Pub Date : 2010-03-05DOI: 10.2174/1877612401002010039
M. Z. Alam, M. Sobhan
The transmitted symbol is affected by the channel noise and diversity is an efficient method to reduce the noise and interference. Recently, the channel noise is cancelled by using a novel beamforming technique, where the transmitted symbols are weighted and the same weighting vectors are used at the receiver. In this paper, the authors use the proposed beamforming technique in antenna diversity to eliminate the channel noise and interference. The authors also compute the bit error rate (BER) performance for different diversity combiner and the result shows that the proposed beamforming algorithm with transmit diversity provide better BER performance than usual antenna diversity.
{"title":"Developing a Novel Beamforming Technique in Antenna Diversity~!2009-06-23~!2009-11-05~!2010-02-02~!","authors":"M. Z. Alam, M. Sobhan","doi":"10.2174/1877612401002010039","DOIUrl":"https://doi.org/10.2174/1877612401002010039","url":null,"abstract":"The transmitted symbol is affected by the channel noise and diversity is an efficient method to reduce the noise and interference. Recently, the channel noise is cancelled by using a novel beamforming technique, where the transmitted symbols are weighted and the same weighting vectors are used at the receiver. In this paper, the authors use the proposed beamforming technique in antenna diversity to eliminate the channel noise and interference. The authors also compute the bit error rate (BER) performance for different diversity combiner and the result shows that the proposed beamforming algorithm with transmit diversity provide better BER performance than usual antenna diversity.","PeriodicalId":104357,"journal":{"name":"Recent Patents on Signal Processing","volume":"418 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117333411","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 : 2010-03-05DOI: 10.2174/1877612401002010047
V. Singh
{"title":"Recent Patents on Image Compression - A Survey~!2009-10-08~!2009-12-04~!2010-02-11~!","authors":"V. Singh","doi":"10.2174/1877612401002010047","DOIUrl":"https://doi.org/10.2174/1877612401002010047","url":null,"abstract":"","PeriodicalId":104357,"journal":{"name":"Recent Patents on Signal Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131760833","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 : 2010-02-01DOI: 10.2174/1877612401002010022
Yevgeny Beiderman, E. Rivlin, M. Teicher, Z. Zalevsky
Image recognition under various changing illumination conditions is an important problem being frequently addressed. The paper presents a new approach based upon combination between spectral manipulation called the HSV and spatial transformation called the K-factor that is applied over the HSV components. Such manipulation allows composing image which is both insensitive to illumination and contains the significant spatial details of the original pattern. A useful application of this algorithm can be applied to pattern recognition problems under variable illumination. Numerical simulations as well as experimental results demonstrate the capability of the proposed algorithm to obtain reduced sensitivity to illumination variations and to increase probability of detection while maintaining the same level of false alarm rate.
{"title":"Illumination Insensitive Reconstraction and Pattern Recognition Using Spectral Manipulation and K-Factor Spatial Transforming","authors":"Yevgeny Beiderman, E. Rivlin, M. Teicher, Z. Zalevsky","doi":"10.2174/1877612401002010022","DOIUrl":"https://doi.org/10.2174/1877612401002010022","url":null,"abstract":"Image recognition under various changing illumination conditions is an important problem being frequently addressed. The paper presents a new approach based upon combination between spectral manipulation called the HSV and spatial transformation called the K-factor that is applied over the HSV components. Such manipulation allows composing image which is both insensitive to illumination and contains the significant spatial details of the original pattern. A useful application of this algorithm can be applied to pattern recognition problems under variable illumination. Numerical simulations as well as experimental results demonstrate the capability of the proposed algorithm to obtain reduced sensitivity to illumination variations and to increase probability of detection while maintaining the same level of false alarm rate.","PeriodicalId":104357,"journal":{"name":"Recent Patents on Signal Processing","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123005986","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 : 2010-02-01DOI: 10.2174/1877612401002010012
M. Z. Alam, M. Sobhan
The software defined radio (SDR) is the heart of the 4G mobile communication to access any network at any time basis. The different wireless networks such as cellular, codeless, wireless local area network (WLAN) having different band of frequency requires individual software to access any call. The SDR device requires more antennas and low noise amplifier (LNA) because it is impossible for single antenna and single band pass filter to operate at all the frequency bands. Large number of antennas, filter and amplifier increased the size of the device. The SDR scan the available network and download the required software from WLAN, memory card, PC server etc. The downloading creates some problem, such as the limited download speed and its reliability. In this paper, the authors study the architecture of SDR based on the recently proposed CI-OFDM multiplexing technique to operate all networks in a particular band-width. We also find the interference among different CI channels of the same and different networks. Finally, we discuss the calling procedure between one user of one network and another user under another network using IP address.
{"title":"Design of Future Software Defined Radio (SDR) for All-IP Heterogeneous Network","authors":"M. Z. Alam, M. Sobhan","doi":"10.2174/1877612401002010012","DOIUrl":"https://doi.org/10.2174/1877612401002010012","url":null,"abstract":"The software defined radio (SDR) is the heart of the 4G mobile communication to access any network at any time basis. The different wireless networks such as cellular, codeless, wireless local area network (WLAN) having different band of frequency requires individual software to access any call. The SDR device requires more antennas and low noise amplifier (LNA) because it is impossible for single antenna and single band pass filter to operate at all the frequency bands. Large number of antennas, filter and amplifier increased the size of the device. The SDR scan the available network and download the required software from WLAN, memory card, PC server etc. The downloading creates some problem, such as the limited download speed and its reliability. In this paper, the authors study the architecture of SDR based on the recently proposed CI-OFDM multiplexing technique to operate all networks in a particular band-width. We also find the interference among different CI channels of the same and different networks. Finally, we discuss the calling procedure between one user of one network and another user under another network using IP address.","PeriodicalId":104357,"journal":{"name":"Recent Patents on Signal Processing","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130367678","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 : 2010-02-01DOI: 10.2174/1877612401002010028
K. Kannan, S. Perumal, K. Arulmozhi
Abstract: The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. In Spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. The most common widely used transform for image fusion at multi scale is Discrete Wavelet Transform since it minimizes structural distortions. But, wavelet transform suffers from lack of shift invariance and poor directional selectivity. These two disadvantages are overcome by Stationary and Complex Wavelet Transform. But they are more expansive and this can be compromised by Double Density Wavelet Transform. Image fusion can be performed using three levels namely Pixel, feature and decision level. This paper evaluates the performance of feature level fusion of multi focused images using Discrete, Stationary and Dual Tree Complex wavelet transform in terms of various performance measures.
{"title":"The Review of Feature Level Fusion of Multi-Focused Images Using Wavelets","authors":"K. Kannan, S. Perumal, K. Arulmozhi","doi":"10.2174/1877612401002010028","DOIUrl":"https://doi.org/10.2174/1877612401002010028","url":null,"abstract":"Abstract: The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. In Spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. The most common widely used transform for image fusion at multi scale is Discrete Wavelet Transform since it minimizes structural distortions. But, wavelet transform suffers from lack of shift invariance and poor directional selectivity. These two disadvantages are overcome by Stationary and Complex Wavelet Transform. But they are more expansive and this can be compromised by Double Density Wavelet Transform. Image fusion can be performed using three levels namely Pixel, feature and decision level. This paper evaluates the performance of feature level fusion of multi focused images using Discrete, Stationary and Dual Tree Complex wavelet transform in terms of various performance measures.","PeriodicalId":104357,"journal":{"name":"Recent Patents on Signal Processing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121547731","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 : 2010-02-01DOI: 10.2174/1877612401002010006
Yevgeny Beiderman, Yaniv Azani, Yoni Cohen, Chen Nisankoren, M. Teicher, V. Micó, Javier García, Z. Zalevsky
A newly developed optical technology for remote recording of voice signal was recently demonstrated. In this paper we present a signal processing approach for improving the quality of the recording and then for classifying the characteristics of the recording done using this system. In both cases the proposed signal processing operations are applied over the spectrogram of the optically recorded signals.
{"title":"Cleaning and Quality Classification of Optically Recorded Voice Signals","authors":"Yevgeny Beiderman, Yaniv Azani, Yoni Cohen, Chen Nisankoren, M. Teicher, V. Micó, Javier García, Z. Zalevsky","doi":"10.2174/1877612401002010006","DOIUrl":"https://doi.org/10.2174/1877612401002010006","url":null,"abstract":"A newly developed optical technology for remote recording of voice signal was recently demonstrated. In this paper we present a signal processing approach for improving the quality of the recording and then for classifying the characteristics of the recording done using this system. In both cases the proposed signal processing operations are applied over the spectrogram of the optically recorded signals.","PeriodicalId":104357,"journal":{"name":"Recent Patents on Signal Processing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130279096","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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