MIMO Systems

In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

A Real and Accurate Ultrasound Fetal Imaging Based Heart Disease Detection Using Deep Learning Technology

The heart anomalies detection is a significant task in cardiac medical research. The CT, ULTRASOUND, CTA and MRI scans have been used to detect heart diseases but giving false experimental outcomes in longer time of conversion (ToC). Therefore, patients haven’t getting better treatment from doctors. So that in this research work an ultrasound image scan-based heart disease prediction and classification is performed with deep learning technology. The LeNet 10 deep learning classifier has been trained Kaggle dataset using appropriate CNN layers. Proposed CNN LeNet -10 is a 165 layers technology consists of flattened layer, dense layer, convolution layer, max pooling layer and etc. Classification and feature extraction has been performed to loading with LeNet-10 architecture. The real time heart ultrasound test images are collecting from Manipal super specialty hospital Vijayawada, these test features are managed to test.CSV file. In pre-processing step, Ostu segmentation and histogram equalization is applied to make heart ultrasound images to be clear. In Segmentation, edge and region-based convolutional steps are applied such that deep features have been identified. LeNet-10 classification is used to find affected area as well as abnormality location. Finally proposed deep learning with confusion matrix can generating application measures. Implementation has been performed on python 3.9 and DL (Deep learning) packages like TensorFlow, keras, sklearn and etc. The measures like Accuracy 98.37%, sensitivity 97.81%, Recall 98.34% and F1 score 98.98% had been attained, proposed heart disease estimation application is more robust and compete with present technology

A Real and Accurate Ultrasound Fetal Imaging Based Heart Disease Detection Using Deep Learning Technology

The heart anomalies detection is a significant task in cardiac medical research. The CT, ULTRASOUND, CTA and MRI scans have been used to detect heart diseases but giving false experimental outcomes in longer time of conversion (ToC). Therefore, patients haven’t getting better treatment from doctors. So that in this research work an ultrasound image scan-based heart disease prediction and classification is performed with deep learning technology. The LeNet 10 deep learning classifier has been trained Kaggle dataset using appropriate CNN layers. Proposed CNN LeNet -10 is a 165 layers technology consists of flattened layer, dense layer, convolution layer, max pooling layer and etc. Classification and feature extraction has been performed to loading with LeNet-10 architecture. The real time heart ultrasound test images are collecting from Manipal super specialty hospital Vijayawada, these test features are managed to test.CSV file. In pre-processing step, Ostu segmentation and histogram equalization is applied to make heart ultrasound images to be clear. In Segmentation, edge and region-based convolutional steps are applied such that deep features have been identified. LeNet-10 classification is used to find affected area as well as abnormality location. Finally proposed deep learning with confusion matrix can generating application measures. Implementation has been performed on python 3.9 and DL (Deep learning) packages like TensorFlow, keras, sklearn and etc. The measures like Accuracy 98.37%, sensitivity 97.81%, Recall 98.34% and F1 score 98.98% had been attained, proposed heart disease estimation application is more robust and compete with present technology

A unified approach to sparse signal processing

A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, compo-nent analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing and rate of innovation. The redundancy introduced by channel coding i

Hyperspectral Imaging for Landmine Detection

This PhD thesis aims at investigating the possibility to detect landmines using hyperspectral imaging. Using this technology, we are able to acquire at each pixel of the image spectral data in hundreds of wavelengths. So, at each pixel we obtain a reflectance spectrum that is used as fingerprint to identify the materials in each pixel, and mainly in our project help us to detect the presence of landmines. The proposed process works as follows: a preconfigured drone (hexarotor or octorotor) will carry the hyperspectral camera. This programmed drone is responsible of flying over the contaminated area in order to take images from a safe distance. Various image processing techniques will be used to treat the image in order to isolate the landmine from the surrounding. Once the presence of a mine or explosives is suspected, an alarm signal is sent to the base station giving information about the type of the mine, its location and the clear path that could be taken by the mine removal team in order to disarm the mine. This technology has advantages over the actually used techniques: • It is safer because it limits the need of humans in the searching process and gives the opportunity to the demining team to detect the mines while they are in a safe region. • It is faster. A larger area could be cleared in a single day by comparison with demining techniques • This technique can be used to detect at the same time objects other than mines such oil or minerals. First, a presentation of the problem of landmines that is expanding worldwide referring to some statistics from the UN organizations is provided. In addition, a brief presentation of different types of landmines is shown. Unfortunately, new landmines are well camouflaged and are mainly made of plastic in order to make their detection using metal detectors harder. A summary of all landmine detection techniques is shown to give an idea about the advantages and disadvantages of each technique. In this work, we give an overview of different projects that worked on the detection of landmines using hyperspectral imaging. We will show the main results achieved in this field and future work to be done in order to make this technology effective. Moreover, we worked on different target detection algorithms in order to achieve high probability of detection with low false alarm rate. We tested different statistical and linear unmixing based methods. In addition, we introduced the use of radial basis function neural networks in order to detect landmines at subpixel level. A comparative study between different detection methods will be shown in the thesis. A study of the effect of dimensionality reduction using principal component analysis prior to classification is also provided. The study shows the dependency between the two steps (feature extraction and target detection). The selection of target detection algorithm will define if feature extraction in previous phase is necessary. A field experiment has been done in order to study how the spectral signature of landmine will change depending on the environment in which the mine is planted. For this, we acquired the spectral signature of 6 types of landmines in different conditions: in Lab where specific source of light is used; in field where mines are covered by grass; and when mines are buried in soil. The results of this experiment are very interesting. The signature of two types of landmines are used in the simulations. They are a database necessary for supervised detection of landmines. Also we extracted some spectral characteristics of landmines that would help us to distinguish mines from background

Selective Darkening Filter and Welding Arc Observation for the Manual Welding Process

An optical see-through LCD (GLCD) with a resolution of n x m pixels gives the ability to selectively control the darkening in the welders view. The setup of such a Selective Auto Darkening Filter is developed and its applicability tested. The setup is done by integrating a camera into the welding operation for extracting the welding arc position properly. A prototype of a GLCD taylored for welding is mounted in the welder's view. The extraction of the welding arc position requires an enhanced video acquisition during welding. The observation of scenes with high dynamic contrast is an outstanding problem which occurs if very high differences between the darkest and the brightest spot in a scene occur. The application to welding with its harsh conditions needs the development of supporting hardware. The synchronization of the camera with the flickering light conditions of pulsed welding processes in Gas Metal Arc Welding (GMAW) stabilizes the acquisition process and allows the scene to be flashed precisely if required by compact high power LEDs. The image acquisition is enhanced by merging two different exposed images for the resulting image. These source images cover a wider histogram range than it is possible by using only a single shot image with optimal camera parameters. After testing different standard contrast enhancement algorithm a novel content based algorithm is developed. It segments the image into areas with similar content and enhances these independently

Automated segmentation of radiodense tissue in digitized mammograms using a constrained Neyman-Pearson classifier

Breast cancer is the second leading cause of cancer related mortality among American women. Mammography screening has emerged as a reliable non-invasive technique for early detection of breast cancer. The radiographic appearance of the female breast consists of radiolucent (dark) regions and radiodense (light) regions due to connective and epithelial tissue. It has been established that the percentage of radiodense tissue in a patient\u27s breast can be used as a marker for predicting breast cancer risk. This thesis presents the design, development and validation of a novel automated algorithm for estimating the percentage of radiodense tissue in a digitized mammogram. The technique involves determining a dynamic threshold for segmenting radiodense indications in mammograms. Both the mammographic image and the threshold are modeled as Gaussian random variables and a constrained Neyman-Pearson criteria has been developed for segmenting radiodense tissue. Promising results have been obtained using the proposed technique. Mammograms have been obtained from an existing cohort of women enrolled in the Family Risk Analysis Program at Fox Chase Cancer Center (FCCC). The proposed technique has been validated using a set of ten images with percentages of radiodense tissue, estimated by a trained radiologist using previously established methods. This work is intended to support a concurrent study at the FCCC exploring the association between dietary patterns and breast cancer risk

Adaptive Communications for Next Generation Broadband Wireless Access Systems

Un dels aspectes claus en el disseny i gestió de les xarxes sense fils d'accés de banda ampla és l'ús eficient dels recursos radio. Des del punt de vista de l'operador, l'ample de banda és un bé escàs i preuat que s´ha d'explotar i gestionar de la forma més eficient possible tot garantint la qualitat del servei que es vol proporcionar. Per altra banda, des del punt de vista del usuari, la qualitat del servei ofert ha de ser comparable al de les xarxes fixes, requerint així un baix retard i una baixa pèrdua de paquets per cadascun dels fluxos de dades entre la xarxa i l'usuari. Durant els darrers anys s´han desenvolupat nombroses tècniques i algoritmes amb l'objectiu d'incrementar l'eficiència espectral. Entre aquestes tècniques destaca l'ús de múltiples antenes al transmissor i al receptor amb l'objectiu de transmetre diferents fluxos de dades simultaneament sense necessitat d'augmentar l'ample de banda. Per altra banda, la optimizació conjunta de la capa d'accés al medi i la capa física (fent ús de l'estat del canal per tal de gestionar de manera optima els recursos) també permet incrementar sensiblement l'eficiència espectral del sistema.L'objectiu d'aquesta tesi és l'estudi i desenvolupament de noves tècniques d'adaptació de l'enllaç i gestió dels recursos ràdio aplicades sobre sistemes d'accés ràdio de propera generació (Beyond 3G). Els estudis realitzats parteixen de la premissa que el transmisor coneix (parcialment) l'estat del canal i que la transmissió es realitza fent servir un esquema multiportadora amb múltiples antenes al transmisor i al receptor. En aquesta tesi es presenten dues línies d'investigació, la primera per casos d'una sola antenna a cada banda de l'enllaç, i la segona en cas de múltiples antenes. En el cas d'una sola antena al transmissor i al receptor, un nou esquema d'assignació de recursos ràdio i priorització dels paquets (scheduling) és proposat i analitzat integrant totes dues funcions sobre una mateixa entitat (cross-layer). L'esquema proposat té com a principal característica la seva baixa complexitat i que permet operar amb transmissions multimedia. Alhora, posteriors millores realitzades per l'autor sobre l'esquema proposat han permès també reduir els requeriments de senyalització i combinar de forma óptima usuaris d'alta i baixa mobilitat sobre el mateix accés ràdio, millorant encara més l'eficiència espectral del sistema. En cas d'enllaços amb múltiples antenes es proposa un nou esquema que combina la selecció del conjunt optim d'antenes transmissores amb la selecció de la codificació espai- (frequència-) temps. Finalment es donen una sèrie de recomanacions per tal de combinar totes dues línies d'investigació, així con un estat de l'art de les tècniques proposades per altres autors que combinen en part la gestió dels recursos ràdio i els esquemes de transmissió amb múltiples antenes.Uno de los aspectos claves en el diseño y gestión de las redes inalámbricas de banda ancha es el uso eficiente de los recursos radio. Desde el punto de vista del operador, el ancho de banda es un bien escaso y valioso que se debe explotar y gestionar de la forma más eficiente posible sin afectar a la calidad del servicio ofrecido. Por otro lado, desde el punto de vista del usuario, la calidad del servicio ha de ser comparable al ofrecido por las redes fijas, requiriendo así un bajo retardo y una baja tasa de perdida de paquetes para cada uno de los flujos de datos entre la red y el usuario. Durante los últimos años el número de técnicas y algoritmos que tratan de incrementar la eficiencia espectral en dichas redes es bastante amplio. Entre estas técnicas destaca el uso de múltiples antenas en el transmisor y en el receptor con el objetivo de poder transmitir simultáneamente diferentes flujos de datos sin necesidad de incrementar el ancho de banda. Por otro lado, la optimización conjunta de la capa de acceso al medio y la capa física (utilizando información de estado del canal para gestionar de manera óptima los recursos) también permite incrementar sensiblemente la eficiencia espectral del sistema.El objetivo de esta tesis es el estudio y desarrollo de nuevas técnicas de adaptación del enlace y la gestión de los recursos radio, y su posterior aplicación sobre los sistemas de acceso radio de próxima generación (Beyond 3G). Los estudios realizados parten de la premisa de que el transmisor conoce (parcialmente) el estado del canal a la vez que se considera que la transmisión se realiza sobre un sistema de transmisión multiportadora con múltiple antenas en el transmisor y el receptor. La tesis se centra sobre dos líneas de investigación, la primera para casos de una única antena en cada lado del enlace, y la segunda en caso de múltiples antenas en cada lado. Para el caso de una única antena en el transmisor y en el receptor, se ha desarrollado un nuevo esquema de asignación de los recursos radio así como de priorización de los paquetes de datos (scheduling) integrando ambas funciones sobre una misma entidad (cross-layer). El esquema propuesto tiene como principal característica su bajo coste computacional a la vez que se puede aplicar en caso de transmisiones multimedia. Posteriores mejoras realizadas por el autor sobre el esquema propuesto han permitido también reducir los requisitos de señalización así como combinar de forma óptima usuarios de alta y baja movilidad. Por otro lado, en caso de enlaces con múltiples antenas en transmisión y recepción, se presenta un nuevo esquema de adaptación en el cual se combina la selección de la(s) antena(s) transmisora(s) con la selección del esquema de codificación espacio-(frecuencia-) tiempo. Para finalizar, se dan una serie de recomendaciones con el objetivo de combinar ambas líneas de investigación, así como un estado del arte de las técnicas propuestas por otros autores que combinan en parte la gestión de los recursos radio y los esquemas de transmisión con múltiples antenas.In Broadband Wireless Access systems the efficient use of the resources is crucial from many points of views. From the operator point of view, the bandwidth is a scarce, valuable, and expensive resource which must be exploited in an efficient manner while the Quality of Service (QoS) provided to the users is guaranteed. On the other hand, a tight delay and link quality constraints are imposed on each data flow hence the user experiences the same quality as in fixed networks. During the last few years many techniques have been developed in order to increase the spectral efficiency and the throughput. Among them, the use of multiple antennas at the transmitter and the receiver (exploiting spatial multiplexing) with the joint optimization of the medium access control layer and the physical layer parameters.In this Ph.D. thesis, different adaptive techniques for B3G multicarrier wireless systems are developed and proposed focusing on the SS-MC-MA and the OFDM(A) (IEEE 802.16a/e/m standards) communication schemes. The research lines emphasize into the adaptation of the transmission having (Partial) knowledge of the Channel State Information for both; single antenna and multiple antenna links. For single antenna links, the implementation of a joint resource allocation and scheduling strategy by including adaptive modulation and coding is investigated. A low complexity resource allocation and scheduling algorithm is proposed with the objective to cope with real- and/or non-real- time requirements and constraints. A special attention is also devoted in reducing the required signalling. However, for multiple antenna links, the performance of a proposed adaptive transmit antenna selection scheme jointly with space-time block coding selection is investigated and compared with conventional structures. In this research line, mainly two optimizations criteria are proposed for spatial link adaptation, one based on the minimum error rate for fixed throughput, and the second focused on the maximisation of the rate for fixed error rate. Finally, some indications are given on how to include the spatial adaptation into the investigated and proposed resource allocation and scheduling process developed for single antenna transmission

Reduced-complexity signal processing techniques for multiple-input multiple-output storage and wireless communication systems

Ph.DNUS-TU/E JOINT PH.D. PROGRAMM