Miracle-dz/UniFAR-FTUs huggingface dataset:
Laser Sintering Control for Metal Additive Manufacturing by PDE Backstepping

Metal additive manufacturing (AM) has been intensively advanced due to numerous industrial applications such as automobiles, aerospace, consumer electronics, and medical devices. The dynamics of the melt pool via laser sintering for metal AM has been studied by means of the thermodynamic phase change model known as "Stefan problem". In this paper, we develop a control design for the laser power to drive the depth of the melt pool to a desired setpoint. The governing equation is described by a partial differential equation (PDE) defined on a time-varying spatial domain which is dependent on the PDE state, and the optical penetration of the laser energy affects the PDE dynamics in domain as well as at surface boundary. The control design is derived via the backstepping method for moving boundary PDEs. The closed loop system is proven to satisfy some conditions to validate the physical model, and its origin is shown to be exponentially stable using Lyapunov method. Numerical simulation illustrates a desired performance of the proposed control law.
Vessel Route Clustering in the Information and Telecommunication System of the Maritime Border Guard

The article solves the problem of data classification concerning the routes of vessel traffic in the marine surface monitoring system. For the purpose of this classification, it is proposed to use clustering, the method developed within the framework of this study. The peculiarity of the method is to take into account the specifics of presenting data on the routes of vessel traffic in the information and telecommunication system of the marine security. This specificity is considered in the routing metric and features of the route clusters formation as well as their centers localization. Such data analysis in the marine surface monitoring system will allow the problem of risk assessment to get solved by detecting spatial anomalies in the routes of vessel traffic.
Similar Face Recognition Using the IE-CNN Model

In the field of face recognition, similar face recognition is difficult due to the high degree of similarity of the face structure. The following two factors are needed to make progress in this field: (i) the availability of large scale similar face training datasets, and (ii) a fine-grained face recognition method. With the above factors fulfilled, we make two contributions. First, we show how a large scale similar face dataset (SFD) can be assembled by a combination of automation and human in the loop, and divide the dataset into five grades according to different degrees of similarity. Second, a new fine-grained face feature extraction method is proposed to solve this problem using the attention mechanism which combines the Internal Features and External Features. The Labeled Faces in the Wild (LFW) database, CASIA-WebFace and similar face dataset (SFD) were selected for experiments. It turns out that the true positive rate is improved by 1.94 - 5.66% and the recognition accuracy rate improved by 2.08 – 5.8% for the LFW and CASIA-WebFace database, respectively. Meanwhile for SFD, the recognition accuracy rate improved by 18.80 – 35.84%.
Joint Optimization of Energy Transfer Scheduling and Power Control in MIMO Wireless Powered Communication Networks

Hybrid beamforming is a low-cost alternative to digital beamforming with its capability to operate with fewer radio frequency (RF) chains than antennas. However, the diminishing degree of freedom from utilizing fewer RF chains results in considerable performance degradation. To circumvent this setback, scheduling of energy harvesting (SEH) has been recently proposed to provide an additional degree of freedom. In this letter, we study the optimization of SEH together with the beamforming weights, energy and data transfer intervals, and uplink transmit power. The objective is minimizing the total duration of uplink and downlink transmission, whereas the constraints include minimum data transfer, maximum allowed transmit power and hybrid beamforming requirements. We formulate the non-convex optimization problem and convert it to the equivalent rank constrained semidefinite programming problem. We then propose efficient solution methodologies based on iteratively moving the rank constraints to the objective function as a penalty. Extensive simulations demonstrate that SEH is an effective circumvention to the RF chain scarcity, culminating in up to 26% delay performance gain.
A Personalized and Approximated Spatial Keyword Query Approach

With the universal use of GPS and rapid increase of spatial Web objects, spatial keyword query has been widely used in Location-Based Services (LBS). Most of the existing spatial keyword query processing models only support location proximity and strict text matching which makes the semantically related objects cannot be provided to users and even may lead to the empty answer problem. In addition, the current index structures (such as IR-tree, Quadtree) cannot process numerical attributes which are usually contained in the descriptive information associated to the spatial objects. To deal with these problems, this paper proposes a spatial keyword query method that can support semantic approximate query processing. Firstly, the user original query is expanded by Conditional Generative Adversarial Nets (CGAN) method to generate a series of query keywords that are semantically related to the original query keywords. And then, a hybrid index structure called AIR-tree is built to facilitate the query matching, which can support the text semantic matching and process numerical attributes with Skyline method. Experimental analysis and results demonstrate that the proposed method achieves higher execution efficiency and better user satisfaction compared with the state-of-the-art methods.
Alternating Optimization Based Low Complexity Hybrid Precoding in Millimeter Wave MIMO Systems

Hybrid precoding is an effective solution to reduce the cost of millimeter wave systems. In recent years, joint optimization has begun to attract the attention of researchers due to its excellent performance. In this letter, we adopt the alternating optimization method to design a low complexity hybrid precoding. In particular, the spectral efficiency optimization problems are simplified to sub-optimization problems under certain constraints. For each sub-optimization problem, we alternatively optimize the columns of the analog precoding matrix and combining matrix by using the projected gradient method. Then, the digital domain matrices are derived from the equivalent channel. Simulation results and complexity analyses show that the performance of the proposed hybrid precoding based on alternating optimization is close to the unconstrained optimal precoder and the algorithm has lower complexity.
Code Design for Non-Coherent Index Modulation

This letter explores the concept of non-coherent index modulation (IM) in a general setting from the standpoint of coding theory. In doing so, we establish a bijective mapping from the set of activation patterns of an IM system to the set of codewords of a binary block code. We show that a non-coherent IM system whose active indices are independently affected by additive white Gaussian noise and Rayleigh fading can achieve a diversity order of <inline-formula> <tex-math notation="LaTeX">$t+1$ </tex-math></inline-formula> if its activation patterns follow a <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-error correcting code designed for a Z-channel. Activation codes employed for IM systems are either constant or variable-weight codes. Using a variable-weight code requires a slightly higher detection complexity, but it can result in a diversity order significantly higher than the maximum achievable diversity order of a constant-weight code.
A Strategy For Relocation of The Mobile Sink in Wireless Sensor Networks

This paper proposes a strategy for movement control of the robot assisted sink node in wireless sensor networks. As a basic condition, the mobile sink node knows the coordinates, the residual energies and the degrees of all the static sensor nodes. A weight is defined for each node dependent on its transmitting power, residual energy and degree. By the relocation strategy, the mobile sink moves towards the weight center (TWC) of all the nodes within its coverage. Average power savings given by our strategy TWC and the Markov chain based robot-assisted weighted relocation algorithm (MRWR) are compared. The comparison shows that TWC can save more than 30 percent total transmission power for those static sensor nodes which are kept connected to the mobile sink before and after its relocation.
Leader-follower Trajectory Tracking Control for a Mobile Robot With Unknown Amplitudes of Reference Velocities and Input Disturbances

This paper studies the leader-follower trajectory tracking control problem of a unicycle-type mobile robot. The follower robot to be controlled is subject to input disturbances, and does not have full access to the reference linear and angular velocities of the leader robot. The leader’s velocities and input disturbances are assumed to be linear combinations of a step signal and a finite number of sinusoidal signals. Within the output regulation framework, a dynamic control law is proposed such that the controlled mobile robot can globally track the leader’s trajectory if the leader’s angular velocity satisfies a certain condition. Meanwhile, the rejection of the input disturbances can also be achieved. Finally, the effectiveness of the dynamic control law is verified with simulation results.
Design of the Primary Side LCC Compensation Network Based on ZVS for Wireless Power Transfer Systems

When a wireless power transfer system (WPTS) needs to be controlled to regulate the output current or voltage, phase-shifting control is a good choice. So, the operation of zero-voltage switching (ZVS) of the system inverter needs to be considered in the process of phase-shifting. We present a parameter design method of the primary LCC compensation network to realize ZVS of the full-bridge inverter for the WPTS in the process of phase-shifting without other auxiliary circuits. In this paper, the condition realizing ZVS of lagging-leg switches is derived and analyzed, and the analytical results are verified by 1kW wireless power transfer prototype.
Segmental Deployment of Neural Network in Cloud Robotic System

In this paper, we describe a new method for ep neural networks in the field of computer vision, which can effectively solve the difficulty of applying deep learning in the cloud robotic system. By segmenting the trained network, most of the computing tasks can be cut out and offloaded to the cloud. By effective feature extraction and compression methods, the computing power of robot and cloud can be integrated and coordinated. A method of selecting the split points of the network model and a method of data transmission and compression in the communication between robots and cloud after segmenting are given based on the characteristics of machine vision tasks, and the theoretical analysis is carried out. In the experiment, the effectiveness of all the above methods is verified by comparing the compression capability, response time and network performance of the actual network model. The experimental results show that with the use of segmental methods in cloud robotic system, the task of deep network is processed in real time, while the performance is almost guaranteed.
Graph Energy Based Centrality Measure to Identify Influential Nodes in Social Networks

One of the measures to analyze complex network is vertex centrality; it can reveal existing network patterns. It helps us in understanding networks. One of the measures to analyze complex network is vertex centrality, and it can reveal existing network patterns. It helps us in understanding networks and their components by analyzing their structural properties. The social network is one of the complex networks which is composed of nodes and relationships. It is growing very vastly due to the addition of new nodes every day. All nodes are not equally important in such a vast network hence, identifying influential nodes becomes a practical problem. Centrality measures were introduced to quantify the importance of nodes in networks. The various criterion is used to select critical nodes in the network. Therefore, different centrality measures like Betweenness Centrality, Degree Centrality, Closeness Centrality, and other well-known centrality measures are used to identify essential nodes. We have proposed an algorithm to compute a centrality using graph energy called Energy-Based-Centrality-Measure (EBCM) in this paper. It identifies the central nodes based on a graph invariant called graph energy. EBCM gives a better understanding of the current network by analyzing the impact of node deletion on graph connectivity and thereby helps us in achieving a better network understanding ability and maintenance.
Energy Disaggregation for NILM applications using Shallow and Deep Networks

Comparative study is conducted by applying deep and shallow network on energy data for non-intrusive load monitoring (NILM) applications. From the total energy consumption, various appliance consumption can be disaggregated using shallow and deep networks. The study highlights the effective method for energy disaggregation from the total energy consumption using algorithms rather than the expensive method of separately metering individual loads. Shallow algorithm support vector regression and deep learning algorithms such as deep neural network (DNN) and long short term memory (LSTM) are used in this study and their performance is evaluated.
Model Predictive Control of Three Level Buck/Boost Converter for Bipolar DC Microgrid Applications

Emergence of bipolar dc microgrids calls for the need of bipolar converter configurations for the integration of battery energy storage system (BESS), electric vehicle dc fast charging stations (EVCS) etc. This paper proposes model predictive control of a bipolar bidirectional buck/boost converter derived from three level converter (TLC) configuration in a bipolar dc microgrid. Bipolar dc microgrid is fed by power from solar PV systems and BESS. State space analysis is done and discrete model is developed. Simulation of the proposed system with model predictive control (MPC) is done in Simulink MATLAB and analysed for the voltage unbalance issues of bipolar dc microgrid under varying conditions of photovoltaic generations and load disturbance. From the simulation results, proposed converter with model predictive control technique gives faster response in mitigating the voltage unbalance and grid voltage regulation issues arising in bipolar dc microgrid.
Blockchain Technology: A Novel Approach in Information Security Research

A blockchain technology originally block chain, is the recent development in the industry gaining great ground. It is the concept by which the digital information is distributed across the network but it is not duplicated. This growing list of records, called blocks, is associated with a hash function. The technology allows any node which is part of ant network to join or leave the chain process execution and the execution process is independent on the identification of each network. Every network can update their entries into a record of, and they can control how the stated records are further processed. This technology is introduced a decade ago considering a concept of unique currency but till date it is facing certain technical challenges like scalability, block size, network propagation, and security and so on. The paper discusses about the challenges, opportunities and recent advances in this area.
Cyber-Physical-Social Systems: A State-of-the-Art Survey, Challenges and Opportunities

It is the overriding trend of the present-day world that traditional systems and mobile devices are currently transforming into intelligent systems and smart devices. Against this backdrop, cyber-physical systems (CPSs) and Internet-of-Things (IoT) emerge as the times require. To achieve the parallel interactions between the human world and the computer network, IoT along with wireless mobile communication and computing open up some future opportunities as well as challenges for constructing a novel cyber-physical-social system (CPSS) that takes human factors into account during the system operation and management. In this article, a brief comprehensive survey is provided on some of the current research work that contributes to enabling CPSSs. Some crucial aspects of CPSSs are identified, including: the development from CPSs to CPSSs, architecture design, applications, standards, real-world case studies, enabling techniques and networks for CPSSs. To lay a foundation for the development of the upcoming smart world, we further propose a virtualization architecture and an integrated framework of caching, computing and networking for CPSSs. Simulations verify the performance improvement of the proposals. At last, some research issues with challenges and possible solutions are unearthed for researchers in the related research areas.
Survey Of Various Challenges Analysis Of Communication Between Devices In Mobile Network Applications

Hybrid Device to Device - Cellular networks provides an effective infrastructure to enhance cellular network communication by means of spectral efficiency, energy efficiency, capacity, extending coverage area, robustness and various quality of services(QoS). In this survey, we are going to analyze various features of Communication between Devices (CBD) applications in cellular networks. Energy consumption in battery operated devices in networks is very serious issue throughout the world and also effective usage of spectrum efficiency due to growing demand for higher capacity in cellular mobile networks. This paper provides an overview of Channel and resource sharing, power saving, routing, relay selecting and security issues in hybrid networks by analyzing various techniques, protocols and algorithms.
Complementary Neural Networks for Managing Innovation Projects

the modelling of neural networks for managing project innovation projects under uncertainty (observability, unverified information and imperfection of loss estimation methods) is considered. The principles and methods of forming innovation projects are defined. The properties of neural networks concerning the parallel processing of information, self-organization, training, generalization, etc. are determined. The general model of the neural network, its elements and training techniques are given. The concepts of neural network and complementarity classes are introduced. The concept of complementary neural network architecture is defined. Within the networks which are determined by elements of the complementary artificial neural network and a signature that forms classes of operations of the neural networks concerning innovation projects.
Motivation of Computer Science Engineering Students: Analysis and Recommendations

This Research Full Paper presents an empirical study about the motivation of Computer Science Engineering students. It is well known that student´s performance is affected by the academic conditions of the students, as well as their aptitudes and motivation. In the case of Engineering disciplines, motivation is particularly critical because the difficulty of Engineering degrees. It can lead to low levels of effectiveness and motivation, resulting in high rates of absenteeism and dropout. Therefore, it is important to reach a complete understanding of Engineering students’ motivation and to draw strategies to boost it. This paper presents a case study performed with 78 Computer Science Engineering students of the Technical University of Madrid. This experience allows to analyze empirically student´s motivation and to establish learning recommendations to encourage it, as well as to compare motivational differences between students from different courses.
Distributed Actuator Fault Detection and Estimation for Linear Multi Agent Systems

This paper proposes a new distributed actuator fault detection and estimation scheme for linear multi agent systems. Information exchange between the agents is described by an undirected graph. The considered faults are time-varying and they have abrupt characteristic. Also, state variables of the system are not available. The proposed distributed fault detection algorithm designs a diagnostic estimator based on the relative local information to monitor the status of each agent, generate residual signal and detect fault. After that, an adaptive fault estimation module is activated to estimate the detected fault. The proposed scheme is formulated as a linear matrix inequality problem and its stability analysis is proved. Finally, to verify the effectiveness of the proposed structure, some simulation results are presented.
IDLP: An Efficient Intrusion Detection and Location-Aware Prevention Mechanism for Network Coding-Enabled Mobile Small Cells

Mobile small cell technology is considered as a 5G enabling technology for delivering ubiquitous 5G services in a cost-effective and energy efficient manner. Moreover, Network Coding (NC) technology can be foreseen as a promising solution for the wireless network of mobile small cells to increase its throughput and improve its performance. However, NC-enabled mobile small cells are vulnerable to pollution attacks due to the inherent vulnerabilities of NC. Although there are several works on pollution attack detection, the attackers may continue to pollute packets in the next transmission of coded packets of the same generation from the source node to the destination nodes. Therefore, in this paper, we present an intrusion detection and location-aware prevention (IDLP) mechanism which does not only detect the polluted packets and drop them but also identify the attacker’s exact location so as to block them and prevent packet pollution in the next transmissions. In the proposed IDLP mechanism, the detection and locating schemes are based on a null space-based homomorphic MAC scheme. However, the proposed IDLP mechanism is efficient because, in its initial phase (i.e., Phase 1), it is not needed to be applied to all mobile devices in order to protect the NC-enabled mobile small cells from the depletion of their resources. The proposed efficient IDLP mechanism has been implemented in Kodo, and its performance has been evaluated and compared with our previous IDPS scheme proposed in [1], in terms of computational complexity, communicational overhead, and successfully decoding probability as well.
Consistent approximation of optimal control problems using Bernstein polynomials

We present a direct method for the solution of nonlinear optimal control problems based on Bernstein polynomial approximations. We show, using a rigorous setting, that the proposed method yields consistent approximations of time continuous optimal control problems. We demonstrate that the method can also be used for the estimation of optimal control problems costates. This result leads to the formulation of the Covector Mapping Theorem for Bernstein polynomial approximation. Finally, we exploit the numerical and geometric properties of Bernstein polynomials, and illustrate the advantages of the method through numerical examples.