Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.

A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications

Most image encryption algorithms based on low-dimensional chaos systems bear security risks and suffer encryption data expansion when adopting nonlinear transformation directly. To overcome these weaknesses and reduce the possible transmission burden, an efficient image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing is proposed. The original image is measured by the measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the cycle shift operation controlled by a hyper-chaotic system. Cycle shift operation can change the values of the pixels efficiently. The proposed cryptosystem decreases the volume of data to be transmitted and simplifies the keys distribution simultaneously as a nonlinear encryption system. Simulation results verify the validity and the reliability of the proposed algorithm with acceptable compression and security performance.

Image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing

The existing ways to encrypt images based on compressive sensing usually treat the whole measurement matrix as the key, which renders the key too large to distribute and memorize or store. To solve this problem, a new image compression–encryption hybrid algorithm is proposed to realize compression and encryption simultaneously, where the key is easily distributed, stored or memorized. The input image is divided into 4 blocks to compress and encrypt, then the pixels of the two adjacent blocks are exchanged randomly by random matrices. The measurement matrices in compressive sensing are constructed by utilizing the circulant matrices and controlling the original row vectors of the circulant matrices with logistic map. And the random matrices used in random pixel exchanging are bound with the measurement matrices. Simulation results verify the effectiveness, security of the proposed algorithm and the acceptable compression performance.

Novel image compression–encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing

An artificial neural network (ANN)-based model for erbium-doped fiber amplifiers (EDFAs) is developed. For a network with two input neurons and a single hidden layer, the number of neurons in the hidden layers is optimized by monitoring the mean square error (MSE) at the outputs. The model is used to optimize the design of EDFAs for central signal gain, 3-dB bandwidth, and noise factors, taking into account the influence of amplifier constructional parameters in the amplifier performance. For a given pump power and pulse width that correspond to the two input neurons, the model computes the constructional parameters to obtain values of central signal gain, 3-dB bandwidth, and noise figures in optimal range. The effects due to intrapulse stimulated Raman scattering (ISRS) are also considered by varying pulse widths from subpicosecond regime to hundreds of picoseconds. Self frequency shift due to ISRS is found to be prominent in the subpicosecond regime. The proposed model generates results with a MSE of 1×10 -4 , and needs relatively very low computational time compared to other analytical models.

Design optimization of erbium-doped fiber amplifiers using artificial neural networks

Moving Object Detection (MOD) is one of the prominent application in the field of computer vision. This paper contributes towards designing a fast and accurate MOD technique for real time applications by addressing the issues like dynamic background, illumination changes, occlusion, camera jitter, moving shadows, etc. The brilliance of the proposed method is confined in the effective utilization of QR decomposition and $l_{2,1}$-norm minimization in the well-known Robust Principal Component Analysis (RPCA) framework. The $l_{2,1}$ norm is robust to outliers and exploits the low rankness of the background component. While the use of $l_{1}$-norm enhances the sparsity in the foreground part. Moreover, Total Variation(TV) regularization technique helps in retaining the details about the edges. Experimental result shows the supremacy of the proposed method w.r.t. the state of the art methods in terms of computational complexity and F-measure score.

A Fast Moving Object Detection Technique using QR Decomposition based TV Regularized RPCA Framework

Ultrasound imaging is one of the safest technique of clinical diagnosis. The presence of speckle noise has made denoising of ultrasound images indispensable for the proper diagnosis of diseases. Non-Local similarity and low-rank approaches is an upcoming area of research in the field of image diagnosis. However, their advantages have not been exploited in the denoising of the ultrasound image. In this work, a technique to denoise the ultrasound images by exploiting the low-rank property of patches grouped together is proposed. The patches are grouped utilising the idea of simultaneous sparse coding technique. This is followed by the singular value decomposition of similarly grouped patches which can be utilised for obtaining local and non-local information between the patches. The singular values are thresholded to obtain the denoised image. The proposed algorithm is compared with the state-of-art techniques existing in the literature and is seen to give the best results regarding both quantitative and qualitative measurements.

Denoising of medical ultrasound images based on non-local similarity: A low-rank approach

A worldwide pandemic, COVID-19 has been caused by a newly discovered strain of coronavirus SARS-Cov-2. Its common symptoms are high fever, coughing, and shortness of breath. With the rising number of COVID-19 cases, manual detection of infectious individuals at public spaces is a hectic task. Artificial Intelligence (AI) based detection systems can be deployed at public places like airports, railway stations, etc. for continuous monitoring of potential infectious individuals and screening based on common symptoms exhibited. In this paper, a new algorithm is developed for detecting repetitive coughing action which is the main symptom in COVID-19 cases, and thus detecting people with COVID-19 based on it. The performance of the proposed system is tested on an existing sneeze-cough dataset and also on a real-time dataset. The evaluation shows that the proposed method has superior performance over the state-of-the-art methods.

A Novel Cough Detection Algorithm for COVID-19 Surveillance at Public Places

Transcoding is the direct digital-to-digital data conversion of one encoding to another. This paper proposes a system of secure image transcoder which mainly focuses on multimedia applications such as web browsing through mobile phones, in order to improve their delivery to client devices with wide range of communication, storage and display capabilities. In this work, we propose a secure image transcoding technique based on a chaotic key. To improve the security of the above mentioned system, we have incorporated a variable block size selection prior to this stage. We analyzed the security of the proposed system and it is proved that the proposed system is highly secure against several forms of attack. The performance of the system is evaluated for different images and it is verified that the system is having less resource complexity with good performance.

Sudhish N. George

Papers

Design optimization of erbium-doped fiber amplifiers using artificial neural networks

A Fast Moving Object Detection Technique using QR Decomposition based TV Regularized RPCA Framework

Denoising of medical ultrasound images based on non-local similarity: A low-rank approach

A Novel Cough Detection Algorithm for COVID-19 Surveillance at Public Places

Secure image transcoding technique using chaotic key based algorithm with improved security