Price and Income Elasticities of Turkish Export Demand : A Panel Data Application

In this paper, price and income elasticities of export demand are calculated. The study is extended to sectoral and country specific export demand functions. The paper presents some panel unit root and cointegration tests, which have been studied extensively in recent years. The major aim of this study is to find the price and foreign income elasticities of aggregate export demand. According to the estimation results, the real exchange rate elasticity of total export demand is found to be less than one, whereas the income elasticity is found to be greater than one.Panel Unit Root Test, Panel Cointegration Test, Income and Real Exchange Rate Elasticities

A group sparsity-driven approach to 3-D action recognition

In this paper, a novel 3-D action recognition method based on sparse representation is presented. Silhouette images from multiple cameras are combined to obtain motion history volumes (MHVs). Cylindrical Fourier transform of MHVs is used as action descriptors. We assume that a test sample has a sparse representation in the space of training samples. We cast the action classification problem as an optimization problem and classify actions using group sparsity based on l1 regularization. We show experimental results using the IXMAS multi-view database and demonstratethe superiority of our method, especially when observations are low resolution, occluded, and noisy and when the feature dimension is reduced

A graphical model based solution to the facial feature point tracking problem

In this paper a facial feature point tracker that is motivated by applications such as human-computer interfaces and facial expression analysis systems is proposed. The proposed tracker is based on a graphical model framework. The facial features are tracked through video streams by incorporating statistical relations in time as well as spatial relations between feature points. By exploiting the spatial relationships between feature points, the proposed method provides robustness in real-world conditions such as arbitrary head movements and occlusions. A Gabor feature-based occlusion detector is developed and used to handle occlusions. The performance of the proposed tracker has been evaluated on real video data under various conditions including occluded facial gestures and head movements. It is also compared to two popular methods, one based on Kalman filtering exploiting temporal relations, and the other based on active appearance models (AAM). Improvements provided by the proposed approach are demonstrated through both visual displays and quantitative analysis

A sparsity-driven approach to multi-camera tracking in visual sensor networks

In this paper, a sparsity-driven approach is presented for multi-camera tracking in visual sensor networks (VSNs). VSNs consist of image sensors, embedded processors and wireless transceivers which are powered by batteries. Since the energy and bandwidth resources are limited, setting up a tracking system in VSNs is a challenging problem. Motivated by the goal of tracking in a bandwidth-constrained environment, we present a sparsity-driven method to compress the features extracted by the camera nodes, which are then transmitted across the network for distributed inference. We have designed special overcomplete dictionaries that match the structure of the features, leading to very parsimonious yet accurate representations. We have tested our method in indoor and outdoor people tracking scenarios. Our experimental results demonstrate how our approach leads to communication savings without significant loss in tracking performance

A Role for TNMD in Adipocyte Differentiation and Adipose Tissue Function: A Dissertation

Adipose tissue is one of the most dynamic tissues in the body and is vital for metabolic homeostasis. In the case of excess nutrient uptake, adipose tissue expands to store excess energy in the form of lipids, and in the case of reduced nutrient intake, adipose tissue can shrink and release this energy. Adipocytes are most functional when the balance between these two processes is intact. To understand the molecular mechanisms that drive insulin resistance or conversely preserve the metabolically healthy state in obese individuals, our laboratory performed a screen for differentially regulated adipocyte genes in insulin resistant versus insulin sensitive subjects who had been matched for BMI. From this screen, we identified the type II transmembrane protein tenomodulin (TNMD), which had been previously implicated in glucose tolerance in gene association studies. TNMD was upregulated in omental fat samples isolated from the insulin resistant patient group compared to insulin sensitive individuals. TNMD was predominantly expressed in primary adipocytes compared to the stromal vascular fraction from this adipose tissue. Furthermore, TNMD expression was greatly increased in human preadipocytes by differentiation, and silencing TNMD blocked adipogenic gene induction and adipogenesis, suggesting its role in adipose tissue expansion. Upon high fat diet feeding, transgenic mice overexpressing Tnmd specifically in adipose tissue developed increased epididymal adipose tissue (eWAT) mass without a difference in mean cell size, consistent with elevated in vitro adipogenesis. Moreover, preadipocytes isolated from transgenic epididymal adipose tissue demonstrated higher BrdU incorporation than control littermates, suggesting elevated preadipocyte proliferation. In TNMD overexpressing mice, lipogenic genes PPARG, FASN, SREBP1c and ACLY were upregulated in eWAT as was UCP-1 in brown fat, while liver triglyceride content was reduced. Transgenic animals displayed improved systemic insulin sensitivity, as demonstrated by decreased inflammation and collagen accumulation and increased Akt phosphorylation in eWAT. Thus, the data we present here suggest that TNMD plays a protective role during visceral adipose tissue expansion by promoting adipogenesis and inhibiting inflammation and tissue fibrosis

Graphical model based facial feature point tracking in a vehicle environment

Facial feature point tracking is a research area that can be used in human-computer interaction (HCI), facial expression analysis, fatigue detection, etc. In this paper, a statistical method for facial feature point tracking is proposed. Feature point tracking is a challenging topic in case of uncertain data because of noise and/or occlusions. With this motivation, a graphical model that incorporates not only temporal information about feature point movements, but also information about the spatial relationships between such points is built. Based on this model, an algorithm that achieves feature point tracking through a video observation sequence is implemented. The proposed method is applied on 2D gray scale real video sequences taken in a vehicle environment and the superiority of this approach over existing techniques is demonstrated

A socio-technical evaluation of the impact of energy demand reduction measures in family homes

Energy consumption in the home depends on appliance ownership and use, space heating systems, control set-points and hot water use. It represents a significant proportion of national demand in the UK. The factors that drive the level of consumption are a complex and interrelated mix of the numbers of people in the home, the building and system characteristics as well as the preferences for the internal environment and service choices of occupants. Reducing the energy demand in the domestic sector is critical to achieving the national 2050 carbon targets, as upward of 60% reduction in demand is assumed by many energy system scenarios and technology pathways. The uptake of reduction measures has been demonstrated to be quite ad hoc and intervention studies have demonstrated considerable variation in the results. Additionally, a limitation of many studies is that they only consider one intervention, whereas a more holistic approach to the assessment of the potential of reduction measures in specific homes may yield a better understanding of the likely impact of measures on the whole house consumption and indeed would shed light on the appropriateness of the assumptions that underpin the decisions that need to be made regarding the future energy supply system and demand strategies. This work presents a systematic approach to modelling potential reductions for a set of seven family homes, feeding back this information to householders and then evaluating the likely reduction potential based on their responses. Carried out through a combination of monitoring and semi-structured interviews, the approach develops a methodology to model energy reduction in specific homes using monitoring data and steady-state heat balance principles to determine ventilation heat loss, improving the assumptions within the energy model regarding those variables affected by human behaviour. The findings suggest that the anticipated reductions in end use energy demand in the domestic sector are possible, but that there is no `one size fits all' solution. A combination of retrofitting and lifestyle change is needed in most homes and smart home technology may potentially be useful in assisting the home owner to achieve reductions where they are attempting to strike a balance between energy efficiency, service and comfort

Human Re-Identification with a Robot Thermal Camera using Entropy-based Sampling

Human re-identification is an important feature of domestic service robots, in particular for elderly monitoring and assistance, because it allows them to perform personalized tasks and human-robot interactions. However vision-based re-identification systems are subject to limitations due to human pose and poor lighting conditions. This paper presents a new re-identification method for service robots using thermal images. In robotic applications, as the number and size of thermal datasets is limited, it is hard to use approaches that require huge amount of training samples. We propose a re-identification system that can work using only a small amount of data. During training, we perform entropy-based sampling to obtain a thermal dictionary for each person. Then, a symbolic representation is produced by converting each video into sequences of dictionary elements. Finally, we train a classifier using this symbolic representation and geometric distribution within the new representation domain. The experiments are performed on a new thermal dataset for human re-identification, which includes various situations of human motion, poses and occlusion, and which is made publicly available for research purposes. The proposed approach has been tested on this dataset and its improvements over standard approaches have been demonstrated

Verification of Localization via Blockchain Technology on Unmanned Aerial Vehicle Swarm

Verification of the geographic location of a moving device is vital. This verification is important in terms of ensuring that the flying systems moving in the swarm are in orbit and that they are able to task completion and manage their energy efficiency. Cyber-attacks on unmanned aerial vehicles (UAV) in a swarm can affect their position and cause various damages. In order to avoid this challenge, it is necessary to share with each other the positions of UAV in the swarm and to increase their accuracy. In this study, it is aimed to increase position accuracy and data integrity of UAV by using blockchain technology in swarm. Experiments were conducted on a virtual UAV network (UAVNet). Successful results were obtained from this proposed study