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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]

We present a method to reduce the number of test patterns (TPs) decoded in the Chase-II algorithm for turbo product codes (TPCs) constructed with multi-error-correcting extended Bose-Chaudhuri-Hocquengem (eBCH) codes. We classify TPs into different conditions based on the relationship between syndromes and the number of errors so that TPs with the same codeword are not decoded except the one with the least number of errors. For eBCH with code length of 64, simulation results show that over 50% of TPs need not to be decoded without any performance degradation. Index Terms--Chase-II algorithm, extended BCH codes, test patterns, turbo product codes.

With the increasing popularity of using wireless local area networks (WLANs) for Internet access, the controlled channel access mechanism in IEEE 802.11e WLANs, i.e., HCF controlled channel access (HCCA), has received much more attention since its inherent centralized mechanism is more efficient in handling time-bounded multimedia traffic. So far, only a few research studies address the admission control problem of variable bit rate (VBR) traffic over HCCA. These existing studies consider each traffic flow individually and, thus, cannot exploit the statistical multiplexing gain among multiple VBR traffic flows. In this paper, we apply the existing statistical multiplexing framework to the studied admission control problem, with all the features of IEEE 802.11e HCCA being taken into consideration. Experimental results show that our proposed admission control scheme achieves significant improvement in network utilization while still satisfying all the quality-of-service (QoS) requirements. Index Terms--Admission control, HCF controlled channel access (HCCA), IEEE 802.11e wireless local area networks (WLANs), medium access control (MAC), variable bit rate (VBR) traffic.

In this paper, we present a novel joint application physical-layer design (JAPLD) strategy to cost-effectively transmit scalable H.264/AVC video over multi-input multi-output (MIMO) wireless systems. With this approach, the application layer cooperates with the physical layer to maximize the system performance. First, in physical layer, we propose a new layered video transmission scheme over MIMO: adaptive channel selection (ACS). ACS-MIMO is fundamentally different from parallel transmission MIMO (PT-MIMO). While each bit stream is continuously transmitted through a fixed antenna in PT-MIMO, ACS-MIMO is able to periodically switch each bit stream among multiple antennas. In application layer, Scalable Video Coding (SVC) generates layered bit streams that need prioritized delivery. Then, we obtain the ordering of each subchannel's SNR strength as partial channel information (CI) at the receiver. The partial CI is acquired via the estimated channel state information based on training sequences. The JAPLD strategy we developed in this research shall switch the bit stream automatically to match the ordering of SNR strength for the subchannels. Essentially, we will launch higher priority layer bit stream into higher SNR strength subchannel by the proposed JAPLD algorithm. In this fashion, we can implicitly achieve automatic unequal error protection (UEP) for layered SVC transmission over MIMO system without power control at the transmitter. Experimental results show that the proposed ACS-MIMO system is able to achieve UEP with the obtained partial CI and the reconstructed video peak signal-to-noise ratio demonstrate the performance improvement of the proposed system as compared with open loop PT-MIMO system. Index Terms--Adaptive channel selection (ACS), joint application physical-layer design (JAPLD), multi-input multi-output (MIMO), partial channel information (CI), Scalable Video Coding (SVC), unequal error protection (UEP).

In variable-bit-rate video coding, the video is preprocessed to collect sequence-level statistics, which are used for global bit allocation in the actual encoding stage to obtain a smoothed video presentation quality. However, in real-time video recording and network streaming, this type of two-pass encoding scheme is not allowed because the access to future frames and global statistics is not available. To address this issue, we introduce the concept of low-pass filtering of rate-distortion functions and develop a smoothed rate control (SRC) framework for real-time video recording and streaming. Theoretically, we prove that, using a geometric averaging filter, the SRC algorithm is able to maintain a smoothed video presentation quality while achieving the target bit rate automatically. We also analyze the buffer requirement of the SRC algorithm in real-time video streaming, and propose a scheme to seamlessly integrate robust buffer control into the SRC framework. The proposed SRC algorithm has very low computational complexity and implementation cost. Our extensive experimental results demonstrate that the SRC algorithm significantly reduces the picture quality variation in the encoded video clips. Index Terms--Bit allocation, quality smoothing, real-time video coding, variable bit rate.

In this letter, we present a novel product channel coding and decoding scheme for image transmission over noisy channels. Two convolutional codes with at least one recursive systematic convolutional code are employed to construct the product code. Received data are decoded alternately in two directions. A constrained Viterbi algorithm is proposed to exploit the detection results of cyclic redundancy check codes so that both reduction in error patterns and fast decoding speed are achieved. Experiments with image data coded by the algorithm of set partitioning in hierarchical trees exhibit results better than those currently reported in the literature. Index Terms--Constrained Viterbi algorithm (C-VA), image transmission, product code, recurrent alternate decoding.

Limited backhaul bandwidth and blockage effects are two main factors limiting the practical deployment of millimeter wave (mmWave) networks. To tackle these issues, we study the feasibility of relaying as well as caching in mmWave networks. A user association and relaying (UAR) criterion dependent on both caching status and maximum biased received power is proposed by considering the spatial correlation caused by the coexistence of base stations (BSs) and relay nodes (RNs). Using stochastic geometry tools, we decouple the UAR and caching placement issues by analyzing the relationship between UAR probabilities and caching placement probabilities. We then optimize the formulated caching placement problem based on polyblock outer approximation by exploiting the monotonic property in the general case and utilizing convex optimization in the noise-limited case. Accordingly, we propose a BS and RN selection algorithm where caching status at BSs and maximum biased received power are jointly considered. Experimental results demonstrate a significant enhancement of backhaul offloading using the proposed algorithms, and show that deploying more RNs and increasing cache size in mmWave networks is a more cost-effective alternative than increasing BS density to achieve similar backhaul offloading performance. [ABSTRACT FROM AUTHOR]

In multi-cell non-orthogonal multiple access (NOMA) systems, designing an appropriate user grouping strategy is an open problem due to diverse quality of service (QoS) requirements and inter-cell interference. In this paper, we exploit both game theory and graph theory to study QoS-aware user grouping strategies, aiming at minimizing power consumption in downlink multi-cell NOMA systems. Under different QoS requirements, we derive the optimal successive interference cancellation (SIC) decoding order with inter-cell interference, which is different from existing SIC decoding order of increasing channel gains, and obtain the corresponding power allocation strategy. Based on this, the exact potential game model of the user grouping strategies adopted by multiple cells is formulated. We prove that, in this game, the problem for each player to find a grouping strategy can be converted into the problem of searching for specific negative loops in the graph composed of users. Bellman-Ford algorithm is expanded to find these negative loops. Furthermore, we design a greedy based suboptimal strategy to approach the optimal solution with polynomial time. Extensive simulations confirm the effectiveness of grouping users with consideration of QoS and inter-cell interference, and show that the proposed strategies can considerably reduce total power consumption comparing with reference strategies. [ABSTRACT FROM AUTHOR]

An innovative scheme is proposed for image transmission over noisy channels. The scheme combines source and channel coding

In this paper, we first develop a rate-distortion (R-D) model for DCT-based video coding incorporating the macroblock (MB) intra refreshing rate. For any given bit rate and intra refreshing rate, this model is capable of estimating the corresponding coding distortion even before a video frame is coded. We then present a theoretical analysis of the picture distortion caused by channel errors and the subsequent inter-frame propagation. Based on this analysis, we develop a statistical model to estimate such channel errors induced distortion for different channel conditions and encoder settings. The proposed analytic model mathematically describes the complex behavior of channel errors in a video coding and transmission system. Unlike other experimental approaches for distortion estimation reported in the literature, this analytic model has very low computational complexity and implementation cost, which are highly desirable in wireless video applications. Simulation results show that this model is able to accurately estimate the channel errors induced distortion with a minimum delay in processing. Based on the proposed source coding R-D model and the analytic channel-distortion estimation, we derive an analytic solution for adaptive intra mode selection and joint source-channel rate control under time-varying wireless channel conditions. Extensive experimental results demonstrate that this scheme significantly improves the end-to-end video quality in wireless video coding and transmission. Index Terms--End-to-end distortion, error propagation, joint source-channel coding, rate-distortion analysis, wireless video.

In this paper, we present a novel joint-source and channel image-coding scheme for noisy channel transmission. The proposed scheme consists of two innovative components: 1) intelligent bidirectional synchronization and 2) layered bit-plane error protection. The bidirectional synchronization is able to recover the coding synchronization when any single or even when two consecutive synchronization codes are corrupted by the channel noise. With synchronized partition, unequal error protection for each bit plane can be designed based on the analysis of bit-plane error sensitivity for compressed image data transmission over noisy channels. Experimental results over extensive channel simulations show that the proposed scheme outperforms the well-known approach proposed by Sherwood and Zeger [1]. Index Terms--Bidirectional synchronization, hierarchical error correction, image transmission, noisy channel.

Recently, robust video transmission system that can eliminate the cliff effect in digital video transmission has attracted great interest from both academia and industry. By linearizing the whole system, robust video transmission is intrinsically scalable to channel conditions in mobile networks. However, the heterogeneity of user devices in terms of viewing resolution has not been well studied for robust video broadcast systems. In this paper, we propose a spatial scalability enabled robust video broadcast (SSRVB) system, aiming at accommodating diverse users with both heterogeneous resolutions and heterogeneous channel conditions. In SSRVB, a novel spatial decomposition method based on linear projection is first designed for robust video transmission. Then the transmission distortion minimization problem with joint subcarrier matching and power allocation is formulated. A near-optimal low-complexity subcarrier matching algorithm based on auction theory and an optimal power allocation strategy are also proposed. Furthermore, an iterative algorithm is designed to solve the problem of joint resource allocation. Simulation results demonstrate that SSRVB can achieve an average of 3 dB gain when compared with the reference schemes (i.e., ECast, MCast, discrete wavelet transform (DWT) based scheme, and scalable video coding (SVC) scheme) in terms of average peak signal-to-noise ratio under heterogeneous scenarios. [ABSTRACT FROM AUTHOR]

A semiautomated feature extraction-algorithm is presented for the extraction and measurement of the hippocampus from volumetric magnetic resonance imaging (MRI) head scans. This algorithm makes use of elements of both deformable model and region growing techniques and allows incorporation of a priori operator knowledge of hippocampal location and shape. Experimental results indicate that the algorithm is able to estimate hippocampal volume and asymmetry with an accuracy which approaches that of laborious manual outlining techniques. Index Terms - Deformable model, feature extraction, MRI, three-dimensional.

Anesthetized mechanically ventilated rats were analyzed after the administration of glibenclamide to determine the role of adenosine triphosphate (ATP)-sensitive K+ channels on microvascular circulation. Inhibition of ATP-sensitive K+ channels partially inhibited diaphragmatic microcirculation by blocking ATP-sensitive K+ channel activity. However, the effects of glibenclamide were altered by the administration of K-sensitive K+ channel agonists indicating the role of K+ channels in mediating diaphragmatic circulation.

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.

A strategy for artifact reduction of low bit rate discrete-cosine-transform-compressed images is shown. First, the DC coefficients are calibrated by employing gradient continuity constraints. Next, an enhanced Huber-Markov-random-field-based smoothing is employed. The constrained optimization is performed by the iterative conditional mode. Final reconstructions of typical images with enhancements in both visual quality and peak signal-to-noise ratio are demonstrated.

We present a human face location technique based on contour extraction within the framework of a wavelet-based video compression scheme for videoconferencing applications. In addition to an adaptive quantization in which spatial constraints are enforced to preserve perceptually important information at low bit rates, semantic information of the human face is incorporated to design a hybrid compression scheme for videoconferencing since the human face is often the most important portion within a frame and should be coded with high fidelity. The human face is detected based on contour extraction and feature point analysis. An approximated face mask is then used in the quantization of the decomposed subbands. At the same total bit rate, coarser quantization of the background enables the face region to be quantized finer and coded with higher quality. Simulation results have shown that the perceptual image quality can be greatly improved using the proposed scheme.

Nowadays, the Internet of Video Things (IoVT) is growing rapidly in terms of quantity and computation demands. In spite of the higher local computation capability on visual processing compared to conventional Internet of Things devices, IoVT devices need to offload partial visual processing tasks to the mobile edge computing (MEC) server wirelessly due to its larger computation demands. However, visual processing task offloading is limited by uplink throughput and computation capability of the MEC server. To break through these limitations, a novel non-orthogonal multiple access (NOMA)-assisted IoVT framework with multiple MEC servers is proposed, where NOMA is exploited to improve uplink throughput, and MEC servers are co-located with base stations to provide enough computation capability for off-loading. In the proposed framework, the association strategy, uplink visual data transmission assisted by NOMA, and division of the visual processing tasks as well as computation resource allocation at the MEC servers are jointly optimized to minimize the total delay of all visual processing tasks, while meeting the delay requirements of all IoVT devices. Simulation results demonstrate that significant performance gains can be achieved by proposed joint optimization with NOMA transmission and multi-MEC offloading in the heterogeneous IoVT network. [ABSTRACT FROM AUTHOR]