Adjacency Matrix Based Energy Efficient Scheduling using S-MAC Protocol in Wireless Sensor Networks

Communication is the main motive in any Networks whether it is Wireless Sensor Network, Ad-Hoc networks, Mobile Networks, Wired Networks, Local Area Network, Metropolitan Area Network, Wireless Area Network etc, hence it must be energy efficient. The main parameters for energy efficient communication are maximizing network lifetime, saving energy at the different nodes, sending the packets in minimum time delay, higher throughput etc. This paper focuses mainly on the energy efficient communication with the help of Adjacency Matrix in the Wireless Sensor Networks. The energy efficient scheduling can be done by putting the idle node in to sleep node so energy at the idle node can be saved. The proposed model in this paper first forms the adjacency matrix and broadcasts the information about the total number of existing nodes with depths to the other nodes in the same cluster from controller node. When every node receives the node information about the other nodes for same cluster they communicate based on the shortest depths and schedules the idle node in to sleep mode for a specific time threshold so energy at the idle nodes can be saved.Comment: 20 pages, 2 figures, 14 tables, 5 equations, International Journal of Computer Networks & Communications (IJCNC),March 2012, Volume 4, No. 2, March 201

APPLICATION OF ATEB-PREDICTION METHOD TO REDUCE THE DOWNLOADING INTENSITY OF NETWORK CHANNELS

Розглянуто сучасний стан зростання обсягів даних у комп'ютерних мережах. Проаналізовано дані корпорації Cisco. Описано розроблену комп'ютерну імітаційну модель мережі за допомогою програмного забезпечення OMNeT++. Здійснено імітаційне моделювання двох топологій комп'ютерних мереж, отриманих з бази даних проекту The Opte Project. Ефективність запропонованого методу Ateb-прогнозування трафіку потоку доведено експериментально, на основі розроблених імітаційних моделей комп'ютерних мереж. Показано, що завдяки застосуванню запропонованого методу Ateb-прогнозування середня затримка передавання пакетів зменшилась на 12-14 %, а максимальна затримка передавання пакетів відповідно зменшилась на 14-19 %. Експерименти проілюстровано графіками.Рассмотрено современное состояние роста объемов данных в компьютерных сетях. Проанализированы данные корпорации Cisco. Описана разработанная компьютерная имитационная модель сети с помощью программного обеспечения OMNeT++. Осуществлено имитационное моделирование двух топологий компьютерных сетей, полученных из базы данных проекта The Opte Project. Эффективность предложенного метода Ateb-прогнозирования трафика потока доказана экспериментально. Показано, что благодаря применению метода Ateb-прогнозирования средняя задержка передачи пакетов уменьшилась на 12-14 %, а максимальная задержка уменьшилась на 14-19 %. Эксперименты проиллюстрированы графиками.This article shows the current state of data volume growth in computer networks. Cisco corporation data were considered and analyzed. Developed computer simulation model of the network through OMNeT++ software was described. Simulation of two topologies of computer networks, obtained from the database of The Opte Project was done. The effectiveness Ateb-prediction method of traffic flows was proved experimentally. It is shown that due to the use of the proposed Ateb-prediction method average delay in the transmission of packets decreased by 12-14 %, and the maximum delay decreased by 14-19 %. The experiments were illustrated by graphs

Learning of Art Style Using AI and Its Evaluation Based on Psychological Experiments

[ICEC 2020]19th IFIP TC 14 International Conference, ICEC 2020, Xi'an, China, November 10–13, 2020, ProceedingsPart of the Lecture Notes in Computer Science book series (LNCS, volume 12523)GANs (Generative adversarial networks) is a new AI technology that has the capability of achieving transformation between two image sets. Using GANs we have carried out a comparison between several art sets with different art styles. We have prepared four image sets; a flower image set with Impressionism art style, one with the Western abstract art style, one with Chinese figurative art style, and one with the art style of Naoko Tosa, one of the authors. Using these four sets we have carried out a psychological experiment to evaluate the difference between these four sets. We have found that abstract drawings and figurative drawings are judged to be different, figurative drawings in West and East were judged to be similar, and Naoko Tosa’s artworks are similar to Western abstract artworks

Developing Japanese Ikebana as a Digital Painting Tool via AI

[ICEC 2020]19th IFIP TC 14 International Conference, ICEC 2020, Xi'an, China, November 10–13, 2020, ProceedingsPart of the Lecture Notes in Computer Science book series (LNCS, volume 12523)In this research, we have carried out various experiments to perform mutual transformation between a domain of Ikebana (Japanese traditional flower arrangement) photos and other domains of images (landscapes, animals, portraits) to create new artworks via CycleGAN, a variation of GANs (Generative Adversarial Networks) - new AI technology that can perform deep learning with less training data. With the capability of achieving transformation between two image sets using CycleGAN, we obtained several interesting results in which Ikebana plays the role of a digital painting tool due to the flexibility and minimality of the Japanese culture form. Our experiments show that Ikebana can be developed as a painting tool in digital art with the help of CycleGAN and opens a new way to create digital artworks of high-abstracted level by applying AI techniques to elements from traditional culture

Route Planning in Transportation Networks

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4, previously published by Microsoft Research. This work was mostly done while the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at Microsoft Research Silicon Valle

Guided Stereo Matching

Stereo is a prominent technique to infer dense depth maps from images, and deep learning further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when enough data is available for training. However, deep networks suffer from significant drops in accuracy when dealing with new environments. Therefore, in this paper, we introduce Guided Stereo Matching, a novel paradigm leveraging a small amount of sparse, yet reliable depth measurements retrieved from an external source enabling to ameliorate this weakness. The additional sparse cues required by our method can be obtained with any strategy (e.g., a LiDAR) and used to enhance features linked to corresponding disparity hypotheses. Our formulation is general and fully differentiable, thus enabling to exploit the additional sparse inputs in pre-trained deep stereo networks as well as for training a new instance from scratch. Extensive experiments on three standard datasets and two state-of-the-art deep architectures show that even with a small set of sparse input cues, i) the proposed paradigm enables significant improvements to pre-trained networks. Moreover, ii) training from scratch notably increases accuracy and robustness to domain shifts. Finally, iii) it is suited and effective even with traditional stereo algorithms such as SGM.Comment: CVPR 201