Chaotic Spreading Sequence for Spread Spectrum Modulation in Stochastic Wireless Channels

In wireless communication system, spread spectrum techniques have been widely used because of the advantages like robustness against interference and noise, low probability of intercept, realization of Code Division Multiple Access (CDMA) and so on. One of the key aspects in such methods is the generation of the spreading sequence which continues to be challenging issue. This paper proposes a scheme for generating binary sequences from chaotic logistic map for use in Direct Sequence Spread Spectrum (DS SS) system in fading environment. The main advantages of such usage are increased security of the data transmission and ease of generation of a extended numbers of chaotic sequences. Generally to spread the bandwidth of the transmitting signals, pseudo-noise (PN) sequences, Gold sequences have been used extensively. We have generated a binary spreading sequences using logistic map. A comparison between Gold sequences and proposed sequences in faded environment have been derived. It is clearly seen that our sequences are comparable and even superior to Gold sequences in several key aspects such as bit error rate (BER), computational time and mutual information for three different spreading code lengths. Therefore, the proposed sequences can be effectively used as spreading sequences in high data rate modulation schemes.Keywords: Logistic map code, Gold code, BER, DS SS.Cite as: Katyayani Kashyap, Manash Pratim Sarma, Kandarpa Kumar Sarma, “Chaotic Spreading Sequence for Spread Spectrum Modulation in Stochastic Wireless Channels†ADBU J.Engg.Tech., 1(2014) 0011402(5pp

Pre-processing and Feature Extraction Techniques for EEGBCI Applications- A Review of Recent Research

The electrical waveforms generated by brain named electroencephalogram (EEG) signals, require certain special processing for using them as part of applications. EEG signals need special pre-processing to enable brain computer interfaces (BCI) capture essential details of the signal and use them for specific applications, including deriving decisions. In this paper, we focus on some of the recent works reported in the area of EEG pre-processing. Further, we discuss some of the reported works related to feature extraction of EEG signals for application in drowsiness detection and development of assistive technologies for persons with special need.Keywords: EEG signals, signal pre-processing, feature extraction, electroencephalography

Detection of Nucleoside/Nucleotide Drug Resistant Mutants in Liver Cancer Cases: An Experience from India

Abstract Use of nucleoside and nucleotide analogues to treat patients infected with hepatitis B virus (HBV) has been found to be associated with mutations in the polymerase gene of the virus. The current study was carried out in HBV-related hepatocellular carcinoma (HCC) cases to trace the presence of drug-related mutants. A total of 75 HBV-related HCC cases were included for the study as per Bruix et al., 2001 EASL guidelines. HBV viral DNA was isolated by the previously standardized manual phenol-chloroform methods. The 3.2 kb genome of HBV was amplified by six sets of overlapping primers. The amplicons were sequenced commercially [Macrogen, South Korea (ABI PRISM)]. Sequences for the polymerase gene were analyzed using commercial bioinformatics software (http://www.hepseq.org/Public/Tool/annotator_tool.php). The different drug-resistant mutations detected were confirmed twice, ahead of reporting. Four drug-resistant mutations were detected in total: L80I (lamivudine), N236T (adefovir), I169T (entecavir) and A181V (lamivudine/adefovir). Interestingly, all four of the drug-resistant mutants were found in genotype D of HBV. The low number (only four) of drug-resistant mutations detected in this study population can be attributed to the fact that most of the cases were not treated and presented late. This study's findings confirm the presence of previously reported drug-resistant mutations in the HBV genome infecting Indian patients; however, its associations with late stage disease and with the virus genotype D, in particular, need to be further studied in a larger population

A SNP STUDY OF HSP GENE AND ITS PROTEIN MODELLING IN LIVER DISEASE CASES FROM NORTH EAST INDIA

HSP gene polymorphism has been widely studied across the globe and particularly with reference to various liver diseases and HCC. Data pertaining to HSP gene polymorphism is lacking from NE India region and there are lacunae of information at the proteomic and bioinformatics level. NE region of India is known for its high incidence of cancer cases. The current study was designed to study the polymorphism of HSP genes in different liver disease cases from Guwahati, India and to predict 3D structure of the proteins from the studied genes by using different bioinformatics tools as well as calculating different physio-chemical information of those studied proteins. The DNA extraction was done followed by PCR amplification and RFLP. EMBOSS Transeq tool and I- TASSER SERVER were used to model the proteins of interest. The results showed that HSPA1B and HSPA1L polymorphisms are significantly associated with advanced stages of liver diseases. Stable protein 2D and 3D models were successfully proposed in this study. The current study highlights the importance of studying cancer critical gene and the need of bioinformatics softwares to generate data

A RAKE RECEIVER DESIGN FOR ULTRA WIDE BAND APPLICATION

In this paper, we describe the design and implementation of a rake receiver for use with ultra wide band (UWB) systems. The rake receiver uses spread spectrum modulation (SSM) aided by kasami sequence generator. The combination is found to be effective in dealing with multipath fading and signal to noise ratio. The design is initially simulated using MATLAB 7.10 and is implemented using a HDL coder. The design is also implemented in a FPGA kit and is found to be effective in interference mitigation as part of a CDMA framework

RF Energy Harvesting in Cognitive Radio: Towards Green Communication

There has been a continuous emphasis on an energy efficient communication system design. With the advent of 5G communication technologies, along with a faster and reliable data transfer mechanisms, energy management and conservation is gaining more attention and is becoming a major and indispensable part of communication research. This papers highlights the contemporary technological developments in the field of RF energy harvesting in a cognitive and high data rate network. It has been observed that an efficient RF energy harvesting technology in a cognitive platform definitely leads towards a greener communication paradigm

RF Energy Harvesting in Cognitive Radio: Towards Green Communication

There has been a continuous emphasis on an energy efficient communication system design. With the advent of 5G communication technologies, along with a faster and reliable data transfer mechanisms, energy management and conservation is gaining more attention and is becoming a major and indispensable part of communication research. This papers highlights the contemporary technological developments in the field of RF energy harvesting in a cognitive and high data rate network. It has been observed that an efficient RF energy harvesting technology in a cognitive platform definitely leads towards a greener communication paradigm

Statistical and Learning Aided Classifier for ECG Based Predictive Diagnostic Tool

Early diagnosis and classification of long term cardiac signals are crucial issues in the treatment of heart related disorders. The available number of medical professional are not sufficient to deal with the increase patients for which design of certain machine based diagnostics tools have been accepted as a viable option. Typical Electrocardiogram (ECG) machine is helpful for monitoring the heart abnormalities only for short interval of time. Therefore, it becomes necessary to design a system which captures relevant features of the ECG signal for use with certain classifiers. In our proposed system, ECG signal elements like Q, R and S peaks are detected and heart rate estimated using Linear Discriminant Analysis (LDA), Adaptive Linear Discriminant Analysis (ALDA) and Support Vector Machine (SVM). For our work we have been used MIT BIH (Standard Arrhythmia Database)

Implementation of Galois Field for Application in Wireless Communication Channels

This paper discusses the implementation of Galois Field based codes for application in wireless communication channel. It discusses the use of Galois Fields outlining the basic performance of a digital communication system in terms of BER curves. The work further discusses the performance of these codes in Gaussian and Rayleigh Fading Channels