In the process of day-to-day learning, expressions are very vital. Detection of human expressions is growing and has caught the interest of many researchers in the past few years, to facilitate research on Affective Computing . Classification of expressions is necessarily needed to keep focus on several expressions for developing an efficient electronic learning system. This classification can provide several solutions in the application of psychology, human computer interaction , artificial intelligence , education, gaming etc. The core attention of this review is to highlight the most recent algorithms used for the extraction of features from multi-modal datasets and various classifiers that are used in the discipline of Multi-modal expression detection (MED). In this study, we review the literature on MED between 2015 and 2022, focusing mainly on feature extraction and classifier schemes. Their advantages, disadvantages and the entire vision have been discussed in brief. This survey also shows that the efficiency of the MED environment is better as compared to single-modal ones.

Particle filters (PFs) are sequential Monte Carlo methods that use particle representation of state‐space model to implement the recursive Bayesian filter for non‐linear and non‐Gaussian systems. Owing to this property, PFs have been extensively used for object tracking in recent years. Although PFs provide a robust object tracking framework, they suffer from shortcomings. Particle degeneracy and particle impoverishment brought by the resampling step result in abysmal construction of posterior probability density function (PDF) of the state. To overcome these problems, this work amalgamates two characteristics of population‐based heuristic optimisation algorithms: exploration and exploitation with PF implementing dynamic resampling method. The aim of optimisation is to distribute particles in high‐likelihood area according to the cognitive effect and improve quality of particles, while the objective of dynamic resampling is to maintain diversity in the particle set. This work uses very efficient spider monkey optimisation to achieve this. Furthermore, to test the efficiency of the proposed algorithm, experiments were carried out on one‐dimensional state estimation problem, bearing only tracking problem, standard videos and synthesised videos. Metrics obtained show that the proposed algorithm outplays simple PF, particle swarm optimisation based PF, and cuckoo search based PF, and effectively handles different challenges inherent in object tracking.

Startling growth in multimedia services over wireless channels emphasizes the need for bandwidth and power efficient modulation techniques. This study investigates the suitability of hierarchical quadrature amplitude modulation (HQAM) for image transmission over erroneous wireless Gaussian channels. HQAM provides unequal error protection, to compressed and coded image data, transmitted without increasing the required bandwidth, unlike channel coding. A discrete cosine transform based image coder is used to divide the image data bit stream into high‐priority (HP) and low‐priority (LP) sub‐streams for significant and non‐significant data respectively. The ratios between the constellation distances are then dynamically adjusted with the objective to achieve an acceptable bit‐error rate (BER) for HP sub‐stream over varying channel conditions at the cost of increased BER for LP sub‐stream. The work highlights HQAM implementation using conventional quadrature amplitude modulation (QAM) demodulator at the receiver which does not require the knowledge of varying constellation distance ratios at the transmitter. An analysis of 16‐HQAM transmission for different values of distance ratios is presented to obtain high peak signal‐to‐noise ratio (PSNR) values of reconstructed images under poor as well as good channel conditions and the performance is compared qualitatively and quantitatively with 16‐QAM.

Recently, much research has been carried out on dimensionality reduction techniques that summarise a large set of features into a smaller set, leading to much less redundancy. Among the various non‐linear dimensionality reduction techniques, Isomap and local linear embedding are the most popular techniques. However, these techniques require optimisation on the size of the neighbourhood set, reducing their computational efficiency. To overcome this limitation, a computational efficient algorithm, which is not dependent on size of the neighbourhood set, has been proposed in this study. In the proposed algorithm, morphological mapping (Morphmap) has been used for visualising an image in three dimensions. Then neighbourhood graph construction is done using intensity attenuation function. The pixels in the layer which comes under the angle of divergence can form a part of group vectors and other vectors are removed. In the proposed method, neighbourhood graph construction is not dependent on size of neighbourhood set and removes all the redundant data that reduces complexity to a great extent. Various facial expression datasets have been used to evaluate the performance of the proposed method. Finally, improvement in recognition accuracy with reduced number of computations using the Morphomap algorithm has been shown in the study.

The aim of this paper is to give an overview of state-of-the-art human detection methods, classify them into reasonable categories and listing new trends in this field. Human detection in static images or video sequences is a challenging area of research with its main application in surveillance, defence, intelligent vehicles and robotics. In this survey paper, we classify the human detection methods based upon the mode of data acquisition and features used for detection of human. The review of different algorithms reported in last few years for human detection is tabulated. Also, we reviewed standard dataset for evaluation of human detection and studied the statistics about these dataset such as number of frames, challenging environments conditions, ground truth structure, camera details, etc. Different performance measures for human detection algorithms both qualitative and quantitative are listed out. Our survey gauges the gap between the present research and future requirements.

The proposed method is based on extraction of non‐linearity from the nearest neighbourhood elements of image. To detect non‐linearity, relation between the nearest neighbourhood elements of the image, have been expressed in terms of Gaussian membership functions. All the elements of the image are connected with the nearest neighbourhood elements with some membership degree of the Gaussian functions. It results in the formation of number of fuzzy lattices. The lattices have been expressed in the form of Schrödinger equation, to find the kinetic energy (KE) used, corresponding to change occurring in the facial activity of a person. Finally, the KE embedded in three dimension space is used to distinguish non‐linear changes during occurrence of various facial activities. Experimental results show that proposed method is effective in recognition of facial expression as it focuses on extracting the non‐linear features corresponding to contours of maximum energy which are appearing because of different expressions.

Detailed modeling of the performance of commercial applications is difficult. The applications can take a very long time to run on real hardware and it is impractical to simulate them to completion on performance models. Furthermore, these applications have complex execution environments that cannot easily be reproduced on a simulator, making porting the applications to simulators difficult. We attack these problems using the well-known SimPoint methodology to find representative portions of an application to simulate, and a dynamic instrumentation framework called Pin to avoid porting altogether. Our system uses dynamic instrumentation instead of simulation to find representative portions - called Pin-Points - for simulation. We have developed a toolkit that automatically detects PinPoints, validates whether they are representative using hardware performance counters, and generates traces for large Itanium® programs. We compared SimPoint-based selection to random selection of simulation points. We found for 95% of the SPEC2000 programs we tested, the PinPoints prediction was within 8% of the actual whole-program CPI, as opposed to 18% for random selection. We measure the end-to-end error, comparing real hardware to a performance model, and have a simple and efficient methodology to determine the step that introduced the error. Finally, we evaluate the system in the context of multiple configurations of real hardware, commercial applications, and industrial-strength performance models to understand the behavior of a complete and practical workload collection system. We have successfully used our system with many commercial Itanium® programs, some running for trillions of instructions, and have used the resulting traces for predicting performance of those applications on future Itanium processors.