BackgroundDifferentiation of canine hookworm species is crucial from both a veterinary and public health standpoint. In Vietnam, three hookworm species, namely Ancylostoma caninum, Ancylostoma braziliense and Uncinaria stenocephala are reported to infect dogs. In light of the emerging distribution of A. ceylanicum in Asia, this study aims to re-evaluate the status of Ancylostoma in dogs in Vietnam.MethodsFaecal samples collected from 200 community dogs in Dak Lak province were subjected to faecal floatation for the detection of hookworm eggs. Hookworm-positive samples were subjected to a PCR-Restriction Fragment Length Polymorphism (PCR-RFLP) assay targeting the internal transcribed spacer (ITS) region of rDNA for hookworm species identification. A subset of hookworm-positive samples was also subject to haplotype characterisation at the cytochrome oxidase-1 (COX-1) gene. Detailed morphological criteria were utilised in addition to molecular markers, to identify adult hookworms recovered from necropsied dogs.ResultsOf 200 canine faecal samples, 111 (55.5 %) were positive for hookworm eggs on faecal flotation. Of these, 94/111 (84.7 %) were successfully amplified and assigned species status by PCR-RFLP targeting the ITS region. In total, 54.3 % (51/94) dogs harboured single infections with A. ceylanicum, 33.0 % (31/94) with A. caninum, and 12.7 % (12/94) harboured mixed infections with both A. ceylanicum and A. caninum. Adult worms recovered from necropsied dogs matched morphological description provided for A. ceylanicum, Looss (1911) for which the mediolateral and posteriolateral rays are parallel. Characterisation of the COX-1 gene placed all Vietnamese canine isolates of A. ceylanicum within the ‘zoonotic’ haplotype.ConclusionBased on this information, it is apparent that the hookworms present in dogs in Vietnam are those of A. ceylanicum and not A. braziliense. Owing to the endemic nature of this significant zoonosis in dogs, the study strongly advocates for specific identification of this hookworm in human hookworm surveys.

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360-degree video streaming for high-quality virtual reality (VR) is challenging for current wireless systems because of the huge bandwidth it requires. However, millimeter wave (mmWave) communications in the 60 GHz band has gained considerable interest from the industry and academia because it promises gigabit wireless connectivity in the huge unlicensed bandwidth (i.e., up to 7 GHz). This massive unlicensed bandwidth offers great potential for addressing the demand for 360-degree video streaming. This paper investigates the problem of 360-degree video streaming for mobile VR using the SHVC, the scalable of High-Efficiency Video Coding (HEVC) standard and PC offloading over 60 GHz networks. We present a conceptual architecture based on advanced tiled-SHVC and mmWave communications. This architecture comprises two main parts. (1) Tile-based SHVC for 360-degree video streaming and optimizing parallel decoding. (2) Personal Computer (PC) offloading mechanism for transmitting uncompressed video (viewport only). The experimental results show that our tiled extractor method reduces the bandwidth required for 360-degree video streaming by more than 47% and the tile partitioning mechanism was improved by up to 25% in terms of the decoding time. The PC offloading mechanism was also successful in offloading 360-degree decoded (or viewport only) video to mobile devices using mmWave communication and the proposed transmission schemes.

Intelligent video analytics systems have come to play an essential role in many fields, including public safety, transportation safety, and many other industrial areas, such as automated tools for data extraction, and analyzing huge datasets, such as multiple live video streams transmitted from a large number of cameras. A key characteristic of such systems is that it is critical to perform real-time analytics so as to provide timely actionable alerts on various tasks, activities, and conditions. Due to the computation-intensive and bandwidth-intensive nature of these operations, however, video analytics servers may not fulfill the requirements when serving a large number of cameras simultaneously. To handle these challenges, we present an edge computing-based system that minimizes the transfer of video data from the surveillance camera feeds on a cloud video analytics server. Based on a novel approach of utilizing the information from the encoded bitstream, the edge can achieve low processing complexity of object tracking in surveillance videos and filter non-motion frames from the list of data that will be forwarded to the cloud server. To demonstrate the effectiveness of our approach, we implemented a video surveillance prototype consisting of edge devices with low computational capacity and a GPU-enabled server. The evaluation results show that our method can efficiently catch the characteristics of the frame and is compatible with the edge-to-cloud platform in terms of accuracy and delay sensitivity. The average processing time of this method is approximately 39 ms/frame with high definition resolution video, which outperforms most of the state-of-the-art methods. In addition to the scenario implementation of the proposed system, the method helps the cloud server reduce 49% of the load of the GPU, 49% that of the CPU, and 55% of the network traffic while maintaining the accuracy of video analytics event detection.

In this paper, we propose a model of subcarrier multiplexing millimeter wave radio-over-fiber SCM/MMW/RoF optical-wireless access system for next generation mobile communications using QPSK modulation. We then calculate signal and total noise power at the end of optical fiber link and at mobile subscribers through wireless medium. Next, system performance showed by SNR, BER at the end of system is determined. They are investigated, compared, evaluated by Matlab in many different scenarios including BER performance versus EDFA’s Gain, optical transmitter power at central office (CO), optical oscillator power in coherent receiver, wireless frequencies, wireless link distance corresponding to two wireless cases of Line of Sight (LoS) and Non Line of Sight (NLoS). Numerical results show that when number of SCM channels increase system performance becomes worse due to nonlinear effect in SCM modulation.