International Conference on Signal Processing 

About: International Conference on Signal Processing is an academic conference. The conference publishes majorly in the area(s): Feature extraction & Wavelet transform. Over the lifetime, 15572 publications have been published by the conference receiving 76203 citations.

Iris recognition: an emerging biometric technology

Abstract: In this paper, iris recognition as one of the important method of biometrics-based identification systems and iris recognition algorithm is described. As technology advances and information and intellectual properties are wanted by many unauthorized personnel. As a result many organizations have being searching ways for more secure authentication methods for the user access. In network security there is a vital emphasis on the automatic personal identification. Due to its inherent advantages biometric based verification especially iris identification is gaining a lot of attention. Iris recognition uses iris patterns for personnel identification. The system steps are capturing iris patterns; determining the location of iris boundaries; converting the iris boundary to the stretched polar coordinate system; extracting iris code based on texture analysis. The system has been implemented and tested using dataset of number of samples of iris data with different contrast quality. The developed algorithm performs satisfactorily on the images, provides 93% accuracy. Experimental results show that the proposed method has an encouraging performance.

CUDA Compatible GPU as an Efficient Hardware Accelerator for AES Cryptography

Abstract: This paper presents a study of the efficiency in applying modern graphics processing units in symmetric key cryptographic solutions. It describes both traditional style approaches based on the OpenGL graphics API and new ones based on the recent technology trends of major hardware vendors. It presents an efficient implementation of the advanced encryption standard (AES) algorithm in the novel CUDA platform by Nvidia. AES is currently the most widely adopted modern symmetric key encryption standard. The performance of the new fastest GPU solution is compared with those of the reference sequential implementations running on an Intel Pentium IV 3.0 GHz CPU. Unlike previous research in this field, the results of this effort show for the first time the GPU can perform as an efficient cryptographic accelerator. The developed solutions run up to 20 times faster than OpenSSL and in the same range of performance of existing hardware based implementations.

A practical path loss model for indoor WiFi positioning enhancement

Abstract: Positioning within a local area refers to technology whereby each node is self-aware of its position. Based on empirical study, this paper proposes an enhancement to the path loss model in the indoor environment for improved accuracy in the relationship between distance and received signal strength. We further demonstrate the potential of our model for the WiFi positioning system, where the mean errors in the distance estimation are 2.3 m and 2.9 m for line of sight and non line of sight environments, respectively.

Performance analysis of NSL-KDD dataset using ANN

Abstract: Anomalous traffic detection on internet is a major issue of security as per the growth of smart devices and this technology. Several attacks are affecting the systems and deteriorate its computing performance. Intrusion detection system is one of the techniques, which helps to determine the system security, by alarming when intrusion is detected. In this paper performance of NSL-KDD dataset is evaluated using ANN. The result obtained for both binary class as well as five class classification (type of attack). Results are analyzed based on various performance measures and better accuracy was found. The detection rate obtained is 81.2% and 79.9% for intrusion detection and attack type classification task respectively for NSL-KDD dataset. The performance of the proposed scheme has been compared with existing scheme and higher detection rate is achieved in both binary class as well as five class classification problems.

Sentiment analysis of Twitter data for predicting stock market movements

Abstract: Predicting stock market movements is a well-known problem of interest Now-a-days social media is perfectly representing the public sentiment and opinion about current events Especially, Twitter has attracted a lot of attention from researchers for studying the public sentiments Stock market prediction on the basis of public sentiments expressed on Twitter has been an intriguing field of research Previous studies have concluded that the aggregate public mood collected from Twitter may well be correlated with Dow Jones Industrial Average Index (DJIA) The thesis of this work is to observe how well the changes in stock prices of a company, the rises and falls, are correlated with the public opinions being expressed in tweets about that company Understanding author's opinion from a piece of text is the objective of sentiment analysis The present paper have employed two different textual representations, Word2vec and N-gram, for analyzing the public sentiments in tweets In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from Twitter and analyze the correlation between stock market movements of a company and sentiments in tweets In an elaborate way, positive news and tweets in social media about a company would definitely encourage people to invest in the stocks of that company and as a result the stock price of that company would increase At the end of the paper, it is shown that a strong correlation exists between the rise and falls in stock prices with the public sentiments in tweets