Dynamic Template Tracking and Recognition

In this paper we address the problem of tracking non-rigid objects whose local appearance and motion changes as a function of time. This class of objects includes dynamic textures such as steam, fire, smoke, water, etc., as well as articulated objects such as humans performing various actions. We model the temporal evolution of the object’s appearance/motion using a linear dynamical system. We learn such models from sample videos and use them as dynamic templates for tracking objects in novel videos. We pose the problem of tracking a dynamic non-rigid object in the current frame as a maximum a-posteriori estimate of the location of the object and the latent state of the dynamical system, given the current image features and the best estimate of the state in the previous frame. The advantage of our approach is that we can specify a-priori the type of texture to be tracked in the scene by using previously trained models for the dynamics of these textures. Our framework naturally generalizes common tracking methods such as SSD and kernel-based tracking from static templates to dynamic templates. We test our algorithm on synthetic as well as real examples of dynamic textures and show that our simple dynamics-based trackers perform at par if not better than the state-of-the-art. Since our approach is general and applicable to any image feature, we also apply it to the problem of human action tracking and build action-specific optical flow trackers that perform better than the state-of-the-art when tracking a human performing a particular action. Finally, since our approach is generative, we can use a-priori trained trackers for different texture or action classes to simultaneously track and recognize the texture or action in the video.     

Consolidating Multi-Criteria Visible-light Personal Network Coordinator Selection Process in Attocells

Wireless Personal Communications - Visible Light Communication (IEEE 802.15.7) implements a centralized topology using a visible-light personal network coordinator (VPNC) for network access and...     

Spatial methane emission modelling from wetlands using geospatial tools

Wetland ecosystems have acquired importance among the scientific community because of their role in biogeochemical cycling and as source and sink of greenhouse gas emissions particularly methane (CH₄) in addition to the ecosystem services that they provide. To estimate the CH₄ emission from wetlands in spatial domain, models incorporating the geospatial tools are required. Accordingly, main focus of this study is to demonstrate the utility of geospatial techniques in assessing the spatial CH₄ emission variability from four different regions of Uttar Pradesh (UP), India, namely, Western, Central, Bundelkhand, and Eastern regions deploying Semi-Automated Empirical CH₄ emission Model (SEMEModel) using Moderate Resolution Imaging Spectro-radiometer data of 2010–2012. SEMEModel is a three-tier model which determines the CH₄ emissions in spatial domain as a function of remote sensing (RS) and Geographic Information System (GIS) derived wetland components including wetland area and corresponding temperature factors coupled with point CH₄ emission coefficients developed via field measurements. Results of the study have shown that eastern region of UP exhibited maximum estimated/modelled CH₄ emissions (43.10 Gg yr^?1) as compared to other regions due to more area being under wetlands whereas central region was found to be the least contributor (0.266 Gg yr^?1) due to the fact that it has minimum wetland area (0.40%) among all the regions. It was observed that estimated/modelled CH₄ emissions depicted an increase by 4.96 orders of magnitude in 2010–2011 and 4.04 orders of magnitude in 2011–2012 when estimated by applying literature-based global CH₄ emission coefficients for UP in place of CH₄ flux values derived in field. It signifies that the upscaling of CH₄ flux values using literature-based CH₄ flux values of one region to another region may not reflect actual values. Therefore, this study not only helps to improve accuracy of CH₄ emission estimates from wetlands but also credibly adjudges that integration of CH₄ flux field measurements with modern tools of RS and GIS will immensely assist to reduce the uncertainties in CH₄ emission predictions done over larger regions.     

Multi scale entropy based adaptive fuzzy contrast image enhancement for crowd images

Multimedia Tools and Applications - Contrast enhancement is a very important issue in image processing, pattern recognition and computer vision. Fuzzy logic based techniques perform enhancement...     

PdCl2(bipy) complex—An efficient catalyst for Heck reaction in glycol–organic biphasic medium

The Palladium complex PdCl₂(bipy) (1) was found to be efficient in catalyzing the Heck reaction of aryl halides in a biphasic system comprising ethylene glycol along with an organic solvent. A proper tailoring of the organic phase ensured that the leaching of the catalyst to the organic phase was negligible. The catalyst was active for the Heck reaction of a number of olefins, aryl halides, in presence of organic and inorganic bases. The PdCl₂(bipy) catalyst was stable and could be recycled for five times without any loss in activity and selectivity. A major advantage of this work is that unlike hitherto reported biphasic systems; it does not employ a water-soluble ligand to achieve biphasic catalysis.     

Thermodynamic Properties of Liquid 3He-4He Mixtures Between 0.15 K and 1.8 K

Thermodynamic property relations for liquid ³He-⁴He mixtures at saturated pressure based on experimental measurements of the specific heat are determined. The relations are valid over the entire concentration range and for temperatures from 0.15 K to 1.8 K. Thermodynamic properties are first determined in the two-phase region, and then extended to the single-phase He-II and He-I regions. The results are in good agreement with some other ³He-⁴He mixture property data, though the scarcity of experimental data in large parts of the region of interest precludes a more thorough comparison. We derive some thermodynamic quantities that may be useful to the analysis of heat exchangers and throttles with superfluid ³He-⁴He flows. We also discuss how these properties can be extended to higher pressures.     

Internet of things and cloud computing‐based energy management system for demand side management in smart grid

A smart grid is an electricity network, which deals with electronic power conditioning and control of production, transmission, and distribution of electrical power by employing digital communication technologies to monitor and manage local changes in electricity usage. In the traditional power grid, energy consumers remain oblivious to their power consumption patterns, resulting in wasted energy as well as money. This issue is severely pronounced in the developing countries where there is a huge gap between demand and supply, resulting in frequent power outages and load‐shedding. For electrical energy savings, the smart grid employs demand side management (DSM), which refers to adaptation in consumer's demand for energy through various approaches such as financial incentives and awareness. The DSM in future smart grid must exploit automated energy management systems (EMS) built upon the state‐of‐the‐art technologies such as the internet of things (IoT) and cloud and/or fog computing. In this paper, we present the architecture framework, design, and implementation of an IoT and cloud computing‐based EMS, which generates load profile of consumer to be accessed remotely by utility company or by the consumer. The consumers' load profiles enable utility companies to regulate and disseminate their incentives and incite the consumers to adapt their energy consumption. Our designed EMS is implemented on a Project Circuit Board (PCB) to be easily installed at the consumer premises where it performs the following tasks: (a) monitors energy consumption of electrical appliances by means of our designed current and voltage sensors, (b) uploads sensed data to Google Firebase cloud over many‐to‐many IoT communication protocol Message Queuing Telemetry Transport (MQTT) where consumer's load profile is generated, which can be accessed via a web portal. These load profiles serve as input for implementing the various DSM approaches. Our results demonstrate generated load profiles of consumer load in terms of current, voltage, energy, and power accessible via a web portal.     

An inter-temporal resource emergency management model

One of the most important ways for promoting the service level during emergency management in public sectors is through enhancing management of strategic resources. From the perspective of vertical integration of operational process in emergency management, emergency resources can be classified as those resources used during the response period as well as those resources that are consumed during the recovery period. An inter-temporal integrated single-period resource model for solving optimal order quantity is proposed that meets the characteristics during the demand for the recovery resources depending on the stock shortage of the response resources. In the light of the classification of the dependent relationship of the two kinds of resources, namely deterministic or stochastic, the research investigates the analytical properties of the model, based on which, a genetic algorithm-based simulation approach is proposed. Finally, a real case with numerical example is provided to assess and validate our model, as well as managerial insights are obtained through key parameters sensitivity analysis.     

Application of genetic algorithms in production and operations management: a review

Over the last decade, there has been a rapid growth of the use of genetic algorithms in the various areas of production and operations management. This paper provides a review of genetic algorithms research published in twenty-one major production and operations management journals from 1990–2001. More specifically, it identifies research trends and publication outlets of genetic algorithms applications. Our findings show that there are only a handful of production and operations management areas to which genetic algorithms have been applied as the solution approach. Furthermore, we recognize and discuss potential research areas and outlets in which researchers may target their work as well as the need for top ranked POM journals to consider publishing genetic algorithms related papers.     

Nutrient composition and the use of solubility to estimate rumen degradability of food proteins in cattle

The use of protein solubility to estimate protein degradability in cattle was tested using 12 by‐products representing agricultural and distillers' food raw materials (RM). Agricultural RM included cereals (rice bran, maize gluten feed), legumes (field beans, soybean hulls) and oil‐seeds (rape meal, sunflower meal). Distillers' RM involved dark grains (DG1, DG2, DG3) and malts (MT1, MT2, MT3). Soluble proteins, as proportions of total soluble N (TSN), were extracted from RM and their undegraded residues (RU) after 18 h of in sacco rumen incubation in cattle (dg₁₈) by using water (albumin), salt (globulin), acid (glutalin 1) and alkali (glutalin 2) in succession. RM and RU differed significantly for all soluble proteins (P < 0.001). While albumin was the major protein in legume RM (0.74TSN), glutalin 1 was the major protein in legume RU (0.54TSN). Cereals were the most variable group, where maize gluten contained five times more albumin and two times more TSN in RM and three times more glutalin 1 + 2 in RU than those in rice bran. Distillers' RM contained slightly less albumin (0.53 vs 0.56TSN) but over twice as much glutalin 1 (0.29 vs 0.12TSN) as agricultural RM. Albumins were the most (0.55TSN, RM; 0.16TSN, RU) and glutalin 1 the least (0.21TSN, RM; 0.48TSN, RU) degradable proteins. There were moderate to strong correlations between protein solubility, dg₁₈ and acid detergent‐insoluble N (ADIN). ADIN correlated satisfactorily with glutalin 1 + 2 (r = ?0.55, RM and ?0.70, RU), TSN (r = ?0.75, RM and RU) and slowly degradable N, (SDN; r = ?0.70, all). TSN in all RM related well with SDN (r = 0.65) and dUN (r = 0.72). TSN in distillers' RM correlated closely with SDN (r = 0.91) and dUN (r = 0.96). Glutalin 1 in distillers' foods correlated extremely well (r = 0.94, RM and 0.93, RU) with dUN. Globulins correlated most consistently with SDN (r = 0.82, all; r = 0.77, agricultural; r = 0.92, distillers' RM). It is possible to predict protein degradability from the presence or absence of soluble proteins in various foods. While ADIN may be more suitable to predict DUN, globulins showed more potential to predict SDN in various foods. However, it may be necessary to be selective in choosing solvents for various foods. Further studies must validate this method by using a greater range of foods before suggesting its routine use for food evaluation. © 2001 Society of Chemical Industry