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Advances in sensing and communication technologies have enabled emerging applications in ocean research. The stringent resource limitations, however, hinder local task processing on maritime devices. Cloud-based task offloading is also not feasible due to the unaffordable costs induced by satellite links. In this work, we propose a visionary architecture on software-defined maritime fog computing (SD-MFC), which adopts fog computing to provide localized services and uses software-defined networking technology to provide the abstraction of underlying resources. In SD-MFC, coastal infrastructures and large-size vessels are turned into maritime fog nodes to process the tasks generated by surrounding maritime devices. By exploration of a real-world vessel trajectory dataset, the feasibility and effectiveness of the proposed SD-MFC are verified. [ABSTRACT FROM AUTHOR]

With the proliferation and diversification of mobile communication services, the current mobile broadband networks face severe challenges. The progressive video service that will make up the majority of future network traffic has more demands for transmission bandwidth, network capacity, and management flexibility. Moreover, denser connectivity, higher reliability, lower latency, and better interaction impose some great challenges to existing networks. In order to meet these growing demands, a promising approach is to integrate a broadcast network with a cellular network. In this article, we briefly set forth the latest progress on the cellular network and the broadcast network, respectively. Then the logic architecture of the integrated network is given to clarify the functional partitioning. In addition, the physical architecture of the integrated network is presented to enable coordination in different aspects. Some feasible strategies are discussed and several promising technologies are presented. [ABSTRACT FROM AUTHOR]

The bootstrap in ATSC 3.0 is expected to act as a universal wake-up signal for various wireless systems in addition to broadcast network. However, the signaling decoding performance of the standardized bootstrap degrades significantly in channels of fast time-variation and strong multipath. Furthermore, part of available bandwidth is reserved to ensure the compatibility with mobile communication network (MCN), which puts a limitation on the performance improvement. In this paper, to make the best of the available bandwidth, we introduce the bandwidth-concerned version information to enable an adaptive bandwidth configuration for the proposed bootstrap. Moreover, a 2-D signaling scheme is used to increase signaling capacity by selecting different gold sequences in frequency domain (FD) and simultaneously applying cyclic shift in time domain (TD). At the receiver side, we first provide improved estimators of symbol timing offset (STO) and fine frequency offset (FFO) for the bootstrap with special TD structure. Meanwhile, a learning-based binary classifier taking the output of STO estimator as training data is provided to generate an SNR-independent threshold for spectrum sensing without requiring the knowledge of channel conditions nor noise estimator. Afterwards, an inverse fast Fourier transform (IFFT)-based algorithm is used to decode FD signaling in the presence of unknown TD signaling, which allows 3-bit higher signaling capacity. Numerical analysis and simulation results demonstrate that the proposed adaptive bootstrap design as well as corresponding receiver algorithms significantly outperform the standardized one in terms of synchronization, detection and signaling decoding. [ABSTRACT FROM AUTHOR]

Given a language query, temporal grounding task is to localize temporal boundaries of the described event in an untrimmed video. There is a long-standing challenge that multiple moments may be associated with one same video-query pair, termed label uncertainty. However, existing methods struggle to localize diverse moments due to the lack of multi-label annotations. In this paper, we propose a novel Diverse Temporal Grounding framework (DTG) to achieve diverse moment localization with only single-label annotations. By delving into the label uncertainty, we find the diverse moments retrieved tend to involve similar actions/objects, driving us to perceive these interest moments. Specifically, we construct soft multi-label through semantic similarity of multiple video-query pairs. These soft labels reveal whether multiple moments in the intra-videos contain similar verbs/nouns, thereby guiding interest moment generation. Meanwhile, we put forward a diverse moment regression network (DMRNet) to achieve multiple predictions in a single pass, where plausible moments are dynamically picked out from the interest moments for joint optimization. Moreover, we introduce new metrics that better reveal multi-output performance. Extensive experiments conducted on Charades-STA and ActivityNet Captions show that our method achieves state-of-the-art performance in terms of both standard and new metrics. [ABSTRACT FROM AUTHOR]

With IT and video technologies evolving rapidly, video services with user interaction emerge, and bring about attracting experiences. The growing bandwidth and latency demands call for advanced transmission techniques which utilize wired, wireless, and 5G cellular network, and cooperate unicast, multicast, and broadcast. Free-viewpoint video, as one of the interactive video applications, can provide users with immersion of real world scenes, and shows potential to be an essential application in future digital life. However, such an application is different from other video services in aspects of computation and communication features, and existing researches are not up to the low frame latency, smooth playback, and high service scalability challenges. In this paper, we target on the above problems, and achieve ultra-low latency and stable video transmission for free-viewpoint video services by improving the performance of multiple system components accordingly. Firstly, the key performance indicator is formulated for such services, and an edge computing based video transmission schema is proposed to achieve ultra-low frame latency. Secondly, a pair of stabilization algorithms at both server and client sides based on application features are introduced to ensure stable encoded video streams before transmission, and smooth playback after that. Thirdly, a cloud-edge-client hybrid free-viewpoint service architecture is proposed, which can provide hierarchical experiences and enlarge the service scale with restricted resources. Experimental results prove the effectiveness of the proposed transmission schema and scheduling algorithms, and feasibility of the proposed service architecture is revealed by qualitative analyses. [ABSTRACT FROM AUTHOR]

Non-uniform constellations (NUCs) successfully utilize geometrical shaping to approach the Shannon capacity, but increase demapping complexity. In this paper, enhanced non-uniform constellations for low-complexity and high-capacity communications are investigated and addressed in two steps: low-complexity demappers for fixed constellations and the constrained design of enhanced NUCs. First, simplified demappers for NUCs are studied, and modified virtual point searching (VPS)-based demappers are proposed. Theoretical analysis and numerical results show that the proposed modified VPS-based demappers reduce demapping complexity with negligible performance degradation. Secondly, two shaping constraint schemes are proposed to NUC design for the purpose of low complexity and high capacity. NUCs optimized under the proposed constraints outperform the uniform constellations, and the proposed demappers have good applicability to the optimized NUCs. The proposed NUC design constraint schemes and simplified demappers enable solutions to achieve low-complexity high-capacity communication. [ABSTRACT FROM AUTHOR]

Mobile services are becoming more diverse, making them have different demands on communications, computing, and caching (3C) resources in mobile systems. Unlike the traditional work that considers only one type of service, this paper designs a unified framework to characterize the different kinds of services, and jointly optimizes the 3C resources of the base station (BS) and mobile devices to provide differentiated quality of service (QoS) for diverse services. In the proposed framework, we model the task required by the mobile device to be generated at the BS, the mobile device, or both of them, which means the requested tasks are served through different paths, consuming different bandwidth, computing and caching resources. Since diverse services have different QoS, we formulate a multi-objective programming (MOP) to optimize the allocation of the 3C resources for minimizing the total delay while maximizing the number of executed tasks requested by the mobile devices. We transform the MOP problem as a multi-objective Markov decision process (MO-MDP) and design a multi-objective proximal policy optimization (MO-PPO) algorithm to solve the MO-MDP. The proposed MO-PPO first trains two sub-policies separately for the two objectives, and then combines them to search for Pareto dominating solutions. By alternately perform the separate training and the combination, we can finally obtain a set of Pareto optimal solutions and the corresponding Pareto front. Simulation results show that the proposed MO-PPO outperforms traditional methods in finding a higher-quality set of Pareto optimal solutions and can more appropriately allocate 3C resources to different types of services. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) which enables communication, computation and storage capabilities at the network edge, is an emerging paradigm for 5G and beyond 5G networks. Resource-limited mobile devices can offload their tasks to the MEC server to reduce computation latency and save energy consumption. However, limited wireless spectrum resources make it hard for MEC to guarantee low latency when a large number of users offload the computation tasks or download the computed results. Full-duplex (FD) communication that allows simultaneous transmission and reception of signals over the same frequency band is a promising solution to the shortage of spectrum resources. In this paper, we study a multi-user FD-MEC system involving all the phases of uploading, computing, and downloading. With the help of an FD-capable base station (BS), two half-duplex (HD) users can form an FD pair and share the same time-frequency resource for task uploading and result downloading. We formulate a latency minimization problem considering power control, time scheduling, offloading decision, and user pairing strategy, which is a mixed-integer non-linear programming (MINLP) problem. The problem can be decomposed into two subproblems. The first subproblem is delay minimization for each possible FD pair, and the second one is to decide the offloading and user pairing strategy, and the user scheduling order. We analyze the problem in two cases: (1) the computing delay at the edge server is negligible and (2) the computing delay at the edge server is non-negligible. For both cases, the first subproblem is solved using a bisection algorithm. Then, we solve the second subproblem in the two cases by a relaxing-based algorithm and a concave-convex procedure (CCCP)-based algorithm, respectively. Finally, numerical results demonstrate that with our proposed strategies, the overall latency can be significantly reduced by integrating FD communication with the MEC system. [ABSTRACT FROM AUTHOR]

Underfill flow control is an important step in flip-chip technology and it can be generalized to fluid flow control in a porous medium, so solving this problem is of generalized implication to many other applications. The size of pores (i.e., clearance between the two solder bumps) in the flip-chip medium is less than 100 micrometers. In this paper, a new analytical model for permeability of the porous medium or solder bump flip-chip package is presented for two configurations of pores or solder of bumps (i.e., quadrilateral and equilateral triangular configurations). The model is shown to be more accurate than the existing model in the literature for the same purpose. Besides, the proposed model explicitly relates permeability with the structural parameters that describe the underfill space in flip-chip technology, namely, diameter of solder bumps, pitch of solder bumps, and gap height. In fact, there are two models, one for Newtonian fluids and the other for non-Newtonian fluids. These models allow us to optimize the structure of flip-chip packages in flip-chip technology or pores mediums in other applications. The models also provide a foundation for more precise control of the underfill flow process in flip-chip packages and the fluid filling process in pores mediums. [ABSTRACT FROM AUTHOR]

This paper investigates the unsourced random access (URA) scheme to accommodate numerous machine-type users communicating to a base station equipped with multiple antennas. Existing works adopt a slotted transmission strategy to reduce system complexity; they operate under the framework of coupled compressed sensing (CCS) which concatenates an outer tree code to an inner compressed sensing code for slot-wise message stitching. We suggest that by exploiting the MIMO channel information in the angular domain, redundancies required by the tree encoder/decoder in CCS can be removed to improve spectral efficiency, thereby an uncoupled transmission protocol is devised. To perform activity detection and channel estimation, we propose an expectation-maximization-aided generalized approximate message passing algorithm with a Markov random field support structure, which captures the inherent clustered sparsity structure of the angular domain channel. Then, message reconstruction in the form of a clustering decoder is performed by recognizing slot-distributed channels of each active user based on similarity. We put forward the slot-balanced $ K $ -means algorithm as the kernel of the clustering decoder, resolving constraints and collisions specific to the application scene. Extensive simulations reveal that the proposed scheme achieves a better error performance at high spectral efficiency compared to the CCS-based URA schemes. [ABSTRACT FROM AUTHOR]

The explosion of mobile communication devices and diversification of emerging media content put forward higher requirements for mobile communication networks in various aspects such as overall throughput, fairness and user experience. Fortunately, non-orthogonal multiplexing (NOM) techniques such as layered-division-multiplexing (LDM) and the flexible compatibility of physical layer design in fifth generation mobile networks (5G) open up the possibility to tackle the challenges. In this paper, we propose to use LDM-based layered multicast to improve network performance, where the multicast content is delivered with differentiated quality in different LDM signal layers. For various performance optimization purposes such as maximum throughput, proportional fairness, minimum dissatisfaction index, and maximum service satisfaction index, a unified analysis framework is developed. Under this framework, we formulate a joint optimization problem of layer-user pairing and power allocation. In order to solve this problem, an optimization algorithm based on subproblem decomposition is proposed, which arranges each multicast subscriber of the multicast content to receive the most suitable LDM layer, and finds the optimal transmit power and data rate for each layer. Simulation results demonstrate the superiority of LDM-based layered multicast over existing multicast approaches while guaranteeing the coverage and the lower limit of user experience. [ABSTRACT FROM AUTHOR]

Many stereoscopic image retargeting (SIR) methods have been developed for automatically and intelligently resizing stereoscopic images and we cannot always rely on time-consuming subjective user studies to validate the performance of different SIR methods. It is therefore required to design reliable objective metrics for SIR quality evaluation. This paper extends our previous 2D aspect ratio similarity (ARS) metric to a stereo 3D version termed as StereoARS where the key idea is to investigate into retargeting inconsistency between the original stereo correspondences. Our proposed StereoARS operates via two stages: monocular quality estimation and binocular inconsistency detection. In the first stage, monocular quality estimation is performed by applying a modified ARS measure on the left and right views separately to quantify the quality degradation within each monocular view. In the second stage, binocular inconsistency detection is performed in both pixel-level and grid-level to characterize the influence of binocular rivalry and stereo visual discomfort on SIR quality. In addition, we also measure to what extent the original pixel visibility relation is preserved after SIR as another binocular quality factor. Finally, these monocular and binocular quality estimates are fused to produce an overall SIR quality score. Extensive experiments have demonstrated that StereoARS achieves better alignment with human subjective ratings than the existing metrics by a large margin. [ABSTRACT FROM AUTHOR]

Low-density parity-check (LDPC) codes are widely used as error correction codes in new generation digital TV standards, such as the second generation of terrestrial digital video broadcasting standard (DVB-T2), Advanced Television Systems Committee (ATSC) 3.0, etc. The nonlinear belief propagation (BP) algorithm has excellent decoding performance for LDPC codes, but is often simplified in hardware implementations by linear min-sum (MS) algorithm due to its high complexity. This simplification also leads to over-estimation problems, which can be corrected by adding factors in conventional algorithms (e.g., normalized min-sum (NMS), offset min-sum (OMS), and variable scaling normalized min-sum (VMS) algorithms). However, the correction factors of these modified MS algorithms cannot adapt to different channels and modulations, and the performance needs further improvement. In this paper, the concepts of over-estimation value (OEV) and over-estimation rate (OER) are introduced to describe the over-estimation problem of the MS algorithm. Then, under the guidance of OEV and OER, a polygonal line min-sum (PMS) algorithm with correction factors adapted to different channels and modulations is proposed according to LLR distribution. Following the properties of OEV and OER, PMS algorithm is further simplified into Simplified PMS (SPMS) algorithm. LDPC codes from ATSC 3.0 are adopted in this paper to evaluate SPMS algorithm in comparison with the conventional algorithms. Extensive simulation results show that the SPMS algorithm for ATSC 3.0 LDPC decoder has at most 1.61dB, 0.24dB and 0.36dB gain over NMS, OMS and VMS algorithms respectively when frame error rate (FER) is at 10−4 level over additive white Gaussian noise (AWGN) channel with QPSK modulation. More importantly, the simulation results show that the SPMS algorithm can achieve much better performance than these modified MS algorithms over AWGN and Rayleigh channel with higher-order modulations or under limited maximum iteration number. [ABSTRACT FROM AUTHOR]

Learning to generate continuous linguistic descriptions for multi-subject interactive videos in great details has particular applications in team sports auto-narrative. In contrast to traditional video caption, this task is more challenging as it requires simultaneous modeling of fine-grained individual actions, uncovering of spatio-temporal dependency structures of frequent group interactions, and then accurate mapping of these complex interaction details into long and detailed commentary. To explicitly address these challenges, we propose a novel framework Graph-based Learning for Multi-Granularity Interaction Representation (GLMGIR) for fine-grained team sports auto-narrative task. A multi-granular interaction modeling module is proposed to extract among-subjects’ interactive actions in a progressive way for encoding both intra- and inter-team interactions. Based on the above multi-granular representations, a multi-granular attention module is developed to consider action/event descriptions of multiple spatio-temporal resolutions. Both modules are integrated seamlessly and work in a collaborative way to generate the final narrative. In the meantime, to facilitate reproducible research, we collect a new video dataset from YouTube.com called Sports Video Narrative dataset (SVN). It is a novel direction as it contains $6K$ 6 K team sports videos (i.e., NBA basketball games) with $10K$ 10 K ground-truth narratives(e.g., sentences). Furthermore, as previous metrics such as METEOR (i.e., used in coarse-grained video caption task) DO NOT cope with fine-grained sports narrative task well, we hence develop a novel evaluation metric named Fine-grained Captioning Evaluation (FCE), which measures how accurate the generated linguistic description reflects fine-grained action details as well as the overall spatio-temporal interactional structure. Extensive experiments on our SVN dataset have demonstrated the effectiveness of the proposed framework for fine-grained team sports video auto-narrative. [ABSTRACT FROM AUTHOR]

5G and edge computing have brought great changes to video industry. Interactive video is becoming an emerging application form of multimedia service, which provides attraction beyond typical scenarios like cloud gaming and remote virtual reality (VR), and puts forward great challenges in resource capacity, response latency, and function flexibility to its service system. In this paper, we propose an elastic system architecture with low latency features to accommodate generic interactive video applications on near user edges. To increase system flexibility, we firstly design a dynamic Directed Acyclic Graph (dDAG) model for efficient task representation. Secondly, based on the model, we present the elastic architecture together with its scalable workflow pipeline. Thirdly, we propose a set of novel latency measurement metrics to analyze and optimize the performance of an interactive video system. Based on the proposed approaches, we disassemble a real world free-viewpoint synthesis application and benchmark its performance with the metrics. Extensive experimental results show the flexibility of our system to handle the stochastic human interactions during a video service session, with less than 5 ms additional scheduling latency introduced. End to end latency is kept within 43 ms for complex functions, and 28 ms for simpler scenarios, which satisfies the restrictions of most interactive video applications provided by an edge. Client of the architecture serves as a pure video player, which is also friendly to power limited terminals such as 5G phones. Efficiency and stability analyses of the system show superiorities over existing work, and also reveal potential optimization directions for future research. [ABSTRACT FROM AUTHOR]

It is a very well-known fact, that the high complexity of the High Efficiency Video Coding standard (HEVC) is the main hurdle for its wide deployment and use. To tackle this problem, a number of recent research outcomes exploit heuristic algorithms and machine learning, including deep learning, to reduce the coding complexity. However, in most cases, each encoder module, i.e., encoding process, is first accelerated individually, and then different acceleration algorithms are manually combined. Without a holistic strategy, the acceleration potential of multi-module combination is not exploited and the Rate-Distortion (RD) loss is generally not well controlled. To tackle these shortcomings, this paper exploits the acceleration properties of different modules, i.e., the numerical representation of potential time saving and possible RD loss, from which a heuristic model is explored. Then a Heuristic Model Oriented Framework (HMOF) is proposed which adapts the properties of modules to underlying acceleration algorithms. In the framework, two advanced acceleration algorithms, including Border Considered CNN (BC-CNN)-based Coding Unit (CU) partition and Naive Bayes-based Prediction Unit (PU) partition, are proposed for the CU and PU modules, respectively. Further, by leveraging the heuristic model as the guidance to combine the proposed acceleration algorithms, HMOF is globally optimized, where different time saving budgets are wisely allocated to different modules and a theoretically minimal RD loss is achieved. According to the experimental results, through fusing a suitable deep learning technique and a Bayes-Based prediction, the proposed acceleration framework HMOF enable multiple acceleration choices. Here the proposed joint optimization strategy help to make a choice leading to the best cost-performance. Furthermore, within the proposed framework, intra coding time can be precisely controlled with negligible Bjøntegaard delta bit-rate (BDBR) loss. In this context, as a complexity control method, HMOF outperforms the state-of-the-art complexity reduction algorithms under a similar complexity reduction ratio. These results partially demonstrate the superiority of the proposed technique. [ABSTRACT FROM AUTHOR]

In this paper, a single frequency network (SFN) application of the digital terrestrial multimedia broadcasting (DTMB) system in Shanghai is presented. A three-stage implementation practice is described in details. Computer simulations of DTMB receiver in severe dynamic multipath channels are presented to verify the method of SFN adjustments. [ABSTRACT FROM PUBLISHER]

Data broadcast is a new kind of value-add service of DTV broadcasting and some data broadcast protocols have already been established. However, these protocols only describe the method for locating files in data streams, method on how to distribute large collection of files in one or more data streams is not provided. Research on this problem mainly focus on how to decrease the wait time and some methods of allocating files on multiple streams based on access probability are proposed, but how to assign the file with a reasonable bandwidth is Ignored. In this paper, we introduced an object multiplex algorithm to optimize the allocation of objects on DTV channel. This method assigns different bandwidth statistically to different object according to its size and access probability. In this method, both download time and wait time are considered. It adopts a modified Virtual Clock (VC) scheduling algorithm to multiplex files accurately and smoothly. [ABSTRACT FROM AUTHOR]

In Multimedia Broadcast/Multicast Service(MBMS), Multicast/Broadcast Single Frequency Network (MBSFN) and Single-cell Point-to-multipoint Network (SC-PTM) are two essential ways to organize networks to provide multicast services. MBSFN shows its unique advantage of enhancing signals at the boundary of two cells, however, highly requiring synchronization of symbols. SC-PTM shows the advantage of the flexibility on the network deployment. The comparison and complementarity of these two modes are explored. In this paper, we analyze the reception performance of MBSFN and SC-PTM from multiple perspectives. We first compare the successful transmission probability (STP) of MBSFN with that of SC-PTM. Then, we consider optimal power control problems in MBSFN and SC-PTM. The corresponding power control optimization problems are with fraction and max-min forms and solved by the Dinkelbach algorithm. Furthermore, we propose a joint mode-selection and power-control method in the hybrid mode of MBSFN and SC-PTM, where each cell could select the multicast mode from MBSFN and SC-PTM. This is a mixed-integer nonlinear programming (MINLP) problem and solved by the concave-convex procedure (CCCP) and the Dinkelbach algorithm. To further improve the throughput, the appropriate successful reception proportion is selected under the opportunistic multicast. Finally, the numerical simulations show the performance of our algorithms and compare the throughput of two modes, which verify our analysis. [ABSTRACT FROM AUTHOR]

In this paper, the backward-compatible coding schemes are explored to achieve the near-capacity performances in dynamic LDPC-coded Gaussian multiple access channels (GMAC). Specifically, some user-load-compatible (ULC) masked edges in a raptor-like protograph are masked with the variation of user load u. To distinguish the edges in a protograph, a multi-user protograph-based extrinsic information transfer (MU-PEXIT) chart is developed. Based on this chart, an iterative edge selection algorithm combined with a theoretical simplification is proposed to facilitate the selection of independent masked edge sets (MESs) for varying u. To further decrease the complexity of the independent masking scheme, a nested version is presented, where the MESs for varying u are nested and derived from the same mother MES. Meanwhile, nested selection algorithms are provided to select the nested MESs. Extensive simulation results demonstrate that the masked 5G-NR LDPC codes can maintain near-capacity performances regardless of the variation of u. Besides, these ULC masked codes achieve comparable performances with the state-of-the-art LDPC codes which are specially re-designed in a static GMAC. [ABSTRACT FROM AUTHOR]

This article investigates the non-uniform constellation (NUC) optimization adapted for Hierarchical modulation (HM) without using Successive Interference Cancellation (SIC). This approach is promising to reduce system demod/decode delay in comparison to Layered Division Multiplexing (LDM). By maximizing the constellation constrained capacity of Enhanced Layer (EL) service in HM, while the capacity of Core Layer (CL) service are approximately the same in HM and LDM, the capacity achieved by HM using NUCs is comparable to LDM. Particle Swarm Optimization (PSO) algorithm is used to resolve this problem. To accelerate the optimization, initial constellation is selected from regular NUCs or the combination of CL and EL constellations of LDM in ATSC 3.0. The results imply that under certain capacity demands, especially when there is a large difference between the SNR thresholds of CL and EL or the power ratio of CL to EL is high (for example, 10 dB or higher), HM, with lower delay compared to LDM, can achieve the capacity comparable or even better than LDM with the help of NUCs. Even if the power ratio of CL to EL is relatively low (for example, 3 dB), the capacity loss can be reduced with properly designed NUCs and the SNR threshold loss of EL can be approximately 0.4 dB with respect to LDM. However, LDM is still superior to HM when the difference between the SNR thresholds of CL and EL is relatively low. [ABSTRACT FROM AUTHOR]

The ever-growing demands for data-hungry services make the multicast play an increasingly important role in service delivery. 3GPP has enabled multicast for cellular network, including the adoption of two distinct multicast modes, Multicast/Broadcast Single Frequency Network (MBSFN) and Single Cell Point to Multipoint (SC-PTM). However, the lack of flexible selection between different multicast modes limits the transmission capacity. In order to further increase system throughput, this article focuses on exploiting the complementarity between MBSFN mode and SC-PTM mode, namely selecting the appropriate multicast mode for each cell in the network. This approach benefits from the trade-off between the utilization of user diversity via SC-PTM and the extra SFN gain from MBSFN, which increases the system throughput from a perspective of enhancing configuration flexibility. By constructing the analytical model of the network comprising cells with different multicast modes, we formulate a multicast mode selection problem aiming to maximize system throughput. Then, the formulated mixed-integer nonlinear programming problem is converted to a Difference of Convex (DC) programming, which is solved by the proposed algorithm based on the concave-convex procedure. Considering potential massive scale networks and high selection frequency, alternative mode selection algorithms with lower complexity are also designed. [ABSTRACT FROM AUTHOR]

Non-uniform constellation (NUC) is an advanced technology in digital terrestrial television broadcasting (DTTB) systems to reduce the shapping gap of BICM capacity to Shannon theoretical limit and provide performance gain. Two-dimensional NUC (2D-NUC) is a kind of NUC providing more gain but bringing higher demapping complexity at the receiver, which hinders its application prospects, especially in power limited systems. This paper proposes three novel demapping algorithms with reduced complexity for low to medium code rate 2D-NUCs in Advanced Television Systems Committee 3rd Generation (ATSC 3.0) standard. The proposed algorithms are based on the introduction of virtual points, the strategy of condensed symbols reduction and some reasonable approximations. There is a trade-off between the demapping complexity and performance. These three algorithms have different degrees of reduction in complexity and performance degradation, so they accommodate for different practical requirements. Theoretical analysis and simulation results are also given in this paper to prove the efficiency of the proposed demapping algorithms with reduced complexity. [ABSTRACT FROM AUTHOR]

Scale variation of pedestrians in a crowd image presents a significant challenge for vision-based people counting systems. Such variations are mainly caused by perspective-related distortions due to the camera pose relative to the ground plane. Following the density-based counting paradigm, we postulate that generating density values adaptive to object scales plays a critical role in the accuracy of the final counting results. Motivated by this, we distill the underlying information from depth cues to obtain scale-aware representations that can respond to object scales considering the fact that the scale is inversely proportional to the object depth. Specifically, we propose a depth embedding module as add-ons into existing networks. This module exploits essential depth cues to spatially re-calibrate the magnitude of the original features. In this way, the objects, although in the same class, will attain distinct representations according to their scales, which directly benefits the estimation of scale-aware density values. We conduct a comprehensive analysis of the effects of the depth embedding module and validate that exploiting depth cues to perceive object scale variations in convolutional neural networks improves crowd counting performances. Our experiments demonstrate the effectiveness of the proposed approach on four popular benchmark datasets. [ABSTRACT FROM AUTHOR]

Being able to accurately predict the common gaze point of a group of persons is of particular interest to precise marketing and automatic group attention assessment. Group gaze estimation faces challenges including small face/head size and outlier observers. To address these challenges, we proposed a novel framework called Multi-stream Group Gaze Learning and Estimation (MUGGLE). The MUGGLE infrastructure includes two inference streams: 1) a holistic stream which utilizes fused attention map as input to a global deep convolutional structure to explore the global geometric configurations and contexts of interesting persons in the scene; and 2) an aggregative stream which robustly aggregates individual gazes via a recurrent structure (e.g., LSTM) to obtain outlier-tolerant estimation. Both streams are seamlessly integrated via a fusion network. Extensive experiments are performed on a fully annotated group gaze image dataset with 8,000+ images and 100,000+ faces (which is publicly releasable). The results demonstrate the effectiveness of the proposed MUGGLE framework in group gaze estimation. [ABSTRACT FROM AUTHOR]

Objective: This study aims to examine, for the first time, the introduction of cationic polymer solutions to improve radiofrequency ablation (RFA) in terms of a potentially enlarged ablation zone. Methods: By using in vivo and computational RFA studies, two cationic polymers, Chitooligosaccharides (COS) and carboxymethyl chitosan (CMC), diluted in deionized water, were injected into tissues separately surrounding the RF bipolar electrode prior to power application. A total of 9 rabbits were used to 1) measure the increase in electrical conductivity of tissues injected with the cationic polymer solutions, and 2) explore the enhancement of the ablation performance in RFA trials. A computer model of RFA comprising a model of the solution diffusion with an RF thermal ablation model was also built, validated by the in vivo experiment, to quantitatively study the effect of cationic polymer solutions on ablation performances. Results: Compared to the control group, the electrical conductivity of rabbit liver tissues was increased by 42.20% (0.282 ± 0.006 vs. 0.401 ± 0.048 S/m, P = 0.001) and 43.97% (0.282 ± 0.006 vs. 0.406 ± 0.042 S/m, P = 0.001) by injecting the COS and CMC solution at the concentration of 100 mg/mL into the tissues, denoted COSDW100 and CMCDW100, respectively. Consequently, the in vivo experiments show that the ablation zone was enlarged by 95% (47.6 ± 6.3 vs. 92.6 ± 11.5 mm2, P < 0.001) and 87% (47.6± 6.3 vs. 88.8 ± 9.6 mm2, P < 0.001) by COSDW100 and CMCDW100, respectively. The computer simulation shows that the ablation zone was enlarged by 71% (51.9 vs. 88.7 mm2) and 63% (51.9 vs. 84.7 mm2) by COSDW100 and CMCDW100, respectively. Conclusion: The injection of the cationic solution can greatly improve the performance of RFA treatment in terms of enlarging the ablation zone, which is due to the increase in the electrical conductivity of liver tissues surrounding the RF electrode. Significance: This study contributes to the improvement of RFA in the treatment of large tumors. [ABSTRACT FROM AUTHOR]

3GPP is now carrying forward the development of the point to multipoint transmission mode of 5G on the basis of the cellular infrastructure and standard. Since the dedicated proposal for multimedia broadcast multicast system (MBMS) was approved, the MBMS technologies are evolving with the update of the requirements from 3G to 5G. This paper reviews the latest progress during the evolved MBMS standardization, which focuses on the LTE-based 5G terrestrial broadcast mode. Some agreements in terms of two widely discussed topics during 3GPP meetings, cell acquisition subframe (CAS) enhancement and numerology refinement, are presented. The enhancement of CAS and the update of numerology aim to deal with the service outage issue in the case of covering large area and serving high-mobility users. To verify the improvement brought by the CAS enhancement and the new numerology, simulations from system level and link level are carried out by several organizations and the representative results are presented and analyzed in this paper. [ABSTRACT FROM AUTHOR]

Broadcasting and broadband network is moving towards integration and LTE-based 5G terrestrial broadcast is now researched in Release 16 in Third Generation Partnership Project (3GPP) standardization meetings. However, the work scope of LTE-based 5G terrestrial broadcast focuses on specifying new numerologies and some minor improvement on cell acquisition subframe, which is insufficient. In this paper, limitations in coding and modulation schemes of LTE-based 5G terrestrial broadcast system, e.g., Turbo codes and Quadrature Amplitude Modulation (QAM), are detailedly analyzed. To further enhance the spectrum efficiency of LTE-based 5G terrestrial broadcast system, LDPC (Low Density Parity Check) codes from 5G new radio (NR) standard and newly designed non-uniform constellations (NUCs) are adopted in this paper to replace Turbo codes and QAM respectively. Extensive simulations and complexity analysis show that the proposed LDPC coding and NUC modulation scheme, either standalone or combined, can provide significant performance gain over Additive White Gaussian Noise (AWGN) and Tapped Delayline (TDL) channels, without additional complexity. To summarize, this paper investigates the weakness of the coding and modulation schemes in current systems and provides potential alternatives for the enhanced future broadcast in 3GPP standard. [ABSTRACT FROM AUTHOR]

Screen content image (SCI) prevails because of the explosive growth of screen oriented applications. This leads to extensive studies on SCI quality assessment and modeling for application optimization. In this paper, we propose a full reference multiscale edge attention (MSEA) similarity index to efficiently measure the perceptual quality of a screen image. This model considers the perceptual impacts of fixation attention, edge structure and edge contrast jointly, to accurately capture the masking phenomena (e.g., frequency selectivity, luminance, contrast, etc.) of our human visual system (HVS) when viewing a screen image. Specifically, we decompose the images using Gaussian and Laplacian pyramids which are then used to derive the edge structure, and edge contrast feature maps. Together with the fixation attention map generated by weighted luminance difference between the reference and distorted SCIs, we could eventually offer a MSEA similarity map for final index score. We have evaluated this model using a publicly accessible screen image database. Simulation results have shown that the MSEA similarity index correlates with the collected subjective mean opinion score (MOS) very well. In fact, it is ranked at the first place for both Pearson linear correlation coefficient (PLCC) and Root mean squared error (RMSE), and ranked at the second place for Spearman rank-order correlation coefficient (SROCC) measurements, among existing quality metrics. [ABSTRACT FROM AUTHOR]

This paper presents an aggregate wavelet-predominant algorithm to measure the distortions in THz security images. The algorithm integrates a spectral-based sharpness estimator, a noise estimator derived alpha-stable model and an overall viewing experience estimator based on free-energy principle. Among them, the greater weight is assigned to the spectral-based sharpness estimator considering that the main quality factor in THz security image is sharpness. To verify the feasibility of the proposed metric, we construct the THz security image dataset including a total of 181 THz security images, and each image has the mean opinion score (MOS) collected via subjective quality evaluation experiment. Quantitative experimental results on the constructed THz security image dataset show that the aggregate wavelet-predominant estimator produces the promising overall performance for the estimation of MOS values, with PLCC, SROCC, and RMSE of 0.900, 0.873, and 0.386, respectively. This performance is superior to other opinion-unaware approaches, viz., FISBLIM, SISBLIM, NIQE, CPBD, SINE, S3, FISH, and noise estimator. The determination coefficient (${R} ^{{2}}$) of linear regression between reference and predicted MOSs is 0.81. The result of Bland–Altman analysis further validates that the aggregate wavelet-predominant estimator can substitute for the subjective IQA of THz security image, with approximately 94.5% of data points locating within the limits of agreement. For usability identification, the wavelet-predominant estimator gives the satisfactory results, with accuracy, precision, recall rate, and false positive rate of 84.0%, 79.8%, 95.0%, and 29.6%, respectively. Furthermore, the potential application perspectives of the proposed metric can refer to commercial applications (guarantee THz security images of good quality) and scientific researches (assist in software development for THz security image analysis). The dataset is available at https://doi.org/10.6084/m9.figshare.7700123.v3. Possible researches on this dataset may include the development of THz quality standards, the selection of the best display mode, the enhancement of images, the modeling of image noise, and the detection of prohibited goods. [ABSTRACT FROM AUTHOR]

Cloud gaming (CG) system could pursue both high-quality gaming experience via intensive computing, and ultimate convenience anywhere at anytime through any energy-constrained mobile devices. Despite the abundance of efforts devoted, state-of-the-art CG systems still suffer from multiple key limitations: expensive deployment cost, high bandwidth consumption and unsatisfied quality of experience (QoE). As a result, existing works are not widely adopted in reality. This paper proposes a Transparent Gaming framework called T-Gaming that allows users to play any popular high-end desktop/console games on-the-fly over the Internet. T-Gaming utilizes the off-the-shelf consumer GPUs without resorting to the expensive proprietary GPU virtualization (vGPU) technology to reduce the deployment cost. Moreover, it enables prioritized video encoding based on the human visual feature to reduce the bandwidth consumption without noticeable visual quality degradation. Last but not least, T-Gaming adopts adaptive real-time streaming based on deep reinforcement learning (RL) to improve user's QoE. To evaluate the performance of T-Gaming, we implement and test a prototype system in the real world. Compared with the existing cloud gaming systems, T-Gaming not only reduces the expense per user by 75 percent hardware cost reduction and 14.3 percent network cost reduction, but also improves the normalized average QoE by 3.6-27.9 percent. [ABSTRACT FROM AUTHOR]

Recently, a new structure of low density parity check (LDPC) code named raptor-like LDPC code has attracted much attention. It has better performance at low code rate. In this paper, a novel decoding scheme for raptor-like LDPC code is proposed. First, the Gaussian approximation density evolution (GADE) algorithm is used to track and analyze the message transmission during the decoding process. It is found that certain log likelihood ratio (LLR) messages can be approximated by “zero” setting in the early iteration of raptor-like LDPC decoding. In other words, some column and row operations could be eliminated without compromise the performance. Next, we propose a new decoding scheme, which can skip unnecessary column and row operations. In comparison with the traditional belief propagation (BP)-based LDPC decoding scheme, the proposed scheme can reduce the decoding complexity. Additionally, a new algorithm is developed to facilitate the selection of the early iteration number. With this novel design, the proposed decoding scheme performs almost the same as the traditional BP-based scheme. To proof the concept, the raptor-like LDPC codes in the ATSC3.0 digital TV system are used to evaluate the proposed scheme, in comparison with the traditional BP-based schemes, i.e., sum-product algorithm (SPA), offset min-sum algorithm (OMSA), and normalized min-sum algorithm (NMSA). The simulation results confirm that the proposed scheme can reduce the decoding complexity without sacrifice in performance for all SPA, OMSA, and NMSA methods. About 10% complexity reduction can be achieved. This will reduce the buttery consumption for Internet of Things (IoT) and handheld devices. [ABSTRACT FROM AUTHOR]

In this paper, we propose a performance analysis algorithm of low density parity check-bit interleaved coded modulation (LDPC-BICM) system. First, we introduce the Gaussian mixture approximation method of the LLR messages output from the demodulator over the AWGN channel. Then, we analyze the density evolution based on these Gaussian mixture approximation LLR messages. During the analysis, the Gaussian approximation is applied to simplify the calculation. Some LDPC-BICM systems (which are adopted by ATSC3.0 standard) are used to evaluate the proposed algorithm. We also employ the actual performances and the thresholds obtained by the multi-edge type (MET) discretized density evolution algorithm as the references. The simulation results prove that our proposed algorithm can work well in most cases. Furthermore, the proposed algorithm is simpler than the MET algorithm because of the Gaussian or Gaussian mixture approximations. [ABSTRACT FROM AUTHOR]

The traditional degree distribution framework is a useful tool to analyze irregular low density parity check (LDPC) code. However, it cannot be used to analyze raptor-like LDPC code as it cannot describe the most important structural feature of raptor-like LDPC code, which is that any check node is connected to at most one degree-one variable node. In this paper, we propose a two-type degree distribution framework for raptor-like LDPC code. The proposed framework can describe the special structural feature of raptor-like LDPC code by dividing the check nodes into two different sets. One is the set of check nodes connected to those degree-one variable nodes, the other is the set of check nodes unconnected to those degree-one variable nodes. Furthermore, we propose a two-type discrete density evolution algorithm and a two-type Gaussian approximation density evolution algorithm based on the proposed two-type degree distribution framework to estimate the threshold of raptor-like LDPC code. During the decoding analysis, the proposed algorithms treat the check nodes belonging the two separate sets differently. Compared with the traditional multi-edge type (MET) algorithm, the proposed algorithms allow us to calculate the threshold more quickly without much sacrifice in accuracy. For some raptor-like LDPC codes adopted by the Advanced Television Systems Committee 3.0 (ATSC3.0) standard, the thresholds estimated by the proposed algorithms can be very close to the thresholds estimated by the traditional MET algorithm. The deviations are less than 0.07 dB. Meanwhile, the proposed algorithms can tremendously reduce the computation complexity because of the two-type degree distribution framework and the Gaussian approximation. [ABSTRACT FROM AUTHOR]

The rapid growth of ocean exploration has led to an increase in the demand of sonar images, since they contain a great amount of information collected by sonar systems such as distributions of underwater creatures and minerals. For further analysis, sonar images usually need to be transmitted via underwater acoustic channel (UAC). However, the hostile nature of UAC makes the transmitted sonar images have high probability to be afflicted with distortions introduced in transmission procedures. To monitor image quality, a partial-reference sonar image quality predictor (PSIQP) is proposed in this paper. A unique property that differentiates the proposed PSIQP metric from existing works is the consideration of the viewing behavior of the human visual system for sonar images, which motivates us to take both image perception and understanding into consideration. The results of experiments based on sonar image quality database show that the proposed metric provides accurate predictions across a variety of compression and transmission distortions. [ABSTRACT FROM AUTHOR]

Currently, many tone mapping operators (TMOs) have been provided to compress high dynamic range images to low dynamic range (LDR) images for visualizing them on the common displays. Since quality degradation is inevitably induced by compression, how to evaluate the obtained LDR images is indeed a headache problem. Until now, only a few full reference (FR) image quality assessment metrics have been proposed. However, they highly depend on reference image and neglect human visual system characteristics, hindering the practical applications. In this paper, we propose an effective blind quality assessment method of tone-mapped image without access to reference image. Inspired by that the performance of existing TMOs largely depend on the brightness and chromatic and structural properties of a scene, we evaluate the perceptual quality from the perspective of color information processing in the brain. Specifically, motivated by the physiological and psychological evidence, we simulate the responses of single-opponent (SO) and double-opponent (DO) cells, which play an important role in the processing of the color information. To represent the textural information, we extract three features from gray-level co-occurrence matrix (GLCM) calculated from SO responses. Meanwhile, both GLCM and local binary pattern descriptor are employed to extract texture and structure in the responses of DO cells. All these extracted features and associated subjective ratings are learned to reveal the connection between feature space and human opinion score. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art blind quality assessment methods and is comparable with the popular FR methods on two recently published tone-mapped image databases. [ABSTRACT FROM PUBLISHER]

In ATSC 3.0, a bootstrap is used to enable initial synchronization and carry transmission parameter signaling. The bootstrap consists of multiple orthogonal frequency division multiplexing symbols which enables variable signaling capacity. Signaling information is conveyed by applying cyclic shift in time-domain. However, the bootstrap suffers from performance loss in the channels of strong multipath and fast time-variation. In this paper, an improved bootstrap enabling separate design of synchronization part and signaling transmission part is proposed. Zero correlation zone sequence is employed in frequency-domain (FD) for signaling transmission by cyclically shifting the sequence in FD. At the receiver, a universal signaling decoding method regardless of the synchronization part design is presented. Besides, to eliminate the performance degradation introduced by non-linear distortion of channel transfer function, signaling validation and correction operations on the previous symbol are required. Numerical simulation indicates that the proposed design achieves better signaling transmission performance than current one in some corner cases. [ABSTRACT FROM PUBLISHER]

In group decision making, it is inevitable that the individual decision maker’s subjectivity is involved, which causes difficulty in reaching a group decision. One of the difficulties is to aggregate a small set of expert opinions with the individual subjectivity or uncertainty modeled with probability theory. This difficult problem is called probability distribution function aggregation (DFA). This paper presents a simple and efficient approach to the DFA problem. The main idea of the proposed approach is that the DFA problem is modeled as a nonlinear function of a set of probability distribution functions, and then a linear feedback iteration scheme is proposed to solve the nonlinear function, leading to a group judgment or decision. Illustration of this new approach is given by a well-known DFA example which was solved with the Delphi method. The DFA problem is a part of the group decision problem. Therefore, the proposed algorithm is also useful to the decision making problem in general. Another contribution of the this paper is the proposed notation of systematically representing the imprecise group decision problem with the classification of imprecise information into three classes, namely incomplete information, vague information, and uncertain information. The particular DFA problem dealt with in this paper is then characterized with this general notation. [ABSTRACT FROM AUTHOR]

Future media transmission as a consistent target is pursued by both next mobile communication system (5G) and advanced digital terrestrial television system. High data rate and flexibility are major considerations for future media transmission. Broadcasting network facilitates high-efficiency transmission of popular or live video in large area, and while, cellular network tends to provide personalized and localized services with a unicast/multicast model. The broadcast-like scheme emerges in 5G to resolve the high demand for bandwidth. However, it requires high deployment cost and imposes much interference on unicast/multicast services. In this paper, a cooperative structure of cellular network and broadcasting network using cloud radio access network (C-RAN) is proposed. The expenses of constructing hybrid network can be significantly cut down by applying the centralization and virtualization of C-RAN. Besides, technical approaches for 3GPP and ATSC cooperation in physical layer is detailed. Dedicated return channel (DRC) of broadcasting network is proposed to enable seamless interaction between broadcasters and few users in a remote area with high expense of cellular tower deployment. To loosen the real-time physical layer pipes period restriction of DRC system, three alternative periods are investigated to provide more flexibility to broadcasters. [ABSTRACT FROM PUBLISHER]

This paper presents a work that was aimed to develop methods along with test facilities for characterizing the arc/contact movement in low-voltage miniature circuit breaker which has a related current under 125 A, and to find its relation with the short-circuit current/voltage in the circuit. The method consists of: 1) the high-speed camera for obtaining the image of the arc or contact movement; 2) the data acquisition system that measures the current during the short-circuit break; and 3) the circuitry system that establishes the synchronization of the arc movement capturing and the current acquisition. In particular, two methods were developed for this purpose: one based on analog signal and the other based on digital signal. The experiment shows that the accuracy of the synchronization, dependent on the sampling rate of the high-speed camera, is about from tens of microseconds to hundreds of microseconds with both methods. The two methods have their own advantages and disadvantages, and they can be tailored to applications for the best performance. [ABSTRACT FROM AUTHOR]

Layered division multiplexing (LDM) is a spectrum efficient non-orthogonal multiplexing technology that has been adopted in the Advanced Television Systems Committee (ATSC) 3.0 Physical Layer Standard as a baseline technology. This paper studies a two-layer LDM with one layer used for providing a global service through a single frequency network (SFN), and the other for providing local coverage/services, such as location targeted advertising or local content insertion. The pilot boosting effect on SNR and co-channel interference is also analyzed. The LDM upper layer can be used to deliver time-division multiplexed mobile-HD and 4k-UHD services. The LDM lower layer with a negative SNR threshold can reliably provide seamless local coverage/service from each SFN transmitter without coverage gaps among adjacent SFN transmitter service areas. No directional receiving antenna is required for the local service reception and receivers simply tune into the stronger signal. In such LDM systems, while the upper layer is operating in a traditional SFN mode, the lower layer operates in a special form of distributed MIMO or gap-filler mode to provide targeted local coverage. For implementing the two-layer system introduced in this paper, only ATSC 3.0 baseline technologies are used, i.e., there is no need to modify the ATSC 3.0 standard. Given the upper and lower layers’ data rate requirements and the SNR thresholds, the lower layer power, with respect to the upper layer (injection level), can be optimized to maximize upper and lower layer performance and coverage. Since the advertisement time of the local service is typically less than 20% of the program time, nonreal time could be used to play-back the local content at five times the transmission bit rate for better (audio/video) service quality. [ABSTRACT FROM AUTHOR]

Due to the nature of a wireless medium, a wireless channel is susceptible to malicious nodes, which encourages the consideration of secrecy in many applications such as satellite networks, social networking services, etc. Motivated by the social characteristic that makes nodes more likely to communicate with nearby neighbors, we study the secrecy capacity of large-scale wireless networks with social relationships. We consider two distributions of legitimate nodes: They are homogeneously placed based on a Poisson point process (PPP), or they are inhomogeneously distributed as multiclustering topology. In particular, we consider two scenarios: 1) the noncolluding case, where each eavesdropper decodes its receiving message independently; and 2) the colluding case, where eavesdroppers can cooperate to decode the message. We adopt a rank-based model to describe the social characteristic and investigate its impact on the secrecy capacity using the technique of self-interference cancelation. We employ a passive attack model, where eavesdroppers can only listen to transmissions among legitimate nodes. Our important findings include the following: 1) In the noncolluding case, secrecy capacity is not influenced by eavesdroppers in order sense, but it varies with the density of eavesdroppers and social impacts in the colluding case. 2) The proposed scheme can achieve optimal secrecy throughput neglecting polylogarithmic factor in homogeneous wireless networks. [ABSTRACT FROM PUBLISHER]

With the increasing video on demand service need in broadcasting television and the development of triple play strategy, the next generation wireless network television system has been presented. With the advantages of interactive capability, next generation broadcasting-wireless (NGB-W) experiment network can cooperate with digital terrestrial multimedia broadcasting and China mobile multimedia broadcasting downlink systems. In this paper, the key technologies of NGB-W are described in detail. Application test and network deployment of NGB-W in Shanghai are presented to validate its extensive use. [ABSTRACT FROM AUTHOR]

In image/video systems, contrast adjustment which manages to enhance visual quality is nowadays an important research topic. Yet very limited struggles have been made to the exploration of visual quality assessment for contrast adjustment. To tackle the issue, this paper proposes a novel reduced-reference (RR) quality metric with the integration of bottom-up and top-down strategies. The former one stems from the recently revealed free energy principle that tells that the human visual system seeks to comprehend an input image via uncertainty removal, while the latter one is toward using the symmetric Kullback–Leibler divergence to compare the histogram of the contrast-altered image with that of the pristine image. The bottom-up and top-down strategies are lastly incorporated to derive the RR contrast-altered image quality measure. A comparison using numerous existing IQA models is carried out on five contrast related databases/subsets in CID2013, CCID2014, CSIQ, TID2008, and TID2013, and experimental results validate the superiority of the proposed technique. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the capacity and delay in mobile ad hoc networks (MANETs), considering the correlation of node mobility (correlated mobility). Previous studies on correlated mobility investigated the maximum capacity with the corresponding delay in several subcases; the problem of optimal capacity under various delay constraints (the optimal capacity–delay tradeoff) still remains open. To this end, we deeply explore the characteristics of correlated mobility and figure out the fundamental relationships between the network performance and the scheduling parameters. Based on that, we establish the overall upper bound of the capacity–delay tradeoff in all the subcases of correlated mobility. Then, we try to obtain the achievable lower bound by identifying the optimal scheduling parameters on certain constraints. Results demonstrate the whole picture of how the correlation of node mobility impacts the capacity, the delay, and the corresponding tradeoff between them. [ABSTRACT FROM PUBLISHER]

Statistical models based on Markov chains, especially mixtures of Markov chains, have recently been studied and demonstrated to be effective in various data mining applications such as tourist flow analysis, animal migration modeling, and transportation administration. Nevertheless, the research so far has mainly focused on analyzing data at individual levels. Due to security and privacy reasons, however, the observations in practice usually consist of coarse-grained statistics of individual data, a.k.a. aggregate data, rendering learning mixtures of Markov chains an even more challenging problem. In this work, we show that this challenging problem, although intractable in its original form, can be solved approximately by posing structural constraints on the transition matrices. The proposed structural constraints include specifying active state sets corresponding to the chains and adding a pairwise sparse regularization term on transition matrices. Based on these two structural constraints, we propose a constrained least-squares method to learn mixtures of Markov chains. We further develop a novel iterative algorithm that decomposes the overall problem into a set of convex subproblems and solves each subproblem efficiently, making it possible to effectively learn mixtures of Markov chains from aggregate data. We propose a framework for generating synthetic data and analyze the complexity of our algorithm. Additionally, the empirical results of the convergence and the robustness of our algorithm are also presented. These results demonstrate the effectiveness and efficiency of the proposed algorithm, comparing with traditional methods. Experimental results on real-world data sets further validate that our algorithm can be used to solve practical problems. [ABSTRACT FROM PUBLISHER]

Image quality assessment (IQA) has been an active research area during last decades. Many existing objective IQA models share a similar two-step structure with measuring local distortion before pooling. Compared with the rapid development for local distortion measurement, seldom effort has been made dedicated to effective pooling schemes. In this paper, we design a new pooling model via the analysis of distortion distribution affected by image content and distortion. That is, distributions of distortion position, distortion intensity, frequency changes, and histogram changes are comprehensively considered to infer an overall quality score. Experimental results conducted on four large-scale image quality databases (LIVE, TID2008, CSIQ, and CCID2014) concluded with three valuable findings. First, the proposed technique leads to consistent improvement in the IQA performance for studied local distortion measures. Second, relative to the traditional pooling, the performance gain of our algorithm is beyond 15% on average. Third, the best overall performance made by the proposed strategy outperforms state-of-the-art competitors. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the problem of image contrast enhancement. Most existing relevant technologies often suffer from the drawback of excessive enhancement, thereby introducing noise/artifacts and changing visual attention regions. One frequently used solution is manual parameter tuning, which is, however, impractical for most applications since it is labor intensive and time consuming. In this research, we find that saliency preservation can help produce appropriately enhanced images, i.e., improved contrast without annoying artifacts. We therefore design an automatic contrast enhancement technology with a complete histogram modification framework and an automatic parameter selector. This framework combines the original image, its histogram equalized product, and its visually pleasing version created by a sigmoid transfer function that was developed in our recent work. Then, a visual quality judging criterion is developed based on the concept of saliency preservation, which assists the automatic parameters selection, and finally properly enhanced image can be generated accordingly. We test the proposed scheme on Kodak and Video Quality Experts Group databases, and compare with the classical histogram equalization technique and its variations as well as state-of-the-art contrast enhancement approaches. The experimental results demonstrate that our technique has superior saliency preservation ability and outstanding enhancement effect. [ABSTRACT FROM PUBLISHER]

Viewing distance and image resolution have substantial influences on image quality assessment (IQA), but this issue has been highly overlooked in the literature so far. In this paper, we examine the problem of optimal resolution adjustment as a preprocessing step for IQA. In general, the sampling of visual information by human eyes’ optics is approximately a low-pass process. For a given visual scene, the amount of the extractable information greatly depends on the viewing distance and image resolution. We first introduce a novel dedicated viewing distance-changed image database (VDID2014) with two groups of typical viewing distances and image resolutions to promote the IQA study for this issue. Then we design a new effective optimal scale selection (OSS) model in dual-transform domains, in which a cascade of adaptive high-frequency clipping in the discrete wavelet transform domain and adaptive resolution scaling in the spatial domain is used. Validation of our technique is conducted on five image databases (LIVE, IVC, Toyama, VDID2014, and TID2008). Experimental results show that the performance of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) can be substantially improved by applying these metrics to OSS model preprocessed images, superior to classical multi-scale-PSNR/SSIM and comparable to the state-of-the-art competitors. [ABSTRACT FROM PUBLISHER]

Min-sum decoding is a low-complexity alternative to the so-called belief propagation and consists in simplification of the nonlinear operation on the log likelihood ratios (LLRs) in the check nodes. The resulting suboptimality may be tempered via appropriate scaling of the LLRs, e.g., the fixed optimal scaling in the normalized min-sum algorithm, and variable scaling algorithms gradually appearing in the literature. However, up to now, none of the papers studied variable scaling both as per iteration and as per different check node degree, due to the prohibitive complexity of multioptimization over space of too many parameters. In this paper, we propose a generalized mutual information (GMI) of LLRs as the criterion to search for the scaling factors for different check node degrees in every iteration in a 1-D thus low-complexity manner. This approach is first analyzed via density evolution, and in addition can be extended to practical LLRs based formulas via Monte Carlo tools to cope with the mismatch issue. Bit error rate simulation results on two low-density parity-check codes show that our proposed GMI metrics have a noticeable gain over the variable scaling schemes that appeared in the literature. [ABSTRACT FROM AUTHOR]