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- Convolutional Neural Network (2)
- Deep Learning (2)
- Deep learning (2)
- Inertial Measurement Unit (2)
- Key management (2)
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- Mobile sink (2)
- Wireless sensor networks (2)
- Analysis (1)
- Beamforming (1)
- Brain–machine interface (1)
- Caputo fractional derivative; conformable fractional derivative; prey–predator system; discretization; stability; bifurcations; chaos (1)
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- Cognitive radio networks (1)
- Computational intelligence (1)
- Computer arithmetic (1)
- Computer vision (1)
- Conformable fractionalderivative; prey–predator–omnivore system;discretization; stability;bifurcations; chaos (1)
- Controller area network (1)
- Convolutional neural network (1)
- Convolutional neural networks (1)
- Cooperative communication (1)
- Cryo-electron microscopy (1)
- Cryptography (1)
- Cyber-attack detection (1)
- Data clustering (1)
- Deep Convolutional Neural Networks · Subspace Separation · Robust Deep Learning (1)
- Deep autoencoders (1)
- Edge device (1)
- Elliptic curve (1)
- Embedded systems (1)
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Articles 1 - 22 of 22
Full-Text Articles in Computer Engineering
Mathematical And Machine Learning Approaches For Classification Of Protein Secondary Structure Elements From Cα Coordinates, Ali Sekmen, Kamal Al Nasr, Bahadir Bilgin, Ahmet Bugra Koku, Christopher Jones
Mathematical And Machine Learning Approaches For Classification Of Protein Secondary Structure Elements From Cα Coordinates, Ali Sekmen, Kamal Al Nasr, Bahadir Bilgin, Ahmet Bugra Koku, Christopher Jones
Computer Science Faculty Research
Determining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information to locate hydrogen bonds to identify SSEs. When some spatial atomic details are missing, locating SSEs becomes a hinder. To address the problem, when some atomic information is missing, three approaches for classifying SSE types using Ca atoms in protein chains were developed: (1) a mathematical approach, (2) a deep learning approach, and (3) an ensemble of five machine learning models. The proposed methods were …
Dynamics And Simulations Of Discretized Caputo-Conformable Fractional-Order Lotka–Volterra Models, Yousef Feras, Semmar Billel, Al Nasr Kamal
Dynamics And Simulations Of Discretized Caputo-Conformable Fractional-Order Lotka–Volterra Models, Yousef Feras, Semmar Billel, Al Nasr Kamal
Computer Science Faculty Research
In this article, a prey–predator system is considered in Caputo-conformable fractional-order derivatives. First, a discretization process, making use of the piecewise-constant approximation, is performed to secure discrete-time versions of the two fractional-order systems. Local dynamic behaviors of the two discretized fractional-order systems are investigated. Numerical simulations are executed to assert the outcome of the current work. Finally, a discussion is conducted to compare the impacts of the Caputo and conformable fractional derivatives on the discretized model.
Incommensurate Conformable-Type Three-Dimensional Lotka–Volterramodel: Discretization, Stability, And Bifurcation, Feras Yousef, Billel Semmar, Kamal Al Nasr
Incommensurate Conformable-Type Three-Dimensional Lotka–Volterramodel: Discretization, Stability, And Bifurcation, Feras Yousef, Billel Semmar, Kamal Al Nasr
Computer Science Faculty Research
The classic Lotka–Volterra model is a two-dimensional system of differential equations used to model population dynamics among two-species: a predator and its prey. In this article, we consider a modified three-dimensional fractional-order Lotka–Volterra system that models population dynamics among three-species: a predator, an omnivore and their mutual prey. Biologically speaking, population models with a discrete and continuous structure often provide richer dynamics than either discrete or continuous models, so we first discretize the model while keeping one time-continuous dependent variable in each equation. Then, we analyze the stability and bifurcation near the equilibria. The results demonstrated that the dynamic behaviors …
Ggnb: Graph-Based Gaussian Naive Bayes Intrusion Detection System For Can Bus, Riadul Islam, Maloy K. Devnath, Manar D. Samad, Syed Md Jaffrey Al Kadry
Ggnb: Graph-Based Gaussian Naive Bayes Intrusion Detection System For Can Bus, Riadul Islam, Maloy K. Devnath, Manar D. Samad, Syed Md Jaffrey Al Kadry
Computer Science Faculty Research
The national highway traffic safety administration (NHTSA) identified cybersecurity of the automobile systems are more critical than the security of other information systems. Researchers already demonstrated remote attacks on critical vehicular electronic control units (ECUs) using controller area network (CAN). Besides, existing intrusion detection systems (IDSs) often propose to tackle a specific type of attack, which may leave a system vulnerable to numerous other types of attacks. A generalizable IDS that can identify a wide range of attacks within the shortest possible time has more practical value than attack-specific IDSs, which is not a trivial task to accomplish. In this …
Robust Feature Space Separation For Deep Convolutional Neural Network Training, Ali Sekmen, Mustafa Parlaktuna, Ayad Abdul-Malek, Erdem Erdemir, Ahmet Bugra Koku
Robust Feature Space Separation For Deep Convolutional Neural Network Training, Ali Sekmen, Mustafa Parlaktuna, Ayad Abdul-Malek, Erdem Erdemir, Ahmet Bugra Koku
Computer Science Faculty Research
This paper introduces two deep convolutional neural network training techniques that lead to more robust feature subspace separation in comparison to traditional training. Assume that dataset has M labels. The first method creates M deep convolutional neural networks called {DCNNi}M i=1 . Each of the networks DCNNi is composed of a convolutional neural network ( CNNi ) and a fully connected neural network ( FCNNi ). In training, a set of projection matrices are created and adaptively updated as representations for feature subspaces {S i}M i=1 . A rejection value is computed for each training based on its projections on …
An Efficient Deep-Learning-Based Detection And Classification System For Cyber-Attacks In Iot Communication Networks, Qasem Abu Al-Haija, Saleh Zein-Sabatto
An Efficient Deep-Learning-Based Detection And Classification System For Cyber-Attacks In Iot Communication Networks, Qasem Abu Al-Haija, Saleh Zein-Sabatto
Electrical and Computer Engineering Faculty Research
With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, the Internet of Things (IoT) has earned wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack that tends to be treated as normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with the cybersecurity …
Survey On Deep Neural Networks In Speech And Vision Systems, M. Alam, Manar D. Samad, Lasitha Vidyaratne, Alexander Glandon, Khan M. Iftekharuddin
Survey On Deep Neural Networks In Speech And Vision Systems, M. Alam, Manar D. Samad, Lasitha Vidyaratne, Alexander Glandon, Khan M. Iftekharuddin
Computer Science Faculty Research
This survey presents a review of state-of-the-art deep neural network architectures, algorithms, and systems in speech and vision applications. Recent advances in deep artificial neural network algorithms and architectures have spurred rapid innovation and development of intelligent speech and vision systems. With availability of vast amounts of sensor data and cloud computing for processing and training of deep neural networks, and with increased sophistication in mobile and embedded technology, the next-generation intelligent systems are poised to revolutionize personal and commercial computing. This survey begins by providing background and evolution of some of the most successful deep learning models for intelligent …
Ahead: Automatic Holistic Energy-Aware Design Methodology For Mlp Neural Network Hardware Generation In Proactive Bmi Edge Devices, Nan-Sheng Huang, Yi-Chung Chen, Jørgen Christian Larsen, Poramate Manoonpong
Ahead: Automatic Holistic Energy-Aware Design Methodology For Mlp Neural Network Hardware Generation In Proactive Bmi Edge Devices, Nan-Sheng Huang, Yi-Chung Chen, Jørgen Christian Larsen, Poramate Manoonpong
Electrical and Computer Engineering Faculty Research
The prediction of a high-level cognitive function based on a proactive brain–machine interface (BMI) control edge device is an emerging technology for improving the quality of life for disabled people. However, maintaining the stability of multiunit neural recordings is made difficult by the nonstationary nature of neurons and can affect the overall performance of proactive BMI control. Thus, it requires regular recalibration to retrain a neural network decoder for proactive control. However, retraining may lead to changes in the network parameters, such as the network topology. In terms of the hardware implementation of the neural decoder for real-time and low-power …
A Deep Learning Approach For Final Grasping State Determination From Motion Trajectory Of A Prosthetic Hand, Cihan Uyanik, Syed F. Hussaini, Erdem Erdemir, Erkan Kaplanoglu, Ali Sekmen
A Deep Learning Approach For Final Grasping State Determination From Motion Trajectory Of A Prosthetic Hand, Cihan Uyanik, Syed F. Hussaini, Erdem Erdemir, Erkan Kaplanoglu, Ali Sekmen
Computer Science Faculty Research
Deep Learning has been gaining popularity due to its numerous implementations and continuous growing capabilities, including the prosthetics industry which has trend of evaluation towards the smart operational decision. The aim of this study is to develop a reliable decision-making system for prosthetic hands which is responsible to grasp or point an object located in the interaction area. In order to achieve this goal, we have exploited the measurements taken from a low-cost inertial measurement unit (IMU) and proposed a convolutional neural network-based decision-making system, which utilizes 9 distinct measurement variables as input, 3 axis accelerometer, 3 axis gyroscope and …
A Deep Learning Approach For Motion Segment Estimation For Pipe Leak Detection Robot, Cihan Uyanik, Erdem Erdemir, Erkan Kaplanoglu, Ali Sekmen
A Deep Learning Approach For Motion Segment Estimation For Pipe Leak Detection Robot, Cihan Uyanik, Erdem Erdemir, Erkan Kaplanoglu, Ali Sekmen
Computer Science Faculty Research
The trajectory motion of a robot can be a valuable information to estimate the localization of an autonomous robotic system, especially in a very dynamic but structurally-known environments like water pipes where the sensor readings are not reliable. The main focus of this research is to estimate the location of meso-scale robots using a deep-learning-based motion trajectory segment detection system from recorded sensory measurements while the robot travels through a pipe system. The idea is based on the classification of the motion measurements, acquired by inertial measurement unit (IMU), by exploiting the deep learning approach. Proposed idea and utilized methodology …
Implementing A Lightweight Schmidt-Samoa Cryptosystem (Ssc) For Sensory Communications, Qasem Abu Al-Haija, Ibrahim Marouf, Mohammad M. Asad, Kamal Al Nasr
Implementing A Lightweight Schmidt-Samoa Cryptosystem (Ssc) For Sensory Communications, Qasem Abu Al-Haija, Ibrahim Marouf, Mohammad M. Asad, Kamal Al Nasr
Computer Science Faculty Research
One of the remarkable issues that face wireless sensor networks (WSNs) nowadays is security. WSNs should provide a way to transfer data securely particularly when employed for mission-critical purposes. In this paper, we propose an enhanced architecture and implementation for 128-bit Schmidt-Samoa cryptosystem (SSC) to secure the data communication for wireless sensor networks (WSN) against external attacks. The proposed SSC cryptosystem has been efficiently implemented and verified using FPGA modules by exploiting the maximum allowable parallelism of the SSC internal operations. To verify the proposed SSC implementation, we have synthesized our VHDL coding using Quartus II CAD tool targeting the …
Analytical Approaches To Improve Accuracy In Solving The Protein Topology Problem, Kamal Al Nasr, Feras Yousef, Ruba Jebril, Christopher Jones
Analytical Approaches To Improve Accuracy In Solving The Protein Topology Problem, Kamal Al Nasr, Feras Yousef, Ruba Jebril, Christopher Jones
Computer Science Faculty Research
To take advantage of recent advances in genomics and proteomics it is critical that the three-dimensional physical structure of biological macromolecules be determined. Cryo-Electron Microscopy (cryo-EM) is a promising and improving method for obtaining this data, however resolution is often not sufficient to directly determine the atomic scale structure. Despite this, information for secondary structure locations is detectable. De novo modeling is a computational approach to modeling these macromolecular structures based on cryo-EM derived data. During de novo modeling a mapping between detected secondary structures and the underlying amino acid sequence must be identified. DP-TOSS (Dynamic Programming for determining the …
Routing Algorithm With Uneven Clustering For Energy Heterogeneous Wireless Sensor Networks, Ying Zhang, Wei Xiong, Dezhi Han, Wei Chen, Jun Wang
Routing Algorithm With Uneven Clustering For Energy Heterogeneous Wireless Sensor Networks, Ying Zhang, Wei Xiong, Dezhi Han, Wei Chen, Jun Wang
Computer Science Faculty Research
Aiming at the “hotspots” problem in energy heterogeneous wireless sensor networks, a routing algorithm of heterogeneous sensor network with multilevel energies based on uneven clustering is proposed. In this algorithm, the energy heterogeneity of the nodes is fully reflected in the mechanism of cluster-heads’ election. It optimizes the competition radius of the cluster-heads according to the residual energy of the nodes. This kind of uneven clustering prolongs the lifetime of the cluster-heads with lower residual energies or near the sink nodes. In data transmission stage, the hybrid multihop transmission mode is adopted, and the next-hop routing election fully takes account …
A Hybrid Key Management Scheme For Wsns Based On Ppbr And A Tree-Based Path Key Establishment Method, Ying Zhang, Jixing Liang, Bingxin Zheng, Wei Chen
A Hybrid Key Management Scheme For Wsns Based On Ppbr And A Tree-Based Path Key Establishment Method, Ying Zhang, Jixing Liang, Bingxin Zheng, Wei Chen
Computer Science Faculty Research
With the development of wireless sensor networks (WSNs), in most application scenarios traditional WSNs with static sink nodes will be gradually replaced by Mobile Sinks (MSs), and the corresponding application requires a secure communication environment. Current key management researches pay less attention to the security of sensor networks with MS. This paper proposes a hybrid key management schemes based on a Polynomial Pool-based key pre-distribution and Basic Random key pre-distribution (PPBR) to be used in WSNs with MS. The scheme takes full advantages of these two kinds of methods to improve the cracking difficulty of the key system. The storage …
A Localization Method For Underwater Wireless Sensor Networks Based On Mobility Prediction And Particle Swarm Optimization Algorithms, Ying Zhang, Jixing Liang, Shengming Jiang, Wei Chen
A Localization Method For Underwater Wireless Sensor Networks Based On Mobility Prediction And Particle Swarm Optimization Algorithms, Ying Zhang, Jixing Liang, Shengming Jiang, Wei Chen
Computer Science Faculty Research
Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO …
Key Management Scheme Based On Route Planning Of Mobile Sink In Wireless Sensor Networks, Ying Zhang, Jixing Liang, Bingxin Zheng, Shengming Jiang, Wei Chen
Key Management Scheme Based On Route Planning Of Mobile Sink In Wireless Sensor Networks, Ying Zhang, Jixing Liang, Bingxin Zheng, Shengming Jiang, Wei Chen
Computer Science Faculty Research
In many wireless sensor network application scenarios the key management scheme with a Mobile Sink (MS) should be fully investigated. This paper proposes a key management scheme based on dynamic clustering and optimal-routing choice of MS. The concept of Traveling Salesman Problem with Neighbor areas (TSPN) in dynamic clustering for data exchange is proposed, and the selection probability is used in MS route planning. The proposed scheme extends static key management to dynamic key management by considering the dynamic clustering and mobility of MSs, which can effectively balance the total energy consumption during the activities. Considering the different resources available …
A Network Topology Control And Identity Authentication Protocol With Support For Movable Sensor Nodes, Ying Zhang, Wei Chen, Jixing Liang, Bingxin Zheng, Shengming Jiang
A Network Topology Control And Identity Authentication Protocol With Support For Movable Sensor Nodes, Ying Zhang, Wei Chen, Jixing Liang, Bingxin Zheng, Shengming Jiang
Computer Science Faculty Research
It is expected that in the near future wireless sensor network (WSNs) will be more widely used in the mobile environment, in applications such as Autonomous Underwater Vehicles (AUVs) for marine monitoring and mobile robots for environmental investigation. The sensor nodes’ mobility can easily cause changes to the structure of a network topology, and lead to the decline in the amount of transmitted data, excessive energy consumption, and lack of security. To solve these problems, a kind of efficient Topology Control algorithm for node Mobility (TCM) is proposed. In the topology construction stage, an efficient clustering algorithm is adopted, which …
Dynamic Deployment For Hybrid Sensor Networks Based On Potential Field-Directed Particle Swarm Optimization, Ying Zhang, Yunlong Qiao, Wei Zhao, Wei Chen, Jinde Cao
Dynamic Deployment For Hybrid Sensor Networks Based On Potential Field-Directed Particle Swarm Optimization, Ying Zhang, Yunlong Qiao, Wei Zhao, Wei Chen, Jinde Cao
Computer Science Faculty Research
For a hybrid sensor network, the effective coverage rate can be optimized by adjusting the location of the mobile nodes. For many deployments by APF (artificial potential field), due to the common problem of barrier effect, it is difficult for mobile nodes to diffuse by the weaker attraction when the nodes initially distribute densely in some places. The proposed deployment algorithm PFPSO (Potential Field-Directed Particle Swarm Optimization) can overcome this problem and guide the mobile nodes to the optimal positions. Normally the requirement is different for the effective coverage rate between the hotspot area and the ordinary area. On the …
Deterministic Versus Randomized Kaczmarz Iterative Projection, Tim Wallace, Ali Sekmen
Deterministic Versus Randomized Kaczmarz Iterative Projection, Tim Wallace, Ali Sekmen
Computer Science Faculty Research
The Kaczmarz’s alternating projection method has been widely used for solving a consistent (mostly over-determined) linear system of equations Ax = b. Because of its simple iterative nature with light computation, this method was successfully applied in computerized tomography. Since tomography generates a matrix A with highly coherent rows, randomized Kaczmarz algorithm is expected to provide faster convergence as it picks a row for each iteration at random, based on a certain probability distribution. It was recently shown that picking a row at random, proportional with its norm, makes the iteration converge exponentially in expectation with a decay constant that …
Nanosensor Data Processor In Quantum-Dot Cellular Automata, Fenghui Yao, Mohamed Saleh Zein-Sabatto, Guifeng Shao, Mohammad Bodruzzaman, Mohan Malkani
Nanosensor Data Processor In Quantum-Dot Cellular Automata, Fenghui Yao, Mohamed Saleh Zein-Sabatto, Guifeng Shao, Mohammad Bodruzzaman, Mohan Malkani
Computer Science Faculty Research
Quantum-dot cellular automata (QCA) is an attractive nanotechnology with the potential alterative to CMOS technology. QCA provides an interesting paradigm for faster speed, smaller size, and lower power consumption in comparison to transistor-based technology, in both communication and computation. This paper describes the design of a 4-bit multifunction nanosensor data processor (NSDP). The functions of NSDP contain (i) sending the preprocessed raw data to high-level processor, (ii) counting the number of the active majority gates, and (iii) generating the approximate sigmoid function. The whole system is designed and simulated with several different input data.
Efficient Cooperative Mimo Paradigms For Cognitive Radio Networks, Wei Chen, Liang Hong, Xiaoqian Chen
Efficient Cooperative Mimo Paradigms For Cognitive Radio Networks, Wei Chen, Liang Hong, Xiaoqian Chen
Computer Science Faculty Research
This paper investigates the benefits that cooperative communication brings to cognitive radio networks. We focus on cooperative Multiple Input Multiple Output (MIMO) technology, where multiple distributed single-antenna secondary users cooperate on data transmission and reception. Three cooperative MIMO paradigms are proposed to maximize the diversity gain and significantly improve the performance of overlay, underlay and interweave systems. In the paradigm for overlay systems the secondary users can assist (relay) the primary transmissions even when they are far away from the primary users. In the paradigm for underlay systems the secondary users can share the primary users’ frequency resources without any …
Reduced Row Echelon Form And Non-Linear Approximation For Subspace Segmentation And High-Dimensional Data Clustering, Akram Aldroubi, Ali Sekmen
Reduced Row Echelon Form And Non-Linear Approximation For Subspace Segmentation And High-Dimensional Data Clustering, Akram Aldroubi, Ali Sekmen
Computer Science Faculty Research
Given a set of data W={w1,…,wN}∈RD drawn from a union of subspaces, we focus on determining a nonlinear model of the form U=⋃i∈ISi, where {Si⊂RD}i∈I is a set of subspaces, that is nearest to W. The model is then used to classify W into clusters. Our approach is based on the binary reduced row echelon form of data matrix, combined with an iterative scheme based on a non-linear approximation method. We prove that, in absence of noise, our approach can find the number of subspaces, their dimensions, and an orthonormal basis for each subspace Si. We provide a comprehensive analysis …