Layered view-dependent texture maps

This paper proposes a novel mesh animation technique which combines the flexible interactive control of skeleton based animation rigs with volumetric mesh deformation to avoid mesh collapse and self-intersection under folding and twisting motion. Our solution combines the industry standard Linear Skin Blending with a mesh based volumetric deformation approach. Linear Skin Blending is used to attach and efficiently animate a small number of points with a skeletal control rig. These points provide constraints for a Laplacian mesh deformation scheme which solves for the mesh which satisfies the constraints and gives minimum volume deformation of a tetrahedralization of the mesh vertices. This approach allows rigging and animation of high-resolution captured surface meshes from multiple view video or 3D scans. Interactive skeleton driven animation is achieved for meshes of several thousand vertices without the known drawbacks of Linear Skin Blending, mesh collapse around joints and the 'candy wrapper effect'.

Summary for only given. Conventional digital video playback systems provide only limited user interactivity, mostly in the form of VCR-like controls. In this model, the temporal ordering and the spatial viewpoints of the video streams being viewed are completely determined at authoring time. In contrast, we have defined a form of interactive video called active video (see http://www.ecsl.cs.sunysb.edu//spl sim/anindya/avs/avs.html, 2002), which supports hyper-linking among related video sequences and interpolation of video sequences with neighboring viewpoints, to offer end users the additional flexibility of choosing the sequencing and the viewing angle (even virtual ones) at playback time. However, active video has a substantially higher storage and transmission cost due to multiple time-synchronized video streams capturing the dynamic scene and the pixel-level correspondence maps encoding the spatial association among the frame-pairs of all adjacent streams. The maps are interpolated at run-time to generate virtual views. We describe and evaluate the following three compression techniques that alleviate the storage and network transmission costs of active video: spatial video compression; lossy map compression; lossless map compression.

Human action recognition is one of the important topic in nowadays research. It is obstructed by several factors, among them we can enumerate: the variation of shapes and postures of a human been, the time and memory space need to capture, store, label and process those images. In addition, recognize a human action from different view point is challenging due to the big amount of variation in each view, one possible solution of mentioned problem is to study different preferential View-invariant features sturdy enough to view variation. Our focus on this paper will be to solve mentioned problem by learning view shared and view specific features applying innovative deep models known as a novel sample-affinity matrix (SAM), able to give a good measurement of the similarities among video samples in different camera views. This will also lead to precisely adjust transmission between views and study more informative shared features involve in cross-view actions classification. In addition, we are proposing in this paper a novel view invariant features algorithm, which will give us a better understanding of the internal processing of our project. We have demonstrated through a series of experiment apply on NUMA and IXMAS (multiple camera view video dataset) that our method out performs state-of-the-art-methods.

In this current era of technology people are interested more in online medical facilities for more faster and comfortable life. Because of huge demand, health care field is growing rapidly. The data in the form of x-rays, diagnosis reports, MRI images, videos is generated. Such form of data consumes large amount of storage space and processing power, therefore nowadays alternate solutions such as cloud storage are used to store such huge data. As the medical data in the form of images or videos is very important and confidential it is very crucial to concentrate on the issues like storage and security. The proposed work discuss about the image security techniques like watermarking using different types combined with data encryption techniques during transmission so as to ensure better security along with reduction in cost for storage with the use of cloud environment. In this study, we offer a deeper insight into the challenges hindering the adoption of this technology. Then we analyze and comp are these findings of the cloud based medical image process implementation with security necessities.

Charging up Stationary Energy Storage

A new design paradigm for nuclear power plants is needed to complement the increasing adoption of low marginal cost variable renewable energy resources. The situation is reflected in the wholesale electricity price–duration curve with four distinct economic opportunities: (a) a hundred or so hours per year of high-value peaking power; (b) about 4000–5000 h of moderate electric prices; (c) about 2000 h per year when renewables set the marginal price at or near zero; and (d) about 1000 h of flexible ramping between the b and c regions. The current approach to the low-carbon energy transition reduces the need for baseload power and requires curtailment of conventional, nuclear, and even renewable generation, decreasing their capacity factors and increasing their fixed charges for electricity generation. Flexible low-carbon dispatchable power plants capable of daily cycling along with storage and time shifting of low-cost nondispatchable renewable power will be needed. Although nuclear power plants have demonstrated load-following capability, cycling can be limited by reactor kinetics (xenon poisoning) as well as by thermal stresses and fatigue considerations in the steam cycle. Storage of nuclear heat is hampered by the relatively low operating temperatures of existing nuclear reactors (but not advanced reactors) that lowers thermal to electric conversion efficiency, which in turn increases the required quantity of storage medium and the cost of storage. The quantity of storage medium can be reduced by integration of thermal energy storage with high-grade heat as in the liquid salt combined cycle (LSCC). The LSCC uses high-temperature gas turbine exhaust heat to increase the electricity output per unit of storage medium, uses the stored energy to add operating flexibility to a bottoming steam cycle, and substantially reduces the fuel heat rate. The low fuel heat rate improves economic competitiveness compared to alternative gas turbine-based power plants, especially when burning expensive fuels such as hydrogen. LSCC could be coupled to a nuclear power plant for time shifting both nuclear and renewable electricity and could support high utilization of a co-located hydrogen electrolysis plant. Further cost reduction could be achieved by using solid media for thermal energy storage, with the liquid salt used as a heat transfer medium.

Seismic acquisitions in urban areas represent a challenge due to the noisy environment during the recording of the data. This often prevents obtaining high-resolution images of the subsurface in the area of interest. One way to achieve better results, despite the challenging circumstances, is full-waveform inversion (FWI). This method iteratively updates a starting model until the simulated data and the observed field data matches to a satisfactory level. This eventually yields the desired subsurface model. However, FWI comes with some major limitations. Because each iteration requires a calculation of the wave propagation several times for each shot, it is highly computationally expensive. Furthermore, due to storage limitations, especially if high performance computing (HPC) clusters are not available, the model size is restricted as well. This work aims to find a solution for these limitations in an elastic 2D case by implementing the random-shot workflow (RSW). In a conventional FWI workflow which is called full-shot workflow (FSW) in this work, each iteration uses all shots of the recorded data before updating the whole model at once which leads to the high computational and storage costs. RSW randomly picks only one shot, applies FWI within the area of the shot and its corresponding receivers and then updates the model only within this subset. This should, in my expectation, split the workload into several smaller subsets without sacrificing the effectiveness of FWI. After the implementation, I compare RSW to FSW in a synthetic benchmark where I verify the performance. Afterwards, the workflows are compared to each other by using a fraction of a field data set that was acquired in Salt Lake City. These data represents a large urban seismic land data set with a low signal-to-noise ratio. They also have suboptimal conditions such as the end-on spread geometry due to the use of a land streamer and occasionally bad coupling of the geophones. This turns out to be a big challenge for the FWI algorithm. The starting model used for the field data inversion is based on the multichannel analysis of surface waves which deals with the inversion of surface-wave dispersion curves. It is a cost-efficient way to obtain an initial model for FWI.With a subset of this model and the data, I investigate the influence of traces near the sources which suffer heavily from the suboptimal acquisition conditions, and if more iterations can improve the results. Removing such traces improve the major part of the results, while more iterations are not beneficial with this particular data set. At the end I show a final comparison of both workflows by applying them to the complete Salt Lake City field data set. An application of FWI on such a large-scale land streamer acquisition with such a large number of shots has, to the best of my knowledge, not been done yet, most likely because of the large computational costs, even in 2D. The comparisons and investigations show that while RSW has a major computational advantage in storage usage and per-CPU performance, it is too sensitive to bad traces and shots which significantly degrade the final model. Therefore, it is unsuitable for field data with a low signal-to-noise ratio or that have not been preprocessed well. Furthermore, RSW cannot utilise the whole power of HPC clusters while FSW is able to do so by optimal shot parallelisation. Thus, FSW could potentially outperform RSW by raw computing power if such excessive power is available.

In this work we present the vectorisation of a new complex numerical algorithm to simulate the lubricant behaviour in an industrial device issued from tribology. This real technological problem leads to the mathematical model of the thin film displacement of a fluid between a rigid plane and an elastic and loaded sphere. The mathematical study and a numerical algorithm to solve the model has been proposed in the previous work [9]. This numerical algorithm mainly combines fixed point techniques, finite elements and duality methods. Nevertheless, in order to obtain a more accurate approach of different real magnitudes, it is interesting to be able to handle finer meshes. As it is well-known in finite element methods, mesh refinement carries out a great increase in the storage cost and the execution time of the algorithm. It is precisely this computational cost problem what has motivated the authors to try to increase the performance of the numerical algorithm by using high performance computing techniques. In this work we mainly apply vectorisation techniques but also we present some preliminary partial results from the design of a parallel version of the algorithm.

E-commerce plays a predominant role in the process of buying and selling of goods and services over the Internet. It helps in establishing businesses and sales with a wide range of customers across the globe, offering the customers to shop online 24/7. This study helps to explore various aspects that contribute to the reduction of operational costs in ecommerce such as inventory management, automation of business processes, logistic and supply chain management, labor cost, warehousing, etc. Additionally, processing orders, payments, and customer services through automation, it helps business to gain more profit by reducing the costs associated with it. Through digital marketing, it can reach customers across the globe efficiently. Data analytics helps to make decisions effortlessly by providing valuable insights. The findings in the article help to understand how the adoption of e-commerce in businesses helps to achieve a significant reduction of administration costs, storage costs, shipping costs, etc by helping the business earn more profitability. The study looks at a number of important ways that e-commerce can save costs, such as through automating corporate processes, enhancing inventory control, requiring less physical infrastructure, having a worldwide reach and being scalable, streamlining communication, and producing less paperwork.

Companies often face various obstacles related to managing raw material inventory to meet demand, one of which is Peuyeum Ketan Istimewa. Working in the food processing industry, of course, raw material inventory management needs to be planned optimally to avoid various risks that can harm the company. The Quantity Discount model is used to take advantage of cost savings provided by suppliers when purchases are made in large quantities, while other efforts that can help manage raw materials in a company are by knowing the safety stock and reorder point of raw materials and also forecasting demand to predict future demand. This study will use the Quantity Discount model which optimizes inventory levels by considering storage costs, ordering costs, and quantity discounts. The calculations carried out are also to find the value of the company's Safety Stock and Reorder Point. The results of this study indicate that the use of the Quantity Discount method can reduce total costs by Rp26,319,267/year, while forecasting using the seasonality method increases the accuracy of demand predictions, thus enabling more efficient inventory management. The implementation of this model is expected to provide a significant contribution to operational efficiency and cost reduction at Peuyeum Ketan Istimewa

This article examines whether the use of electrical or thermal storage is economically feasible for photovoltaic plants supplying power to a cold store. It is assumed that the PV plant does not receive a feed-in tariff for the electricity generated and that the PV plant cannot feed excess electricity into the grid. It is shown that economic operation with storage is possible, but a reduction of the storage costs is necessary. In the case of a storage tank with phase change material and an installed PV peak power of 112 kWp, lower levelized cost of cooling energy (LCCE) can be achieved at specific additional costs for storage below € 44 / kWhth, which corresponds to a required cost reduction of -39%. If a PV peak power of 224 kWp is available this will result in lower LCCE at specific additional costs for storage below € 76 / kWhth, which corresponds to a required cost reduction of -16%. The use of battery storage currently generates higher costs. Lower LCCE for the battery scenario with 112 kWp can only be achieved with additional costs for storage below € 120/kWhel, which corresponds to a necessary cost reduction of -84%. In the battery scenario with 224 kWp, lower LCCE can be achieved below additional costs for storage of € 220/kWhel, which equals a cost reduction of -71%. It can be summarized that both battery and PCM storage allow economic operation for the described cold store if the calculated cost reductions for the individual storage component can be achieved in the near future.

Optimal strategies for production plan and carbon emission reduction in a hydrogen supply chain under cap-and-trade policy

The preservation of temporal dependencies among a group of processes that exchange continuous media at runtime is a key issue for emerging mobile distributed systems (MDS), such as monitoring of biosignals and interactive multiuser games. Although several works are oriented to satisfy temporal dependencies, most of them are not suitable for MDSs. In general, an MDS is characterized by the absence of global references (e.g. shared memory and wall clock), host mobility, limited processing and storage capabilities in mobile hosts, and limited bandwidth on wireless communication channels. This paper proposes an asymmetric synchronization protocol to be used at runtime in an MDS without using a common reference. One main aspect of our synchronization protocol is that it translates temporal constraints to causal dependencies of the continuous media data as seen by the mobile hosts. We simulate the protocol by considering a cellular network environment and by using MPEG-4 encoders. The simulation results show that our protocol is effective in reducing the synchronization error. In addition, the protocol is efficient in terms of processing and storage costs at the mobile devices, as well as in the overhead attached per message across the wired and wireless channels.

Flash memory based solid state drives (SSDs) are widely adopted in mobile devices, PCs and data centers, making their reliability critical. Although periodically creating checkpoints is a well-developed technique for traditional hard disk drives, very few work has exploited the unique properties of SSDs to accelerate checkpoint creation and reduce storage cost. More specifically, the remap-on-write property of SSD creates a trail of multiple versions of data, which can be exploited to create multiple checkpoints without engendering extra writes. However, efficiently managing the metadata to support multiple checkpoints is challenging. In this paper, we propose a low storage cost and high performance scheme to support multiple checkpoints in SSDs. Instead of storing a log-based snapshot of the entire Flash Translation Layer (FTL) per checkpoint, we efficiently keep track of the changes to the FTL across multiple checkpoints, thus accelerating the creation, deletion, and activation of checkpoints within minimum storage overhead and minimum impact on regular SSD operations. Experiments on Microsoft real-world benchmarks confirm the advantage of the proposed scheme over a fully snapshot scheme in terms of storage and performance overhead.

While Deep Neural Networks (DNNs) quantization leads to a significant reduction in computational and storage costs, it reduces model capacity and therefore, usually leads to an accuracy drop. One of the possible ways to overcome this issue is to use different quantization bit-widths for different layers. The main challenge of the mixed-precision approach is to define the bit-widths for each layer, while staying under memory and latency requirements. Motivated by this challenge, we introduce a novel technique for explicit complexity control of DNNs quantized to mixed-precision, which uses smooth optimization on the surface containing neural networks of constant size. Furthermore, we introduce a family of smooth quantization regularizers, which can be used jointly with our complexity control method for both post-training mixed-precision quantization and quantization-aware training. Our approach can be applied to any neural network architecture. Experiments show that the proposed techniques reach state-of-the-art results. KeywordsNeural network quantizationMixed-precision quantizationRegularization for quantization