Special Issue "Symmetry in Systems Design and Analysis"

A special issue of Symmetry (ISSN 2073-8994).

Deadline for manuscript submissions: closed (31 October 2016)

Special Issue Editors

Guest Editor
Prof. Dr. Ka Lok Man

Department of Computer Science and Software Engineering, Xi’an Jiaotong Liverpool University, Suzhou Dushu Lake Higher Education Town, Suzhou Industrial Park, Jiangsu Province, China
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Guest Editor
Dr. Yo-Sub Han

Dept. of Computer Science, Yonsei University, Seoul 120-749, Korea
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Guest Editor
Dr. Hai-Ning Liang

Dept. of Computer Science and Software Engineering, Xi’an Jiaotong Liverpool University, Suzhou Dushu Lake Higher Education Town, Suzhou Industrial Park, Jiangsu Province, China
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Special Issue Information

Dear Colleagues,

Recent times have witnessed many technological improvements. Underlying these improvements are the internal components of the electronic devices we use. Despite these improvements, there are still many challenges ahead. This Special Issue concerns symmetric properties and issues that arise in the design and analysis of systems and circuits. Symmetry, as the agreement in dimensions, due to proportion and arrangement, is a property that occurs in many natural phenomena. There could also be symmetry properties in the design and analysis process of systems and circuits, as well as the actual physical elements derived from the process.

This Special Issue aims to gather original papers and in-depth reviews of the current developments, paying special attention to symmetry issues and properties.

Prof. Dr. Ka Lok Man
Dr. Yo-Sub Han
Dr. Hai-Ning Liang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Low Power Design, Simulation and Test of Digital, Analog, Mixed Mode and RF Circuits and Systems
  • Low Power Processor Design and Embedded Systems
  • VLSI, ASIC, FPGA, SoC and MPSoC
  • Computer Aided Design and Electronic Design Automation for Low Power Design
  • Circuits and Systems for Low Power Communications
  • Nonlinear Circuits and Systems for Low Power Applications
  • Control Theory Topics in Circuits and Systems
  • Signal Processing
  • Low Power Circuits and Systems for Biomedical Applications
  • Energy Harvesting for Energy Constrained Applications
  • Circuits and Systems for Cryptography
  • Circuit/Device Modeling and Simulation
  • Battery Management Systems
  • Photovoltaic System Design
  • Intelligent systems
  • Real-Time, Hybrid, Embedded and Cyber-Physical Systems
  • Self-Correcting/Self-Healing Circuits and Systems
  • Information Visualization Systems
  • Big Data and Sensing Systems
  • Ubiquitous and Pervasive Systems

Published Papers (25 papers)

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Research

Open AccessArticle Adaptive Job Load Balancing Scheme on Mobile Cloud Computing with Collaborative Architecture
Symmetry 2017, 9(5), 65; doi:10.3390/sym9050065
Received: 28 January 2017 / Revised: 18 April 2017 / Accepted: 25 April 2017 / Published: 29 April 2017
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Abstract
The adaptive mobile resource offloading (AMRO) proposed in this paper is a load balancing scheme for processing large-scale jobs using mobile resources without a cloud server. AMRO is applied in a mobile cloud computing environment based on collaborative architecture. A load balancing scheme
[...] Read more.
The adaptive mobile resource offloading (AMRO) proposed in this paper is a load balancing scheme for processing large-scale jobs using mobile resources without a cloud server. AMRO is applied in a mobile cloud computing environment based on collaborative architecture. A load balancing scheme with efficient job division and optimized job allocation is needed because the resources for mobile devices will not always be provided consistently in this environment. Therefore, a job load balancing scheme is proposed that considers personal usage patterns and the dynamic resource state of the mobile devices. The delay time for computer job processing is minimized through dynamic job reallocation and adaptive job allocation in the disability state that occurs due to unexpected problems and to excessive job allocations by the mobile devices providing the resources for the mobile cloud computing. In order to validate the proposed load balancing scheme, an adaptive mobile resource management without cloud server (AMRM) protocol was designed and implemented, and the improved processing speed was verified in comparison with the existing offloading method. The improved job processing speed in the mobile cloud environment is demonstrated through job allocation based on AMRM and by taking into consideration the idle resources of the mobile devices. Furthermore, the resource waste of the mobile devices is minimized through adaptive offloading and consideration of both insufficient and idle resources. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle BSLIC: SLIC Superpixels Based on Boundary Term
Symmetry 2017, 9(3), 31; doi:10.3390/sym9030031
Received: 24 November 2016 / Revised: 16 February 2017 / Accepted: 22 February 2017 / Published: 26 February 2017
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Abstract
A modified method for better superpixel generation based on simple linear iterative clustering (SLIC) is presented and named BSLIC in this paper. By initializing cluster centers in hexagon distribution and performing k-means clustering in a limited region, the generated superpixels are shaped into
[...] Read more.
A modified method for better superpixel generation based on simple linear iterative clustering (SLIC) is presented and named BSLIC in this paper. By initializing cluster centers in hexagon distribution and performing k-means clustering in a limited region, the generated superpixels are shaped into regular and compact hexagons. The additional cluster centers are initialized as edge pixels to improve boundary adherence, which is further promoted by incorporating the boundary term into the distance calculation of the k-means clustering. Berkeley Segmentation Dataset BSDS500 is used to qualitatively and quantitatively evaluate the proposed BSLIC method. Experimental results show that BSLIC achieves an excellent compromise between boundary adherence and regularity of size and shape. In comparison with SLIC, the boundary adherence of BSLIC is increased by at most 12.43% for boundary recall and 3.51% for under segmentation error. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine
Symmetry 2017, 9(3), 30; doi:10.3390/sym9030030
Received: 12 November 2016 / Revised: 15 February 2017 / Accepted: 20 February 2017 / Published: 26 February 2017
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Abstract
Nowadays, the increasing demands of location-based services (LBS) have spurred the rapid development of indoor positioning systems (IPS). However, the performance of IPSs is affected by the fluctuation of the measured signal. In this study, a Gaussian filtering algorithm based on an extreme
[...] Read more.
Nowadays, the increasing demands of location-based services (LBS) have spurred the rapid development of indoor positioning systems (IPS). However, the performance of IPSs is affected by the fluctuation of the measured signal. In this study, a Gaussian filtering algorithm based on an extreme learning machine (ELM) is proposed to address the problem of inaccurate indoor positioning when significant Received Signal Strength Indication (RSSI) fluctuations happen during the measurement process. The Gaussian filtering method is analyzed and compared, which can effectively filter out the fluctuant signals that were caused by the environment effects in an RFID-based positioning system. Meanwhile, the fast learning ability of the proposed ELM algorithm can reduce the time consumption for the offline and online service, and establishes the network positioning regression model between the signal strengths of the tags and their corresponding positions. The proposed positioning system is tested in a real experimental environment. In addition, system test results demonstrate that the positioning algorithms can not only provide higher positioning accuracy, but also achieve a faster computational efficiency compared with other previous algorithms. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle Single Image Super-Resolution by Non-Linear Sparse Representation and Support Vector Regression
Symmetry 2017, 9(2), 24; doi:10.3390/sym9020024
Received: 31 July 2016 / Accepted: 2 February 2017 / Published: 10 February 2017
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Abstract
Sparse representations are widely used tools in image super-resolution (SR) tasks. In the sparsity-based SR methods, linear sparse representations are often used for image description. However, the non-linear data distributions in images might not be well represented by linear sparse models. Moreover, many
[...] Read more.
Sparse representations are widely used tools in image super-resolution (SR) tasks. In the sparsity-based SR methods, linear sparse representations are often used for image description. However, the non-linear data distributions in images might not be well represented by linear sparse models. Moreover, many sparsity-based SR methods require the image patch self-similarity assumption; however, the assumption may not always hold. In this paper, we propose a novel method for single image super-resolution (SISR). Unlike most prior sparsity-based SR methods, the proposed method uses non-linear sparse representation to enhance the description of the non-linear information in images, and the proposed framework does not need to assume the self-similarity of image patches. Based on the minimum reconstruction errors, support vector regression (SVR) is applied for predicting the SR image. The proposed method was evaluated on various benchmark images, and promising results were obtained. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle Fully Integrated on-Chip Switched DC–DC Converter for Battery-Powered Mixed-Signal SoCs
Symmetry 2017, 9(1), 18; doi:10.3390/sym9010018
Received: 6 November 2016 / Revised: 27 December 2016 / Accepted: 10 January 2017 / Published: 22 January 2017
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Abstract
This paper presents a fully integrated on-chip switched-capacitor (SC) DC–DC converter that supports a programmable regulated power supply ranging from 2.6 to 3.2 V out of a 5 V input supply. The proposed 4-to-3 step-down topology utilizes two conventional 2-to-1 step-down topologies; each
[...] Read more.
This paper presents a fully integrated on-chip switched-capacitor (SC) DC–DC converter that supports a programmable regulated power supply ranging from 2.6 to 3.2 V out of a 5 V input supply. The proposed 4-to-3 step-down topology utilizes two conventional 2-to-1 step-down topologies; each of them (2-to-1_up and 2-to-1_dw) has a different flying capacitance to maximize the load current driving capability while minimizing the bottom-plate capacitance loss. The control circuits use a low power supply provided by a small internal low-drop output (LDO) connected to the internal load voltage (VL_dw) from the 2-to-1_dw, and low swing level-shifted gate-driving signals are generated using the internal load voltage (VL_dw). Therefore, the proposed implementation reduces control circuit and switching power consumptions. The programmable power supply voltage is regulated by means of a pulse frequency modulation (PFM) technique with the compensated two-stage operational transconductance amplifier (OTA) and the current-starved voltage controlled oscillator (VCO) to maintain high efficiency over a wide range of load currents. The proposed on-chip SC DC–DC converter is designed and simulated using high-voltage 0.35 μm bipolar, complementary metal-oxide-semiconductor (CMOS) and DMOS (BCDMOS) technology. It achieves a peak efficiency of 74% when delivering an 8 mA load current at a 3.2 V supply voltage level, and it provides a maximum output power of 48 mW (IL = 15 mA at VL_up = 3.2 V) at 70.5% efficiency. The proposed on-chip SC voltage regulator shows better efficiency than the ideal linear regulator over a wide range of output power, from 2.6 mW to 48 mW. The 18-phase interleaving technique enables the worst-case output voltage ripple to be less than 5.77% of the load voltage. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle State of Health Estimation of Li-ion Batteries with Regeneration Phenomena: A Similar Rest Time-Based Prognostic Framework
Symmetry 2017, 9(1), 4; doi:10.3390/sym9010004
Received: 31 October 2016 / Revised: 17 December 2016 / Accepted: 21 December 2016 / Published: 24 December 2016
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Abstract
State of health (SOH) prediction in Li-ion batteries plays an important role in intelligent battery management systems (BMS). However, the existence of capacity regeneration phenomena remains a great challenge for accurately predicting the battery SOH. This paper proposes a novel prognostic framework to
[...] Read more.
State of health (SOH) prediction in Li-ion batteries plays an important role in intelligent battery management systems (BMS). However, the existence of capacity regeneration phenomena remains a great challenge for accurately predicting the battery SOH. This paper proposes a novel prognostic framework to predict the regeneration phenomena of the current battery using the data of a historical battery. The global degradation trend and regeneration phenomena (characterized by regeneration amplitude and regeneration cycle number) of the current battery are extracted from its raw SOH time series. Moreover, regeneration information of the historical battery derived from corresponding raw SOH data is utilized in this framework. The global degradation trend and regeneration phenomena of the current battery are predicted, and then the prediction results are integrated together to calculate the overall SOH prediction values. Particle swarm optimization (PSO) is employed to obtain an appropriate regeneration threshold for the historical battery. Gaussian process (GP) model is adopted to predict the global degradation trend, and linear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated using experimental data from the degradation tests of Li-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framework. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle THD Reduction of Distribution System Based on ASRFC and HVC Method for SVC under EV Charger Condition for Power Factor Improvement
Symmetry 2016, 8(12), 156; doi:10.3390/sym8120156
Received: 31 October 2016 / Revised: 12 December 2016 / Accepted: 13 December 2016 / Published: 20 December 2016
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Abstract
Electric vehicles (EVs) have been gaining popularity in recent years due to growing concerns about fuel depletion and increasing petrol prices. Random uncoordinated charging of multiple EVs at residential distribution feeders with moderate penetration levels is expected in the near future. This paper
[...] Read more.
Electric vehicles (EVs) have been gaining popularity in recent years due to growing concerns about fuel depletion and increasing petrol prices. Random uncoordinated charging of multiple EVs at residential distribution feeders with moderate penetration levels is expected in the near future. This paper describes a high performance voltage controller for the EVs charging system, and proposes a scheme of asymmetric synchronous reference frame controller (ASRFC) in order to compensate for the voltage distortions and unbalance distribution system due to EVs charger. This paper explores the power factor of distribution and residential network under random EVs charger on the bus load. ASRFC and harmonic voltage compensator (HVC) are employed for static VAR compensator (SVC) in this paper. The proposed scheme can improve the power factor and total harmonic distortion (THD) of the smart grid due to the EVs charger in grid. The effectiveness of the scheme was investigated and verified through computer simulations of a 22.9-kV grid. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle EMG Pattern Classification by Split and Merge Deep Belief Network
Symmetry 2016, 8(12), 148; doi:10.3390/sym8120148
Received: 18 April 2016 / Revised: 21 November 2016 / Accepted: 1 December 2016 / Published: 6 December 2016
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Abstract
In this paper; we introduce an enhanced electromyography (EMG) pattern recognition algorithm based on a split-and-merge deep belief network (SM-DBN). Generally, it is difficult to classify the EMG features because the EMG signal has nonlinear and time-varying characteristics. Therefore, various machine-learning methods have
[...] Read more.
In this paper; we introduce an enhanced electromyography (EMG) pattern recognition algorithm based on a split-and-merge deep belief network (SM-DBN). Generally, it is difficult to classify the EMG features because the EMG signal has nonlinear and time-varying characteristics. Therefore, various machine-learning methods have been applied in several previously published studies. A DBN is a fast greedy learning algorithm that can identify a fairly good set of weights rapidly—even in deep networks with a large number of parameters and many hidden layers. To reduce overfitting and to enhance performance, the adopted optimization method was based on genetic algorithms (GA). As a result, the performance of the SM-DBN was 12.06% higher than conventional DBN. Additionally, SM-DBN results in a short convergence time, thereby reducing the training epoch. It is thus efficient in reducing the risk of overfitting. It is verified that the optimization was improved using GA. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle Design of a Sustainable and Efficient Transportation Station (SETS) Based on Renewable Sources and Efficient Electric Drives
Symmetry 2016, 8(12), 146; doi:10.3390/sym8120146
Received: 31 October 2016 / Revised: 20 November 2016 / Accepted: 28 November 2016 / Published: 2 December 2016
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Abstract
The need for reduction in power consumption for public facilities has increased after the occurrences of multiple blackout events. In an effort to enable the development of green and smart social infrastructure, this paper introduces a design for a sustainable and efficient transportation
[...] Read more.
The need for reduction in power consumption for public facilities has increased after the occurrences of multiple blackout events. In an effort to enable the development of green and smart social infrastructure, this paper introduces a design for a sustainable and efficient transportation system (SETS). For this design, renewable power sources and efficient electric drives are considered to be crucial technologies. Considering the subway station as an illustrative example, a power system design that uses wind and solar energy as major power sources is studied. The adjustable speed electric drive system that uses synchronous reluctance machines for ventilation systems contributes to increasing the overall power consumption efficiency. The effectiveness of the proposed SETS system is verified through a set of various field measurement data and simulation results. While the verification results demonstrate that operation of SETS is enabled by effective integration of renewable sources and efficient ventilation systems, future research directions have also been identified. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle Data Aggregation Gateway Framework for CoAP Group Communications
Symmetry 2016, 8(12), 138; doi:10.3390/sym8120138
Received: 7 August 2016 / Revised: 2 November 2016 / Accepted: 18 November 2016 / Published: 24 November 2016
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Abstract
In this paper, a data aggregation gateway framework (DA-GW) for constrained application protocol (CoAP) group communications is proposed. The DA-GW framework is designed to improve the throughput performance and energy efficiency of group communication to monitor and control multiple sensor devices collectively with
[...] Read more.
In this paper, a data aggregation gateway framework (DA-GW) for constrained application protocol (CoAP) group communications is proposed. The DA-GW framework is designed to improve the throughput performance and energy efficiency of group communication to monitor and control multiple sensor devices collectively with a single user terminal. The DA-GW consists of four function blocks—the message analyzer, group manager, message scheduler and data handler—and three informative databases—the client database, resource database and information database. The DA-GW performs group management and group communication through each functional block and stores resources in the informative databases. The DA-GW employs international standard-based data structures and provides the interoperability of heterogeneous devices used in various applications. The DA-GW is implemented using a Java-based open source framework called jCoAP to evaluate the functions and performance of the DA-GW. The experiment results showed that the DA-GW framework revealed better performance than existing group communication methods in terms of throughput and energy consumption. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle The Design and Analysis of a Secure Personal Healthcare System Based on Certificates
Symmetry 2016, 8(11), 129; doi:10.3390/sym8110129
Received: 12 September 2016 / Revised: 12 October 2016 / Accepted: 4 November 2016 / Published: 14 November 2016
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Abstract
Due to the development of information technology (IT), it has been applied to various fields such as the smart home, medicine, healthcare, and the smart car. For these fields, IT has been providing continuous prevention and management, including health conditions beyond the mere
[...] Read more.
Due to the development of information technology (IT), it has been applied to various fields such as the smart home, medicine, healthcare, and the smart car. For these fields, IT has been providing continuous prevention and management, including health conditions beyond the mere prevention of disease, improving the quality of life. e-Healthcare is a health management and medical service to provide prevention, diagnosis, treatment, and the follow-up management of diseases at any time and place in connection with information communication technology, without requiring patients to visit hospitals. However, e-Healthcare has been exposed to eavesdropping, manipulation, and the forgery of information that is personal, biological, medical, etc., and is a security threat from malicious attackers. This study suggests a security service model to exchange personal health records (PHRs) for e-Healthcare environments. To be specific, this study suggests a scheme in which communicators are able to securely authorize and establish security channels by constituting the infrastructure each organization relies on. In addition, the possibility of establishing a security service model is indicated by suggesting an e-Healthcare system for a secure e-Healthcare environment as a secure personal health record system. This is anticipated to provide securer communication in e-Healthcare environments in the future through the scheme suggested in this study. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle Collaborative Spectrum Sensing Algorithm Based on Exponential Entropy in Cognitive Radio Networks
Symmetry 2016, 8(11), 112; doi:10.3390/sym8110112
Received: 14 June 2016 / Revised: 8 August 2016 / Accepted: 21 October 2016 / Published: 26 October 2016
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Abstract
Traditional detectors for spectrum sensing in cognitive radio networks always become disabled when noise uncertainty is severe. Shannon entropy-based detection methods have aroused widespread attention in recent years due to the characteristics of effective anti-noise uncertainty. However, in existing entropy-based sensing schemes, the
[...] Read more.
Traditional detectors for spectrum sensing in cognitive radio networks always become disabled when noise uncertainty is severe. Shannon entropy-based detection methods have aroused widespread attention in recent years due to the characteristics of effective anti-noise uncertainty. However, in existing entropy-based sensing schemes, the uniform quantization method cannot guarantee the maximum entropy distribution when primary users do not exist, and cannot effectively distinguish between two hypotheses, which severely limits the promotion of detection performance. Moreover, the Shannon entropy-based sensing schemes are prone to misconvergence occurring when estimating entropy values, thus causing failure detection, which leads to system detection inefficiency and resource waste. These are the two major serious defects in Shannon entropy-based detectors, which restrict the performance improvement. In this paper, a novel non-uniform quantized exponential entropy-based (NQEE) detector is proposed for local sensing to deal with the problems of maximum entropy distribution and detection failure. To further improve the reliability of the detection, a collaborative spectrum sensing algorithm based on an NQEE detector with multiple fusion rules is presented. Simulation results verify that the detection performance of the improved local entropy-based detector is superior to the existing Shannon entropy-based detectors and is proved to be robust to noise power uncertainty. In addition, the novel collaborative detection algorithm outperforms the traditional collaborative spectrum detection method to a great degree. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model
Symmetry 2016, 8(11), 109; doi:10.3390/sym8110109
Received: 31 August 2016 / Revised: 10 October 2016 / Accepted: 19 October 2016 / Published: 25 October 2016
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Abstract
Texture feature description is a remarkable challenge in the fields of computer vision and pattern recognition. Since the traditional texture feature description method, the local binary pattern (LBP), is unable to acquire more detailed direction information and always sensitive to noise, we propose
[...] Read more.
Texture feature description is a remarkable challenge in the fields of computer vision and pattern recognition. Since the traditional texture feature description method, the local binary pattern (LBP), is unable to acquire more detailed direction information and always sensitive to noise, we propose a novel method based on generalized Gabor direction pattern (GGDP) and weighted discrepancy measurement model (WDMM) to overcome those defects. Firstly, a novel patch-structure direction pattern (PDP) is proposed, which can extract rich feature information and be insensitive to noise. Then, motivated by searching for a description method that can explore richer and more discriminant texture features and reducing the local Gabor feature vector’s high dimension problem, we extend PDP to form the GGDP method with multi-channel Gabor space. Furthermore, WDMM, which can effectively measure the feature distance between two images, is presented for the classification and recognition of image samples. Simulated experiments on olivetti research laboratory (ORL), Carnegie Mellon University pose, illumination, and expression (CMUPIE) and Yale B face databases under different illumination or facial expression conditions indicate that the proposed method outperforms other existing classical methods. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle A Search Complexity Improvement of Vector Quantization to Immittance Spectral Frequency Coefficients in AMR-WB Speech Codec
Symmetry 2016, 8(10), 104; doi:10.3390/sym8100104
Received: 21 June 2016 / Revised: 29 August 2016 / Accepted: 27 September 2016 / Published: 30 September 2016
Cited by 1 | PDF Full-text (858 KB) | HTML Full-text | XML Full-text
Abstract
An adaptive multi-rate wideband (AMR-WB) code is a speech codec developed on the basis of an algebraic code-excited linear-prediction (ACELP) coding technique, and has a double advantage of low bit rates and high speech quality. This coding technique is widely used in modern
[...] Read more.
An adaptive multi-rate wideband (AMR-WB) code is a speech codec developed on the basis of an algebraic code-excited linear-prediction (ACELP) coding technique, and has a double advantage of low bit rates and high speech quality. This coding technique is widely used in modern mobile communication systems for a high speech quality in handheld devices. However, a major disadvantage is that a vector quantization (VQ) of immittance spectral frequency (ISF) coefficients occupies a significant computational load in the AMR-WB encoder. Hence, this paper presents a triangular inequality elimination (TIE) algorithm combined with a dynamic mechanism and an intersection mechanism, abbreviated as the DI-TIE algorithm, to remarkably improve the complexity of ISF coefficient quantization in the AMR-WB speech codec. Both mechanisms are designed in a way that recursively enhances the performance of the TIE algorithm. At the end of this work, this proposal is experimentally validated as a superior search algorithm relative to a conventional TIE, a multiple TIE (MTIE), and an equal-average equal-variance equal-norm nearest neighbor search (EEENNS) approach. With a full search algorithm as a benchmark for search load comparison, this work provides a search load reduction above 77%, a figure far beyond 36% in the TIE, 49% in the MTIE, and 68% in the EEENNS approach. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment
Symmetry 2016, 8(10), 103; doi:10.3390/sym8100103
Received: 31 July 2016 / Revised: 14 September 2016 / Accepted: 20 September 2016 / Published: 29 September 2016
Cited by 1 | PDF Full-text (1666 KB) | HTML Full-text | XML Full-text
Abstract
A fundamental key for enterprise users is a cloud-based parameter-driven statistical service and it has become a substantial impact on companies worldwide. In this paper, we demonstrate the statistical analysis for some certain criteria that are related to data and applied to the
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A fundamental key for enterprise users is a cloud-based parameter-driven statistical service and it has become a substantial impact on companies worldwide. In this paper, we demonstrate the statistical analysis for some certain criteria that are related to data and applied to the cloud server for a comparison of results. In addition, we present a statistical analysis and cloud-based resource allocation method for a heterogeneous platform environment by performing a data and information analysis with consideration of the application workload and the server capacity, and subsequently propose a service prediction model using a polynomial regression model. In particular, our aim is to provide stable service in a given large-scale enterprise cloud computing environment. The virtual machines (VMs) for cloud-based services are assigned to each server with a special methodology to satisfy the uniform utilization distribution model. It is also implemented between users and the platform, which is a main idea of our cloud computing system. Based on the experimental results, we confirm that our prediction model can provide sufficient resources for statistical services to large-scale users while satisfying the uniform utilization distribution. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle ANFIS-Based Modeling for Photovoltaic Characteristics Estimation
Symmetry 2016, 8(9), 96; doi:10.3390/sym8090096
Received: 30 July 2016 / Revised: 10 September 2016 / Accepted: 12 September 2016 / Published: 16 September 2016
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Abstract
Due to the high cost of photovoltaic (PV) modules, an accurate performance estimation method is significantly valuable for studying the electrical characteristics of PV generation systems. Conventional analytical PV models are usually composed by nonlinear exponential functions and a good number of unknown
[...] Read more.
Due to the high cost of photovoltaic (PV) modules, an accurate performance estimation method is significantly valuable for studying the electrical characteristics of PV generation systems. Conventional analytical PV models are usually composed by nonlinear exponential functions and a good number of unknown parameters must be identified before using. In this paper, an adaptive-network-based fuzzy inference system (ANFIS) based modeling method is proposed to predict the current-voltage characteristics of PV modules. The effectiveness of the proposed modeling method is evaluated through comparison with Villalva’s model, radial basis function neural networks (RBFNN) based model and support vector regression (SVR) based model. Simulation and experimental results confirm both the feasibility and the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle Energy Conservation Law in Industrial Architecture: An Approach through Geometric Algebra
Symmetry 2016, 8(9), 92; doi:10.3390/sym8090092
Received: 5 May 2016 / Revised: 25 July 2016 / Accepted: 29 August 2016 / Published: 6 September 2016
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Abstract
Since 1892, the electrical engineering scientific community has been seeking a power theory for interpreting the power flow within electric networks under non-sinusoidal conditions. Although many power theories have been proposed regarding non-sinusoidal operation, an adequate solution is yet to be found. Using
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Since 1892, the electrical engineering scientific community has been seeking a power theory for interpreting the power flow within electric networks under non-sinusoidal conditions. Although many power theories have been proposed regarding non-sinusoidal operation, an adequate solution is yet to be found. Using the framework based on complex algebra in non-sinusoidal circuit analysis (frequency domain), the verification of the energy conservation law is only possible in sinusoidal situations. In this case, reactive energy turns out to be proportional to the energy difference between the average electric and magnetic energies stored in the loads and its cancellation is mathematically trivial. However, in industrial architecture, apparent power definition of electric loads (non-sinusoidal conditions) is inconsistent with the energy conservation law. Up until now, in the classical complex algebra approach, this goal is only valid in the case of purely resistive loads. Thus, in this paper, a new circuit analysis approach using geometric algebra is used to develop the most general proof of energy conservation in industrial building loads. In terms of geometric objects, this powerful tool calculates the voltage, current, and apparent power in electrical systems in non-sinusoidal, linear/nonlinear situations. In contrast to the traditional method developed by Steinmetz, the suggested powerful tool extends the concept of phasor to multivector-phasors and is performed in a new Generalized Complex Geometric Algebra structure (CGn), where Gn is the Clifford algebra in n-dimensional real space and C is the complex vector space. To conclude, a numerical example illustrates the clear advantages of the approach suggested in this paper. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle The Algorithm of Continuous Optimization Based on the Modified Cellular Automaton
Symmetry 2016, 8(9), 84; doi:10.3390/sym8090084
Received: 20 March 2016 / Revised: 4 August 2016 / Accepted: 22 August 2016 / Published: 25 August 2016
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Abstract
This article is devoted to the application of the cellular automata mathematical apparatus to the problem of continuous optimization. The cellular automaton with an objective function is introduced as a new modification of the classic cellular automaton. The algorithm of continuous optimization, which
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This article is devoted to the application of the cellular automata mathematical apparatus to the problem of continuous optimization. The cellular automaton with an objective function is introduced as a new modification of the classic cellular automaton. The algorithm of continuous optimization, which is based on dynamics of the cellular automaton having the property of geometric symmetry, is obtained. The results of the simulation experiments with the obtained algorithm on standard test functions are provided, and a comparison between the analogs is shown. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle Automatic Frequency Identification under Sample Loss in Sinusoidal Pulse Width Modulation Signals Using an Iterative Autocorrelation Algorithm
Symmetry 2016, 8(8), 78; doi:10.3390/sym8080078
Received: 31 March 2016 / Revised: 28 July 2016 / Accepted: 29 July 2016 / Published: 10 August 2016
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Abstract
In this work, we present a simple algorithm to calculate automatically the Fourier spectrum of a Sinusoidal Pulse Width Modulation Signal (SPWM). Modulated voltage signals of this kind are used in industry by speed drives to vary the speed of alternating current motors
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In this work, we present a simple algorithm to calculate automatically the Fourier spectrum of a Sinusoidal Pulse Width Modulation Signal (SPWM). Modulated voltage signals of this kind are used in industry by speed drives to vary the speed of alternating current motors while maintaining a smooth torque. Nevertheless, the SPWM technique produces undesired harmonics, which yield stator heating and power losses. By monitoring these signals without human interaction, it is possible to identify the harmonic content of SPWM signals in a fast and continuous manner. The algorithm is based in the autocorrelation function, commonly used in radar and voice signal processing. Taking advantage of the symmetry properties of the autocorrelation, the algorithm is capable of estimating half of the period of the fundamental frequency; thus, allowing one to estimate the necessary number of samples to produce an accurate Fourier spectrum. To deal with the loss of samples, i.e., the scan backlog, the algorithm iteratively acquires and trims the discrete sequence of samples until the required number of samples reaches a stable value. The simulation shows that the algorithm is not affected by either the magnitude of the switching pulses or the acquisition noise. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle Adaptive Image Matching Using Discrimination of Deformable Objects
Symmetry 2016, 8(7), 68; doi:10.3390/sym8070068
Received: 20 April 2016 / Revised: 11 July 2016 / Accepted: 13 July 2016 / Published: 21 July 2016
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Abstract
We propose an efficient image-matching method for deformable-object image matching using discrimination of deformable objects and geometric similarity clustering between feature-matching pairs. A deformable transformation maintains a particular form in the whole image, despite local and irregular deformations. Therefore, the matching information is
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We propose an efficient image-matching method for deformable-object image matching using discrimination of deformable objects and geometric similarity clustering between feature-matching pairs. A deformable transformation maintains a particular form in the whole image, despite local and irregular deformations. Therefore, the matching information is statistically analyzed to calculate the possibility of deformable transformations, and the images can be identified using the proposed method. In addition, a method for matching deformable object images is proposed, which clusters matching pairs with similar types of geometric deformations. Discrimination of deformable images showed about 90% accuracy, and the proposed deformable image-matching method showed an average 89% success rate and 91% accuracy with various transformations. Therefore, the proposed method robustly matches images, even with various kinds of deformation that can occur in them. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle A Model-Driven Framework to Develop Personalized Health Monitoring
Symmetry 2016, 8(7), 65; doi:10.3390/sym8070065
Received: 3 June 2016 / Revised: 7 July 2016 / Accepted: 11 July 2016 / Published: 18 July 2016
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Abstract
Both distributed healthcare systems and the Internet of Things (IoT) are currently hot topics. The latter is a new computing paradigm to enable advanced capabilities in engineering various applications, including those for healthcare. For such systems, the core social requirement is the privacy/security
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Both distributed healthcare systems and the Internet of Things (IoT) are currently hot topics. The latter is a new computing paradigm to enable advanced capabilities in engineering various applications, including those for healthcare. For such systems, the core social requirement is the privacy/security of the patient information along with the technical requirements (e.g., energy consumption) and capabilities for adaptability and personalization. Typically, the functionality of the systems is predefined by the patient’s data collected using sensor networks along with medical instrumentation; then, the data is transferred through the Internet for treatment and decision-making. Therefore, systems creation is indeed challenging. In this paper, we propose a model-driven framework to develop the IoT-based prototype and its reference architecture for personalized health monitoring (PHM) applications. The framework contains a multi-layered structure with feature-based modeling and feature model transformations at the top and the application software generation at the bottom. We have validated the framework using available tools and developed an experimental PHM to test some aspects of the functionality of the reference architecture in real time. The main contribution of the paper is the development of the model-driven computational framework with emphasis on the synergistic effect of security and energy issues. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
Open AccessArticle Power Spectral Deviation-Based Voice Activity Detection Incorporating Teager Energy for Speech Enhancement
Symmetry 2016, 8(7), 58; doi:10.3390/sym8070058
Received: 11 April 2016 / Accepted: 1 July 2016 / Published: 6 July 2016
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Abstract
In this paper, we propose a robust voice activity detection (VAD) algorithm to effectively distinguish speech from non-speech in various noisy environments. The proposed VAD utilizes power spectral deviation (PSD), using Teager energy (TE) to provide a better representation of the PSD, resulting
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In this paper, we propose a robust voice activity detection (VAD) algorithm to effectively distinguish speech from non-speech in various noisy environments. The proposed VAD utilizes power spectral deviation (PSD), using Teager energy (TE) to provide a better representation of the PSD, resulting in improved decision performance for speech segments. In addition, the TE-based likelihood ratio and speech absence probability are derived in each frame to modify the PSD for further VAD. We evaluate the performance of the proposed VAD algorithm by objective testing in various environments and obtain better results that those attained by of the conventional methods. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
Open AccessArticle Top-N Recommender Systems Using Genetic Algorithm-Based Visual-Clustering Methods
Symmetry 2016, 8(7), 54; doi:10.3390/sym8070054
Received: 6 April 2016 / Revised: 16 June 2016 / Accepted: 17 June 2016 / Published: 24 June 2016
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Abstract
The drastic increase of websites is one of the causes behind the recent information overload on the internet. A recommender system (RS) has been developed for helping users filter information. However, the cold-start and sparsity problems lead to low performance of the RS.
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The drastic increase of websites is one of the causes behind the recent information overload on the internet. A recommender system (RS) has been developed for helping users filter information. However, the cold-start and sparsity problems lead to low performance of the RS. In this paper, we propose methods including the visual-clustering recommendation (VCR) method, the hybrid between the VCR and user-based methods, and the hybrid between the VCR and item-based methods. The user-item clustering is based on the genetic algorithm (GA). The recommendation performance of the proposed methods was compared with that of traditional methods. The results showed that the GA-based visual clustering could properly cluster user-item binary images. They also demonstrated that the proposed recommendation methods were more efficient than the traditional methods. The proposed VCR2 method yielded an F1 score roughly three times higher than the traditional approaches. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
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Open AccessArticle Exact and Numerical Solutions of a Spatially-Distributed Mathematical Model for Fluid and Solute Transport in Peritoneal Dialysis
Symmetry 2016, 8(6), 50; doi:10.3390/sym8060050
Received: 18 April 2016 / Revised: 27 May 2016 / Accepted: 4 June 2016 / Published: 16 June 2016
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Abstract
The nonlinear mathematical model for solute and fluid transport induced by the osmotic pressure of glucose and albumin with the dependence of several parameters on the hydrostatic pressure is described. In particular, the fractional space available for macromolecules (albumin was used as a
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The nonlinear mathematical model for solute and fluid transport induced by the osmotic pressure of glucose and albumin with the dependence of several parameters on the hydrostatic pressure is described. In particular, the fractional space available for macromolecules (albumin was used as a typical example) and fractional fluid void volume were assumed to be different functions of hydrostatic pressure. In order to find non-uniform steady-state solutions analytically, some mathematical restrictions on the model parameters were applied. Exact formulae (involving hypergeometric functions) for the density of fluid flux from blood to tissue and the fluid flux across tissues were constructed. In order to justify the applicability of the analytical results obtained, a wide range of numerical simulations were performed. It was found that the analytical formulae can describe with good approximation the fluid and solute transport (especially the rate of ultrafiltration) for a wide range of values of the model parameters. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
Open AccessArticle A Data Mining Approach for Cardiovascular Disease Diagnosis Using Heart Rate Variability and Images of Carotid Arteries
Symmetry 2016, 8(6), 47; doi:10.3390/sym8060047
Received: 20 April 2016 / Revised: 31 May 2016 / Accepted: 6 June 2016 / Published: 13 June 2016
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Abstract
In this paper, we proposed not only an extraction methodology of multiple feature vectors from ultrasound images for carotid arteries (CAs) and heart rate variability (HRV) of electrocardiogram signal, but also a suitable and reliable prediction model useful in the diagnosis of cardiovascular
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In this paper, we proposed not only an extraction methodology of multiple feature vectors from ultrasound images for carotid arteries (CAs) and heart rate variability (HRV) of electrocardiogram signal, but also a suitable and reliable prediction model useful in the diagnosis of cardiovascular disease (CVD). For inventing the multiple feature vectors, we extract a candidate feature vector through image processing and measurement of the thickness of carotid intima-media (IMT). As a complementary way, the linear and/or nonlinear feature vectors are also extracted from HRV, a main index for cardiac disorder. The significance of the multiple feature vectors is tested with several machine learning methods, namely Neural Networks, Support Vector Machine (SVM), Classification based on Multiple Association Rule (CMAR), Decision tree induction and Bayesian classifier. As a result, multiple feature vectors extracted from both CAs and HRV (CA+HRV) showed higher accuracy than the separative feature vectors of CAs and HRV. Furthermore, the SVM and CMAR showed about 89.51% and 89.46%, respectively, in terms of diagnosing accuracy rate after evaluating the diagnosis or prediction methods using the finally chosen multiple feature vectors. Therefore, the multiple feature vectors devised in this paper can be effective diagnostic indicators of CVD. In addition, the feature vector analysis and prediction techniques are expected to be helpful tools in the decisions of cardiologists. Full article
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)

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