A study of SPRT algorithm and New-Guard for radiation detection

A novel and efficient radiation detection algorithm combined with a measuring unit will produce an ideal detector to battle field radiation measurement problems. Studies of Sequential Probability Ratio Test (SPRT) for radiation detection are essential towards developing efficient and accurate radiation detection algorithms. In this study, the performance of the classical Single-Threshold-Test (STT) and the SPRT First-In-First-Out (FIFO) algorithms is considered. Next, improvements made by the Last-In-First-Elected-Last-Out (LIFELO) algorithm are analyzed. Further, enhancements to the LIFELO algorithm, using the Dynamic Background Updating and Maximum Likelihood Estimator (MLE), are performed; The thesis also provides detailed requirements for an innovative hand-held radiation detection system and underlines additional features available on a New Generation User Adaptable Radiation Detector (New-GUARD) to help the field survey processes. Currently available technologies are studied to rationalize the need for the New-GUARD prototype. The New-GUARD is compared to similar products that are already available in the market to show its completeness as a radiation detector incorporated with Global Positioning System (GPS), wireless communication, and a self-correcting system. Primary performance evaluations of the algorithms are executed using Mathematica and further analysis is carried out with Matlab and C

Blind recognition of analog modulation schemes for software defined radio

With the emergence of software defined radios (SDRs), an adaptive receiver is needed that can configure various parameters, such as carrier frequency, bandwidth, symbol timing, and signal to noise ratio (SNR), and automatically identify modulation schemes. In this dissertation research, several fundamental SDR tasks for analog modulations are investigated, since analog radios are often used by civil government agencies and some unconventional military forces. Hence, the detection and recognition of old technology analog modulations remain an important task both for civil and military electronic support systems and for notional cognitive radios. In this dissertation, a Cyclostationarity-Based Decision Tree classifier is developed to separate between analog modulations and digital modulations, and classify signals into several subsets of modulation types. In order to further recognize the specific modulation type of analog signals, more effort and work are, however, needed. For this purpose, two general methods for automatic modulation classification (AMC), feature- based method and likelihood-based method, are investigated in this dissertation for analog modulation schemes. For feature-based method, a multi-class SVM-based AMC classifier is developed. After training, the developed classifier can achieve high classification accuracy in a wide range of SNR. While the likelihood-based methods for digital modulation types have been well developed, it is noted that the likelihood-based methods for analog modulation types are seldom explored in the literature. Average-Likelihood-Ratio-Testing based AMC algorithms have been developed to automatically classify AM, DSB and FM signals in both coherent and non-coherent situations In addition, the Non-Data-Aided SNR estimation algorithms are investigated, which can be used to estimate the signal power and noise power either before or after modulation classification

Pattern recognition using genetic programming for classification of diabetes and modulation data

The field of science whose goal is to assign each input object to one of the given set of categories is called pattern recognition. A standard pattern recognition system can be divided into two main components, feature extraction and pattern classification. During the process of feature extraction, the information relevant to the problem is extracted from raw data, prepared as features and passed to a classifier for assignment of a label. Generally, the extracted feature vector has fairly large number of dimensions, from the order of hundreds to thousands, increasing the computational complexity significantly. Feature generation is introduced to handle this problem which filters out the unwanted features. The functionality of feature generation has become very important in modern pattern recognition systems as it not only reduces the dimensions of the data but also increases the classification accuracy. A genetic programming (GP) based framework has been utilised in this thesis for feature generation. GP is a process based on the biological evolution of features in which combination of original features are evolved. The stronger features propagate in this evolution while weaker features are discarded. The process of evolution is optimised in a way to improve the discriminatory power of features in every new generation. The final features generated have more discriminatory power than the original features, making the job of classifier easier. One of the main problems in GP is a tendency towards suboptimal-convergence. In this thesis, the response of features for each input instance which gives insight into strengths and weaknesses of features is used to avoid suboptimal-convergence. The strengths and weaknesses are utilised to find the right partners during crossover operation which not only helps to avoid suboptimal-convergence but also makes the evolution more effective. In order to thoroughly examine the capabilities of GP for feature generation and to cover different scenarios, different combinations of GP are designed. Each combination of GP differs in the way, the capability of the features to solve the problem (the fitness function) is evaluated. In this research Fisher criterion, Support Vector Machine and Artificial Neural Network have been used to evaluate the fitness function for binary classification problems while K-nearest neighbour classifier has been used for fitness evaluation of multi-class classification problems. Two Real world classification problems (diabetes detection and modulation classification) are used to evaluate the performance of GP for feature generation. These two problems belong to two different categories; diabetes detection is a binary classification problem while modulation classification is a multi-class classification problem. The application of GP for both the problems helps to evaluate the performance of GP for both categories. A series of experiments are conducted to evaluate and compare the results obtained using GP. The results demonstrate the superiority of GP generated features compared to features generated by conventional methods

Strategies for Devising Automatic Signal Recognition Algorithms in a Shared Radio Environment

In an increasingly congested and complex radio environment interference is to be expected, which poses problems for Automatic Signal Recognition (ASR) systems. This thesis explores strategies for improving ASR performance in the presence of interference. The thesis breaks the overall research question down into a number of subquestions and explores each of these in turn. A Phase-symmetric Cross Recurrence Plot is developed and used to show how a radio signal can be manipulated to separate information about the modulation from the information being carried. The Logarithmic Cyclic frequency Domain Profile is introduced to illustrate how a logarithmic representation can be used for analysing mixtures of signals with very different cyclic frequencies. After defining a canonical ASR system architecture, the concepts of an Ideal Feature and Interference Selectivity are introduced and applied to typical features used in ASR processing. Finally it is shown how these algorithmic developments can be combined in a Bayesian chain implementation that can accommodate a wide variety of feature extraction algorithms. It is concluded that future ASR systems will require features that can handle a wide range of signal types with much higher levels of interference selectivity if they are to achieve acceptable performance in shared spectrum bands. Intelligent segmentation is shown to be a requirement for future ASR systems unless features can be developed that have near ideal performance

Identificação e caracterização da modulação dos sinais digitais em RF

Mestrado em Engenharia Electrónica e TelecomunicaçõesActualmente, o consumo de informação assume enormes proporções, tornando a necessidade de comunicar cada vez mais, e mais depressa, uma pressão constante sobre quem desenvolve a tecnologia. A evolução da tecnologia analógica para digital oferece amplas possibilidades de inovação e melhoramento das comunicações. O desejo crescente que a sociedade desenvolveu de comunicar de forma fiável e rápida levou ao desenvolvimento de redes complexas de comunicação que asseguram quase instantaneamente meios de comunicação entre variadíssimos locais, independentemente da sua localização geográfica. A proliferação de sistemas de comunicações de complexidade crescente, que têm em comum a partilha do espectro radioeléctrico implica o aumentando de situações de interferência entre sinais/sistemas e a dificuldade em efectuar a despistagem do sinal/sistema interferidor. Devido à complexidade dos sinais digitais, a sua identificação e caracterização tornou-se um desafio. Esta dissertação tem como objectivo o estudo das modulações digitais mais comuns, a sua caracterização espectral e as métricas associadas. O estudo dos Analisador Espectral, Analisador Vectorial de Sinal e do Analisador Espectral Em Tempo Real. Por fim apresenta-se um estudo de técnicas desenvolvidas para a identificação automática da modulação de sinais digitais.Nowadays, the information consumption assumes enormous proportions, the increasing necessity to communicate, each time more and faster, becomes a constant pressure on who develops the technology. The evolution from analogue to digital technology offers huge possibilities of innovation and improvement on communications systems. The increasing desire that the society developed to communicate in fast and reliably way, led to the development of complex communications systems that, almost instantaneously, can provide connection anywhere and everywhere, no matter where you are. The proliferation of communications systems with increasing complexity, sharing the radioelectric spectrum, leads to increasing interference situations between signals/systems. In the other hand, it becomes harder to find and identify the interfering signal/system. Due to complexity of the digital modulations, signals identification and characterization becomes a challenge. The main propose of this dissertation is the study of the more common digital modulations, its spectral characterization and its associated metrics. The study of Spectral Analyzers, Vector Signal Analysers and Real Time Spectrum Analyzers. Finally is shown a study of techniques to improve the identification of the digital modulations

An improvement of automatic modulation classification based on sixth-order cumulant for QAM signals

Automatska klasifikacija modulacija (AKM) predstavlja proces prepoznavanja tipa modulacije nepoznatog primljenog signala, veoma bitan za savremene telekomunikacione sisteme, i ključan za veliki broj kako vojnih tako i civilnih primena. U poslednjih nekoliko decenija razvijen je veliki broj različitih algoritama za AKM. Savremena rešenja uobičajeno pretpostavljaju kompleksne strukture kao što su neuralne mreže, ili druge adaptivne mehanizme za postizanje bolje preciznosti. Međutim, još uvek je, sa tačke gledišta implementacije u praksi, veoma poželjno da se za AKM koriste algoritmi male kompleksnosti koji se brzo izvršavaju i koji ekonomično koriste resurse. Ove poželjne osobine mogu se prepoznati u algoritmima za AKM zasnovanim na kumulantima višeg reda kao statističkim klasifikacionim obeležjima. U ovoj disertaciji je prikazan novi pristup zasnovan na kumulantima šestog reda, koji poboljšava tačnost klasifikacionog procesa QAM signala u odnosu na postojeće algoritme. Predloženi pristup koristi dvokoračnu strukturu za izdvajanje obeležja, tako što primenjuje nov metod za redukciju reda modulacije opserviranog signala, praćen pragovskim odlučivanjem. Predložen je i odgovarajući rekurzivni algoritam, usmeren ka uspešnoj klasifikaciji signala visokog reda modulacije. Prilikom testiranja pomoću sveobuhvatnih računarskih simulacija, predložena rešenja pokazuju izvanredne performanse klasifikacije ‑ u nekim slučajevima bolje i od sofisticiranih modela dubokog učenja koji zahtevaju znatno veće procesorske i memorijske resurse; takođe, predloženi metod zadržava dobre statističke osobine kumulanata, tako da se može primeniti udružen i sa drugim algoritmima za AKM. Pokazane prednosti predloženih rešenja otvaraju nekoliko smerova za dalja istraživanja, bilo kroz dalje usavršavanje algoritama zasnovanih na obeležjima, ili kroz uparivanje sa savremenim metodama koje koriste veće računarske resurse

Design of spectrum sensing and mac in cognitive radio networks

Ph.DDOCTOR OF PHILOSOPH

Optimal Cooperative Spectrum Sensing for Cognitive Radio

The rapid increasing interest in wireless communication has led to the continuous development of wireless devices and technologies. The modern convergence and interoperability of wireless technologies has further increased the amount of services that can be provided, leading to the substantial demand for access to the radio frequency spectrum in an efficient manner. Cognitive radio (CR) an innovative concept of reusing licensed spectrum in an opportunistic manner promises to overcome the evident spectrum underutilization caused by the inflexible spectrum allocation. Spectrum sensing in an unswerving and proficient manner is essential to CR. Cooperation amongst spectrum sensing devices are vital when CR systems are experiencing deep shadowing and in a fading environment. In this thesis, cooperative spectrum sensing (CSS) schemes have been designed to optimize detection performance in an efficient and implementable manner taking into consideration: diversity performance, detection accuracy, low complexity, and reporting channel bandwidth reduction. The thesis first investigates state of the art spectrums sensing algorithms in CR. Comparative analysis and simulation results highlights the different pros, cons and performance criteria of a practical CSS scheme leading to the problem formulation of the thesis. Motivated by the problem of diversity performance in a CR network, the thesis then focuses on designing a novel relay based CSS architecture for CR. A major cooperative transmission protocol with low complexity and overhead - Amplify and Forward (AF) cooperative protocol and an improved double energy detection scheme in a single relay and multiple cognitive relay networks are designed. Simulation results demonstrated that the developed algorithm is capable of reducing the error of missed detection and improving detection probability of a primary user (PU). To improve spectrum sensing reliability while increasing agility, a CSS scheme based on evidence theory is next considered in this thesis. This focuses on a data fusion combination rule. The combination of conflicting evidences from secondary users (SUs) with the classical Dempster Shafter (DS) theory rule may produce counter-intuitive results when combining SUs sensing data leading to poor CSS performance. In order to overcome and minimise the effect of the counter-intuitive results, and to enhance performance of the CSS system, a novel state of the art evidence based decision fusion scheme is developed. The proposed approach is based on the credibility of evidence and a dissociability degree measure of the SUs sensing data evidence. Simulation results illustrate the proposed scheme improves detection performance and reduces error probability when compared to other related evidence based schemes under robust practcial scenarios. Finally, motivated by the need for a low complexity and minmum bandwidth reporting channels which can be significant in high data rate applications, novel CSS quantization schemes are proposed. Quantization methods are considered for a maximum likelihood estimation (MLE) and an evidence based CSS scheme. For the MLE based CSS, a novel uniform and optimal output entropy quantization scheme is proposed to provide fewer overhead complexities and improved throughput. While for the Evidence based CSS scheme, a scheme that quantizes the basic probability Assignment (BPA) data at each SU before being sent to the FC is designed. The proposed scheme takes into consideration the characteristics of the hypothesis distribution under diverse signal-to-noise ratio (SNR) of the PU signal based on the optimal output entropy. Simulation results demonstrate that the proposed quantization CSS scheme improves sensing performance with minimum number of quantized bits when compared to other related approaches

Recent Advances in Wireless Communications and Networks

This book focuses on the current hottest issues from the lowest layers to the upper layers of wireless communication networks and provides "real-time" research progress on these issues. The authors have made every effort to systematically organize the information on these topics to make it easily accessible to readers of any level. This book also maintains the balance between current research results and their theoretical support. In this book, a variety of novel techniques in wireless communications and networks are investigated. The authors attempt to present these topics in detail. Insightful and reader-friendly descriptions are presented to nourish readers of any level, from practicing and knowledgeable communication engineers to beginning or professional researchers. All interested readers can easily find noteworthy materials in much greater detail than in previous publications and in the references cited in these chapters