Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal registration method based on ICP algorithm from head CT and MR image sets

Image registration is an important problem and a fundamental task for understanding the internal organ or internal structure of human body, analyzing the temporal change from the image sets which is obtained different time series in computer vision and image processing field. Especially, CT and MR imaging of the head for diagnosis and surgical planning indicate that physicians and surgeons gain important information from these modalities. In radiotherapy planning, manual registration techniques are performed on MR and CT images of the brain. In general, physicians segment the volumes of interest (VOIs) from each set of slices on the MR and CT images manually. However, manual registration of the object area may require several hours for analysis based on anatomical knowledge. In this paper, to register two images which are obtained from different modalities we develop a new method for automatic registration of the head CT and MR images by using ICP (iterative closest point) algorithm in several extracted data and maximization of mutual information. One of the benefits of using the ICP algorithm is that computational costs can be reduced on the registration. The primary objective of this study is to increase the registration accuracy and reduce the computational processing time. The technique was applied to five real image sets which are obtained from the two different modalities and the satisfactory results are obtained. Some experimental results are shown with discussion.
On the Minimax Approach to Overcoming Prior Uncertainty and Application to Pattern Recognition Problems - eScholarship

The usual consistent estimator of A/^(/2) is i where {X , n v = 1,. , n} is a sequence of random variables with probability u distribution P^dx). max, Ap\ \jl). Evidently, a consistent estimator of F(jl) is F (jl) n This method can reduce computational time by the factor of n tens and hundreds. References [1] Repin, V.G. and Tartakovsky, G.P. (1978). Statistical Prior Uncertainty and Adaptation of Information Synthesis Systems. Under Sovetskoe Radio, Moscow (In Russian). [2] Wald, A. (1950). Statistical Decision John Wiley & Functions. Sons, Inc., New York. [3] Tartakovsky, A.G. (1991). Sequential mation Systems. Methods in the Theory of Infor- Radio i Svyaz, Moscow (In Russian). [4] Tartakovskii, G.P. (1984). Multiple-alternative adaptive pattern recog- nition with parameter estimation, when non-single-valued decisions are allowed. Problems of Information Transmission, [5] Verdenskaya, N.V. and Tartakovskii, A.G. (1991). Asymptotically opti- mal sequential testing multiple hypotheses for nonhomogeneous Gaus- sian processes in an asymmetric situation. Theory Application of Probability and Its
Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks

This paper presents a novel change detection approach for synthetic aperture radar images based on deep learning. The approach accomplishes the detection of the changed and unchanged areas by designing a deep neural network. The main guideline is to produce a change detection map directly from two images with the trained deep neural network. The method can omit the process of generating a difference image (DI) that shows difference degrees between multitemporal synthetic aperture radar images. Thus, it can avoid the effect of the DI on the change detection results. The learning algorithm for deep architectures includes unsupervised feature learning and supervised fine-tuning to complete classification. The unsupervised feature learning aims at learning the representation of the relationships between the two images. In addition, the supervised fine-tuning aims at learning the concepts of the changed and unchanged pixels. Experiments on real data sets and theoretical analysis indicate the advantages, feasibility, and potential of the proposed method. Moreover, based on the results achieved by various traditional algorithms, respectively, deep learning can further improve the detection performance.
Efficient customer selection for sustainable demand response in smart grids

Regulating the power consumption to avoid peaks in demand is a common practice. Demand Response(DR) is being used by utility providers to minimize costs or ensure system reliability. Although it has been used extensively there is a shortage of solutions dealing with dynamic DR. Past attempts focus on minimizing the load demand without considering the sustainability of the reduced energy. In this paper an efficient algorithm is presented which solves the problem of dynamic DR scheduling. Data from the USC campus micro grid were used to evaluate the efficiency as well as the robustness of the proposed solution. The targeted energy reduction is achieved with a maximum average approximation error of ≈ 0.7%. Sustainability of the reduced energy is achieved with respect to the optimal available solution providing a maximum average error less than 0.6%. It is also shown that a solution is provided with a low computational cost fulfilling the requirements of dynamic DR.
Implementation of Interceptor Based Resource Server for Software Product Line

The responsibility of resource server is to store and manage the reusable and product-specific assets produced in the process of software development applying software product line principals. Separately designed for each specific resource type, classical resource server could not satisfy the demands for mass storage of various resource types in collaborative development environment. Interception design pattern is introduced as a novel means to implement resource server. Interceptors with specific functionalities are developed and configured for various resource types in domain to provide a uniform framework to deal with distinct types of resource, including customized resource type. The description of the resource handling process using Pi calculus demonstrates the validity of the approach.
LD-STBC-VBLAST Receiver for WLAN systems

In this article, a multistream orthogonal frequency division multiplexing (OFDM) transmission is analyzed, in which a part of spatial streams is space time block coded (STBC) and linear dispersion (LD) theory is employed. Using a simulation model made in MATLAB environment, the quality of transmission of the wireless-LAN (WLAN) system with channel type E is investigated. We present the simulation results for two models of receivers WLAN 802.11n data transmission. The performance of ZF and LD-STBC-VBLAST receivers has been assessed and compared. The bit error rate (BER) and packet error rate (PER) has been determined for different numbers of spatial streams in use. The results show how the LD-STBC structure employed in the transmitting and receiving parts may improve the quality of WLAN transmissions. Key-Words: MIMO transmission, wireless receiver, space time block coding, bit error rate, WLAN, OFDM
From Data to Actionable Knowledge: Applying Data Mining to the Problem of Intrusion Detection

Much of the research in the area of Knowledge Discovery in Databases (KDD) has focused on the development of more efficient and effective data mining algorithms, but recently issues related to the usability of these techniques in extracting exploitable knowledge from databases has drawn significant attention. In this paper we describe the progress we are making in building an interactive knowledge discovery system to help assist in developing knowledge for activity monitoring tasks such as intrusion detection. We describe why data mining in support of problems such as intrusion detection is hard and why it is so important to make optimal use of the human in the KDD process for guiding the exploration and evaluating its results. In particular, we describe how inductively generated simulations can be used to help understand the relationships amongst disparate events.
A framework for inferring field‐scale rock physics relationships through numerical simulation

Rock physics attempts to relate the geophysical response of a rock to geologic properties of interest, such as porosity, lithology, and fluid content. The geophysical properties estimated by field‐scale surveys, however, are impacted by additional factors, such as complex averaging of heterogeneity at the scale of the survey and artifacts introduced through data inversion, that are not addressed by traditional approaches to rock physics. We account for these field‐scale factors by creating numerical analogs to geophysical surveys via Monte Carlo simulation. The analogs are used to develop field‐scale rock physics relationships that are appropriate for transforming the geophysical properties estimated from a survey into geologic properties. We demonstrate the technique using a synthetic example where radar tomography is used to estimate water content.
TEMPORAL PHASE UNWRAPPING USING BAYESIAN INFERENCE

When analyzing a stack of radar images using persistent scatterer techniques, one of the major limitations that temporal unwrapping methods have is that they frequently do not take advantage of the information provided by the rest of the detected scatterers. Here, we propose to use this information in two different ways. First, with an iterative method that utilizes the estimations from previous runs to constrain the solution using Bayesian inference. By doing so, solutions given by local maxima can be avoided. Second, we estimate the variance of the observations from the phase variability of the pixels around the PS in question. These variances are then used to weight the observations. The method is first validated using simulated data. Then, we applied it to a small area in southern Netherlands and compare with conventional unwrapping methods (e.g. periodogram and bootstrapping). The method shows a decrease in the number of rejected arcs after statistical testing. It also reduces the spatial variability of the estimations and renders a smooth solution. Spatially smooth estimations are , in principle, logic since the unknown parameters (deformation rates and heights) are usually spatially correlated and the uncorrelated part is normally small. However, it can happen that in the selection process included pixels that are not truly PS , i.e. we committed type II errors. These wrongly selected pixels whose phase contain mostly noise will have a solution artificially assigned that is similar to the surroundings and which makes it difficult to reject for further analysis. Other methods to detect these pixels should be used which estimates the noise of individual time series.
Schemas Based Universal Code Generation Framework for Distributed CCA Applications

Code generators for distributed computing systems shield users from the low level communication details of the underlying run-time systems by isolating those details in a library against which code can be linked. Distributed systems have complex run-time systems designed to run in heterogeneous environments with dynamically varying loads, multiple communication protocols, and they adhere to different Quality of Service (QoS) requirements. Our focus is on the code-generation requirements of distributed CCA systems. To leverage the strengths of each distributed component system, a user may want to develop an application that spans multiple frameworks. Currently, in such cases the user needs to use a different code generator for each one of these distributed component systems that the application incorporates. Learning the details of the interface specification and code generation tool for each one of these distributed systems is tedious and error-prone. Instead, it is desirable to present the user with a flexible code generation framework that accepts a simple yet powerful interface definition language and is capable of generating code for all distributed CCA systems. In this paper we present the design and implementation of an XML schema based toolkit that can serve as the universal code generation toolkit for distributed CCA frameworks. 1
Computation-oriented fault-tolerance schemes for RRAM computing systems

The emerging metal-oxide resistive switching random-access memory (RRAM) devices and RRAM crossbar arrays have demonstrated their potential in enormously boosting the speed and energy-efficiency of analog matrix-vector multiplication. Unfortunately, due to the immature fabrication technology, commonly occurring Stuck-At-Faults (SAFs) seriously degrade the computational accuracy of RRAM crossbar based Computing System (RCS). In this paper, we propose a Mapping Algorithm with inner fault-tolerant ability (MAO) to convert matrix parameters into RRAM conductances in RCS by providing larger mapping space and fully exploring the available mapping space. Furthermore, we present two computation-oriented redundancy schemes — ‘Redundant Crossbars’ (RX) and ‘Independent Redundant Columns’ (IRC) to alleviate the loss of computational accuracy due to SAFs. RX adds redundant RRAM crossbar arrays and IRC introduces independent redundant RRAM columns to compensate the computational errors brought by SAFs.
Image Inpainting Using Field of Experts Model

Image Inpainting is a technique which recovers the deteriorated parts of a digital image. The process is carried out by filling the information from the area surrounding the damaged part of an image. The application areas of Image Inpainting includes scratch removal, image compression, image denoising, object and stamped date removal from photographs and creating visually stunning effect. In ancient era, this art was carried out manually by the artists, but now this process has been digitized to speed up the task and improve the accuracy of work done. This paper uses Field of Experts model to carry out image inpainting process and achieve the desired results. This model can also be used for image denoising application.
Multi-touch steering wheel for in-car tertiary applications using infrared sensors

This paper proposes a multi-touch steering wheel for in-car tertiary applications. Existing interfaces for in-car applications such as buttons and touch displays have several operating problems. For example, drivers have to consciously move their hands to the interfaces as the interfaces are fixed on specific positions. Therefore, we developed a steering wheel where touch positions can correspond to different operating positions. This system can recognize hand gestures at any position on the steering wheel by utilizing 120 infrared (IR) sensors embedded in it. The sensors are lined up in an array surrounding the whole wheel. An Support Vector Machine (SVM) algorithm is used to learn and recognize the different gestures through the data obtained from the sensors. The gestures recognized are flick, click, tap, stroke and twist. Additionally, we implemented a navigation application and an audio application that utilizes the torus shape of the steering wheel. We conducted an experiment to observe the possibility of our proposed system to recognize flick gestures at three positions. Results show that an average of 92% of flick could be recognized.
A Modified Incremental Learning Approach for Data Stream Classification

Data mining for data stream becomes important in academic areas. Due to large-scale data, people utilize incremental learning approach to handle the data. In this paper, a modified Support Vector Machine (SVM) incremental learning model is proposed. Through experiments of selecting kernel function for the SVM method, we optimize several parameters. Real network dataset is used in our experiments to verify the model's feasibility and applicability. The experimental results show that the modified SVM incremental learning model can improve the accuracy of classification and increase performance.
COSIMA-DSS Evaluation System: A new Decision Support System for Large-Scale Transport Infrastructure Projects

This paper presents a new decision support model COSIMA-DSS that examines socio-economic feasibility risks involved in the implementation of transport infrastructure projects. The model makes use of conventionally costbenefit analysis embedded within a wider multi-criteria analysis. The basic approach set out in the paper looks upon the mix between so-called “hard” and “soft” evaluation criteria. Finally, a Monte-Carlo simulation is used to take account of the varying information relating to the different criteria.
Determining digital image origin using sensor imperfections

In this paper, we demonstrate that it is possible to use the sensor’s pattern noise for digital camera identification from images. The pattern noise is extracted from the images using a wavelet-based denoising filter. For each camera under investigation, we first determine its reference pattern, which serves as a unique identification fingerprint. This could be done using the process of flat-fielding, if we have the camera in possession, or by averaging the noise obtained from multiple images, which is the option taken in this paper. To identify the camera from a given image, we consider the reference pattern noise as a high-frequency spread spectrum watermark, whose presence in the image is established using a correlation detector. Using this approach, we were able to identify the correct camera out of 9 cameras without a single misclassification for several thousand images. Furthermore, it is possible to perform reliable identification even from images that underwent subsequent JPEG compression and/or resizing. These claims are supported by experiments on 9 different cameras including two cameras of exactly the same model.
Thermal conductivity of GaN films: Effects of impurities and dislocations

We report details of the calculation of the lattice thermal conductivity κ in wurtzite GaN. Numerical simulations are performed for n-type wurtzite GaN with different density of silicon dopants, point defects and threading dislocations. Using the material specific model we verified the experimentally observed linear decrease of the room-temperature thermal conductivity with the logarithm of the carrier density n. The decrease was attributed mostly to the increased phonon relaxation on dopants. Our calculations show that the increase in the doping density from 1017 to 1018 cm−3 leads to about a factor of 2 decrease in thermal conductivity from 1.77 W/cm K to 0.86 W/cm K. We have also established that the room-temperature thermal conductivity in GaN can be limited by dislocations when their density is high, e.g., ND>1010 cm−2. The obtained results are in good agreement with experimental data. The developed calculation procedure can be used for accurate simulation of self-heating effects in GaN-based devices.
Regional myocardial function: advances in MR imaging and analysis.

Cardiovascular magnetic resonance (MR) imaging can provide three-dimensional analysis of global and regional cardiac function with great accuracy and reproducibility. Quantitative assessment of regional function with cardiac MR imaging previously was limited by long acquisition times and time-consuming analysis. The use of steady-state free precession cine MR imaging substantially improves assessment of myocardial wall motion. Advances in gradient technology and reconstruction techniques have increased MR image acquisition speed and made real-time cine MR imaging possible. Myocardial deformation may be measured with cine MR tagging, and interpretation of the resultant tagged MR images by means of harmonic phase analysis enables prompt and precise strain measurements. Velocity-encoded and stimulated-echo techniques such as phase-contrast MR imaging and displacement encoding with stimulated echoes, or DENSE, provide high-resolution strain maps. Clinical validation of these strain imaging techniques will depend on future assessments of their effect on the management of cardiac disease.
A null model for testing thermodynamic optimization in ecological systems

Several authors have hypothesized that ecological systems are subject to thermodynamic optimization, which, if proven correct, could represent a long sought general principle of organization in ecology. Although there have been recent advances, this still remains as an unresolved topic, and ecologists lack a general method to test thermodynamic optimization hypotheses in specific systems. Here we present a general, novel approach that allows generating a null model for testing thermodynamic optimization on ecological systems. We first describe the general methodology, which is based in the analysis of a parametrized mathematical model of the system and the explicit consideration of constraints. Next we present an application example to an animal population using a general age-structured population model and physiological parameters from the literature. We finalize discussing the relevance of this work in the context of the current state of ecology, and implications for the further development of a thermodynamic ecological theory.
Coulomb-oscillator duality in spaces of constant curvature

In this paper we construct generalizations to spheres of the well-known Levi-Civita, Kustaanheimo–Steifel, and Hurwitz regularizing transformations in Euclidean spaces of dimensions two, three, and five. The corresponding classical and quantum mechanical analogs of the Kepler–Coulomb problem on these spheres are discussed.
Pulsatile flow increases the expression of eNOS, ET-1, and prostacyclin in a novel in vitro coculture model of the retinal vasculature.

PURPOSE By the development of a novel retinal microvascular endothelial and pericyte cell coculture system, this study determined the effects of pulsatile flow on the activation of the endothelial cell markers nitric oxide (NO), prostacyclin (PGI2), and endothelin (ET)-1. METHODS Monocultured bovine retinal endothelial cells (BRECs) and cocultured BRECs with bovine retinal pericytes (BRPs) were exposed to low flow (flow rate, 0.3 mL/min; pulse pressure, 6 mmHg; shear stress, 0.5 dyne/cm2) or high flow (flow rate, 25 mL/min; pulse pressure, 56 mmHg; shear stress, 23 dynes/cm2) for 24 hours, by using a novel perfused transcapillary culture system. The cells were characterized by immunohistochemistry and electron and confocal microscopy. Endothelial nitric oxide synthase (eNOS) and phosphorylated-eNOSSer1179 (pp-eNOS) were determined by Western blot analysis. Nitrate, PGI2, and ET-1 levels were quantified in the medium perfusate by using fluorometric and enzyme-linked immunosorbent assays, respectively. Activation of cyclooxygenase (COX)-2 in BRECs was determined by measuring COX-2 promoter activity with a luciferase reporter assay. RESULTS The presence of BRPs and BRECs was confirmed by Western blot, immunocytochemistry, and scanning electron microscopy. Phosphorylated eNOS (pp-eNOS) protein levels in BRECs were significantly increased from low to high flow in both mono- and cocultures, concomitant with a significant increase in nitrate levels in the conditioned medium after exposure to pulsatile flow. In parallel cultures, PGI2 levels were also significantly enhanced concomitant with an increase in the transactivation of a COX-2 promoter BREC after exposure to pulsatile flow. ET-1 levels were also increased in both mono- and cocultured cells. conclusions. In this study a novel, functioning, in vitro model of retinal microvascular endothelial and pericyte cells that respond to changes in pulsatile flow was established.
Fuzzy control design for nonlinear dynamic systems using constrained H∞/LTR

Stabilization problems for nonlinear systems are difficult, especially when not all the state are available. This study proposed a combination of constrained H∞ and loop transfer recovery (LTR) to control nonlinear systems via Takagi-Sugeno fuzzy model. First, the Takagi-Sugeno fuzzy model is employed to represent a nonlinear system. Next, based on the fuzzy model, a Kalman filter is developed. The controller gain is calculated by using constrained H∞ theory. LTR is then applied to reinforce the robust property of the controller. Simulation results for application to a two-link robot system are demonstrated. The performance of the designed system is assessed in the frequency domain and via the time-domain simulation.