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Pendelluft, the shift of air from non-dependent to dependent lung regions, is known to occur during active breathing in ventilated patients. However, information about pendelluft in ARDS patients under assisted mechanical ventilation is limited. In this prospectively collected and retrospectively analyzed study, we combined electrical impedance tomography and respiratory mechanics monitoring to quantitatively examine pendelluft in trigger and reverse triggering breaths in 20 mechanically ventilated patients with ARDS during the transition from controlled to active breaths under volume-cycled ventilation. Besides the 10 resting breaths in each patient, 20% of the counted active breaths were selected based on three levels of esophageal pressure swing (∆Pes): low (< 5 cm H2O, breaths = 471), moderate (≥ 5, < 10 cm H2O, breaths = 906), and high effort (≥ 10 cm H2O, breaths = 565). The pendelluft response to breathing efforts was significantly greater in trigger breaths than in reverse triggering breaths (p < 0.0001). Based on the pendelluft-∆Pes slope (ml/cmH2O), there were two distinct patterns of effort-related pendelluft (high vs. low pendelluft group). For trigger breaths, the high pendelluft group (n = 9, slope 0.7–2.4 ml/cmH2O) was significantly associated with lower peak airway/plateau pressure and lower respiratory system/lung elastance than the low pendelluft group (n = 11, slope − 0.1 to 0.3 ml/cmH2O). However, there was no difference in respiratory mechanics between high and low pendelluft groups for reverse triggering breathes. The use of ∆Pes to predict pendelluft was found to have a low positive predictive value. [ABSTRACT FROM AUTHOR]

Kallistatin, an endogenous serine proteinase inhibitor, is protective against sepsis in animal models. The aim of this study was to determine the plasma concentration of kallistatin in intensive care unit (ICU) patients with severe sepsis and septic shock and to determine their potential correlation with disease severity and outcomes. We enrolled 86 ICU patients with severe sepsis and septic shock. Their plasma concentrations of kallistatin, kallikrein, tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, and IL-8 were measured by enzyme-linked immunosorbent assay. The association of kallistatin levels with disease severity and patient outcomes was evaluated. The relationship between kallistatin and other biomarkers was also analyzed. Plasma kallistatin levels on day 1 of ICU admission were lower in patients with septic shock compared with patients with severe sepsis (p = 0.004). Twenty-nine patients who died in the hospital had significantly lower day 1 kallistatin levels than patients who survived (p = 0.031). Using the optimal cutoff value (4 μg/ml) of day 1 plasma kallistatin determined by receiver operating characteristic curves for 60-day mortality, we found that high kallistatin levels were associated with a preferable 60-day survival (p = 0.012) by Kaplan-Meier analysis and lower Sequential Organ Failure Assessment (SOFA) scores over the first 5 days in the ICU (p = 0.001). High kallistatin levels were also independently associated with a decreased risk of septic shock, the development of acute respiratory distress syndrome, and positive blood cultures. In addition, there were inverse correlations between day 1 kallistatin levels and the levels of TNF-α, IL-1β, IL-6, and C-reactive protein, and SOFA scores on day 1. Our results indicate that during severe sepsis and septic shock, a decrease in plasma concentrations of kallistatin reflects increased severity and poorer outcome of disease. [ABSTRACT FROM AUTHOR]

In this paper, we present Prius, a hybrid edge cloud and client adaptation framework for HTTP adaptive streaming (HAS) by taking advantage of the new capabilities empowered by recent advances in edge cloud computing. In particular, emerging edge clouds are capable of accessing an application layer and radio access networks (RANs) information in real time. Coupled with powerful computation support, an edge cloud-assisted strategy is expected to significantly enrich mobile services. Meanwhile, although HAS has established itself as the dominant technology for video streaming, one key challenge for adapting HAS to mobile cellular networks is in overcoming the inaccurate bandwidth estimation and unfair bitrate adaptation under the highly dynamic cellular links. Edge cloud-assisted HAS presents a new opportunity to resolve these issues and achieve systematic enhancement of quality of experience (QoE) and QoE fairness in cellular networks. To explore this new opportunity, Prius overlays a layer of adaptation intelligence at the edge cloud to finalize the adaptation decisions while considering the initial bandwidth-irrelevant bitrate selection at the clients. Prius is able to exploit RAN channel status, client device characteristics, and application-layer information in order to jointly adapt the bitrate of multiple clients. Prius also adopts a QoE continuum model to track the cumulative viewing experience and an exponential smoothing estimation to accurately estimate a future channel under different moving patterns. Extensive trace-driven simulation results show that Prius with hybrid edge cloud and client adaptation is promising under both slow and fast-moving environments. Furthermore, the Prius adaptation algorithm achieves a near-optimal performance that outperforms the exiting strategies. [ABSTRACT FROM PUBLISHER]

This paper presents a novel smart downlink scheduling scheme to enhance the performance of multimedia transmission over long-term evolution (LTE) networks. LTE represents a promising framework for the next-generation broadband multimedia services because of its significantly increased data rate over 3G cellular networks. However, the current LTE scheduling scheme has largely been designed for general data traffic without adequate consideration of multimedia characteristics. Moreover, the hard handoff (HO) procedure adopted in LTE will further degrade the multimedia services when the mobile user moves from one cell to another. Even with increased data rate, current LTE systems still cannot deliver the expected quality of service (QoS) to the mobile users under various mobility scenarios, especially when the hard HO is evoked. Aiming at overcoming these major challenges, we develop in this paper a QoS-driven smart downlink scheduling scheme for enhanced multimedia transmission over LTE networks. The proposed design shall take the following QoS metrics into consideration: 1) the delay constraint of voice-over-Internet protocol flow; 2) the packet deadline of video flow; and 3) the service degradation induced by the hard HO procedure. We achieve the design objectives by creating three QoS-driven operational control modules: 1) a transmission delay control module to ensure the on-time arrival of various types of multimedia data; 2) an HO control module to warrant continuous multimedia services when the user moves across a cell boundary; and 3) a resource allocation module to strategically map the requested flows to best fit radio resource blocks. The simulation results confirm the performance gain of the proposed scheme. [ABSTRACT FROM AUTHOR]

Electromagnetic simulation and multiple light extraction concepts for solution-processed organic light-emitting diodes were explored in this report. Based on the optical simulation results, the device architecture was optimized for maximization of the light out-coupling efficiency. By utilizing a high refractive index substrate, a high light scattering electrode, and an external light extraction structure, trapped photons in the wave-guided and substrate modes were extracted effectively. The best solution-processed device exhibited a maximum external quantum efficiency of 42.9%, a current efficiency as high as 150.1 cd/A, and a power efficacy up to 125.1 lm/W. [ABSTRACT FROM AUTHOR]

In multimedia processing area, image/video frame resizing is recognized as one of the key techniques and has received more and more attention from the research community. It is necessary and very challenging for image/video distribution systems to be able to generate higher resolution video to meet high-end users’ requirements. In this paper, we analyze the bottleneck of hybrid DCT-Wiener scheme firstly and propose a w-norm signal reconstruction scheme from partial frequency coefficients for upsampling based on uncertainty principles. In the proposed scheme, we design a w-norm model to reconstruct up-sized image/video frames by solving convex optimization with constraints. The proposed scheme combines Wiener filter with compressive sensing (CS) theory seamlessly to automatically control the accuracy of both low- and high-frequency components. This scheme can efficiently avoid the fake contours and ringing effects which are the main defects of hybrid DCT-Wiener methods (hybrid methods). Experiments have been carried out to demonstrate that the proposed scheme can achieve significant PSNR and SSIM gains over typical CS schemes and original hybrid method. On the other hand, the proposed scheme controls the accuracy of both low- and high-frequency components by calculating the correlation between reconstructed signals and sampling matrixes during reconstruction which is a good solution to reduce fake contours and ringing effect. This scheme can also achieve noticeable visual quality improvement compared with hybrid methods and typical CS methods. [ABSTRACT FROM AUTHOR]

Highlights: [•] Different types of interconnection layers for tandem solar cells were investigated. [•] Spatial positions of sub-cells were designed to balance the photocurrent. [•] Highly efficient tandem cell with efficiency up to 7.4% were demonstrated. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present a novel scalable video transmission strategy over multi-input multi-output (MIMO) wireless systems with time-varying channel capacity. It is a great challenge to simultaneously guarantee the QoS for video delivery and maximize the system throughput over time-varying MIMO channel. We demonstrate that, by making full use of estimated channel state information (CSI) through feedback, a cascade of adaptive operations can be designed to satisfy maximum throughput for scalable video over MIMO systems. These operations include power allocation based on water-filling (WF), adaptive channel selection (ACS), and novel throughput maximizing power reallocation (PR). The proposed ACS transmission scheme enables overall increase in data throughput among enhancement layers by adaptively launching base layer bit-stream to proper sub-channel. Then, after initial power allocation with WF and proper adaptive mode selection, we obtain the surplus power across enhancement layer sub-channels which can be reallocated to some sub-channels by the proposed PR scheme. With such power reallocation, certain enhancement layers will be able to reach new level of QAM modulation through PR so as to maximize the system data throughput. We present in this paper some detailed analysis on these adaptive operations. We also present some simulation results to demonstrate that maximum throughput video transmission over MIMO wireless systems indeed can be achieved based on scalable video coding (SVC) and a sequence of appropriately designed adaptive operations. [Copyright &y& Elsevier]

The article presents a study that investigated the feasibility of using computerized algorithm in detecting ineffective triggering (IT) in the expiratory phase (ITE) in mechanically-ventilated patients. A total of 14 mechanically ventilated adult patients were evaluated at a medical intensive care unit (MICU) of a university hospital. It found that the accuracy of detecting and quantifying ITEs can be feasible with the use of a computerized algorithm.

An optimal joint source-channel coding (OJSCC) scheme is developed for encoding memoryless generalized Gaussian distribution (GGD) sources and transmission over noisy channels, including memoryless channels and bursty channels. In particular, we are able to incorporate both transition probability and bit error rate of a bursty channel model into an end-to-end rate-distortion (R-D) function. This R-D function results in an optimum tradeoff between source coding accuracy and channel error protection under a fixed transmission rate. Based on the results of OJSCC, we further proposed a robust joint source-channel coding (RJSCC) scheme based on a combination of OJSCC with allpass filtering source reshaping. Experimental results based on a broad class of GGD sources suggest that the RJSCC scheme consistently outperforms the OJSCC scheme when the shape factor, a parameter of GGD, is less than 2.0. The comparison of RJSCC with the state-of-the-art robust quantization (RQ) [IEEE Trans. Image Process. 7 (1998) 496-505] shows that RJSCC is comparable with RQ at low bit rate and significantly better than RQ at high bit rate. In addition, an image transmission system using RJSCC demonstrates that the RJSCC system can be efficiently employed in practical applications. [ABSTRACT FROM AUTHOR]

Discrete wavelet transform provides an advantageous framework of multiresolution space-frequency representation with promising applications in image processing. An efficient modeling of the spatial and frequency characteristics of the wavelet transform coefficients has been recognized as a key to designing efficient quantization for wavelet-based image coding. We have proposed a Bayesian estimation framework to provide a unified, simultaneous modeling of both the spatial distribution and the intensity distribution of the wavelet transform coefficients [IEEE Trans. Circuit Syst. Video Technol. 7 (1997) 343], where the spatially localized characteristics of a given subband are modeled by Gibbs random fields (GRF). In this study, we investigate the modeling of individual cluster intensity distribution of the subband coefficients, within the context of a joint scene and signal modeling. This joint modeling decomposes the overall distribution of the coefficients into the superposition of individual cluster distributions. Among Gaussian, Laplacian, and generalized Gaussian distributions, we conclude that the composite of multiple generalized Gaussian distributions is most consistent with the overall distribution of the coefficients for any given high frequency subband. Meanwhile, modeling by Laplacian distributions has its advantage in terms of low computational complexity. The joint modeling enables us to devise a novel scene adaptive and signal adaptive quantization that fully exploits the coding redundancies resulting from wavelet transform. The concept and the mechanism of the proposed Bayesian estimation framework are applicable and tunable to other wavelet-based image processing tasks. [Copyright &y& Elsevier]

In this paper, we present a multiple hierarchical image coding and transmission scheme in which both the fragile structure of the set partitioning in hierarchical trees (SPIHT) source coding and unequal bit error sensitivity in the coded bitstreams have been taken into account. Multiple subsampling is applied to split the wavelet coefficients of the source image into multiple subsources so that each subsource is a crude representation of the original image. Therefore, the sequential dependence in the coded bitstream is broken and the error propagation is limited to a single subsource. Each subsource is coded using the SPIHT algorithm to achieve the high source coding performance. Cyclic redundancy code and rate compatible punctured convolutional (RCPC) code have been employed as the channel coding method to offer unequal error protection to the source coded bits. In each substream, all the bits in the same bit-layer are protected with the same channel coding rate. The higher the bit-layer, the more the channel coding protection is provided. Experimental results show that the proposed scheme has an excellent error resilient performance for image transmission over wireless channels with time varying and bursty error characteristics. In particular, the basic image structure is preserved even in highly noisy channels. [Copyright &y& Elsevier]

Presents information on a study which investigated the mechanism of adenosine-induced vasodilation in rat diaphragm microcirculation using laser Doppler flowmetry. Methodology of the study; Results and discussion on the study.

Motions in videos are typically a mixture of local dynamic object motions and global camera motion, which are inconsistent in some cases, and even interfere with each other, causing difficulties in various downstream applications, such as video stabilization that requires the global motion, and action recognition that consumes local motions. Therefore, it is crucial to estimate them separately. Existing methods separate two motions from the mixed motion fields, such as optical flow. However, the quality of mixed motion determines the higher bounds of the performance. In this work, we propose a framework, GLOCAL, to directly estimate global and local motions simultaneously from adjacent frames in a self-supervised manner. Our GLOCAL consists of a Global Motion Estimation (GME) module and a Local Motion Estimation (LME) module. The GME module involves a mixed motion estimation backbone, an implicit bottleneck structure for feature dimension reduction, and an explicit bottleneck for global motion recovery based on the global motion bases with foreground mask under the training guidance of proposed global reconstruction loss. An attention U-Net is adopted for LME which produces local motions while excluding motion of irrelevant regions under the guidance of proposed local reconstruction loss. Our method can achieve better performance than the existing methods on the homography estimation dataset DHE and the action recognition dataset NCAA and UCF-101. • A unified framework to estimate global and local motion simultaneously. • An implicit–explicit bottleneck for global motion without irrelevant information. • Two loss functions to learn global and local motion in a self-supervised manner. • Achieves SOTA in regular homography estimation scenes and action recognition task. [Display omitted] [ABSTRACT FROM AUTHOR]

Describes a novel three-dimensional image segmentation technique capable of robust segmentation using K-mean clustering and knowledge-based morphological operations. Description of the volumetric data; Information on the adaptive K-mean clustering algorithm; Results of the segmentation; Conclusion.

Analyzes the role of adenosine triphosphate-sensitive potassium (KATP) channels on the regulation of diaphragmatic microvascular flow during hemorrhagic hypotension. Characterizes KATP channels in the resting diaphragmatic microcirculation; Modulation of the microcirculation in the resting diaphragm during hemorrhagic hypotension.

Assesses the effects of glibenclamide (GLB), a blocker of ATP-sensitive potassium channel (KATP), and tetraethylammonium on modulating the regulation of diaphragmatic microcirculation in anesthetized mechanically ventilated rats. Recording of microcirculatory blood flow; Induction of hemorrhagic hypotension; Abolition of vasodilation effects.

Presents a scheme for artifact reduction of low bit rate discrete-cosine-transform-compressed images. Optimization technique; Calibration of coefficients; Anchor blocks and the double spiral scan; Gradient continuity constraint; Image compression model; Formulation of MAP estimation.

Proposes a novel scene adaptive and signal adaptive quantization scheme capable of exploiting both the spectral and spatial localization properties resulting from wavelet transform. Subband scheme for image and video coding; Adaptive quantization of high-frequency subbands; Issues beyond quantization; Experimental results; Conclusions.

Presents a novel cellular connectionist model for the implementation of a clustering-based adaptive quantization in video coding applications. Adaptive quantization algorithm; Features of overall video coding scheme based on wavelet transform and adaptive quantization; Proof of network convergence.

Looks at a human face location technique based on contour extraction within the framework of a wavelet-based video compression scheme for videoconferencing applications. Use of semantic information of the human face to design a hybrid compression scheme for videoconferencing; Effectiveness of location technique in improving perceptual image quality.

Network morphism is an effective learning scheme to morph a well-trained neural network to a new one with the network function completely preserved. However, existing network morphism scheme addresses only basic morphing types on the layer level. In this research, we address the central problem of network morphism at a higher level, i.e., how a convolutional layer can be morphed into an arbitrary module of a neural network. To simplify the representation of a network, we abstract a module as a graph with blobs as vertices and convolutional layers as edges. Based on this graph, the morphing process can be formulated as a graph transformation problem. Two atomic morphing operations are introduced to construct the graphs, based on which modules are classified into two families, i.e., simple morphable modules and complex modules. We present practical morphing solutions for both families, and prove that any module can be morphed from a single convolutional layer. Extensive experiments have been conducted based on the state-of-the-art ResNet on benchmarks to verify the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

With the exponential growth of video-based services, the development of video transmission systems, and the advancement of consumer video technologies, it is becoming increasingly important to improve visual applications in order to meet the quality expectations of end users. While monitoring and controlling QoS parameters of the video transmission system is important for achieving high video quality, it is more crucial to evaluate video quality directly from the users? perspective, which is known as quality of experience (QoE), or user-level quality of service (QoS). QoE-based video quality assessment is difficult because user experience is subjective, and hence hard to quantify and measure. [ABSTRACT FROM AUTHOR]

Wireless body area network (WBAN) has become a promising network for continuous health monitoring of various diseases. The limited energy of sensors in WBAN cannot support the long-term work with the high requirements of Quality of Service (QoS) for health applications. Energy harvesting (EH)-powered WBAN, which can provide uninterrupted work, has attracted more attention from both academic and industry. However, the time-varying and heterogeneous EH states of different sensors become an important factor when designing the resource allocation schemes in EH-powered WBAN. In this paper, we propose a novel two-phase resource allocation scheme, which optimizes the allocation of the transmission power, the source rate and the time slots to improve the QoS performances of EH-powered WBAN. In the first phase, we investigate that the statistical knowledge of energy harvesting to measure the long-term QoS performances, and then a joint power-rate control scheme is proposed to optimize the transmission power and the source rate for ensuring the long-term QoS performance. Moreover, we design a QoS aware slot allocation scheme to dynamically adjust the time slot allocation to cope with the time-varying and heterogeneous EH states for obtaining better short-term QoS performances in the second phase. Finally, numerical simulation results demonstrate that the proposed joint power-rate-slot resource allocation scheme of EH-powered WBANs can efficiently exploit the time-varying EH to improve both the long-term and short-term QoS performances. [ABSTRACT FROM AUTHOR]

Acute respiratory distress syndrome (ARDS) is characterized by acute-onset rapid-deteriorating inflammatory lung injury. Although the preservation of spontaneous breathing may have physiological benefits in oxygenation, increasing evidence shows that vigorous spontaneous breathing may aggravate lung injury (i.e., patient self-inflicted lung injury). Increased lung stress and pendelluft, which is defined as intrapulmonary gas redistribution without a significant change in tidal volume, are important mechanisms of patient self-inflicted lung injury. The presence of pendelluft may be considered a surrogate marker of vigorous inspiratory effort, which can cause the dependent lung to overstretch. In this review, we summarized three major methods for electrical impedance tomography–based pendelluft monitoring. Future studies are warranted to compare and validate the different methods of pendelluft estimation in patients with ARDS. [ABSTRACT FROM AUTHOR]

Kallistatin, an endogenous plasma protein, exhibits pleiotropic properties in inhibiting inflammation, oxidative stress and apoptosis, as evidenced in various animal models and cultured cells. Here, we demonstrate that kallistatin levels were positively correlated with the concentration of total protein in bronchoalveolar lavage fluids (BALF) from patients with sepsis-related acute respiratory distress syndrome (ARDS), indicating a compensatory mechanism. Lower ratio of kallistatin to total protein in BALF showed a significant trend toward elevated neutrophil counts (P = 0.002) in BALF and increased mortality (P = 0.046). In lipopolysaccharide (LPS)-treated mice, expression of human kallistatin in lung by gene transfer with human kallistatin-encoding plasmid ameliorated acute lung injury (ALI) and reduced cytokine/chemokine levels in BALF. These mice exhibited attenuated lung epithelial apoptosis and decreased Fas/FasL expression compared to the control mice. Mouse survival was improved by kallistatin gene transfer or recombinant human kallistatin treatment after LPS challenge. In LPS-stimulated A549 human lung epithelial cells, kallistatin attenuated apoptosis, down-regulated Fas/FasL signaling, suppressed intracellular reactive oxygen species (ROS) and inhibited ROS-mediated NF-κB activation and inflammation. Furthermore, LPS-induced apoptosis was blocked by antioxidant N-acetylcysteine or NF-κB inhibitor via down-regulating Fas expression. These findings suggest the therapeutic potential of kallistatin for sepsis-related ALI/ARDS. [ABSTRACT FROM AUTHOR]

We present in this paper a novel proactive interference avoidance scheme for mobile-to-mobile (M2M) communication as an underlay in Long-Term Evolution (LTE) downlink networks. The proposed scheme introduces an intelligent user-selective resource allocation algorithm that aims to proactively avoid interference between M2M pairs and LTE regular users while achieving a satisfactory link budget for both M2M and LTE regular links. The proposed resource allocation algorithm is based on a novel low-complexity interference estimation approach that requires only channel quality indicator (CQI) information (CSI). Since the CQI reporting mechanism has been already implemented in current LTE systems, the proposed algorithm is indeed practical and standard compatible, compared with the existing works that require perfect knowledge of the channel state information (CSI). In this research, achievable data throughput with LTE-compliant adaptive modulation and coding has been adopted as the optimization measurement, rather than the information-theoretic capacity. Therefore, the performance of the proposed scheme is practically realizable in LTE communication systems. Since the performance of the proposed scheme highly relies on the accuracy of CQI feedback, we also carry out performance analysis to evaluate the impact of CQI estimation error and delay. The simulation results show that the total achievable data throughput for LTE networks is dramatically enhanced by M2M communication with this proposed interference avoidance resource allocation scheme. [ABSTRACT FROM PUBLISHER]

The state-of-the-art High Efficiency Video Coding (HEVC) standard adopts a hierarchical coding structure to improve its coding efficiency. This allows for the quantization parameter cascading (QPC) scheme that assigns quantization parameters (Qps) to different hierarchical layers in order to further improve the rate-distortion (RD) performance. However, only static QPC schemes have been suggested in HEVC test model, which are unable to fully explore the potentials of QPC. In this paper, we propose an adaptive QPC scheme for an HEVC hierarchical structure to code natural video sequences characterized by diversified textures, motions, and encoder configurations. We formulate the adaptive QPC scheme as a non-linear programming problem and solve it in a scientifically sound way with a manageable low computational overhead. The proposed model addresses a generic Qp assignment problem of video coding. Therefore, it also applies to group-of-picture-level, frame-level and coding unit-level Qp assignments. Comprehensive experiments have demonstrated that the proposed QPC scheme is able to adapt quickly to different video contents and coding configurations while achieving noticeable RD performance enhancement over all static and adaptive QPC schemes under comparison as well as HEVC default frame-level rate control. We have also made valuable observations on the distributions of adaptive QPC sets in the videos of different types of contents, which provide useful insights on how to further improve static QPC schemes. [ABSTRACT FROM PUBLISHER]

We propose an adaptive hybrid digital–analog video transmission (A-HDAVT) scheme for robust video streaming in mobile networks with realistic fading channels. This scheme is fundamentally different from recent research in hybrid digital–analog video transmission in which wireless channels are unrealistically assumed to be Gaussian. For fading channels, it is critical to take full advantage of diversity in both video contents and multiuser channels. Like all hybrid approaches, A-HDAVT is designed to exploit the benefits from both digital and analog systems. To achieve this goal, each group of pictures is first transformed into one low-pass frame and several high-pass frames with motion-compensated temporal filtering. The critical low-pass frame is reliably transmitted as base layer in a digital mode, while high-pass frames are transmitted as enhancement layers in an analog mode to achieve desired graceful degradation performance. In analog transmission, we introduce a channel prediction-based adaptive power-distortion optimization (P-APDO) scheme to combat channel fading in mobile networks. The basic idea behind P-APDO is to perform power allocation based on the video content as well as the predicted channel status. Furthermore, we also investigate the multiuser scenarios in which the content diversity and channel diversity among users are appropriately exploited. Extensive simulations have been carried out to evaluate the performance of A-HDAVT under various degrees of channel fading. The results show that A-HDAVT achieves significant performance gains over competing schemes Robust Uncoded Video Transmission, Parcast, and Softcast in both single-user and multiuser scenarios. [ABSTRACT FROM PUBLISHER]

In contemporary multiuser multiple-input multiple-output systems, it is common for the reception devices to have a varying number of antennas. When multicast is performed, the number of concurrent spatial streams is limited by the device with the least number of antennas, which prevents more capable devices from getting higher rates. In this paper, we address the antenna heterogeneity in wireless video multicast by the innovative design of multiple similar description (MSD) video coding and multiplexed space–time block coding (M-STBC). MSD coding generates multiple descriptions of a video and features that any linear combinations of the descriptions are decodable. The descriptions comprising of real numbers are further processed by transform and power allocation steps for efficient transmission in a power-constrained system. M-STBC puts symbols in similar descriptions to corresponding space–time positions and ensures decodability under any antenna settings and channel conditions. As a result, we build up a scalable video multicast system, named AirScale, which allows receivers with a various number of antennas to decode from a single transmission, and the reconstructed video quality improves with the number of equipped antennas. Evaluations on Sora shows that, in a \1,2,3,4\ \times 4 system, AirScale provides baseline quality for one-antenna receiver and a much higher quality for multiantenna receivers. The gain over SoftCast is up to 3.5, 3.9, and 4.1 dB for two-, three-, and four-antenna receivers, respectively. [ABSTRACT FROM PUBLISHER]

Video transmission using Scalable Video Coding (SVC) enables the functionalities of adaptation for both channel bandwidth and terminal device. However, link adaptation, which requires the video bitstream to provide error resilient scalability (ERS), is currently not supported in SVC. In this paper, we propose a robust SVC bitstream transmission framework, which integrates both error resilient (ER) coding and error concealment (EC) to enable the desired link adaptability. First, at the encoder, we design an ER coding scheme for SVC using redundant pictures, in which redundant picture information (RPI) is generated under rate-distortion criteria and transmitted to the media gateway together with the SVC bitstream. Then, ERS can be effectively achieved at the media gateway by selectively adding or removing coded pictures of different video coding layers according to the RPI and current network link status. Finally, at the decoder, we take advantage of the unique characteristics of SVC to develop an EC scheme based on both the proposed Virtual-BLSkip method and Wiener filtering to recover the lost or removed enhancement layer pictures. The proposed framework has negligible impact on the coding efficiency because it transmits RPIs rather than directly adding redundancy into the original bitstream. More importantly, it successfully strikes a balance between coding efficiency, error resiliency, and operation complexity. It is capable of providing the desired ERS with extremely low complexity, and maintaining virtually the same bit rate as input video bitstream. The experimental results show that the proposed framework achieves significant performance gains over existing ER transmission schemes. [ABSTRACT FROM PUBLISHER]

This research addresses the problem of robust linear video transmission over the Rayleigh fading channel, where only statistical channel state information (CSI) is available to the sender. We observe that discarding low-priority (LP) data and saving the channel uses for high-priority (HP) data can significantly improve the quality of the received video. We formulate an optimization problem that aims to minimize the total squared error of a multi-variant Gaussian random vector under the given bandwidth and power resources. To tame the complexity of this NP-hard problem, we analyze two sub-problems, namely power allocation and bandwidth allocation, and propose an iterative algorithm to approximate the solution. Subsequently, we propose a one-pass two-step fast algorithm that further reduces both algorithmic and computational complexity. A linear video transmission system is implemented based on the proposed algorithm. Simulations show that our system significantly outperforms SoftCast, and the PSNR gain at 5th percentile of 1000 test runs is between 4.0 dB and 7.5 dB under varying noise levels. [ABSTRACT FROM PUBLISHER]

Background and objective Previous studies have demonstrated that positive blood culture could contribute to poorer outcomes in patients with pneumonia. However, the impact of positive blood culture on the outcomes of patients with sepsis-induced acute respiratory distress syndrome ( ARDS) has not been evaluated. Methods An observational study that prospectively screened 4861 patients admitted to medical or surgical intensive care units ( ICUs) of a tertiary referral centre was performed. Results Among 4861 admitted patients, 146 diagnosed with sepsis-induced ARDS were enrolled (mean age: 66.1 years). Lower PaO2/ FiO2, decreased respiratory system compliance, and higher lung injury scores ( LIS) on the day of ARDS diagnosis were associated with positive blood cultures ( n = 68) rather than negative blood cultures ( n = 78). There was no relationship between positive blood culture and in-hospital mortality. Kaplan-Meier estimates also revealed that positive blood culture was not associated with 60-day mortality but with an increased length of stay in the hospital and in the ICU ( P = 0.007 and P = 0.016, respectively). Using multivariate logistic regression, higher LIS was independently associated with positive blood culture. In addition, chronic pulmonary disease, lower platelet count, higher LIS, and the development of shock on the diagnosis of ARDS, were independent risk factors for in-hospital mortality. Conclusions This study suggests that the presence of positive blood culture is not associated with increased mortality; however, the mean durations of hospital and ICU stays in patients with sepsis-induced ARDS are increased. [ABSTRACT FROM AUTHOR]

Spectral clustering has been an attractive topic in the field of computer vision due to the extensive growth of applications, such as image segmentation, clustering and representation. In this problem, the construction of the similarity matrix is a vital element affecting clustering performance. In this paper, we propose a multi-view joint learning (MVJL) framework to achieve both a reliable similarity matrix and a latent low-dimensional embedding. Specifically, the similarity matrix to be learned is represented as a convex hull of similarity matrices from different views, where the nuclear norm is imposed to capture the principal information of multiple views and improve robustness against noise/outliers. Moreover, an effective low-dimensional representation is obtained by applying local embedding on the similarity matrix, which preserves the local intrinsic structure of data through dimensionality reduction. With these techniques, we formulate the MVJL as a joint optimization problem and derive its mathematical solution with the alternating direction method of multipliers strategy and the proximal gradient descent method. The solution, which consists of a similarity matrix and a low-dimensional representation, is ultimately integrated with spectral clustering or K-means for multi-view clustering. Extensive experimental results on real-world datasets demonstrate that MVJL achieves superior clustering performance over other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose an integrated robust multiple description coding (MDC) scheme for compressed still images over error-prone channels with both packet loss and random bit errors. Multiple descriptions are first generated independently by using multiple description scalar quantizer (MDSQ). To achieve an excellent error resilient capability for each description, multiple bitstreams are then generated based on wavelet trees along the spatial orientations. The spatial orientation trees in the wavelet domain are individually encoded using SPIHT. Error propagation is thus limited within each bitstreams. However, synchronization words are usually needed to avoid error propagation across multiple independent bitstreams. In order to maintain high compression efficiency robust synchronization, we adopt error resilient entropy coding (EREC) to re-organize these variable-length bitstreams into fixed-length data slots before multiplexing and transmission. Therefore, the synchronization of the start of each bitstream can be automatically obtained at the receiver. Finally, to alleviate the devastating image degradation resulted from errors in the beginning of the bitstreams, we propose an error concealment technique to both constrain the EREC/MDC decoding and post-process the decoded wavelet coefficients. With analysis and experimental results, we have demonstrated how each of these components has contributed to the overall robustness of the proposed transmission of compressed images over wireless channels. [Copyright &y& Elsevier]

Abstract: For leaky prediction based FGS (Fine Granularity Scalability), constant quality constrained rate adaptation, i.e., how to optimally truncate/allocate bits given the current channel bandwidth, is still an open problem. The difficulty lies in obtaining accurate R–D (rate–distortion) curves for leaky prediction based FGS (L-FGS) due to the dependency among video frames. In this paper, we propose an accurate R–D model, which considers not only the distortion introduced in the current frame and the propagated distortion from the reference frame due to rate adaptation, but also the correlation between them. An excellent property of our proposed R–D model is that even when applying the model for a long video sequence without any update of the actual distortion values, the estimation error is still negligible and the error is not accumulated. Based on our proposed R–D model, a sliding window technique is further developed to solve the problem of constant quality constrained bit allocation. Experimental results show that the proposed R–D model is very accurate and the corresponding bit allocation algorithm can achieve much more smooth video quality than the traditional uniform bit allocation under both CBR (constant bit rate) and VBR (variable bit rate) channels. [Copyright &y& Elsevier]

Abstract: Current rate control schemes in video coding standards do not have efficient frame-level bit allocation due to the limitation of real-time encoding. In this paper, by taking advantage of offline video encoding, we proposed a frame-level bit allocation scheme based on a latest developed rate–distortion (R-D) model, the ρ-domain R-D model [IEEE Trans. Circuits Syst. Video Technol. (2001) 928], for low bit rate streaming applications. Specifically, at the encoder, we code each video sequence twice. In the first pass, we generate the R-D information of video sequences. Then, in the second pass, according to the available channel bandwidth, by exploiting the pre-generated R-D information we are able to implement frame-level bit allocation in an optimal way so that video sequences can be coded at low bit rate with an improved quality. We apply the proposed bit allocation scheme for both offline video coding and rate-reduction transcoding. Experimental results demonstrate the proposed scheme is able to achieve not only reduced average distortion but also much smoother visual quality. [Copyright &y& Elsevier]

We propose a local force model based on the kinetics of the left ventricle (LV) surfaces to characterize the complex nonrigid motion that the heart undergoes. The left ventricle motion is analyzed in a coarse-to-fine fashion. First, global motion and deformation are analyzed and compensated with hierarchical surface modeling. Then, we propose a physics-based model of local deformation derived from the dynamics of independent point masses driven by local external force. Experimental results show that the ensembles of the estimated point mass trajectories match well with the realistic left ventricle surface dynamics. [Copyright &y& Elsevier]

Many semantic events in team sport activities e.g. basketball often involve both group activities and the outcome (score or not). Motion patterns can be an effective means to identify different activities. Global and local motions have their respective emphasis on different activities, which are difficult to capture from the optical flow due to the mixture of global and local motions. Hence it calls for a more effective way to separate the global and local motions. When it comes to the specific case for basketball game analysis, the successful score for each round can be reliably detected by the appearance variation around the basket. Based on the observations, we propose a scheme to fuse global and local motion patterns (MPs) and key visual information (KVI) for semantic event recognition in basketball videos. Firstly, an algorithm is proposed to estimate the global motions from the mixed motions based on the intrinsic property of camera adjustments. And the local motions could be obtained from the mixed and global motions. Secondly, a two-stream 3D CNN framework is utilized for group activity recognition over the separated global and local motion patterns. Thirdly, the basket is detected and its appearance features are extracted through a CNN structure. The features are utilized to predict the success or failure. Finally, the group activity recognition and success/failure prediction results are integrated using the kronecker product for event recognition. Experiments on NCAA dataset demonstrate that the proposed method obtains state-of-the-art performance. [ABSTRACT FROM AUTHOR]

In Cache-enabled dense small cell networks (DSCNs), energy consumption and file delivery delay are two critical performance metrics. On the other hand, power control is an essential mechanism to mitigate inter-cell interference in DSCNs. However, the impact of power control at small base stations (SBSs) on the system performance when caches are involved has not been well studied. Observing energy consumption and file delivery delay are coupled with each other in cache-enabled DSCNs, different from existing studies, we attempt to optimize these two performance metrics at the same time, taking into account sophisticated power control at SBSs. Firstly, we formulate the energy-delay optimization problem as a utility maximization problem, where cooperative power control among SBSs, file placement and user association are considered. Then, we solve the optimization problem in two stages (i.e., caching stage and delivery stage), based on the fact that caching is performed during off-peak time. At the caching stage, a local popular file placement policy is proposed by estimating user preference at each SBS. At the delivery stage, with given caching status at SBSs, the optimization problem is further decomposed by Benders’ decomposition method. An efficient algorithm is proposed to approach the optimal association and power solution by iteratively shrinking the gap of the upper and lower bounds. Finally, extensive simulations are performed to validate our analytical and algorithmic work. The results demonstrate that the proposed algorithms can achieve a better tradeoff between energy consumption and file delivery delay than the existing algorithms. [ABSTRACT FROM AUTHOR]

In multi-person videos, especially team sport videos, a semantic event is usually represented as a confrontation between two teams of players, which can be represented as collective motion. In broadcast basketball videos, specific camera motions are used to present specific events. Therefore, a semantic event in broadcast basketball videos is closely related to both the global motion (camera motion) and the collective motion. A semantic event in basketball videos can be generally divided into three stages: pre-event, event occurrence (event-occ), and post-event. By analyzing the influence of different stages of video segments to semantic events discrimination, it is observed that the pre-event and event-occ segments are effective for classification, while the post-events are effective for event success/failure classification. In this paper, we propose an ontology-based global and collective motion pattern (On_GCMP) algorithm for the basketball event classification. First, a two-stage GCMP-based event classification scheme is proposed. The GCMP is extracted using the optical flow. The two-stage scheme progressively combines a five-class event classification algorithm on event-occs and a two-class event classification algorithm on pre-events. Both algorithms utilize the sequential convolutional neural networks (CNNs) and the long short-term memory (LSTM) networks to extract the spatial and temporal features of GCMP for event classification. Second, we utilize the post-event segments to predict success/failure using deep features of images in the video frames (RGB_DF_VF)-based algorithms. Finally, the event classification results and success/failure classification results are integrated to obtain the final results. To evaluate the proposed scheme, we collected a new dataset called NCAA+, which is automatically obtained from the NCAA dataset by extending the fixed length of video clips forward and backward of the corresponding semantic events. The experimental results demonstrate that the proposed scheme achieves the mean average precision of 58.10% on NCAA+. It is higher by 6.50% than the state of the art on NCAA. [ABSTRACT FROM AUTHOR]

Self-supervised representation learning (SSL) has achieved remarkable success in its application to natural images while falling behind in performance when applied to whole-slide pathological images (WSIs). This is because the inherent characteristics of WSIs in terms of gigapixel resolution and multiple objects in training patches are fundamentally different from natural images. Directly transferring the state-of-the-art (SOTA) SSL methods designed for natural images to WSIs will inevitably compromise their performance. We present a novel scheme SGCL: Spatial Guided Contrastive Learning, to fully explore the inherent properties of WSIs, leveraging the spatial proximity and multi-object priors for stable self-supervision. Beyond the self-invariance of instance discrimination, we expand and propagate the spatial proximity for the intra-invariance from the same WSI and inter-invariance from different WSIs, as well as propose the spatial-guided multi-cropping for inner-invariance within patches. To adaptively explore such spatial information without supervision, we propose a new loss function and conduct a theoretical analysis to validate it. This novel scheme of SGCL is able to achieve additional improvements over the SOTA pre-training methods on diverse downstream tasks across multiple datasets. Extensive ablation studies have been carried out and visualizations of these results have been presented to aid understanding of the proposed SGCL scheme. As open science, all codes and pre-trained models are available at https://github.com/HHHedo/SGCL. • Two intrinsic properties behind the WSIs are leveraged for designing the pretext tasks. • A novel self-supervised learning method is proposed for whole-slide pathological images. • A new loss function is proposed and validated via a theoretical analysis. • The proposed method achieves state-of-the-art performance on multiple datasets and tasks. [ABSTRACT FROM AUTHOR]

Video delivery has been playing an essential role in video services over edge networks. Although HTTP segment-based streaming, e.g., Dynamic Adaptive Streaming over HTTP (DASH), has become the prevailing technique, it cannot provide guaranteed video playback in terms of bitrate to mobile users. In essence, HTTP streaming downloads the video segments in a best effort fashion, i.e., passively responding to the channel dynamics. This can cause unstable playback with frequent rebuffer and multi-client competition that degrades a network-wide performance. In this paper, we present a network-assisted streaming framework for Guaranteed Playback-Experience Streaming over HTTP (GESH) that leverages the proactive control of network resources and joint coordination among multiple clients for service-aware network resource allocation. Specifically, GESH is empowered by a new weighted proportional fair scheduling without modifying existing cellular infrastructure, a per-segment channel variation model, and a suite of algorithms to seek the optimal weights for the scheduling. Extensive evaluations show that GESH can maximally guarantee the video playback of multiple users, as well as significantly outperforming conventional HTTP streaming and current DASH systems. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy set is a useful tool to handle the uncertainty in data. In order to deal with the uncertainty in an image and meanwhile overcome the sensitivity to image noise, a multi-objective evolutionary intuitionistic fuzzy clustering algorithm with multiple image spatial information (MOEIFC-MSI) is proposed to perform image segmentation in this paper. The important innovations of the method are listed as follows: (1) an intuitionistic fuzzy set of the image is first constructed by using a generalized fuzzy complement function; (2) this intuitionistic fuzzy set of the image is then utilized to compute fitness functions and a fuzzy evaluation index for selecting the optimal solution; (3) two kinds of complementary image spatial information are also introduced into the fitness functions and the fuzzy evaluation index to make the proposed method robust to image noise; (4) a real-coded variable string length technique is utilized to encode the cluster centers to automatically determine the number of clusters. Experimental results on synthetic, Berkeley and magnetic resonance (MR) images show that the proposed method outperforms state-of-the-art methods in noise robustness and segmentation performance. [ABSTRACT FROM AUTHOR]

Generating an interior support structure is a key issue in 3D model geometric optimization for 3D printing. Most existing interior support structures have been designed by simulating lightweight structures naturally exist. One limitation of the existing method is that only one single structure is used for the model. However, different parts of a 3D model have different shapes and mechanical properties and different structures demonstrate distinctive advantages for supporting the model. Based on such observation, we propose to use hybrid structures for designing an optimal support structure. In this paper, we present a novel scheme of generating hybrid interior support structures for 3D printing. The proposed approach first partitions an input model into parts with different physical behaviors. Different interior structures are generated for each part, and the interior structures are finally joined together. Experimental results demonstrate that the proposed hybrid structure obtains higher strength-to-weight ratio than recent competing approaches that use single types of interior structures. [ABSTRACT FROM AUTHOR]

Image retargeting as a basic trick has been successfully applied to various computer vision problems. In this work, we propose a fast non-continuous seam carving with seam context (FN2SC) for robust and efficient image retargeting. FN2SC first conducts content aware image partition to separate an image into several strips with different target sizes from their content importance. It helps the removed seams in key objects distribute in a relatively uniform manner and prevents distortions to key objects. Then FN2SC performs fast non-continuous seam carving controlled by seam context of both neighboring relationship and touch bound relationship of seams. The seam context makes the removed seams distribute scattered and relieves artifacts caused by seam carving. During seam carving, image distortion is monitored by fast content aware image distance. Finally, FN2SC switches seam carving to scaling when the distortion meets the tolerance, which resizes the strips to the target sizes for image retargeting. Specifically, fast seam searching and image distortion based switching make FN2SC a fast and effective hybrid scheme. Experimental results demonstrate that the proposed FN2SC approach achieves good performance in terms of image quality and efficiency comprehensively. [ABSTRACT FROM AUTHOR]

Rate control typically involves two steps: bit allocation and bitrate control. The bit allocation step can be implemented in various fashions depending on how many levels of allocation are desired and whether or not an optimal rate–distortion (R-D) performance is pursued. The bitrate control step has a simple aim in achieving the target bitrate as precisely as possible. In our recent research, we have developed a \lambda -domain rate control algorithm that is capable of controlling the bitrate precisely for High Efficiency Video Coding (HEVC). The initial research showed that the bitrate control in the \lambda -domain can be more precise than the conventional schemes. However, the simple bit allocation scheme adopted in this initial research is unable to achieve an optimal R-D performance reflecting the inherent R-D characteristics governed by the video content. In order to achieve an optimal R-D performance, the bit allocation algorithms need to be developed taking into account the video content of a given sequence. The key issue in deriving the video-content-guided optimal bit allocation algorithm is to build a suitable R-D model to characterize the R-D behavior of the video content. In this paper, to complement the R- \lambda model developed in our initial work, a D- \lambda model is properly constructed to complete a comprehensive framework of \lambda -domain R-D analysis. Based on this comprehensive \lambda -domain R-D analysis framework, a suite of optimal bit allocation algorithms are developed. In particular, we design both picture-level and basic-unit-level bit allocation algorithms based on the fundamental R-D optimization theory to take full advantage of the content-guided principles. The proposed algorithms are implemented in HEVC reference software, and the experimental results demonstrate that they can achieve an obvious R-D performance improvement with a smaller bitrate control error. The proposed bit allocation algorithms have already been adopted by the Joint Collaborative Team on Video Coding and integrated into the HEVC reference software. [ABSTRACT FROM PUBLISHER]