structured, and non-configured data of various types, known as "Big Data". The given papers deals with the both problems. A special problem is Security and Privacy in potentially "dangerous" IoT-scenarios. Anyway, this subject needs as special discussion for risks evaluation and cooperative intrusion detection.Some advanced approaches for optimization of Performance, Reliability and Scalability for IoT-solutions are offered within the paper. The paper discusses the Best Practises and Case Studies aimed to solution of the established problems.
Charge-flow and field in soil due to return-stroke

The evaluation of the electric field gradient along the air-soil interface, generally termed as the step-potential, is an important parameter of practical interest. Generally, the peak value of the step-potential is one of the primary target variables in the design of the earth termination. Two of the most popular approaches used in the literature for its evaluation are the quasi-static approach, in which a lumped current source is employed to inject the return-stroke current to the ground/grounding system and the antenna-model approach, in which a voltage source at the ground end of a monopole is employed to set up the return-stroke current. When viewed in terms of the charge dynamics and in terms of the channel current evolution, both these approaches are found to have certain limitations. This work, by employing a self-consistent return stroke model along with a full representation of the soil, intends to address the above lacunae. It is shown that the step-potential predicted are different for different models and so is the current flow patterns in the soil. Based on the same, the advantage of employing a self-consistent model for the return stroke is demonstrated.
Zeno-Free Stochastic Distributed Event-Triggered Consensus Control for Multi-Agent Systems

This paper investigates the problem of multiagent consensus control with input disturbances. We propose a stochastic distributed event-triggereing law, based on an existing deterministic law, for the said problem. It is shown that both event-triggering laws achieve practical consensus exponentially fast under the influence of disturbance. We further derive an analytical upper bound for the consensus error and show that the stochastic law excludes Zeno behavior. A numerical simulation is also provided to illustrate the proposed consensus control law compared with the deterministic law.
Mobile Robot Capable of Crossing Floors for Library Management

In order to reduce the labor and material of library management and contribute to the development of unmanned library, an intelligent mobile robot for library management is proposed. The robot consists of a mobile platform, a three-DOF rise-and-fall robotic arm and a multisource image recognition information fusion system. Besides recognizing data and position of the book, the robot is also able to conduct book grasp and shelving in order task. To adapt to the existing library environment and enlarge its move area, an efficient autonomous elevator button recognition system based on neural networks is designed, which enables the mobile robot span floors by elevator. Combined with visual recognition technology, the mobile robot has functions of autonomous navigation and obstacle avoidance. There is also a data monitoring center and graphical user interface corresponding to the mobile robot, which is developed to simplify the operation instructions. The effectiveness of the proposed research is examined by simulation and experiments. The innovation of the robot is that it can complete shelving book task on the premise of adapting present library to the most extent.
Graph Matching Pose SLAM based on Road Network Information

This paper presents a Graph Matching Pose SLAM to build the perception map which is a prerequisite of localization and environment perception of the intelligent vehicle. Graph-based simultaneous localization and mapping (SLAM) is widely used to build a map with global consistency and requires loop closure to eliminate the accumulative errors. However, loop closure forces the vehicle to re-visit a previously entered area. In this paper, a road network-based graph SLAM method without the requirement of loop closure is proposed to build a consistent map for the real environment. The estimated poses from the original SLAM are linked with the available road network based on the graph matching. Thus the available road network introduces the real topology of the environment to Pose SLAM regularly. In this way, the map is optimized by using the factor graph inference frequently. The proposed method is validated on the KITTI dataset and the real-world experiments. This method outperforms the state-of-the-art methods.
Novel Approach for Image Text Recognition and Translation

One of the most concerned problems of today is to exactly translate the text present in an image to a human readable text. This has been gaining attention these days because of the immense work done by the Computer Vision Community. The main important concept behind this technology is something called as OCR – Optical Character Recognition. With the help of the OCR, we can search and recognize the text in electronic documents and can easily convert them into human readable text. It converts electronic documents’ text into related ASCII character and if the document is a handwritten one, then the OCR uses database to recognize what character it is and try to solve it to its highest accuracy.
A Function Clustering Algorithm for Resource Utilization in Service Function Chaining

Abstract-Virtualized service and network functions are deployed on virtual machines (VMs) to realize essential processing to realize service function chaining (SFC). Issues on SFC is SF allocation to a VM and to minimize the response time and number of function instances. In this paper, we propose an SF clustering-based scheduling algorithm, called "SF-clustering for utilizing virtual CPUs" (SFCUV), to solve the SF allocation and SF selection problems simultaneously. Experimental results show that SF-CUV can utilize vCPUs to minimize the response time.
Generalized Simplicial Decomposition for Optimal Sensor Selection in Parameter Estimation of Spatiotemporal Processes*

We propose an algorithm based on general sim-plicial decomposition for optimal node activation in large-scale sensor networks. The resulting measurements are to be used to estimate unknown parameters of a spatiotemporal process described by a partial differential equation under some nondifferentiable metrics of estimation accuracy. In this setting, continuous relaxations of the sensor subset selection problem quickly become computationally intractable for extraordinarily large numbers of sensors. We show how generalized simplicial decomposition, which is a recent extension of the classical simplicial decomposition to nondifferentiable optimization, can be employed to drastically reduce the dimensionality of the design problem. Computationally, the resulting algorithm alternates between solving a linear programming subproblem which has an explicit solution and a convex programming subproblem whose dimensionality is typically moderate. A simple separability characterization of optimal solutions is provided and illustrated with a numerical example.
Optimization of PID Controller for Single Phase Inverter Based on ABC

We optimize the Proportional-Integral-Derivative (PID) control parameters for the single-phase inverter using Artificial Bee Colony Algorithm (ABC). The parameters of the PID controller are regarded as the honey source, and the integral time absolute error (ITAE) performance index is selected as the objective function of parameter optimization. The simulation results show that the proposed algorithm can improve the dynamic performance of the control system, enhance the rapidity and stability of the system, and find the global optimal solution effectively. The optimized PID controller can be applied to single-phase inverters with good system stability, which proves the practicability and effectiveness of ABC application in power electronic system control optimization.
A Compact Skin-Shear Device using a Lead-Screw Mechanism

We present a skin-shear actuator based on the lead screw mechanism. The lead screw mechanism is simple, reliable, offers fewer components, and accommodates into compact form- factors. We show mechanical design of a single assembly unit and implement multiple units in a single handheld device. We evaluate the actuator in one instrumentation-based test and one preliminary user study. Tests show that the actuator performance matches with the open-loop control scheme when no load is placed on the actuator. The performance deteriorates with loading, particularly when quicker and high amplitude stroke are required. The user study shows that information throughput with the skin-shear is comparable to vibrations through three digits on the hand. It is shown that small compact actuators (~5g) with efficient mechanisms can render displacements (>3mm) and forces (>1N) for easily differentiating skin-shear cues.
Analysis of taxi driving behavior and driving risk based on trajectory data *

Understanding human driving style and classifying driver's risk pattern is the basis of traffic risk management. The recent rapid increase of the availability of taxi trajectory data, combined with the popular analysis techniques for big data, gives the chance of thorough analysis of taxi drivers' driving style and risk pattern. In this paper, the driving characteristics of 10674 taxies (at Qiangsheng Taxi Corporation) in a month are extracted from trajectory data. The trajectory data includes time, position, motion, as well as operating status. The method adopted in this paper is entropy weight-analytic hierarchy process (Entropy-AHP) with speed, over speed behavior, driving stability, mileage and time, and fatigue driving as first-grade indexes. The weights of indexes and risk value are calculated, then all taxi drivers are grouped into five risk grades. The risk pattern recognized from the data could be particularly helpful for insurance companies to formulate differentiated pricing strategy.
Degradation Estimation of Turbines in Wind Farm Using Denoising Autoencoder Model

We propose a method to estimate the power performance degradation in wind turbines (WTs) that arises from damage in the turbine blade and other components. In general, the single anemometer mounted on the nacelle is unable to measure precise wind speed distributions that the WT receives, thus, degradation of the power output is difficult to evaluate. By focusing on the fact that the power output data of adjacent WTs have some correlation although the wake effect on downstream turbines sometimes exists, our method uses the power output data of every WT in a farm to estimate the amount of degraded power performance of each turbine by the introduction of the virtual variable which corresponds to each turbine’s degraded amount. The feature of the correlation among each WT’s non-degradation data was learned by a denoising autoencoder (DAE). The virtual variables along with the power output are fed into the trained DAE model and these variables were updated by minimizing the reconstruction error. Moreover, the proposed method can perform the estimation even when some WTs are down, i.e., due to the periodical maintenance, and can classify between non-degraded and degraded WTs without enforcing diagnostics to set suitable threshold parameters. We demonstrated the superiority of this novel method over traditional methods by using real and artificial data inputs.
Credit Card Fraud Prediction And Detection using Artificial Neural Network And Self-Organizing Maps

The credit card business has increased speedily over the last two decades. Corporations and establishments are moving towards various online services, which aims to permit their customers with high potency and accessibility. The evolution is a huge step towards potency, accessibility and profitableness of view. Nevertheless, it additionally has some downsides. These smart services are recently prone to significant security related vulnerabilities. Developing business through card depends on the fact that neither the card nor the user needs to be present at the point of transaction. Thus, it is impossible for merchandiser to check weather the cardholder is real or not. Companies’ loss in recent times are majorly due to the credit card fraud and the fraudsters who ceaselessly obtain new ways to commit the unlawful activities. As we know that Artificial Neural Network has the ability to work as a human brain when trained properly. We have also implemented SOM for accuracy purpose. In this paper, we discuss about the performance of the network and their accuracy.
Implementation of Sentiment Classification of Movie Reviews by Supervised Machine Learning Approaches

Entertainment is crucial part of human life entertainments like songs, music, drama and movies etc. So for watching good movies most of the peoples generally prefer theater. If movie is not good then we feel nervous and we think wasted our money and time for watching bad movie so people prefer to go to movie by reading reviews and rating for that movie on various apps like IMDb, flixter and voice etc. But by reading one or two reviews we cannot say movie is good or bad because different peoples have different opinions some peoples like action or some like thrill or romance so people gives reviews based on their area of interest. So we cannot predict movie for that we proposed movie reviews based on sentiment analysis and classification algorithms such as Naïve bays and Random forest (RF). Sentiment analysis generally utilized to identify the sentiment of huge amount of text. We compare naïve bayes and RF machine learning techniques for measuring negative, positive and neutral reviews.
Networked Control under DoS Attacks: Trade-off between Resilience and Data Rate

We study communication-constrained networked control problems for linear time-invariant systems in the presence of Denial-of-Service (DoS) attacks, namely attacks that prevent transmissions over the communication network. Our work aims at exploring the relationship between system resilience and network bandwidth. Given a class of DoS attacks, we characterize the bit-rate bounds that are dependent on the unstable eigenvalues of the dynamic matrix of the plant and the parameters of DoS attacks, beyond which exponential stability of the closed-loop system can be guaranteed. Our characterization clearly shows the trade-off between the communication bandwidth and resilience against DoS. An example is given to illustrate the proposed solution approach.
Crossover Service Fusion Approach Based on Microservice Architecture

Crossover cooperation and fusion between services is becoming very common in the modern service industry. Crossover service fusion can create value that cannot be provided by single-domain services, thus achieving the value-emergence effect of "1+1>2". However, semantic inconsistencies in business and interface make crossover service fusion difficult and time-consuming. This paper proposes an interactive crossover service fusion approach based on microservice architecture to enable smooth and rapid integration of domain services. This approach takes service fusion requirements as the driving force to detect business inconsistencies between the services to be fused, and carries out business process reengineering by human-computer interaction. Then, semantic inconsistencies in service interface matching are detected and solved by splitting and completing parameter concepts to obtain the service fusion design scheme. Finally, the implementation scheme based on microservice architecture transforms the business coupling between domain services into asynchronous data communication, which facilitates the crossover fusion of complex business services. The elderly healthcare application is used to demonstrate and validate our approach.
The Method of the Road Surface Crack Detection by the Improved Otsu Threshold

With great economic value and practical value, the road crack detection is critical for road rehabilitation. In this paper, an improved Otsu threshold crack detection method based on gray histogram is proposed. By modifying the probability weighting factor of the gray histogram, the proposed method improves the accuracy of target extraction, avoids the problem of prominent peaks, solves the problem of road image processing of various crack types. The experiment results show that the proposed method not only has good consistency in detecting cracks on different pavements, but also has fast calculation speed.
A Probabilistic Approach for WiFi Fingerprint Localization in Severely Dynamic Indoor Environments

Taking advantage of widely deployed access points (AP), WiFi fingerprint based localization is of importance in indoor internet-of-things (IOT) environments. Nevertheless, spatio-temporal variation is one of its intractable problems, indicating severely environmental dynamics and uncertainty of decision. In this case, the localization accuracy drops significantly. In this paper, we attempt to overcome effects of spatio-temporal variations from two aspects: filtering of the training data and selection of partially valuable APs for matching in test phase. The key idea is to match partial unaffected measurements with ‘clean’ unaffected fingerprints. Bayesian framework and category model are presented for WiFi fingerprints. Two binary hidden variables with different dimensions are introduced to identify singular fingerprints and affected measurements respectively by employing expectation-maximization (EM) algorithms. EM based filter and simultaneous AP selection and localization are then proposed to obtain an optimal matching. Experimental results show that our proposed scheme greatly improves the localization accuracy in severely dynamic indoor environments.
A 4-year Timeliness Analysis of the Lightning Waming in Shenzhen China

In this paper, a statistical analysis of the lightning warning in Shenzhen from 2014 to 2017 is conducted. Based on 115 lightning cases with the number of lightning larger than 100, the duration of lightning warning signals and the lifecycle of lightning activities are detailedly analyzed. The results show that, the issuing time of lightning warning signals by Shenzhen Municipal Meteorological Observatory is averagely 31.7 minutes earlier than the initial lightning. Moreover, most of the lightning warning signals are issued between June and August each year, accounting for more than 60% of the cases in the whole year. It is also found that, the lag time of cancelling warning signals in the early rainy season is significantly shorter than that in the latter rainy season. Also, according to the observational data, there is still ample room for the improvement of the lag time, especially when the lightning frequency is relatively small. Finally, the conclusions put forward effective strategies to improve the timeliness of the lightning warning signals.
Development of a Hybrid Locomotion Robot for Earthquake Search and Rescue in Partially Collapsed Building

In this paper, we developed a hybrid locomotion robot for earthquake search and rescue in partially collapsed buildings. The objective of this research is to find a new method to avoid rescuers from accessing partially collapsed buildings, and instead access these buildings using a robot, searching for trapped victims and transporting necessary goods (food, water, communication system and so on) to them. To achieve this goal, we design a robot with several functions which include: (1) hybrid locomotion (flying and moving on flat surfaces), (2) goods transportation mechanism, (3) victim detection sensing system, (4) victims position estimation system. In this paper, the details of the robot and some experimental results are presented.