A Remote Markerless Human Gait Tracking for E-Healthcare Based on Content-Aware Wireless Multimedia Communications

Remote human motion tracking and gait analysis over wireless networks can be used for various e-healthcare systems for fast medical prognosis and diagnosis. However, most existing gait tracking systems rely on expensive equipment and take lengthy processes to collect gait data in a dedicated biomechanical environment, limiting their accessibility to small clinics located in remote areas. In this work we propose a new accurate and cost-effective e­ healthcare system for fast human gait tracking over wireless networks, where gait data can be collected by using advanced video content analysis techniques with low-cost cameras in a general clinic environment. Furthermore, based on video content analysis, the extracted human motion region is coded, transmitted, and protected in video encoding with a higher priority against the insignificant background area to cope with limited communication bandwidth. In this way the encoder behavior and the modulation and coding scheme are jointly optimized in a holistic way to achieve the best user-perceived video quality over wireless networks. Experimental results using H.264/AVC demonstrate the validity and efficacy of the proposed system

Wireless Mesh Networks to Support Video Surveillance: Architecture, Protocol, and Implementation Issues

Current video-surveillance systems typically consist of many video sources distributed over a wide area, transmitting live video streams to a central location for processing and monitoring. The target of this paper is to present an experience of implementation of a large-scale video-surveillance system based on a wireless mesh network infrastructure, discussing architecture, protocol, and implementation issues. More specifically, the paper proposes an architecture for a video-surveillance system, and mainly centers its focus on the routing protocol to be used in the wireless mesh network, evaluating its impact on performance at the receiver side. A wireless mesh network was chosen to support a video-surveillance application in order to reduce the overall system costs and increase scalability and performance. The paper analyzes the performance of the network in order to choose design parameters that will achieve the best trade-off between video encoding quality and the network traffic generated

Effects of habitat and livestock on nest productivity of the Asian houbara Chlamydotis macqueenii in Bukhara Province, Uzbekistan

To inform population support measures for the unsustainably hunted Asian houbara Chlamydotis macqueenii (IUCN Vulnerable) we examined potential habitat and land-use effects on nest productivity in the Kyzylkum Desert, Uzbekistan. We monitored 177 nests across different semi-arid shrub assemblages (clay-sand and salinity gradients) and a range of livestock densities (0–80 km-2). Nest success (mean 51.4%, 95% CI 42.4–60.4%) was similar across four years; predation caused 85% of those failures for which the cause was known, and only three nests were trampled by livestock. Nesting begins within a few weeks of arrival when food appears scarce, but later nests were more likely to fail owing to the emergence of a key predator, suggesting foraging conditions on wintering and passage sites may be important for nest productivity. Nest success was similar across three shrub assemblages and was unrelated to landscape rugosity, shrub frequency or livestock density, but was greater with taller mean shrub height (range 13–67 cm) within 50 m. Clutch size (mean = 3.2 eggs) and per-egg hatchability in successful nests (87.5%) did not differ with laying date, shrub assemblage or livestock density. We therefore found no evidence that livestock density reduced nest productivity across the range examined, while differing shrub assemblages appeared to offer similar habitat quality. Asian houbara appear well-adapted to a range of semi-desert habitats and tolerate moderate disturbance by pastoralism. No obvious in situ mitigation measures arise from these findings, leaving regulation and control as the key requirement to render hunting sustainable

Frame-based multiple-description video coding with extended orthogonal filter banks

We propose a frame-based multiple-description video coder. The analysis filter bank is the extension of an orthogonal filter bank which computes the spatial polyphase components of the original video frames. The output of the filter bank is a set of video sequences which can be compressed with a standard coder. The filter bank design is carried out by taking into account two important requirements for video coding, namely, the fact that the dual synthesis filter bank is FIR, and that loss recovery does not enhance the quantization error. We give explicit results about the required properties of the redundant channel filter and the reconstruction error bounds in case of packet errors. We show that the proposed scheme has good error robustness to losses and good performance, both in terms of objective and visual quality, when compared to single description and other multiple description video coders based on spatial subsampling. PSNR gains of 5 dB or more are typical for packet loss probability as low as 5%

Building the Full Annual Cycle Picture for Long-Billed Curlews: Correlates of Nest Success in the Breeding Grounds and Spatial Distribution and Site Fidelity in the Wintering Grounds

Migratory birds face threats throughout the annual cycle, and cumulative effects from linkages between the breeding and non-breeding grounds may impact species at the population level. Long-billed Curlews (Numenius americanus) are a migratory shorebird of conservation concern associated with grasslands that show breeding population declines at some regional and local scales. Curlews exhibit high site fidelity to breeding territories, but also spend approximately 75% of the year on the wintering grounds. Therefore, localized population declines could indicate localized threats, in the breeding or wintering grounds. However, little information is available regarding the spatial distribution of curlews on the wintering grounds, especially for Mexico. Furthermore, breeding ground studies which examine habitat selection and nest success in the context of predator and anthropogenic pressures are lacking. We add critical information that could help pinpoint conservation issues, including understanding limitations to nesting success and mapping spatial distribution and habitat use patterns during the non-breeding season. On the breeding grounds, we used a conditional logistic regression model to compare used nest-sites to available random sites and examine habitat selection within territories. We also studied correlates of nesting success with a generalized linear model for 128 curlew nests at five sites in the Intermountain West. During the non-breeding season, we attached satellite transmitters to track 21 curlews that bred in the Intermountain West and wintered in California and Mexico and quantified 95% home range and 50% core use size via utilization distributions created with dynamic Brownian Bridge Movement Models. For 14 individuals, we tracked multiple winter seasons and compared inter-annual site fidelity among winter areas, sexes, and habitat type with a Utilization Distribution Overlap Index. We documented four main wintering areas: (1) Central Valley of California, (2) the adjoining Imperial and Mexicali Valleys of California and Mexico, (3) the Chihuahuan Desert of inland Mexico, and (4) coastal areas of western Mexico and the Baja Peninsula. Curlews wintering in coastal areas had significantly smaller home ranges and fewer core use areas than inland-wintering curlews. Home ranges in the Central Valley were larger than other inland areas, and Central Valley females had larger home ranges than Central Valley males. Inter-annual site fidelity for wintering curlews was high, regardless of habitat type or sex. On the breeding grounds, curlews selected habitats for nest-sites with lower vegetation height and lower percent cover of grasses, bare ground, and shrubs than available sites. Nest-sites were six times more likely to have a cowpie within 50 cm than random sites. Higher probability of nest success was associated with higher curlew density in the nesting area, increasing percent cover of conspicuous objects such as cowpies within approximately two meters of the nest, and – surprisingly – higher densities of American Crows and Black-billed Magpies in the breeding area. In a separate analysis with a subset of nests (n = 100), we found nests had higher probability of success when they were farther from roads and perches. Given the central role of working lands to breeding curlews in much of the Intermountain West, an understanding of limitations to nesting success in these diverse landscapes is necessary to guide adaptive management strategies in increasingly human-modified habitats. Similarly, foundational understanding of winter spatial ecology is essential for understanding population declines which may be related to linkages between breeding and non-breeding seasons. Overall, these findings provide valuable information for full annual cycle conservation and will be particularly constructive for conservation planning once range-wide migratory connectivity is mapped

PCPT and ACPT: Copyright Protection and Traceability Scheme for DNN Models

Deep neural networks (DNNs) have achieved tremendous success in artificial intelligence (AI) fields. However, DNN models can be easily illegally copied, redistributed, or abused by criminals, seriously damaging the interests of model inventors. The copyright protection of DNN models by neural network watermarking has been studied, but the establishment of a traceability mechanism for determining the authorized users of a leaked model is a new problem driven by the demand for AI services. Because the existing traceability mechanisms are used for models without watermarks, a small number of false-positives are generated. Existing black-box active protection schemes have loose authorization control and are vulnerable to forgery attacks. Therefore, based on the idea of black-box neural network watermarking with the video framing and image perceptual hash algorithm, a passive copyright protection and traceability framework PCPT is proposed that uses an additional class of DNN models, improving the existing traceability mechanism that yields a small number of false-positives. Based on an authorization control strategy and image perceptual hash algorithm, a DNN model active copyright protection and traceability framework ACPT is proposed. This framework uses the authorization control center constructed by the detector and verifier. This approach realizes stricter authorization control, which establishes a strong connection between users and model owners, improves the framework security, and supports traceability verification

Error Resilient Video Coding Using Bitstream Syntax And Iterative Microscopy Image Segmentation

There has been a dramatic increase in the amount of video traffic over the Internet in past several years. For applications like real-time video streaming and video conferencing, retransmission of lost packets is often not permitted. Popular video coding standards such as H.26x and VPx make use of spatial-temporal correlations for compression, typically making compressed bitstreams vulnerable to errors. We propose several adaptive spatial-temporal error concealment approaches for subsampling-based multiple description video coding. These adaptive methods are based on motion and mode information extracted from the H.26x video bitstreams. We also present an error resilience method using data duplication in VPx video bitstreams. A recent challenge in image processing is the analysis of biomedical images acquired using optical microscopy. Due to the size and complexity of the images, automated segmentation methods are required to obtain quantitative, objective and reproducible measurements of biological entities. In this thesis, we present two techniques for microscopy image analysis. Our first method, “Jelly Filling” is intended to provide 3D segmentation of biological images that contain incompleteness in dye labeling. Intuitively, this method is based on filling disjoint regions of an image with jelly-like fluids to iteratively refine segments that represent separable biological entities. Our second method selectively uses a shape-based function optimization approach and a 2D marked point process simulation, to quantify nuclei by their locations and sizes. Experimental results exhibit that our proposed methods are effective in addressing the aforementioned challenges