Temporal Scalability Research Articles

Discussion on Reliability and Power Quality in the Smart Grid: a prosumer approach of a time scalable index

This article presents a discussion related to the measurement techniques of the electric energy quality within the smart grid (SG) context, and considering the producer-consumer (prosumer) approach. It establishes the basis where new power quality (PQ) indexes should be proposed, in order to complement the current recommendations regarding consumer dissatisfaction indexes (CDI) on which the network should be assessed and planned. Indeed, the current indices only consider the number of interruptions and the time during which they occur, and do not take into account neither the type nor the non-linear loads (distributed energy resources, DER) connected to the SG, which are giving rise to new kinds of transients coupled to the electrical power supply signal. In this sense, the paper provides reasons on why temporal evolution of energy and its deviation from the contractual specifications should be monitored and assessed, with the goal of making real-time decisions. Based on advanced statistical signal processing, forecasting methods and artificial intelligence, this focus conveys the conclusive need for a dual measurement index of PQ reliability, on the basis of dynamic temporal scalability, and time-space compressions. A practical example is outlined.

Scalable Multi-FPGA HPC Architecture for Associative Memory System.

Associative memory is a cornerstone of cognitive intelligence within the human brain. The Bayesian confidence propagation neural network (BCPNN), a cortex-inspired model with high biological plausibility, has proven effective in emulating high-level cognitive functions like associative memory. However, the current approach using GPUs to simulate BCPNN-based associative memory tasks encounters challenges in latency and power efficiency as the model size scales. This work proposes a scalable multi-FPGA high performance computing (HPC) architecture designed for the associative memory system. The architecture integrates a set of hypercolumn unit (HCU) computing cores for intra-board online learning and inference, along with a spike-based synchronization scheme for inter-board communication among multiple FPGAs. Several design strategies, including population-based model mapping, packet-based spike synchronization, and cluster-based timing optimization, are presented to facilitate the multi-FPGA implementation. The architecture is implemented and validated on two Xilinx Alveo U50 FPGA cards, achieving a maximum model size of 200×10 and a peak working frequency of 220 MHz for the associative memory system. Both the memory-bounded spatial scalability and compute-bounded temporal scalability of the architecture are evaluated and optimized, achieving a maximum scale-latency ratio (SLR) of 268.82 for the two-FPGA implementation. Compared to a two-GPU counterpart, the two-FPGA approach demonstrates a maximum latency reduction of 51.72× and a power reduction exceeding 5.28× under the same network configuration. Compared with the state-of-the-art works, the two-FPGA implementation exhibits a high pattern storage capacity for the associative memory task.

Cozie Apple: An iOS mobile and smartwatch application for environmental quality satisfaction and physiological data collection

Collecting feedback from people in indoor and outdoor environments is traditionally challenging and complex to achieve in a reliable, longitudinal, and non-intrusive way. This paper introduces Cozie Apple, an open-source mobile and smartwatch application for iOS devices. This platform allows people to complete a watch-based micro-survey and provide real-time feedback about environmental conditions via their Apple Watch. It leverages the inbuilt sensors of the smartwatch to collect physiological (e.g., heart rate, activity) and environmental (sound level) data. This paper outlines data collected from 48 research participants who used the platform to report perceptions of urban-scale environmental comfort (noise and thermal) and contextual factors such as who they were with and what activity they were doing. The results of 2,400 micro-surveys across various urban settings are illustrated in this paper, showing the variability of noise-related distractions, thermal comfort, and associated context. The results show that participants experienced at least a little noise distraction 58% of the time, with people talking being the most common reason (46%). This effort is novel due to its focus on spatial and temporal scalability and the collection of noise, distraction, and associated contextual information. These data set the stage for larger deployments, deeper analysis, and more helpful prediction models toward better understanding the occupants’ needs and perceptions. These innovations could result in real-time control signals to building systems or nudges for people to change their behavior.

High Resolution Forest Masking for Seasonal Monitoring with a Regionalized and Colourimetrically Assisted Chorologic Typology

Comparisons of recent global forest products at higher resolutions that are only available annually have shown major disagreements among forested areas in highly fragmented landscapes. A holistic reductionist framework and colourimetry were applied to create a chorologic typology of environmental indicators to map forest extent with an emphasis on large-scale performance, interpretability/communication, and spatial–temporal scalability. Interpretation keys were created to identify forest and non-forest features, and a set of candidate tree cover indices were developed and compared with a decision matrix of prescribed criteria. The candidate indices were intentionally limited to those applying only the visible and NIR bands to obtain the highest possible resolution and be compatible with commonly available multispectral satellites and higher resolution sensors, including aerial and potentially UAV/drone sensors. A new High-Resolution Tree Cover Index (HRTCI) in combination with the Green band was selected as the best index based on scores from the decision matrix. To further improve the performance of the indices, the chorologic typology included two insolation indices, a water index and a NIR surface saturation index, to exclude any remaining spectrally similar but unrelated land cover features such as agriculture, water, and built-up features using a process of elimination. The approach was applied to the four seasons across a wide range of ecosystems in south-eastern Australia, with and without regionalisation, to identify which season produces the most accurate results for each ecoregion and to assess the potential for mitigating the spatial–temporal scaling effects of the Modifiable Spatio-Temporal Unit Problem. Autumn was found to be the most effective season, yielding overall accuracies of 94.19% for the full extent, 95.79% for the temperate zone, and 95.71% for the arid zone. It produced the greatest spatial agreement between two recognised global products, the GEDI forest heights extent and the ESA WorldCover Tree cover class. The performance, transparency, and scalability of the approach should provide the basis for a framework for globally relatable forest monitoring.

Distributed Quantum Computing with Photons and Atomic Memories

AbstractThe promise of universal quantum computing requires scalable single‐ and inter‐qubit control interactions. Currently, three of the leading candidate platforms for quantum computing are based on superconducting circuits, trapped ions, and neutral atom arrays. However, these systems have strong interaction with environmental and control noises that introduce decoherence of qubit states and gate operations. Alternatively, photons are well decoupled from the environment and have advantages of speed and timing for quantum computing. Photonic systems have already demonstrated capability for solving specific intractable problems like Boson sampling, but face challenges for practically scalable universal quantum computing solutions because it is extremely difficult for a single photon to “talk” to another deterministically. Here, a universal distributed quantum computing scheme based on photons and atomic‐ensemble‐based quantum memories is proposed. Taking the established photonic advantages, two‐qubit nonlinear interaction is mediated by converting photonic qubits into quantum memory states and employing Rydberg blockade for the controlled gate operation. Spatial and temporal scalability of this scheme is demonstrated further. These results show photon‐atom network hybrid approach can be a potential solution to universal distributed quantum computing.

Holistic Reduction to Compare and Create New Indices for Global Inter-Seasonal Monitoring: Case Study for High Resolution Surface Water Mapping

A remote sensing method that integrates virtual sampling from formalized visual interpretations is proposed to facilitate land cover mapping and enhance its accuracy, with an emphasis on spatial and temporal scalability. Indices are widely used for mapping and monitoring surface water across space and time; however, they typically display some kind of limitation across different environments and seasons. A decision matrix framework based on observations derived from interpretation keys was designed to compare the performance of existing indices alongside a set of newly developed indices. This comparison helped to shortlist indices that warranted further evaluation and accuracy assessment to identify effective indices for global inter-seasonal surface water extent mapping. Additional visual inspections were conducted for criteria that remained unresolved by the decision matrix to examine index consistency across the seasons in a wide range of geographic settings around the world, and further reduce the shortlist. An accuracy assessment was performed for three new shortlisted indices. On a global scale, CAWI (Comprehensive Automatic Water Index) was the best-performing index. Its distinct binary data distribution provides the possibility of regional automatic Otsu thresholding. CAWI was determined to be compatible for Sentinel-2 and Landsat 8 sensors, providing the highest possible spatial resolution as well as the longest time series for retrospective analyses with freely available multispectral imagery. Two alternative indices were identified for sensors limited to the visible and NIR bands. The first index, CATWIC (Clear and Turbid Water Index Combination), split the classification of water into two components, with one index for generally clear water and another index for turbid water. The second, NDCHRWI (Normalized Difference Colourimetric High Resolution Water Index), applied the hue angle from a normalized difference RGB. Masking indices based on modified HSV Saturation equations were developed to reduce misclassification due to other high reflectance features. The indices’ overall accuracies, respectively, were: 94.97%, 94.51%, and 94.85%. This study concludes with recommendations for the application of different indices for sensors possessing shortwave infrared bands and for sensors limited to the visible and NIR bands, with a simple stratification of six zones for Global Surface Water monitoring.

Evaluation of Hybrid Models for Maize Chlorophyll Retrieval Using Medium- and High-Spatial-Resolution Satellite Images

Accurate estimation of the leaf or canopy chlorophyll content is crucial for monitoring crop growth conditions. Remote sensing monitoring of crop chlorophyll is a non-destructive, large-area, and real-time method that requires reliable retrieval models and satellite data. High-resolution satellite imagery generally has better object recognition capabilities. However, the influence of the spectral and spatial resolution of medium- and high-spatial-resolution satellite imagery on chlorophyll retrieval is currently unexplored, especially in conjunction with radiative transfer models (RTMs). This has important implications for the accurate quantification of crop chlorophyll over large areas. Therefore, the objectives of this study were to establish an RTM for the retrieval of maize chlorophyll and to compare the chlorophyll retrieval capability of the model using medium- and high-spatial-resolution satellite images. We constructed a hybrid model consisting of the PROSAIL model and the Gaussian process regression (GPR) algorithm to retrieve maize leaf and canopy chlorophyll contents (LCC and CCC). In addition, an active learning (AL) strategy was incorporated into the hybrid model to enhance the model’s accuracy and efficiency. Sentinel-2 imagery with a spatial resolution of 10 m and 3 m-resolution Planet imagery were utilized for the LCC and CCC retrieval, respectively, using the hybrid model. The accuracy of the model was verified using field-measured maize chlorophyll data obtained in Dajianchang Town, Wuqing District, Tianjin City, in 2018. The results showed that the AL strategy increased the accuracy of the chlorophyll retrieval. The hybrid model for LCC retrieval with 10-band Sentinel-2 without AL had an R2 of 0.567 and an RMSE of 5.598, and the model with AL had an R2 of 0.743 and an RMSE of 3.964. Incorporating the AL strategy improved the model performance (R2 = 0.743 and RMSE = 3.964). The Planet imagery provided better results for chlorophyll retrieval than 4-band Sentinel-2 imagery but worse performance than 10-band Sentinel-2 imagery. Additionally, we tested the model using maize chlorophyll data obtained from Youyi Farm in Heilongjiang Province in 2021 to evaluate the model’s robustness and scalability. The test results showed that the hybrid model used with 10-band Sentinel-2 images achieved good accuracy in the Youyi Farm area (LCC: R2 = 0.792, RMSE = 2.8; CCC: R2 = 0.726, RMSE = 0.152). The optimal hybrid model was applied to images from distinct periods to map the spatiotemporal distribution of the chlorophyll content. The uncertainties in the chlorophyll content retrieval results from different periods were relatively low, demonstrating that the model had good temporal scalability. Our research results can provide support for the precise management of maize growth.

Data-driven building energy modelling – An analysis of the potential for generalisation through interpretable machine learning

Data-driven building energy modelling techniques have proven to be effective in multiple applications. However, the debate around the possibility of generalisation is open. Generalisation involves the ability of a machine-learning model to adapt to previously unseen data and perform in a satisfactory way. Besides that, while machine-learning techniques are extremely powerful, interpretability, i.e. the ability for humans to predict how the model output will change in response to a change in input data or algorithmic parameters, is essential to attain a “human-in-the-loop” approach and creating feedback loops aimed at continuous improvement of efficiency measures in buildings.A flexible regression-based approach is developed and tested on a Passive House building in this study. The formulation employs dummy (binary) variables as a piecewise linearization method, and the rules for creating them are explicitly stated to ensure interpretability. Furthermore, the possibility of automating the model selection process using statistical indicators is described, including specific indicators used in Measurement and Verification (M&V) for the acceptance of calibrated energy models.The valuable insights that can be found using data-driven methods are reported and discussed, emphasizing limitations and constraints, as well as the potential for future research focused on systems of (interpretable data-driven) models that can exploit the techniques' spatial and temporal scalability. Finally, the physical interpretation of model coefficients and the analytical formulations for energy model decomposition can be used to supplement the scalability of data-driven techniques and create more sophisticated systems of interconnected models.

Monitoring the Invasive Plant Spartina alterniflora in Jiangsu Coastal Wetland Using MRCNN and Long-Time Series Landsat Data

Jiangsu coastal wetland has the largest area of the invasive plant, Spartina alterniflora (S. alterniflora), in China. S. alterniflora has been present in the wetland for nearly 40 years and poses a substantial threat to the safety of coastal wetland ecosystems. There is an urgent need to control the distribution of S. alterniflora. The biological characteristics of the invasion process of S. alterniflora contribute to its multi-scale distribution. However, the current classification methods do not deal successfully with multi-scale problems, and it is also difficult to perform high-precision land cover classification on multi-temporal remote sensing images. In this study, based on Landsat data from 1990 to 2020, a new deep learning multi-scale residual convolutional neural network (MRCNN) model was developed to identify S. alterniflora. In this method, features at different scales are extracted and concatenated to obtain multi-scale information, and residual connections are introduced to ensure gradient propagation. A multi-year data unified training method was adopted to improve the temporal scalability of the MRCNN. The MRCNN model was able to identify the annual S. alterniflora distribution more accurately, overcame the disadvantage that traditional CNNs can only extract feature information at a single scale, and offered significant advantages in spatial characterization. A thematic map of S. alterniflora distribution was obtained. Since it was introduced in 1982, the distribution of S. alterniflora has expanded to approximately 17,400 ha. In Jiangsu, the expansion process of S. alterniflora over time was divided into three stages: the growth period (1982–1994), the outbreak period (1995–2004), and the plateau period (2005–2020). The spatial expansion direction was mainly parallel and perpendicular to the coastline. The hydrodynamic conditions and tidal flat environment on the coast of Jiangsu Province are suitable for the growth of S. alterniflora. Reclamation of tidal flats is the main factor affecting the expansion of S. alterniflora.

The superiority of the normalized difference phenology index (NDPI) for estimating grassland aboveground fresh biomass

Accurate monitoring of grassland aboveground fresh biomass (called AGB in the study) and its spatial-temporal dynamics is indispensable for sustainable grassland management. The most common method used in estimating AGB with remotely sensed data is based on the relationship between field AGB measurements and vegetation indices (VIs); however, the existing VIs do not deliver adequate results due to the soil background and spatial, temporal and sampling size variability. In this study, the AGB estimation model with the normalized difference phenology index (NDPI) was evaluated in terms of model robustness and spatial and temporal scalability based on comparisons with the widely used ratio vegetation index (RVI), difference vegetation index (DVI), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), and optimized soil-adjusted vegetation index (OSAVI). The field measurements of AGB of the natural grassland in Inner Mongolia, China, collected in 2013, 2016, and 2017 and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance products were used for analysis. The results based on training and independent validation data showed the following: (1) The R2 value between AGB and the NDPI was the highest (0.73) among all VIs, followed by soil-line-adjusted VIs, while the R2 values of the RVI and DVI were the lowest; (2) The NDPI-based model had the best robustness for different sampling sizes; (3) The NDPI-based model also had superior spatial and temporal scalability. The results from simulation experiments using the PROSAIL model also support the superiority of the NDPI in estimating AGB. The simulation analysis further reveals that the overall superiority of the NDPI originates from the fact that the NDPI overcomes the adverse impacts of the heterogeneity of the soil background and accounts for changes in the leaf water content that contribute substantially to AGB in grassland. These findings suggest that the NDPI-based AGB estimation model is advantageous for monitoring AGB in large grasslands with significant spatial-temporal heterogeneity.

Light Field Image Coding Using VVC Standard and View Synthesis Based on Dual Discriminator GAN

Light field (LF) technology is considered as a promising way for providing a high-quality virtual reality (VR) content. However, such an imaging technology produces a large amount of data requiring efficient LF image compression solutions. In this paper, we propose a LF image coding method based on a view synthesis and view quality enhancement techniques. Instead of transmitting all the LF views, only a sparse set of reference views are encoded and transmitted, while the remaining views are synthesized at the decoder side. The transmitted views are encoded using the versatile video coding (VVC) standard and are used as reference views to synthesize the dropped views. The selection of non-reference dropped views is performed using a rate-distortion optimization based on the VVC temporal scalability. The dropped views are reconstructed using the LF dual discriminator GAN (LF-D2GAN) model. In addition, to ensure that the quality of the views is consistent, at the decoder, a quality enhancement procedure is performed on the reconstructed views allowing smooth navigation across views. Experimental results show that the proposed method provides high coding performance and overcomes the state-of-the-art LF image compression methods by –36.22% in terms of BD-BR and 1.35 dB in BD-PSNR. The web page of this work is available at https://naderbakir79.github.io/LFD2GAN.html .

Combinatorial Q-Learning for Condition-Based Infrastructure Maintenance

Infrastructure maintenance planning is a large-scale optimization problem of planning when and on which components to carry out maintenance so as to keep the whole infrastructure in good condition with minimal maintenance cost. Recent advances in condition monitoring techniques have enabled timely maintenance in response to the condition of each part regardless of age. In addition to the condition, the spatial structure is also important for cost-efficiency in infrastructure maintenance since traveling costs and/or setup costs can be saved by simultaneous maintenance of neighboring components, which is called economic dependency. This optimization problem naively has a high computational complexity of $O(2^{nH})$ , where $n$ is the number of components and $H$ is the planning horizon, and the predictive modeling of degradation is also a big issue. To solve this problem efficiently at scale, our proposed method utilizes two kinds of dynamic programming for temporal and spatial scalability and consequently enjoys $O(n)$ complexity at each time step. For temporal scalability, we utilize a direct modeling approach for the action value of maintenance instead of modeling degradation, namely, Q-learning. For spatial scalability, we exploit locality in economic dependency by means of a reasonable approximation of the Q-function. A typical baseline approach is to divide the whole infrastructure into fixed groups of neighboring components beforehand and determine if maintenance should be performed for all the components in each group at each time step. In contrast, our scalable method enables fully combinatorial optimization for each component at each time step. We demonstrate the advantage of our method in a simulated environment, and the resulting maintenance history intuitively illustrates the benefit of our dynamic grouping approach. We also show that our method has a kind of interpretability in the optimization at each time step.

A fast temporal and hybrid SHVC encoder

In the last few years new devices with different computational capabilities and network connections has emerged, and this leads to the need for more adaptable video streams. The last two video coding standards, namely H.264/Advanced Video Coding and High Efficiency Video Coding, have defined scalable versions of themselves that provide this adaptability by the use of several layers with different levels of quality, frame rates, and resolutions. Moreover, HEVC introduced hybrid scalability, which allows a non-HEVC base layer (e.g., H.264/AVC) in combination with HEVC enhancement layers, what provides backwards compatibility with older devices. Nevertheless, scalable video coding is very computationally expensive, making it necessary to accelerate the video encoding. This paper presents a fast Coding Unit size algorithm for the combination of hybrid and temporal scalabilities that is based on machine learning. Experimental results show that an acceleration of 54% is achieved at the cost of a slight increment in the bit rate.

Escalabilidade nos filmes de Alexander Kluge: estratégia para evitar a catástrofe

Resumo O artigo reflete sobre o conceito de escalabilidade na obra do cineasta alemão Alexander Kluge, pensando nos efeitos provocados pelo deslocamento do filme analógico para o filme computadorizado. Por meio de citações em filmes e entrevistas, o artigo aponta momentos e formas de escalabilidade na obra de Kluge, analisando as escalabilidades temporais e espaciais que também se deslocam para uma escalabilidade a partir de dados digitais. Ao final, o artigo apresenta parte de entrevista inédita feita com o cineasta sobre a internet e os novos meios digitais de comunicação e representação.

A conceptual framework for the real-time monitoring and diagnostic system for the optimal operation of smart building: A case study in Hotel ICON of Hong Kong

This study proposes a conceptual framework for the real-time monitoring and diagnostic system for the optimal operation of smart building, focusing on the energy-efficient, occupant-oriented, and comfortable indoor environment. The proposed framework aims to improve the energy efficiency in a room of building while achieving the healthy and comfortable indoor environmental quality and occupants’ satisfaction. The proposed framework consists of a three-phase cyclic process (i.e. monitoring, diagnostic, and intervention), and it can be simply replicated and extensively applied to the different levels of physical entities (spatial scalability) in the different time resolutions in the whole life cycle processes (temporal scalability). To elaborate the feasibility of the proposed framework, the Hotel ICON in Hong Kong are chosen as a case study. For three rooms in the Hotel ICON, several sensors are installed for monitoring the energy efficiency and indoor environmental quality in real time. With the collected dataset, it is planned to carry out the diagnostic process (e.g. anomaly detection, time-series analysis, and occupancy schedule pattern analysis) and the intervention process (e.g. automatic control, occupant behaviour change, and optimal operation). The conceptual framework provides a standardized and systematic research approach toward a big picture of an intelligent building systems for the energy-efficient, occupant-oriented, and comfortable indoor environment.

Tampering detection and localization in digital video using temporal difference between adjacent frames of actual and reconstructed video clip

The scientific, generalized and automatic methods for detecting forgery became the biggest challenge for scientists and researchers. This problem is true in case of all multimedia contents including audios, graphics and videos. It is harder when one doesn’t know the source and background of video in hand and still expected to establish authenticity of it. However, there are algorithms suggested which can work for such tampering in videos captured with static GOP structure. The problem becomes even more difficult when video is captured using adaptive GOP structure (AGS) scheme in which variable sizes of GOP structures are used to improve coding efficiency and to provide strong temporal scalability. In this paper, an algorithm is proposed which is a passive tampering detection algorithm based on comparison of temporal difference between adjacent video frames of actual video clip and its reconstructed version using intrinsic temporal fingerprints, which can work on videos captured using variable size GOP structures. Firstly, all the video frames are extracted from given video sequence. Then, temporal difference is calculated for each pair of adjacent frames in video’s actual and reconstructed from. Video is reconstructed using frame prediction error. Lastly, the calculated differences are used to find and localize tampering. Our proposed algorithm can effectively classify a video, irrespective of whether captured with fixed or AGSs, as genuine or forged using temporal difference between adjacent video frames in its actual and reconstructed form. Extensive experimental results show that the proposed method achieves promising accuracy in classifying genuine videos and forgeries. The results show that the proposed tampering detection algorithm can detect and precisely locate tampering with an average accuracy of 87.5%.

A 120 fps High Frame Rate Real-time HEVC Video Encoder with Parallel Configuration Scalable to 4K

This paper describes a new 120 fps (frames per second) real-time HEVC (High Efficiency Video Coding) encoder for HFR (high frame rate) video encoding and transmission. HFR provides more immersive viewing experience features by solving the problems created by fast moving scenes. Temporally scalable encoding with backward compatibility for legacy non-HFR systems is suitable for the rapid spread of HFR content delivery, avoiding the need to distribute multiple bitstreams of the same video with different frame rates. Such temporal scalability requires flexible encoder control functionalities to support newly-customized reference picture structures and dual-stream bitrate control. In this paper, modification in the customizable software architecture of encoder LSIs makes it possible to achieve 120 fps temporally scalable HEVC encoding for existing 60 fps-based systems. The encoder also achieves ${4\mathrm{K}/ 120\;\mathrm{fps}}$ video encoding in real time through the synchronized operation of multiple ${2\mathrm{K}/ 120\;\mathrm{fps}}$ encoders working in parallel. Our evaluations show that the bitrate increase rate from 60 fps to 120 fps under the same objective image quality condition are within the range of less than 57.2 percent in all video sequences and its average value is 53.8 percent. Both values are lower than that of the HM (HEVC reference software). The proposed encoder systems will open the door to the next generation high frame rate UHDTV (ultra high definition television) services.

Temporal Scalability of Dynamic Volume Data Using Mesh Compensated Wavelet Lifting.

Due to their high resolution, dynamic medical 2D+t and 3D+t volumes from computed tomography (CT) and magnetic resonance tomography (MR) reach a size which makes them very unhandy for teleradiologic applications. A lossless scalable representation offers the advantage of a down-scaled version which can be used for orientation or previewing, while the remaining information for reconstructing the full resolution is transmitted on demand. The wavelet transform offers the desired scalability. A very high quality of the lowpass sub-band is crucial in order to use it as a down-scaled representation. We propose an approach based on compensated wavelet lifting for obtaining a scalable representation of dynamic CT and MR volumes with very high quality. The mesh compensation is feasible to model the displacement in dynamic volumes which is mainly given by expansion and contraction of tissue over time. To achieve this, we propose an optimized estimation of the mesh compensation parameters to optimally fit for dynamic volumes. Within the lifting structure, the inversion of the motion compensation is crucial in the update step. We propose to take this inversion directly into account during the estimation step and can improve the quality of the lowpass sub-band by 0.63 and 0.43 dB on average for our tested dynamic CT and MR volumes at the cost of an increase of the rate by 2.4% and 1.2% on average.

Multilevel Ultrafast Flexible Nanoscale Nonvolatile Hybrid Graphene Oxide–Titanium Oxide Memories

Graphene oxide (GO) resistive memories offer the promise of low-cost environmentally sustainable fabrication, high mechanical flexibility and high optical transparency, making them ideally suited to future flexible and transparent electronics applications. However, the dimensional and temporal scalability of GO memories, i.e., how small they can be made and how fast they can be switched, is an area that has received scant attention. Moreover, a plethora of GO resistive switching characteristics and mechanisms has been reported in the literature, sometimes leading to a confusing and conflicting picture. Consequently, the potential for graphene oxide to deliver high-performance memories operating on nanometer length and nanosecond time scales is currently unknown. Here we address such shortcomings, presenting not only the smallest (50 nm), fastest (sub-5 ns), thinnest (8 nm) GO-based memory devices produced to date, but also demonstrate that our approach provides easily accessible multilevel (4-level, 2-bit per cell) storage capabilities along with excellent endurance and retention performance-all on both rigid and flexible substrates. Via comprehensive experimental characterizations backed-up by detailed atomistic simulations, we also show that the resistive switching mechanism in our Pt/GO/Ti/Pt devices is driven by redox reactions in the interfacial region between the top (Ti) electrode and the GO layer.

Cross-Layer Optimization for Efficient Delivery of Scalable Video over WiMAX

Video delivery application over wireless network has become increasingly important in recent years. For achieving more flexible video streams transmission, the scalable extension of the H.264/AVC standard has been developed, as well as which is combined with temporal, spatial and quality scalabilities to adapt efficiently the wireless bandwidth resource. In this paper, a cross-layer optimization for robust H.264 scalable video delivery over WiMAX network is proposed. In order to obtain a better delivery performance, the proposed method is used the hierarchical B-picture approach to classify video packets into different priorities. In the cross-layer optimization, the scalable video and packet classifier are proposed at the application layer, and the packet priority and queue allocation are used at the MAC layer. Finally, it is shown by NS2 simulations that the proposed method provides more stable wireless bandwidth and also achieves a better quality of the video streaming delivery.