Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646107
E. Manor, S. Greenberg
This paper presents a novel model-based hardware/software co-design methodology applied to heterogeneous embedded platforms. A time-predictable hardware and software co-design architecture design is proposed. The proposed technique is based on floating point operations analysis and is intended to be applied for real-time applications at an early stage of the design, to assist the designer taking the right considerations in choosing the most effective Hardware/Software partitioning. The design analysis is carried out on the MATLAB model of the application, and is demonstrated for a specific voice activation algorithm. A Data Flow Graph (DFG) representation is used to represent the various operational blocks of the chosen algorithm. An efficient decomposition of the design operational blocks into a fixed software processor and alternative extensible hardware components is carefully carried out to find the correct balance between flexibility and performance with respect to power consumption and size, and the demands related to time predictability. Experimental results demonstrate that the proposed algorithm performs a significant area saving factor of 39% and power consumption reduction of 19%, while applied to voice activation module within the same system constraints.
{"title":"Efficient Hardware/Software partitioning for Heterogeneous Embedded Systems","authors":"E. Manor, S. Greenberg","doi":"10.1109/ICSEE.2018.8646107","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646107","url":null,"abstract":"This paper presents a novel model-based hardware/software co-design methodology applied to heterogeneous embedded platforms. A time-predictable hardware and software co-design architecture design is proposed. The proposed technique is based on floating point operations analysis and is intended to be applied for real-time applications at an early stage of the design, to assist the designer taking the right considerations in choosing the most effective Hardware/Software partitioning. The design analysis is carried out on the MATLAB model of the application, and is demonstrated for a specific voice activation algorithm. A Data Flow Graph (DFG) representation is used to represent the various operational blocks of the chosen algorithm. An efficient decomposition of the design operational blocks into a fixed software processor and alternative extensible hardware components is carefully carried out to find the correct balance between flexibility and performance with respect to power consumption and size, and the demands related to time predictability. Experimental results demonstrate that the proposed algorithm performs a significant area saving factor of 39% and power consumption reduction of 19%, while applied to voice activation module within the same system constraints.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122522762","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}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646189
Yaron Laufer, S. Gannot
In this paper, the problem of speech dereverberation in a noiseless scenario is addressed in a hierarchical Bayesian framework. Our probabilistic approach relies on a Gaussian model for the early speech signal combined with a multichannel Gaussian model for the relative early transfer function (RETF). The late reverberation is modelled as a Gaussian additive interference, and the speech and reverberation precisions are modelled with Gamma distribution. We derive a variational Expectation-Maximization (VEM) algorithm which uses a variant of the multichannel Wiener filter (MCWF) to infer the early speech component while suppressing the late reverberation. The proposed algorithm was evaluated using real room impulse responses (RIRs) recorded in our acoustic lab with a reverberation time set to 0.36 s and 0.61 s. It is shown that a significant improvement is obtained with respect to the reverberant signal, and that the proposed algorithm outperforms a baseline algorithm. In terms of channel alignment, a superior channel estimate is demonstrated.
{"title":"A Bayesian Hierarchical Model for Speech Dereverberation","authors":"Yaron Laufer, S. Gannot","doi":"10.1109/ICSEE.2018.8646189","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646189","url":null,"abstract":"In this paper, the problem of speech dereverberation in a noiseless scenario is addressed in a hierarchical Bayesian framework. Our probabilistic approach relies on a Gaussian model for the early speech signal combined with a multichannel Gaussian model for the relative early transfer function (RETF). The late reverberation is modelled as a Gaussian additive interference, and the speech and reverberation precisions are modelled with Gamma distribution. We derive a variational Expectation-Maximization (VEM) algorithm which uses a variant of the multichannel Wiener filter (MCWF) to infer the early speech component while suppressing the late reverberation. The proposed algorithm was evaluated using real room impulse responses (RIRs) recorded in our acoustic lab with a reverberation time set to 0.36 s and 0.61 s. It is shown that a significant improvement is obtained with respect to the reverberant signal, and that the proposed algorithm outperforms a baseline algorithm. In terms of channel alignment, a superior channel estimate is demonstrated.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131405115","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}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646302
Oded Yechiel, G. Israeli, H. Guterman
Developing a control system, that brings a plant to a desired state in finite time, can be a tedious task. In traditional control theory, one must first analytically analyze the plant, take into consideration the uncertainties and finally construct a controller that keeps the plant stable and meet certain design requirements. For many plants, designing a controller is extremely challenging, and existing control theory and practice are unable to cope with the uncertainty and complexity of the plant. Modern control systems are increasingly trying to address the problem of designing controllers using adaptive methods and machine learning techniques, and in fact, classical adaptive control theory has shown marvelous strength when applied to uncertain plants. Indeed, adaptive machine learning techniques such as, adaptive fuzzy logic control, neural networks, reinforcement learning, and, evolutionary algorithms have been an asset in the control system community when applied in practice. These machine learning techniques are able to cope with the uncertainties and nonlinearities of plants. In this paper, a method for developing a direct adaptive control system to tune the gains of a PID controller to control a vehicle’s speed is investigated. This method does not use any a-priori knowledge about the plant. The control system is a two stage process: identification and controller generation. The identification is performed using a neural network, that learns the behavior of the plant and, once trained, allows to run virtual simulation on different controllers. After the neural network is trained, an evolutionary algorithm is used to generate a wide population of controllers, and evaluate the performance of each controller. The evolutionary algorithm runs several generations to achieve good performing controllers. Preliminary results of this approach are shown as a method to generate a speed control for a vehicle in a physics simulation.
{"title":"Direct Adaptive Control Using a Neuro-evolutionary Algorithm for Vehicle Speed Control","authors":"Oded Yechiel, G. Israeli, H. Guterman","doi":"10.1109/ICSEE.2018.8646302","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646302","url":null,"abstract":"Developing a control system, that brings a plant to a desired state in finite time, can be a tedious task. In traditional control theory, one must first analytically analyze the plant, take into consideration the uncertainties and finally construct a controller that keeps the plant stable and meet certain design requirements. For many plants, designing a controller is extremely challenging, and existing control theory and practice are unable to cope with the uncertainty and complexity of the plant. Modern control systems are increasingly trying to address the problem of designing controllers using adaptive methods and machine learning techniques, and in fact, classical adaptive control theory has shown marvelous strength when applied to uncertain plants. Indeed, adaptive machine learning techniques such as, adaptive fuzzy logic control, neural networks, reinforcement learning, and, evolutionary algorithms have been an asset in the control system community when applied in practice. These machine learning techniques are able to cope with the uncertainties and nonlinearities of plants. In this paper, a method for developing a direct adaptive control system to tune the gains of a PID controller to control a vehicle’s speed is investigated. This method does not use any a-priori knowledge about the plant. The control system is a two stage process: identification and controller generation. The identification is performed using a neural network, that learns the behavior of the plant and, once trained, allows to run virtual simulation on different controllers. After the neural network is trained, an evolutionary algorithm is used to generate a wide population of controllers, and evaluate the performance of each controller. The evolutionary algorithm runs several generations to achieve good performing controllers. Preliminary results of this approach are shown as a method to generate a speed control for a vehicle in a physics simulation.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132705950","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}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646231
Mattan Serry, David Sriker, Avi Caciularu, R. Machlev, Y. Beck, D. Raz
Nonintrusive load monitoring (NILM) algorithms may suggest different approaches for solving the NILM problem: the disintegrating of total power consumption to the discrete appliances comprising it. All of these algorithms incorporate some cost function to discriminate between the possible options at each sample time. For the estimation process of such algorithms, and the selection of the most likely possibility, we propose a new formulation of a family of cost functions, on the set of the possible assertions per each appliance. The proposed design, abbreviated ARPM (Additive, Retentive Penalty Method), emphasizes two major properties that were discovered to be significant when performing real-time estimation in a NILM system. The first is a granular calculation of Hamming distances between possibilities, and the second is the processing of the changes in the measured power consumption, rather than the consumption value itself. This design consists of a low number of free parameters, and can be integrated additively and seamlessly with existing cost functions already embedded in NILM systems. It had been evaluated with a series of experiments and proven to enhance the success rate by all measured criteria and on various datasets, with no parameter adjustments.
{"title":"ARPM: Additive, Retentive Penalty Method for Multidimensional NILM Algorithms","authors":"Mattan Serry, David Sriker, Avi Caciularu, R. Machlev, Y. Beck, D. Raz","doi":"10.1109/ICSEE.2018.8646231","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646231","url":null,"abstract":"Nonintrusive load monitoring (NILM) algorithms may suggest different approaches for solving the NILM problem: the disintegrating of total power consumption to the discrete appliances comprising it. All of these algorithms incorporate some cost function to discriminate between the possible options at each sample time. For the estimation process of such algorithms, and the selection of the most likely possibility, we propose a new formulation of a family of cost functions, on the set of the possible assertions per each appliance. The proposed design, abbreviated ARPM (Additive, Retentive Penalty Method), emphasizes two major properties that were discovered to be significant when performing real-time estimation in a NILM system. The first is a granular calculation of Hamming distances between possibilities, and the second is the processing of the changes in the measured power consumption, rather than the consumption value itself. This design consists of a low number of free parameters, and can be integrated additively and seamlessly with existing cost functions already embedded in NILM systems. It had been evaluated with a series of experiments and proven to enhance the success rate by all measured criteria and on various datasets, with no parameter adjustments.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115133117","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}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646074
O. Shalom, Amir Leshem, A. Scaglione, A. Nedić
This paper describes a distributed statistical estimation problem, corresponding to a network of agents. The network may be vulnerable to data injection attacks, in which the attackers’ main goal is to steer the network’s final state to a state of their choice. We show that the detection metric of the straightforward attack scheme proposed by Wu et. at in [1], is vulnerable to a more sophisticated attack. To overcome this attack we propose a novel metric that can be computed locally by each agent to detect the presence of an attacker in the network, as well as a metric that localizes the attackers in the network. We conclude the paper with simulations supporting our findings.
{"title":"Detection of Data Injection Attacks on Decentralized Statistical Estimation","authors":"O. Shalom, Amir Leshem, A. Scaglione, A. Nedić","doi":"10.1109/ICSEE.2018.8646074","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646074","url":null,"abstract":"This paper describes a distributed statistical estimation problem, corresponding to a network of agents. The network may be vulnerable to data injection attacks, in which the attackers’ main goal is to steer the network’s final state to a state of their choice. We show that the detection metric of the straightforward attack scheme proposed by Wu et. at in [1], is vulnerable to a more sophisticated attack. To overcome this attack we propose a novel metric that can be computed locally by each agent to detect the presence of an attacker in the network, as well as a metric that localizes the attackers in the network. We conclude the paper with simulations supporting our findings.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124505940","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}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646205
David Levy, A. Reichman, D. Wulich
Filter Bank Multi Carrier (FBMC) is considered as promising candidate solution to the air interference problem in the fifth-generation communication (5G). Since the performance of multicarrier transmission is affected by peak to average power ratio (PAPR) therefore, research on PAPR reduction methods is essential. The paper presents the effect of the simplest method for reduction the PAPR for FBMC modulation by using the iterative clipping and filtering (ICF) method. A significant improvement in PAPR reduction can be obtained with a modest complexity and low degradation in Bit Error Ratio (BER).
{"title":"Peak to Average Power Ratio Reduction for Filter Bank Multicarrier Modulation using Iterative Clipping and Filtering","authors":"David Levy, A. Reichman, D. Wulich","doi":"10.1109/ICSEE.2018.8646205","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646205","url":null,"abstract":"Filter Bank Multi Carrier (FBMC) is considered as promising candidate solution to the air interference problem in the fifth-generation communication (5G). Since the performance of multicarrier transmission is affected by peak to average power ratio (PAPR) therefore, research on PAPR reduction methods is essential. The paper presents the effect of the simplest method for reduction the PAPR for FBMC modulation by using the iterative clipping and filtering (ICF) method. A significant improvement in PAPR reduction can be obtained with a modest complexity and low degradation in Bit Error Ratio (BER).","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126297402","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}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646309
Aviel Adler, Ofer Schwartz, S. Gannot
Speech enhancement and source separation are well-known challenges in the context of hands-free communication and automatic speech recognition. The multichannel Wiener filter (MCWF) that satisfies the minimum mean square error (MMSE) criterion, is a fundamental speech enhancement tool. However, it can suffer from speech distortion, especially when the noise level is high. The speech distortion weighted multichannel Wiener filter (SDW-MWF) was therefore proposed to control the tradeoff between noise reduction and speech distortion for the single-speaker case. In this paper, we generalize this estimator and propose a method for controlling this tradeoff in the multi-speaker case. The proposed estimator is decomposed into two successive stages: 1) a multi-speaker linearly constrained minimum variance (LCMV), which is solely determined by the spatial characteristics of the speakers; and 2) a multi-speaker Wiener postfilter (PF), which is responsible for reducing the residual noise. The proposed PF consists of several controlling parameters that can almost independently control the tradeoff between the distortion of each speaker and the total noise reduction.
{"title":"A Weighted Multichannel Wiener Filter and its Decomposition to LCMV Beam Former and Post-Filter for Source Separation and Noise Reduction","authors":"Aviel Adler, Ofer Schwartz, S. Gannot","doi":"10.1109/ICSEE.2018.8646309","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646309","url":null,"abstract":"Speech enhancement and source separation are well-known challenges in the context of hands-free communication and automatic speech recognition. The multichannel Wiener filter (MCWF) that satisfies the minimum mean square error (MMSE) criterion, is a fundamental speech enhancement tool. However, it can suffer from speech distortion, especially when the noise level is high. The speech distortion weighted multichannel Wiener filter (SDW-MWF) was therefore proposed to control the tradeoff between noise reduction and speech distortion for the single-speaker case. In this paper, we generalize this estimator and propose a method for controlling this tradeoff in the multi-speaker case. The proposed estimator is decomposed into two successive stages: 1) a multi-speaker linearly constrained minimum variance (LCMV), which is solely determined by the spatial characteristics of the speakers; and 2) a multi-speaker Wiener postfilter (PF), which is responsible for reducing the residual noise. The proposed PF consists of several controlling parameters that can almost independently control the tradeoff between the distortion of each speaker and the total noise reduction.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130891176","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}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646294
Samah Khawaled, Mohamad Khateeb, Hadas Benisty
Existing sound retrieval systems are mostly based on a textual query. Using text to describe a sound signal is not intuitive and is often inaccurate due to subjective impression of the user; different people may use different words to describe the same sound which makes theses system complex to design and unintuitive to use. Vocal imitation, however, is the most natural human way to describe a sound. In this paper we consider a newly rising approach for sound retrieval based on vocal imitations, where the user records himself imitating the desired sound, and the system retrieves a ranked list of the most similar sounds in the dataset. In this work we represent sound signals using histograms, obtained with respect to a Gaussian Mixture Model (GMM), representing the spectral domain. This recently proposed approach was successfully applied for word representation in a keyword spotting task. Having a fixed length representation for vocal imitation signals allows us to train a robust classifier using support vector machine (SVM). Given a test imitation signal, we apply the classifier and use the output score to rank the retrieved signals, based on a majority vote. Our simulation results show that the proposed system yields a more accurate ranking compared with other existing solutions.
{"title":"Audio Retrieval By Voice Imitation","authors":"Samah Khawaled, Mohamad Khateeb, Hadas Benisty","doi":"10.1109/ICSEE.2018.8646294","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646294","url":null,"abstract":"Existing sound retrieval systems are mostly based on a textual query. Using text to describe a sound signal is not intuitive and is often inaccurate due to subjective impression of the user; different people may use different words to describe the same sound which makes theses system complex to design and unintuitive to use. Vocal imitation, however, is the most natural human way to describe a sound. In this paper we consider a newly rising approach for sound retrieval based on vocal imitations, where the user records himself imitating the desired sound, and the system retrieves a ranked list of the most similar sounds in the dataset. In this work we represent sound signals using histograms, obtained with respect to a Gaussian Mixture Model (GMM), representing the spectral domain. This recently proposed approach was successfully applied for word representation in a keyword spotting task. Having a fixed length representation for vocal imitation signals allows us to train a robust classifier using support vector machine (SVM). Given a test imitation signal, we apply the classifier and use the output score to rank the retrieved signals, based on a majority vote. Our simulation results show that the proposed system yields a more accurate ranking compared with other existing solutions.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131241822","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}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646018
O. Barzilai, Nadav Voloch, Alon Hasgall, O. L. Steiner
Smart devices and their connections to the Internet of Things (IoT) have been the subject of many papers in the past decade. One of the transportation subjects of IoT is a smart junction. This research deals with the case of this junction, where several cars approach the intersection from various directions, and a smart traffic light must decide about the time intervals of RED and GREEN in each direction, with our novel approach that is based not only on the number of vehicles in each lane, but also on other factors such as the type of vehicles (e.g. emergency vehicles), and the social characteristics of the passengers (e.g. a handicapped person, a student who is late for an exam). Those factors will be gleaned from the IoT network amongst cars, traffic lights, individuals, municipality data, and more. Once those priorities have been examined, they are fed into the algorithm we have devised, and outputted as a timing schedule for the different sides of the intersection, taking also into consideration the cars physical attributes such as length and speed. In this paper we present the algorithm, the prioritizing research, its implementation in the algorithm and our experimental results.
{"title":"Real life applicative timing algorithm for a smart junction with social priorities and multiple parameters","authors":"O. Barzilai, Nadav Voloch, Alon Hasgall, O. L. Steiner","doi":"10.1109/ICSEE.2018.8646018","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646018","url":null,"abstract":"Smart devices and their connections to the Internet of Things (IoT) have been the subject of many papers in the past decade. One of the transportation subjects of IoT is a smart junction. This research deals with the case of this junction, where several cars approach the intersection from various directions, and a smart traffic light must decide about the time intervals of RED and GREEN in each direction, with our novel approach that is based not only on the number of vehicles in each lane, but also on other factors such as the type of vehicles (e.g. emergency vehicles), and the social characteristics of the passengers (e.g. a handicapped person, a student who is late for an exam). Those factors will be gleaned from the IoT network amongst cars, traffic lights, individuals, municipality data, and more. Once those priorities have been examined, they are fed into the algorithm we have devised, and outputted as a timing schedule for the different sides of the intersection, taking also into consideration the cars physical attributes such as length and speed. In this paper we present the algorithm, the prioritizing research, its implementation in the algorithm and our experimental results.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"03 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130062379","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}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646221
Y. Mogilevsky, M. Mellincovsky
This paper presents the basic concepts regarding active power filters. An active filter generates a compensating signal that mitigates the harmonics, thus improving the power quality of the grid. Some of the most recent applications of these filters in several fields of electrical engineering are depicted. And a practical example is shown.
{"title":"Active Power Filter Applications: State of the Art","authors":"Y. Mogilevsky, M. Mellincovsky","doi":"10.1109/ICSEE.2018.8646221","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646221","url":null,"abstract":"This paper presents the basic concepts regarding active power filters. An active filter generates a compensating signal that mitigates the harmonics, thus improving the power quality of the grid. Some of the most recent applications of these filters in several fields of electrical engineering are depicted. And a practical example is shown.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121274485","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}
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