Pub Date : 2017-12-01DOI: 10.1109/CCUBE.2017.8394146
K.S. Vanitha, S. Uma, S.K. Mahidhar
A Distributed Denial of Service (DDoS) attack is an attempt to make a service unavailable by overwhelming the server with malicious traffic. DDoS attacks have become the most tedious and cumbersome issue in recent past. The number and magnitude of attacks have increased from few megabytes of data to 100s of terabytes of data these days. Due to the differences in the attack patterns or new types of attack, it is hard to detect these attacks effectively. In this paper, we devise new techniques for causing DDoS attacks and mitigation which are clearly shown to perform much better than the existing techniques. We also categorize DDoS attack techniques as well as the techniques used in their detection and thus attempt an extensive scoping of the DDoS problem. We also compare our attack module with a couple of tools available.
{"title":"Distributed denial of service: Attack techniques and mitigation","authors":"K.S. Vanitha, S. Uma, S.K. Mahidhar","doi":"10.1109/CCUBE.2017.8394146","DOIUrl":"https://doi.org/10.1109/CCUBE.2017.8394146","url":null,"abstract":"A Distributed Denial of Service (DDoS) attack is an attempt to make a service unavailable by overwhelming the server with malicious traffic. DDoS attacks have become the most tedious and cumbersome issue in recent past. The number and magnitude of attacks have increased from few megabytes of data to 100s of terabytes of data these days. Due to the differences in the attack patterns or new types of attack, it is hard to detect these attacks effectively. In this paper, we devise new techniques for causing DDoS attacks and mitigation which are clearly shown to perform much better than the existing techniques. We also categorize DDoS attack techniques as well as the techniques used in their detection and thus attempt an extensive scoping of the DDoS problem. We also compare our attack module with a couple of tools available.","PeriodicalId":443423,"journal":{"name":"2017 International Conference on Circuits, Controls, and Communications (CCUBE)","volume":"30 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120927985","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 : 2017-12-01DOI: 10.1109/CCUBE.2017.8394144
R. Kanakaraju, A. Singh
Nowadays wireless sensor Networks (WSNs) have significant task into diverse field. Sensor network include several sensor nodes designed for the principle of sensing the information from the surroundings of a particular area. Sensor nodes include restricted battery time with their recharging be difficult. As a result, to rest the time span of WSNs various optimization techniques have been introduced. Cluster be the organized as a set of groups of sensor nodes. Clustering is a procedure toward utilize the energy of system resourcefully. The primary hierarchical based clustering algorithm was LEACH wherein Cluster Heads (CHs) are determined through means of probabilistic approach into a distributed method. several new protocols have be introduce which make use of select the cluster heads along with rotating them toward sense of steadiness the energy practice. In this paper investigates configuration of cluster, predominantly promote a novel scheme; mobility based LEACH-ERE along by Handover method is used for proficient cluster head choice for efficient the network life span.
{"title":"Clustering enhanced approach for network lifetime in wireless sensor network","authors":"R. Kanakaraju, A. Singh","doi":"10.1109/CCUBE.2017.8394144","DOIUrl":"https://doi.org/10.1109/CCUBE.2017.8394144","url":null,"abstract":"Nowadays wireless sensor Networks (WSNs) have significant task into diverse field. Sensor network include several sensor nodes designed for the principle of sensing the information from the surroundings of a particular area. Sensor nodes include restricted battery time with their recharging be difficult. As a result, to rest the time span of WSNs various optimization techniques have been introduced. Cluster be the organized as a set of groups of sensor nodes. Clustering is a procedure toward utilize the energy of system resourcefully. The primary hierarchical based clustering algorithm was LEACH wherein Cluster Heads (CHs) are determined through means of probabilistic approach into a distributed method. several new protocols have be introduce which make use of select the cluster heads along with rotating them toward sense of steadiness the energy practice. In this paper investigates configuration of cluster, predominantly promote a novel scheme; mobility based LEACH-ERE along by Handover method is used for proficient cluster head choice for efficient the network life span.","PeriodicalId":443423,"journal":{"name":"2017 International Conference on Circuits, Controls, and Communications (CCUBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129473158","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 : 2017-12-01DOI: 10.1109/CCUBE.2017.8394134
M. Rakesh
Barrel shifters perform the shifting functions and are used in floating point arithmetic operations. The logarithmic barrel shifter designed using Fredkin and Feynman reversible gates result in lesser power consumption but there is a considerable increase in path delay, garbage outputs and ancilla inputs which reduces the performance efficiency. This paper presents an implementation of 8 bit and 32 bit logarithmic barrel shifter using SCRL(super conservative reversible logic) and also fredkin gates and a performance comparison is made in terms of garbage outputs, ancilla inputs, power consumption and path delay. The behavioural simulation is checked using verilog language and implementation is done on Xilinx 13.1 tool to find the power consumption. The implemented design with SCRL gate shows lesser garbage outputs, ancilla inputs and path delay.
{"title":"Performance comparison of 8 bit & 32 bit logarithmic barrel shifter using Fredkin & SCRL gates","authors":"M. Rakesh","doi":"10.1109/CCUBE.2017.8394134","DOIUrl":"https://doi.org/10.1109/CCUBE.2017.8394134","url":null,"abstract":"Barrel shifters perform the shifting functions and are used in floating point arithmetic operations. The logarithmic barrel shifter designed using Fredkin and Feynman reversible gates result in lesser power consumption but there is a considerable increase in path delay, garbage outputs and ancilla inputs which reduces the performance efficiency. This paper presents an implementation of 8 bit and 32 bit logarithmic barrel shifter using SCRL(super conservative reversible logic) and also fredkin gates and a performance comparison is made in terms of garbage outputs, ancilla inputs, power consumption and path delay. The behavioural simulation is checked using verilog language and implementation is done on Xilinx 13.1 tool to find the power consumption. The implemented design with SCRL gate shows lesser garbage outputs, ancilla inputs and path delay.","PeriodicalId":443423,"journal":{"name":"2017 International Conference on Circuits, Controls, and Communications (CCUBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129734480","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 : 2017-12-01DOI: 10.1109/CCUBE.2017.8394150
Himanshu Padole, S. Joshi
In recent years, it is now well established that for the data like MRI images that admit the sparse representation in some transformed domain, Compressed Sensing (CS) approach is well suited for the accurate restoration tasks. Various analytical sparsifying transforms such as wavelets, finite differences and curvelets are used extensively in many CS methods. In this paper, a general framework for the adaptive learning of the sparsifying transform (dictionary) and reconstruction of the MR image from undersampled k-space data simultaneously is proposed. Here, we also propose the supervised dictionary learning framework adapted to specific task of MR image reconstruction and an efficient algorithm to solve the corresponding optimization problem. In this framework, overlapping image patches are used to exploit the local structure in the image to enforce the sparsity. Dictionary is trained using training images corresponding to particular class the given image belongs to. This results in better sparsities hence the higher undersampling rate. In this alternating reconstruction algorithm, firstly the sparsifying dictionary is learnt to remove aliasing effect and then restoring and filling of the k-space data is performed in the other step. Experiments are conducted on the brain MR image data set with different sampling methods. Results of these experiments show the improvement of around 2.5 dB in PSNR and improvement of around 0.1 in the HFEN value of the reconstructed image. Performance with various sampling schemes is evaluated and the results show that 2D variable density random undersampling scheme is best suited for the MRI application.
{"title":"Application of dictionary learning in compressed sensing of data in MRI","authors":"Himanshu Padole, S. Joshi","doi":"10.1109/CCUBE.2017.8394150","DOIUrl":"https://doi.org/10.1109/CCUBE.2017.8394150","url":null,"abstract":"In recent years, it is now well established that for the data like MRI images that admit the sparse representation in some transformed domain, Compressed Sensing (CS) approach is well suited for the accurate restoration tasks. Various analytical sparsifying transforms such as wavelets, finite differences and curvelets are used extensively in many CS methods. In this paper, a general framework for the adaptive learning of the sparsifying transform (dictionary) and reconstruction of the MR image from undersampled k-space data simultaneously is proposed. Here, we also propose the supervised dictionary learning framework adapted to specific task of MR image reconstruction and an efficient algorithm to solve the corresponding optimization problem. In this framework, overlapping image patches are used to exploit the local structure in the image to enforce the sparsity. Dictionary is trained using training images corresponding to particular class the given image belongs to. This results in better sparsities hence the higher undersampling rate. In this alternating reconstruction algorithm, firstly the sparsifying dictionary is learnt to remove aliasing effect and then restoring and filling of the k-space data is performed in the other step. Experiments are conducted on the brain MR image data set with different sampling methods. Results of these experiments show the improvement of around 2.5 dB in PSNR and improvement of around 0.1 in the HFEN value of the reconstructed image. Performance with various sampling schemes is evaluated and the results show that 2D variable density random undersampling scheme is best suited for the MRI application.","PeriodicalId":443423,"journal":{"name":"2017 International Conference on Circuits, Controls, and Communications (CCUBE)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129188955","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 : 2017-12-01DOI: 10.1109/CCUBE.2017.8394148
Akshay Krishnan, Ananya Hs
Robotic vision is an ideal sensor for many robot platforms. By making robots perform adverse tasks, engineers today are working towards the eternal goal of bringing robots closer to human life. One such task is recognition or authentication of a person which is essential in both social and industrial domains. Upon finding a face, the face recognition robot either recognizes it to be one from the database, or in case of a new person, adds it to the database. Indeed, there are a number of existing algorithms that have been used to achieve this goal. For vision-based autonomous robots in dynamic domain it is crucial that the processing algorithms are fast in addition to being robust. This paper compares the efficiency of three algorithms - Eigenfaces, Fisherfaces and Local Binary Patterns Histograms. It also compares the implementation of these algorithms on a Raspberry Pi against that on a PC. Empirical results demonstrating the robotic platform performing face recognition under various circumstances, justify the validity of the proposed design of a face recognition robot.
{"title":"Design and implementation of robotic vision for face recognition","authors":"Akshay Krishnan, Ananya Hs","doi":"10.1109/CCUBE.2017.8394148","DOIUrl":"https://doi.org/10.1109/CCUBE.2017.8394148","url":null,"abstract":"Robotic vision is an ideal sensor for many robot platforms. By making robots perform adverse tasks, engineers today are working towards the eternal goal of bringing robots closer to human life. One such task is recognition or authentication of a person which is essential in both social and industrial domains. Upon finding a face, the face recognition robot either recognizes it to be one from the database, or in case of a new person, adds it to the database. Indeed, there are a number of existing algorithms that have been used to achieve this goal. For vision-based autonomous robots in dynamic domain it is crucial that the processing algorithms are fast in addition to being robust. This paper compares the efficiency of three algorithms - Eigenfaces, Fisherfaces and Local Binary Patterns Histograms. It also compares the implementation of these algorithms on a Raspberry Pi against that on a PC. Empirical results demonstrating the robotic platform performing face recognition under various circumstances, justify the validity of the proposed design of a face recognition robot.","PeriodicalId":443423,"journal":{"name":"2017 International Conference on Circuits, Controls, and Communications (CCUBE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123606600","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 : 2017-12-01DOI: 10.1109/CCUBE.2017.8394173
Praharsha Sirsi, Kelvin Chelli, T. Herfet
The dynamic environment of a vehicular communication system poses a difficult task of estimating the channel at minimal complexity. A time-varying multipath channel is estimated by computationally intensive algorithms that are generally not suitable for implementation on resource limited consumer hardware. Compressed Sensing (CS) schemes have been established to provide an accurate estimate by exploiting the inherent sparsity of a wireless communication channel. Correspondingly, the Rake-Matching Pursuit (RMP) and its low complexity variant, the Gradient Rake-Matching Pursuit (GRMP) algorithm, first identify different delay taps in the environment. The Doppler is then implicitly estimated by a tracking stage of respective tap coefficients. Although their performance is encouraging even under high Doppler shifts, its adoption for a static multipath environment is excessive due to the required computational resources. A low complexity scheme, like Least Squares (LS), is sufficient to estimate and compensate such channels. The cognitive framework envisages the switch between a high mobility scheme, like RMP, and a low mobility scheme, like LS, based on the channel conditions. In this paper, an enhanced cognitive framework is proposed to interchange between the channel estimation schemes to provide an adequate Bit Error Rate (BER) performance at optimum complexity. Even though the experimentation is performed for the IEEE 802.11p standard, the proposed metrics are relevant for any Orthogonal Frequency- Division Multiplexing (OFDM) based wireless communication system.
{"title":"Tap coefficient based cognitive framework for estimating a dynamic channel","authors":"Praharsha Sirsi, Kelvin Chelli, T. Herfet","doi":"10.1109/CCUBE.2017.8394173","DOIUrl":"https://doi.org/10.1109/CCUBE.2017.8394173","url":null,"abstract":"The dynamic environment of a vehicular communication system poses a difficult task of estimating the channel at minimal complexity. A time-varying multipath channel is estimated by computationally intensive algorithms that are generally not suitable for implementation on resource limited consumer hardware. Compressed Sensing (CS) schemes have been established to provide an accurate estimate by exploiting the inherent sparsity of a wireless communication channel. Correspondingly, the Rake-Matching Pursuit (RMP) and its low complexity variant, the Gradient Rake-Matching Pursuit (GRMP) algorithm, first identify different delay taps in the environment. The Doppler is then implicitly estimated by a tracking stage of respective tap coefficients. Although their performance is encouraging even under high Doppler shifts, its adoption for a static multipath environment is excessive due to the required computational resources. A low complexity scheme, like Least Squares (LS), is sufficient to estimate and compensate such channels. The cognitive framework envisages the switch between a high mobility scheme, like RMP, and a low mobility scheme, like LS, based on the channel conditions. In this paper, an enhanced cognitive framework is proposed to interchange between the channel estimation schemes to provide an adequate Bit Error Rate (BER) performance at optimum complexity. Even though the experimentation is performed for the IEEE 802.11p standard, the proposed metrics are relevant for any Orthogonal Frequency- Division Multiplexing (OFDM) based wireless communication system.","PeriodicalId":443423,"journal":{"name":"2017 International Conference on Circuits, Controls, and Communications (CCUBE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127902910","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 : 2017-12-01DOI: 10.1109/CCUBE.2017.8394156
K. N. Hittanagi, M. Ramesh, K. N. R. Kumar, S. Mahadeva
DC Drives are serving in industries with their efficient motion control ability. These Drives can operate in Local Mode as well as in Overriding Control mode. In Local Mode the Drive is controlled by its control panel where as in Overriding Control mode, it can be controlled remotely by a Programmable Logic Controller (PLC). This PLC based control of Drive is essential for process control in industries like Sugar Mill. Further, this control can be employed using any of the industrial communication protocols which reduce the wiring complexity. Modbus Remote Terminal Unit (RTU) is one such protocol which is open, robust and widely used in industries because of its simplicity. Therefore the same protocol has been implemented in the presented work. This paper depicts initially about configuring a DC Drive (DCS800 of ABB) for Modbus RTU communication and a preliminary test to verify the proper working of intermediate components and cables by communicating the Drive with PC using Modscan32 software. Later, the PLC and Drive communication details and development of ladder program are presented. Further, the results obtained from testing PLC-Drive communication considering the two applications of Sugar Mill are put forth. These ensure the successful implementation of PLC-Drive communication. The applications considered are; speed raise and lower of a DC motor by PLC and secondly, achieving a speed pattern (speed versus time graph) required by a process of Centrifugal Machine in Sugar Mill.
{"title":"PLC based DC drive control using Modbus RTU communication for selected applications of sugar mill","authors":"K. N. Hittanagi, M. Ramesh, K. N. R. Kumar, S. Mahadeva","doi":"10.1109/CCUBE.2017.8394156","DOIUrl":"https://doi.org/10.1109/CCUBE.2017.8394156","url":null,"abstract":"DC Drives are serving in industries with their efficient motion control ability. These Drives can operate in Local Mode as well as in Overriding Control mode. In Local Mode the Drive is controlled by its control panel where as in Overriding Control mode, it can be controlled remotely by a Programmable Logic Controller (PLC). This PLC based control of Drive is essential for process control in industries like Sugar Mill. Further, this control can be employed using any of the industrial communication protocols which reduce the wiring complexity. Modbus Remote Terminal Unit (RTU) is one such protocol which is open, robust and widely used in industries because of its simplicity. Therefore the same protocol has been implemented in the presented work. This paper depicts initially about configuring a DC Drive (DCS800 of ABB) for Modbus RTU communication and a preliminary test to verify the proper working of intermediate components and cables by communicating the Drive with PC using Modscan32 software. Later, the PLC and Drive communication details and development of ladder program are presented. Further, the results obtained from testing PLC-Drive communication considering the two applications of Sugar Mill are put forth. These ensure the successful implementation of PLC-Drive communication. The applications considered are; speed raise and lower of a DC motor by PLC and secondly, achieving a speed pattern (speed versus time graph) required by a process of Centrifugal Machine in Sugar Mill.","PeriodicalId":443423,"journal":{"name":"2017 International Conference on Circuits, Controls, and Communications (CCUBE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132163259","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 : 2017-12-01DOI: 10.1109/CCUBE.2017.8394152
S. Singh, Ajai Kumar, H. Darbari
Named entity recognition (NER) is an important and very effective for the Machine Translation, Retrieval (IR), Information Extraction (IE) from huge corpus, Question Answering (QA) system, text Mining and text clustering and etc. NER help us to classify or identify the Noun and its types such place /location, people, department, Ministry, organization, times and etc. The huge data available on social Media, websites, news channels and many more sources can be classified so that it can be used in research for NLP processes such as in Machine Translation, Speech Technology, Information Extraction and etc. To process this huge data or corpus we propose recent techniques of Machine Learning and Deep Neural Network. The Deep Neural Network approach will help to identify the Named entity (NE) from huge corpus or text by training the corpus using Word2vec approach. On the basis of fetched tokens and tag. We categorize these tokens into different Grammar categories based of cosine similarity concept of Deep Neural Network. Cosine similarity help to find the tag of unknown token or phases by finding its neared Vectors which are not trained earlier in Word2vec database. We have used the supervised learning (SL) techniques to train the network.
{"title":"Deep neural based name entity recognizer and classifier for English language","authors":"S. Singh, Ajai Kumar, H. Darbari","doi":"10.1109/CCUBE.2017.8394152","DOIUrl":"https://doi.org/10.1109/CCUBE.2017.8394152","url":null,"abstract":"Named entity recognition (NER) is an important and very effective for the Machine Translation, Retrieval (IR), Information Extraction (IE) from huge corpus, Question Answering (QA) system, text Mining and text clustering and etc. NER help us to classify or identify the Noun and its types such place /location, people, department, Ministry, organization, times and etc. The huge data available on social Media, websites, news channels and many more sources can be classified so that it can be used in research for NLP processes such as in Machine Translation, Speech Technology, Information Extraction and etc. To process this huge data or corpus we propose recent techniques of Machine Learning and Deep Neural Network. The Deep Neural Network approach will help to identify the Named entity (NE) from huge corpus or text by training the corpus using Word2vec approach. On the basis of fetched tokens and tag. We categorize these tokens into different Grammar categories based of cosine similarity concept of Deep Neural Network. Cosine similarity help to find the tag of unknown token or phases by finding its neared Vectors which are not trained earlier in Word2vec database. We have used the supervised learning (SL) techniques to train the network.","PeriodicalId":443423,"journal":{"name":"2017 International Conference on Circuits, Controls, and Communications (CCUBE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121929184","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 : 2017-12-01DOI: 10.1109/CCUBE.2017.8394131
T. Lakshmi, K. B. Ramesh, V. Niranjan, Aishwarya Shetty, N. Monica, Aishwarya Rao
Rheumatic Arthritis (RA) is a chronic, autoimmune, inflammatory disease involving primarily the peripheral synovial joints. The diagnosis of RA in its pre-clinical phase is of at most importance as it can prevent progressive and irreversible joint damage if treated early. As RA is a genetic disorder, diagnosis through genomic sequence analysis has proven to be an appropriate solution to achieve the above goal [2]. Digital Signal Processing (DSP) applications in bio- informatics has received great attention in recent years, where computationally efficient methods for genome sequence analysis have been developed by utilizing existing signal processing algorithms. In the proposed work, a software module that uses signal processing techniques to predict probability of the future occurrence of RA has been developed. This is done by reviewing medical literature to identify the genes responsible for causing the disease and subsequently obtaining the nucleotide sequences of these genes through GenBank, a standard open-access gene database. The nucleotides are then mapped onto a unit circle in the complex plane so that complimentary base pairs are complex conjugates of each other and the magnitudes of the nucleotides are normalized at unity. Risk gene patterns are then searched in the chromosome sequence under test. Cross-correlation, which is a signal processing algorithm, was used for recognition of presence of risk genes in the chromosome sequence. The usage of cross- correlation not only allowed the identification of mutated sequences but also reduced the time complexity to O[Nlog2(N)].A relative genetic risk score and overall genetic risk score of probability of developing RA was then calculated using statistical methods. In order to test the system, a genome sequence simulator whose underlying architecture is that of a state machine, was created. Using this simulator multiple datasets containing several combinations of risk genes were generated. The system tested using the datasets thus obtained was found to be 95% accurate when the risk magnitudes obtained by the system was compared against the ground truth values given in RAVariome database for the same set of genes chosen. Hence by ensuring early diagnosis, the system will assist doctors to formulate effective treatment plans and thus prevent joint deterioration and permanent functional disability.
{"title":"Design of a state-machine based genomic simulator and development of a system for prediction of Rheumatoid Arthritis (RA) using signal processing techniques.","authors":"T. Lakshmi, K. B. Ramesh, V. Niranjan, Aishwarya Shetty, N. Monica, Aishwarya Rao","doi":"10.1109/CCUBE.2017.8394131","DOIUrl":"https://doi.org/10.1109/CCUBE.2017.8394131","url":null,"abstract":"Rheumatic Arthritis (RA) is a chronic, autoimmune, inflammatory disease involving primarily the peripheral synovial joints. The diagnosis of RA in its pre-clinical phase is of at most importance as it can prevent progressive and irreversible joint damage if treated early. As RA is a genetic disorder, diagnosis through genomic sequence analysis has proven to be an appropriate solution to achieve the above goal [2]. Digital Signal Processing (DSP) applications in bio- informatics has received great attention in recent years, where computationally efficient methods for genome sequence analysis have been developed by utilizing existing signal processing algorithms. In the proposed work, a software module that uses signal processing techniques to predict probability of the future occurrence of RA has been developed. This is done by reviewing medical literature to identify the genes responsible for causing the disease and subsequently obtaining the nucleotide sequences of these genes through GenBank, a standard open-access gene database. The nucleotides are then mapped onto a unit circle in the complex plane so that complimentary base pairs are complex conjugates of each other and the magnitudes of the nucleotides are normalized at unity. Risk gene patterns are then searched in the chromosome sequence under test. Cross-correlation, which is a signal processing algorithm, was used for recognition of presence of risk genes in the chromosome sequence. The usage of cross- correlation not only allowed the identification of mutated sequences but also reduced the time complexity to O[Nlog2(N)].A relative genetic risk score and overall genetic risk score of probability of developing RA was then calculated using statistical methods. In order to test the system, a genome sequence simulator whose underlying architecture is that of a state machine, was created. Using this simulator multiple datasets containing several combinations of risk genes were generated. The system tested using the datasets thus obtained was found to be 95% accurate when the risk magnitudes obtained by the system was compared against the ground truth values given in RAVariome database for the same set of genes chosen. Hence by ensuring early diagnosis, the system will assist doctors to formulate effective treatment plans and thus prevent joint deterioration and permanent functional disability.","PeriodicalId":443423,"journal":{"name":"2017 International Conference on Circuits, Controls, and Communications (CCUBE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127118560","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 : 2017-12-01DOI: 10.1109/CCUBE.2017.8394167
Raksha Ghosh, R. Pragathi, S. Ullas, Surekha Borra
Transport system is recognized as one of the important factors for a country's progress and economic growth. Further, public transportation reduces the traffic which has been the major concern and cost efficient. Two main problems being faced by today's public transportation system include possible threats to personal safety and irregularities in scheduling. Existing intelligent transportation systems provides details about the present location of the bus, its expected arrival time and expected waiting time mainly based on GPS and GSM systems. The primary focus of this paper is to provide an overview of such systems, and suggest a system with which, a user can reach his/her desired destination safely, easily and efficiently. These objectives can be achieved by collecting, processing and providing all the necessary details regarding the arrival/departure time of the bus, its real location time of arrival, availability of seats, accident/breakdown detection and alerting systems. Once this data is collected, it can be communicated along with other custom data to the users via a wireless communication system using GSM model. Thus, the intelligent transportation systems satisfy the users and improve usage of public transportation.
{"title":"Intelligent transportation systems: A survey","authors":"Raksha Ghosh, R. Pragathi, S. Ullas, Surekha Borra","doi":"10.1109/CCUBE.2017.8394167","DOIUrl":"https://doi.org/10.1109/CCUBE.2017.8394167","url":null,"abstract":"Transport system is recognized as one of the important factors for a country's progress and economic growth. Further, public transportation reduces the traffic which has been the major concern and cost efficient. Two main problems being faced by today's public transportation system include possible threats to personal safety and irregularities in scheduling. Existing intelligent transportation systems provides details about the present location of the bus, its expected arrival time and expected waiting time mainly based on GPS and GSM systems. The primary focus of this paper is to provide an overview of such systems, and suggest a system with which, a user can reach his/her desired destination safely, easily and efficiently. These objectives can be achieved by collecting, processing and providing all the necessary details regarding the arrival/departure time of the bus, its real location time of arrival, availability of seats, accident/breakdown detection and alerting systems. Once this data is collected, it can be communicated along with other custom data to the users via a wireless communication system using GSM model. Thus, the intelligent transportation systems satisfy the users and improve usage of public transportation.","PeriodicalId":443423,"journal":{"name":"2017 International Conference on Circuits, Controls, and Communications (CCUBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123133402","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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