Spatio-Temporal Information for Action Recognition in Thermal Video Using Deep Learning Model

Researchers can evaluate numerous information to ensure automated monitoring due to the widespread use of surveillance cameras in smart cities. For the monitoring of violence or abnormal behaviors in smart cities, schools, hospitals, residences, and other observational domains, an enhanced safety and security system is required to prevent any injuries that might result in ecological, economic and social losses. Automatic detection for prompt actions is vital and may help the respective departments effectively. Based on thermal imaging, several researchers have concentrated on object detection, tracking, and action identification. Few studies have simultaneously extracted spatial-temporal information from a thermal image and utilized it to recognize human actions. This research provides a novelty based on frame-level and spatial and temporal features which combines richer context temporal information to address the issue of poor efficiency and less accuracy in detecting abnormal/violent behavior in thermal monitoring devices. The model can locate (bounded box) video frame areas involving different human activities and recognize (classify) the actions. The dataset on human behavior includes videos captured with infrared cameras in both indoor and outdoor environments. The experimental results using the publicly available benchmark datasets reveal the proposed model\u27s efficiency. Our model achieves 98.5% and 94.85% accuracy on IITR Infrared Action Recognition (IITR-IAR) and Thermal Simulated Fall (TSF) datasets, respectively. In addition, the proposed method may be evaluated in more realistic conditions, such as zooming in and out etc

Stochastic Approximation Approach to Federated Machine Learning

This paper examines Federated learning (FL) in a Stochastic Approximation (SA) framework. FL is a collaborative way to train neural network models across various participants or clients without centralizing their data. Each client will train a model on their respective data and send the weights across to a the server periodically for aggregation. The server aggregates these weights which are then used by the clients to re-initialize their neural network and continue the training. SA is an iterative algorithm that uses approximate sample gradients and tapering step size to locate a minimizer of a cost function. In this paper the clients use a stochastic approximation iterate to update the weights of its neural network. It is shown that the aggregated weights track an autonomous ODE. Numerical simulations are performed and the results are compared with standard algorithms like FedAvg and FedProx. It is observed that the proposed algorithm is robust and gives more reliable estimates of the weights, in particular when the clients data are not identically distributed

The Influence of GRA and TOPSIS for Assortment of Green Supply Chain Management Strategies in Cement Industry

The present paper aimed at proposing new strategies for evaluating the green supply chain management for enhancing the priority to environmental factors in cement manufacturing life cycle analysis, there by reducing the carbon foot prints. These strategies help in producing eco-friendly products there striking the balance between economy and environment. Initially green supply chain priorities are defined by using grey relational analysis (GRA). The priority weights obtained by GRA method is used to determine the weight for each indicator selected in the present study and then GRA is combined with TOPSIS methodology to obtain the priority for level-II measurement indicators used in the present study. These strategies will influence the decision making priorities during cement manufacturing. Keywords - Green Supply Chain Management (GSCM), strategy prioritization, Grey Relational Analysis (GRA), TOPSIS, Life Cycle Analysis (LCA

Identifying collateral and synthetic lethal vulnerabilities within the DNA-damage response.

BackgroundA pair of genes is defined as synthetically lethal if defects on both cause the death of the cell but a defect in only one of the two is compatible with cell viability. Ideally, if A and B are two synthetic lethal genes, inhibiting B should kill cancer cells with a defect on A, and should have no effects on normal cells. Thus, synthetic lethality can be exploited for highly selective cancer therapies, which need to exploit differences between normal and cancer cells.ResultsIn this paper, we present a new method for predicting synthetic lethal (SL) gene pairs. As neighbouring genes in the genome have highly correlated profiles of copy number variations (CNAs), our method clusters proximal genes with a similar CNA profile, then predicts mutually exclusive group pairs, and finally identifies the SL gene pairs within each group pairs. For mutual-exclusion testing we use a graph-based method which takes into account the mutation frequencies of different subjects and genes. We use two different methods for selecting the pair of SL genes; the first is based on the gene essentiality measured in various conditions by means of the "Gene Activity Ranking Profile" GARP score; the second leverages the annotations of gene to biological pathways.ConclusionsThis method is unique among current SL prediction approaches, it reduces false-positive SL predictions compared to previous methods, and it allows establishing explicit collateral lethality relationship of gene pairs within mutually exclusive group pairs

Multi-Channel Scheduling with Optimal Spectrum Channel Hole Filling (MCS-OSHF) for Cognitive Radio Wireless Networks

In this study, a contemporary method of scheduling algorithm has been proposed for working on scheduling of varying size data-frames transmission in CR based wireless networks. The objective of the proposed model is to achieve maximum throughput, and also reduction of loss of dataframes in the transmission. Some of the key elements that are considered in the development of the model are optimal bandwidth and idle channel availability. Using the three level hierarchical approach, the scheduling strategy is constructed. The optimal idle channel allocation, allocation with considerable transmission intervals allocation and optimal multiple channels models are considered at respective levels in the hierarchy in the proposed algorithm. The proposed model while tested under simulated environment in comparison to the other two bench marking models, the outcome depicts that the process is more efficient and supports in improving the overall process of scheduling of data-frames as per the desired objectives of the model

Analyzing intramolecular vibrational energy redistribution via the overlap intensity-level velocity correlator

Numerous experimental and theoretical studies have established that intramolecular vibrational energy redistribution (IVR) in isolated molecules has a heirarchical tier structure. The tier structure implies strong correlations between the energy level motions of a quantum system and its intensity-weighted spectrum. A measure, which explicitly accounts for this correaltion, was first introduced by one of us as a sensitive probe of phase space localization. It correlates eigenlevel velocities with the overlap intensities between the eigenstates and some localized state of interest. A semiclassical theory for the correlation is developed for systems that are classically integrable and complements earlier work focusing exclusively on the chaotic case. Application to a model two dimensional effective spectroscopic Hamiltonian shows that the correlation measure can provide information about the terms in the molecular Hamiltonian which play an important role in an energy range of interest and the character of the dynamics. Moreover, the correlation function is capable of highlighting relevant phase space structures including the local resonance features associated with a specific bright state. In addition to being ideally suited for multidimensional systems with a large density of states, the measure can also be used to gain insights into the phase space transport and localization. It is argued that the overlap intensity-level velocity correlation function provides a novel way of studying vibrational energy redistribution in isolated molecules. The correlation function is ideally suited to analyzing the parametric spectra of molecules in external fields.Comment: 16 pages, 13 figures (low resolution

Formulation and Evaluation of Pulsatile Drug Delivery System of Zafirlukast

 In the current scenario of pharmaceutical research much attention has been focused on patients health in terms of therapeutic efficacy and economical standards (price factor).The formulation design consist of core tablets designed by direct compression method. Core tablets were coated with an naturally occurring swelling agent (carbopol & Karaya gum). Evaluation studies were performed for prepared pulsatile tablets hardness. In in vitro release profile of 6 hours study in first 5 hours it shows minimum drug release and at the end of six hours rapid and transient release was observed. Stability studies proved that coating of tablets seems to decrease the effect of temperature and moisture on degradation of Zafirlukast. The pulsatile release has been achieved from tablet over a 7-8 hour period. &nbsp

Numerical Study of Pulse Detonation Engine with One-step Overall Reaction Model

This paper presents an insight for the study of transient, compressible, intermittent pulsed detonation engine with one-step overall reaction model to reduce the computational complexity in detonation simulations. Investigations are done on flow field conditions developing inside the tube with the usage of irreversible one-step chemical reactions for detonations. In the present simulations 1-D and 2-D axisymmetric tubes are considered for the investigation. The flow conditions inside the detonation tube are estimated as a function of time and distance. Studies are also performed with different grid sizes which influence the prediction of Von-Neumann spike, CJ Pressure and detonation velocity. The simulation result from the single-cycle reaction model agrees well with the previous published literature of multi-step reaction models. The present studies shows that one-step overall reaction model is sufficient to predict the flow properties with reasonable accuracy. Finally, the results from the present study were compared and validated using NASA CEA.Defence Science Journal, Vol. 65, No. 4, July 2015, pp. 265-271, DOI: http://dx.doi.org/10.14429/dsj.65.873