The Experts below are selected from a list of 263739 Experts worldwide ranked by ideXlab platform
Lajos Hanzo - One of the best experts on this subject based on the ideXlab platform.
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cross layer network lifetime optimisation considering transmit and signal Processing Power in wireless sensor networks
IET wireless sensor systems, 2014Co-Authors: Halil Yetgin, Kent Tsz Kan Cheung, Mohammed Elhajjar, Lajos HanzoAbstract:Maintaining high energy efficiency is essential for increasing the lifetime of wireless sensor networks (WSNs), where the battery of the sensor nodes cannot be routinely replaced. Nevertheless, the energy budget of the WSN strictly relies on the communication parameters, where the choice of both the transmit Power as well as of the modulation and coding schemes (MCSs) plays a significant role in maximizing the network lifetime (NL). In this paper, we optimize the NL of WNSs by analysing the impact of the physical layer parameters as well as of the signal Processing Power (SPP) P_sp on the NL. We characterize the underlying trade-offs between the NL and bit error ratio (BER) performance for a predetermined set of target signal-to-interference-plus-noise ratio (SINR) values and for different MCSs using periodic transmit-time slot (TS) scheduling in interference-limited WSNs. For a per-link target BER requirement (PLBR) of 10^?3, our results demonstrate that a ’continuous-time’ NL in the range of 0.58?4.99 years is achieved depending on the MCSs, channel configurations, and SPP.
Ravi Sankar - One of the best experts on this subject based on the ideXlab platform.
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Advanced two tier User Authentication scheme for heterogeneous Wireless Sensor Networks
2011 IEEE Consumer Communications and Networking Conference (CCNC), 2011Co-Authors: Ismail Butun, Ravi SankarAbstract:In this paper, we propose a novel User Authentication (UA) scheme for heterogeneous Wireless Sensor Networks (WSNs), which employs both Public Key Cryptography (PKC) and Symmetric Key Cryptography (SKC) approaches, such that it takes advantage of both schemes. Our analysis results have shown that, our scheme is not only more secure and scalable than existing SKC based schemes, but also requires lesser Processing Power and provides higher energy efficiency than existing PKC based schemes.
Hamid Soltanianzadeh - One of the best experts on this subject based on the ideXlab platform.
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web based interactive 2d 3d medical image Processing and visualization software
Computer Methods and Programs in Biomedicine, 2010Co-Authors: Seyyed Ehsan Mahmoudi, Alireza Akhondiasl, Roohollah Rahmani, Shahrooz Faghihroohi, Vahid Taimouri, Ahmad Sabouri, Hamid SoltanianzadehAbstract:There are many medical image Processing software tools available for research and diagnosis purposes. However, most of these tools are available only as local applications. This limits the accessibility of the software to a specific machine, and thus the data and Processing Power of that application are not available to other workstations. Further, there are operating system and Processing Power limitations which prevent such applications from running on every type of workstation. By developing web-based tools, it is possible for users to access the medical image Processing functionalities wherever the internet is available. In this paper, we introduce a pure web-based, interactive, extendable, 2D and 3D medical image Processing and visualization application that requires no client installation. Our software uses a four-layered design consisting of an algorithm layer, web-user-interface layer, server communication layer, and wrapper layer. To compete with extendibility of the current local medical image Processing software, each layer is highly independent of other layers. A wide range of medical image preProcessing, registration, and segmentation methods are implemented using open source libraries. Desktop-like user interaction is provided by using AJAX technology in the web-user-interface. For the visualization functionality of the software, the VRML standard is used to provide 3D features over the web. Integration of these technologies has allowed implementation of our purely web-based software with high functionality without requiring Powerful computational resources in the client side. The user-interface is designed such that the users can select appropriate parameters for practical research and clinical studies.
Emanuel Marom - One of the best experts on this subject based on the ideXlab platform.
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depth estimation from a single image using deep learned phase coded mask
IEEE Transactions on Computational Imaging, 2018Co-Authors: Harel Haim, Shay Elmalem, Raja Giryes, Alexander M Bronstein, Emanuel MaromAbstract:Depth estimation from a single image is a well-known challenge in computer vision. With the advent of deep learning, several approaches for monocular depth estimation have been proposed, all of which have inherent limitations due to the scarce depth cues that exist in a single image. Moreover, these methods are very demanding computationally, which makes them inadequate for systems with limited Processing Power. In this paper, a phase-coded aperture camera for depth estimation is proposed. The camera is equipped with an optical phase mask that provides unambiguous depth-related color characteristics for the captured image. These are used for estimating the scene depth map using a fully convolutional neural network. The phase-coded aperture structure is learned jointly with the network weights using backpropagation. The strong depth cues (encoded in the image by the phase mask, designed together with the network weights) allow a much simpler neural network architecture for faster and more accurate depth estimation. Performance achieved on simulated images as well as on a real optical setup is superior to the state-of-the-art monocular depth estimation methods (both with respect to the depth accuracy and required Processing Power), and is competitive with more complex and expensive depth estimation methods such as light-field cameras.
Halil Yetgin - One of the best experts on this subject based on the ideXlab platform.
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cross layer network lifetime optimisation considering transmit and signal Processing Power in wireless sensor networks
IET wireless sensor systems, 2014Co-Authors: Halil Yetgin, Kent Tsz Kan Cheung, Mohammed Elhajjar, Lajos HanzoAbstract:Maintaining high energy efficiency is essential for increasing the lifetime of wireless sensor networks (WSNs), where the battery of the sensor nodes cannot be routinely replaced. Nevertheless, the energy budget of the WSN strictly relies on the communication parameters, where the choice of both the transmit Power as well as of the modulation and coding schemes (MCSs) plays a significant role in maximizing the network lifetime (NL). In this paper, we optimize the NL of WNSs by analysing the impact of the physical layer parameters as well as of the signal Processing Power (SPP) P_sp on the NL. We characterize the underlying trade-offs between the NL and bit error ratio (BER) performance for a predetermined set of target signal-to-interference-plus-noise ratio (SINR) values and for different MCSs using periodic transmit-time slot (TS) scheduling in interference-limited WSNs. For a per-link target BER requirement (PLBR) of 10^?3, our results demonstrate that a ’continuous-time’ NL in the range of 0.58?4.99 years is achieved depending on the MCSs, channel configurations, and SPP.
