Pub Date : 2017-08-01DOI: 10.1109/IC3.2017.8284294
A. Ramanathan, L. Pullum, Zubir Husein, Sunny Raj, N. Torosdagli, S. Pattanaik, Sumit Kumar Jha
Verifying the correctness of intelligent embedded systems is notoriously difficult due to the use of machine learning algorithms that cannot provide guarantees of deterministic correctness. In this paper, our validation efforts demonstrate that the OpenCV Histogram of Oriented Gradients (HOG) implementation for human detection is susceptible to errors due to both malicious perturbations and naturally occurring fog phenomena. To the best of our knowledge, we are the first to explicitly employ a natural perturbation (like fog) as an adversarial attack using methods from computer graphics. Our experimental results show that computer vision algorithms are susceptible to errors under a small set of naturally occurring perturbations even if they are robust to a majority of such perturbations. Our methods and results may be of interest to the designers, developers and validation teams of intelligent cyber-physical systems such as autonomous cars.
{"title":"Adversarial attacks on computer vision algorithms using natural perturbations","authors":"A. Ramanathan, L. Pullum, Zubir Husein, Sunny Raj, N. Torosdagli, S. Pattanaik, Sumit Kumar Jha","doi":"10.1109/IC3.2017.8284294","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284294","url":null,"abstract":"Verifying the correctness of intelligent embedded systems is notoriously difficult due to the use of machine learning algorithms that cannot provide guarantees of deterministic correctness. In this paper, our validation efforts demonstrate that the OpenCV Histogram of Oriented Gradients (HOG) implementation for human detection is susceptible to errors due to both malicious perturbations and naturally occurring fog phenomena. To the best of our knowledge, we are the first to explicitly employ a natural perturbation (like fog) as an adversarial attack using methods from computer graphics. Our experimental results show that computer vision algorithms are susceptible to errors under a small set of naturally occurring perturbations even if they are robust to a majority of such perturbations. Our methods and results may be of interest to the designers, developers and validation teams of intelligent cyber-physical systems such as autonomous cars.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122170016","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284301
Surbhi Jain, J. Dhar
During previous couple of years, the World Wide Web (WWW) has become an extremely well-liked information source. To successfully utilize the vast quantity of information that the web provides, we want an effective way to explore it. Image data is much more voluminous than textual data, and visual information cannot be indexed by traditional strategies developed for indexing textual information. Therefore, Content-Based Image Retrieval (CBIR) has received an excellent deal of interest within the research community. A CBIR system operates on the visible features at low-level of a user's input image that makes it troublesome for the users to devise the input and additionally doesn't offer adequate retrieval results. In CBIR system, the study of the useful representation of features and appropriate similarity metrics is extremely necessary for improving the performance of retrieval task. Semantic gap has been the main issue which occurs between image pixels at low-level and semantics at high-level interpreted by humans. Among varied methods, machine learning (ML) has been explored as a feasible way to reduce the semantic gap. Inspired by the current success of deep learning methods for computer vision applications, in this paper, we aim to confront an advance deep learning method, known as Convolutional Neural Network (CNN), for studying feature representations and similarity measures. In this paper, we explored the applications of CNNs towards solving classification and retrieval problems. For retrieval of similar images, we agreed on using transfer learning to apply the GoogleNet deep architecture to our problem. Extracting the last-but-one fully connected layer from the retraining of GoogleNet CNN model served as the feature vectors for each image, computing Euclidean distances between these feature vectors and that of our query image to return the closest matches in the dataset.
{"title":"Image based search engine using deep learning","authors":"Surbhi Jain, J. Dhar","doi":"10.1109/IC3.2017.8284301","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284301","url":null,"abstract":"During previous couple of years, the World Wide Web (WWW) has become an extremely well-liked information source. To successfully utilize the vast quantity of information that the web provides, we want an effective way to explore it. Image data is much more voluminous than textual data, and visual information cannot be indexed by traditional strategies developed for indexing textual information. Therefore, Content-Based Image Retrieval (CBIR) has received an excellent deal of interest within the research community. A CBIR system operates on the visible features at low-level of a user's input image that makes it troublesome for the users to devise the input and additionally doesn't offer adequate retrieval results. In CBIR system, the study of the useful representation of features and appropriate similarity metrics is extremely necessary for improving the performance of retrieval task. Semantic gap has been the main issue which occurs between image pixels at low-level and semantics at high-level interpreted by humans. Among varied methods, machine learning (ML) has been explored as a feasible way to reduce the semantic gap. Inspired by the current success of deep learning methods for computer vision applications, in this paper, we aim to confront an advance deep learning method, known as Convolutional Neural Network (CNN), for studying feature representations and similarity measures. In this paper, we explored the applications of CNNs towards solving classification and retrieval problems. For retrieval of similar images, we agreed on using transfer learning to apply the GoogleNet deep architecture to our problem. Extracting the last-but-one fully connected layer from the retraining of GoogleNet CNN model served as the feature vectors for each image, computing Euclidean distances between these feature vectors and that of our query image to return the closest matches in the dataset.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125197250","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284344
Surbhi Saxena, Dalpat Songara
Automatic counting of people, entering or exiting a region of interest, is very important for both business and security applications. To properly manage security in various places, it is of great importance to introduce video surveillance. This paper introduces an automatic people counting system which can count multiple people who interact in the region of interest, by using only one camera. The algorithm uses Viola Jones method of facial recognition to detect people. Using a single overhead mounted camera, the system counts the number of people going in to an observed area. Counting is performed by analyzing the image to detect faces. We have tested the performance of the system, achieving a correct people counting rate of 85%.
{"title":"Design of people counting system using MATLAB","authors":"Surbhi Saxena, Dalpat Songara","doi":"10.1109/IC3.2017.8284344","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284344","url":null,"abstract":"Automatic counting of people, entering or exiting a region of interest, is very important for both business and security applications. To properly manage security in various places, it is of great importance to introduce video surveillance. This paper introduces an automatic people counting system which can count multiple people who interact in the region of interest, by using only one camera. The algorithm uses Viola Jones method of facial recognition to detect people. Using a single overhead mounted camera, the system counts the number of people going in to an observed area. Counting is performed by analyzing the image to detect faces. We have tested the performance of the system, achieving a correct people counting rate of 85%.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117312510","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284354
Suyash Somani, Somya Jain
In the current time zone, the utilization of online web-based social networking systems has developed exponentially with clients investing the vast majority of the energy of their life associating with the world by means of web-based social networking. Systems like Twitter, Facebook, Instagram, LinkedIn and so forth are the most acclaimed ones with greatest number of clients over the world. The un-linking of the systems enables clients to contaminate the web-based social networking by undesirable materials and still stay mysterious. With the utilization of a worldwide identifier, it will be anything but difficult to inquiry them. In the writing proposed, personality determination has been broken into 2 sections i.e. Identity Search and identity matching with various traits mulled over. Properties like Username, Profile picture, Content of the posts and specified URLs are contemplated and a group of these gave enhanced outcomes.
{"title":"Resolving identities on Facebook and Twitter","authors":"Suyash Somani, Somya Jain","doi":"10.1109/IC3.2017.8284354","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284354","url":null,"abstract":"In the current time zone, the utilization of online web-based social networking systems has developed exponentially with clients investing the vast majority of the energy of their life associating with the world by means of web-based social networking. Systems like Twitter, Facebook, Instagram, LinkedIn and so forth are the most acclaimed ones with greatest number of clients over the world. The un-linking of the systems enables clients to contaminate the web-based social networking by undesirable materials and still stay mysterious. With the utilization of a worldwide identifier, it will be anything but difficult to inquiry them. In the writing proposed, personality determination has been broken into 2 sections i.e. Identity Search and identity matching with various traits mulled over. Properties like Username, Profile picture, Content of the posts and specified URLs are contemplated and a group of these gave enhanced outcomes.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127243971","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284290
Vandana Bhatia, Rinkle Rani
Graph Databases offer a very influential way to provide an instinctual representation for many applications spanning from social networks, web networks to biological networks. In the current era of big data, the size of the graph is increasing exponentially. It is difficult for the conventional machines to analyze a whole graph. To overcome this, the characteristics of the large graphs are estimated via sampling in order to identify trends and patterns in the large graph. The existing sampling techniques such as random node and random walk do not provide consistent efficiency over the graphs. In this paper, an efficient sampling algorithm named Influence sampling (IS) is proposed which sample the graphs by analyzing the degree of the vertices of the graph such that the most influential vertices remain in the graph sample. The experiments are performed over three real life datasets and the performance is compared with the three existing sampling algorithms. It is shown that IS performs well in the terms of accuracy.
{"title":"An efficient algorithm for sampling of a single large graph","authors":"Vandana Bhatia, Rinkle Rani","doi":"10.1109/IC3.2017.8284290","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284290","url":null,"abstract":"Graph Databases offer a very influential way to provide an instinctual representation for many applications spanning from social networks, web networks to biological networks. In the current era of big data, the size of the graph is increasing exponentially. It is difficult for the conventional machines to analyze a whole graph. To overcome this, the characteristics of the large graphs are estimated via sampling in order to identify trends and patterns in the large graph. The existing sampling techniques such as random node and random walk do not provide consistent efficiency over the graphs. In this paper, an efficient sampling algorithm named Influence sampling (IS) is proposed which sample the graphs by analyzing the degree of the vertices of the graph such that the most influential vertices remain in the graph sample. The experiments are performed over three real life datasets and the performance is compared with the three existing sampling algorithms. It is shown that IS performs well in the terms of accuracy.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124969628","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}
With crimes increasing at an alarming rate, it becomes essential to impart justice to the victims readily. To come to a final decision, lawyers need to study several previous judgments for research purposes. Reducing the time spent on research can speed up the judicial process drastically. The time consumption mostly happens in two areas — searching for the right document and understanding that document. To start with, being able to get hold of the appropriate judgments or other legal documents is the most essential task for any legal professional, especially lawyers. Once a document is obtained, the next most integral and inevitable task is to read and re-read it, and come to necessary as well as needful conclusions after a comprehensive analysis. To resolve the first issue, there is a need for an efficient search system which can provide searching options based upon multiple views. This system is an effort at improving the search for users by providing them with search options based upon either the semantics of the word or based upon the IPC sections. It is important that laymen can access all the related judgments by entering just one keyword or phrase without bothering about the legal jargon. Post retrieval of documents, the lengthy texts have to be scrutinized for meaningful inferences. To reduce the time spent in reading texts, we intend to present the information in the judgments visually through semantic networks. Lawyers will be benefitted by this system because it will enable them to skip the complexity of the often verbose language of the legal documents. This IR System provides the features of semantic and IPC section based search to users, deriving information from semantic networks that are representative of the documents, so that a more efficient search system on legal documents can be put in place.
{"title":"Approaches for information retrieval in legal documents","authors":"Rachayita Giri, Yosha Porwal, Vaibhavi Shukla, Palak Chadha, Rishabh Kaushal","doi":"10.1109/IC3.2017.8284324","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284324","url":null,"abstract":"With crimes increasing at an alarming rate, it becomes essential to impart justice to the victims readily. To come to a final decision, lawyers need to study several previous judgments for research purposes. Reducing the time spent on research can speed up the judicial process drastically. The time consumption mostly happens in two areas — searching for the right document and understanding that document. To start with, being able to get hold of the appropriate judgments or other legal documents is the most essential task for any legal professional, especially lawyers. Once a document is obtained, the next most integral and inevitable task is to read and re-read it, and come to necessary as well as needful conclusions after a comprehensive analysis. To resolve the first issue, there is a need for an efficient search system which can provide searching options based upon multiple views. This system is an effort at improving the search for users by providing them with search options based upon either the semantics of the word or based upon the IPC sections. It is important that laymen can access all the related judgments by entering just one keyword or phrase without bothering about the legal jargon. Post retrieval of documents, the lengthy texts have to be scrutinized for meaningful inferences. To reduce the time spent in reading texts, we intend to present the information in the judgments visually through semantic networks. Lawyers will be benefitted by this system because it will enable them to skip the complexity of the often verbose language of the legal documents. This IR System provides the features of semantic and IPC section based search to users, deriving information from semantic networks that are representative of the documents, so that a more efficient search system on legal documents can be put in place.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130059562","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284340
K. Rai, Krishna Asawa
The routing protocol — RPL for low power and lossy networks is the fundamental routing convention of 6LoWPAN. The introduction of rank idea in RPL serves numerous intentions, including path optimization, prevention of loops, and managing control overhead. Since the RPL is currently used as the main routing protocol, for large scale low-power and lossy networks, the concept of “Rank” can lead to a vulnerable performance due to the internal threats in RPL based networks. It is studied that an attack on the rank property with legitimate IP can degrade the network performance in the aspect of the delivery ratio and end-to-end delay. And this can be further degraded if node's IP gets spoofed by the attacker node. This work discuss the impact of rank attack when the attack is launched by the node having a spoofed IP address. Performance is measured in terms of packet delivery ratio and end-to-end delay of packets. The observations show that the Rank attack with spoofed IP has more impact on the degradation of delivery ratio.
{"title":"Impact analysis of rank attack with spoofed IP on routing in 6LoWPAN network","authors":"K. Rai, Krishna Asawa","doi":"10.1109/IC3.2017.8284340","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284340","url":null,"abstract":"The routing protocol — RPL for low power and lossy networks is the fundamental routing convention of 6LoWPAN. The introduction of rank idea in RPL serves numerous intentions, including path optimization, prevention of loops, and managing control overhead. Since the RPL is currently used as the main routing protocol, for large scale low-power and lossy networks, the concept of “Rank” can lead to a vulnerable performance due to the internal threats in RPL based networks. It is studied that an attack on the rank property with legitimate IP can degrade the network performance in the aspect of the delivery ratio and end-to-end delay. And this can be further degraded if node's IP gets spoofed by the attacker node. This work discuss the impact of rank attack when the attack is launched by the node having a spoofed IP address. Performance is measured in terms of packet delivery ratio and end-to-end delay of packets. The observations show that the Rank attack with spoofed IP has more impact on the degradation of delivery ratio.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130167204","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284361
Mahmoud Pourmehrab, L. Elefteriadou, S. Ranka
The pace at which autonomous vehicle technology is reaching consumers is accelerating. Furthermore, the future driverless cars are intended to be talkative with regard to exchanging information with other vehicles, infrastructure, or even cloud. More recent attention has focused on the provision of frameworks to take advantage of automated and connected vehicle technology. This study proposes a system design to allow safe and efficient traffic of both automated/connected and conventional vehicles approaching an isolated intersection. The benefit mainly comes from the ability of automated/connected vehicles to maintain shorter and more stable headways to the vehicle in ahead compared to conventional vehicles. Simulation experiments are conducted to gain insight into the influence of flow and automated vehicle ratio over the performance of intersection.
{"title":"Smart intersection control algorithms for automated vehicles","authors":"Mahmoud Pourmehrab, L. Elefteriadou, S. Ranka","doi":"10.1109/IC3.2017.8284361","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284361","url":null,"abstract":"The pace at which autonomous vehicle technology is reaching consumers is accelerating. Furthermore, the future driverless cars are intended to be talkative with regard to exchanging information with other vehicles, infrastructure, or even cloud. More recent attention has focused on the provision of frameworks to take advantage of automated and connected vehicle technology. This study proposes a system design to allow safe and efficient traffic of both automated/connected and conventional vehicles approaching an isolated intersection. The benefit mainly comes from the ability of automated/connected vehicles to maintain shorter and more stable headways to the vehicle in ahead compared to conventional vehicles. Simulation experiments are conducted to gain insight into the influence of flow and automated vehicle ratio over the performance of intersection.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131994863","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284303
R. Niyogi, Tarun Kulshrestha, Dhaval Patel
Crowd Sensing collects users' local knowledge such as local information, ambient context, and traffic conditions, etc., using their sensor-enabled devices. The collected information is further aggregated and transferred to the cloud for detailed analysis, such as places / friends recommendation, human behavior, criminal activities, etc. These tracking and monitoring systems must be scalable, fast, and easy to deploy to meet the requirements of a real-time system. In this paper, we propose a fast and scalable crowdsensing based trajectory tracking system which can track any person having the smartphone and can provide a complete analysis of her visited locations in a given time span. We use the Redis in-memory database and XMPP at the sensing units for fast data retrieval and exchange. When a person moves to a new location, WebSocket server updates that person's new location automatically among all sensing units to make the system analysis in real-time. We develop and deploy a real prototype testbed in IIT Roorkee campus and evaluate it extensively to demonstrate the efficiency and scalability of our proposed system.
{"title":"A fast and scalable crowd sensing based trajectory tracking system","authors":"R. Niyogi, Tarun Kulshrestha, Dhaval Patel","doi":"10.1109/IC3.2017.8284303","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284303","url":null,"abstract":"Crowd Sensing collects users' local knowledge such as local information, ambient context, and traffic conditions, etc., using their sensor-enabled devices. The collected information is further aggregated and transferred to the cloud for detailed analysis, such as places / friends recommendation, human behavior, criminal activities, etc. These tracking and monitoring systems must be scalable, fast, and easy to deploy to meet the requirements of a real-time system. In this paper, we propose a fast and scalable crowdsensing based trajectory tracking system which can track any person having the smartphone and can provide a complete analysis of her visited locations in a given time span. We use the Redis in-memory database and XMPP at the sensing units for fast data retrieval and exchange. When a person moves to a new location, WebSocket server updates that person's new location automatically among all sensing units to make the system analysis in real-time. We develop and deploy a real prototype testbed in IIT Roorkee campus and evaluate it extensively to demonstrate the efficiency and scalability of our proposed system.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133883931","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 : 2017-08-01DOI: 10.1109/IC3.2017.8284302
Alok Kumar, P. Thakur, S. Pandit, G. Singh
Spectrum sensing is a key step in cognitive radio communication systems. The probability of detection and probability of false-alarm are two crucial performance parameters in spectrum sensing. In this paper, we have presented the effect of selection of fixed and dynamic threshold scheme of energy detection technique on various spectrum sensing performance parameters such as probability of detection, probability of false-alarm, and probability of error, considering the constant noise variance in the channel. In addition to this, we have also computed the minimum value of time-bandwidth product when the noise variance is constant or variable in the channel.
{"title":"Fixed and dynamic threshold selection criteria in energy detection for cognitive radio communication systems","authors":"Alok Kumar, P. Thakur, S. Pandit, G. Singh","doi":"10.1109/IC3.2017.8284302","DOIUrl":"https://doi.org/10.1109/IC3.2017.8284302","url":null,"abstract":"Spectrum sensing is a key step in cognitive radio communication systems. The probability of detection and probability of false-alarm are two crucial performance parameters in spectrum sensing. In this paper, we have presented the effect of selection of fixed and dynamic threshold scheme of energy detection technique on various spectrum sensing performance parameters such as probability of detection, probability of false-alarm, and probability of error, considering the constant noise variance in the channel. In addition to this, we have also computed the minimum value of time-bandwidth product when the noise variance is constant or variable in the channel.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126518019","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：