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Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
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 …
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 …
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 …
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 …