Naima Kaabouch

A spectrum sensing technique based on autocorrelation and Euclidean distance and its comparison with energy detection for cognitive radio networks

Display Omitted Gaussian noise samples are delta correlated; ACF(t)=ź(t).We can do spectrum sensing with the Euclidean distance between ACF(t) and a reference line.The Euclidean distance method performs better than the ED and ACF(1) methods. Spectrum sensing is an important aspect of cognitive radios. This paper describes a method for spectrum sensing based on the autocorrelation of the received samples. The proposed method was evaluated by means of experiments wherein the probabilities of detection and false alarm at different signal-to-noise ratios (SNRs) were observed. The platform used for the experiments was a set of Universal Software Radio Peripheralź (USRPź) devices acting as radio frequency front ends in combination with GNU Radio software. Since the signal processing was performed in the software domain, Gaussian noise of different levels was emulated by changing the standard deviation of a Python random number generator. In addition, the output power of a signal generator was varied to obtain different levels of SNR. A metric called the Euclidean distance was derived to analyze the autocorrelation of the samples received by the USRPź device in order to decide between two possible situations: only noise present or signal plus noise present. The proposed method was compared with two methods: one based on the value of the autocorrelation at the first lag and another one based on the power of the signal, known as energy detection spectrum sensing technique.

A survey on compressive sensing techniques for cognitive radio networks

In cognitive radio, one of the main challenges is wideband spectrum sensing. Existing spectrum sensing techniques are based on a set of observations sampled by an analog/digital converter (ADC) at the Nyquist rate. However, those techniques can sense only one band at a time because of the hardware limitations on sampling rate. In addition, in order to sense a wideband spectrum, the band is divided into narrow bands or multiple frequency bands. Secondary users (SU) have to sense each band using multiple RF frontends simultaneously, which results in a very high processing time, hardware cost, and computational complexity. In order to overcome this problem, the signal sampling should be as fast as possible, even with high dimensional signals. Compressive sensing has been proposed as one of the solutions to reduce the processing time and accelerate the scanning process. It allows reducing the number of samples required for high dimensional signal acquisition while keeping the important information. Over the last decade, a number of papers related to compressive sensing techniques have been published. However, most of these papers describe techniques corresponding to one process either sparse representation, sensing matrix, or recovery. This paper provides an in depth survey on compressive sensing techniques and classifies these techniques according to which process they target, namely, sparse representation, sensing matrix, or recovery algorithms. It also discusses examples of potential applications of these techniques including in spectrum sensing, channel estimation, and multiple-input multiple-output (MIMO) based cognitive radio. Metrics to evaluate the efficiencies of existing compressive sensing techniques are provided as well as the benefits and challenges in the context of cognitive radio networks.

A survey on image mosaicing techniques

We perform a comprehensive survey of the existing image mosaicing algorithms.We classify the state-of-the-art mosaicing techniques into major groups.We present a comparative overview of the different mosaicing categories. Image mosaicing, the process of obtaining a wider field-of-view of a scene from a sequence of partial views, has been an attractive research area because of its wide range of applications, including motion detection, resolution enhancement, monitoring global land usage, and medical imaging. A number of image mosaicing algorithms have been proposed over the last two decades. This paper provides an in-depth survey of the existing image mosaicing algorithms by classifying them into several groups. For each group, the fundamental concepts are first explained and then the modifications made to the basic concepts by different researchers are explained. Furthermore, this paper also discusses the advantages and disadvantages of all the mosaicing groups.

Spectrum occupancy measurement: An autocorrelation based scanning technique using USRP

This paper presents a technique for scanning and evaluating the radio spectrum use. This technique determines the average occupancy of a channel over a specific duration. The technique was implemented using Software Defined Radio units and GNU Radio software. The survey was conducted in Grand Forks, North Dakota, over a frequency range of 824 MHz to 5.8 GHz. The results of this technique were compared to those of two existing techniques, energy detection and autocorrelation, that were also implemented. The results show that the proposed technique is more efficient at scanning the radio spectrum than the other two techniques.

Matched filter detection with dynamic threshold for cognitive radio networks

In cognitive radio networks, spectrum sensing aims to detect the unused spectrum channels in order to use the radio spectrum more efficiently. Various methods have been proposed in the past, such as energy, feature detection, and matched filter. These methods are characterized by a sensing threshold, which plays an important role in the sensing performance. Most of the existing techniques used a static threshold. However, the noise is random, and, thus the threshold should be dynamic. In this paper, we suggest an approach with an estimated and dynamic sensing threshold to increase the efficiency of the sensing detection. The matched filter method with dynamic threshold is simulated and its results are compared to those of other existing techniques.

Predicting neuropathic ulceration: analysis of static temperature distributions in thermal images

Foot ulcers affect millions of Americans annually. Conventional methods used to assess skin integrity, including inspection and palpation, may be valuable approaches, but they usually do not detect changes in skin integrity until an ulcer has already developed. We analyze the feasibility of thermal imaging as a technique to assess the integrity of the skin and its many layers. Thermal images are analyzed using an asymmetry analysis, combined with a genetic algorithm, to examine the infrared images for early detection of foot ulcers. Preliminary results show that the proposed technique can reliably and efficiently detect inflammation and hence effectively predict potential ulceration.

Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management

In this chapter, the concepts of Cognitive Radio (CR) and multi-dimensional spectrum sensing are introduced. Spectrum sensing methodologies, energy efficiency consideration, resources scheduling, and self-management and learning mechanisms in cognitive radio networks are also discussed. The entailed challenges of CR RF front-end architectures are looked into. The synthesis and design performance analysis of a tunable RF front-end sensing receiver for CR applications are presented. The chapter also discusses how sensing performance degradation, which is due to RF impairments, is analytically evaluated. Spectrum sensing algorithms that correct imperfect RF issues by compensating induced error effects through digital baseband processing are also illustrated.

A Bayesian network model of the bit error rate for cognitive radio networks

In addition to serve as platforms for dynamic spectrum access, cognitive radios can also serve as a method for improving the performance of wireless communication systems by smartly adjusting their operating parameters according to the environment and requirements. The uncertainty always present in the environment makes the practical implementation of the latter application difficult. In this paper, we propose a probabilistic graphical model, Bayesian network that captures the causal relationships among the variables bit energy to noise spectral density ratio (EbN0), carrier to interference ratio (C/I), modulation scheme (MOD), Doppler phase shift (Dop_Phi), and bit error rate (BER). BER indicates how the communication link is performing. The goal of our proposed Bayesian network is to use the BER as evidence in order to infer the behavior of the other variables, so the cognitive radio can learn how the conditions of the environment are, and based on that knowledge make better informed decisions. This model along with the method used to build it are described in this paper.

Quantitative evaluation of image mosaicing in multiple scene categories

Image mosaicing has been practiced in several computer vision and scientific research areas. There is a clear indication of the advancement of the state of the art of mosaicing algorithms. However, the methods of quantitative evaluation of mosaicing algorithms are still inadequate. Furthermore, a majority of the previous evaluation methodologies lack a sufficient number of performance metrics, while others suffer from computational complication. Therefore, this paper proposes an evaluation method to assess the performance of mosaicing algorithms. This method involves four metrics: percentage of mismatches, difference of pixel intensities, peak signal-to-noise ratio, and mutual information to measure the quality of the mosaicing outputs. These outputs are obtained using a mosaicing algorithm based on the Scale Invariant Feature Transform, Best Bins First, and Random Sample Consensus, reprojection and stitching algorithms. In order to evaluate mosaicing performance objectively, the proposed method compares mosaicing images with the ground-truth images that depict the same scene view. Evaluation has been performed using 36 test sequences from 3 different categories: images of 2D surfaces, images of outdoor 3D scenes, and airborne images from an Unmanned Aerial Vehicle. Exhaustive testing has shown that the proposed metrics are effective in assessing the quality of mosaicing outputs.

Enhancement of the asymmetry-based overlapping analysis through features extraction

In this paper, an enhanced algorithm is proposed to detect foot inflammation and, hence, predict ulcers before they can develop. This algorithm is based on an asymmetry analysis combined with a segmentation technique with a genetic algorithm to achieve higher efficiency in the detection of inflammation. The analysis involves several steps: segmentation, geometry transformation, overlapping, and abnormality identification. To enhance the results of this analysis, an additional step, features extraction, is performed. In this step, low and high order statistics are computed for each foot. Preliminary results show that the proposed algorithm combined with features extraction can be reliable and efficient to predict potential ulceration.