Automated extraction of water bodies from NIR and RGB aerial imagery in northern Alaska using supervised and unsupervised machine learning techniques

Thawing and freezing of permafrost ground are affected by various reasons: air temperature, vegetation, snow accumulation, subsurface physical properties, and moisture. Due to the rising of air temperature, the permafrost temperature and the thermokarst activity increase. Thermokarst instability causes an imbalance for the hydrology system, topography, soils, sediment and nutrient cycle to lakes and streams. Hence the lakes and ponds are ubiquitous in permafrost region. The plants and animals fulfil their nutrient needs from water in the environment. Other animals acquire their needs from the plants and animals that they consume. Therefore the influence of degradation of lakes and ponds strongly affect biogeochemical cycles. This research aims to implement an automated workflow to map the water bodies caused by permafrost thawing. The scientific challenge is to test the machine learning techniques adaptability to assist the observation and mapping of the water bodies using aerial imagery. The study area is mainly located in northern Alaska and consists of five different locations: Ikpikpuk, Teschekpuk Central, Teshekpuk East, Tesheckpuk West, Meade East, and Meade West. To estimate the degradation of the high centred polygons distribution and potential degradation of ice wedges, I mapped the polygonal terrain and ice-wedge melt ponds using areal photogrammetry data of NIR and RGB bands captured by Thaw Trend Air 2019 flight campaign. The techniques used are unsupervised K-mean classification, supervised segment mean shift, and supervised random forest classification to model the water polygons from airborne photogrammetry. There are two phases to perform the machine learning classification; the first step is to test the accuracy of each technique and get to a conclusion about the most adapted method. The second is to prepare the Orthomosaic data, run the chosen workflow, and visualize the final results. The morphology filter with opening option application and clean boundary filters are practical before classification as they sharpen the image features. The conclusion is to use the Random forest classification as it was helpful in all NIR Orthomosaics; however, the RGB images required downsampling to provide adequate accuracy

Efficient and Virtualized Scheduling for OFDM-Based High Mobility Wireless Communications Objects

Services providers (SPs) in the radio platform technology standard long term evolution (LTE) systems are enduring many challenges in order to accommodate the rapid expansion of mobile data usage. The modern technologies demonstrate new challenges to SPs, for example, reducing the cost of the capital and operating expenditures while supporting high data throughput per customer, extending battery life-per-charge of the cell phone devices, and supporting high mobility communications with fast and seamless handover (HO) networking architecture. In this thesis, a variety of optimized techniques aimed at providing innovative solutions for such challenges are explored. The thesis is divided into three parts. The first part outlines the benefits and challenges of deploying virtualized resource sharing concept. Wherein, SPs achieving a different schedulers policy are sharing evolved network B, allowing SPs to customize their efforts and provide service requirements; as a promising solution for reducing operational and capital expenditures, leading to potential energy savings, and supporting higher peak rates. The second part, formulates the optimized power allocation problem in a virtualized scheme in LTE uplink systems, aiming to extend the mobile devices’ battery utilization time per charge. While, the third part extrapolates a proposed hybrid-HO (HY-HO) technique, that can enhance the system performance in terms of latency and HO reliability at cell boundary for high mobility objects (up to 350 km/hr; wherein, HO will occur more frequent). The main contributions of this thesis are in designing optimal binary integer programmingbased and suboptimal heuristic (with complexity reduction) scheduling algorithms subject to exclusive and contiguous allocation, maximum transmission power, and rate constraints. Moreover, designing the HY-HO based on the combination of soft and hard HO was able to enhance the system performance in term of latency, interruption time and reliability during HO. The results prove that the proposed solutions effectively contribute in addressing the challenges caused by the demand for high data rates and power transmission in mobile networks especially in virtualized resources sharing scenarios that can support high data rates with improving quality of services (QoSs)

Wave Interaction With Epsilon-znd-Mu-Near-Zero (emnz) Platforms and Nonreciprocal Metastructures

The concept of metamaterials has offered platforms for unconventional tailoring and manipulation of the light-matter interaction. In this dissertation, we explore several concepts and designs within this scope. We investigate some of the electromagnetic characteristics of the concept of “static optics”, i.e., wave interaction with structures in which both the relative effective permittivity and permeability attain near-zero values at a given operating frequency and thus the spatial distributions of the electric and magnetic fields exhibit curl-free features, while the fields are temporally dynamic. Using such structures, one might in principle ‘open up’ and ‘stretch’ the space, and have regions behaving electromagnetically as ‘single points’ despite being electrically large. We study some of the wave-matter interaction in these platforms and suggest possible designs for implementation of such structures in different frequency regimes and experimentally verify our findings in the microwave regime. Another research direction that is explored in this dissertation is the development of some nonreciprocal metaplatforms. We investigate theoretically an approach through which one-way electromagnetic wave flow can be achieved using properly designed nonlinearity combined with structural asymmetry. The approach is rather general and applicable for any desired frequency regime and opens doors for high performance “electromagnetic diodes” and nonreciprocal metasurfaces and metastructures. We also theoretically study the usage of time-dependent materials in achieving wave flow isolation within plasmonic waveguides environments. We also provide physical remarks on our various findings

Secure Control of Cyber-Physical Systems

Cyber-Physical Systems (CPS) are smart co-engineered interacting networks of physical and computational components. They refer to a large class of technologies and infrastructure in almost all life aspects including, for example, smart grids, autonomous vehicles, Internet of Things (IoT), advanced medical devices, and water supply systems. The development of CPS aims to improve the capabilities of traditional engineering systems by introducing advanced computational capacity and communications among system entities. On the other hand, the adoption of such technologies introduces a threat and exposes the system to cyber-attacks. Given the unique properties of CPSs, i.e. physically interacting with its environment, malicious parties might be interested in exploiting the physical properties of the system in the form of a cyber-physical attack. In a large class of CPSs, the physical systems are controlled using a feedback control loop. In this thesis, we investigate, from many angles, how CPSs' control systems can be prone to cyber-physical attacks and how to defend them against such attacks using arguments drawn from control theory. In our first contribution, by considering Smart Grid applications, we address the problem of designing a Denial of Service (DoS)-resilient controller for recovering the system's transient stability robustly. We propose a Model Predictive Control (MPC) controller based on the set-theoretic (ST) arguments, which is capable of dealing with both model uncertainties, actuator limitations, and DoS. Unlike traditional MPC solutions, the proposed controller has the capability of moving most of the required computations into an offline phase. The online phase requires the solution of a quadratic programming problem, which can be efficiently solved in real-time. Then, stemming from the same ST based MPC controller idea, we propose a novel physical watermarking technique for the active detection of replay attacks in CPSs. The proposed strategy exploits the ST-MPC paradigm to design control inputs that, whenever needed, can be safely and continuously applied to the system for an apriori known number of steps. Such a control scheme enables the design of a physical watermarked control signal. We prove that, in the attack-free case, the generators' transient stability is achieved for all admissible watermarking signals and that the closed-loop system enjoys uniformly ultimately bounded stability. In our second contribution, we address the attacker's ability to collect useful information about the control system in the reconnaissance phase of a cyber-physical attack. By using existing system identification tools, an attacker who has access to the control loop can identify the dynamics of the underlying control system. We develop a decoy-based moving target defense mechanism by leveraging an auxiliary set of virtual state-based decoy systems. Simulation results show that the provided solution degrades the attacker's ability to identify the underlying state-space model of the considered system from the intercepted control inputs and sensor measurements. It also does not impose any penalty on the control performance of the underlying system. Finally, in our third contribution, we introduce a covert channel technique, enabling a compromised networked controller to leak information to an eavesdropper who has access to the measurement channel. We show that this can be achieved without establishing any additional explicit communication channels by properly altering the control logic and exploiting robust reachability arguments. A dual-mode receding horizon MPC strategy is used as an illustrative example to show how such an undetectable covert channel can be established

Market Orientation in the Sudanese Manufacturing Firms (An Empirical Study)

The researcher used the stratified random sampling method. A total number of 91 companies out of 114companies participated (with a response rate of 80%).To analyze the data, the researcher used descriptive statistics including methods like tables, frequencies, and averages to assess the were tested using sophisticated methods like multiple regression analysis (stepwise regression analysis) and multivariate analysis of variance (MANOVA). The findings of this study confirm the applicability of the empirical model in Sudan. Ten antecedents/factors determine the market orientation of the manufacturing companies in Sudan that including, top management emphasis, management training, risk aversion, formalization, market based reward system, interdepartmental conflict, interdepartmental connectedness, competition, market turbulence, and general economy. Only three antecedents, formal marketing education, centralization and technological turbulence are not effective in determining the MO and its components in Sudan.  The findings also confirm that the MO significantly contributes to the achievement of superior performance, both the economic and non-economic. The most important recommendations of this research indicate that the model of MO developed for Sudan makes a significant contribution to the MO literature in that country particularly, to the manufacturing companies. The Sudanese organizations can use this model as a guide for their businesses. This model confirms that both superior economic and non-economic performance of business are determined by the level of MO of an organization. The empirical findings of this investigation have policy implications for large companies in developing countries in general and Sudan in particular. It is expected that this model will be used as a starting point for further researches and to be tested in other countries in the world, both developed and developing

Sublytic Terminal Complement Components Induce Eryptosis in Autoimmune Haemolytic Anaemia Related to IgM Autoantibodies

BACKGROUND/AIMS: Eryptosis, the suicidal death of red blood cells (RBCs), is characterized by phosphatidylserine (PS) exposure at the cell surface. It can be catalysed by a variety of abnormal conditions and diseases. Until now, the many questions surrounding the physiology and pathophysiology of eryptosis have not been sufficiently answered. Recently, we demonstrated IgM and IgA autoantibodies (aab) to induce PS exposure on circulating RBCs of patients with autoimmune haemolytic anaemia (AIHA). However, it remained unclear how these aab lead to eryptosis. METHODS: Serum and plasma samples from patients with clinically relevant AIHA of cold type were used to induce eryptosis in O RBCs. Serum containing fresh complement from healthy donors, antibodies to complement component, and complement factor depleted sera were added to examine the influence of the complement on PS-exposure. RBC bound annexin V PE were analysed by flow cytometry. RESULTS: Eryptosis related to IgM aab was found to be dependent on complement activation and could be effectively inhibited by EDTA, serum heat inactivation and anti-C5. PS exposure increased with sequential activation of the sublytic terminal complement components C5b6, C5b-7 and was most significant at the C5b-8 stage. A decrease was observed following the formation of the lytic membrane attack complex C5b-9, either because of lysis of eryptotic RBCs or because of inhibition of eryptosis by C9. CONCLUSION: Our findings reflect new aspects on RBC destruction in AIHA as well the impact of the terminal complement complexes on the RBC membrane. The striking differences to nucleated cell apoptosis may even have physiological meaning of RBC acting as a buffer of the complement system

Aortic Root abscess repair in a patient with previous mechanical aortic valve replacement, mitral regurgitation and ischemic heart disease

We present a complex case of aortic root abscess and fistula formation between the aorta and left ventricle, 20 years after aortic valve replacement with a mechanical prosthesis. The patient had concomitant severe rheumatic mitral valve regurgitation, significant left anterior descending and left circumflex coronary artery stenosis, with non-viable anterior wall and apex. In addition to replacing the mitral valve with a mechanical prosthesis, our decision was to treat the abscess and patch the fistula, while keeping the well functioning mechanical aortic valve to use its cuff to fix the patch. We have chosen to delay revascularization of the marginal branches with DES till after surgery to avoid extensive dissection under clopidogrel. We think that those decisions simplified the operative procedure and were essential to reach the positive outcome we had in this case

Enabling Efficient Communications Over Millimeter Wave Massive MIMO Channels Using Hybrid Beamforming

The use of massive multiple-input multiple-output (MIMO) over millimeter wave (mmWave) channels is the new frontier for fulfilling the exigent requirements of next-generation wireless systems and solving the wireless network impending crunch. Massive MIMO systems and mmWave channels offer larger numbers of antennas, higher carrier frequencies, and wider signaling bandwidths. Unleashing the full potentials of these tremendous degrees of freedom (dimensions) hinges on the practical deployment of those technologies. Hybrid analog and digital beamforming is considered as a stepping-stone to the practical deployment of mmWave massive MIMO systems since it significantly reduces their operating and implementation costs, energy consumption, and system design complexity. The prevalence of adopting mmWave and massive MIMO technologies in next-generation wireless systems necessitates developing agile and cost-efficient hybrid beamforming solutions that match the various use-cases of these systems. In this thesis, we propose hybrid precoding and combining solutions that are tailored to the needs of these specific cases and account for the main limitations of hybrid processing. The proposed solutions leverage the sparsity and spatial correlation of mmWave massive MIMO channels to reduce the feedback overhead and computational complexity of hybrid processing. Real-time use-cases of next-generation wireless communication, including connected cars, virtual-reality/augmented-reality, and high definition video transmission, require high-capacity and low-latency wireless transmission. On the physical layer level, this entails adopting near capacity-achieving transmission schemes with very low computational delay. Motivated by this, we propose low-complexity hybrid precoding and combining schemes for massive MIMO systems with partially and fully-connected antenna array structures. Leveraging the disparity in the dimensionality of the analog and the digital processing matrices, we develop a two-stage channel diagonalization design approach in order to reduce the computational complexity of the hybrid precoding and combining while maintaining high spectral efficiency. Particularly, the analog processing stage is designed to maximize the antenna array gain in order to avoid performing computationally intensive operations such as matrix inversion and singular value decomposition in high dimensions. On the other hand, the low-dimensional digital processing stage is designed to maximize the spectral efficiency of the systems. Computational complexity analysis shows that the proposed schemes offer significant savings compared to prior works where asymptotic computational complexity reductions ranging between $80\%$ and $98\%$. Simulation results validate that the spectral efficiency of the proposed schemes is near-optimal where in certain scenarios the signal-to-noise-ratio (SNR) gap to the optimal fully-digital spectral efficiency is less than $1$ dB. On the other hand, integrating mmWave and massive MIMO into the cellular use-cases requires adopting hybrid beamforming schemes that utilize limited channel state information at the transmitter (CSIT) in order to adapt the transmitted signals to the current channel. This is so mainly because obtaining perfect CSIT in frequency division duplexing (FDD) architecture, which dominates the cellular systems, poses serious concerns due to its large training and excessive feedback overhead. Motivated by this, we develop low-overhead hybrid precoding algorithms for selecting the baseband digital and radio frequency (RF) analog precoders from statistically skewed DFT-based codebooks. The proposed algorithms aim at maximizing the spectral efficiency based on minimizing the chordal distance between the optimal unconstrained precoder and the hybrid beamformer and maximizing the signal to interference noise ratio for the single-user and multi-user cases, respectively. Mathematical analysis shows that the proposed algorithms are asymptotically optimal as the number of transmit antennas goes to infinity and the mmWave channel has a limited number of paths. Moreover, it shows that the performance gap between the lower and upper bounds depends heavily on how many DFT columns are aligned to the largest eigenvectors of the transmit antenna array response of the mmWave channel or equivalently the transmit channel covariance matrix when only the statistical channel knowledge is available at the transmitter. Further, we verify the performance of the proposed algorithms numerically where the obtained results illustrate that the spectral efficiency of the proposed algorithms can approach that of the optimal precoder in certain scenarios. Furthermore, these results illustrate that the proposed hybrid precoding schemes have superior spectral efficiency performance while requiring lower (or at most comparable) channel feedback overhead in comparison with the prior art