nils-herrmann/clean_scirepeval huggingface dataset:
A scene adaptive and signal adaptive quantization for subband image and video compression using wavelets

The discrete wavelet transform (DWT) provides an advantageous framework of multiresolution space-frequency representation with promising applications in image processing. The challenge as well as the opportunity in wavelet-based compression is to exploit the characteristics of the subband coefficients with respect to both spectral and spatial localities. A common problem with many existing quantization methods is that the inherent image structures are severely distorted with coarse quantization. Observation shows that subband coefficients with the same magnitude generally do not have the same perceptual importance. We propose in this paper a scene adaptive and signal adaptive quantization scheme capable of exploiting the spectral and spatial localization properties resulting from the wavelet transform. The quantization is implemented as maximum a posteriori probability estimation-based clustering in which subband coefficients are quantized to their cluster means, subject to local spatial constraints. The intensity distribution of each cluster within a subband is modeled by an optimal Laplacian source to achieve signal adaptivity, while spatial constraints are enforced by appropriate Gibbs random fields (GRF) to achieve scene adaptivity. With spatially isolated coefficients removed and clustered coefficients retained at the same time, the available bits are allocated to visually important scene structures so that the information loss is least perceptible. Furthermore, the reconstruction noise in the decompressed image can be suppressed using another GRF-based enhancement algorithm.
Mobile video transmission via Wyner-Ziv video coding and rate compatible modulation

This work refers to mobile video transmission at the physical layer. We develop a video communication system using transform-domain Wyner-Ziv video coding and rate compatible modulation. Because Wyner-Ziv video coding is employed, the encoder and transmitter of this system have low complexity and therefore are applicable to mobile devices. In addition, rate compatible modulation proves more efficient than the conventional adaptive modulation and coding, and thus achieves a significantly higher throughput. This increase of throughput leads to an improvement of the video quality by up to 1.5 dB in PSNR under the same bandwidth constraint and transmission channel state. Above all, the combination of Wyner-Ziv video coding and rate compatible modulation turns out to be a competitive candidate solution for mobile video communication with low encoding complexity.
CA-MAC: A Hybrid context-aware MAC protocol for wireless body area networks

In wireless body area network (WBAN), various data sources are generated by different type of sensors and transmitted to the master node, which may result in temporally different traffic loads. With time-varying channel caused by frequent body movement, transmitted data may also experience deep fading and packet loss. To meet the complex transmission requirements in WBAN, a traffic-aware and channel-aware MAC protocol is required. In this paper, we introduce CA-MAC, a context-aware MAC protocol using a hybrid of contention-based and TDMA-based approaches. A dynamic control mechanism is proposed to address fading channel by adaptively modifying MAC frame structure. Schedule-based and polling-based techniques are also used to manage periodic, bursty, and emergency traffic requirement. Simulation results confirm the advantage of CA-MAC on reliability over existing TDMA-based approach.
A new method for block effect removal in low bit-rate image compression

At low bit-rate, most block-based image compression schemes generate artifacts known as block effect. Many existing approaches concentrated on concealing the discontinuity around the block boundary regions to produce a visually more pleasant image with no or little improvement in PSNR. We proposed a novel approach which attempts to recover the DC components of each block from the coded ones in order to calibrate the reference level for all the pixels within the block. The calibration of DC components starts from two anchor blocks centered in the image with their DC components coded error free. It then proceeds in a spiral scan fashion to exploit the maximum constraints imposed by the already updated neighboring blocks. After DC components calibration, a Huber-Markov random field model based POCS algorithm is applied to obtain good reconstruction both in terms of PSNR improvement and visual observation.<<ETX>>
Are you what you look like? Exploring correlations in personality type and their wearing

Everyday, people choose their preferred clothing to wear before leaving home for various activities. It has been recognized that each person may consciously or unconsciously express individual personality through their wearing styles. It will be interesting to ask: Are you what you look like? This paper presents a novel scheme developed to infer personality type from their wearing. The proposed research is justifiably rooted in the psychological findings that reveal intrinsic correlations between clothing style and wearer's inner self in self-image, mood, and social aspirations. First, we build a relatively large dataset with more than 300 persons and over 10,000 portraits, each is labeled with personality type. Then, personality-related clothing features are explored through statistical analysis based on psychological theories. To extract the clothing features from the images, a suite of algorithms, including body detection, GrabCut algorithm and saliency detection have been developed. Binary logistic regression is then applied to verify the Significance Level of the extracted features for predicting personality types. Experimental results demonstrate that the proposed scheme is able to predict several types of personality combination or type pairs with relatively high precisions.
QoS-driven network coded wireless multicast

Emerging wireless multicast applications simultaneously impose two requirements to the underlying communication networks: to provide sufficient bandwidth and to support a variety of quality-of-service (QoS) sensitivities. For the first requirement, network coding has been proposed recently as an effective way of improving bandwidth utilization. However, almost all previous works about network coding focus on the throughput gain without considering the QoS requirements. Optimal network code construction in wireless multicast under different QoS constraints remains as a significant challenge. In this research, we study the QoS-driven network coding problem. We use large deviation principle to establish the relationship among source rate, link condition, QoS requirement, and network code. Using this relationship, under given QoS requirements, we solve the optimal network code construction problem. The proposed network code supports maximal source rate without violating the QoS requirements. These results constitute the foundations for future designing and implementing network coding based wireless multicast protocols.
A cellular neural network for clustering-based adaptive quantization in subband video compression

This paper presents a novel cellular connectionist model for the implementation of a clustering-based adaptive quantization in video coding applications. The adaptive quantization has been designed for a wavelet-based video coding system with a desired scene adaptive and signal adaptive quantization. Since the adaptive quantization is accomplished through a maximum a posteriori probability (MAP) estimation-based clustering process, its massive computation of neighborhood constraints makes it difficult for a software-based real-time implementation of video coding applications. The proposed cellular connectionist model aims at designing an architecture for the real-time implementation of the clustering-based adaptive quantization. With a cellular neural network architecture mapping onto the image domain, the powerful Gibbs spatial constraints are realized through interactions among neurons connected with their neighbors. In addition, the computation of coefficient distribution is designed as an external input to each component of a neuron or processing element (PE). We prove that the proposed cellular neural network does converge to the desired steady state with the proposed, update scheme. This model also provides a general architecture for image processing tasks with Gibbs spatial constraint-based computations.
Message From the Editor-in-Chief

The official 2011 impact factor for the Journal of Epidemiology is 1.858. Our journal placed 66th among the 157 journals in the category of Public, Environmental & Occupational Health and top in the Asia-Pacific region. The proportion of self-citation was only 6%, which indicates that ours is a sound and promising journal in epidemiology. The 10 most frequently cited papers in the Journal of Epidemiology in 2010 and 2011 are shown below. The average time between submission and first response is 27 days. For this achievement, I sincerely thank the editorial committee members and reviewers for their outstanding efforts. In addition to our regular review articles, we published 7 joint or single review articles in 2012 for various research fields, Epidemiology—Scientific Wisdom and Perspectives from East and West, which are written by leading epidemiologists from Asian and Western countries. Such review series will continue this year. Our editorial team looks forward to receiving new high-quality submissions from around the world in a broad range of topics in epidemiology. In this way, our journal can continue to advance research in basic and clinical science, public health science, and health policy.
Enabling Adaptive High-Frame-Rate Video Streaming in Mobile Cloud Gaming Applications

High-frame-rate (HFR) video is emerging in popular gaming applications to enhance the smooth experience perceived by end users. However, it is challenging to guarantee the delivery quality of HFR video in mobile cloud gaming scenarios because of the high transmission rate and limited wireless resources. To address this critical problem, we develop a novel transmission scheduling framework dubbed AdaPtive HFR vIdeo Streaming (APHIS). The term adaptive indicates this scheme's capability in dynamically adjusting the video traffic load and forward error correction (FEC) coding. First, we propose an online video frame selection algorithm to minimize the total distortion based on the network status, input video data, and delay constraint. Second, we introduce an unequal FEC coding scheme to provide differentiated protection for Intra (I) and Predicted (P) frames with low-latency cost. The proposed APHIS framework is able to appropriately filter video frames and adjust data protection levels to optimize the quality of HFR video streaming. We conduct extensive emulations in Exata involving HFR video encoded with H.264 codec. Experimental results show that APHIS outperforms the reference transmission schemes in terms of video peak signal-to-noise ratio, end-to-end delay, and goodput. Therefore, we recommend APHIS for delivering HFR video streaming in mobile cloud gaming systems.
JSM-2 based joint ECG compressed sensing with partially known support establishment

Compressed sensing (CS) is a technique that enables sparse signal reconstruction from much fewer samples. In this paper, we propose ECG compressed sensing methods based on distributed compressed sensing to exploit the joint sparsity for both single- and multi-lead ECG signals. We apply JSM-2 (joint sparse model type 2) for jointly sparse ECG signals and formulate how to establish a partially known support based on this type of sparse model. Through careful analysis of joint partially known support, two-step ECG signal reconstruction schemes for single-lead and multi-lead ECG signals are developed. Simulation results show that the proposed schemes based on partially known support establishment outperforms existing schemes with enhanced performance measured by percentage root mean square difference (PRD).
A privacy-aware cloud-assisted healthcare monitoring system via compressive sensing

Wireless sensors are being increasingly used to monitor/collect information in healthcare medical systems. For resource-efficient data acquisition, one major trend today is to utilize compressive sensing, for it unifies traditional data sampling and compression. Despite the increasing popularity, how to effectively process the ever-growing healthcare data and simultaneously protect data privacy, while maintaining low overhead at sensors, remains challenging. To address the problem, we propose a privacy-aware cloud-assisted healthcare monitoring system via compressive sensing, which integrates different domain techniques with following benefits. By design, acquired sensitive data samples never leave sensors in unprotected form. Protected samples are later sent to cloud, for storage, processing, and disseminating reconstructed data to receivers. The system is privacy-assured where cloud sees neither the original samples nor underlying data. It handles well sparse and general data, and data tampered with noise. Theoretical and empirical evaluations demonstrate the system achieves privacy-assurance, efficiency, effectiveness, and resource-savings simultaneously.
A Low PAPR Constellation Mapping Scheme for Rate Compatible Modulation

This letter proposes a novel nonlinear constellation mapping (NLCM) scheme for the rate compatible modulation (RCM) approach in low-to-moderate signal to noise ratios (SNRs). In NLCM, each RCM coded symbol is mapped to a complex-valued amplitude phase shift keying (APSK) constellation point. Moreover, we provide a novel method of designing code degree and weight set for a wide SNR range. The proposed NLCM is developed to increase the throughput and decrease the peak to average power ratio (PAPR) at the same time. Both analysis and simulation results show that the NLCM scheme outperforms the original RCM in terms of throughput and PAPR.
DAQ system for LEPS experiment

A data acquisition (DAQ) system for experiments with the Laser Electron Photons at SPring-8 (LEPS) has been developed based on network distributed architecture. The system is designed so as to transfer digitized data with a 1-kHz trigger rate from various front-end electronics. Four local DAQ systems with VME board computers at the front end play a role to provide modularity, scalability, and flexibility. Data from each local DAQ system are sent through fast Ethernet to the data server. With this DAQ system, experiments to measure /spl phi/ photoproduction at LEPS started on May 2000. Signals from scintillation counters, drift chambers, and silicon strip detectors were successfully stored on disk. The performance of the present DAQ system is found to be good enough for the measurement.
Modeling and Optimization of High Frame Rate Video Transmission Over Wireless Networks

High frame rate (HFR) video is emerging as a new paradigm in popular multimedia applications (e.g., cloud gaming) to achieve smooth viewing experience perceived by end-users. In the context of HFR streaming video, end-to-end distortion and sending frame rate are equally important to the perceptual quality. This study presents a modeling-based approach to optimizing the HFR video transmission over wireless networks. First, we develop an analytical model dubbed FRIED (Frame Rate versus vIdEo Distortion) to characterize the tradeoff between sending frame rate and end-to-end video distortion. Second, we propose a Joint frAme Selection and FEC (Forward Error Correction) cOding (JASCO) approach based on the FRIED model to optimize the transmission performance. The efficacy of the proposed JASCO is evaluated through extensive semi-physical emulations in Exata involving H.264 video streaming. Experimental results show that JASCO outperforms the reference approaches in improving video peak signal-to-noise ratio (PSNR) at the same frame rate. Or conversely, JASCO is able to achieve higher received frame rate while guaranteeing the same video PSNR.
An energy-efficient and QoS-effective resource allocation scheme in WBANs

Wireless Body Area Networks (WBANs) represent one of the most promising networks to provide health applications for improving the quality of life, such as ubiquitous e-Health services and real-time health monitoring. The resource allocation of an energy-constrained, heterogeneous WBAN is a critical issue that should consider both energy efficiency and Quality of Service (QoS) requirements with the dynamic link characteristics, especially when the limited resource cannot satisfy the expected QoS requirements. In this paper, we propose an Energy-efficient and QoS-effective resource allocation that considers a mix-cost parameter characterizing both energy cost and QoS cost between attainable QoS support and QoS requirements. Based on the mix-cost parameter, we first formulate the resource allocation problem as a mixed integer nonlinear programming (MINP) for optimizing the transmission power, the transmission rate and allocated time slots for each sensor to minimize total mix-cost of the system. Then we propose a sub-optimal greedy resource allocation algorithm, which has a much lower complexity compared to exhaustive search. Simulation results demonstrate the advantage of the mix-cost parameter to evaluate energy efficiency and attainable QoS support, as well as verifying the effectiveness of the proposed resource allocation algorithm.
A hierarchical anti-occlusion tracking algorithm based on DMPF and ORB

An important issue in video target tracking is to deal with occlusion problem. In this paper, a hierarchical anti-occlusion tracking algorithm based on Dual Mode Particle Filter (DMPF) and Oriented FAST and Rotated BRIEF (ORB) is proposed to improve the location accuracy under different occlusion conditions in video target tracking. In the first layer, DMPF is used to track target and preliminarily locate its position under various occlusion status. By using corner matching in the second layer, the target is precisely located according to ORB similarity and corner coordinate, thus a more accurate position of target is obtained. A modularized occlusion judgment scheme is also presented to switch tracking mode timely and accurately in DMPF and a Quantified Matrix based on Weighted RGB color space (WQM) is introduced for both color feature creation and corner detection to save operation time. Simulation results show that the proposed algorithm could provide high tracking accuracy in a real-time manner for different occlusion status.
Automatic suggestion of presentation image for storytelling

Digital storytelling applications are playing an increasingly important role in people's daily life. In contemporary storytelling applications such as PowerPoint presentation and macro/micro blogs, good presentation images are always highly desired by content creators to boost their presentation in an intuitive and attractive way. Existing studies, however, have not yet addressed the challenging problem of how to select the most appropriate presentation images for storytelling. In this paper, we formulate this problem of presentation image suggestion (given a textual query) as selecting images by maximizing visual and semantic diversity from web image search results of suggested queries. The proposed framework consists of two novel components: 1) click-through-based query suggestion, which is designed to suggest textual queries by searching relevant queries in a constructed query graph that can reflect diverse aspects of a given query, and 2) query-based image selection, which selects the most appropriate presentation images by keeping semantic relevance while maximizing visual diversity and quality, using a novel model based on Conditional Random Field (CRF) by individual and correlation characters. We evaluate the proposed approach by comparing with several baselines and a thorough subjective survey. The evaluations show inspiring results using the proposed approach for automatic suggestion of images for storytelling.
QoE continuum driven HTTP adaptive streaming over multi-client wireless networks

Different from traditional HTTP adaptive streaming (HAS) in which only one client is considered, HAS over multi-client wireless networks faces new challenges. The Quality of Experience (QoE) of users becomes unstable due to users' competition for shared bandwidth. It is thus important to accurately estimate the perceived experience of users and then adapt the streaming process accordingly. Furthermore, the QoE fairness among multiple clients subscribing to the same services shall also be addressed. In this research, we propose a QoE continuum driven HAS adaptation algorithm to address these challenges. We model the QoE continuum as an integrated consideration of cumulative playback quality and playback smoothness. Based on this model, we jointly optimize the quality adaptation of multiple users by considering both QoE history and channel status. Moreover, we propose to use quantization parameter and segment size to represent the video files in a fine-grained fashion, in order to more effectively capture the bandwidth fluctuation. The results from extensive simulations show that the proposed scheme can provide balanced and satisfactory QoE among multiple clients.
A Cross-Layer Approach to Multichannel MAC Protocol Design for Video Streaming Over Wireless Ad Hoc Networks

This paper presents a cross-layer design for a reliable video transmission over wireless ad hoc networks based on multichannel MAC protocol with TDMA. First, we conduct a study of the multichannel MAC protocol through Markov chain model. Based on this study, two novel cross-layer modules are adopted for the design of multichannel MAC protocol. First, we adopt maximum latency rate (MLR) as the channel quality metric. Unlike the traditional MAC design based on network allocation vector (NAV), MLR is implemented to provide differentiated traffic so that the channel with smaller MLR time is initiated for higher priority traffic. Second, we adopt two congestion-aware metrics, namely MAC utilization and queue length of MAC layer, to improve the congestion-aware routing protocols with AODV and DSR. These two novel modules allow the proposed MAC protocol design to achieve high performance video transmission over wireless ad hoc networks. Experimental results show that the proposed scheme outperforms the state-of-the-art schemes under multichannel environments in wireless ad hoc networks for as much as 3.6 dB in PSNR. Such significant performance enhancement confirms that the cross-layer approach is very effective for multichannel MAC protocol design.
New TCP video streaming proxy design for last-hop wireless networks

TCP based HTTP video streaming is becoming more and more popular in recent years. However, for last-hop wireless access, due to TCP's additive increase/multiplicative decrease (AIMD) congest control mechanism, when interference and fading are strong, TCP streaming behaves poorly. In this research, we propose a new design of TCP video streaming proxy at the wireless base station. The new proxy's mission is two folds. First, it transparently isolates the wireless network from the wired network. Specifically, for the last-hop wireless transmission, it adopts a new variant of TCP, we call it raw-TCP, which in combination with a fair scheduler component in the proxy, results in an increased average throughput with low dynamics. Second, through video adaptation, i.e. priority guided packet reordering and deadline driven adaptive truncation, the proxy eliminates playback jitter while maintaining small visual quality variation between successive frames. These two functions fundamentally differentiate our research from the vanilla split-TCP schemes. In our research, the TCP proxy is a multimedia aware element while in traditional split-TCP, the relay node is multimedia oblivious. Simulation results using ns-2 verify the effectiveness of the proposed solution.