作者：
HERR, DONALDBLUMENSTOCK, NORMANHONORARY MEMBERTHE AUTHORS MR. HERR
Honorary Member of the A.S.N.E. has the B.S. in E.E. M.S. in E.E. and E.E. degrees. He was National Coffin Foundation Fellow of the General Electric Company National Tau Beta Pi Fellow and National Sigma Tau Fellow at the Moore School of Electrical Engineering University of Pennsylvania and at M.I.T. prior to World War II. He was also awarded a National Gordon McKay Fellowship by Harvard University and received the A. Atwater Kent Award in Electrical Engineering from the University of Pennsylvania. A licensed radio amateur at 12 Mr. Herr first worked summers at RCA and Bell Laboratories and was with the General Electric Company in 1939 and 1940 as development engineer before volunteering for over five years of active Naval duty. He served as Officer-in-Charge Electrical Minesweeping Group Bureau of Ships December 1940 to April 1943 as Acting Design Superintendent and Officer-in-Charge
Los Angeles-Long Beach Harbor Surge Investigation U. S. Naval Shipyard Terminal Island to November 1944 and as Research-Patents Liaison Officer
Office of Naval Research to January 1946 returning to inactive duty as lieutenant commander U.S.N.R. Mr. Herr received two Navy letters of commendation. Since 1946 he was assistant to vice president in charge of the engineering division of Control Instrument Company Brooklyn New York and is project engineer at the Reeves Instrument Corporation responsible for new servo and computer component developments. Mr. Herr has been associated with Dean Harold Pender and Professor Ernst Guillemin in advanced network theory and has specialized for 12 years in development and design of servomechanisms differential analyzers computers and fire control systems utilizing advanced network analysis and synthesis methods. Mr. Herr is also presently teaching servomechanisms network-synthesis and feedback amplifier design in the Graduate School of the Polytechnic Institute of Brooklyn. He has contributed frequently to the JOURNAL OF THE AMERICA
This book gathers a collection of high-quality peer-reviewed research papers presented at the 4th International Conference on Data and Information sciences (ICDIS 2022), held at Raja Balwant Singh engineering Technica...
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ISBN:
(数字)9789811952920
ISBN:
(纸本)9789811952913
This book gathers a collection of high-quality peer-reviewed research papers presented at the 4th International Conference on Data and Information sciences (ICDIS 2022), held at Raja Balwant Singh engineering Technical Campus, Agra, India, on May 6 – 7, 2022. The book covers all aspects of computational sciences and information security, including central topics like artificial intelligence, cloud computing, and big data. Highlighting the latest developments and technical solutions, it will show readers from the computer industry how to capitalize on key advances in next-generation computer and communication technology.
作者：
B. BhaskerS. MuraliResearch Scholar
School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Associate Professor
School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India
Extensive and exhaustive water utilization for agriculture, industries and ground water consumption for domestic purposes has heavily deterioted the water bodies. Cloud and sensor technology is widely deployed in a se...
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Extensive and exhaustive water utilization for agriculture, industries and ground water consumption for domestic purposes has heavily deterioted the water bodies. Cloud and sensor technology is widely deployed in a several real-time applications, especially in agriculture. The transformation of data obtained from large sensor networks into a valuable knowledge and assests for applications can effectively leverage the techniques like Cloud Computing (CC). In CC, scheduling the workflow is the major concern that focuses on comprehensive execution of workflows without compromising the Quality of Service (QoS). But workflow scheduling augmented with resource allocation is extremely challenging task because of its inherent computational intensity, task dependencies, and heterogeneous cloud resources. In this article, a novel Optimum Energy and Resource Aware Workflow Scheduling (OERES) scheme that is motivated by popular Fuzzy Membership Mutation Elephant Herding Optimization (FMMEHO) algorithm is proposed, that aims to schedule the task workflow to Virtual Machines (VMs) that are involved in computation. This also concentrates on dynamically deploying and un-deploying the VMs pertaining to the task requirements. The FMMEHO algorithm is a popular nature inspired technique, which is rooted on herding patterns of the giant mammals, the elephants. This algorithm employs a clan operator that updates the location and distance of elephants depending resource and energy usage of each clan in the context of matriarch elephant. The proposed OERES schema elevates the resource utilization and simultaneously mitigates the energy usage without compromising the dependency and deadline constraints. This work uses the famous Cloud Sim simulator to simulate the underlying cloud environment to investigate the effectiveness of proposed model. The efficacy of the scheduling methods is examined based on important parameters like mean Resource Utilization (RU), Energy utilization or Consumpti
作者：
Asha P.Hemamalini V.Poongodaia.Swapna N.Soujanya K. L. S.Vaishali Gaikwad (Mohite)Associate Professor
Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai TN India Assistant Professor
Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur India Assistant Professor
School of Computers Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India. Associate Professor
Head of the Department Department of Computer Science and Engineering Vijay Rural Engineering College Nizamabad Telangana India Professor
Department of Computer Science and Engineering CMR College of Engineering & Technology Hyderabad Telangana India Associate Professor
Department of Computer Engineering Xavier Institute of Engineering Mumbai Maharashtra India
It is difficult to discover significant audio elements and conduct systematic comparison analyses when trying to automatically detect emotions in speech. In situations when it is desirable to reduce memory and process...
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It is difficult to discover significant audio elements and conduct systematic comparison analyses when trying to automatically detect emotions in speech. In situations when it is desirable to reduce memory and processing constraints, this research deals with emotion recognition. One way to achieve this is by reducing the amount of features. In this study, propose "Active Feature Selection" (AFS) method and compares it against different state-of-the-art techniques. According to the results, smaller subsets of features than the complete feature set can produce accuracy that is comparable to or better than the full feature set. The memory and processing requirements of an emotion identification system will be reduced, which can minimise the hurdles to using health monitoring technology. The results show by using 696 characteristics, the AFS technique for emobase yields a Unweighted average recall (UAR) of 75.8%.
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