Browsing by Author "Al-Dhahir, Naofal"
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Item A Joint Unsupervised Learning and Genetic Algorithm Approach for Topology Control in Energy-Efficient Ultra-Dense Wireless Sensor Networks(Institute of Electrical and Electronics Engineers Inc.) Chang, Y.; Yuan, X.; Li, B.; Niyato, D.; Al-Dhahir, Naofal; Al-Dhahir, NaofalEnergy efficiency is a key performance metric for ultra-dense wireless sensor networks. In this letter, an unsupervised learning approach for topology control is proposed to prolong the lifetime of ultra-dense wireless sensor networks by balancing energy consumption. By encoding sensors as genes according to the network clusters, the proposed genetic-based algorithm learns an optimum chromosome to construct a close-to-optimum network topology using unsupervised learning in probability. Moreover, it schedules some of the cluster members to sleep to conserve the node energy using geographically adaptive fidelity. Simulation results demonstrate the superior performance of the proposed algorithm by improving energy efficiency in comparison with state-of-the-art algorithms at an acceptable computational complexity.Item ADMM for Joint Data and Off-Grid NBI Recovery in OFDM Systems(Institute of Electrical and Electronics Engineers Inc.) Al-Tous, H.; Barhumi, I.; Kalbat, A.; Al-Dhahir, Naofal; 113149196515374792028 (Al-Dhahir, N); Al-Dhahir, NaofalJoint data and off-grid narrow-band interference (NBI) recovery is investigated in orthogonal-frequency-division-multiplexing (OFDM) systems using compressive-sensing (CS) framework. The joint recovery problem is formulated as a convex optimization problem of three weighted norms. A reduced computational complexity and scalable algorithm is proposed to solve the recovery problem based on the alternating-direction-method-of-multipliers (ADMM). Simulation results, show that the average-run-time of solving the joint recovery problem using the proposed ADMM algorithm is much less than the average-run-time of the interior point method.Item Advanced Signal Processing Algorithms for Security and Safety in Vehicular Applications(2020-11-18) Marzban, Mohamed; Al-Dhahir, NaofalAdvanced driver assistance systems (ADAS) have witnessed a significant growth in the last decade. To complement the ADAS technologies, wireless vehicular communications emerged as a promising solution providing vehicles with an all-directions non-line of sight view of their surroundings. This dissertation focuses on the security aspects of vehicular communications in addition to in-vehicle safety aspects of ADAS applications. Wireless communications are vulnerable to potential security attacks due to their broadcast nature. Vehicles exchange critical safety information about their speed, direction and location which necessities efficient algorithms to secure the ongoing communications. This motivates us to investigate the physical layer (PHY) security for wireless systems having fast fading channels, few transmit antennas and stringent transmit power constraints as imposed by the vehicular communication standards. We study the PHY security in a single-user orthogonal frequency-division multiplexing (OFDM) and multi-user single-carrier frequency-division multiple-access (SC-FDMA) systems where transmitter(s), denoted by Alice(s), transmit confidential data messages to a neighbouring receiver, which we refer to as Bob, in the presence of eavesdropping node(s), denoted by Eve(s). Alice(s) send a temporal artificial-noise (AN) signal superimposed over their information signal to secure the ongoing communications. We investigate the impact of the channel delay spread, cyclic prefix, information/AN signals power allocations, channel state information (CSI) knowledge and information and AN precoders design on the achievable average secrecy rate in single-user and multi-users systems. We highlight the trade-offs between the computational complexity and the average secrecy rate performance for different detection strategies. In addition, we enhance the system’s security by proposing a hybrid scheme that combines temporal AN with channel-based secret-key extraction. Furthermore, we propose a novel secure scheme that utilizes the sub-channel orthogonality of OFDM systems to simultaneously transmit data and secret key symbols in two opposite directions. Bob, shares secret key symbols with Alice using wiretap coding over a portion of the sub-channels. Simultaneously, Alice uses the accumulated secret keys in her secret-key queue to encrypt data symbols using a one time pad (OTP) cipher and transmits them to Bob over the remaining sub-channels. We drive closed-form expressions for the average secrecy rate and the secrecy outage probabilities for the different proposed schemes. In the second part of this dissertation, we focus on the in-vehicle safety aspects of ADAS applications with emphasis on the driver’s gaze estimation which is essential to asses the visual attention of drivers allowing alarming drivers in different distraction scenarios. We present a 3D head based coordinate system whose center is the driver’s head center of mass (CoM). Then, we utilize fiducial markers to precisely annotate the driver’s gaze into elevation and azimuth angles. We increase the realism of the discrete driving data by augmenting it to continuous gaze data collected while the car is parked. Then, we propose deep-learning based gaze estimation algorithms that utilizes all features on the driver’s face to produce new statistical customizable gaze regions estimates. Our statistical gaze estimations provide the level of confidence in estimation which can be used a useful parameter for different ADAS applications. We perform large scale cross-driver testing utilizing the data from 54 different subjects collected in naturalistic driving environments.Item Advanced Signal Processing Techniques for Smart Grid Communications(2016-12) ElSamadouny, Ahmed Hossny Anis Hossny; Al-Dhahir, NaofalSmart grid (SG) networks require a reliable communication infrastructure for bi-directional communication. The well-established network infrastructure which covers a vast geographical area motivates SG communications. The SG is expected to incorporate a hybrid mesh of different communication technologies over different physical media to create an efficient and reliable communication network. This dissertation focuses on the unlicensed wireless communication and power-line communication (PLC) approaches to SG communications. Wireless SG smart utility networks (SUNs) use unlicensed frequency bands which motivate the need for wireless coexistence mechanisms and reliable spectrum sensing. We investigate the spectrum sensing problem in orthogonal frequency-division multiplexing (OFDM) based cognitive radio networks under In-phase/Quadrature (I/Q) imbalance. We derive the likelihood ratio test (LRT) in the presence of I/Q imbalance at the analog front-ends of both the primary and secondary users. In addition, we derive closed-form expressions for the probabilities of detection and false alarm and the receiver operating characteristics and examine their dependence on both transmit and receive I/Q imbalance levels. Furthermore, we compare the performance of the LRT with that of the energy detector and demonstrate the superiority of the former over the latter. Next, we generalize our analysis to the blind case where we derive simple closed-form expressions for the generalized likelihood ratio test (GLRT) and its false alarm probability as a function of the received signal only, i.e., without requiring any knowledge of the primary-to-secondary channel response, noise statistics, or I/Q imbalance parameters. Our results demonstrate that the correlation properties of the primary user's signal induced by transmit I/Q imbalance are signal features that can be exploited in a blind fashion at the secondary user to enhance the detection probability significantly compared to the conventional energy detector. Furthermore, we propose and analyze a multiple-input multiple-output (MIMO) OFDM-based bi-directional communication system for SG applications in medium-voltage (MV) PLC. Multi-conductor transmission line theory is used for accurate narrowband (NB) MV-PLC channel characterization and evaluation of the spatial correlation between the phase conductors. The proposed MV MIMO-OFDM design can achieve significant data rate gains over single-input single-output OFDM transmission by optimizing the indices of the active OFDM sub-channels, the bit allocation across them, and the transmit power allocation across the spatial beams. The achievable MIMO-OFDM data rates are evaluated for an MV overhead network with different line lengths, coupler impedances, and branches. In addition, we thoroughly investigate the possibility of reliable data transmission through distribution transformers, utilizing a MIMO NB-PLC OFDM-based approach. The achievable data rates are quantified for NB MV-PLC line in cascade connection with distribution transformers. We investigate the multi-user sum-rate optimization for point-to-multipoint Multicasting MV PLC network. We design the optimal spatial precoder and equalizer which maximize the achievable sum-rate for all users in the network. In addition, we propose two schemes to secure data transmission through the two-group multicasting network. The first scheme secures the network when the eavesdropper is a user in one of the two multicasting groups. The second scheme secures the multicasting network from external passive eavesdroppers. The secrecy rate is evaluated for both scenarios.Item Coherence diversity: A novel source of gain in wireless multiuser networks(2017-12) Shady, Mohamed Fadel; Nosratinia, Aria; Al-Dhahir, Naofal; Minn, HlaingThis dissertation investigates multiuser networks where the fading links experience unequal coherence conditions as well as dissimilar link CSI availability. It is shown that the disparity in coherence conditions for multiple users leads to a novel gain in the transmission rates compared with techniques that do not explicitly take advantage of this disparity. This gain is denoted coherence diversity and is demonstrated by product superposition transmission. First, a frequency-selective broadcast channel is considered, where two users have a disparity in coherence time and coherence bandwidth. This channel is analyzed under three broad scenarios of the disparity between the link qualities: when the disparity is in coherence time, in coherence bandwidth, and in both coherence time and coherence bandwidth. For each scenario, an analysis is provided and coherence diversity gain is demonstrated. The results are obtained in the framework of OFDM transmission covering a variety of pilot transmission schemes and different channel estimation techniques. Numerical simulations are presented to show coherence diversity gains. Second, coherence diversity is investigated in broadcast and multiple access channels with an arbitrary number of users. The users experience unequal fading block lengths, and CSI is not available. In the broadcast channel, product superposition is employed to find the achievable degrees of freedom. The case of multiple users experiencing fading block lengths of arbitrary ratio or alignment is studied. Also, in the multiple-access channel with unequal coherence times, achievable and outer bounds on the degrees of freedom are obtained. Third, a MISO broadcast channel is considered where some receivers experience longer coherence intervals and have CSIR, while some other receivers experience shorter coherence intervals and do not enjoy free CSIR. A variety of CSIT availability models is considered, including no CSIT, delayed CSIT, or hybrid CSIT. For each model, coherence diversity gains are merged with interference alignment and beamforming to achieve degrees of freedom. For several cases, inner and outer bounds are established that either partially meet, or the gap diminishes with increasing coherence times.Item Cyclostationary Noise Mitigation for SIMO Powerline Communications(IEEE-Inst Electrical Electronics Engineers Inc) Elgenedy, Mahmoud; Sayed, Mostafa; Al-Dhahir, Naofal; Chabaan, Rakan C.; 0000 0003 5178 379X (Al-Dhahir, N); 0000-0002-1866-0272 (Sayed, M); 113149196515374792028 (Al-Dhahir, N); Elgenedy, Mahmoud; Sayed, Mostafa; Al-Dhahir, NaofalThe cyclostationary noise in low-voltage narrowband powerline communications (NB-PLC) severely degrades the communication reliability. In this paper, we adopt single-input multi-output (SIMO) transmission to enhance the reliability of NB-PLC. Considering the SIMO receiver structure, we exploit the NB-PLC noise cyclostationarity and the high spatial correlations across multiple receive phases to design practical and efficient noise mitigation techniques. In particular, we propose two time-domain frequency shift (FRESH) filtering-based cyclostationary signal recovery techniques with different performance and complexity levels. The proposed time-domain-based FRESH filtering techniques minimize the mean squared error in estimating the orthogonal frequency division multiplexing (OFDM) information signal in the time-domain The FRESH filtering exploits the cyclic auto-correlation of both the NB-PLC noise and the OFDM information signal in addition to their cyclic cross-correlation across the receive phases. Moreover, we propose a frequency-domain-based cyclostationary noise mitigation technique that minimizes the mean squared error in estimating the OFDM information signal in the frequency-domain The proposed frequency domain-based technique exploits the cyclostationarity of the noise to estimate its power spectral density as well as the cross-correlation, per frequency subchannel, over multiple stationary noise temporal regions. Our proposed SIMO NB-PLC noise mitigation techniques are shown via simulation results conducted using noise field measurements to achieve considerable performance gains over single-input single-output techniques. In addition, we show that our proposed techniques achieve considerable performance gains over the conventional SIMO maximal-ratio-combiner designed assuming stationary noise.Item Data Transport in an Integrated NGSO Satellite Communication and Radio Astronomy System(2022-12-01T06:00:00.000Z) Mohamed, Ahmed Mohamed Magdy Ahmed 1991-; Minn, Hlaing; Hays, Seth; Fonseka, John P.; Al-Dhahir, Naofal; Saquib, MohammadAn integrated mega-constellation non-geostationary orbit (NGSO) satellite communication and radio astronomy system (ISCRAS), was recently proposed in the literature as a new coexistence paradigm. We consider ISCRAS and focus on radio astronomical observation (RAO) data transport from the RAO-conducting satellites (called source satellites) to the ground gateways where each gateway is directly connected to a certain number of satellites (called destination satellites). This transportation of RAO data is an important problem and an existing work addressed it by applying a linear programming optimization based algorithm. However, the computational complexity of this algorithm is quite high which is exacerbated by the need to re-compute the algorithm frequently due to the time-varying characteristics of the RAO-conducting satellites, the Earth stations, and the RAO region. To address this high complexity issue, we develop a low-complexity RAO data transport algorithm. In addition to the static spectrum resource constraint scenario considered in the existing work, we introduce a dynamic spectrum resource constraint scenario to exploit the knowledge of the satellite communication system (SCS) traffic statistics. We also present a modified version of the optimization based algorithm in the literature for the dynamic resource constraint scenario. In addition to the number of inter-satellite link (ISL) hops as the RAO data transport cost metric, we also evaluate the metric of the sum of the squared ISL hop distances which reflects the transmission energy cost. Furthermore, computational complexity analysis and data transport costs of the algorithms are presented. Our results show that the proposed algorithm yields several orders of computational complexity saving over the existing method while incurring a modest increase in the data transport cost. Our proposed dynamic resource constraint scenario provides plausible reduction of the RAO data transport cost. Next, we introduce a generalized ISL connection model which extends the existing approach of adjacent-only ISL connections to include non-adjacent ISL connections. This model is implemented with an upper limit on the ISL distance which can be adjusted for performance tradeoffs. Next, we develop analytical expressions to study two impacts of satellite dynamics on the RAO data transport. The first one concerns with the beam misalignment of individual ISL hops. The second one corresponds to whether destination satellites move outside the gateway coverage during the end-to-end delay interval of a data packet transport. Furthermore, we introduce delay, energy, and spectrum usage performance metrics and evaluate them to yield performance tradeoff guidelines. A simplified data transport path model is also presented to facilitate analytical performance evaluation and initial system design settings. Then, we present a new RAO data transport design which incorporates system dynamics and desired performance tradeoffs.Item Enabling Technologies for Next Generation Wireless Local Area Networks (WLANs)Mohamed, Ahmed Gamal Helmy; Al-Dhahir, NaofalNext-generation wireless local area networks (WLANs) address two major challenges. The first is the flexibility to provide significantly increased users’ throughput due to the current evolution of the Internet usage toward real-time high-definition video content. Multi-input multi-output (MIMO) transmission at both access points (APs) and stations (STAs) is one of the key technologies to achieve high throughput in WLANs for both single-user MIMO (SUMIMO) and downlink multi-user MIMO (MU-MIMO). This requires APs and STAs to efficiently communicate while addressing challenging design trade-offs between energy efficiency, implementation complexity, and overall network spectral efficiency. In current WLANs, implementing MIMO techniques for SU-MIMO, which utilizes multiple radio-frequency (RF) chains, has become the norm. Thus, using a small number of RF chains, and ideally a single RF chain, is highly desirable for future low-power devices. The second challenge is dense deployment scenarios where many heterogeneous devices, from high-end laptops to low-power Internet of Things (IoT) devices and wearables, must coexist and operate reliably. In these dense scenarios, most relevant challenges in MU-MIMO are related to interference issues, which increase the packet error. In this dissertation, we focus on enhancing the user experience in SU-MIMO transmission and improving interference management techniques in MU-MIMO transmission for dense deployment WLAN scenarios. For SU-MIMO, we adopt Spatial Modulation (SM) as a single- (or few-) RF MIMO transmission technique that efficiently uses multiple antennas while addressing challenging design trade-offs between energy efficiency, implementation complexity, and overall network spectral efficiency. This motivates SM-based transmission for low-power IoT devices providing a better user experience for dense environments. We analyze the robustness of SM-based direct-conversion transceivers under transmit in-phase/quadrature (I/Q) imbalance. Then, we propose temporal modulation as a new dimension to enhance the performance of spatially modulated space-time block codes (STBC) while achieving a full transmit diversity order. Based on our proposed codebook, we propose the first differential transmission scheme for spatial modulation with multiple active transmit antennas. For the multi-stream MU-MIMO interference networks, we study the problem of per-stream maximum sum-rate (MSR) joint precoder and minimum mean-squared error (MMSE) equalizer design for the scenarios where multiple independent transmitters send data streams to corresponding different receivers via a shared channel forming an interference environment. We propose a generalized iterative algorithm which directly maximizes the sum-rate without assuming the signal-to-noise ratio (SNR) to be infinite. To reduce complexity, which can become prohibitive for large network size, we examine the performance-complexity tradeoffs involved in a sparse equalizer design. Joint precoder and equalizer optimization requires alternation between the forward and reverse links and assumes perfect synchronization between the transmitters and receivers at each network node, resulting in extensive overhead and spectral efficiency loss. To overcome this serious drawback, we propose a new design approach based on weighted-sum-rate maximization assuming a virtual equalizer type at the transmitter to limit the optimization process to the transmitter side. Finally, we quantify the sum-rate loss due to mismatched equalizer types and demonstrate the robustness of our proposed sum-rate weighting strategy to such mismatches with perfect or imperfect channel knowledge.Item Energy Efficiency Analysis of Collaborative Compressive Sensing for Cognitive Radio Networks(Institute of Electrical and Electronics Engineers Inc.) Kishore, R.; Gurugopinath, S.; Muhaidat, S.; Sofotasios, P. C.; Dianati, M.; Al-Dhahir, Naofal; 113149196515374792028 (Al-Dhahir, N); Al-Dhahir, NaofalWe investigate the energy efficiency of a conventional collaborative compressed sensing (CCCS) scheme in cognitive radio networks. In particular, we derive expressions for the throughput, energy consumption and energy efficiency, and analyze the trade-off between the achievable throughput and the energy consumption of the underlying CCCS scheme. Furthermore, we formulate a multiple variable non-convex optimization problem to determine the optimum compression level that maximizes the energy efficiency, subject to interference constraints. We propose a sub-optimal solution based on tight approximations to simplify the aforementioned optimization problem, and further demonstrate that the energy efficiency achieved by the CCCS scheme is higher than that of conven- tional collaborative sensing scheme, under the same predefined conditions. It is further shown that the increase in the energy efficiency of CCCS scheme is due to the considerable decrease in the energy consumption, which is particularly noticeable with a large number of sensors. © 2018 IEEE.Item Enhancing Energy Efficiency and Ranging Accuracy in IoT Networks(May 2023) Helwa, Sherief Mohammad Salaheldin 1989-; Al-Dhahir, Naofal; Ryu, Ill; Fonseka, John P.; Heins, Matthew; Torlak, MuratWe consider an IoT network and try to address some of the issues and limitations of this new wireless system paradigm. Some of the issues come from the design requirements of IoT devices such as low cost, low energy consumption, and extended battery lifetime. These requirements limit the ability of such devices to operate at large bandwidth or use complex receiver circuitry that consume high energy. We focus on these two problems and investigate possible solutions to overcome such issues. We address the problem of receiver power consumption by introducing the usage of low-resolution Analog to Digital Converters (ADCs) under Differential-Phase Shift Keying (D-PSK) modulation. Outage-constrained receiver energy efficiency is then used as our metric to ensure low power consumption while operating at reasonable rates which are reflected in achieving low transmission latency values. Additionally, the effects of bandwidth limitations on ranging accuracy are taken into consideration. The idea of Channel Frequency Response (CFR) stitching is applied to expand the bandwidth, where two-way CFR is introduced to ensure CFR coherency upon stitching. Two-way CFR is further studied in details highlighting its advantages and disadvantages. Moreover, two alternative approaches are proposed to overcome two-way CFR’s accuracy degradation drawback by working with one-way response instead. The first techniques is a two-way to one-way CFR conversion where we apply signal processing techniques to detect and correct any phase errors in the CFR after conversion. While the second approach utilizes the novel idea of frequency overlap-based CFR alignment to mitigate the system’s impairments we experience while operating with the one-way approach. Significant ranging accuracy gains were achieved by the proposed techniques and verified by means of accurate system simulation as well as hardware prototyping.Item Enhancing Rate and Reliability of PLC in LV/MV Smart Grid Networks(2019-05) Elgenedy, Mahmoud A; 0000-0003-3402-5188 (Elgenedy, MA); Al-Dhahir, NaofalPower line communications (PLC) is a promising solution for smart grid communications due to low deployment cost over the existing power line infrastructure. In this dissertation, we propose enhancing the PLC reliability and/or data rate by exploiting the noise and channel statistical properties in time, frequency and spatial domains. For the Narrowband PLC (NB-PLC) in the 3-500 kHz frequency band, a major challenge is the presence of cyclostationary noise whose statistics vary periodically with a period of half the AC cycle. We propose two techniques to mitigate the cyclostationary noise, namely, erasure decoding and noise cancellation. For erasure decoding, we enhance the channel decoding capabilities by feeding the positions of the noise impulses to the channel decoder. Erasure decoding has been investigated for different modes including the Reed-Solomon decoder only, the Viterbi decoder only, and the concatenated decoding modes. Next, we investigate two cyclostationary noise cancellation techniques that offer better performance but at higher complexity, namely, the temporal-region-based and the frequency-shift-filtering-based (FRESH-filtering-based) techniques. The temporal-region-based technique assumes that the cyclostationary noise is stationary over each of the multiple temporal-regions, and employs a per sub-channel linear minimum mean square (LMMSE) estimator in the frequency domain. The FRESH-filtering-based technique aims to filter the cyclostationary noise in the time domain using FRESH filters that utilize time average MMSE (TA-MMSE) estimator. Furthermore, we extend both cyclostationary noise cancellation techniques to exploit the available spatial dimensions, i.e., multiple receive powerline phases. Moreover, to ensure realistic results, we develop novel methods to generate the cyclostationary noise based on FRESH filtering where the filter coefficients are extracted based on field measurements. For the broadband powerline communication (BB-PLC) in the 1.8-250 MHz frequency band, additional diversity can be achieved through simultaneous transmissions over powerline and unlicensed wireless frequency bands, namely, hybrid PLC-wireless system. However, a hybrid PLC-wireless system faces two main challenges. First, in-home BB-PLC systems suffer from impulsive noise (IN). Second, unlicensed wireless transmissions are subject to narrowband interference (NBI) from other in-band wireless communication systems. Therefore, we propose a new sparsity-aware framework to model and mitigate the joint effects of NBI and IN in hybrid PLC-wireless system. In addition, we explore different cases of the NBI and IN including the block sparse NBI/IN and asynchronous NBI cases. For further mitigation performance enhancements, we investigate a Bayesian LMMSE-based approach. Numerical results show superiority of our proposed joint processing of NBI and IN sparsity-based mitigation techniques versus separate processing. Lastly, we investigate the application of multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) to NB-PLC over medium-voltage (MV) underground networks. We study different MIMO transmission scenarios with different injection configurations utilizing both the cable conductor and sheath phases. Multi-conductor transmission line theory is used to characterize the underground MV NB-PLC channel transfer function. The achievable data rates are evaluated after optimizing the transmit energy allocation across different spatial information streams subject to a power constraint. The achievable data rates for MIMO configurations are shown to be significantly higher compared to singleinput single-output OFDM transmission.Item Fault-proneness Prediction Based on Analysis of Human Aspects in Software Developing Process(2022-05-01T05:00:00.000Z) Lee, Shou-Yu; Wong, W. Eric; Al-Dhahir, Naofal; Sarac, Kamil; Chung, Lawrence; Du, Ding-ZhuNowadays, Quality Assurance and debugging defective software is becoming gradually costly and time-consuming. As a result, applying techniques such as fault-proneness prediction can help in this regard in large scale software system development. The specific fault-proneness prediction usually starts with software metrics, which are introduced to quantify and evaluated different aspects of attributes in software processes and productions. Once we have software metrics as indicators, a fault-proneness prediction model can be built using statistics or machine learning methods. However, since software programs are pure cognitive products of human developers; the flaws in it are caused by erroneous behaviors of human involved. Therefore, we feel the importance of introducing quantitative analysis on the human factors to enhance fault-proneness prediction. With that in mind, we come up with two approaches to achieve more precise prediction: firstly, separating the consistent characteristics of human individuals and evaluate their future and classifying certain working activities, and secondly, interaction during development process that could affect the performance of developers. For the first approach, we proposed a new metric based on historical activities: the Developer Risk Score, which is a performance indicator for the developers’ history of making mistakes during their working period on related software projects. Different from previous approaches, the Developer Risk Score further takes software complexity and the severity of bug into account when evaluating the performance of a developer. Our approach proved that for a software module that more high-risk developers involved more tend to be faultprone, and it is more efficient than other approaches when predicting such fault in software modules. For the second approach, we considered a software network with human aspects. Software network is a graphical model that constructed by several elements in modern software systems to symbolize the patterns of activities between them. To integrate human aspects into the model, we proposed the Composite Developer-Module Network. The network combines developers’ network and software modules’ network. By using network structure categorization comparation on the sub-structures within the network, deeper and indirect dependencies between developers and software modules can be included. Our evaluation proved that the more complex sub-structures of software network, that is, more developers or more function calls include in the sub-structures, can be more correlated to possible bug introduction.Item Hybrid Powerline/Wireless Diversity for Smart Grid Communications(2017-12) Ibrahim, Mostafa; Al-Dhahir, NaofalSmart Grids refer to evolved power grids that can intelligently monitor and control energy flows in order to improve the efficiency and reliability of power delivery. Adding such intelligence throughout the grid requires deployment of a two-way communication network between smart meters at the consumer residential sites, and command and control centers operated by local/regional utility. This communication network enables the utility to collect data about network events which can be leveraged to optimally manage the power grid. Smart Grids are supported by heterogeneous networks that employ both wireless and powerline communication (PLC) technologies, since no single solution fits all scenarios. The channel and noise statistics experienced by powerline and wireless transmissions are independent and of a nonidentical nature. In this dissertation, I propose to exploit the diversity provided by the simultaneous trans- mission of the same information over both powerline and wireless links as a way to enhance the overall system reliability. In particular, I propose efficient techniques to combine the received signals of the NB-PLC and wireless links for both coherent and differential modulation schemes while considering the impulsive nature of the noise on both links. In addition, I derived closed-form expressions for the average bit-error-rate of the proposed combining techniques. Furthermore, I present simulation results to quantify the performance gains achieved by the proposed receive diversity combining techniques compared to conventional combining techniques. In addition, I describe a real-time NB-PLC/wireless testbed proto- type for the proposed combining techniques to demonstrate the enhanced performance over a single link. I considered practical implementation issues such as packet synchronization for both PLC and wireless and correction of the frequency offset due to oscillator mismatch.Item Impact of Passive and Active Security Attacks on MIMO Smart Grid Communications(Institute of Electrical and Electronics Engineers Inc.) El Shafie, Ahmed; Chihaoui, H.; Hamila, R.; Al-Dhahir, Naofal; Gastli, A.; Ben-Brahim, L.; 0000-0002-7315-8242 (El Shafie, A); El Shafie, Ahmed; Al-Dhahir, NaofalWe consider multiple source nodes (consumers) communicating wirelessly their energy demands to the meter data-management system (MDMS) over the subarea gateway(s). We quantify the impact of passive and active security attacks on the reliability and security of the wireless communications system and the energy-demand estimation error cost in dollars incurred by the utility. We adopt a multiple-input multiple-output (MIMO) multiantenna-eavesdropper wiretap channel model. To secure the MIMO wireless communication system, the legitimate nodes generate artificial noise signals to mitigate the effect of the passive eavesdropping security attacks. Furthermore, we propose a redundant gateway design where multiple gateways are assumed to coexist in each subarea to forward the consumers’ energy-demand messages. We quantify the redundant designs impact on the communication reliability between the consumers and the MDMS and on the energy-demand estimation error cost.Item Joint Blind Identification of the Number of Transmit Antennas and MIMO Schemes Using Gerschgorin Radii and FNN(Institute of Electrical Electronics Engineers Inc, 2018-11-13) Gao, Mingjun; Li, Yongzhao; Dobre, Octavia A.; Al-Dhahir, Naofal; 113149196515374792028 (Al-Dhahir, N); Al-Dhahir, NaofalBlind enumeration of the number of transmit antennas and blind identification of multiple-input multiple-output (MIMO) schemes are two pivotal steps in MIMO signal identification for both military and commercial applications. Conventional approaches treat them as two independent problems, namely the source number enumeration and the presence detection of space-time redundancy. In this paper, we develop a joint blind identification algorithm to determine the number of transmit antennas and MIMO schemes simultaneously. By restructuring the received signals, we derive three subspace-rank features based on the signal subspace-rank to determine the number of transmit antennas and identify space-time redundancy. Then, a Gerschgorin radii-based method and a feed-forward neural network are employed to calculate these three features, and a minimal weighted norm-1 distance metric is utilized for decision making. In particular, our approach can identify additional MIMO schemes, which most previous works have not considered, and is compatible with both single-carrier and orthogonal frequency division multiplexing (OFDM) systems. The simulation results verify the viability of our proposed approach for single-carrier and OFDM systems and demonstrate its favorable identification performance for a short observation period with acceptable complexity.Item Joint Frame Synchronization and Channel Estimation: Sparse Recovery Approach and USRP Implementation(Institute of Electrical and Electronics Engineers Inc., 2019-03-25) Ozdemir, O.; Anjinappa, C. K.; Hamila, R.; Al-Dhahir, Naofal; Guvenc, I.; 113149196515374792028 (Al-Dhahir, N); Al-Dhahir, NaofalCorrelation-based techniques used for frame synchronization can suffer significant performance degradation over multi-path frequency-selective channels. In this paper, we propose a joint frame synchronization and channel estimation (JFSCE) framework as a remedy to this problem. This framework, however, increases the size of the resulting combined channel vector which should capture both the channel impulse response vector and the frame boundary offset and, therefore, its estimation becomes more challenging. On the other hand, because the combined channel vector is sparse, sparse channel estimation methods can be applied. We propose several JFSCE methods using popular sparse signal recovery algorithms which exploit the sparsity of the combined channel vector. Subsequently, the sparse channel vector estimate is used to design a sparse equalizer. Our simulation results and experimental measurements using software defined radios show that in some scenarios our proposed method improves the overall system performance significantly, in terms of the mean square error between the transmitted and the equalized symbols compared to the conventional method. © 2013 IEEE.Item Machine-Learning-Based Parallel Genetic Algorithms for Multi-Objective Optimization in Ultra-Reliable Low-Latency WSNs(Institute of Electrical Electronics Engineers Inc, 2018-12-10) Chang, Yuchao; Yuan, Xiaobing; Li, Baoqing; Niyato, Dusit; Al-Dhahir, Naofal; Al-Dhahir, NaofalDifferent from conventional wireless sensor networks (WSNs), ultra-reliable and low-latency WSNs (uRLLWSNs), being an important application of 5G networks, must meet more stringent performance requirements. In this paper, we propose a novel algorithm to improve uRLLWSNs' performance by applying machine learning techniques and genetic algorithms. Using the K -means clustering algorithm to construct a 2-tier network topology, the proposed algorithm designs the fetal dataset, denoted by the population, and develops a clustering method of energy conversion to prevent overloaded cluster heads. A multi-objective optimization model is formulated to simultaneously satisfy multiple optimization objectives including the longest network lifetime and the highest network connectivity and reliability. Under this model, the principal component analysis algorithm is adopted to eliminate the various optimization objectives' dependencies and rank their importance levels. Considering the NP-hardness of wireless network scheduling, the genetic algorithm is used to identify the optimal chromosome for designing a near-optimal clustering network topology. Moreover, we prove the convergence of the proposed algorithm both locally and globally. Simulation results are presented to demonstrate the viability of the proposed algorithm compared to stateof-the-art algorithms at an acceptable computational complexity.Item Modeling of Driver Attention in Real World Scenarios Using Probabilistic Salient Maps(2021-05-01T05:00:00.000Z) Jha, Sumit; Busso-Recabarren, Carlos A.; Schweitzer, Haim; Hansen, John H. L.; Al-Dhahir, Naofal; Kehtarnavaz, NasserMonitoring driver behavior can play a vital role in combating various road hazards. The majority of accidents can be avoided if the driver gets an adequate warning few seconds prior to the event. Monitoring driver actions can provide insights about the driver’s intent, attention and vigilance. This information can be helpful in designing smart interfaces in the vehicle that provides necessary warning to the driver or take control when necessary. Visual attention is one of the most important factors in driver monitoring, since most driving maneuvers strongly rely on vision. An inattentive driver may lack awareness about the factors in the environment such as pedestrians, other vehicles and trac changes. Visual attention of a driver can be monitored by either tracking the driver’s head pose or by tracking their eye movement. While advancement in computer vision have inspired various studies that can eciently track head and eye movement from the face, these models face challenges in a naturalistic driving environment because of the changes in illumination, high head rotation and occlusions. This dissertation discusses various methods to predict the driver’s visual attention using probabilistic visual maps. We collect a large scale multimodal dataset where 59 drivers are recording when performing various secondary activities while driving, to capture the vi diversity of data in a naturalistic driving environment. The subjects fixate their gaze at predetermined location which help us establish a correspondence between the driver’s face and their gaze target. Using this dataset, we have performed various analysis that guided our proposed models to predict the driver’s visual attention. We establish that while the head pose of the driver has a strong correlation with the driver’s visual attention the relationship is not one to one. Hence, it is not feasible to design models that can predict a single value of driver’s gaze from the head pose. Therefore, we take a probabilistic approach where the driver’s visual attention is predicted as a probabilistic visual map whose value at each point depend on the probability that the driver is looking at a certain direction. First, we design parametric regression models that provide a Gaussian distribution of the driver’s gaze from the driver’s head pose. The model is heteroscedastic based on Gaussian Process Regression (GPR) which learns the distribution of gaze as a gaussian random process which is function of the head pose in 6 degrees of freedom. Next, we propose deep networks with convolutional and upsampling layers that performs classification on a 2D grid to obtain visual map. The model is non-parametric and learns the distribution from the data. We propose two di↵erent models. The first model takes the head pose of the driver as the input and passes it through a fully connected layer followed by convolution and upsampling to predict the visual attention at di↵erent resolutions. The second model takes an image of the eye patch as an input and passes it through multiple layers of convolution and maxpooling to obtain a low dimensional representation of the visual attention. Consecutively, this low dimensional representation is passed through upsampling and convolution layers to obtain a high dimension representation of visual attention. In our final approach, We design a fusion model that integrates the information from the driver’s head pose as well as their eye appearance to predict a visual attention map at multiple resolution. This model follows an encoder-decoder architecture with two encoders, one each for the head pose and the gaze and a decoder that concatenates the information from both the head pose and gaze to obtain the final visual map. We project the model prediction onto the road and evaluate it on data when the subject looks at the landmarks on the road.Item Multi-antenna Millimeter-wave Radars: Algorithms and Performance Analysis(May 2023) Baral, Ashwin Bhobani 1989-; Torlak, Murat; Fox, Emily; Al-Dhahir, Naofal; Henderson, Rashaunda; Fonseka, John P.Millimeter-wave (mmWave) radars with multi-antenna systems have become popular in numerous automotive and industrial sensing applications. For these applications, target estimation is a crucial function. However, accurately estimating a target’s parameters becomes challenging due to either the limitations of the system parameters or the presence of clutter and interference. To address these challenges, this dissertation focuses on developing robust signal processing algorithms and studying their performance analyses. Direction of arrival (DOA) estimation of a target is a fundamental problem for radar sensors. Super-resolution algorithms like MUltiple SIgnal Classification (MUSIC) have been proposed for better DOA estimation performance compared to classical approaches. MUSIC relies on accurate partitioning of the eigenvectors of the spatial correlation matrix between the signal eigenvectors (i.e., signal subspace) and noise eigenvectors (i.e., noise subspace). In the first part of this dissertation, we present a novel statistical framework for analyzing the resolution performance of the MUSIC algorithm in resolving two closely spaced targets according to the number of noise eigenvectors used in the spectrum evaluation. Using this framework, we derive an analytical expression for the probability of resolution of the MUSIC algorithm. Multiple-input multiple-output (MIMO) radar achieves high angular resolution at the expense of certain limitations of its systems realized using different multiplexing techniques such as time division multiplexing (TDM), frequency division multiplexing (FDM), and code division multiplexing (CDM). Of all these multiplexing techniques, TDM is the appropriate choice for automotive applications due to its low hardware complexity. The latter part of this dissertation focuses on the Doppler ambiguity problem associated with a standard TDM MIMO radar. In a standard TDM MIMO radar, transmitters are activated sequentially according to their natural spatial order. The drawback of the standard TDM MIMO approach is the coupling of velocity and DOA information of the targets. The coupling reduces the unambiguous estimation interval of the Doppler frequencies of the targets by the number of transmit antennas being multiplexed. To solve this problem, we propose a novel cost function for jointly estimating the Doppler frequency and DOA of the targets. In the last part of this dissertation, we address the mutual interference problem between automotive radars. With the increasing demand for radar sensors in automotive applications, this mutual interference between them is inevitable due to their unregulated transmissions. To reliably estimate the target’s parameters, this interference needs to be detected and mitigated. To mitigate automotive interference, we propose a two-stage signal decomposition approach.Item Novel bidirectional single-phase single-stage isolated AC-DC converter with PFC for charging of electric vehicles(2016-12) Singh, Anant Kumar; Rajashekara, Kaushik; Al-Dhahir, NaofalThis thesis proposes a novel bidirectional single-phase single-stage AC-DC converter for Electric Vehicle (EV) charging application. AC side of the proposed converter consists of a current-fed half bridge converter. This is connected to the full-bridge converter on secondary side of a high-frequency (HF) transformer. Power Factor Correction (PFC) can be attained by regulating the current at the input of the ac side. In addition to that, the proposed converter achieves Zero Current Switching (ZCS) of primary side switches and zero current turn-on for secondary side devices throughout the operation without any additional components. Furthermore, a novel modulation technique and control algorithm is implemented. This ensures soft-switching throughout the operation range of the converter during bidirectional power flow. Design equations are derived to help suitable selection of components for a given specification. The proposed converter is designed for 1.5KW capacity for EV charging application. The simulation and experimental results are presented.