K. B. Sindhuri, G. S. C. Teja, K. Madhusudhan, N. Kumar
{"title":"A distinct carry celect adder design approach for area and delay reduction using modified full adder","authors":"K. B. Sindhuri, G. S. C. Teja, K. Madhusudhan, N. Kumar","doi":"10.1201/9780429340710-1","DOIUrl":"https://doi.org/10.1201/9780429340710-1","url":null,"abstract":"","PeriodicalId":231525,"journal":{"name":"Computer-Aided Developments: Electronics and Communication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129989083","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}
K. Sai, Plabini Jibanjyoti Nayak, S. Yallamandaiah
{"title":"VLSI Architecture of DNN neuron for face recognition","authors":"K. Sai, Plabini Jibanjyoti Nayak, S. Yallamandaiah","doi":"10.1201/9780429340710-6","DOIUrl":"https://doi.org/10.1201/9780429340710-6","url":null,"abstract":"","PeriodicalId":231525,"journal":{"name":"Computer-Aided Developments: Electronics and Communication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133887732","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}
Human-machine interaction is calling for a sophisticated understanding of subjects’ behavior performed by smartphones, home automation and entertainment devices, and many service robots. During an interaction with human beings in their environment, a service robot has to be capable to perceive and process visual and sound information of the scene that he observes. To capture salient elements in such different signals many semi-supervised deep learning methods have been proposed. In this article, it is proposed a new convolutional neural network, endowed with a mechanism of attention in order not only to classify, but also to localize temporally a sound event, and in a semi-supervised way.
{"title":"Sound classification and localization in service robots with attention mechanisms","authors":"Matteo Bodini","doi":"10.1201/9780429340710-9","DOIUrl":"https://doi.org/10.1201/9780429340710-9","url":null,"abstract":"Human-machine interaction is calling for a sophisticated understanding of subjects’ behavior performed by smartphones, home automation and entertainment devices, and many service robots. During an interaction with human beings in their environment, a service robot has to be capable to perceive and process visual and sound information of the scene that he observes. To capture salient elements in such different signals many semi-supervised deep learning methods have been proposed. In this article, it is proposed a new convolutional neural network, endowed with a mechanism of attention in order not only to classify, but also to localize temporally a sound event, and in a semi-supervised way.","PeriodicalId":231525,"journal":{"name":"Computer-Aided Developments: Electronics and Communication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129340835","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 : 2019-09-30DOI: 10.1201/9780429340710-34
S. V. V. Satyanarayana, Sridevi Sriadibhatla, N. Amarnath
{"title":"Gate diffusion input (Gdi) technique based CAM cell design for low power and high performance","authors":"S. V. V. Satyanarayana, Sridevi Sriadibhatla, N. Amarnath","doi":"10.1201/9780429340710-34","DOIUrl":"https://doi.org/10.1201/9780429340710-34","url":null,"abstract":"","PeriodicalId":231525,"journal":{"name":"Computer-Aided Developments: Electronics and Communication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130436321","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 : 2019-09-30DOI: 10.1201/9780429340710-11
P. Illavarason, Renjith J Arokia, P. M. Kumar
{"title":"Comparative study and an improved algorithm for iris and eye corner detection in real time application","authors":"P. Illavarason, Renjith J Arokia, P. M. Kumar","doi":"10.1201/9780429340710-11","DOIUrl":"https://doi.org/10.1201/9780429340710-11","url":null,"abstract":"","PeriodicalId":231525,"journal":{"name":"Computer-Aided Developments: Electronics and Communication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128575014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Eigenface recognition using PCA","authors":"K. Anitha, V. Susmitha, M. Rao","doi":"10.1201/9780429340710-8","DOIUrl":"https://doi.org/10.1201/9780429340710-8","url":null,"abstract":"","PeriodicalId":231525,"journal":{"name":"Computer-Aided Developments: Electronics and Communication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116697683","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 : 2019-09-30DOI: 10.1201/9780429340710-31
Rohit Bhargav Peesa, Pydimarri Manoj Kumar, D. Panda
{"title":"Simulation of GaN MOS-HEMT based bio-sensor for breast cancer detection","authors":"Rohit Bhargav Peesa, Pydimarri Manoj Kumar, D. Panda","doi":"10.1201/9780429340710-31","DOIUrl":"https://doi.org/10.1201/9780429340710-31","url":null,"abstract":"","PeriodicalId":231525,"journal":{"name":"Computer-Aided Developments: Electronics and Communication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123748797","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 : 2019-09-30DOI: 10.1201/9780429340710-15
G. Rao, P. J. Rao, Rajesh Duvvuru, K. Beulah, Venkateswarlu Sunkari
{"title":"Internet of things for wildfire disasters","authors":"G. Rao, P. J. Rao, Rajesh Duvvuru, K. Beulah, Venkateswarlu Sunkari","doi":"10.1201/9780429340710-15","DOIUrl":"https://doi.org/10.1201/9780429340710-15","url":null,"abstract":"","PeriodicalId":231525,"journal":{"name":"Computer-Aided Developments: Electronics and Communication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132124889","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 : 2019-09-30DOI: 10.1201/9780429340710-21
N. K. Prasad, P. A. Kumar
{"title":"Frequency domain speech bandwidth extension","authors":"N. K. Prasad, P. A. Kumar","doi":"10.1201/9780429340710-21","DOIUrl":"https://doi.org/10.1201/9780429340710-21","url":null,"abstract":"","PeriodicalId":231525,"journal":{"name":"Computer-Aided Developments: Electronics and Communication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131505765","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 : 2019-09-30DOI: 10.1201/9780429340710-10
Matteo Bodini
From an algorithmic complexity point of view, machine learning methods scale and generalize better when using a few key features: using lots is computationally expensive, and overfitting can occur. High dimensional data is often counterintuitive to perceive and process, but unfortunately it is common for observed data to be in a representation of greater dimensionality than it requires. This gives rise to the notion of dimensionality reduction, a sub-field of machine learning that is motivated to find a descriptive low-dimensional representation of data. In this review it is explored a way to perform dimensionality reduction, provided by a class of Latent Variable Models (LVMs). In particular, the aim is to establish the technical foundations required for understanding the Gaussian Process Latent Variable Model (GP-LVM), a probabilistic nonlinear dimensionality reduction model. The review is organized as follows: after an introduction to the problem of dimensionality reduction and LVMs, Principal Component Analysis (PCA) is recalled and it is reviewed its probabilistic equivalent that contributes to the derivation of GP-LVM. Then, GP-LVM is introduced, and briefly a remarkable extension of the latter, the Bayesian Gaussian Process Latent Variable Model (BGP-LVM) is described. Eventually, and the main advantages of using GP-LVM are summarized.
{"title":"Probabilistic nonlinear dimensionality reduction through gaussian process latent variable models: An overview","authors":"Matteo Bodini","doi":"10.1201/9780429340710-10","DOIUrl":"https://doi.org/10.1201/9780429340710-10","url":null,"abstract":"From an algorithmic complexity point of view, machine learning methods scale and generalize better when using a few key features: using lots is computationally expensive, and overfitting can occur. High dimensional data is often counterintuitive to perceive and process, but unfortunately it is common for observed data to be in a representation of greater dimensionality than it requires. This gives rise to the notion of dimensionality reduction, a sub-field of machine learning that is motivated to find a descriptive low-dimensional representation of data. In this review it is explored a way to perform dimensionality reduction, provided by a class of Latent Variable Models (LVMs). In particular, the aim is to establish the technical foundations required for understanding the Gaussian Process Latent Variable Model (GP-LVM), a probabilistic nonlinear dimensionality reduction model. The review is organized as follows: after an introduction to the problem of dimensionality reduction and LVMs, Principal Component Analysis (PCA) is recalled and it is reviewed its probabilistic equivalent that contributes to the derivation of GP-LVM. Then, GP-LVM is introduced, and briefly a remarkable extension of the latter, the Bayesian Gaussian Process Latent Variable Model (BGP-LVM) is described. Eventually, and the main advantages of using GP-LVM are summarized.","PeriodicalId":231525,"journal":{"name":"Computer-Aided Developments: Electronics and Communication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115687891","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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