Multi-resolution approach to human activity recognition in video sequence based on combination of complex wavelet transform,Local Binary Pattern and Zernike moment

Human activity recognition is a challenging problem of computer vision and it has different emerging applications. The task of recognizing human activities from video sequence exhibits more challenges because of its highly variable nature and requirement of real time processing of data. This paper proposes a combination of features in a multiresolution framework for human activity recognition. We exploit multiresolution analysis through Daubechies complex wavelet transform (DCxWT). We combine Local binary pattern (LBP) with Zernike moment (ZM) at multiple resolutions of Daubechies complex wavelet decomposition. First, LBP coefficients of DCxWT coefficients of image frames are computed to extract texture features of image, then ZM of these LBP coefficients are computed to extract the shape feature from texture feature for construction of final feature vector. The Multi-class support vector machine classifier is used for classifying the recognized human activities. The proposed method has been tested on various standard publicly available datasets. The experimental results demonstrate that the proposed method works well for multiview human activities as well as performs better than some of the other state-of-the-art methods in terms of different quantitative performance measures.

     

Analysis of energy saving in user equipment in LTE-A using stochastic modelling

Telecommunication Systems - Energy saving in User Equipment (UE) is one of the important issues for limited sources of power in the device. It is critical for the UE to maximize its energy...     

Attitudinal choice models with applications in human decision making

A new family of attitudinal discrete choice models is proposed by considering the attitudinal character and the weight vector, both of which are specific to a decision maker (DM). Given the attribute values of different alternatives, the proposed models give varying choice probabilities, as per the DM's-specific attitudinal character and the weight vector. It is also shown that the conventional discrete choice models are the special cases of the proposed attitudinal models. The proposed choice models are also generalized through an additional parameter to add to their capabilities. An application on real data is included to demonstrate their usefulness in the real world.     

Pull-Down of Metalloproteins in Their Native States by Using Desthiobiotin-Based Probes

One-third of all proteins are estimated to require metals for structural stability and/or catalytic activity. Desthiobiotin probes containing metal binding groups can be used to capture metalloproteins with exposed active-site metals under mild conditions so as to prevent changes in metallation state. The proof-of-concept was demonstrated with carbonic anhydrase (CA), an open active site, Zn²⁺-containing protein. CA was targeted by using sulfonamide derivatives. Linkers of various lengths and structures were screened to determine the optimal structure for capture of the native protein. The optimized probes could selectively pull down CA from red blood cell lysate and other protein mixtures. Pull-down of differently metallated CAs was also investigated.     

Brain tumor detection and segmentation using mesh-free super-diffusive model

Multimedia Tools and Applications - Brain tumor detection and segmentation is a complex and challenging task in image processing. Most of the techniques used for brain tumor detection and...     

Deployment of a cloud pipeline for real-time visual inspection using fast streaming high-definition images

We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.     

Uncertainty Assessment in Cognitive Load for Multiple Object Tracking Based on EEG

Cognitive load varies the attention level, which has serious consequences in complex dynamic situations. Assessment of uncertainty in cognitive load during multiple object tracking task is necessary, as it is used to improve the cognitive capabilities. The present research work investigates the uncertainty in cognitive load of multiple object tracking task using electroencephalograph (EEG) on 25 football players. A d2 test of neuropsychological measure of attention was employed before starting the experiment. Each player participated in four levels of the task with variation in the cognitive load, which varies in terms of the targets from 2 to 5. Percentage changes in the power spectral density were estimated for the cognitive levels. Results show that the percentage changes were much more in high cognitive load than in low cognitive load. Significant changes (p < 0.05) were observed in level 1 (− 11.07 to 1.91%), level 2 (− 3.13 to − 14.51%), level 3 (− 6.33 to − 19.46%) and level 4 (− 8.10 to − 20.88%). Variation in the EEG data in terms of the combined uncertainty corresponds very well with low to high cognitive loads. The fourth level of the task with high cognitive load has more uncertainty than the low cognitive load levels. The results are useful for assessing the cognitive state of the player, which is valuable for the design of the effective training model.     

IoT Based Multimode Sensing Platform for Underground Coal Mines

Wireless Personal Communications - Real-time information of the underground environment plays a vital role for underground coal mines. For this purpose, a number of systems have been developed by...