Miracle-dz/UniFAR-FTUs huggingface dataset:
Change detection using distance-based algorithms between synthetic aperture radar polarimetric decompositions

ABSTRACT The present study introduces distance based change detection (CD) algorithms in polarimetric synthetic aperture radar (PolSAR) data. PolSAR images, due to interactions between electromagnetic waves and target and because of the high spatial resolution, can be used to study changes in the Earth’s surface. The purpose of this paper is to use features extracted from the fully-polarimetry imaging radar that involved Yamaguchi four-component and H/A/α decomposition based on the distance between the vectors of features for CD. We first extract features from polarimetric decompositions of multi-looked covariance (or coherency) matrix data. We then use two well-known distance measures namely Canberra and Euclidean distances for measuring the similarity between the vectors of polarimetric decompositions at different times. Assessment of incorporated methods is performed using different criteria, such as overall accuracy, area under the receiver operating characteristic curve, and false alarms rate. The results of the experiments show that Canberra distance has better performance with high overall accuracy and low false alarm rate than Euclidean distance and other compared algorithms to detect changes.
THE ROLE OF QUANTITATIVE METHODS IN SUPPLY CHAIN MANAGEMENT Introduction

The supply chain of both manufacturing and commercial enterprises comprises a highly distributed environment, in which complex processes evolve in a network of companies (see Figure 1). Such processes include materials procurement and storage, production of intermediate and final products, warehousing, sales, customer service, and distribution. The role of the supply chain in a company’s competitiveness is critical, since the supply chain affects directly customer satisfaction, inventory and distribution costs, and responsiveness to the ever changing markets. This role becomes more critical in today’s distributed manufacturing environment, in which companies focus on core competencies and outsource supportive tasks, thus creating large supply networks. Within this environment, there are strong interactions of multiple entities, processes, and data. For each process in isolation, it is usually feasible to identify those decisions that are locally optimal, especially in a deterministic setting. However, decision making in supply chain systems should consider intrinsic uncertainties, while coordinating the interests and goals of the multitude of processes involved. Operational Research-based Management Science (OR/MS) methods as well as Computational Intelligence (i.e. nature inspired techniques), offer effective techniques for modelling, analyzing and
Thick Film Accelerometers in LTCC Technology—Design Optimization, Fabrication, and Characterization

Diaphragms and beams for force and pressure sensors, e.g., are state of the art in mechanical elements of MEMS in LTCC technology. These elements sustain small strains and small deformations under load. A number of sensor and actuator applications, however, require movable elements that allow higher deformations while the local strains are still low. Springs, accelerometers, actuators, positioners, and valves are examples of such applications. For an accelerometer we developed an approach fabricate leaf springs, integrated into the LTCC technology. The working principle of the accelerometer is based on a seismic mass disposed on two parallel leaf springs that carry piezoresistors connected such that they form a measuring bridge. In the first design optimization step, we used an FEA model for finding an optimized design meeting our sensitivity requirements, inclusiding resonance frequency. In the second step, we made a tolerance analysis that calculates the probability distributions of functional variables.
Timing and crosstalk driven area routing

We present a timing and crosstalk driven router for the chip assembly task that is applied between global and detailed routing. Our new approach aims to process the crosstalk and timing constraints by ordering nets and tuning wire spacing in a quantitative way. Our graph-based optimizer preroutes wires on the global routing grids incrementally in two stages-net order assignment and space relaxation. The timing delay of each critical path is calculated taking into account interconnect coupling capacitance. The objective is to reduce the delays of critical nets with negative timing slack values, by tuning net ordering and adding extra wire spacing. It shows a remarkable 8.4-25% delay reduction for MCNC benchmarks for wire geometric ratio=2.0, against a 33% delay reduction if interconnect interference disappear.
A bi-illuminant dichromatic reflection model for understanding images

This paper presents a new model for understanding the appearance of objects that exhibit both body and surface reflection under realistic illumination. Specifically, the model represents the appearance of surfaces that interact with a dominant illuminant and a non-negligible ambient illuminant that may have different spectral power distributions. Real illumination environments usually have an ambient illuminant, and the current dynamic range of consumer cameras is sufficient to capture significant information in shadows. The bi-illuminant dichromatic reflection model explains numerous empirical findings in the literature and has implications for commonly used chromaticity spaces that claim to be illumination invariant but are not in many natural situations. One outcome of the model is the first 2-D chromaticity space for an RGB image that is robust to illumination change given dominant and ambient illuminants with different spectral power distributions.
Transimpedance amplifier for very high sensitivity current detection over 5MHz bandwidth

The paper presents a transimpedance amplifier made on standard 0.35 mum CMOS technology specifically conceived to measure the impedance of low-conductivity nano-bio devices. The circuit combines a bandwidth of 5 MHz with an extremely low noise of 3 fA/sqrt (Hz) by using an integrator-differentiator scheme. An innovative feedback network continuously discharges the integrator capacitance to ensure an unlimited measuring time irrespective of the do input current up to 20 nA. An highly linear 300 GOmega active resistance, used in the feedback network to extend the lower limit of the bandwidth down to 100 Hz, will be also described.
Adding haptic feedback to mobile tv

With the abundance of large-screen displays, mobile device users currently have little motivation to stream video content and TV broadcasts to their device - the desire to watch content 'on the move' does not currently outweigh the necessity of viewing this content on a miniaturised screen. However, the value and appeal of mobile TV broadcasts can be increased by the addition of a haptic-feedback channel to supplement the traditional video and audio streams. This paper discusses the development of mobile haptic TV systems. It describes the design constraints for these systems and presents one concept implementation, UltraTV. UltraTV is a mobile device that provides mid-air, multi-point, back-of-device ultrasonic haptic feedback to enhance the mobile TV experience (see Figure 1). The paper concludes with a look at avenues for further exploration within the realm of mobile haptic TV.
Decision Tree Rule Reduction Using Linear Classifiers in Multilayer Perceptron

It has been shown that a neural network is better than a direct application of induction trees in modeling complex relations of input attributes in sample data. We propose that concise rules be extracted to support data with input variable relations over continuous-valued attributes. Those relations as a set of linear classifiers can be obtained from neural network modeling based on back-propagation. A linear classifier is derived from a linear combination of input attributes and neuron weights in the first hidden layer of neural networks. It is shown in this paper that when we use a decision tree over linear classifiers extracted from a multilayer perceptron, the number of rules can be reduced. We have tested this method over several data sets to compare it with decision tree results.
Discrete-time spectrum occupancy model based on Markov Chain and duty cycle models

This paper presents an empirical time-dimension model of spectrum use in the context of dynamic spectrum access. Concretely, a discrete-time two-state Markov chain with novel duty cycle models is proposed as an adequate mean to accurately describe spectrum occupancy in the time domain. The validity and accuracy of the proposed model is assessed and corroborated with extensive empirical data from a multi-band spectrum measurement campaign. The obtained results demonstrate that the proposed approach is able to capture and reproduce with significant accuracy the statistical properties of spectrum use observed in real channels of various technologies.
Intracellular Recognition of Lipopolysaccharide by Toll-like Receptor 4 in Intestinal Epithelial Cells

Toll-like receptor (TLR)4 has recently been shown to reside in the Golgi apparatus of intestinal crypt epithelial m-ICcl2 cells, colocalizing with internalized lipopolysaccharide (LPS). Here we demonstrate that disruption of the integrity of the Golgi apparatus significantly reduced LPS-mediated nuclear factor κB activation. Also, the TLR4 adaptor protein MyD88 and the serine/threonine kinase IRAK-1 were rapidly recruited to the Golgi apparatus upon stimulation. LPS-mediated activation required lipid raft formation and intact clathrin-dependent internalization. In contrast to macrophages, prevention of ligand internalization by use of LPS-coated beads significantly impaired recognition by epithelial cells. The localization of TLR4 to the Golgi apparatus was abrogated by expression of a genetically modified form of the TLR4 binding chaperone gp96. Thus, our data provide evidence that in contrast to the situation in macrophages, LPS recognition in intestinal epithelial cells may occur in the Golgi apparatus and require LPS internalization.
Analytical model of thin-body InGaAs-on-InP MOSFET low-field electron mobility for integration in TCAD tools

A simple analytical model of thin-body In<sub>0.53</sub>Ga<sub>0.47</sub>As-on-InP MOSFET low-field electron mobility suitable for integration in Technology-CAD (TCAD) tools is presented. Phonon, Coulomb and surface roughness scattering are accounted for. In order to characterize the phonon scattering contribution, an expression for the device effective thickness is derived from 1-D Schroedinger-Poisson simulations. The model is validated through comparison with experimental C<sub>G</sub>-V<sub>gs</sub> and I<sub>d</sub>-V<sub>gs</sub> curves collected on transistors with body thicknesses down to 5 nm.
Cholesterol reduction impairs exocytosis of synaptic vesicles

Cholesterol and sphingolipids are abundant in neuronal membranes, where they help the organisation of the membrane microdomains involved in major roles such as axonal and dendritic growth, and synapse and spine stability. The aim of this study was to analyse their roles in presynaptic physiology. We first confirmed the presence of proteins of the exocytic machinery (SNARES and Cav2.1 channels) in the lipid microdomains of cultured neurons, and then incubated the neurons with fumonisin B (an inhibitor of sphingolipid synthesis), or with mevastatin or zaragozic acid (two compounds that affect the synthesis of cholesterol by inhibiting HMG-CoA reductase or squalene synthase). The results demonstrate that fumonisin B and zaragozic acid efficiently decrease sphingolipid and cholesterol levels without greatly affecting the viability of neurons or the expression of synaptic proteins. Electron microscopy showed that the morphology and number of synaptic vesicles in the presynaptic boutons of cholesterol-depleted neurons were similar to those observed in control neurons. Zaragozic acid (but not fumonisin B) treatment impaired synaptic vesicle uptake of the lipophilic dye FM1-43 and an antibody directed against the luminal epitope of synaptotagmin-1, effects that depended on the reduction in cholesterol because they were reversed by cholesterol reloading. The time-lapse confocal imaging of neurons transfected with ecliptic SynaptopHluorin showed that cholesterol depletion affects the post-depolarisation increase in fluorescence intensity. Taken together, these findings show that reduced cholesterol levels impair synaptic vesicle exocytosis in cultured neurons.
Carbon nanotubes/low density polyethylene composite films for strain sensing

It is a pleasure to thank those who made this thesis possible. I especially want to thank my advisers Dr. Amal Esawi and Dr. Mustafa Arafa for their help and guidance during the research. Special thanks to Eng. Rami Wasfi, Eng. Ehab Salama, Eng. Hanady Hussein, Eng. Ahmed Abdel Gawad, and Mr. Essam Aziz for their help with different samples testing and analyses at the different stages of this research. Also, thanks are due to Dr. Hosny Omar, Professor at the Physics department, for his helpful advice and fruitful discussions. I would like as well to thank the Science and Technology Research Center (STRC) for the financial help and support.
Mobile Geographic Information Services Challenges in Pervasive Computing Environment

Mobile Geographic Information Service concept and the necessaries to begin related research are proposed firstly. Then the discussion about the innovational research challenges of Mobile Geographic Information Services in pervasive computing environment are introduced, including Ubiquitous Positioning technologies, context modeling and adapting, next-generation GIS. The need and importance of many GIScience research topics see new challenges with Mobile Geographic Information Services. More cross-disciplinary endeavors are anticipated in the future particularly at the intersection of pervasive computing technology, geospatial technology, and increasing awareness of social impacts of the technologies.
Transform Domain Fingerprint Identification Based on DTCWT

The physiological biometric characteristics are better compared to behavioral biometric identification of human beings to identify a person. In this paper, we propose Transform Domain Fingerprint Identification Based on DTCWT. The original Fingerprint is cropped and resized to suitable dimension to apply DTCWT. The DTCWT is applied on Fingerprint to generate coefficient which form features. The performance analysis is discussed with different levels of DTCWT and also with different sizes of Fingerprint database. It is observed that the recognition rate is better in the case of level 7 compared to other levels of DTCWT. Keywords-Fingerprint; DTCWT; Euclidean Distance; Preprocessing
The Lyman-Alpha Forest in the Cold Dark Matter Model

Cosmological simulations with gas provide a detailed description of the intergalactic medium, which makes possible predictions of neutral hydrogen absorption in the spectra of background QSOs. We present results from a high-resolution calculation of an Ω = 1 cold dark matter model. Our simulation reproduces many of the observed properties of the Lyα forest surprisingly well. The distribution of H I column densities agrees with existing data to within a factor of ~2 over most of the range from 1014 cm-2 to 1022 cm-2; i.e., from unsaturated Lyα forest lines to damped Lyα systems. The equivalent width distribution matches the observed exponential form with a characteristic width W* ≈ 0.3 Å. The distribution of b-parameters appears consistent with that derived from QSO spectra. Most of the low column density absorption arises in large, flattened structures of moderate or even relatively low overdensity, so there is no sharp distinction between the Lyα forest and the “Gunn-Peterson” absorption produced by the smooth intergalactic medium. Our results demonstrate that a Lyα forest like that observed develops naturally in a hierarchical clustering scenario with a photoionizing background. Comparison between simulations and high-resolution QSO spectra should open a new regime for testing theories of cosmic structure formation.
Using Australian Medicines Terminology (AMT) and SNOMED CT-AU to better support clinical research

A large scale, long term clinical study faced significant quality issues with its medications use data which had been collected from participants using paper forms and manually entered into a data capture system. A method was developed that automatically mapped 72.2% of the unique medication names collected for the study to the AMT and SNOMED CT-AU using Ontoserver, a terminology server for clinical ontologies. These initial results are promising and, with further improvements to the algorithms and evaluation, are expected to greatly improve the analysis of medication data gathered from the study.
Statistics of high-level scene context

Context is critical for recognizing environments and for searching for objects within them: contextual associations have been shown to modulate reaction time and object recognition accuracy, as well as influence the distribution of eye movements and patterns of brain activations. However, we have not yet systematically quantified the relationships between objects and their scene environments. Here I seek to fill this gap by providing descriptive statistics of object-scene relationships. A total of 48, 167 objects were hand-labeled in 3499 scenes using the LabelMe tool (Russell et al., 2008). From these data, I computed a variety of descriptive statistics at three different levels of analysis: the ensemble statistics that describe the density and spatial distribution of unnamed “things” in the scene; the bag of words level where scenes are described by the list of objects contained within them; and the structural level where the spatial distribution and relationships between the objects are measured. The utility of each level of description for scene categorization was assessed through the use of linear classifiers, and the plausibility of each level for modeling human scene categorization is discussed. Of the three levels, ensemble statistics were found to be the most informative (per feature), and also best explained human patterns of categorization errors. Although a bag of words classifier had similar performance to human observers, it had a markedly different pattern of errors. However, certain objects are more useful than others, and ceiling classification performance could be achieved using only the 64 most informative objects. As object location tends not to vary as a function of category, structural information provided little additional information. Additionally, these data provide valuable information on natural scene redundancy that can be exploited for machine vision, and can help the visual cognition community to design experiments guided by statistics rather than intuition.
On non-canonical solving the Satisfiability problem

We study the non-canonical method for solving the Satisfiability problem which given by a formula in the form of the conjunctive normal form. The essence of this method consists in counting the number of tuples of Boolean variables, on which at least one clause of the given formula is false. On this basis the solution of the problem obtains in the form YES or NO without constructing tuple, when the answer is YES. It is found that if the clause in the given formula has pairwise contrary literals, then the problem can be solved efficiently. However, when in the formula there are a long chain of clauses with pairwise non-contrary literals, the solution leads to an exponential calculations.
Cooperative ambient intelligence: towards autonomous and adaptive cooperative ubiquitous environments

Cooperative ambient intelligence provides users and groups of users with seamless and flexible support for communication and work in groups at one location and among groups at distributed locations. Autonomous and adaptive cooperative ubiquitous environments that are conceptually elegant, technologically sound and user-centred provide vital contributions for their success. This article characterises the evolution of interactive systems based on five foci, identifies the respective strengths of autonomous and adaptive behaviour from a users' perspective, and derives research issues for future cooperative ambient intelligence environments.
Modelling slotted multi-channel ring all-optical networks

This paper presents an approximate analytical model for the evaluation of throughput and access delays in high-speed all-optical networks with slotted multi-channel ring topology, where each channel is shared in statistical time division by all nodes transmitting to one destination using a collision- and contention-free access protocol based on a channel inspection capability. Nodes are assumed to have only one queue to store packets for all destinations; two configurations are considered: the single buffer queue and the infinite buffer queue. Simulation results are used to assess the accuracy of the approximate model.
Tsunami Hazard Assessment of the Strait of Juan de Fuca

This study presents and discusses the tsunami hazard posed by an updated CSZ earthquake scenerio to the coastal communities of Port Angeles and Port Townsend, based on the results of a high resolution GeoClaw simulation with 2/3 arc second resolution (about 20.56 meters) surrounding these towns. In addition, we will also present the results of a coarse regional simulation of the Strait of Juan de Fuca. This coarse study encompasses 28 regions that span the Strait’s coast, including the communities of Anacortes, Bellingham, Friday Harbor, and Victoria, BC in addition to extended areas around Port Angeles and Port Townsend. The finest grid for these 28 regions where we collected results had 2 arc sec resolution (around 62 meters). Finally, we will discuss some inherent uncertainties in the specification of the earthquake scenario, the limitations of the GeoClaw model, and the associated uncertainites in the results.
AC-coupled front-end for biopotential measurements

AC coupling is essential in biopotential measurements. Electrode offset potentials can be several orders of magnitude larger than the amplitudes of the biological signals of interest, thus limiting the admissible gain of a dc-coupled front end to prevent amplifier saturation. A high-gain input stage needs ac input coupling. This can be achieved by series capacitors, but in order to provide a bias path, grounded resistors are usually included, which degrade the common mode rejection ratio (CMRR). This paper proposes a novel balanced input ac-coupling network that provides a bias path without any connection to ground, thus resulting in a high CMRR. The circuit being passive, it does not limit the differential dc input voltage. Furthermore, differential signals are ac coupled, whereas common-mode voltages are dc coupled, thus allowing the closed-loop control of the dc common mode voltage by means of a driven-right-leg circuit. This makes the circuit compatible with common-mode dc shifting strategies intended for single-supply biopotential amplifiers. The proposed circuit allows the implementation of high-gain biopotential amplifiers with a reduced number of parts, thus resulting in low power consumption. An electrocardiogram amplifier built according to the proposed design achieves a CMRR of 123 dB at 50 Hz.