ON RELAY NODE PLACEMENT PROBLEM FOR SURVIVABLE WIRELESS SENSOR NETWORKS

Wireless sensor networks are widely applied to many fields such as animal habitat monitoring, air traffic control, and health monitoring. One of the current problems with wireless sensor networks is the ability to overcome communication failures due to hardware failure, distributing sensors in an uneven geographic area, or unexpected obstacles between sensors. One common solution to overcome this problem is to place a minimum number of relay nodes among sensors so that the communication among sensors is guaranteed. This is called Relay Node Placement Problem (RNP). This problem has been proved as NP-hard for a simple connected graph. Therefore, many algorithms have been developed based on Steiner graphs. Since RNP for a connected graph is NP-hard, the RNP for a survivable network has been conjectured as NP-hard and the algorithms for a survivable network have also been developed based on Steiner graphs. In this study, we show the new approximation bound for the survivable wireless sensor networks using the Steiner graphs based algorithm. We prove that the approximation bound is guaranteed in an environment where some obstacles are laid, and also propose the newly developed algorithm which places fewer relay nodes than the existing algorithms. Consequently, the main purpose of this study is to find the minimum number of relay nodes in order to meet the survivability requirements of wireless sensor networks

The Minimum Scheduling Time for Convergecast in Wireless Sensor Networks

We study the scheduling problem for data collection from sensor nodes to the sink node in wireless sensor networks, also referred to as the convergecast problem. The convergecast problem in general network topology has been proven to be NP-hard. In this paper, we propose our heuristic algorithm (finding the minimum scheduling time for convergecast (FMSTC)) for general network topology and evaluate the performance by simulation. The results of the simulation showed that the number of time slots to reach the sink node decreased with an increase in the power. We compared the performance of the proposed algorithm to the optimal time slots in a linear network topology. The proposed algorithm for convergecast in a general network topology has 2.27 times more time slots than that of a linear network topology. To the best of our knowledge, the proposed method is the first attempt to apply the optimal algorithm in a linear network topology to a general network topology

Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss

Reducing bit-widths of activations and weights of deep networks makes it efficient to compute and store them in memory, which is crucial in their deployments to resource-limited devices, such as mobile phones. However, decreasing bit-widths with quantization generally yields drastically degraded accuracy. To tackle this problem, we propose to learn to quantize activations and weights via a trainable quantizer that transforms and discretizes them. Specifically, we parameterize the quantization intervals and obtain their optimal values by directly minimizing the task loss of the network. This quantization-interval-learning (QIL) allows the quantized networks to maintain the accuracy of the full-precision (32-bit) networks with bit-width as low as 4-bit and minimize the accuracy degeneration with further bit-width reduction (i.e., 3 and 2-bit). Moreover, our quantizer can be trained on a heterogeneous dataset, and thus can be used to quantize pretrained networks without access to their training data. We demonstrate the effectiveness of our trainable quantizer on ImageNet dataset with various network architectures such as ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve the state-of-the-art accuracy

Major medical causes by breed and life stage for dogs presented at veterinary clinics in the Republic of Korea: a survey of electronic medical records

Background Age and breed are considered the greatest risk factors for disease prevalence and mortality in companion dogs. Understanding the prevalence of diseases, in relation to age and breed, would support appropriate guidance for future health care strategies and provide useful information for the early diagnosis of diseases. The purpose of this study was to investigate the major medical causes for dogs visiting primary-care veterinary clinics in the Republic of Korea, stratified by age and breed. Methods A total of 15,531 medical records of canine patients were analyzed from 11 veterinary clinics who shared data from January 1, 2016 to December 31, 2016. An electronic medical record (EMR) system was used for data collection, which included the animal identification number, age, breed, gender, neuter status, clinical information, and diagnosis. EMR data were classified using the International Classification of Disease system from the World Health Organization; presenting signs or diagnoses were identified according to breed and life stage. Results Within the age groups, preventive medicine (16.7% confidence intervals (CI) [15.9–17.5]) was the most common cause for clinic visits for the 10 year), the prevalences of heart disease, kidney disease, Cushing’s disease, and mammary tumors were higher than in the other age groups. Small and toy breed dogs comprised 67.7% of all dogs in this analysis. For all breeds, otitis externa, dermatitis or eczema, vomiting, and diarrhea were common medical problems. Discussion This study identified the most common medical disorders and differences in prevalences of diseases, according to age and breeds. The information from EMRs for dogs visiting primary-care veterinary clinics can provide background knowledge that is required to enable a better understanding of disease patterns and occurrence by age and breeds. The information from this study could enable the creation of strategies for preventing diseases and enable the identification of health problems for more effective disease management in companion dogs

The Minimum Scheduling Time for Convergecast in Wireless Sensor Networks

We study the scheduling problem for data collection from sensor nodes to the sink node in wireless sensor networks, also referred to as the convergecast problem. The convergecast problem in general network topology has been proven to be NP-hard. In this paper, we propose our heuristic algorithm (finding the minimum scheduling time for convergecast (FMSTC)) for general network topology and evaluate the performance by simulation. The results of the simulation showed that the number of time slots to reach the sink node decreased with an increase in the power. We compared the performance of the proposed algorithm to the optimal time slots in a linear network topology. The proposed algorithm for convergecast in a general network topology has 2.27 times more time slots than that of a linear network topology. To the best of our knowledge, the proposed method is the first attempt to apply the optimal algorithm in a linear network topology to a general network topology

Efficient characterization of the beam by nanoparticle diffraction in XFEL

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Direct autolink

© 2017 ACM. Direct Autolink is an interconnected system of programs that establish both hardware and software connections between an ARM-based computer and handhold devices designed with ease of modularization. These programs are capable of sending and receiving commands and files over this connection. Similar systems exist using the internet, which presents both speed and safety concerns. The proposed system establishes a direct communication channel without Internet enabled, so that it can release the concerns raised from the Internet-based connection