Error Propagation Problem Research Articles

Performance Evaluation of Frame-loss Error-concealment Solutions for the SHVC Standard

Scalable High Efficiency Video Coding (SHVC) has been emerging as an efficient video coding solution for adaptive video streaming and conferencing. SHVC is designed with a layered coding structure in which High Efficiency Video Coding (HEVC) tools are employed as core elements. However, compression efficiency and error sensitivity associated with efficient HEVC tools make this scalable codec less attractive for practical video transmission, especially when the loss of packets or video frames occurs. This problem severely impacts the display quality of received video. To address this problem, we propose efficient error concealment (EC) methods that compensate for a whole frame that is lost and that mitigate the error propagation problem occurring in practical video transmissions using SHVC. The presented EC methods mainly rely on the decoded information; thus, it is easily integrated into the SHVC decoder as a post-processing component. The experimental results obtained for both subjective and objective quality assessments show that the inter-layer correlation-based EC approach typically provides the highest concealed frame quality; thus, it is highly recommended for practical video transmission.

Solution for error propagation in a NOMA-based VLC network: symmetric superposition coding.

Non-orthogonal multiple access (NOMA) has recently attracted significant attention as a promising multiple access scheme for the 5th generation (5G) wireless communication due to its superior spectral efficiency, which has also been studied and shown to achieve a superior performance in visible light communication (VLC) networks. However, the error propagation (EP) problem due to successive interference cancellation (SIC) decoding has not yet been resolved, which degrades the system BER performance and causes user unfairness. In this work, symmetric superposition coding (SSC) and symmetric SIC (SSIC) decoding are proposed for a downlink NOMA-based VLC network, in which the distribution of the demodulation regions of the user allocated with more power will be symmetrical in terms of the decision threshold of the user allocated with less power. Furthermore, the proposed method is experimentally tested and the results show that more than 90% demodulation errors caused by EP are eliminated compared with traditional NOMA VLC.

Hybrid profilometry using a single monochromatic multi-frequency pattern.

In this paper, we propose a novel profilometry scheme to acquire high quality depth, where only a single shot of a monochromatic pattern is utilized. We design a band-wise pattern consisting of fringe bands spatially modulated with coprime periods. After that, with the designed pattern, depth is obtained in a hybrid manner, where both phase-based profilometry and active stereo are incorporated. To be specific, pixels in smooth regions obtain their depth values through phases analysis. Especially, based on depth smooth property, we propose a novel phase unwrapping algorithm, which avoids the problem of error propagation and yields accurate unwrapping phases. On the other hand, for boundary regions, spatial stereo, which is more robust to depth discontinuities, is utilized to modify incorrect depth values. Both theoretical verification and experimental results demonstrate that the proposed scheme can generate high quality depth maps, even for complex scenes and isolated objects.

An Improved Frequency Domain Turbo Equalizer for Single-Carrier Spatial Modulation Systems

In this paper, a new time-domain soft-decision feedback (TDSDF) aided frequency domain turbo equalizer (FDTE) is proposed for broadband single-carrier (SC) spatial modulation (SM) systems. Specifically, the proposed equalizer operates on a frame-by-frame basis and is designed based on the minimum block-averaged mean-square error (MBMSE) criterion. To tackle the error propagation problem, a metric in consideration of the reliability of the feedback information is considered. The optimal coefficients of the forward and feedback filters involved in the TDSDF-FDTE design are derived analytically. Simulation results show that the proposed nonlinear TDSDF-FDTE detector is capable of offering a better tradeoff between the bit-error-rate (BER) performance and the imposed computational complexity than the conventional linear frequency domain turbo demapper (FDTD) conceived for SC-SM systems. Furthermore, a 4 dB improvement is observed for the unbalanced channel.

Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics

BackgroundGenetic variant effect prediction algorithms are used extensively in clinical genomics and research to determine the likely consequences of amino acid substitutions on protein function. It is vital that we better understand their accuracies and limitations because published performance metrics are confounded by serious problems of circularity and error propagation. Here, we derive three independent, functionally determined human mutation datasets, UniFun, BRCA1-DMS and TP53-TA, and employ them, alongside previously described datasets, to assess the pre-eminent variant effect prediction tools.ResultsApparent accuracies of variant effect prediction tools were influenced significantly by the benchmarking dataset. Benchmarking with the assay-determined datasets UniFun and BRCA1-DMS yielded areas under the receiver operating characteristic curves in the modest ranges of 0.52 to 0.63 and 0.54 to 0.75, respectively, considerably lower than observed for other, potentially more conflicted datasets.ConclusionsThese results raise concerns about how such algorithms should be employed, particularly in a clinical setting. Contemporary variant effect prediction tools are unlikely to be as accurate at the general prediction of functional impacts on proteins as reported prior. Use of functional assay-based datasets that avoid prior dependencies promises to be valuable for the ongoing development and accurate benchmarking of such tools.

Formation Control of Agricultural Mobile Robots: A Bidirectional Weighted Constraints Approach

AbstractThis paper focuses on the formation control of several mobile robots in off‐road conditions. A control strategy based on bidirectional referencing is proposed, where each robot combines a velocity control w.r.t. the immediate preceding neighbor with a control w.r.t. the immediate following neighbor. Two virtual leaders, respectively, at the head and at the tail of the fleet, are propelling the fleet at the desired velocity. The fleet stability is investigated using Lyapunov techniques, pending on combination coefficients. The stability is theoretically proven for constant coefficients as weights of the respective velocity controls and tested through simulations and full‐scale experiments. It is shown that this control strategy permits us to reduce the error propagation problem often encountered in formation control, while limiting the communication problems of the global strategies. As a result, a stable formation control architecture is defined, which requires each vehicle to be refereed only to two other robots. This limited communication need is particularly interesting for agricultural operations. The proposed approach is implemented in this paper on agricultural tractors.

Hierarchical Latent Concept Discovery for Video Event Detection.

Semantic information is important for video event detection. How to automatically discover, model, and utilize semantic information to facilitate video event detection has been a challenging problem. In this paper, we propose a novel hierarchical video event detection model, which deliberately unifies the processes of underlying semantics discovery and event modeling from video data. Specially, different from most of the approaches based on manually pre-defined concepts, we devise an effective model to automatically uncover video semantics by hierarchically capturing latent static-visual concepts in frame-level and latent activity concepts (i.e., temporal sequence relationships of static-visual concepts) in segment-level. The unified model not only enables a discriminative and descriptive representation for videos, but also alleviates error propagation problem from video representation to event modeling existing in previous methods. A max-margin framework is employed to learn the model. Extensive experiments on four challenging video event datasets, i.e., MED11, CCV, UQE50, and FCVID, have been conducted to demonstrate the effectiveness of the proposed method.

Linear Precoded THP-FDE for Single-Carrier Broadband MIMO Systems

Tomlinson–Harashima precoding with frequency-domain equalization (THP-FDE) is a promising approach for multiple-input multiple-output (MIMO) single-carrier broadband systems. By performing interference cancelation at the transmitter, THP-FDE performs better than frequency-domain decision-feedback equalization (FD-DFE) via eliminating the error propagation problem resulting from incorrect symbol decisions. One of the drawbacks for MIMO THP-FDE systems is that the spatial stream with the largest mean square error (MSE) dominates the error performance. To alleviate this problem, conventional design adopts a sorting algorithm (a permutation matrix) to minimize the worst-case stream MSE. Since the difference of stream MSEs roots in the nonequal diagonal entries of the MSE matrix in the time domain, we are motivated to apply a general linear precoder based on geometric-mean decomposition to balance these MSEs. By jointly optimizing the precoder and equalizer, we are able to minimize the arithmetic MSE, geometric MSE, and the worst stream MSE simultaneously for the MIMO THP-FDE system. Simulation results are presented to demonstrate the superior bit-error-rate (BER) performance of the proposed transceiver architecture for MIMO THP-FDE systems.

A Simplified Joint Source and Channel Coding System for Data Transmission over Fading Channel

The major challenge with multimedia communication lies in the demand for higher data rates with better quality of service and lower latency over the wireless channel. Multimedia content is usually source encoded with variable length codes (VLCs) for optimizing bandwidth utilization. VLCs, even though uniquely decodable, suffer from the problem of error propagation. A single bit error can cause an entire block of data to be decoded wrongly, because the ending of a codeword and the beginning of the next can no longer be determined correctly. Several joint source-channel coding (JSCC) techniques are studied in literature to have efficient source coding with resilience to channel errors, but such methods pose a prohibitively high computational complexity. A variant of JSCC for efficient transmission of information is presented in this paper. The information text is encoded by reversible VLC (RVLC) and corresponding to it, a header sequence is generated. Since the headers are critical in decoding a block, they are channel coded with convolution code to protect from errors. Orthogonal frequency division multiplexing is used to provide high data rate communication and robustness to multipath fading. The performance of the system is analysed over Rayleigh fading channel in terms letter error rate (LER) and computational complexity. The proposed method is seen to provide a good LER performance at moderate to high signal to noise ratio, along with minimal computational complexity, when compared with the contemporary JSCC based schemes. Theoretical performance bounds for the error rate of the proposed system are also derived.

On the error performance of network-coded error-prone DF-relaying in multiple access relay channel

This paper deals with bit error performance analysis of network-coded (NC) multiple access relay channels that operate in the decode and forward mode with error-prone relaying under quasi-static Rayleigh fading channels. It shows that the combination of channel coding and NC relaying can be regarded as a distributed coding scheme (DCS) if the error propagation problem is adequately addressed. For this purpose, methods based on NC soft information relaying (NC-SIR) and NC hard information relaying (NC-HIR) are investigated. In the framework of NC-SIR, the recently proposed Rayleigh-Gaussian log-likelihood-ratio-based Model is used for modelling the soft estimated symbols at the output of the relay soft encoder. The resulting scheme is referred to as NC-SIR-DCS. On the other hand, two NC-HIR-based schemes are studied and analyzed. The first employs automatic repeat request (ARQ) protocols between the relay node and each source node to ensure error-free propagation and is referred to as NC-ARQ-DCS, while the second performs a limiter function at the destination during iterative decoding as introduced in Thobaben (1) distributed and is referred to as NC-LFD-DCS. The SNR of the one-hop link, the equivalent to the sources-relay-destination links, is estimated in the case of NC-SIR-DCS and is lower bounded in the case of NC-LFD-DCS, while in the case of NC-ARQ-DCS, the average SNR cost per information bit due to the ARQ protocol is estimated. These processes allow tractable analysis of the bit error rate performances of the presented network-coded DCSs. Simulation results are carried out to assess the accuracy of the proposed bounds for different relay positions between source nodes and destination.

Decentralized thermal modeling of multi-zone buildings for control applications and investigation of submodeling error propagation

Centralized model predictive control is a popular and optimal approach for thermal control of multi-zone buildings. In centralized control of large-scale, multi-zone buildings, decentralized thermal modeling (including identification) may be useful since it may hard and computationally demanding to obtain a single model for the overall system. Success of a centralized predictive controller depends highly on the prediction accuracy of the used control model during control design phase.In decentralized modeling, control-oriented thermal models of individual zones are obtained by taking the thermal interactions with adjacent zones as extra input signals. These submodels can be very accurate when validated at the zone level. However, when all submodels developed in a decentralized approach are combined to construct a model of the overall building system, modeling errors in submodels may be amplified through thermal interactions and the resulting multi-zone model may not have enough prediction accuracy to be used in a centralized model predictive control algorithm. The opposite is also possible: submodel errors may cancel out, to some degree, each other and hence the multi-zone model may have a better prediction accuracy than submodels.In this paper, first a set of state-of-the-art decentralized control-oriented thermal modeling/identification approaches (composable zones approach, graph theory-based decentralized identification approach, and determination of weakly-interacting zones approach) for multi-zone buildings is presented. Second, multi-zone building thermal models based on the composable zones approach and multi-input single output (MISO) ARMAX (AutoRegressive Moving Average with eXogenous input) model structure for submodels are obtained, and then the problem of submodeling error propagation is investigated through a set of case studies. The case studies are arranged with an increasing number of thermal interactions. For the considered case studies, it was found that the combination of composable zones approach with MISO ARMAX model structure for submodels was an appropriate way to obtain accurate control-oriented prediction models for multi-zone buildings. Moreover, it was observed that the error propagation sometimes can be significant when a multi-zone model was composed from submodels, and that there was no correlation between the number of thermal interactions and the level of submodeling error propagation.

SAR Ground Moving Target Imaging Algorithm Based on Parametric and Dynamic Sparse Bayesian Learning

In this paper, a novel synthetic aperture radar (SAR) ground moving target imaging (GMTIm) algorithm is presented within a parametric and dynamic sparse Bayesian learning (SBL) framework. A new time–frequency representation, which is known as Lv's distribution (LVD), is employed on the moving targets to determine the parametric dictionary used in the SBL framework. To combat the inherent accuracy limitations of the LVD and extrinsic perturbation errors, a dynamical refinement process is further developed and incorporated into the SBL framework to obtain highly focused SAR image of multiple moving targets. An emerging inference technique, which is known as variational Bayesian expectation–maximization, is applied to achieve an efficient Bayesian inference for the focused SAR moving target image. A remarkable advantage of the proposed algorithm is to provide a fully posterior distribution (Bayesian inference) for the SAR moving target image, rather than a poor point estimate used in conventional methods. Because of utilizing high-order statistical information, the error propagation problem is desirably ameliorated in an iterative manner. The perturbations, known as the multiplicative phase error and additive clutter and noise, are both well adjusted for further improving the image quality. Experimental results by using simulated spotlight-SAR data and real Gotcha data have demonstrated the superiority of the proposed algorithm over other reported ones.

Joint Inference over a Lightly Supervised Information Extraction Pipeline: Towards Event Coreference Resolution for Resource-Scarce Languages

We address two key challenges in end-to-end event coreference resolution research: (1) the error propagation problem, where an event coreference resolver has to assume as input the noisy outputs produced by its upstream components in the standard information extraction (IE) pipeline; and (2) the data annotation bottleneck, where manually annotating data for all the components in the IE pipeline is prohibitively expensive. This is the case in the vast majority of the world's natural languages, where such annotated resources are not readily available. To address these problems, we propose to perform joint inference over a lightly supervised IE pipeline, where all the models are trained using either active learning or unsupervised learning. Using our approach, only 25% of the training sentences in the Chinese portion of the ACE 2005 corpus need to be annotated with entity and event mentions in order for our event coreference resolver to surpass its fully supervised counterpart in performance.

A Hybrid Fault-Tolerant Architecture for Highly Reliable Processing Cores

Increasing vulnerability of transistors and interconnects due to scaling is continuously challenging the reliability of future microprocessors. Lifetime reliability is gaining attention over performance as a design factor even for lower-end commodity applications. In this work we present a low-power hybrid fault tolerant architecture for reliability improvement of pipelined microprocessors by protecting their combinational logic parts. The architecture can handle a broad spectrum of faults with little impact on performance by combining different types of redundancies. Moreover, it addresses the problem of error propagation in nonlinear pipelines and error detection in pipeline stages with memory interfaces. Our case-study implementation of a fault tolerant MIPS microprocessor highlights four main advantages of the proposed solution. It offers (i) 11.6 % power saving, (ii) improved transient error detection capability, (iii) lifetime reliability improvement, and (iv) more effective fault accumulation effect handling, in comparison with TMR architectures. We also present a gate-level fault-injection framework that offers high fidelity to model physical defects and transient faults.

Uncertainty analysis of space–time prisms based on the moment-design method

ABSTRACTSpace–time prism (STP) is an important concept for the modeling of object movements in space and time. An STP can be conceptualized as the result of the potential path of a moving object revolving around in the three-dimensional space. Though the concept has found applications in time geography, research on the analysis and propagation of uncertainty in STPs, particularly under high degree of nonlinearity, is scanty. Based on the efficiency and effectiveness of the moment-design (M-D) method, this paper proposes an approach to deal with nonlinear error propagation problems in the potential path areas (PPAs) of STPs and their intersections. Propagation of errors to the PPA and its boundary, and to the intersection of two PPAs is investigated. Performance of the proposed method is evaluated via a series of experimental studies. In comparison with the Monte Carlo method and the implicit function method, simulation results show the advantages of the M-D method in the analysis of error propagation in STPs.

Global optimization for coupled detection and data association in multiple object tracking

We present a novel framework for tracking multiple objects imaged from one or more static cameras, where the problems of object detection and data association are expressed by a single objective function. Particularly, we combine a sparsity-driven detector with the network-flow data association technique. The framework follows the Lagrange dual decomposition strategy, taking advantage of the often complementary nature of the two subproblems. Our coupling formulation avoids the problem of error propagation from which traditional “detection-tracking approaches” to multiple object tracking suffer. We also eschew common heuristics such as “non-maximum suppression” of hypotheses by modeling the joint image likelihood as opposed to applying independent likelihood assumptions. Our coupling algorithm is guaranteed to converge and can resolve the ambiguities in track maintenance due to frequent occlusion and indistinguishable appearance between objects. Furthermore, our method does not have severe scalability issues but can process hundreds of frames at the same time. Our experiments involve challenging, notably distinct datasets and demonstrate that our method can achieve results comparable to or better than those of state-of-art approaches.

BER‐modified regenerative protocols for OFDM‐based two‐way wireless relay channel

SummaryIt is well known that symbol‐level regenerative relay protocols suffer the error propagation problem because receiver decodes blindly and overlooks the probability of relay forwarding wrong bits. In a two‐way relay networks, the problem still exists in both network coding (decode‐and‐forward) and physical network coding (denoise‐and‐forward) protocols. For today's widely adopted wide band Orthogonal frequency‐division multiplexing (OFDM) systems, error propagation will dramatically restrict the system's end‐to‐end performance especially when frequency selective fading exists. In this paper, we propose a bit error rate (BER) modified decoding algorithm for these OFDM‐based two‐way symbol‐level regenerative relay strategies. By confining the confidence level of demodulated soft information according to the likelihood of relay having made an error on each bit, this proposed algorithm significantly boosts the end‐to‐end BER performance of the system. Copyright © 2015 John Wiley & Sons, Ltd.

HEVC 스트림 상에서의 객체 추적 방법

Video object tracking is important for variety of applications, such as security, video indexing and retrieval, video surveillance, communication, and compression. This paper proposes an object tracking method in HEVC bitstreams. Without pixel reconstruction, motion vector (MV) and size of prediction unit in the bitstream are employed in an Spatio-Temporal Markov Random Fields (ST-MRF) model which represents the spatial and temporal aspects of the object's motion. Coefficient-based object shape adjustment is proposed to solve the over-segmentation and the error propagation problems caused in other methods. In the experimental results, the proposed method provides on average precision of 86.4%, recall of 79.8% and F-measure of 81.1%. The proposed method achieves an F-measure improvement of up to 9% for over-segmented results in the other method even though it provides only average F-measure improvement of 0.2% with respect to the other method. The total processing time is 5.4ms per frame, allowing the algorithm to be applied in real-time applications.

Receiver Design and Performance Analysis of Multiplicative Superposition Signaling Scheme for Heterogeneous-Speed Users

On the two-user multiple-input multiple-output (MIMO) broadcast channel, when one user with low mobility (called the static user) knows the exact channel state information (CSI) whereas the other user with high mobility (called the dynamic user) does not know CSI, a novel multiplicative superposition signaling scheme could obtain additive degree of freedom. Based on this novel signaling scheme, different types of receivers over an two-Heterogeneous-Speed-User MIMO broadcast channel are investigated in this study. At the dynamic user, the non-coherent maximum likelihood detection (NCMLD) is used. At the static user, we propose two hard detection scheme based on NCMLD-ML and NCMLD-Minimum-Mean-Square-Error (MMSE), respectively. We derive the union bound of the symbol error rate for the optimal hard detection scheme and the upper bound of that for the MMSE-based hard detection scheme. To avoid the error-propagation problem in the hard detection schemes, we also propose a low-complexity soft detection scheme based on the conditional expectation and MMSE (CE-MMSE). Simulation results show that the NCMLD-ML scheme outperforms the NCMLD-MMSE scheme and CE-MMSE scheme. Furthermore, CE-MMSE scheme has a better error performance than NCMLD-MMSE in the low signal-to-noise ratio region at the expense of higher complexity which is inconsiderable when compared with NCMLD-ML scheme. The numerical and simulation results verify the correctness of the theoretical analysis.

Spatial Error Concealment With an Adaptive Linear Predictor

In this paper, a novel spatial error concealment (EC) algorithm is proposed. Under the sequential recovery framework, pixels in missing blocks are successively reconstructed based on adaptive linear predictor. The predictor automatically tunes its order and support shape according to local contexts. The predictor order and support shape are determined using Bayesian information criterion, which is able to strike a balance between the bias and variance of the prediction errors. The flexibility of the order-adaptive predictor is able to recover more important features or structures. A novel scan order based on the uncertainty of each pixel is also proposed to alleviate error propagation problem. Compared with the state-of-the-art EC algorithms, experimental results show that the proposed method gives better reconstruction performance in terms of objective and subjective evaluations.