bluuebunny/crossref_metadata_2025 huggingface dataset:
3D CNN-Based Speech Emotion Recognition Using K-Means Clustering and Spectrograms
Authors: Hajarolasvadi Noushin , Demirel Hasan
Published: May 2019

Detecting human intentions and emotions helps improve human–robot interactions. Emotion recognition has been a challenging research direction in the past decade. This paper proposes an emotion recognition system based on analysis of speech signals. Firstly, we split each speech signal into overlapping frames of the same length. Next, we extract an 88-dimensional vector of audio features including Mel Frequency Cepstral Coefficients (MFCC), pitch, and intensity for each of the respective frames. In parallel, the spectrogram of each frame is generated. In the final preprocessing step, by applying k-means clustering on the extracted features of all frames of each audio signal, we select k most discriminant frames, namely keyframes, to summarize the speech signal. Then, the sequence of the corresponding spectrograms of keyframes is encapsulated in a 3D tensor. These tensors are used to train and test a 3D Convolutional Neural network using a 10-fold cross-validation approach. The proposed 3D CNN has two convolutional layers and one fully connected layer. Experiments are conducted on the Surrey Audio-Visual Expressed Emotion (SAVEE), Ryerson Multimedia Laboratory (RML), and eNTERFACE’05 databases. The results are superior to the state-of-the-art methods reported in the literature.
On Properties of Distance-Based Entropies on Fullerene Graphs
Authors: Ghorbani Modjtaba , Dehmer Matthias , Rajabi-Parsa Mina , Mowshowitz Abbe , Emmert-Streib Frank
Published: May 2019

In this paper, we study several distance-based entropy measures on fullerene graphs. These include the topological information content of a graph I a ( G ) , a degree-based entropy measure, the eccentric-entropy I f σ ( G ) , the Hosoya entropy H ( G ) and, finally, the radial centric information entropy H e c c . We compare these measures on two infinite classes of fullerene graphs denoted by A 12 n + 4 and B 12 n + 6 . We have chosen these measures as they are easily computable and capture meaningful graph properties. To demonstrate the utility of these measures, we investigate the Pearson correlation between them on the fullerene graphs.
Quantum Probes for Ohmic Environments at Thermal Equilibrium
Authors: Salari Sehdaran Fahimeh , Bina Matteo , Benedetti Claudia , Paris Matteo
Published: May 2019

It is often the case that the environment of a quantum system may be described as a bath of oscillators with an ohmic density of states. In turn, the precise characterization of these classes of environments is a crucial tool to engineer decoherence or to tailor quantum information protocols. Recently, the use of quantum probes in characterizing ohmic environments at zero-temperature has been discussed, showing that a single qubit provides precise estimation of the cutoff frequency. On the other hand, thermal noise often spoil quantum probing schemes, and for this reason we here extend the analysis to a complex system at thermal equilibrium. In particular, we discuss the interplay between thermal fluctuations and time evolution in determining the precision attainable by quantum probes. Our results show that the presence of thermal fluctuations degrades the precision for low values of the cutoff frequency, i.e., values of the order ω c ≲ T (in natural units). For larger values of ω c , decoherence is mostly due to the structure of environment, rather than thermal fluctuations, such that quantum probing by a single qubit is still an effective estimation procedure.
Discriminative Structure Learning of Bayesian Network Classifiers from Training Dataset and Testing Instance
Authors: Wang Limin , Liu Yang , Mammadov Musa , Sun Minghui , Qi Sikai
Published: May 2019

Over recent decades, the rapid growth in data makes ever more urgent the quest for highly scalable Bayesian networks that have better classification performance and expressivity (that is, capacity to respectively describe dependence relationships between attributes in different situations). To reduce the search space of possible attribute orders, k-dependence Bayesian classifier (KDB) simply applies mutual information to sort attributes. This sorting strategy is very efficient but it neglects the conditional dependencies between attributes and is sub-optimal. In this paper, we propose a novel sorting strategy and extend KDB from a single restricted network to unrestricted ensemble networks, i.e., unrestricted Bayesian classifier (UKDB), in terms of Markov blanket analysis and target learning. Target learning is a framework that takes each unlabeled testing instance P as a target and builds a specific Bayesian model Bayesian network classifiers (BNC) P to complement BNC T learned from training data T . UKDB respectively introduced UKDB P and UKDB T to flexibly describe the change in dependence relationships for different testing instances and the robust dependence relationships implicated in training data. They both use UKDB as the base classifier by applying the same learning strategy while modeling different parts of the data space, thus they are complementary in nature. The extensive experimental results on the Wisconsin breast cancer database for case study and other 10 datasets by involving classifiers with different structure complexities, such as Naive Bayes (0-dependence), Tree augmented Naive Bayes (1-dependence) and KDB (arbitrary k-dependence), prove the effectiveness and robustness of the proposed approach.
Negentropy Spectrum Decomposition and Its Application in Compound Fault Diagnosis of Rolling Bearing
Authors: Xu Yonggang , Chen Junran , Ma Chaoyong , Zhang Kun , Cao Jinxin
Published: May 2019

The rolling bearings often suffer from compound fault in practice. Compared with single fault, compound fault contains multiple fault features that are coupled together and make it difficult to detect and extract all fault features by traditional methods such as Hilbert envelope demodulation, wavelet transform and empirical node decomposition (EMD). In order to realize the compound fault diagnosis of rolling bearings and improve the diagnostic accuracy, we developed negentropy spectrum decomposition (NSD), which is based on fast empirical wavelet transform (FEWT) and spectral negentropy, with cyclic extraction as the extraction method. The infogram is constructed by FEWT combined with spectral negentropy to select the best band center and bandwidth for band-pass filtering. The filtered signal is used as a new measured signal, and the fast empirical wavelet transform combined with spectral negentropy is used to filter the new measured signal again. This operation is repeated to achieve cyclic extraction, until the signal no longer contains obvious fault features. After obtaining the envelope of all extracted components, compound fault diagnosis of rolling bearings can be realized. The analysis of the simulation signal and the experimental signal shows that the method can realize the compound fault diagnosis of rolling bearings, which verifies the feasibility and effectiveness of the method. The method proposed in this paper can detect and extract all the fault features of compound fault completely, and it is more reliable for the diagnosis of compound fault. Therefore, the method has practical significance in rolling bearing compound fault diagnosis.
Entropy Generation and Heat Transfer Analysis in MHD Unsteady Rotating Flow for Aqueous Suspensions of Carbon Nanotubes with Nonlinear Thermal Radiation and Viscous Dissipation Effect
Authors: Jawad Muhammad , Shah Zahir , Khan Aurungzeb , Khan Waris , Kumam Poom , Islam Saeed
Published: May 2019

The impact of nonlinear thermal radiations rotating with the augmentation of heat transfer flow of time-dependent single-walled carbon nanotubes is investigated. Nanofluid flow is induced by a shrinking sheet within the rotating system. The impact of viscous dissipation is taken into account. Nanofluid flow is assumed to be electrically conducting. Similarity transformations are applied to transform PDEs (partial differential equations) into ODEs (ordinary differential equations). Transformed equations are solved by the homotopy analysis method (HAM). The radiative source term is involved in the energy equation. For entropy generation, the second law of thermodynamics is applied. The Bejan number represents the current investigation of non-dimensional entropy generation due to heat transfer and fluid friction. The results obtained indicate that the thickness of the boundary layer decreases for greater values of the rotation parameter. Moreover, the unsteadiness parameter decreases the temperature profile and increases the velocity field. Skin friction and the Nusselt number are also physically and numerically analyzed.
Is Political Ideology Stable? Evidence from Long-Serving Members of the United States Congress
Authors: Mixon Franklin G. , Sankaran Chandini , Upadhyaya Kamal P.
Published: May 2019

This study extends the political science and political psychology literature on the political ideology of lawmakers by addressing the following question: How stable is a legislator’s political ideology over time? In doing so, we employ Nokken–Poole scores of legislators’ political ideology for members of the United States (U.S.) House of Representatives and the U.S. Senate who were elected prior to the 103rd Congress that began in early 1991 and who served consecutively through the 115th Congress, which ended in early 2019. Results from individual time-series estimations suggest that political ideology is unstable over time for a sizable portion of the members of both major political parties who serve in the U.S. Congress, while analysis of the pooled data suggests that, after accounting for inertia in political ideology and individual legislator effects, Republican legislators become more conservative over time. These results run somewhat counter to the finding in prior studies that the political ideologies of lawmakers and other political elites are stable over time.
Growth in Agricultural Productivity and Its Components in Bangladeshi Regions (1987–2009): An Application of Bootstrapped Data Envelopment Analysis (DEA)
Authors: Bagchi Mita , Rahman Sanzidur , Shunbo Yao
Published: May 2019

The present study applies a bootstrapped data envelopment analysis (DEA) procedure to compute bias-corrected measures of agricultural total factor productivity (TFP) change and its components (technical change and technical efficiency change) using a panel data of 19 regions of Bangladesh covering a 23-year period (1987–2009), thereby overcoming the limitation of the lack of statistical inference of the conventional non-parametric DEA. Results revealed that overall productivity grew at a modest rate of 0.03%, mainly powered by technological progress at 0.03% and a negligible decline in technical efficiency at 0.004% with large disparities amongst regions. Six regions in the middle order shifted ranks with regard to TFP change following bias correction. The estimated confidence intervals demonstrated that many regions underwent either progress or regress in productivity performance over time. Investments in research and development (R&D), agricultural extension, and crop diversification are suggested to improve regional inequality and declining technical efficiency.
Analysis of the Effect of Industrial Transformation of Resource-Based Cities in Northeast China
Authors: Hou Gaolan , Zou Zhou , Zhang Tianran , Meng Yue
Published: May 2019

Based on the development of the industrial structure of prefecture-level resource-based cities in Northeast China, this paper selects three indicators of industrial structure—diversification, rationalization and upgrading—conducting empirical analysis on the transformation of the industrial structure of these resource-based cities. The effects of the industrial transformation of resource-based cities of the same kind are analysed and the development of industrial structure in different provinces is compared. It indicates that the transformation process and adjustment effect of the industrial structures in resource-based cities of Northeast China were different and were not very satisfactory on the whole and the secondary industry was still the leading industry in most cities. In addition, the article analyses the impact of the industrial transformation on the economic growth of resource-based cities in Northeast China. It can be concluded that the rationalization and upgrading of industrial structure generally promoted the cities’ economic growth. The promotion of industrial structure rationalization was greater than that of upgrading. In order to promote the sustainable development of resource-based cities’ economy in Northeast China and solve problems in the process of the cities’ development, two suggestions are proposed. One is to pay attention to the rational development of the industrial structure, so it is necessary to strengthen the overall planning of resource-based cities in the Northeast region, promote regional coordination and rationally arrange and adjust the industrial structure from the overall scope of the region. Another is to continue to promote the process of industrial structure upgrading by introducing high-tech industries through preferential policies and to develop relevant industries based on the advantages of the cities, instead of just extending the original industry.
The Effect of Continued Team Randomization on Student’s Perception and Performance in a Blended Team-Based Teaching Approach
Authors: Malekigorji Maryam
Published: May 2019

The purpose of this study is to investigate how constant changes in team allocation within a modified flipped team-based learning (FTBL) study can affect student’s perception of a course (gathered by an online questionnaire) and academic performance. This teaching strategy is a team-based learning (TBL) approach combined with flipped classroom learning methodology, in which BSc students studying pharmaceutical science/biotechnology courses in a UK satellite campus in China preview online lectures and apply their knowledge in different in-class activities. The students are randomly assigned into teams in each session. The project was run across the full academic year (sixteen sessions). Students’ perceptions regarding modified FTBL were statistically analyzed, and their academic performance was compared with previous results obtained by the initial FTBL study. Although students initially showed reluctance to leave their ‘comfort zone’—the main limitation of this study—our findings show that learners perceived benefits to the adoption of continued random allocation, which resulted in the removal of limitations from their social clustering and eventual accustomization to this learning approach. Modified FTBL assisted students in enhancing their team-work skills, improving their academic performance, developing their reflective capabilities, improving their rapport building skills, learning and academic performance. Learners also believed that this learning strategy creates critical incidents that can simulate their future work environment, as they might be expected to work in unfamiliar situations. Therefore, the present study indicated strong support for the modified FTBL method, which was seen to work exceptionally well despite some minor problems that students experienced working in a team with different teammates in every session.
HPEFT for Hierarchical Heterogeneous Multi-DAG in a Multigroup Scan UPA System
Authors: Li Yuzhong , Tang Wenming , Liu Guixiong
Published: May 2019

Multidirected acyclic graph (DAG) workflow scheduling is a key problem in the heterogeneous distributed environment in the distributed computing field. A hierarchical heterogeneous multi-DAG workflow problem (HHMDP) was proposed based on the different signal processing workflows produced by different grouping and scanning modes and their hierarchical processing in specific functional signal processing modules in a multigroup scan ultrasonic phased array (UPA) system. A heterogeneous predecessor earliest finish time (HPEFT) algorithm with predecessor pointer adjustment was proposed based on the improved heterogeneous earliest finish time (HEFT) algorithm. The experimental results denote that HPEFT reduces the makespan, ratio of the idle time slot (RITS), and missed deadline rate (MDR) by 3.87–57.68%, 0–6.53%, and 13–58%, respectively, and increases relative relaxation with respect to the deadline (RLD) by 2.27–8.58%, improving the frame rate and resource utilization and reducing the probability of exceeding the real-time period. The multigroup UPA instrument architecture in multi-DAG signal processing flow was also provided. By simulating and verifying the scheduling algorithm, the architecture and the HPEFT algorithm is proved to coordinate the order of each group of signal processing tasks for improving the instrument performance.
Slot-Loaded Microstrip Patch Sensor Antenna for High-Sensitivity Permittivity Characterization
Authors: Yeo Junho , Lee Jong-Ig
Published: May 2019

A slot-loaded microstrip patch sensor antenna is proposed to enhance sensitivity in measuring the permittivity of planar materials. A thin rectangular slot was etched along the radiating edge of a rectangular patch antenna fed by a microstrip transmission line. Two resonant frequencies were created at a lower frequency compared to the single resonant frequency of a conventional ordinary patch antenna. The sensitivity of the proposed slot-loaded patch antenna was measured by the shift in the resonant frequency of the input reflection coefficient when the planar dielectric superstrate was placed above the patch, and was compared with that of a conventional patch antenna without the slot. The two antennas were designed and fabricated on a 0.76 mm-thick RF-35 substrate for the first resonant frequency to resonate at 2.5 GHz under unloaded conditions. Five different standard dielectric samples with dielectric constants ranging from 2.17 to 10.2 were tested for sensitivity comparison. The experiment results showed that the measured sensitivity of the proposed patch antenna were 3.54 to 4.53 times higher, compared to a conventional patch antenna, for the five samples.
Shaping SiC MOSFET Voltage and Current Transitions by Intelligent Control for Reduced EMI Generation
Authors: Xu Congwen , Ma Qishuang , Xu Ping , Cui Tongkai
Published: May 2019

In power converters, the fast switching of the power conversion components results in rapid changes in voltage and current, which results in oscillations and high-level electromagnetic interference (EMI), so the power components become a source of internal electromagnetic interference. Taking SiC Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) as an example, an intelligent control method to suppressing interference sources is proposed in this paper. The combination of open-loop and closed-loop methods can simultaneously reduce the electromagnetic interference generated by voltage and current. Firstly, this paper analyzes how to select a reference signal. The relationship between the time domain and the frequency domain of the noise signal is analyzed. The convolution of the trapezoidal signal and the Gaussian signal is selected as the reference signal, which is named S-shaped signal in this paper. The S-shaped signal has continuous infinitely conductive characteristics, so its spectrum has a large attenuation in the high frequency region. Secondly, a new topology is proposed. Based on the closed-loop gate control, a current control signal is added, which can simultaneously shape the output voltage and control the output current slope. Both the simulation results and the experimental results show that the output voltage can follow the reference signal, S-shaped signal, and the slope and overshoot of output current can be changed. Compared with classical gate driver method, the spectrum of output voltage and output current obtained by the method proposed in this paper has a large attenuation, in other words, the electromagnetic interference is significantly reduced.
Some Structures of Parallel VLSI-Oriented Processing Units for Implementation of Small Size Discrete Fractional Fourier Transforms
Authors: Cariow Aleksandr , Papliński Janusz , Majorkowska-Mech Dorota
Published: May 2019

Discrete orthogonal transforms such as the discrete Fourier transform, discrete cosine transform, discrete Hartley transform, etc., are important tools in numerical analysis, signal processing, and statistical methods. The successful application of transform techniques relies on the existence of efficient fast algorithms for their implementation. A special place in the list of transformations is occupied by the discrete fractional Fourier transform (DFrFT). In this paper, some parallel algorithms and processing unit structures for fast DFrFT implementation are proposed. The approach is based on the resourceful factorization of DFrFT matrices. Some parallel algorithms and processing unit structures for small size DFrFTs such as N = 2, 3, 4, 5, 6, and 7 are presented. In each case, we describe only the most important part of the structures of the processing units, neglecting the description of the auxiliary units and the control circuits.
A Literature Survey on Open Platform Communications (OPC) Applied to Advanced Industrial Environments
Authors: González Isaías , Calderón Antonio José , Figueiredo João , Sousa João M. C.
Published: May 2019

Extensive digitization and interconnection through networks have ushered in a number of new paradigms over the last years: Internet of Things, cyber–physical systems, Industry 4.0, etc. These challenging systems rely on an effective information communication between distributed components. Therefore, the heterogeneity of entities, both hardware and software, must be handled to achieve an operative interoperability and a proper behavior. However, there is also a heterogeneous availability of solutions; different technologies, protocols, and architectures aim to achieve a seamless interconnection. Henceforth, the standardization still requires great efforts from industrial and scientific environments. In this sense, the interface of the open platform communications (OPC) has supported connectivity for automation and supervision infrastructures for more than two decades. The OPC comprises the so-called classic OPC, the original protocol, as well as the last specification, unified architecture (UA). The widespread utilization of the classic OPC together with the powerful functionalities of OPC UA, make the latter one of the main candidates to lead the standardization and systems integration. This paper presents a survey of recent OPC-based systems reported in scientific literature for different domains as well as research projects. The goal of this paper is to provide a broad perspective about the OPC’ applicability and capabilities in order to support the decision about communication interfaces. The results are analyzed and discussed putting special attention on the aforementioned new paradigms. Finally, the main conclusions and open research directions are highlighted.
An Efficient Hardware Accelerator for the MUSIC Algorithm
Authors: Chen Hui , Chen Kai , Cheng Kaifeng , Chen Qinyu , Fu Yuxiang , Li Li
Published: May 2019

As a classical DOA (direction of arrival) estimation algorithm, the multiple signal classification (MUSIC) algorithm can estimate the direction of signal incidence. A major bottleneck in the application of this algorithm is the large computation amount, so accelerating the algorithm to meet the requirements of high real-time and high precision is the focus. In this paper, we design an efficient and reconfigurable accelerator to implement the MUSIC algorithm. Initially, we propose a hardware-friendly MUSIC algorithm without the eigenstructure decomposition of the covariance matrix, which is time consuming and accounts for about 60% of the whole computation. Furthermore, to reduce the computation of the covariance matrix, this paper utilizes the conjugate symmetry property of it and the way of iterative storage, which can also lessen memory access time. Finally, we adopt the stepwise search method to realize the spectral peak search, which can meet the requirements of 1° and 0.1° precision. The accelerator can operate at a maximum frequency of 1 GHz with a 4,765,475.4 μm2 area, and the power dissipation is 238.27 mW after the gate-level synthesis under the TSMC 40-nm CMOS technology with the Synopsys Design Compiler. Our implementation can accelerate the algorithm to meet the high real-time and high precision requirements in applications. Assuming that the case is an eight-element uniform linear array, a single signal source, and 128 snapshots, the computation times of the algorithm in our architecture are 2.8 μs and 22.7 μs for covariance matrix estimation and spectral peak search, respectively.
Two-Stage Energy Management of Multi-Smart Homes With Distributed Generation and Storage
Authors: Tezde Efe Isa , Okumus Halil Ibrahim , Savran Ibrahim
Published: May 2019

This study presents a new two-stage hybrid optimization algorithm for scheduling the power consumption of households that have distributed energy generation and storage. In the first stage, non-identical home energy management systems (HEMSs) are modeled. HEMS may contain distributed generation systems (DGS) such as PV and wind turbines, distributed storage systems (DSS) such as electric vehicle (EV), and batteries. HEMS organizes the controllable appliances considering user preferences, amount of energy generated/stored and electricity price. A group of optimum consumption schedules for each HEMS is calculated by a Genetic Algorithm (GA). In the second stage, a neighborhood energy management system (NEMS) is established based on Bayesian Game (BG). In this game, HEMSs are players and their pre-determined optimal schedules are their actions. NEMS regulates the total power fluctuations by allowing the energy transfer among households. In the proposed algorithm, HEMS decreases the electricity cost of the users, while NEMS flats the load curve of the neighborhood to prevent overloading of the distribution transformer. The proposed HEMS and NEMS models are implemented from scratch. A survey of 250 participants was conducted to determine user habits. The results of the survey and the proposed system were compared. In conclusion, the proposed hybrid energy management system saves power by up to 25% and decreases cost by 8.7% on average.
A Novel Modular Radiation Hardening Approach Applied to a Synchronous Buck Converter
Authors: Banteywalu Solomon , Khan Baseem , De Smedt Valentijn , Leroux Paul
Published: May 2019

Radiation and extreme temperature are the main inhibitors for the use of electronic devices in space applications. Radiation challenges the normal and stable operation of DC-DC converters, used as power supply for onboard systems in satellites and spacecrafts. In this situation, special design techniques known as radiation hardening or radiation tolerant designs have to be employed. In this work, a module level design approach for radiation hardening is addressed. A module in this sense is a constituent of a digital controller, which includes an analog to digital converter (ADC), a digital proportional-integral-derivative (PID) controller, and a digital pulse width modulator (DPWM). As a new Radiation Hardening by Design technique (RHBD), a four module redundancy technique is proposed and applied to the digital voltage mode controller driving a synchronous buck converter, which has been implemented as hardware-in-the-loop (HIL) simulation block in MATLAB/Simulink using Xilinx system generator based on the Zynq-7000 development board (ZYBO). The technique is compared, for reliability and hardware resources requirement, with triple modular redundancy (TMR), five modular redundancy (FMR) and the modified triplex–duplex architecture. Furthermore, radiation induced failures are emulated by switching all duplicated modules inputs to different signals, or to ground during simulation. The simulation results show that the proposed technique has 25% and 30%longer expected life compared to TMR and FMR techniques, respectively, and has the lowest hardware resource requirement compared to FMR and the modified triplex–duplex techniques.
Reversible Data Hiding Using Inter-Component Prediction in Multiview Video Plus Depth
Authors: Lee Jin Young , Kim Cheonshik , Yang Ching-Nung
Published: May 2019

With the advent of 3D video compression and Internet technology, 3D videos have been deployed worldwide. Data hiding is a part of watermarking technologies and has many capabilities. In this paper, we use 3D video as a cover medium for secret communication using a reversible data hiding (RDH) technology. RDH is advantageous, because the cover image can be completely recovered after extraction of the hidden data. Recently, Chung et al. introduced RDH for depth map using prediction-error expansion (PEE) and rhombus prediction for marking of 3D videos. The performance of Chung et al.’s method is efficient, but they did not find the way for developing pixel resources to maximize data capacity. In this paper, we will improve the performance of embedding capacity using PEE, inter-component prediction, and allowable pixel ranges. Inter-component prediction utilizes a strong correlation between the texture image and the depth map in MVD. Moreover, our proposed scheme provides an ability to control the quality of depth map by a simple formula. Experimental results demonstrate that the proposed method is more efficient than the existing RDH methods in terms of capacity.
Laplace Prior-Based Bayesian Compressive Sensing Using K-SVD for Vibration Signal Transmission and Fault Detection
Authors: Ma Yunfei , Jia Xisheng , Hu Qiwei , Xu Daoming , Guo Chiming , Wang Qiang , Wang Shuangchuan
Published: May 2019

Vibration signal transmission plays a fundamental role in equipment prognostics and health management. However, long-term condition monitoring requires signal compression before transmission because of the high sampling frequency. In this paper, an efficient Bayesian compressive sensing algorithm is proposed. The contribution is explicitly decomposed into two components: a multitask scenario and a Laplace prior-based hierarchical model. This combination makes full use of the sparse promotion under Laplace priors and the correlation between sparse blocks to improve the efficiency. Moreover, a K-singular value decomposition (K-SVD) dictionary learning method is used to find the best sparse representation of the signal. Simulation results show that the Laplace prior-based reconstruction performs better than typical algorithms. The comparison between a fixed dictionary and learning dictionary also illustrates the advantage of the K-SVD method. Finally, a fault detection case of a reconstructed signal is analyzed. The effectiveness of the proposed method is validated by simulation and experimental tests.
A New Hybrid Fault Tolerance Approach for Internet of Things
Authors: Nazari Cheraghlou Mehdi , Khadem-Zadeh Ahmad , Haghparast Majid
Published: May 2019

In the Distributed Management Task Force, DMTF, the management software in the Internet of things (IoT) should have five abilities including Fault Tolerance, Configuration, Accounting, Performance, and Security. Given the importance of IoT management and Fault Tolerance Capacity, this paper has introduced a new architecture of Fault Tolerance. The proposed hybrid architecture has used all of the reactive and proactive policies simultaneously in its structure. Another objective of the current paper was to develop a measurement indicator to measure the fault tolerance capacity in different architectures. The CloudSim simulator has been used to evaluate and compare the proposed architecture. In addition to CloudSim, another simulator was implemented that was based on the Pegasus-Workflow Management System (WMS) in order to validate the architecture that is proposed in this article. Finally, fuzzy inference systems were designed in a third step to model and evaluate the fault tolerance in various architectures. Based on the results, the positive effect of using various combined Reactive and Proactive policies in increasing the fault tolerance in the proposed architecture has been prominently evident and confirmed.
Design of Fuzzy-PI and Fuzzy-Sliding Mode Controllers for Single-Phase Two-Stages Grid-Connected Transformerless Photovoltaic Inverter
Authors: Zeb Kamran , Islam Saif Ul , Din Waqar Ud , Khan Imran , Ishfaq Muhammad , Busarello Tiago Davi Curi , Ahmad Iftikhar , Kim Hee Je
Published: May 2019

Conventional Energy Resources (CER) are being rapidly replaced by Renewable Energy Resources (RER) due to their abundant, environmentally friendly, clean, and inexhaustible nature. In recent years, Solar Photovoltaic (SPV) energy installation is booming at a rapid rate among various RER. Grid-Connected PVS required advance DC-link controllers to overcome second harmonic ripple and current controllers to feed-in high-quality current to the grid. This paper successfully presents the design of a Fuzzy-Logic Based PI (F-PI) and Fuzzy-Logic based Sliding Mode Controller (F-SMC) for the DC-link voltage controller and Proportional Resonant (PR) with Resonant Harmonic Compensator (RHC) as a current controller for a Single-Phase Two-Stages Grid-connected Transformerless (STGT) Photovoltaic (PV) Inverter. The current controller is designed with and without a feedforward PV power loop to improve dynamics and control. A Second Order General Integral (SOGI)-based Phase Lock Loop (PLL) is also designed that has a fast-dynamic response, fast-tracking accuracy, and harmonic immunity. A 3 kW STGT-PV system is used for simulation in Matlab/Simulink. A comparative assessment of designed controllers is carried out with a conventionally well-tuned PI controller. The designed controllers improve the steady-state and dynamic performance of the grid-connected PV system. In addition, the results, performance measure analysis, and harmonics contents authenticate the robustness, fastness, and effectiveness of the designed controllers, related to former works.
Joint Space and Time Processing for Unknown Mutual Coupling Blind Calibration and Mixed Sources Identification Using Uniform Circular Array
Authors: Wang Kai , Wang Ling , Xie Jian , Wang Yuexian , Zhang Zhanolin
Published: May 2019

In classification and localization of mixed far-field and near-field sources, the unknown mutual coupling degrades the performance of most high-resolution algorithms. In practice, the assumption of an ideal receiving sensor array is rarely satisfied. This paper proposes an effective algorithm of mixed sources identification using uniform circular array under unknown mutual coupling. Firstly, according to rank reduction and joint space–time processing, the directions of arrival of far-field sources is estimated directly without mutual coupling elimination. Addition, the joint space–time processing can improve the estimation results in the case of low signal noise ratio of incoming signal sources and small number of snapshots. Then, these estimates are adopted to reconstruct the mutual coupling matrix. Finally, both direction and range parameters of near-field sources are obtained through spatial search after mutual coupling effects and far-field components elimination. The proposed algorithm is described in detail, and its behavior is illustrated by numerical examples.
Predictive Energy Control Strategy for Peak Shaving and Shifting Using BESS and PV Generation Applied to the Retail Sector
Authors: Barchi Grazia , Pierro Marco , Moser David
Published: May 2019

As is known, a reduction in CO 2 emissions is closely related to the improvement of energy efficiency and the increasing use of renewable energy sources in building stock due to its high contribution to worldwide energy consumption. The retail sector has become particularly interesting in this sense, because commercial buildings are no longer just places where a variety of services are offered to customers. In fact, they can be beacons of energy efficiency. In this paper, we propose a predictive energy control strategy that, through the combination of production and demand forecasting, can effectively shave and shift the peak consumption of shopping malls equipped with battery energy storage systems (BESS). The adopted optimization strategy takes into account the variability of electricity tariffs over time, as is customary in some European countries. The performed energy and economic simulations based on the experimental data collected in an Italian shopping mall clearly highlight the benefits in terms of energy and economic savings. Moreover, the reported results lead to the conclusion that BESS management, photovoltaic (PV) generation, and peak switch strategies can have a reasonable pay-back investment time even for buildings with a large energy demand.
High-Performance Time Server Core for FPGA System-on-Chip
Authors: Viejo Julian , Juan-Chico Jorge , Bellido Manuel J. , Ruiz-de-Clavijo Paulino , Guerrero David , Ostua Enrique , Cano German
Published: May 2019

This paper presents the complete design and implementation of a low-cost, low-footprint, network time protocol server core for field programmable gate arrays. The core uses a carefully designed modular architecture, which is fully implemented in hardware using digital circuits and systems. Most remarkable novelties introduced are a hardware-optimized timekeeping algorithm implementation, and a full-hardware protocol stack and automatic network configuration. As a result, the core is able to achieve similar accuracy and performance to typical high-performance network time protocol server equipment. The core uses a standard global positioning system receiver as time reference, has a small footprint and can easily fit in a low-range field-programmable chip, greatly scaling down from previous system-on-chip time synchronization systems. Accuracy and performance results show that the core can serve hundreds of thousands of network time clients with negligible accuracy degradation, in contrast to state-of-the-art high-performance time server equipment. Therefore, this core provides a valuable time server solution for a wide range of emerging embedded and distributed network applications such as the Internet of Things and the smart grid, at a fraction of the cost and footprint of current discrete and embedded solutions.
Enabling Green Wireless Sensor Networks: Energy Efficient T-MAC Using Markov Chain Based Optimization
Authors: Ram Mahendra , Kumar Sushil , Kumar Vinod , Sikandar Ajay , Kharel Rupak
Published: May 2019

Due to the rapidly growing sensor-enabled connected world around us, with the continuously decreasing size of sensors from smaller to tiny, energy efficiency in wireless sensor networks has drawn ample consideration in both academia as well as in industries’ R&D. The literature of energy efficiency in wireless sensor networks (WSNs) is focused on the three layers of wireless communication, namely the physical, Medium Access Control (MAC) and network layers. Physical layer-centric energy efficiency techniques have limited capabilities due to hardware designs and size considerations. Network layer-centric energy efficiency approaches have been constrained, in view of network dynamics and available network infrastructures. However, energy efficiency at the MAC layer requires a traffic cooperative transmission control. In this context, this paper presents a one-dimensional discrete-time Markov chain analytical model of the Timeout Medium Access Control (T-MAC) protocol. Specifically, an analytical model is derived for T-MAC focusing on an analysis of service delay, throughput, energy consumption and power efficiency under unsaturated traffic conditions. The service delay model calculates the average service delay using the adaptive sleep wakeup schedules. The component models include a queuing theory-based throughput analysis model, a cycle probability-based analytical model for computing the probabilities of a successful transmission, collision, and the idle state of a sensor, as well as an energy consumption model for the sensor’s life cycle. A fair performance assessment of the proposed T-MAC analytical model attests to the energy efficiency of the model when compared to that of state-of-the-art techniques, in terms of better power saving, a higher throughput and a lower energy consumption under various traffic loads.
A Novel Multi-Input Bidirectional LSTM and HMM Based Approach for Target Recognition from Multi-Domain Radar Range Profiles
Authors: Gao Fei , Huang Teng , Wang Jun , Sun Jinping , Hussain Amir , Zhou Huiyu
Published: May 2019

Radars, as active detection sensors, are known to play an important role in various intelligent devices. Target recognition based on high-resolution range profile (HRRP) is an important approach for radars to monitor interesting targets. Traditional recognition algorithms usually rely on a single feature, which makes it difficult to maintain the recognition performance. In this paper, 2-D sequence features from HRRP are extracted in various data domains such as time-frequency domain, time domain, and frequency domain. A novel target identification method is then proposed, by combining bidirectional Long Short-Term Memory (BLSTM) and a Hidden Markov Model (HMM), to learn these multi-domain sequence features. Specifically, we first extract multi-domain HRRP sequences. Next, a new multi-input BLSTM is proposed to learn these multi-domain HRRP sequences, which are then fed to a standard HMM classifier to learn multi-aspect features. Finally, the trained HMM is used to implement the recognition task. Extensive experiments are carried out on the publicly accessible, benchmark MSTAR database. Our proposed algorithm is shown to achieve an identification accuracy of over 91% with a lower false alarm rate and higher identification confidence, compared to several state-of-the-art techniques.
A CMOS W-Band Amplifier with Tunable Neutralization Using a Cross-Coupled MOS–varactor Pair
Authors: Yook Byungho , Park Kwangwon , Park Seungwon , Lee Hyunkyu , Kim Taehoon , Park Jong Sung , Jeon Sanggeun
Published: May 2019

This paper presents a CMOS W-band amplifier adopting a novel neutralization technique for high gain and stability. The W-band amplifier consists of four common-source differential gain cells that are neutralized by a cross-coupled MOS–varactor pair. Contrary to conventional neutralizations, the proposed technique enables tunable neutralization, so that the gate-to-drain capacitance of transistors is accurately tracked and neutralized as the varactor voltage is adjusted. This makes the neutralization tolerant of capacitance change caused by process–voltage–temperature (PVT) variation or transistor model inaccuracy, which commonly occurs at mm-wave frequencies. The proposed tunable neutralization is experimentally confirmed by measuring gain and stability of the W-band amplifier fabricated in a 65-nm CMOS process. The amplifier achieves a measured gain of 17.5 dB at 79 GHz and a 3-dB bandwidth from 77.5 to 84 GHz without any stability issue. The DC power consumption is 56.7 mW and the chip area is 0.85 mm2.
Congestion Control in V2V Safety Communication: Problem, Analysis, Approaches
Authors: Liu Xiaofeng , Jaekel Arunita
Published: May 2019

The emergence of Vehicular Ad Hoc Networks (VANETs) is expected to be an important step toward achieving safety and efficiency in intelligent transportation systems (ITS). One important requirement of safety applications is that vehicles are able to communicate with neighboring vehicles, with very low latency and packet loss. The high mobility, unreliable channel quality and high message rates make this a challenging problem for VANETs. There have been significant research activities in recent years in the development of congestion control algorithms that ensure reliable delivery of safety messages in vehicle-to-vehicle (V2V) communication. In this paper, we present a comprehensive survey of congestion control approaches for VANET. We identify the relevant parameters and performance metrics that can be used to evaluate these approaches and analyze each approach based a number of factors such as the type of traffic, whether it is proactive or reactive, and the mechanism for controlling congestion. We conclude this paper with some additional considerations for designing V2V communication protocols and interesting and open research problems and directions for future work.
Low-Level Control of Hybrid Hydromechanical Transmissions for Heavy Mobile Working Machines
Authors: Larsson L. Viktor , Ericson Liselott , Uebel Karl , Krus Petter
Published: May 2019

Fuel efficiency has become an increasingly important property of heavy mobile working machines. As a result, Hybrid Hydromechanical Transmissions (HMTs) are often considered for the propulsion of these vehicles. The introduction of hybrid HMTs does, however, come with a number of control-related challenges. To date, a great focus in the literature has been on high-level control aspects, concerning optimal utilization of the energy storage medium. In contrast, the main topic of this article is low-level control, with the focus on dynamic response and the ability to realize requested power flows accurately. A static decoupled Multiple-Input-Multiple-Output (MIMO) control strategy, based on a linear model of a general hybrid HMT, is proposed. The strategy is compared to a baseline approach in Hardware-In-the-Loop (HWIL) simulations of a reference wheel loader for two drive cycles. It was found that an important benefit of the decoupled control approach is that the static error caused by the system’s cross-couplings is minimized without introducing integrating elements. This feature, combined with the strategy’s general nature, motivates its use for multiple-mode transmissions in which the transmission configuration changes between the modes.
Column Leaching Tests to Valorize a Solid Waste from the Decommissioning of Coal-Fired Power Plants
Authors: Rivas Ernesto , Martín-Lara María Ángeles , Blázquez Gabriel , Pérez Antonio , Calero Mónica
Published: May 2019

Solid waste from the decommissioning of coal-fired power plants collected from a power plant in Spain (Puertollano, Ciudad Real) was subjected to acid leaching tests in columns to evaluate the leachability of several valuable and toxic metals (Al, Ca, Fe, K, Mg, Na, Ti, V, Cr, Mn, Ni, and Zn). First, the contaminated waste, delivered by a national company, was chemically characterized. Second, column-leaching tests were conducted using two different acid solutions (nitric and sulfuric acid). The effect of the leaching agent concentration and time of leaching were examined. The results of column leaching tests showed that different concentrations of the acid solutions leached different proportions of Al, Fe, Mg, Mn, Ni, V, and Zn, which were leached by acid solutions from the solid waste sample. In general, use of sulfuric acid at pH 0.5 resulted in better leaching. Next, a comparison between three different configurations (one single stage without recirculation, one single stage with total recirculation of leachate and leaching in two consecutive stages: one with total recirculation of leachate and another one with acid set to a pH value of 0.5 and without recirculation) was performed. At the end of the experiments, all leaching methods resulted in comparable yields for Al (0.36–0.48%), Fe (5.99–6.40%), Mg (4.43–5.11%), Mn (2.71–2.83%), Ni (12.08–12.75%), V (0.08–0.34%), and Zn (23.62–25.28%). However, better results were obtained when two consecutive stages were carried out. Additionally, the effect of forced aeration on leachability was studied. Finally, this investigation showed that hydrometallurgical treatment of contaminated solid by means of acid leaching followed by basic leaching and a water wash between these stages was a potentially feasible method for reducing hazardous levels of the residue.