Sherif Kishk - Academia.edu (original) (raw)

Papers by Sherif Kishk

Wireless Sensor Networks (WSNs) is a group of sensor nodes with lightweight, inexpensive, low bat... more Wireless Sensor Networks (WSNs) is a group of sensor nodes with lightweight, inexpensive, low battery nodes distributed in interesting field to sense the environment and send collected data to Base Station (BS) for further processing. Communication distance between sensor node and BS affects the battery of sensor node, as distance between sensor node and BS increased, the energy consumed in data sending increased. There are many different routing algorithm designed for efficient data route from sensor node to BS, Cluster based routing is one of famous technique recently used in data transmission . Cluster Head (CH) node elected to gather data from other nodes in the cluster and this in turn reduce distance from sensor node to BS and perform data aggregation before sending to BS, This paper discuss the impact of network size and BS location in different Efficient-Energy protocols for WSN clustering on the network Performance. Simulation Results show Network life time, total energy co...

Wireless sensor network (WSN) has numerous applications in our daily life. This paper discusses t... more Wireless sensor network (WSN) has numerous applications in our daily life. This paper discusses the usage of wireless sensors in monitoring and surveillance of one or more intruders in a pre-defined area of interest. Detection probability of intruder is analyzed versus different parameters. Results show the intrusion detection probability versus some parameters such as sensor sensing radius, number of nodes, radius of the coverage area, node availability, probability of active nodes and node density. Furthermore, we investigated the performance of the expected time of detecting an intruder by changing the node density and at different scenarios of intruder velocities.

Multi-hop Relay networks are proposed to fulfill the demanding coverage and capacity requirements... more Multi-hop Relay networks are proposed to fulfill the demanding coverage and capacity requirements for current applications in a cost efficient way. It can be used in IMT-Advanced technologies such as 3GPP LTE-Advanced. In this paper, LTE-A Uplink performance will be improved using Synchronous direct and multi-hop transmission (SDMT) between user equipment and Base Station. Also the effect of adding RS to the network and its position will be discussed. The total LTE-A uplink Throughput, average throughput per user, and Mean File Transfer Time (MFTT) in the LTE-A network are considered as performance measures. This is done and measured as a function of different arrival rates of UEs assigned from random positions in the cell. Adaptive Modulation, and Coding (AMC) scheme with high order 2x4MIMO is used to maximize network throughput with low bit error rate (BER). Simulation results show LTE-A uplink performance improvement using 2x4MIMO-AMC and SDMT scheme with RS at 50% of cell radius...

Multi-hop Relay networks are proposed to fulfill the demanding coverage and capacity requirements... more Multi-hop Relay networks are proposed to fulfill the demanding coverage and capacity requirements in both uplink and downlink for current applications in a cost efficient way. It can be used in IMT-Advanced technologies such as 3GPP LTE-Advanced. Spatial Diversity (SD) and Multiplexing (SM) have been adopted to improve the Signal to interference Noise Ratio (SINR) and spectrum efficiency in wireless networks. The objective of this paper is to evaluate the performance of Spatial Diversity and Multiplexing based on Adaptive modulation and coding (AMC) schemes in Uplink LTE-A. The average throughput, Throughput gain, and Mean File Transfer Time (MFTT) are considered as performance measures. The best Relay Station (RS) location in the cell was also estimated in order to improve LTE-A Uplink performance. The study takes into consideration the impact of Asymmetry between multi-hop links, the effect of different arrival rates from random positions of UEs in the cell. Resource blocks schedu...

Wireless sensor networks is a field of research which is vastly spreading because of the advancem... more Wireless sensor networks is a field of research which is vastly spreading because of the advancement of new technologies in relatively cheap sensors. These electronic devices have been recently improved with respect to their memory size, communication networking capabilities and their processing speed. In the last few years, a new subcategory of sensor networks known as linear wireless sensor networks (LWSN) is rising as a great focus area of research. Such wireless sensor networks have a large number of applications such as border monitoring, railway track monitoring, structural health monitoring of bridges, health care and machines surveillance. This paper focus on the use of this new technology in the monitoring and protection of the essential and critical pipelines infrastructures carrying water, oil, gas and other vital resources. The paper introduces a linear wireless sensor network model that can be used to facilitate this control and monitoring functions. Besides the paper i...

This paper proposes a palmprint-based identification system using Radon transform. Palmprint is a... more This paper proposes a palmprint-based identification system using Radon transform. Palmprint is a reliable identification method because the print patterns are unique even in the monozygotic twins, has permanent ridge structure, has fixed line structure, user friendliness, low cost capturing devices, and low resolution imaging. The proposed system is applied to CASIA database. Radon transform is used for extracting the features because it can be used in palm lines detection with high precision and efficiency. Live and enrolled palmprint are matched using Euclidean distance for verification. This system can achieve Equal Error Rate (EER) of 0.47% and Genuine Acceptance Rate (GAR) of 99.56%. The results of this study showed that the proposed system achieves higher verification accuracy than other palmprint identification systems.

2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), 2017

Recently, the sparse representations are one of the most active research areas. Here, the problem... more Recently, the sparse representations are one of the most active research areas. Here, the problem of single image super-resolution is revisited with sparse and low rank priors. The introduced algorithm employs a self-learning approach. This self-learning approach is applied on cluster domain rather than the common used patch domain. For supporting the self-learning approach, the learning model adopts an incoherence property with the classical sparse priors. In addition, to compensate the weakness of the high frequency details of the underlying low-resolution image, an edge preserving low lark model is proposed. Hence, the low rank representation guarantees the global structure constraints in the recovered high-resolution images. Experimental results, on different datasets, show that the proposed algorithm can recover high-resolution images compared with the state-of-the art.

Wireless Personal Communications, 2021

In this paper, allocation memory algorithms (i.e. First Fit, Next Fit and Best Fit) are redesigne... more In this paper, allocation memory algorithms (i.e. First Fit, Next Fit and Best Fit) are redesigned to offload the tasks of end devices in multi-device multi-task D2D communication. The proposed algorithms offload each task to single device, which enhance the performance by 4x at their maximum performance. To enhance the performance, the next fit algorithm is redesigned to offload the task to multiple devices, which enhances the performance to 88x at its maximum performance. The proposed algorithms are evaluated for different cell scenarios (i.e. femto, pico, micro and macro cells). Simulation results demonstrates that utilizing computation offloading minimizes the latency.

Journal of The Egyptian Society of Nephrology and Transplantation, 2021

The birth of distance learning courses in nephrology was set up in Mansoura (Mansoura Nephrology ... more The birth of distance learning courses in nephrology was set up in Mansoura (Mansoura Nephrology Club) in 2005. This activity was endorsed by the Egyptian Society of Nephrology and Renal Transplantation (ESNT) in early 2006. Tele-education was restricted to sharing presentations and the facilities to ask the experts. All questions and answers were uploaded to the website. The year 2012 witnessed a revolution in the ESNT continuous medical education chapter to include outreach programs followed by the ESNT virtual academy takeoff, with evolving experience through the following years, including enormous amounts of presentations, conferences, assignments, quizzes, as well as the full curriculum of Mansoura Nephrology Doctorate degree. More than 28 000 users from all continents and countries use this service on a daily basis. From May 2019, the ESNT activated online webinars to ensure interactive teaching that extended to a real Tele-medicine and teleconsultation to improve nephrology practice. In coronavirus disease 2019 era, where social distancing has become a necessity, the ESNT in collaboration with the international nephrology experts adopted a very active international distance nephrology education. A total number of 4596 lectures as well as links to 1850 videos are free of charges for all nephrologists all over the world, making ESNT virtual academy one of the largest open-source nephrology educational facilities.

Bulletin of the Faculty of Engineering. Mansoura University, 2020

Multi-hop Relay networks are proposed to fulfill the demanding coverage and capacity requirements... more Multi-hop Relay networks are proposed to fulfill the demanding coverage and capacity requirements for current applications in a cost efficient way. It can be used in IMT-Advanced technologies such as 3GPP LTE-Advanced. In this paper, Resource Blocks (RBs) proposed scheduling scheme is considered for zero multi-hop links overflow in Uplink LTE-Advanced. Then based on this scheduling scheme and other network assumptions the Optimum Relay Station (RS) placement in the cell was estimated using nonlinear optimization problem in order to improve LTE-A Uplink performance. The average throughput, Throughput gain, and Mean File Transfer Time (MFTT) are considered as performance measures. The study takes into consideration the impact of Asymmetry between multi-hop links, the effect of different arrival rates from random positions of UEs in the cell. Adaptive MIMO, Modulations, and Coding Switching (AMMCS) scheme is used to maximize network throughput with low bit error rate (BER). Simulation results show effective improvement in uplink network performance using the proposed scheduling scheme and AMMCS with RS at optimum location in the cell.

Wireless Personal Communications, 2020

Nowadays, the recent developments in the field of wireless sensor networks (WSNs) have initiated ... more Nowadays, the recent developments in the field of wireless sensor networks (WSNs) have initiated new applications of WSNs which can be used in many fields, such as military, environment, health, home and industry. One of the emerged wireless sensor networks topologies are linear wireless sensor networks (LWSNs). They have been rising as a great focus area of research. Such wireless sensor networks have a large number of applications such as border monitoring, railway track monitoring, structural health monitoring of bridges, health care and machines surveillance. LWSNs are widely applied in oil and gas pipelines infrastructure monitoring applications to enable the automatic measurement, analyses, storage and transmission of real-time data. Minimization of energy consumption of LWSNs is crucial for their proper usage. Using two different system models, this research investigates the minimization of LWSNs energy consumption using optimal node placement strategies compared to simple equal-distance placement scheme.

Progress In Electromagnetics Research C, 2018

In this paper linear and nonlinear properties of graphene at millimeter wave frequency band are i... more In this paper linear and nonlinear properties of graphene at millimeter wave frequency band are investigated. The nonlinear properties of the graphene are utilized to design frequency multiplier and mixer for millimeter wave applications. A patch of graphene is deposited on the dielectric image guide that will generate higher order harmonics. The amplitude of harmonics is optimized based on the dimensions of the graphene patch on top of the dielectric image guide. A frequency multiplier and mixer are designed, which utilize the second harmonics generated through graphene. The nonlinear behavior of the proposed designs has been simulated in the 50-75 GHz input signal frequency range. A conversion efficiency of −23 dB is obtained for the second harmonic for the frequency doubler. The frequency mixer is designed to mix two frequencies in V-band using dielectric image guide as the waveguide. A −28 dB conversion efficiency is simulated on a dielectric image-guide platform.

Future Generation Computer Systems, 2020

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Neurocomputing, 2019

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Physical Communication, 2018

Due to the increasing demand for wireless communications, millimeter-wave band has gained a great... more Due to the increasing demand for wireless communications, millimeter-wave band has gained a great attention recently. Also, achieving secure wireless communications is of high importance. Antenna subset modulation is a low complexity single beam directional modulation technique suitable for millimeter-wave wireless communications, whereas multi-beam antenna subset modulation is a multi-directional, generalized form of antenna subset modulation. In this paper, interference mitigation for multi-beam antenna subset modulation via side lobe level reduction is introduced. A method for designing thinned arrays with minimum side lobe levels for antenna subset modulation is introduced and generalized for multi-beam antenna subset modulation. A new variable constraint is applied to the optimization problem to control the localization of optimum solution within the antenna array. Two solutions are introduced, convex optimization combined with local search and local search assisted genetic algorithm. Simulation results show the superiority of the proposed algorithms compared to simulated annealing algorithm and traditional genetic algorithm.

Concurrency and Computation: Practice and Experience, 2019

With the ever-increasing popularity of resource-intensive mobile applications, today, Fog-to-Clou... more With the ever-increasing popularity of resource-intensive mobile applications, today, Fog-to-Cloud (F2C) computing system becomes a prominent technology for the next generation wireless networks. Despite the benefits of fog computing regarding localized storage and processing, it suffers from restricted power allowance and computational capability of the edge nodes. User nodes also may suffer from extensive delay, especially in offloading periods. Therefore, it is essential to develop a distributed mechanism for users' computation in offloading periods. According to this mechanism, not only the computational servers are exploited at their best capacity but also the users' latency constraints fulfilled. Consequently, this paper develops automated distributed fog computing for computational offloading using the theory of minority game. The proposed scheme achieves user satisfaction latency deadline as well as Quality-of-Experience. Moreover, it guarantees an adaptive equilibrium level of F2C computing system, which is suitable for heterogeneous wireless networks.

International Journal of Online and Biomedical Engineering (iJOE), 2019

In this paper, a computer-aided detection system is developed to detect lung nodules at an early... more In this paper, a computer-aided detection system is developed to detect lung nodules at an early stage using Computed Tomography (CT) scan images where lung nodules are one of the most important indicators to predict lung cancer. The developed system consists of four stages. First, the raw Computed Tomography lung images were preprocessed to enhance the image contrast and eliminate noise. Second, an automatic segmentation procedure for human's lung and pulmonary nodule canddates (nodules, blood vessels) using a two-level thresholding technique and morphological operations. Third, a feature fusion technique that fuses four feature extraction techniques: the statistical features of first and second order, value histogram features, histogram of oriented gradients features, and texture features of gray level co-occurrence matrix based on wavelet coefficients was utilised to extract the main features. The fourth stage is the classifier. Three classifiers were used and their perform...

2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), 2017

Recently, depth maps introduce a very effective representation for solving many fundamental compu... more Recently, depth maps introduce a very effective representation for solving many fundamental computer vision problems. However, modern 3D scanning devices, such as TOF (Time Of Flight) cameras and Microsoft Kinect sensor, provide a huge unbalance between the resolution of the intensity image and its corresponding depth map. Here, we address the problem of single depth map up-sampling using a new non-local total variation decomposition process with a self-learning structured sparsity model. This technique considers the fact of the decomposed components should be regularized by different constraints, hence better representation for depth maps in sparse domain can be achieved. Using different datasets, experimental results demonstrate superior effectiveness in terms of qualitative and quantitative measures.

The Visual Computer, 2018

Recently, RGB-D sensors have gained significant popularity due to their affordable cost. Compared... more Recently, RGB-D sensors have gained significant popularity due to their affordable cost. Compared to their associated highresolution (HR) color images, their depth maps counterparts are typically with lower resolution. In addition, the quality of those maps is still inadequate for further applications due to the existing holes, noises and artifacts. In this paper, we propose a clustering graph-based framework for depth map super-resolution. This framework uses the guidance of HR textured-intensity layer to support and compel high-frequency details in the depth map recovery process. This textured layer is extracted from the consolidated HR intensity image in a texture-structure separation process via a new relative total variation technique. Furthermore, instead of the standard sparse representation that does not consider the local structural information effectively, we propose a novel clustered-graph sparse representation with a low-rank prior. With this joint representation, any signal can be coded effectively, as the low-rank property reveals the global structure information while the intrinsic information is kept by a novel multiclass incoherence self-learning between classes. At the same time, a grouped coherence within each class dictionary is preserved. We optimize that joint objective function using state-of-the-art split Bregman algorithm. Experimental results on Middleburry 2005, 2007, 2014 and real-world datasets demonstrate that the proposed algorithm is very efficient and outperforms the state-of-the-art approaches in terms of objective and subjective quality.

Wireless Sensor Networks (WSNs) is a group of sensor nodes with lightweight, inexpensive, low bat... more Wireless Sensor Networks (WSNs) is a group of sensor nodes with lightweight, inexpensive, low battery nodes distributed in interesting field to sense the environment and send collected data to Base Station (BS) for further processing. Communication distance between sensor node and BS affects the battery of sensor node, as distance between sensor node and BS increased, the energy consumed in data sending increased. There are many different routing algorithm designed for efficient data route from sensor node to BS, Cluster based routing is one of famous technique recently used in data transmission . Cluster Head (CH) node elected to gather data from other nodes in the cluster and this in turn reduce distance from sensor node to BS and perform data aggregation before sending to BS, This paper discuss the impact of network size and BS location in different Efficient-Energy protocols for WSN clustering on the network Performance. Simulation Results show Network life time, total energy co...

Wireless sensor network (WSN) has numerous applications in our daily life. This paper discusses t... more Wireless sensor network (WSN) has numerous applications in our daily life. This paper discusses the usage of wireless sensors in monitoring and surveillance of one or more intruders in a pre-defined area of interest. Detection probability of intruder is analyzed versus different parameters. Results show the intrusion detection probability versus some parameters such as sensor sensing radius, number of nodes, radius of the coverage area, node availability, probability of active nodes and node density. Furthermore, we investigated the performance of the expected time of detecting an intruder by changing the node density and at different scenarios of intruder velocities.

Multi-hop Relay networks are proposed to fulfill the demanding coverage and capacity requirements... more Multi-hop Relay networks are proposed to fulfill the demanding coverage and capacity requirements for current applications in a cost efficient way. It can be used in IMT-Advanced technologies such as 3GPP LTE-Advanced. In this paper, LTE-A Uplink performance will be improved using Synchronous direct and multi-hop transmission (SDMT) between user equipment and Base Station. Also the effect of adding RS to the network and its position will be discussed. The total LTE-A uplink Throughput, average throughput per user, and Mean File Transfer Time (MFTT) in the LTE-A network are considered as performance measures. This is done and measured as a function of different arrival rates of UEs assigned from random positions in the cell. Adaptive Modulation, and Coding (AMC) scheme with high order 2x4MIMO is used to maximize network throughput with low bit error rate (BER). Simulation results show LTE-A uplink performance improvement using 2x4MIMO-AMC and SDMT scheme with RS at 50% of cell radius...

Multi-hop Relay networks are proposed to fulfill the demanding coverage and capacity requirements... more Multi-hop Relay networks are proposed to fulfill the demanding coverage and capacity requirements in both uplink and downlink for current applications in a cost efficient way. It can be used in IMT-Advanced technologies such as 3GPP LTE-Advanced. Spatial Diversity (SD) and Multiplexing (SM) have been adopted to improve the Signal to interference Noise Ratio (SINR) and spectrum efficiency in wireless networks. The objective of this paper is to evaluate the performance of Spatial Diversity and Multiplexing based on Adaptive modulation and coding (AMC) schemes in Uplink LTE-A. The average throughput, Throughput gain, and Mean File Transfer Time (MFTT) are considered as performance measures. The best Relay Station (RS) location in the cell was also estimated in order to improve LTE-A Uplink performance. The study takes into consideration the impact of Asymmetry between multi-hop links, the effect of different arrival rates from random positions of UEs in the cell. Resource blocks schedu...

Wireless sensor networks is a field of research which is vastly spreading because of the advancem... more Wireless sensor networks is a field of research which is vastly spreading because of the advancement of new technologies in relatively cheap sensors. These electronic devices have been recently improved with respect to their memory size, communication networking capabilities and their processing speed. In the last few years, a new subcategory of sensor networks known as linear wireless sensor networks (LWSN) is rising as a great focus area of research. Such wireless sensor networks have a large number of applications such as border monitoring, railway track monitoring, structural health monitoring of bridges, health care and machines surveillance. This paper focus on the use of this new technology in the monitoring and protection of the essential and critical pipelines infrastructures carrying water, oil, gas and other vital resources. The paper introduces a linear wireless sensor network model that can be used to facilitate this control and monitoring functions. Besides the paper i...

This paper proposes a palmprint-based identification system using Radon transform. Palmprint is a... more This paper proposes a palmprint-based identification system using Radon transform. Palmprint is a reliable identification method because the print patterns are unique even in the monozygotic twins, has permanent ridge structure, has fixed line structure, user friendliness, low cost capturing devices, and low resolution imaging. The proposed system is applied to CASIA database. Radon transform is used for extracting the features because it can be used in palm lines detection with high precision and efficiency. Live and enrolled palmprint are matched using Euclidean distance for verification. This system can achieve Equal Error Rate (EER) of 0.47% and Genuine Acceptance Rate (GAR) of 99.56%. The results of this study showed that the proposed system achieves higher verification accuracy than other palmprint identification systems.

2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), 2017

Recently, the sparse representations are one of the most active research areas. Here, the problem... more Recently, the sparse representations are one of the most active research areas. Here, the problem of single image super-resolution is revisited with sparse and low rank priors. The introduced algorithm employs a self-learning approach. This self-learning approach is applied on cluster domain rather than the common used patch domain. For supporting the self-learning approach, the learning model adopts an incoherence property with the classical sparse priors. In addition, to compensate the weakness of the high frequency details of the underlying low-resolution image, an edge preserving low lark model is proposed. Hence, the low rank representation guarantees the global structure constraints in the recovered high-resolution images. Experimental results, on different datasets, show that the proposed algorithm can recover high-resolution images compared with the state-of-the art.

Wireless Personal Communications, 2021

In this paper, allocation memory algorithms (i.e. First Fit, Next Fit and Best Fit) are redesigne... more In this paper, allocation memory algorithms (i.e. First Fit, Next Fit and Best Fit) are redesigned to offload the tasks of end devices in multi-device multi-task D2D communication. The proposed algorithms offload each task to single device, which enhance the performance by 4x at their maximum performance. To enhance the performance, the next fit algorithm is redesigned to offload the task to multiple devices, which enhances the performance to 88x at its maximum performance. The proposed algorithms are evaluated for different cell scenarios (i.e. femto, pico, micro and macro cells). Simulation results demonstrates that utilizing computation offloading minimizes the latency.

Journal of The Egyptian Society of Nephrology and Transplantation, 2021

The birth of distance learning courses in nephrology was set up in Mansoura (Mansoura Nephrology ... more The birth of distance learning courses in nephrology was set up in Mansoura (Mansoura Nephrology Club) in 2005. This activity was endorsed by the Egyptian Society of Nephrology and Renal Transplantation (ESNT) in early 2006. Tele-education was restricted to sharing presentations and the facilities to ask the experts. All questions and answers were uploaded to the website. The year 2012 witnessed a revolution in the ESNT continuous medical education chapter to include outreach programs followed by the ESNT virtual academy takeoff, with evolving experience through the following years, including enormous amounts of presentations, conferences, assignments, quizzes, as well as the full curriculum of Mansoura Nephrology Doctorate degree. More than 28 000 users from all continents and countries use this service on a daily basis. From May 2019, the ESNT activated online webinars to ensure interactive teaching that extended to a real Tele-medicine and teleconsultation to improve nephrology practice. In coronavirus disease 2019 era, where social distancing has become a necessity, the ESNT in collaboration with the international nephrology experts adopted a very active international distance nephrology education. A total number of 4596 lectures as well as links to 1850 videos are free of charges for all nephrologists all over the world, making ESNT virtual academy one of the largest open-source nephrology educational facilities.

Bulletin of the Faculty of Engineering. Mansoura University, 2020

Multi-hop Relay networks are proposed to fulfill the demanding coverage and capacity requirements... more Multi-hop Relay networks are proposed to fulfill the demanding coverage and capacity requirements for current applications in a cost efficient way. It can be used in IMT-Advanced technologies such as 3GPP LTE-Advanced. In this paper, Resource Blocks (RBs) proposed scheduling scheme is considered for zero multi-hop links overflow in Uplink LTE-Advanced. Then based on this scheduling scheme and other network assumptions the Optimum Relay Station (RS) placement in the cell was estimated using nonlinear optimization problem in order to improve LTE-A Uplink performance. The average throughput, Throughput gain, and Mean File Transfer Time (MFTT) are considered as performance measures. The study takes into consideration the impact of Asymmetry between multi-hop links, the effect of different arrival rates from random positions of UEs in the cell. Adaptive MIMO, Modulations, and Coding Switching (AMMCS) scheme is used to maximize network throughput with low bit error rate (BER). Simulation results show effective improvement in uplink network performance using the proposed scheduling scheme and AMMCS with RS at optimum location in the cell.

Wireless Personal Communications, 2020

Nowadays, the recent developments in the field of wireless sensor networks (WSNs) have initiated ... more Nowadays, the recent developments in the field of wireless sensor networks (WSNs) have initiated new applications of WSNs which can be used in many fields, such as military, environment, health, home and industry. One of the emerged wireless sensor networks topologies are linear wireless sensor networks (LWSNs). They have been rising as a great focus area of research. Such wireless sensor networks have a large number of applications such as border monitoring, railway track monitoring, structural health monitoring of bridges, health care and machines surveillance. LWSNs are widely applied in oil and gas pipelines infrastructure monitoring applications to enable the automatic measurement, analyses, storage and transmission of real-time data. Minimization of energy consumption of LWSNs is crucial for their proper usage. Using two different system models, this research investigates the minimization of LWSNs energy consumption using optimal node placement strategies compared to simple equal-distance placement scheme.

Progress In Electromagnetics Research C, 2018

In this paper linear and nonlinear properties of graphene at millimeter wave frequency band are i... more In this paper linear and nonlinear properties of graphene at millimeter wave frequency band are investigated. The nonlinear properties of the graphene are utilized to design frequency multiplier and mixer for millimeter wave applications. A patch of graphene is deposited on the dielectric image guide that will generate higher order harmonics. The amplitude of harmonics is optimized based on the dimensions of the graphene patch on top of the dielectric image guide. A frequency multiplier and mixer are designed, which utilize the second harmonics generated through graphene. The nonlinear behavior of the proposed designs has been simulated in the 50-75 GHz input signal frequency range. A conversion efficiency of −23 dB is obtained for the second harmonic for the frequency doubler. The frequency mixer is designed to mix two frequencies in V-band using dielectric image guide as the waveguide. A −28 dB conversion efficiency is simulated on a dielectric image-guide platform.

Future Generation Computer Systems, 2020

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Neurocomputing, 2019

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Physical Communication, 2018

Due to the increasing demand for wireless communications, millimeter-wave band has gained a great... more Due to the increasing demand for wireless communications, millimeter-wave band has gained a great attention recently. Also, achieving secure wireless communications is of high importance. Antenna subset modulation is a low complexity single beam directional modulation technique suitable for millimeter-wave wireless communications, whereas multi-beam antenna subset modulation is a multi-directional, generalized form of antenna subset modulation. In this paper, interference mitigation for multi-beam antenna subset modulation via side lobe level reduction is introduced. A method for designing thinned arrays with minimum side lobe levels for antenna subset modulation is introduced and generalized for multi-beam antenna subset modulation. A new variable constraint is applied to the optimization problem to control the localization of optimum solution within the antenna array. Two solutions are introduced, convex optimization combined with local search and local search assisted genetic algorithm. Simulation results show the superiority of the proposed algorithms compared to simulated annealing algorithm and traditional genetic algorithm.

Concurrency and Computation: Practice and Experience, 2019

With the ever-increasing popularity of resource-intensive mobile applications, today, Fog-to-Clou... more With the ever-increasing popularity of resource-intensive mobile applications, today, Fog-to-Cloud (F2C) computing system becomes a prominent technology for the next generation wireless networks. Despite the benefits of fog computing regarding localized storage and processing, it suffers from restricted power allowance and computational capability of the edge nodes. User nodes also may suffer from extensive delay, especially in offloading periods. Therefore, it is essential to develop a distributed mechanism for users' computation in offloading periods. According to this mechanism, not only the computational servers are exploited at their best capacity but also the users' latency constraints fulfilled. Consequently, this paper develops automated distributed fog computing for computational offloading using the theory of minority game. The proposed scheme achieves user satisfaction latency deadline as well as Quality-of-Experience. Moreover, it guarantees an adaptive equilibrium level of F2C computing system, which is suitable for heterogeneous wireless networks.

International Journal of Online and Biomedical Engineering (iJOE), 2019

In this paper, a computer-aided detection system is developed to detect lung nodules at an early... more In this paper, a computer-aided detection system is developed to detect lung nodules at an early stage using Computed Tomography (CT) scan images where lung nodules are one of the most important indicators to predict lung cancer. The developed system consists of four stages. First, the raw Computed Tomography lung images were preprocessed to enhance the image contrast and eliminate noise. Second, an automatic segmentation procedure for human's lung and pulmonary nodule canddates (nodules, blood vessels) using a two-level thresholding technique and morphological operations. Third, a feature fusion technique that fuses four feature extraction techniques: the statistical features of first and second order, value histogram features, histogram of oriented gradients features, and texture features of gray level co-occurrence matrix based on wavelet coefficients was utilised to extract the main features. The fourth stage is the classifier. Three classifiers were used and their perform...

2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), 2017

Recently, depth maps introduce a very effective representation for solving many fundamental compu... more Recently, depth maps introduce a very effective representation for solving many fundamental computer vision problems. However, modern 3D scanning devices, such as TOF (Time Of Flight) cameras and Microsoft Kinect sensor, provide a huge unbalance between the resolution of the intensity image and its corresponding depth map. Here, we address the problem of single depth map up-sampling using a new non-local total variation decomposition process with a self-learning structured sparsity model. This technique considers the fact of the decomposed components should be regularized by different constraints, hence better representation for depth maps in sparse domain can be achieved. Using different datasets, experimental results demonstrate superior effectiveness in terms of qualitative and quantitative measures.

The Visual Computer, 2018

Recently, RGB-D sensors have gained significant popularity due to their affordable cost. Compared... more Recently, RGB-D sensors have gained significant popularity due to their affordable cost. Compared to their associated highresolution (HR) color images, their depth maps counterparts are typically with lower resolution. In addition, the quality of those maps is still inadequate for further applications due to the existing holes, noises and artifacts. In this paper, we propose a clustering graph-based framework for depth map super-resolution. This framework uses the guidance of HR textured-intensity layer to support and compel high-frequency details in the depth map recovery process. This textured layer is extracted from the consolidated HR intensity image in a texture-structure separation process via a new relative total variation technique. Furthermore, instead of the standard sparse representation that does not consider the local structural information effectively, we propose a novel clustered-graph sparse representation with a low-rank prior. With this joint representation, any signal can be coded effectively, as the low-rank property reveals the global structure information while the intrinsic information is kept by a novel multiclass incoherence self-learning between classes. At the same time, a grouped coherence within each class dictionary is preserved. We optimize that joint objective function using state-of-the-art split Bregman algorithm. Experimental results on Middleburry 2005, 2007, 2014 and real-world datasets demonstrate that the proposed algorithm is very efficient and outperforms the state-of-the-art approaches in terms of objective and subjective quality.