MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

School of Computer Engineering & Technology

Digital voice systems have been in use for many applications including secure military voice communications, hands-free or cellular mobile telephone systems. In such applications, the speech signal is corrupted by environmental ambient noise. This acoustically added background noise degrades the performance of digital voice processors used for various functions like speech compressors, encoders etc. Further, due to dramatic variance in characteristics of noise in different environments, a single algorithm cannot prove equally beneficial in all circumstances. Finally, the complexity is a major barrier when dealing with systems involving runtime signal processing for mobile phones, as issues such as time complexity and MIPS as involved. Hence, some kind of noise reduction technique has to be employed in order to maintain the performance level of the system at par with that in case of a noise-free environment, regardless of the actual environment in which it is employed. To overcome these problems this paper propose to design a pre-processor system for noise suppression which should adapt to the environmental ambient noise and consequently apply a suitable algorithm. ©2007 IEEE.

Engineering and Technology

World University root node

2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007

Adaptive preprocessor system for noise suppression in cellular phones

Multi-Antenna systems are expected to play very important role in future multimedia wireless communication systems. Such systems are predicted to provide tremendous improvement in spectrum utilization. In this paper we consider the capacity analysis of Multiple-Input Multiple-Output (MIMO) systems. Space-Time coding schemes are the practical signal design techniques to realize the information theoretic capacity limits of MIMO systems. Here the orthogonal space-time block codes are considered for the capacity and error probability analysis of MIMO systems as a case study. The numerical and simulation results obtained using MATLAB are presented for the Multi-antenna system channel capacity and bit-error probability in Rayleigh fading channels.

WSEAS Transactions on Communications

Capacity and performance analysis of space-time block codes in Rayleigh fading channels

The main concern dealing with Content-based Image Retrieval (CBIR) is to bridge the semantic gap. The high level query posed by the user and low level features extracted by the machine illustrates the problem of semantic gap. To solve the problem of semantic gap, this paper presents a hybrid technique using an emergence index and fuzzy logic for efficient retrieval of images based on the colour feature. Emergence Index (EI) is proposed to understand the hidden meaning of the image. Fuzzy Similarity Measure is developed to calculate the similarity between the target image and the images in the database. The images were ranked based on their similarity along with the fuzzy similarity distance measure. The preliminary experiments conducted on small set of images and promising results were obtained. © 2007 IEEE.

Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP

Human perception based image retrieval using emergence index and fuzzy similarity measure

This chapter introduces neural networks for content-based image retrieval (CBIR) systems. It presents a critical literature review of both the traditional and neural network-based techniques that are used to retrieve images based on their content. It shows how neural networks and fuzzy logic can be used in the interpretation of queries, feature extraction, and classification of features by describing a detailed research methodology. It investigates a neural network-based technique in conjunction with fuzzy logic to improve the overall performance of CBIR systems. The results of the investigation on a benchmark database with a comparative analysis are presented in this chapter. The methodologies and results presented in this chapter will allow researchers to improve and compare their methods, and it also will allow system developers to understand and implement the neural network and fuzzy logic-based techniques for content-based image retrieval. © 2007, Idea Group Inc.

Semantic-Based Visual Information Retrieval

Neural networks for content-based image retrieval

Sorting of integers by “D” method.

The paper presents a novel fuzzy logic-based approach for interpretation of color queries and a technique for fusion of multiple queries. Currently, in most real world systems, users have to provide the content of color in terms of percentage or many real numbers to retrieve an image. In this paper a fuzzy logic-based interface is presented which allows user to express queries in terms of phrases like mostly red, many green, few red. The paper also presents a novel way of fusing multiple queries. By incorporating the fusion of multiple queries, it was noticed that there was a prominent increase in the confidence factor for the images. Fusion experiments were conducted with neuro-fuzzy, fuzzy AND and binary AND techniques. A comparative analysis showed that neuro-fuzzy has outperformed other two techniques. © 2003 Elsevier B.V. All rights reserved.

Fuzzy Sets and Systems

Fuzzy logic based interpretation and fusion of color queries

As digital video databases become more and more pervasive, finding video in large databases becomes a major problem. Because of the nature of video (streamed objects), accessing the content of such databases is inherently a time-consuming operation. This paper proposes a novel neural-fuzzy based approach for retrieving a specific video clip from a video database. Fuzzy logic is used for expressing queries in terms of natural language and neural network is designed to learn the meaning of these queries. The queries are designed based on the features such as colour and texture of shots, scenes and objects in video clips. Error back propagation algorithm is proposed to learn the meaning of queries in fuzzy terms such as "very similar", "similar" and "some-what similar." The preliminary experiments were conducted on a small video database and different combinations of queries using colour and texture features along with visual video clip and achieved very promising results.

Canadian Conference on Electrical and Computer Engineering

Neural-fuzzy approach for content-based retrieval of digital video

This paper presents a fuzzy-neural approach for interpretation and fusion of colour and texture features for CBIR systems. The presented approach uses fuzzy logic to interpret queries expressed in natural language such as mostly red, many green, few red for colour feature. Tamura feature is used to represent the texture of an image in the database. A term set on each Tamura feature is generated using a fuzzy clustering algorithm to pose a query in terms of natural language. The query can be expressed as a logic combination of natural language terms and Tamura feature values. A fusion of multiple queries is incorporated into the proposed approach. The performance of the technique was evaluated on Brodatz texture benchmark database and it was noticed that there was a prominent increase in the confidence factor for the images. Fusion experiments were conducted using neuro-fuzzy, fuzzy AND and binary AND techniques. A comparative analysis showed that fuzzy-neural approach has significantly improved the performance of CBIR system. © 2004 Elsevier B.V. All rights reserved.

Fulltext

Applied Soft Computing Journal

A fuzzy-neural approach for interpretation and fusion of colour and texture features for CBIR systems

The main aim of the proposed approach presented in this paper is to improve Web information retrieval effectiveness by overcoming the problems associated with a typical keyword matching retrieval system, through the use of concepts and an intelligent fusion of confidence values. By exploiting the conceptual hierarchy of the WordNet (G. Miller, 1995) knowledge base, we show how to effectively encode the conceptual information in a document using the semantic information implied by the words that appear within it. Rather than treating a word as a string made up of a sequence of characters, we consider a word to represent a concept. © 2003 IEEE.

Proceedings - 5th International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2003

Intelligent encoding of concepts in Web document retrieval

This paper presents a novel fuzzy logic based approach for the interpretation of texture queries. Tamura feature extraction technique is used to extract each texture feature of an image in the database. A term set on each Tamura feature is generated by a fuzzy clustering algorithm to pose a query in terms of natural language. The query can be expressed as a logic combination of natural language terms and tamura feature values. The performance of the technique was evaluated on Brodatz texture benchmark database. Experimental results show that the proposed technique is effective and the retrieved images indicate that those images are suitable for the specific queries. © 2003 IEEE.