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21 pages, 3057 KiB

Open AccessArticle

A Hybrid Degradation Modeling and Prognostic Method for the Multi-Modal System

by Jun Peng, Shengnan Wang, Dianzhu Gao, Xiaoyong Zhang, Bin Chen, Yijun Cheng, Yingze Yang, Wentao Yu and Zhiwu Huang

Appl. Sci. 2020, 10(4), 1378; https://doi.org/10.3390/app10041378 - 18 Feb 2020

Cited by 4 | Viewed by 2272

Abstract

Engineering systems typically go through complicated degradation processes, partially due to their multiple operating modes. Therefore, how to accurately estimate their remaining useful life is a critical issue. To address this challenge, a hybrid degradation modeling and prognostic method for the multi-modal system [...] Read more.

Engineering systems typically go through complicated degradation processes, partially due to their multiple operating modes. Therefore, how to accurately estimate their remaining useful life is a critical issue. To address this challenge, a hybrid degradation modeling and prognostic method for the multi-modal system is proposed. Firstly, the cumulative dynamic differential health indicator is constructed for the multi-modal switching system using a multi-objective optimization approach. The long-term cumulative degradation assessment model is constructed based on the gated recurrent unit. Then, considering that the damage in the latest stage has a significant impact on the remaining useful life, the time window is used to extract local features of the sequence, including energy features and statistical features. The latest stage degradation is predicted based on the light gradient boosting machine. Finally, model averaging is used to integrate the two predicted results, which is expected to improve the prognostic robustness. The proposed model is evaluated with synthetic analysis and NASA turbofan aero-engine datasets. Extensive experimental results demonstrate the proposed method provides a better characterization of the degradation status of the system and provides a higher estimation accuracy than existing methods. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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23 pages, 7152 KiB

Open AccessArticle

Apparatus and Method of Defect Detection for Resin Films

by Ruey-Kai Sheu, Ya-Hsin Teng, Chien-Hao Tseng and Lun-Chi Chen

Appl. Sci. 2020, 10(4), 1206; https://doi.org/10.3390/app10041206 - 11 Feb 2020

Cited by 3 | Viewed by 3718

Abstract

A defect inspection of resin films involves processes of detecting defects, size measuring, type classification and reflective action planning. It is not only a process requiring heavy investment in workforce, but also a tension between quality assurance with a 50-micrometer tolerance and visibility [...] Read more.

A defect inspection of resin films involves processes of detecting defects, size measuring, type classification and reflective action planning. It is not only a process requiring heavy investment in workforce, but also a tension between quality assurance with a 50-micrometer tolerance and visibility of the naked eye. To solve the difficulties of the workforce and time consumption processes of defect inspection, an apparatus is designed to collect high-quality images in one shot by leveraging a large field-of-view microscope at 2K resolution. Based on the image dataset, a two-step method is used to first locate possible defects and predict their types by a defect-shape-based deep learning model using the LeNet-5-adjusted network. The experimental results show that the proposed method can precisely locate the position and accurately inspect the fine-grained defects of resin films. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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24 pages, 5927 KiB

Open AccessArticle

Control Strategy of a Hybrid Renewable Energy System Based on Reinforcement Learning Approach for an Isolated Microgrid

by Bao Chau Phan and Ying-Chih Lai

Appl. Sci. 2019, 9(19), 4001; https://doi.org/10.3390/app9194001 - 24 Sep 2019

Cited by 33 | Viewed by 6195

Abstract

Due to the rising cost of fossil fuels and environmental pollution, renewable energy (RE) resources are currently being used as alternatives. To reduce the high dependence of RE resources on the change of weather conditions, a hybrid renewable energy system (HRES) is introduced [...] Read more.

Due to the rising cost of fossil fuels and environmental pollution, renewable energy (RE) resources are currently being used as alternatives. To reduce the high dependence of RE resources on the change of weather conditions, a hybrid renewable energy system (HRES) is introduced in this research, especially for an isolated microgrid. In HRES, solar and wind energies are the primary energy resources while the battery and fuel cells (FCs) are considered as the storage systems that supply energy in case of insufficiency. Moreover, a diesel generator is adopted as a back-up system to fulfill the load demand in the event of a power shortage. This study focuses on the development of HRES with the combination of battery and hydrogen FCs. Three major parts were considered including optimal sizing, maximum power point tracking (MPPT) control, and the energy management system (EMS). Recent developments and achievements in the fields of machine learning (ML) and reinforcement learning (RL) have led to new challenges and opportunities for HRES development. Firstly, the optimal sizing of the hybrid renewable hydrogen energy system was defined based on the Hybrid Optimization Model for Multiple Energy Resources (HOMER) software for the case study in an island in the Philippines. According to the assessment of EMS and MPPT control of HRES, it can be concluded that RL is one of the most emerging optimal control solutions. Finally, a hybrid perturbation and observation (P&O) and Q-learning (h-POQL) MPPT was proposed for a photovoltaic (PV) system. It was conducted and validated through the simulation in MATLAB/Simulink. The results show that it showed better performance in comparison to the P&O method. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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12 pages, 13821 KiB

Open AccessArticle

A Size-Controlled AFGAN Model for Ship Acoustic Fault Expansion

by Linke Zhang, Na Wei, Xuhao Du and Shuping Wang

Appl. Sci. 2019, 9(11), 2292; https://doi.org/10.3390/app9112292 - 3 Jun 2019

Cited by 1 | Viewed by 2575

Abstract

Identifying changes in the properties of acoustical sources based on a small number of sample data from measurements has been a challenge for decades. Typical problems are the increasing sound power from a vibrating source, decreasing transmission loss of a structure, and decreasing [...] Read more.

Identifying changes in the properties of acoustical sources based on a small number of sample data from measurements has been a challenge for decades. Typical problems are the increasing sound power from a vibrating source, decreasing transmission loss of a structure, and decreasing insertion loss of vibration mounts. Limited access to structural and acoustical data from complex acoustical systems makes it challenging to extract complete information of the system and, in practice, often only a small amount of test data is available for detecting changes. Although sample expansion via interpolation can be implemented based on the priori knowledge of the system, the size of the expanded samples also affects identification performance. In this paper, a generative adversarial network (GAN) is employed to expand the acoustic fault vibration signals, and an Acoustic Fault Generative Adversarial Network (AFGAN) model is proposed. Moreover, a size-controlled AFGAN is designed, which includes two sub-models: the generator sub-model generates expanded samples and also determines the optimal sample size based on the information entropy equivalence principle, while the discriminator sub-model outputs the probabilities of the input samples belonging to the real samples and provides the generator with information to guide sample size considerations. Some real data experiments have been conducted to verify the effectiveness of this method. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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16 pages, 5516 KiB

Open AccessArticle

Object-Based Approach for Adaptive Source Coding of Surveillance Video

by Tung-Ming Pan, Kuo-Chin Fan and Yuan-Kai Wang

Appl. Sci. 2019, 9(10), 2003; https://doi.org/10.3390/app9102003 - 16 May 2019

Cited by 6 | Viewed by 2555

Abstract

Intelligent analysis of surveillance videos over networks requires high recognition accuracy by analyzing good-quality videos that however introduce significant bandwidth requirement. Degraded video quality because of high object dynamics under wireless video transmission induces more critical issues to the success of smart video [...] Read more.

Intelligent analysis of surveillance videos over networks requires high recognition accuracy by analyzing good-quality videos that however introduce significant bandwidth requirement. Degraded video quality because of high object dynamics under wireless video transmission induces more critical issues to the success of smart video surveillance. In this paper, an object-based source coding method is proposed to preserve constant quality of video streaming over wireless networks. The inverse relationship between video quality and object dynamics (i.e., decreasing video quality due to the occurrence of large and fast-moving objects) is characterized statistically as a linear model. A regression algorithm that uses robust M-estimator statistics is proposed to construct the linear model with respect to different bitrates. The linear model is applied to predict the bitrate increment required to enhance video quality. A simulated wireless environment is set up to verify the proposed method under different wireless situations. Experiments with real surveillance videos of a variety of object dynamics are conducted to evaluate the performance of the method. Experimental results demonstrate significant improvement of streaming videos relative to both visual and quantitative aspects. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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15 pages, 3114 KiB

Open AccessArticle

Regularized Urdu Speech Recognition with Semi-Supervised Deep Learning

by Mohammad Ali Humayun, Ibrahim A. Hameed, Syed Muslim Shah, Sohaib Hassan Khan, Irfan Zafar, Saad Bin Ahmed and Junaid Shuja

Appl. Sci. 2019, 9(9), 1956; https://doi.org/10.3390/app9091956 - 13 May 2019

Cited by 11 | Viewed by 5656

Abstract

Automatic Speech Recognition, (ASR) has achieved the best results for English, with end-to-end neural network based supervised models. These supervised models need huge amounts of labeled speech data for good generalization, which can be quite a challenge to obtain for low-resource languages like [...] Read more.

Automatic Speech Recognition, (ASR) has achieved the best results for English, with end-to-end neural network based supervised models. These supervised models need huge amounts of labeled speech data for good generalization, which can be quite a challenge to obtain for low-resource languages like Urdu. Most models proposed for Urdu ASR are based on Hidden Markov Models (HMMs). This paper proposes an end-to-end neural network model, for Urdu ASR, regularized with dropout, ensemble averaging and Maxout units. Dropout and ensembles are averaging techniques over multiple neural network models while Maxout are units in a neural network which adapt their activation functions. Due to limited labeled data, Semi Supervised Learning (SSL) techniques are also incorporated to improve model generalization. Speech features are transformed into a lower dimensional manifold using an unsupervised dimensionality-reduction technique called Locally Linear Embedding (LLE). Transformed data along with higher dimensional features is used to train neural networks. The proposed model also utilizes label propagation-based self-training of initially trained models and achieves a Word Error Rate (WER) of 4% less than that reported as the benchmark on the same Urdu corpus using HMM. The decrease in WER after incorporating SSL is more significant with an increased validation data size. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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14 pages, 3188 KiB

Open AccessArticle

A Robust Brain MRI Segmentation and Bias Field Correction Method Integrating Local Contextual Information into a Clustering Model

by Zhe Zhang and Jianhua Song

Appl. Sci. 2019, 9(7), 1332; https://doi.org/10.3390/app9071332 - 29 Mar 2019

Cited by 9 | Viewed by 5783

Abstract

The segmentation results of brain magnetic resonance imaging (MRI) have important guiding significance for subsequent clinical diagnosis and treatment. However, brain MRI segmentation is a complex and challenging problem due to the inevitable noise or intensity inhomogeneity. A novel robust clustering with local [...] Read more.

The segmentation results of brain magnetic resonance imaging (MRI) have important guiding significance for subsequent clinical diagnosis and treatment. However, brain MRI segmentation is a complex and challenging problem due to the inevitable noise or intensity inhomogeneity. A novel robust clustering with local contextual information (RC_LCI) model was used in this study which accurately segmented brain MRI corrupted by noise and intensity inhomogeneity. For pixels in the neighborhood of the central pixel, a weighting scheme combining local contextual information was used to generate the corresponding anisotropic weight to update the current central pixel and thus remove noisy pixels. Then, a multiplicative framework consisting of the product of a real image and a bias field could effectively segment brain MRI and estimate the bias field. Further, a linear combination of basis functions was introduced to guarantee the bias field properties. Therefore, compared with state-of-the-art models, the segmentation result obtained by RC_LCI was increased by 0.195 ± 0.125 in terms of the Jaccard similarity coefficient. Both visual experimental results and quantitative evaluation demonstrate the superiority of RC_LCI on real and synthetic images. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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17 pages, 19053 KiB

Open AccessArticle

Grapheme-to-Phoneme Conversion with Convolutional Neural Networks

by Sevinj Yolchuyeva, Géza Németh and Bálint Gyires-Tóth

Appl. Sci. 2019, 9(6), 1143; https://doi.org/10.3390/app9061143 - 18 Mar 2019

Cited by 25 | Viewed by 17835

Abstract

Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. In this paper, we investigate convolutional neural networks (CNN) for [...] Read more.

Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. In this paper, we investigate convolutional neural networks (CNN) for G2P conversion. We propose a novel CNN-based sequence-to-sequence (seq2seq) architecture for G2P conversion. Our approach includes an end-to-end CNN G2P conversion with residual connections and, furthermore, a model that utilizes a convolutional neural network (with and without residual connections) as encoder and Bi-LSTM as a decoder. We compare our approach with state-of-the-art methods, including Encoder-Decoder LSTM and Encoder-Decoder Bi-LSTM. Training and inference times, phoneme and word error rates were evaluated on the public CMUDict dataset for US English, and the best performing convolutional neural network-based architecture was also evaluated on the NetTalk dataset. Our method approaches the accuracy of previous state-of-the-art results in terms of phoneme error rate. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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14 pages, 5018 KiB

Open AccessArticle

Concrete Object Anomaly Detection Using a Nondestructive Automatic Oscillating Impact-Echo Device

by Hsi-Chiang Chou

Appl. Sci. 2019, 9(5), 904; https://doi.org/10.3390/app9050904 - 4 Mar 2019

Cited by 3 | Viewed by 2612

Abstract

The goal of this study was to develop an impact-echo device that can conduct automatic oscillation tests, process signals rapidly, and apply it to concrete object anomaly analysis. The system presented in this study comprises three parts, namely the impact device, the oscillator [...] Read more.

The goal of this study was to develop an impact-echo device that can conduct automatic oscillation tests, process signals rapidly, and apply it to concrete object anomaly analysis. The system presented in this study comprises three parts, namely the impact device, the oscillator circuit, and signal processing software. The design concept of the impact-echo device was inspired by a pendulum clock, and its implementation used a nondestructive wooden hammer instead of a conventional manual steel hammer. In this study, we used a pulse generator in the adjustable oscillator circuit to produce delayed changes. The delayed changes would activate the wooden hammer that struck the surface of the object. To process the signal, our lab used a built-in sound card in the computer to transfer the reflection soundwave from striking the wall to MATLAB software to analyze the energy of the frequency spectrum. This was conducted to evaluate whether the object contained anomalies and, if so, to determine the location of the anomalies to serve as a reference for real-life implementation. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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24 pages, 11899 KiB

Open AccessArticle

Multimedia Vision for the Visually Impaired through 2D Multiarray Braille Display

by Seondae Kim, Eun-Soo Park and Eun-Seok Ryu

Appl. Sci. 2019, 9(5), 878; https://doi.org/10.3390/app9050878 - 1 Mar 2019

Cited by 6 | Viewed by 4111

Abstract

Visual impairments cause very limited and low vision, leading to difficulties in processing information such as obstacles, objects, multimedia contents (e.g., video, photographs, and paintings), and reading in outdoor and indoor environments. Therefore, there are assistive devices and aids for visually impaired (VI) [...] Read more.

Visual impairments cause very limited and low vision, leading to difficulties in processing information such as obstacles, objects, multimedia contents (e.g., video, photographs, and paintings), and reading in outdoor and indoor environments. Therefore, there are assistive devices and aids for visually impaired (VI) people. In general, such devices provide guidance or some supportive information that can be used along with guide dogs, walking canes, and braille devices. However, these devices have functional limitations; for example, they cannot help in the processing of multimedia contents such as images and videos. Additionally, most of the available braille displays for the VI represent the text as a single line with several braille cells. Although these devices are sufficient to read and understand text, they have difficulty in converting multimedia contents or massive text contents to braille. This paper describes a methodology to effectively convert multimedia contents to braille using 2D braille display. Furthermore, this research also proposes the transformation of Digital Accessible Information SYstem (DAISY) and electronic publication (EPUB) formats into 2D braille display. In addition, it introduces interesting research considering efficient communication for the VI. Thus, this study proposes an eBook reader application for DAISY and EPUB formats, which can correctly render and display text, images, audios, and videos on a 2D multiarray braille display. This approach is expected to provide better braille service for the VI when implemented and verified in real-time. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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18 pages, 9129 KiB

Open AccessArticle

Efficient Transcoding and Encryption for Live 360 CCTV System

by Tuan Thanh Le, JongBeom Jeong and Eun-Seok Ryu

Appl. Sci. 2019, 9(4), 760; https://doi.org/10.3390/app9040760 - 21 Feb 2019

Cited by 6 | Viewed by 4525

Abstract

In recent years, the rapid development of surveillance information in closed-circuit television (CCTV) has become an indispensable element in security systems. Several CCTV systems designed for video compression and encryption need to improve for the best performance and different security levels. Specially, the [...] Read more.

In recent years, the rapid development of surveillance information in closed-circuit television (CCTV) has become an indispensable element in security systems. Several CCTV systems designed for video compression and encryption need to improve for the best performance and different security levels. Specially, the advent of 360 video makes the CCTV promising for surveillance without any blind areas. Compared to current systems, 360 CCTV requires the large bandwidth with low latency to run smoothly. Therefore, to improve the system performance, it needs to be more robust to run smoothly. Video transmission and transcoding is an essential process in converting codecs, changing bitrates or resizing the resolution for 360 videos. High-performance transcoding is one of the key factors of real time CCTV stream. Additionally, the security of video streams from cameras to endpoints is also an important priority in CCTV research. In this paper, a real-time transcoding system designed with the ARIA block cipher encryption algorithm is presented. Experimental results show that the proposed method achieved approximately 200% speedup compared to libx265 FFmpeg in transcoding task, and it could handle multiple transcoding sessions simultaneously at high performance for both live 360 CCTV system and existing 2D/3D CCTV system. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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18 pages, 5519 KiB

Open AccessArticle

An Embedded Gateway with Communication Extension and Backup Capabilities for ZigBee-Based Monitoring and Control Systems

by Ke-Feng Lin, Shih-Sung Lin, Min-Hsiung Hung, Chung-Hsien Kuo and Ping-Nan Chen

Appl. Sci. 2019, 9(3), 456; https://doi.org/10.3390/app9030456 - 29 Jan 2019

Cited by 6 | Viewed by 3391

Abstract

ZigBee wireless sensor devices possess characteristics of small size, light weight, low power consumption, having up to 65535 nodes in a sensor network, in theory. Therefore, the ZigBee wireless sensor network (WSN) is very suitable for use in developing monitoring and control (MC) [...] Read more.

ZigBee wireless sensor devices possess characteristics of small size, light weight, low power consumption, having up to 65535 nodes in a sensor network, in theory. Therefore, the ZigBee wireless sensor network (WSN) is very suitable for use in developing monitoring and control (MC) applications, such as remote healthcare, industrial control, fire detection, environmental monitoring, and so on. This dissertation is directed towards the research on the issues of communication extension and backup, encountered in creating ZigBee-based MC systems for military storerooms, together with providing associated solutions. We design an embedded gateway that possesses wired network (Ethernet) and wireless communication (GSM) backup capability. The gateway can not only easily extend the monitoring distance of the ZigBee-based MCS, but can also solve the problem that some military zones do not have wire networks or possess communication blind spots. The results of this dissertation have been practically applied in constructing a paradigm monitoring system of a military storeroom. It is believed that the research results could be a useful reference for developing ZigBee-based MCSs in the future. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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11 pages, 1726 KiB

Open AccessArticle

Efficient Detection Approach for DTMF Signal Detection

by Cheng-Yu Yeh and Shaw-Hwa Hwang

Appl. Sci. 2019, 9(3), 422; https://doi.org/10.3390/app9030422 - 27 Jan 2019

Cited by 6 | Viewed by 5042

Abstract

A novel tone detection approach, designated as the multi-frequency detecting (MFD) algorithm, is presented in this work as an alternative to conventional single point detection approaches but it is an efficient way to achieve the aim of further computational load reduction for a [...] Read more.

A novel tone detection approach, designated as the multi-frequency detecting (MFD) algorithm, is presented in this work as an alternative to conventional single point detection approaches but it is an efficient way to achieve the aim of further computational load reduction for a dual-tone multi-frequency (DTMF) signal detection. The idea is that an optimal phase search is performed over the frequency band of interest in each tone detection, and then the optimal frequency response of a detector is built accordingly. In this manner, a DTMF detection task is done following one-time detection computation. This proposal demonstrates an overall computational load reduction of 80.49% and 74.06% in comparison with a discrete Fourier transform (DFT) approach and the Goertzel algorithm, respectively. This detection complexity reduction is an advantage and an important issue for applying DTMF detection technique to embedded devices. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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14 pages, 961 KiB

Open AccessArticle

Optimal Unit Commitment by Considering High Penetration of Renewable Energy and Ramp Rate of Thermal Units-A case study in Taiwan

by Shiue-Der Lu, Meng-Hui Wang, Ming-Tse Kuo, Ming-Chang Tsou and Rui-Min Liao

Appl. Sci. 2019, 9(3), 421; https://doi.org/10.3390/app9030421 - 27 Jan 2019

Cited by 9 | Viewed by 3435

Abstract

When large amounts of wind power and solar photovoltaic (PV) power are integrated into an independent power grid, the intermittent renewable energy destabilizes power output. Therefore, this study explored the unit commitment (UC) optimization problem; the ramp rate was applied to solve problems [...] Read more.

When large amounts of wind power and solar photovoltaic (PV) power are integrated into an independent power grid, the intermittent renewable energy destabilizes power output. Therefore, this study explored the unit commitment (UC) optimization problem; the ramp rate was applied to solve problems with 30 and 10 min of power shortage. The data of actual unit parameters were provided by the Taiwan Power Company. The advanced priority list method was used together with a combination of a generalized Lagrangian relaxation algorithm and a random feasible directions algorithm to solve a large-scale nonlinear mixed-integer programming UC problem to avoid local and infeasible solutions. The results showed that the proposed algorithm was superior to improved particle swarm optimization (IPSO) and simulated annealing (SA) in terms of the minimization of computation time and power generation cost. The proposed method and UC results can be effective information for unit dispatch by power companies to reduce the investment costs of power grids and the possibility of renewable energy being disconnected from the power system. Thus, the proposed method can increase the flexibility of unit dispatch and the proportion of renewable energy in power generation. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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15 pages, 2259 KiB

Open AccessArticle

A MAP Overhead Aware Two-Dimensional OFDMA Burst Construction Algorithm

by Lin-Kung Chen, Pi-Tzong Jan, Yen-Hung Chen, Rui-Ze Hung and Yen-Jung Lee

Appl. Sci. 2019, 9(2), 354; https://doi.org/10.3390/app9020354 - 21 Jan 2019

Cited by 3 | Viewed by 2936

Abstract

Conventional orthogonal frequency division multiple access (OFDMA) burst construction methods can only support limited numbers of connections due to the map overhead and corresponding limitations in the numbers of orthogonal resources blocks, which limits the capacity of current 4G and the following 5th [...] Read more.

Conventional orthogonal frequency division multiple access (OFDMA) burst construction methods can only support limited numbers of connections due to the map overhead and corresponding limitations in the numbers of orthogonal resources blocks, which limits the capacity of current 4G and the following 5th generation (5G) networks. This study therefore provides a novel OFDMA burst construction algorithm and enhanced burst indexing aware algorithm (EHA), which try to maximize the throughput while considering the subchannel diversity and optimizing burst indexing issues. The EHA not only allocates the subchannels with the best channel quality for each burst, but also groups the bursts to alleviate the MAP overhead. Simulation results showed that the EHA yields two times the throughput that has been achieved using previous algorithms under a heavy load. Two contributions of the EHA are: (1) the overhead of burst indexing decreases because massive numbers of connections can be accommodated by one burst; and (2) the overall throughput increases due to that one connection with large data transferring requirements can be split and distributed into several bursts and placed on the subchannels with good channel quality to adopt better modulation coding scheme (MCS), if the saved bandwidth in this burst construction is more than the increased overhead of burst indexing. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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14 pages, 3201 KiB

Open AccessArticle

Channel-Quality Aware RFID Tag Identification Algorithm to Accommodate the Varying Channel Quality of IoT Environment

by Yen-Hung Chen, Rui-Ze Hung, Lin-Kung Chen, Pi-Tzong Jan and Yin-Rung Su

Appl. Sci. 2019, 9(2), 321; https://doi.org/10.3390/app9020321 - 17 Jan 2019

Cited by 6 | Viewed by 2649

Abstract

Radio Frequency Identification (RFID) technique is broadly adopted as the automated identification system for the Internet of Things (IoT). Many RFID anti-collision algorithms were proposed to accelerate the tag identification process. However, they misjudged some unreadable slots which were due to collision instead [...] Read more.

Radio Frequency Identification (RFID) technique is broadly adopted as the automated identification system for the Internet of Things (IoT). Many RFID anti-collision algorithms were proposed to accelerate the tag identification process. However, they misjudged some unreadable slots which were due to collision instead of the bad channel condition, causing low bandwidth usage. This study proposes the Channel-quality Aware Query Tree algorithm (CAQT) to improve the identification performance in an error-prone channel environment. CAQT has three novel features: (1) it estimates the channel quality continuously and statistically in the rapidly changing channel quality environment; (2) it asks the tag for retransmission or to split the collide tags based on the channel quality; (3) the number of the groups which it splits tags is based on the estimated number of tags collide in current slot. The simulation results show that CAQT uses less than 31% slots compared with the conventional algorithms. The simulation results also demonstrate that CAQT provides enhanced performance when the channel quality is varying especially in outdoor environment, for example, ticket checking for railway or subway system. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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18 pages, 5227 KiB

Open AccessArticle

Hiding Stealth Optical CDMA Signals in Public BPSK Channels for Optical Wireless Communication

by Chih-Ta Yen, Jen-Fa Huang and Wen-Zong Zhang

Appl. Sci. 2018, 8(10), 1731; https://doi.org/10.3390/app8101731 - 25 Sep 2018

Cited by 12 | Viewed by 3660

Abstract

A new optical steganography scheme is proposed that transmits a stealth optical code-division multiple-access (OCDMA) signal through a public binary phase-shift keying (BPSK) channel. Polarization beam splitters and arrayed waveguide gratings are used to implement a spectral-polarization coding (SPC) system with an incoherent [...] Read more.

A new optical steganography scheme is proposed that transmits a stealth optical code-division multiple-access (OCDMA) signal through a public binary phase-shift keying (BPSK) channel. Polarization beam splitters and arrayed waveguide gratings are used to implement a spectral-polarization coding (SPC) system with an incoherent optical source. We employ a Walsh–Hadamard code as the signature code of the user who wants to transmit stealth information using the system. A free space optical link applied to this system maintains the polarization states of light during propagation. The secret data are extracted using correlation detection and balanced subtraction in the OCDMA decoder of the intended receiver, and the other signal from the public channel is reduced by the OCDMA decoder. At the demodulator of the public channel, BPSK demodulation eliminates the stealth signal so that the public channel is not affected by the stealth signal. The two signals cannot interfere with each other. The results of this study show that our proposed optical steganography system is highly secure. The stealth signal can be favorably hidden in the public channel when the average source power of the stealth signal, public noise, and public signal are −5, −3, and 0 dBm, respectively. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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17 pages, 5328 KiB

Open AccessArticle

Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD

by Yiting Li, Haisong Huang, Qingsheng Xie, Liguo Yao and Qipeng Chen

Appl. Sci. 2018, 8(9), 1678; https://doi.org/10.3390/app8091678 - 17 Sep 2018

Cited by 264 | Viewed by 17741

Abstract

This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) [...] Read more.

This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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Review

Jump to: Research

26 pages, 1425 KiB

Open AccessReview

A Survey on Robust Video Watermarking Algorithms for Copyright Protection

by Xiaoyan Yu, Chengyou Wang and Xiao Zhou

Appl. Sci. 2018, 8(10), 1891; https://doi.org/10.3390/app8101891 - 11 Oct 2018

Cited by 53 | Viewed by 9640

Abstract

With the development and popularization of the Internet and the rise of various live broadcast platforms, digital videos have penetrated into all aspects of people’s life. At the same time, all kinds of pirated videos are also flooding the Internet, which seriously infringe [...] Read more.

With the development and popularization of the Internet and the rise of various live broadcast platforms, digital videos have penetrated into all aspects of people’s life. At the same time, all kinds of pirated videos are also flooding the Internet, which seriously infringe the rights and interests of video copyright owners and hinder the healthy development of the video industry. Therefore, robust video watermarking algorithms for copyright protection have emerged as these times require. In this paper, we review robust video watermarking algorithms for copyright protection based on original videos and compressed videos. Basic models and properties of video watermarking algorithms are described, and the evaluation indexes corresponding to each property are also introduced. To help researchers understand various existing robust watermarking algorithms quickly, some basic information and the quantitative estimation of several performances are analyzed and compared. Finally, we discuss the challenges in the research of robust video watermarking algorithms, and give possible development directions for the future. Full article

(This article belongs to the Special Issue Recent Advances on Signal Processing and Deep Learning for Public Security Applications)

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Recent Advances on Signal Processing and Deep Learning for Public Security Applications

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