We study the throughput capacity of wireless networks which employ (asynchronous) random-access scheduling as opposed to deterministic scheduling. The central question we answer is: how should we set the channel-acces...
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We study the throughput capacity of wireless networks which employ (asynchronous) random-access scheduling as opposed to deterministic scheduling. The central question we answer is: how should we set the channel-access probability for each link in the network so that the network operates close to its optimal throughput capacity? We design simple and distributed channel-access strategies for random-access networks which are provably competitive with respect to the optimal scheduling strategy, which is deterministic, centralized, and computationally infeasible. We show that the competitiveness of our strategies are nearly the best achievable via random-access scheduling, thus establishing fundamental limits on the performance of random- access. A notable outcome of our work is that random access compares well with deterministic scheduling when link transmission durations differ by small factors, and much worse otherwise. The distinguishing aspects of our work include modeling and rigorous analysis of asynchronous communication, asymmetry in link transmission durations, and hidden terminals under arbitrary link-conflict based wireless interference models.
This book constitutes the refereed proceedings of the 21st International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2018, held in Tokyo, Japan, in October/November 2018. The 27...
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ISBN:
(数字)9783030030988
ISBN:
(纸本)9783030030971
This book constitutes the refereed proceedings of the 21st International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2018, held in Tokyo, Japan, in October/November 2018. The 27 full papers presented and 31 short papers were carefully reviewed and selected from 103 submissions.;PRIMA presents subjects in many application domains, particularly in e-commerce, and also in planning, logistics, manufacturing, robotics, decision support, transportation, entertainment, emergency relief and disaster management, and data mining and analytics.
A cancer blood disorder can be dangerous if not detected in time. It causes aberrant white blood cell production in the blood by the bone marrow. Using image processing of microscopic images of the blood, it may be qu...
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ISBN:
(纸本)9798400709418
A cancer blood disorder can be dangerous if not detected in time. It causes aberrant white blood cell production in the blood by the bone marrow. Using image processing of microscopic images of the blood, it may be quickly diagnosed. Deep learning techniques are a practical approach to cancer blood disorders in early diagnosis. In this study, we have proposed a novel method to identify cancer blood disorders in the early stage using the deep convolutional neural network (DCNN). Using filtering techniques, the microscopic images are first preprocessed. The 2D Adaptive Anisotropic Diffusion Filter (2DAADF) technique is used for image filtering to eliminate noise from the input images. Feature extraction is carried out utilizing the Grey Level Co-Occurrence Matrix (GLCM) from the filtered images to increase the identification accuracy. Finally, the proposed DCNN classifier is used to detect the cancer blood disorder. In comparison to the current approaches, the proposed methodology attained the maximum accuracy of 97%. According to the results, the proposed method can identify blood cancer with high accuracy and may help with its diagnosis and treatment in the early stages.
Approximate nearest-neighbor search is a fundamental algorithmic problem that continues to inspire study due its essential role in numerous contexts. In contrast to most prior work, which has focused on point sets, we...
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The problem of selecting an adequate set of variables from a given data set of a sampled function becomes crucial by the time of designing the model that will approximate it. Several approaches have been presented in ...
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Software designers face many challenges when developing applications for embedded systems. One major challenge is meeting the conflicting constraints of speed, code size, and power consumption. Embedded application de...
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In this era of openness, educational achievement is a top priority for students at all levels;however, what worked in the past to promote accountability in face-to-face settings does not necessarily work in online env...
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In a system comprising of human-machine interaction, the emotion recognition of speech has always been a wide area of research since the machines can never analyze the emotion of a speaker on its own. To recognize the...
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ISBN:
(纸本)9781728102122
In a system comprising of human-machine interaction, the emotion recognition of speech has always been a wide area of research since the machines can never analyze the emotion of a speaker on its own. To recognize the speaker's emotion, numerous systems were developed and tested. In this research study, an enhanced human speech emotion recognition system using a hybrid of PRNN and KNN algorithms is designed. The six basic emotions like neutral, anger, happiness, sadness, surprise and fear over the speech emotions are classified and studied for their accuracy with other previously developed systems. The database for this study is taken as the emotional speech samples of numbers. A cascaded system of Mel Frequency Cepstral Coefficient (MFCC) and Gray Level Co-occurrence Matrix (GLCM) was used for feature extraction process along with a Wiener filter for filtering the noise in speech. Also, a hybrid of Pattern Recognition Neural Network (PRNN) and K-Nearest Neighbour (KNN) is used for prediction accuracy of outcomes. The outcomes are compared with previously developed recognition systems and better efficiency is observed.
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