Reliability Enhancement of Fault-prone Many-core Systems Combining Spatial and Temporal Redundancy

Data Reconciliation via Temporal and Spatial Redundancies

CHAPTER 9 - Model-based Multiview Video Compression Using Distributed Source Coding Principles

This paper presents a new video compression technique which is based on adaptive vector quantization of multiwavelet coefficients. Three types of redundancies that are common in video sequences are spatial, temporal and psycho visual redundancies. In this work, the spatial redundancy is minimized using Multiwavelet transform, temporal redundancy is minimized using Kite Cross Diamond Search motion estimation algorithm, and the psycho visual redundancy is minimized using adaptive vector quantization technique. The objective of the paper is to develop a low bit rate video coder with acceptable visual quality. The performance of the proposed scheme is compared with wavelet based video coder. Simulation results show that multiwavelet based adaptive vector quantization gives better coding performance than wavelet based adaptive vector quantization scheme.

In the process of marketization, the lack of redundancy evaluation of the MSW incineration treatment capacity leads to the regional imbalance of treatment capacity and waste of resources. Therefore, this study aimed to develop a spatial-temporal redundancy evaluation method for the MSW incineration treatment capacity based on the accurate MSW generation prediction using artificial intelligence. To achieve this aim, this study first proposed and finalized a prediction model of the provincial MSW generation by applying the artificial neuron network (ANN) technology and using the statistical data of Jiangsu Province of China from 1990 to 2020. In the finalized model, the input variables consist of three demographic variables, three social variables, and five economic variables; the model structure that includes four hidden layers and 16 neurons in each hidden layer performed best with a coefficient of determination (R2) of 0.995 on the training samples and an R2 of 0.974 on the test set, respectively. Using the finalized model and statistical data of all provinces in China, this study proposed a redundancy evaluation method for the MSW incineration treatment capacity and evaluated the spatial and temporal redundancy status of China. The results first confirm the effectiveness of the proposed method to model and quantify the redundancy problem. Second, according to the evaluation results, even if no new treatment plant will be built before 2025, 10 of China's 31 provinces still have redundancy problems, indicating the severity of this problem. This study first contributes to the body of knowledge by modeling the redundancy problem of the MSW incineration treatment capacity. Moreover, this study provides a tool to quantify temporal and spatial redundancy using advanced technology and publicly available data. Furthermore, the results can help waste-related authorities and organizations make optimal strategies and actions to better match MSW treatment capacity and MSW generation volume.

This article presents a hybrid pixel dedicated to event-based image sensors, which suppresses spatial and temporal redundancies. This pixel is a combination between two kinds of pixels, known as TFS and DVS. The first one is able to reduce - associated to a dedicated readout - spatial redundancies, while the second manages the temporal redundancies. Each pixel is classically built with a photodiode but this hybrid structure shares the photodiode between the DVS and the TFS pixels. In our design, 4 TFS pixel photodiodes are shared with 1 DVS pixel, which exploits the average current of the latter. In order to evaluate such a pixel, a comparison is made between a hybrid pixel matrix and a matrix composed of TFS and DVS pixels. The evaluation is made in terms of activity and power but also by analyzing their fill-factor, circuitry and layout. A testchip including the 2 event-based image sensors has been designed in FDSOI 28 nm technology from STMicroelecronics.

Reputation of the sensors are highly essential in authenticating the sensing data, especially when sensors are deployed in a hostile environment. We refer to the term data-trust as the degree of confidence, which can be represented as a quantitative score, based on the reputation of the sensor, where the reputation is comprehended with spatial and temporal redundancy. In this paper, we analyzed the vulnerability of the sensors subject to radical environmental conditions, and we have derived a first-order differential equation utilizing a linear combination of trust factors to quantify the trust value. The trust value is expressed as a weighted combination of two trust factors: coherent data (spatial redundancy) and periodic behavior (temporal redundancy) of the sensors. The selection of weights are automated based on a cost value suited for the operating environment, and it is treated as a combinatorial optimization problem, with an objective function is to maximize the confidence of the sensor. We employed Tabu Search to find the better combination of weights to be associated with the trust factors, in order to find the positive subspace reflecting the domain of trusted sensor operations. We carried out many experiments with varying proportions of the selected trust factors; and the experimental outcomes were analyzed in drawing boundaries of trusted domain (trust space). Our experimental results with varying malfunctioning sensor readings showed that Tabu Search reduced the search space by 22% in comparison to the local search utilizing Simulated Annealing.

Reliable spatial and temporal data redundancy reduction approach for WSN

In a pilot project, a spatial and temporal algorithm (geostatistical temporal–spatial or GTS) was developed for optimizing long‐term monitoring (LTM) networks. Data from two monitored ground‐water plumes were used to test the algorithm. The primary objective was to determine the degree to which sampling, laboratory analysis, and/or well construction resources could be pared without losing key statistical information concerning the plumes. Optimization of an LTM network requires an accurate assessment of both ground‐water quality over time and trends or other changes in individual monitoring wells. Changes in interpolated concentration maps over time indicate whether ground‐water quality has improved or declined. GTS separately identifies temporal and spatial redundancies. Temporal redundancy may be reduced by lengthening the time between sample collection. Spatial redundancy may be reduced by removing wells from the network which do not significantly impact assessment of ground‐water quality.Part of the temporal algorithm in GTS involves computation of a composite temporal variogram to determine the least redundant overall sampling interval. Under this measure of autocorrelation between sampling events, the lag time at which the variogram reaches a sill is the sampling interval at which same‐well measurements lack correlation and are therefore non‐redundant. The spatial algorithm assumes that well locations are redundant if nearby wells offer nearly the same statistical information about the underlying plume. A well was considered redundant if its removal did not significantly change: (i) an interpolated map of the plume; (ii) the local kriging variances in that section of the plume; and (iii) the average global kriging variance. To identify well redundancy, local kriging weights were accumulated into global weights and used to gauge each well's relative contribution to the interpolated plume map. By temporarily removing that subset of wells with the lowest global kriging weights and re‐mapping the plume, it was possible to determine how many wells could be removed without losing critical information.Test results from the Massachusetts Military Reserve (MMR) indicated that substantial savings in sampling, analysis and operational costs could be realized by utilizing GTS. Annual budgetary savings that would accrue were estimated at between 35 per cent and 5 per cent for both LTM networks under study.Copyright © 2002 John Wiley & Sons, Ltd.

In this paper, we propose a computationally efficient algorithm for video denoising that exploits temporal and spatial redundancy. The proposed method is based on non-local means (NLM). NLM methods have been applied successfully in various image denoising applications. In the single-frame NLM method, each output pixel is formed as a weighted sum of the center pixels of neighboring patches, within a given search window. The weights are based on the patch intensity vector distances. The process requires computing vector distances for all of the patches in the search window. Direct extension of this method from 2D to 3D, for video processing, can be computationally demanding. Note that the size of a 3D search window is the size of the 2D search window multiplied by the number of frames being used to form the output. Exploiting a large number of frames in this manner can be prohibitive for real-time video processing. Here, we propose a novel recursive NLM (RNLM) algorithm for video processing. Our RNLM method takes advantage of recursion for computational savings, compared with the direct 3D NLM. However, like the 3D NLM, our method is still able to exploit both spatial and temporal redundancy for improved performance, compared with 2D NLM. In our approach, the first frame is processed with single-frame NLM. Subsequent frames are estimated using a weighted sum of pixels from the current frame and a pixel from the previous frame estimate. Only the single best matching patch from the previous estimate is incorporated into the current estimate. Several experimental results are presented here to demonstrate the efficacy of our proposed method in terms of quantitative and subjective image quality.

In this paper, a new optimised method of coding stereoscopic image sequences is presented and compared with already known methods. Two basic methods of coding a stereoscopic image sequence are the compatible and joint. The first one uses MPEC for coding the left channel and takes advantage of the spatial disparity redundancy between the two sequences for coding the right channel. The second one employs MPEG for coding the left channel but takes advantage of both temporal redundancy among the right channel frames and spatial redundancy between the corresponding frames of the two channels The proposed method, which is called IMDE, estimates the P and B type of frames of the right channel by an interpolative scheme that takes in to account both the temporal and disparity characteristics. Investigating the effectiveness of the joint motion and disparity vectors estimation as well as the choice of the weighting factors that participate in the proposed interpolative scheme optimises the whole framework.

With the continous decrease in the minimum feature size and increase in the chip density, modern processors are being increasingly susceptible to soft errors. In the past, the technique of lockstep execution with redundant threads on duplicated pipelines have been used for soft error rate reduction which can achieve high error coverage but at the cost of large overheads in terms of area and performance. In this paper, we propose techniques for protection against soft errors in multi-core designs using (i) the properties of spatial and temporal redundancy and (ii) value based detection. We utilize temporal redundancy by using the “latency use slack” (LSC) of an instruction, which we de£ne as the number of cycles before the computed result from the instruction becomes the source operand of a subsequent instruction, while spatial redundancy is exploited by duplicating the instruction to a nearby idle processor core. Further, the value based detection technique is explored by exploiting the width of the operands with small data values and the generation of residue code check bits for the source operands. When a soft error is detected, error correction is achieved by rolling back the execution to a previous checkpoint state and re-executing the instructions. The proposed techniques have been implemented on the RSIM simulation framework and validated using the SPLASH benchmarks. Our results indicate that the soft error detection schemes proposed in this work, can be implemented, on average, with less than 10% increase in CPI on modern multi-core designs.

A novel hierarchical light field coding scheme based on hybrid stacked multiplicative layers and Fourier disparity layers for glasses-free 3D displays

We present an adaptive lossless video compression algorithm based on predictive coding. The proposed algorithm exploits temporal, spatial, and spectral redundancies in a backward adaptive fashion with extremely low side information. The computational complexity is further reduced by using a caching strategy. We also study the relationship between the operational domain for the coder (wavelet or spatial) and the amount of temporal and spatial redundancy in the sequence being encoded. Experimental results show that the proposed scheme provides significant improvements in compression efficiencies.

In this paper, we present local and nonlocal algorithms for video denoising and simplification based on discrete regularization on graphs. The main difference between video and image denoising is the temporal redundancy in video sequences. Recent works in the literature showed that motion compensation is counter-productive for video denoising. Our algorithms do not require any motion estimation. In this paper, we consider a video sequence as a volume and not as a sequence of frames. Hence, we combine the contribution of temporal and spatial redundancies in order to obtain high quality results for videos. To enhance the denoising quality, we develop a robust method that benefits from local and nonlocal regularities within the video. We propose an optimized method that is faster than the nonlocal approach, while producing equally attractive results. The experimental results show the efficiency of our algorithms in terms of both Peak Signal to Noise Ratio and subjective visual quality.

Building distributed embedded systems that will be fault-free for all their lifetime is virtually impossible, thus the systems must deal with them if a continued correct behavior is needed. This is the case of safety-critical systems, such as X-by-wire systems in the automotive domain. Concerning transient communication faults in particular, they can be dealt with at various levels of the protocol stacks, with different techniques, e.g., temporal and spatial redundancy. In this paper we focus on temporal redundancy and we address the limitations imposed by typical time-triggered systems, commonly found in safety-critical systems, arising from their static traffic definition. In these systems the use of temporal redundancy to handle communication errors requires the pre-allocation of communication resources that, in the absence of errors, are wasted. Therefore, we propose an online traffic scheduling approach in which retransmissions are consistently scheduled with the remaining time-triggered traffic, using the unique flexibility provided by the FTT-CAN protocol (Flexible Time-Triggered communication on CAN). We address the integration of appropriate fault detectors in the FTT-CAN protocol to monitor the bus activity and re-schedule omitted messages. We show that this approach is more efficient than the static allocations, since communication resources are only allocated when necessary. We also discuss alternative realizations and validate the approach with initial results from a prototype implementation.