Intelligent System and Computing最新文献

{"title":"Quantized Neural Networks and Neuromorphic Computing for Embedded Systems","authors":"Shiya Liu, Y. Yi","doi":"10.5772/intechopen.91835","DOIUrl":"https://doi.org/10.5772/intechopen.91835","url":null,"abstract":"Deep learning techniques have made great success in areas such as computer vision, speech recognition and natural language processing. Those breakthroughs made by deep learning techniques are changing every aspect of our lives. However, deep learning techniques have not realized their full potential in embedded systems such as mobiles, vehicles etc. because the high performance of deep learning techniques comes at the cost of high computation resource and energy consumption. Therefore, it is very challenging to deploy deep learning models in embedded systems because such systems have very limited computation resources and power constraints. Extensive research on deploying deep learning techniques in embedded systems has been conducted and considerable progress has been made. In this book chapter, we are going to introduce two approaches. The first approach is model compression, which is one of the very popular approaches proposed in recent years. Another approach is neuromorphic computing, which is a novel computing system that mimicks the human brain.","PeriodicalId":258328,"journal":{"name":"Intelligent System and Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128561050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Study of Interval Type-2 and Type-1 Fuzzy Genetic and Flower Pollination Algorithms in Optimization of Fuzzy Fractional Order PIλDμ Controllers","authors":"H. Patel, V. Shah","doi":"10.5772/intechopen.90359","DOIUrl":"https://doi.org/10.5772/intechopen.90359","url":null,"abstract":"In this chapter, a comparison between fuzzy genetic optimization algorithm (FGOA) and fuzzy flower pollination optimization algorithm (FFPOA) is bestowed. In extension, the prime parameters of each algorithm adapted using interval type-2 and type-1 fuzzy logic system (FLS) are presented. The key feature of type-2 fuzzy system is alimenting the modeling uncertainty to the algorithms, and hence it is a prime motivation of using interval type-2 fuzzy systems for dynamic parameter adaption. These fuzzy algorithms (type-1 and type-2 fuzzy system versions) are compared with the design of fuzzy control systems used for controlling the dihybrid level control process subject to system component (leak) fault. Simulation results reveal that interval type-2 fuzzy-based FPO algorithm outperforms the results of the type-1 and type-2 fuzzy GO algorithm.","PeriodicalId":258328,"journal":{"name":"Intelligent System and Computing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126788493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graphene Nanowire Based TFETs","authors":"Jayabrata Goswami, A. Ganguly, Anirudhha Ghosal, J. Banerjee","doi":"10.5772/intechopen.89315","DOIUrl":"https://doi.org/10.5772/intechopen.89315","url":null,"abstract":"The present work is aimed at improving the performance potential of tunnel field effect transistors (TFETs), where the carriers are transported by the process of band to band tunneling. The nanoscale TFETs serves the purpose of ULSI integration with high speed and memory. The requirements of new device technology are challenging: for logical switching. In this paper, a p-channel graphene nanoribbon (GNR) TFETs has been analyzed and designed for low power and high performance digital switching application. The energy band diagram of the device is obtained from self-consistent iterative method for numerical solution of one-dimensional Poisson ’ s equation subject to appropriate boundary conditions. It is observed that the optimized p + channel GNR TFET provides high ON – OFF current ratio, low sub-threshold slope for a channel length of 85 nm and channel width of 4 nm.","PeriodicalId":258328,"journal":{"name":"Intelligent System and Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131133588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Routing Mechanisms in IoT","authors":"Gowrishankar Subhrahmanyam, Ishwari Ginimav","doi":"10.5772/intechopen.89392","DOIUrl":"https://doi.org/10.5772/intechopen.89392","url":null,"abstract":"Wireless sensor networks (WSN) works on battery in order to communicate with each other. Energy consumption is the challenging issue in WSNs. In the recent few years, green communications have become a major concern in communication research and industries. Its major goal is to minimize the energy consumed by the nodes of the WSN. In order to save energy we need to switch off the extra components that are not in use during low traffic period. The technique where the unused extra components are switched off is called as sleep-scheduling, and the routing algorithm used to implement this is called as sleep-scheduling routing algorithm. In WSN the network is divided into multiple clusters. In each cluster one of the sensor nodes is elected as cluster head (CH) and other sensor nodes act as cluster members (CM). The cluster head collects the data form all the other nodes, removes the redundant data and transmits it to the destination. As the amount of workload is much more on the cluster head, the energy consumed by the cluster head is also more. Therefore to equalize the energy consumption among all the nodes, cluster head rotation is done. This chapter deals with different energy consumption techniques.","PeriodicalId":258328,"journal":{"name":"Intelligent System and Computing","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123323354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Techniques to Mitigate Nonlinear Phase Noise in Moderate Baud Rate Optical Communication Systems","authors":"E. A. Fernandez, Ana Maria Cardenas Soto, N. G. González, G. Serafino, P. Ghelfi, A. Bogoni","doi":"10.5772/intechopen.88871","DOIUrl":"https://doi.org/10.5772/intechopen.88871","url":null,"abstract":"Nonlinear phase noise (NLPN) is the most common impairment that degrades the performance of radio-over-fiber networks. The effect of NLPN in the constellation diagram consists of a shape distortion of symbols that increases the symbol error rate due to symbol overlapping when using a conventional demodulation grid. Symbol shape characterization was obtained experimentally at a moderate baud rate (250 MBd) for constellations impaired by phase noise due to a mismatch between the optical carrier and the transmitted radio frequency signal. Machine learning algorithms have become a powerful tool to perform monitoring and to identify and mitigate distortions introduced in both the electrical and optical domains. Clustering-based demodulation assisted with Voronoi contours enables the definition of non-Gaussian boundaries to provide flexible demodulation of 16-QAM and 4+12 PSK modulation formats. Phase-offset and in-phase and quadrature imbalance may be detected on the received constellation and compensated by applying thresholding boundaries obtained from impairment characterization through statistical analysis. Experimental results show increased tolerance to the optical signal-to-noise ratio (OSNR) obtained from clustering methods based on k-means and fuzzy c-means Gustafson-Kessel algorithms. Improvements of 3.2 dB for 16-QAM, and 1.4 dB for 4+12 PSK in the OSNR scale as a function of the bit error rate are obtained without requiring additional compensation algorithms. A classifier, is introduced and range A proposal of a bit-based SVM as a non-parameter nonlinear mitigation approach in the millimeter-wave Radio-over-Fiber (mm-RoF) system for different modulation formats is demonstrated. Experimental results outperform the k -means algorithm and show improvements of 1.2 dB for 16-QAM, 1.3 dB for 64-QAM, 1.8 dB for 16-APSK, and 1.3 dB for 32-APSK at BER of 1e-3 with the SVM detector, respectively. Convolutional An intelligent constellation diagram analyzer is proposed to implement both modulation format recognition (MFR) and OSNR estimation by using a CNN-based deep learning technique. The experimental results showed that the OSNR estimation errors for all the signals were less than 0.7 dB and the accuracy of MFR was 100%, proving the feasibility of the proposed scheme. Maximization of capacity over deployed links require operation regime estimation based on precise understanding of transmission conditions through linear and nonlinear SNR from the received signal. The extraction of NLPN and second-order statistical moments by a neural network is trained to estimate SNR from extensive realistic fiber transmissions. Measured performance of 0.04 and 0.2 dB of standard error for the linear and nonlinear SNRs, respectively,","PeriodicalId":258328,"journal":{"name":"Intelligent System and Computing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116271885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wax Deposition in Crude Oil Transport Lines and Wax Estimation Methods","authors":"F. Alnaimat, M. Ziauddin, B. Mathew","doi":"10.5772/INTECHOPEN.89459","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.89459","url":null,"abstract":"Petroleum industry is one of the major industries serving the energy demands. Flow assurance is essential for providing continuous fuel supply. Wax deposition is the main issue that affects flow assurance or reduces the efficiency of transporting crude oil. As the maintenance cost of repairing and troubleshooting is very high, addressing issues related to flow assurance becomes critical in the petroleum industry. This chapter will explore methods used for reducing, cleaning, and monitoring deposition of wax. Wax dissolved in the crude oil gets crystallized causing accumulation across the pipe walls once the bulk temperature of the crude oil gets lower than wax appearance temperature (WAT). Mechanical, thermal, chemical, and microbial methods highlighting general practice in the industry are discussed in this chapter. Next, the direct techniques providing information about the numerical wax deposition models used along with scientific measurement techniques are emphasized. Later, the indirect measurement techniques are discussed providing information about the external probing and nondestructive techniques to obtain information about wax layer deposition inside the pipe. The role of artificial intelligence and use of fuzzy logic for effective wax prediction or in developing the existing wax numerical models are emphasized in the last section.","PeriodicalId":258328,"journal":{"name":"Intelligent System and Computing","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122116637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parallel Processing for Range Assignment Problem in Wireless Sensor Networks","authors":"M. Lakshmi, D. Shetty","doi":"10.5772/INTECHOPEN.89368","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.89368","url":null,"abstract":"Wireless sensor network is a collection of autonomous devices called sensor nodes which sense the environmental factors such as temperature, pressure, humidity, moisture, etc. The nodes sense the data, process it and transmit to the other nodes within their transmission range through radio propagation. Energy minimization in wireless sensor networks is a significant problem since the nodes are powered by a small battery of limited capacity. In case of networks with several thousand nodes, the simulation of algorithms can be very slow. The parallel computing model provides significantly faster simulation time for larger networks. Parallel processing involves executing the program instructions by dividing them among multiple processors with the objective of reducing the running time. So, we propose algorithms for the range assignment problem in wireless sensor networks using the parallel processing techniques. We also discuss the complexity of the proposed algorithms and significance of the parallel processing techniques in detail. The proposed techniques will be useful for implementing the distributed algorithms in WSNs.","PeriodicalId":258328,"journal":{"name":"Intelligent System and Computing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130395182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vehicle Tracking Using Video Surveillance","authors":"S. Shrestha","doi":"10.5772/intechopen.89405","DOIUrl":"https://doi.org/10.5772/intechopen.89405","url":null,"abstract":"In numerous applications including the security of individual vehicles as well as public transportation frameworks, the ability to follow or track vehicles is very helpful. Using computer vision and deep learning algorithms, the project deals with the concept of vehicle tracking in real-time based on continuous video stream from a CCTV camera to track the vehicles. The tracking system is tracking by detection paradigm. YOLOv3 object detection is applied to achieve faster object detection for real-time tracking. By implementing and improving the ideas of Deep SORT tracking for better occlusion handling, a better tracking system suitable for real-time vehicle tracking is presented. So as to demonstrate the achievability and adequacy of the framework, this chapter presents exploratory consequences of the vehicle following framework and a few encounters on handy executions.","PeriodicalId":258328,"journal":{"name":"Intelligent System and Computing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126039980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Novel Applications of Deep Reservoir Computing in Cyber-Security and Wireless Communication","authors":"K. Hamedani, Zhou Zhou, Kangjun Bai, Lingjia Liu","doi":"10.5772/intechopen.89328","DOIUrl":"https://doi.org/10.5772/intechopen.89328","url":null,"abstract":"This chapter introduces the novel applications of deep reservoir computing (RC) systems in cyber-security and wireless communication. The RC systems are a new class of recurrent neural networks (RNNs). Traditional RNNs are very challenging to train due to vanishing/exploding gradients. However, the RC systems are easier to train and have shown similar or even better performances compared with traditional RNNs. It is very essential to study the spatio-temporal correlations in cyber-security and wireless communication domains. Therefore, RC models are good choices to explore the spatio-temporal correlations. In this chapter, we explore the applications and performance of delayed feedback reservoirs (DFRs), and echo state networks (ESNs) in the cyber-security of smart grids and symbol detection in MIMO-OFDM systems, respectively. DFRs and ESNs are two different types of RC models. We also introduce the spiking structure of DFRs as spiking artificial neural networks are more energy efficient and biologically plausible as well.","PeriodicalId":258328,"journal":{"name":"Intelligent System and Computing","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123790644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Based Prediction of Transfer Probability of Shared Bikes Data","authors":"Wenwen Tu","doi":"10.5772/intechopen.89835","DOIUrl":"https://doi.org/10.5772/intechopen.89835","url":null,"abstract":"In the pile-free bicycle sharing scheme, the parking place and time of the bicycle are arbitrary. The distribution of the pile does not constrain the origin and destination of the journey. The travel demand of the user can be derived from the use of the shared bicycle. The goal of this article is to predict the probability of transition for a shared bicycle user destination based on a deep learning algorithm and a large amount of trajectory data. This study combines eXtreme Gradient Boosting (XGBoost) algorithm, stacked Restricted Boltzmann Machines (RBM), support vector regression (SVR), Differential Evolution (DE) algorithm, and Gray Wolf Optimization (GWO) algorithm. In an experimental case, the destinations of the cycling trips and the probability of traffic flow transfer for shared bikes between traffic zones were predicted by computing 2.46 million trajectory points recorded by shared bikes in Beijing. The hybrid algorithm can improve the accuracy of prediction, analyze the importance of various factors in the prediction of transfer probability, and explain the travel preferences of users in the pile free bicycle-sharing scheme.","PeriodicalId":258328,"journal":{"name":"Intelligent System and Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131352524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}