Sadiq Ali

Evaluating the Impact of Color on Texture Recognition

State-of-the-art texture descriptors typically operate on grey scale images while ignoring color information. A common way to obtain a joint color-texture representation is to combine the two visual cues at the pixel level. However, such an approach provides sub-optimal results for texture categorisation task. In this paper we investigate how to optimally exploit color information for texture recognition. We evaluate a variety of color descriptors, popular in image classification, for texture categorisation. In addition we analyze different fusion approaches to combine color and texture cues. Experiments are conducted on the challenging scenes and 10 class texture datasets. Our experiments clearly suggest that in all cases color names provide the best performance. Late fusion is the best strategy to combine color and texture. By selecting the best color descriptor with optimal fusion strategy provides a gain of 5% to 8% compared to texture alone on scenes and texture datasets.

Spectrum sensing with spatial signatures in the presence of noise uncertainty and shadowing

In this paper, we consider a system of cognitive radios that collaborate with each other with the aim of detecting the random waveforms being emitted from licensed users. We study the problem of fusing the statistics from collaborating sensors, assuming that they send their statistics to a base station, where the final decision is made. The main contribution of this work is the derivation of a cognitive detector based on the generalized likelihood ratio test and the use of spatial signatures, a novel concept that allows the detector to capture the spatial correlation inherently embedded in measurements coming from neighboring sensors. The problem is formulated in terms of a model order detection problem, where a set of active and inactive sensors can be distinguished, thus allowing the detector to operate with a rank-reduced version of the observed covariance matrix. Since the estimation of this matrix may be a challenge in large-scale networks, we study the application of shrinkage techniques to cope with the problem of having more sensors than available observations. Finally, we analyze the performance of the proposed detection scheme in the presence of log-normal shadowing effects and noise power uncertainties, the latter due to presence of interferences. For the proposed detector, numerical results are drawn, showing a significant gain in performance compared to traditional approaches.

Multi-antenna spectrum sensing by exploiting spatio-temporal correlation

In this paper, we propose a novel mechanism for spectrum sensing that leads us to exploit the spatio-temporal correlation present in the received signal at a multi-antenna receiver. For the proposed mechanism, we formulate the spectrum sensing scheme by adopting the generalized likelihood ratio test (GLRT). However, the GLRT degenerates in the case of limited sample support. To circumvent this problem, several extensions are proposed that bring robustness to the GLRT in the case of high dimensionality and small sample size. In order to achieve these sample-efficient detection schemes, we modify the GLRT-based detector by exploiting the covariance structure and factoring the large spatio-temporal covariance matrix into spatial and temporal covariance matrices. The performance of the proposed detectors is evaluated by means of numerical simulations, showing important advantages over existing detectors.

Sparsity-Aware Adaptive Directional Time–Frequency Distribution for Source Localization

Multi-component characteristics and missing data samples introduce artifacts and cross-terms in quadratic time–frequency distributions, thus affecting their readability. In this study, we propose a new time–frequency method that employs directional smoothing and compressive sensing to reduce cross-terms and mitigate artifacts associated with missing samples. The efficacy of the proposed time–frequency distribution for solving real-life problems is illustrated by employing it to estimate direction of arrival of sparsely sampled sources in under-determined scenario. Numerical results show that the proposed method is superior to other state-of-the-art methods both in terms of obtaining clear time–frequency representation and accurately estimating direction of arrival.

Exploiting Temporal Correlation for Detection of Non-stationary Signals Using a De-chirping Method Based on Time–Frequency Analysis

Novel time–frequency (t–f) methods are developed for the detection of non-stationary signals in the presence of noise with uncertain power. The proposed method uses instantaneous frequency estimation and de-chirping procedure to convert a non-stationary signal into a stationary signal, thus allowing us to exploit temporal correlation as an extra feature for signal detection in addition to the signal energy. The proposed method can be used for both mono-sensor and multi-sensor recordings. Area under receiver operating characteristic curve and probability of signal detection are used as criteria for comparing the performance of the proposed signal detection methods with the state of the art in the presence of noise power uncertainty. Simulation results indicate the superiority of the proposed approach.

Instantaneous frequency estimation of intersecting and close multi-component signals with varying amplitudes

Instantaneous frequency (IF) estimation of multi-component signals with closely spaced and intersecting signal components of varying amplitudes is a challenging task. This paper presents a novel iterative time–frequency (TF) filtering approach to address this problem. The proposed algorithm first adopts a high-resolution time–frequency distribution to resolve close components in the TF domain. Then, IF of the strongest signal component is estimated by a new peak detection and tracking algorithm that takes into account both the amplitude and the direction of peaks in the TF domain. The estimated IF is used to remove the strongest component from the mixture, and this process is repeated till the IFs of all signal components are estimated. Experimental results show the superiority of the proposed method as compared to other state-of-the-art methods.

Sensor-to-sensor assistance for distributed signal detection

This paper analyzes the problem of distributed composite signal detection in a sensor-to-sensor (S2S) scenario. Based on the classical Generalized Likelihood Ratio Test (GLRT) and Bayesian approaches, some insights are provided for extending classical detection theory to cooperative environments. As a result, innovative decision rules are proposed by taking advantage of prior information from neighboring sensors (for instance, using maximum likelihood estimates of the unknown parameters). Simulation results are provided confirming the outperforming behavior of the proposed collaborative techniques.