Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems

Artificial Immune Systems: Principle, Algorithms and Applications

The present thesis aims to make an in-depth study of adaptive identification, digital channel equalization, functional link artificial neural network (FLANN) and Artificial Immune Systems (AIS).Two learning algorithms CPSO and IPSO are also developed in this thesis. These new algorithms are employed to train the weights of a low complexity FLANN structure by way of minimizing the squared error cost function of the hybrid model. These new models are applied for adaptive identification of complex nonlinear dynamic plants and equalization of nonlinear digital channel. Investigation has been made for identification of complex Hammerstein models. To validate the performance of these new models simulation study is carried out using benchmark complex plants and nonlinear channels. The results of simulation are compared with those obtained with FLANN-GA, FLANN-PSO and MLP-BP based hybrid approaches. Improved identification and equalization performance of the proposed method have been observed in all cases

Improved detection techniques in autonomous vehicles for increased road safety

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2020.A futura adoção em massa de Veículos Autônomos traz um potencial significativo para aumentar a segurança no trânsito para ambos os motoristas e pedestres. Como reportado pelo Departamento de Transportes dos E.U.A., cerca de 94% dos acidentes de trânsito são causados por erro humano. Com essa realidade em mente, a indústria automotiva e pesquisadores acadêmicos ambicionam alcançar direção totalmente automatizada em cenários reais nos próximos anos. Para tal, algorit- mos mais precisos e sofisticados são necessários para que os veículos autônomos possam tomar decisões corretas no tráfego. Nesse trabalho, é proposta uma técnica melhorada de detecção de pedestres, com um aumento de precisão de até 31% em relação aos benchmarks atuais. Em seguida, de forma a acomodar a infraestrutura de trânsito já existente, avançamos a precisão na detecção de placas de trânsito com base em Redes Neurais Convolucionais. Nossa abordagem melhora substancialmente a acurácia em relação ao modelo-base considerado. Finalmente, ap- resentamos uma proposta de fusão de dados precoce, a qual mostramos surpassar abordagens de detecção com um só sensor e fusão de dados tardia em até 20%.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).The future widespread use of Autonomous Vehicles has a significant potential to increase road safety for drivers and pedestrians alike. As reported by the U.S. Department of Transportation, up to 94% of transit accidents are caused by human error. With that reality in mind, the auto- motive industry and academic researches are striving to achieve fully automated driving in real scenarios in the upcoming years. For that, more sophisticated and precise detection algorithms are necessary to enable the autonomous vehicles to take correct decisions in transit. This work proposes an improved technique for pedestrian detection that increases precision up to 31% over current benchmarks. Next, in order to accommodate current traffic infrastructure, we enhance performance of a traffic sign recognition algorithm based on Convolutional Neural Networks. Our approach substantially raises precision of the base model considered. Finally, we present a proposal for early data fusion of camera and LiDAR data, which we show to surpass detection using individual sensors and late fusion by up to 20%

Fair relay selection in wireless rural networks using game theory

Access to Internet is the key to facilitate the economic growth and development of the rural communities and to bridge the digital-divide between the urban and rural population. The traditional broadband access technologies are not always suitable for the rural areas due to their difficult topography and sparsely populated communities. Specialized relay stations can be deployed to extend the coverage of a wireless rural network but they come with an inherited increase in the infrastructural cost. An alternative is to utilize the in-range users as relays to enhance the coverage range of the wireless rural network. In this thesis, the in-range ordinary users termed as primary users (PUs) are used to act as relays for the out-of-range users called the secondary users (SUs). Two relay selection solutions, the Fair Battery Power Consumption (FBPC) algorithm and the Credit based Fair Relay Selection (CF-RS) protocol have been proposed with the aim of providing fair chance to every PU to assist the SUs, thus resulting in fair utilization of battery power of all relays along with the coverage extension. The FBPC algorithm uses the concept of proportional fairness as the relay selection criterion. However, if only proportionally fair consumption of battery power is taken as the relay selection parameter, the FBPC algorithm may result in selecting relays with poor channel conditions. The rural network may also consist of selfish PUs which need to be incentivized to use their resources for the SUs. The CF-RS protocol is developed which takes into account both the achievable data rate and consumption of battery power for selection of a relay. The CF-RS protocol is formulated using Stackelberg game which employs a credit-based incentive mechanism to motivate the self-interested PUs to help the SUs by providing instantaneous as well as long term benefit to the PUs. A basic network model consisting of PUs and SUs has been simulated and the performance of the FBPC algorithm and the CF-RS protocol have been evaluated in terms of data rate and utility achievable at the SUs, dissipation of battery power of the PUs and Jain’s fairness index to determine fairness in utilization of battery power. The results obtained show that the FBPC algorithm achieves approximately 100% fairness for utilization of battery power of relays but compromises the data rate attainable by the SUs. Thus the FBPC algorithm shall be viewed as a trade-off between the fair battery power dissipation of relays and the data rate achievable by the SUs. Whereas, the CF-RS protocol provides 55% better utility and longer service time to the SUs without harming the attainable data rate and achieves 80% fairness. When the CF-RS protocol is used for relay selection, it is advantageous even for the self-interested users to participate in the relaying process to earn some benefit to utilize it when needed to buy assistance from other users

Development of a fault detection and diagnosis approach for a binary ice system

Fault detection and diagnosis (FDD) is an important part to maintain the performance, improve the reliability and prevent energy wastage of the refrigeration systems. Binary ice systems, which have become more commonly employed in both industry and domestic applications, are essentially refrigeration systems using water-ice slurry mixture as a secondary refrigerant. The existence of the ice makes binary ice systems different from conventional liquid chillers, leading to the requirement of a specified FDD method. Therefore, the current research focuses on developing a model based dynamic FDD approach that can capture the unique features of binary ice systems in order to detect some pre-selected faults, including binary ice flow restriction, cooling water flow restriction, incorrect solution concentration, ice generator scraper fault and ice generator motor failure. To provide fault free predictions for the FDD, a dynamic hybrid model of the binary ice system was proposed. The model consisted of an analytical sub-model of the scraped surface ice generator, which was an essential component of the binary ice system that produced ice, and an artificial neural network (ANN) sub-model of the primary refrigeration circuit. The two sub-models were coupled by using two of the ANN model’s outputs as the inputs to the analytical model, namely the evaporating temperature and the compressor power consumption, as well as sharing some of the input parameters. The coupled model was validated with data from a 2.5kW laboratory binary ice test rig. The FDD was carried out by monitoring the changes of the residuals of some carefully chosen parameters, using CUmulative SUM (CUSUM) test. Two parameters, namely cooling water temperature difference and evaporating temperature, were monitored for fault detection purpose, and condenser outlet temperature, cooling water temperature difference, discharge temperature and binary ice outlet temperature were observed for fault diagnosis function. An ANN fault classifier was developed to identify the type of the fault by analysing the combinations of the fault diagnosis parameter variations. This FDD method was found to be able to detect and diagnose successfully the pre-selected faults without raising any false alarm, and in addition it was capable of diagnosing three pairs of double faul

Passive Planar Microwave Devices

The aim of this book is to highlight some recent advances in microwave planar devices. The development of planar technologies still generates great interest because of their many applications in fields as diverse as wireless communications, medical instrumentation, remote sensing, etc. In this book, particular interest has been focused on an electronically controllable phase shifter, wireless sensing, a multiband textile antenna, a MIMO antenna in microstrip technology, a miniaturized spoof plasmonic antipodal Vivaldi antenna, a dual-band balanced bandpass filter, glide-symmetric structures, a transparent multiband antenna for vehicle communications, a multilayer bandpass filter with high selectivity, microwave planar cutoff probes, and a wideband transition from microstrip to ridge empty substrate integrated waveguide

Faculty Publications and Creative Works 2005

Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. Published by the Office of the Vice President for Research and Economic Development, it serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM. In 2005, UNM faculty produced over 1,887 works, including 1,887 scholarly papers and articles, 57 books, 127 book chapters, 58 reviews, 68 creative works and 4 patented works. We are proud of the accomplishments of our faculty which are in part reflected in this book, which illustrates the diversity of intellectual pursuits in support of research and education at the University of New Mexico