Pub Date : 2021-10-21DOI: 10.1109/ICACC-202152719.2021.9708329
V. P. Reshma, V. R. Adersh
In this proposed system, a two speed modified radix-4 booth multiplier algorithm with different adder configurations are implemented for the applications like digital signal processing, digital filters and other neural networks. This multiplier is a modified version of a n-bit serial multiplier which performs booth multiplication algorithm which adds the nonzero computations and skips all other all-zero all-one computations. The datapath of the multiplier is partitioned into two subcircuits operating with two different critical paths which includes a control circuit for operating skipping and a combinational circuit for multiplication. Various adder configurations are utilized in this two speed multiplier and compared in terms of speed by propagation delay and area by number of slice LUTs. The proposed multiplier is designed using verilog HDL and evaluated on Xilinx ISE 14.7 Simulator.
在该系统中，实现了一种具有不同加法器配置的双速改进基数-4 booth乘法器算法，用于数字信号处理、数字滤波器和其他神经网络等应用。该乘法器是n位串行乘法器的改进版本，该乘法器执行booth乘法算法，该算法添加非零计算并跳过所有其他全零全一计算。该乘法器的数据路径被划分为两个子电路，其中包括用于操作跳变的控制电路和用于乘法的组合电路，这些子电路具有两条不同的关键路径。在这两个速度乘法器中使用了各种加法器配置，并根据传播延迟的速度和片lut数量的面积进行了比较。该乘法器采用verilog HDL进行设计，并在Xilinx ISE 14.7模拟器上进行了仿真。
{"title":"Two Speed Modified Radix-4 Booth Multiplier With Different Adder Configurations","authors":"V. P. Reshma, V. R. Adersh","doi":"10.1109/ICACC-202152719.2021.9708329","DOIUrl":"https://doi.org/10.1109/ICACC-202152719.2021.9708329","url":null,"abstract":"In this proposed system, a two speed modified radix-4 booth multiplier algorithm with different adder configurations are implemented for the applications like digital signal processing, digital filters and other neural networks. This multiplier is a modified version of a n-bit serial multiplier which performs booth multiplication algorithm which adds the nonzero computations and skips all other all-zero all-one computations. The datapath of the multiplier is partitioned into two subcircuits operating with two different critical paths which includes a control circuit for operating skipping and a combinational circuit for multiplication. Various adder configurations are utilized in this two speed multiplier and compared in terms of speed by propagation delay and area by number of slice LUTs. The proposed multiplier is designed using verilog HDL and evaluated on Xilinx ISE 14.7 Simulator.","PeriodicalId":198810,"journal":{"name":"2021 International Conference on Advances in Computing and Communications (ICACC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117239122","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 : 2021-10-21DOI: 10.1109/ICACC-202152719.2021.9708172
V. Vinod, Muhsin Ahamed Fazal, Tom Thomas, T. Jose, Ms.Amitha Mathew
It’s been more than a year since the world is struggling with the COVID-19 pandemic. Mutation of the virus leads to a new wave of infection in a lot of countries. The virus has a very high spreading rate, so all the infected patients won’t be able to treat in the hospitals and chances of it spreading among healthcare workers is also high. So we propose a system to monitor COVID-19 patients undergoing quarantine from their own homes during the pandemic, so as to save the hospital bed spaces for the patients with a critical health condition, who need immediate medical attention. The proposed system helps us to avoid overcrowding in hospitals and thereby avoiding the spreading of the virus from highly infected patients to the unaffected individuals. The methodology utilizes LSTM model which is a recurrent neural network (RNN) architecture used in the field of deep learning.
{"title":"E-Quarantine: Remote Real Time Monitoring of COVID-19 Patients","authors":"V. Vinod, Muhsin Ahamed Fazal, Tom Thomas, T. Jose, Ms.Amitha Mathew","doi":"10.1109/ICACC-202152719.2021.9708172","DOIUrl":"https://doi.org/10.1109/ICACC-202152719.2021.9708172","url":null,"abstract":"It’s been more than a year since the world is struggling with the COVID-19 pandemic. Mutation of the virus leads to a new wave of infection in a lot of countries. The virus has a very high spreading rate, so all the infected patients won’t be able to treat in the hospitals and chances of it spreading among healthcare workers is also high. So we propose a system to monitor COVID-19 patients undergoing quarantine from their own homes during the pandemic, so as to save the hospital bed spaces for the patients with a critical health condition, who need immediate medical attention. The proposed system helps us to avoid overcrowding in hospitals and thereby avoiding the spreading of the virus from highly infected patients to the unaffected individuals. The methodology utilizes LSTM model which is a recurrent neural network (RNN) architecture used in the field of deep learning.","PeriodicalId":198810,"journal":{"name":"2021 International Conference on Advances in Computing and Communications (ICACC)","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134222106","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 : 2021-10-21DOI: 10.1109/ICACC-202152719.2021.9708404
Jinsi Jose, Deepa V. Jose, Karna Srinivasa Rao, Justin Janz
The importance of security aspects is increased recently due to the enormous usage of IoT devices. Securing the system from all sorts of vulnerabilities is inevitable to use IoT applications. Intrusion detection systems are power mechanism which provides this service. The introduction of artificial intelligence into intrusion detection systems can further enhance its power. This paper is an attempt to understand the impact of machine learning algorithms in attack detection. Using the UNSW-NB 15 dataset, the impact of different machine learning algorithms is assessed.
{"title":"Impact of Machine Learning Algorithms in Intrusion Detection Systems for Internet of Things","authors":"Jinsi Jose, Deepa V. Jose, Karna Srinivasa Rao, Justin Janz","doi":"10.1109/ICACC-202152719.2021.9708404","DOIUrl":"https://doi.org/10.1109/ICACC-202152719.2021.9708404","url":null,"abstract":"The importance of security aspects is increased recently due to the enormous usage of IoT devices. Securing the system from all sorts of vulnerabilities is inevitable to use IoT applications. Intrusion detection systems are power mechanism which provides this service. The introduction of artificial intelligence into intrusion detection systems can further enhance its power. This paper is an attempt to understand the impact of machine learning algorithms in attack detection. Using the UNSW-NB 15 dataset, the impact of different machine learning algorithms is assessed.","PeriodicalId":198810,"journal":{"name":"2021 International Conference on Advances in Computing and Communications (ICACC)","volume":"78 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129959300","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 : 2021-10-21DOI: 10.1109/ICACC-202152719.2021.9708272
Nipun Anoob, Sanju Jacob Ebey, P. Praveen, Prasidh Prabudhan, P. Augustine
Object detection is an important process in computer vision projects/tasks. It is a technique which tries to predict the location of an object by drawing a bounding box around it. This however does not give us any idea about the actual shape of the object. For this we must employ the next stage of computer vision task which is known as Instance Segmentation. This task can be used to find the shape of an object along with its bounding box. In this survey paper, we discuss some of the models that can achieve the task of instance segmentation and a dataset has been discussed. The goal of the paper is to give the reader an idea about the field of instance segmentation.
{"title":"A Comparison on Instance Segmentation Models","authors":"Nipun Anoob, Sanju Jacob Ebey, P. Praveen, Prasidh Prabudhan, P. Augustine","doi":"10.1109/ICACC-202152719.2021.9708272","DOIUrl":"https://doi.org/10.1109/ICACC-202152719.2021.9708272","url":null,"abstract":"Object detection is an important process in computer vision projects/tasks. It is a technique which tries to predict the location of an object by drawing a bounding box around it. This however does not give us any idea about the actual shape of the object. For this we must employ the next stage of computer vision task which is known as Instance Segmentation. This task can be used to find the shape of an object along with its bounding box. In this survey paper, we discuss some of the models that can achieve the task of instance segmentation and a dataset has been discussed. The goal of the paper is to give the reader an idea about the field of instance segmentation.","PeriodicalId":198810,"journal":{"name":"2021 International Conference on Advances in Computing and Communications (ICACC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126966634","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 : 2021-10-21DOI: 10.1109/ICACC-202152719.2021.9708300
M. Anitha, K. Sherly
In the last few decades, Feature selection is one of the most challenging and open problem to researchers. The rapid progress in computational techniques causes the generation and recording of data in huge size. Though there exists various feature ranking methods, the processing of data is still a challenging task due to its computational complexity. The filter method has many advantages over the wrapper method. The filter methods are classifier independent and have better computational efficiency. Here, a subset of features is selected based on a certain goal function. Most of these goal functions employs the principle of information theory. Most of the algorithms in earlier studies addressed two factors, that is, maximization of relevancy and minimization of redundancy without considering the interaction among the features. This paper developed a new forward filter feature selection algorithm based on mutual information known as Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR). This method considers all the three factors: relevance, redundancy, and feature interaction. This method is experimented on three datasets and compares the performance with existing methods. The results show that MDIMFR outperforms the existing competitive feature selection methods of recent studies: mRMR, JMIM and CMIM. MDIMFR also achieves good stability in average classification accuracy for a certain number of features, say k and above. Hence, these k features can be considered as an optimal feature set.
{"title":"A Novel Forward Filter Feature Selection Algorithm Based on Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR) for Machine Learning","authors":"M. Anitha, K. Sherly","doi":"10.1109/ICACC-202152719.2021.9708300","DOIUrl":"https://doi.org/10.1109/ICACC-202152719.2021.9708300","url":null,"abstract":"In the last few decades, Feature selection is one of the most challenging and open problem to researchers. The rapid progress in computational techniques causes the generation and recording of data in huge size. Though there exists various feature ranking methods, the processing of data is still a challenging task due to its computational complexity. The filter method has many advantages over the wrapper method. The filter methods are classifier independent and have better computational efficiency. Here, a subset of features is selected based on a certain goal function. Most of these goal functions employs the principle of information theory. Most of the algorithms in earlier studies addressed two factors, that is, maximization of relevancy and minimization of redundancy without considering the interaction among the features. This paper developed a new forward filter feature selection algorithm based on mutual information known as Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR). This method considers all the three factors: relevance, redundancy, and feature interaction. This method is experimented on three datasets and compares the performance with existing methods. The results show that MDIMFR outperforms the existing competitive feature selection methods of recent studies: mRMR, JMIM and CMIM. MDIMFR also achieves good stability in average classification accuracy for a certain number of features, say k and above. Hence, these k features can be considered as an optimal feature set.","PeriodicalId":198810,"journal":{"name":"2021 International Conference on Advances in Computing and Communications (ICACC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126498089","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 : 2021-10-21DOI: 10.1109/ICACC-202152719.2021.9708376
K. A. Safa Hassan Ali, S. Chinchu Krishna
Generative Adversarial Network (GANs) has become one of the most interesting ideas in the last years in Machine Learning. Generative Adversarial Network is a very exciting area and that’s why researchers are so excited about building generative models as they are set to vary what machines can do for humans. This paper proposes the generation of realistic images according to their semantics based on text description using a Knowledge Graph alongside Knowledge Guided Generative Adversarial Network (KG-GAN) that comes with the embeddings generated from the Knowledge Graph (KG) into GAN. The Knowledge Graph is made from the text description by making the machine understand from the Natural Language Processing (NLP) techniques. The Knowledge Graph produced from the text description is converted to its embeddings by utilizing a Graph Convolutional Networks (GCN) and is fed into the GAN for generating realistic images by training the generators and discriminators and also the performance is evaluated. The experimental study is completed on a Caltech-UCSD Birds 200-2011 (CUB-200-2011) dataset and results that the approach using the knowledge graph for image generation using GAN has performed well and with high accuracy in comparison to the other established techniques generated in the past years for text to image generation in GAN.
{"title":"Generating Text to Realistic Image using Generative Adversarial Network","authors":"K. A. Safa Hassan Ali, S. Chinchu Krishna","doi":"10.1109/ICACC-202152719.2021.9708376","DOIUrl":"https://doi.org/10.1109/ICACC-202152719.2021.9708376","url":null,"abstract":"Generative Adversarial Network (GANs) has become one of the most interesting ideas in the last years in Machine Learning. Generative Adversarial Network is a very exciting area and that’s why researchers are so excited about building generative models as they are set to vary what machines can do for humans. This paper proposes the generation of realistic images according to their semantics based on text description using a Knowledge Graph alongside Knowledge Guided Generative Adversarial Network (KG-GAN) that comes with the embeddings generated from the Knowledge Graph (KG) into GAN. The Knowledge Graph is made from the text description by making the machine understand from the Natural Language Processing (NLP) techniques. The Knowledge Graph produced from the text description is converted to its embeddings by utilizing a Graph Convolutional Networks (GCN) and is fed into the GAN for generating realistic images by training the generators and discriminators and also the performance is evaluated. The experimental study is completed on a Caltech-UCSD Birds 200-2011 (CUB-200-2011) dataset and results that the approach using the knowledge graph for image generation using GAN has performed well and with high accuracy in comparison to the other established techniques generated in the past years for text to image generation in GAN.","PeriodicalId":198810,"journal":{"name":"2021 International Conference on Advances in Computing and Communications (ICACC)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121171106","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}
In recent years we have seen a huge surge in benchmark tasks for Deep Reinforcement learning algorithms and a tremendous growth in the field of reinforcement learning itself but oftentimes the stochasticity and real time decision making of real world strategic and competitive games and also the choice between a multitude of actions are not mirrored in the environments used for RL agents. To address this issue Google released Gfootball, a football game engine based environment which was popularized by Manchester City FC sponsoring a Kaggle competition but the majority methods revolved around Rule based RL agent, imitation learning, reward modifications etc. and the pure reinforcement learning included feature extractors which had parallel neural networks on costly hardware. We propose a much simpler method involving depthwise separable convolutions as the base feature extractor which yields competitive results across a lot of benchmarks in very few episodes compared to the original paper. We also used Quantile regression DQN due to highly stochastic nature of the environment to exploit the quantiles of the return distribution to improve performance.
{"title":"Learning to play Football using Distributional Reinforcement Learning and Depthwise separable convolution feature extraction","authors":"Aniruddha Datta, Swapnamoy Bhowmick, Kunal Kulkarni","doi":"10.1109/ICACC-202152719.2021.9708400","DOIUrl":"https://doi.org/10.1109/ICACC-202152719.2021.9708400","url":null,"abstract":"In recent years we have seen a huge surge in benchmark tasks for Deep Reinforcement learning algorithms and a tremendous growth in the field of reinforcement learning itself but oftentimes the stochasticity and real time decision making of real world strategic and competitive games and also the choice between a multitude of actions are not mirrored in the environments used for RL agents. To address this issue Google released Gfootball, a football game engine based environment which was popularized by Manchester City FC sponsoring a Kaggle competition but the majority methods revolved around Rule based RL agent, imitation learning, reward modifications etc. and the pure reinforcement learning included feature extractors which had parallel neural networks on costly hardware. We propose a much simpler method involving depthwise separable convolutions as the base feature extractor which yields competitive results across a lot of benchmarks in very few episodes compared to the original paper. We also used Quantile regression DQN due to highly stochastic nature of the environment to exploit the quantiles of the return distribution to improve performance.","PeriodicalId":198810,"journal":{"name":"2021 International Conference on Advances in Computing and Communications (ICACC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132668365","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 : 2021-10-21DOI: 10.1109/icacc-202152719.2021.9708208
{"title":"[ICACC-2021 2021 Copyright notice]","authors":"","doi":"10.1109/icacc-202152719.2021.9708208","DOIUrl":"https://doi.org/10.1109/icacc-202152719.2021.9708208","url":null,"abstract":"","PeriodicalId":198810,"journal":{"name":"2021 International Conference on Advances in Computing and Communications (ICACC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114209300","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 : 2021-10-21DOI: 10.1109/ICACC-202152719.2021.9708082
V. Karthikraj, Varsha A. Patil, Senthil Thanneermalai, Thirumalainambi Yadav
Due to the Covid-19 pandemic, more than 1.2 billion students around the world were out of the classrooms in the year 2020. Nevertheless, in this age of technological innovation, schools and pedagogical institutions have resorted to online teaching through various video conferencing applications to maintain continuity in the crucial impartation of knowledge in the leaders of the upcoming generations. However, online teaching is a difficult medium for the teachers as well as students to adapt to. Due to its shortcomings, students fail to pay attention during the lecture which results in teacher’s teaching in vain. Therefore, it is the need of the hour to implement an effective teaching system that can quantify the attention of an individual student as well as of the entire class during the online lecture. This will not only encourage the students to pay attention during the lecture but also assist the teacher with a powerful tool of determining the effectiveness of their teaching and thereby make changes to it to increase the attention of the entire class.
{"title":"Attention Span Detection for Online Lectures","authors":"V. Karthikraj, Varsha A. Patil, Senthil Thanneermalai, Thirumalainambi Yadav","doi":"10.1109/ICACC-202152719.2021.9708082","DOIUrl":"https://doi.org/10.1109/ICACC-202152719.2021.9708082","url":null,"abstract":"Due to the Covid-19 pandemic, more than 1.2 billion students around the world were out of the classrooms in the year 2020. Nevertheless, in this age of technological innovation, schools and pedagogical institutions have resorted to online teaching through various video conferencing applications to maintain continuity in the crucial impartation of knowledge in the leaders of the upcoming generations. However, online teaching is a difficult medium for the teachers as well as students to adapt to. Due to its shortcomings, students fail to pay attention during the lecture which results in teacher’s teaching in vain. Therefore, it is the need of the hour to implement an effective teaching system that can quantify the attention of an individual student as well as of the entire class during the online lecture. This will not only encourage the students to pay attention during the lecture but also assist the teacher with a powerful tool of determining the effectiveness of their teaching and thereby make changes to it to increase the attention of the entire class.","PeriodicalId":198810,"journal":{"name":"2021 International Conference on Advances in Computing and Communications (ICACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114504992","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 : 2021-10-21DOI: 10.1109/ICACC-202152719.2021.9708201
J. H. Kishore, B. Yamuna, Karthi Balasubramanian
In this paper a high speed turbo product code (TPC) decoder based on fast Chase algorithm is proposed. The improvement in speed is achieved by exploiting the parallelism in the decoder design. A fully parallel soft-in soft-out (SISO) module based on low complexity fast Chase algorithm is designed. The designed parallel SISO module is used to construct the turbo product decoder using sub block parallelism without any interleaving resources. With this design, a 15 times increase in speed is achieved with a $(31,26)^{2}$ Hamming TPC when compared to that of the decoder that uses sequential SISO module with barrel shifter as interleaving resource. However there is a marginal increase in the area utilized. It is envisaged that, with the use of area efficient adders and comparators, the speed-area trade-off can be mitigated.
{"title":"Design of a Fast Chase Algorithm based High Speed Turbo Product Code Decoder","authors":"J. H. Kishore, B. Yamuna, Karthi Balasubramanian","doi":"10.1109/ICACC-202152719.2021.9708201","DOIUrl":"https://doi.org/10.1109/ICACC-202152719.2021.9708201","url":null,"abstract":"In this paper a high speed turbo product code (TPC) decoder based on fast Chase algorithm is proposed. The improvement in speed is achieved by exploiting the parallelism in the decoder design. A fully parallel soft-in soft-out (SISO) module based on low complexity fast Chase algorithm is designed. The designed parallel SISO module is used to construct the turbo product decoder using sub block parallelism without any interleaving resources. With this design, a 15 times increase in speed is achieved with a $(31,26)^{2}$ Hamming TPC when compared to that of the decoder that uses sequential SISO module with barrel shifter as interleaving resource. However there is a marginal increase in the area utilized. It is envisaged that, with the use of area efficient adders and comparators, the speed-area trade-off can be mitigated.","PeriodicalId":198810,"journal":{"name":"2021 International Conference on Advances in Computing and Communications (ICACC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130145912","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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