This paper presents a new technique for designing a jointly optimized residual vector quantizer (RVQ). In conventional stage-by-stage design procedure, each stage codebook is optimized for that particular stage distortion and does not consider the distortion from the subsequent stages. However, the overall performance can be improved if each stage codebook is optimized by minimizing the distortion from the subsequent stage quantizers as well as the distortion from the previous stage quantizers. This can only be achieved when stage codebooks are jointly designed for each other. In this paper, the proposed codebook design procedure is based on a multilayer competitive neural network where each layer of this network represents one stage of the RVQ. The weight connecting these layers form the corresponding stage codebooks of the RVQ. The joint design problem of the RVQ's codebooks (weights of the multilayer competitive neural network) is formulated as a nonlinearly constrained optimization task which is based on a Lagrangian error function. This Lagrangian error function includes all the constraints that are imposed by the joint optimization of the codebooks. The proposed procedure seeks a locally optimal solution by iteratively solving the equations for this Lagrangian error function. Simulation results show an improvement in the performance of an RVQ when designed using the proposed joint optimization technique as compared to the stage-by-stage design, where both generalized Lloyd algorithm (GLA) and the Kohonen learning algorithm (KLA) were used to design each stage codebook independently, as well as the conventional joint-optimization technique 相似文献
Nonalcoholic fatty liver disease (NAFLD) is strongly associated to the features of metabolic syndrome which can progress to cirrhosis, liver failure and hepatocellular carcinoma. However, the most common cause of mortality in people with NAFLD is not liver-related but stems from atherosclerotic cardiovascular disease (CVD). The prevalence of NAFLD is on the rise, mainly as a consequence of its close association with two major worldwide epidemics, obesity and type 2 diabetes (T2D). The exact pathogenesis of NAFLD and especially the mechanisms leading to disease progression and CVD have not been completely elucidated. Human fetuin-A (alpha-2-Heremans Schmid glycoprotein), a glycoprotein produced by the liver and abundantly secreted into the circulation appears to play a role in insulin resistance, metabolic syndrome and inflammation. This review discusses the links between NAFLD and CVD by specifically focusing on fetuin-A’s function in the pathogenesis of NAFLD and atherosclerotic CVD. 相似文献
With the tremendous applications of the wireless sensor network, self-localization has become one of the challenging subject matter that has gained attention of many researchers in the field of wireless sensor network. Localization is the process of assigning or computing the location of the sensor nodes in a sensor network. As the sensor nodes are deployed randomly, we do not have any knowledge about their location in advance. As a result, this becomes very important that they localize themselves as manual deployment of sensor node is not feasible. Also, in WSN the main problem is the power as the sensor nodes have very limited power source. This paper provides a novel solution for localizing the sensor nodes using controlled power of the beacon nodes such that we will have longer life of the beacon nodes which plays a vital role in the process of localization as it is the only special nodes that has the information about its location when they are deployed such that the remaining ordinary nodes can localize themselves in accordance with these beacon node. We develop a novel model that first finds the distance of the sensor nodes then it finds the location of the unknown sensor nodes in power efficient manner. Our simulation results show the effectiveness of the proposed methodology in terms of controlled and reduced power. 相似文献
Silk fibroin (SF), an organic material obtained from the cocoons of a silkworm Bombyx mori, is used in several applications and has a proven track record in biomedicine owing to its superior compatibility with the human body, superb mechanical characteristics, and its controllable propensity to decay. Due to its robust biocompatibility, less immunogenic, non-toxic, non-carcinogenic, and biodegradable properties, it has been widely used in biological and biomedical fields, including wound healing. The key strategies for building diverse SF-based drug delivery systems are discussed in this review, as well as the most recent ways for developing functionalized SF for controlled or redirected medicines, gene therapy, and wound healing. Understanding the features of SF and the various ways to manipulate its physicochemical and mechanical properties enables the development of more effective drug delivery devices. Drugs are encapsulated in SF-based drug delivery systems to extend their shelf life and control their release, allowing them to travel further across the bloodstream and thus extend their range of operation. Furthermore, due to their tunable properties, SF-based drug delivery systems open up new possibilities for drug delivery, gene therapy, and wound healing. 相似文献
The requirements for barrier properties of packaging materials against environmental factors vary with food products. The chemical, physical, and biological mechanisms of food deterioration due to environmental factors, vital properties required in packaging materials, and developments in progress and future trends to maintain the required standard of food quality have been critically reviewed. Theoretical and experimental results for a variety of food products in relation to the properties of the packaging materials are discussed. Methods of prediction of food stability and their industrial applications are also emphasized by specific examples. 相似文献
We present the development of a bias compensating reinforcement learning (RL) algorithm that optimizes thermal comfort (by minimizing tracking error) and control utilization (by penalizing setpoint deviations) in a multi-zone heating, ventilation, and air-conditioning (HVAC) lab facility subject to unmeasurable disturbances and unknown dynamics. It is shown that the presence of unmeasurable disturbance results in an inconsistent learning equation in traditional RL controllers leading to parameter estimation bias (even with integral action support), and in the extreme case, the divergence of the learning algorithm. We demonstrate this issue by applying the popular Q-learning algorithm to linear quadratic regulation (LQR) of a multi-zone HVAC environment and showing that, even with integral support, the algorithm exhibits bias issue during the learning phase when the HVAC disturbance is unmeasurable due to unknown heat gains, occupancy variations, light sources, and outside weather changes. To address this difficulty, we present a bias compensating learning equation that learns a lumped bias term as a result of disturbances (and possibly other sources) in conjunction with the optimal control parameters. Experimental results show that the proposed scheme not only recovers the bias-free optimal control parameters but it does so without explicitly learning the dynamic model or estimating the disturbances, demonstrating the effectiveness of the algorithm in addressing the above challenges.
Wireless Networks - Communication systems play an important role in smart grid (SG). Advanced Metering Infrastructure (AMI) is hybrid architecture in smart grid comprising of smart meters and... 相似文献