M. I. Sohaib

Bio: M. I. Sohaib is an academic researcher from COMSATS Institute of Information Technology. The author has contributed to research in topics: Image fusion & Image processing. The author has an hindex of 1, co-authored 1 publications receiving 7 citations.

Image fusion using multivariate and multidimensional EMD

Abstract: We present a novel methodology for the fusion of multiple (two or more) images using the multivariate extension of empirical mode decomposition (MEMD). Empirical mode decomposition (EMD) is a data-driven method which decomposes input data into its intrinsic oscillatory modes, known as intrinsic mode functions (IMFs), without making a priori assumptions regarding the data. We show that the multivariate and multidimensional extensions of EMD are suitable for image fusion purposes. We further demonstrate that while multidimensional extensions, by design, may seem more appropriate for tasks related to image processing, the proposed multivariate extension outperforms these in image fusion applications owing to its mode-alignment property for IMFs. Case studies involving multi-focus image fusion and pan-sharpening of multi-spectral images are presented to demonstrate the effectiveness of the proposed method.

A Multivariate Empirical Mode DecompositionBased Approach to Pansharpening

Abstract: We propose a novel class of schemes for the pansharpening of multispectral (MS) images using a multivariate empirical mode decomposition (MEMD) algorithm. MEMD is an extension of the empirical mode decomposition (EMD) algorithm, which enables the decomposition of multivariate data into its intrinsic oscillatory scales. The ability of MEMD to process multichannel data directly by performing data-driven, local, and multiscale analysis makes it a perfect match for pansharpening applications, a task for which standard univariate EMD is ill-equipped due to the nonuniqueness, mode-mixing, and mode-misalignment issues. We show that MEMD overcomes the limitations of standard EMD and yields improved spatial and spectral performance in the context of pansharpening of MS images. The potential of the proposed schemes is further demonstrated through comparative analysis against a number of standard pansharpening algorithms on both simulated Pleiades and real-world IKONOS data sets.

Artificial Intelligence-Based Technique for Fault Detection and Diagnosis of EV Motors: A Review

Abstract: The motor drive system plays a significant role in the safety of electric vehicles as a bridge for power transmission. Meanwhile, to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis (FDD) of the motor drive system. This article reviews the application of AI techniques in motor FDD in recent years. AI-based FDD is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed are summarized. Finally, the latest developments, research gaps, and future challenges in fault monitoring and diagnosis of motor faults are discussed.

Bidimensional Multivariate Empirical Mode Decomposition With Applications in Multi-Scale Image Fusion

Abstract: Empirical mode decomposition (EMD) is a fully data-driven technique designed for multi-scale decomposition of signals into their natural scale components, called intrinsic mode functions (IMFs). When EMD is directly applied to perform fusion of multivariate data from multiple and heterogeneous sources, the problem of uniqueness, that is, different numbers of decomposition levels for different sources, is likely to occur, due to the empirical nature of EMD. Although the multivariate EMD (MEMD) has been proposed for temporal data, which employs real-valued projections along multiple directions on a unit hypersphere in the $n$ -dimensional space to calculate the envelope and the local mean of multivariate signals, in order to guarantee the uniqueness of the scales, its direct usefulness in 2D multi-scale image fusion is still limited, due to its inability to maintain the spatial information. To address this issue, we propose a novel bidimensional MEMD (BMEMD) which directly projects a bidimensional multivariate signal, which is composed of multiple images, on the unit hypersphere in the $n$ -dimensional space. This is achieved via real-valued surface projections and the mean surface is estimated by interpolating the multivariate scatter data so as to extract common spatio-temporal scales across multiple images. Case studies involving texture analysis and multi-focus image fusion are presented to demonstrate the effectiveness of the proposed method.

GPU-Accelerated Multivariate Empirical Mode Decomposition for Massive Neural Data Processing

Abstract: This paper presents an efficient implementation of multivariate empirical mode decomposition (MEMD) algorithm, a multivariate extension of EMD algorithm. Analogous to EMD, MEMD decomposes a multivariate signal into its intrinsic mode functions using joint rotational mode. The algorithm is computationally intensive because of its recursive nature and any increase in input data size results in a non-linear increase in its execution time. Therefore, it is extremely time-consuming to obtain a decomposition of signal, such as EEG into its intrinsic modes using MEMD. As the interest in applying MEMD algorithm in various domains is increasing, there is a need to develop an optimized implementation of the algorithm, since it requires repeated execution of the same operations and computationally extensive interpolations on each projected vector. This can be done using GPGPU, because it has the power to process similar function on different blocks of data. We have compared the optimized implementation of MEMD, using GPU, with the MATLAB implementation for hexa-variate and hexa-deca-variate data sets, and observed that the GPU-based optimized implementation results in approximately $6\times \sim 16\times $ performance improvements in terms of time consumption.