<title>Accurate infrared scene simulation by means of microlithographically deposited substrate</title>

This paper describes a distributed test capability developed at the Redstone Technical Test Center (RTTC) in support of the Javelin Fire-and-Forget missile system. Javelin has a target acquisition sensor for surveying the battlefield, and a missile seeker staring focal plane array (FPA) for target tracking. Individually, these systems can be tested using scene projector capabilities at RTTC. But when combined, using this distributed capability, complete system acquisition to missile performance can be tested with the gunner in the loop. This capability was demonstrated at the Association of the United States Army (AUSA) conference in October of 2000. The Defense Research and Engineering Network (DREN) was used to connect the tactical target acquisition sensor in Washington, D.C. to the missile seeker and guidance section in Redstone Arsenal, in Huntsville, AL. The RTTC High Performance Computer provided scene projector control, distributed architecture support and real-time visible and infrared scene generation.

The importance of hard-ware-in-the-loop infrared seekers testing with realistic infrared scenes warrants a review of the current technologies used in dynamic infrared scene simulation. Infrared seeker dynamic scene simulation technology includes dynamic infrared simulator technology and infrared scene image generation technology. Infrared simulator includes resistor arrays, liquid crystal light valves, laser writers, laser diode arrays, CRT and digital mirror devices (DMD). The technology that may provide high performance for testing seekers is the DMD approach. Other technologies such as resistor arrays, LCLVs, laser diodes, and the CRTs are still under development. Infrared scene generation can be divided into the stage of data generation and the stage of programming. Target 3D model is completed by the software of Multigen Creator and dynamic scene image generation is completed by the software of Vega. The SensorVision module of Vega can supply the atmosphere model parameter and target texture material.

The fastest published infrared (IR) scene simulation called IMIRST by BAE Systems takes 32 minutes to construct a single 352 x 352 IR scene using a Pentium 4 (2.8GHz) dual core processor. In defence and homeland security, IR scene simulators are designed to test infrared missile approach warning systems (MAWS). A typical MAWS requires input scenes at the rate of over 50 scenes per second to effectively run key IR search and track algorithms. Current IR scene simulations fall well short in meeting this requirement. Advanced computing based on programmable logic provides a platform for parallel computing with complete freedom of choice about the granularity and structure of the parallel algorithm. In this paper we identify the key computationally intensive infrared algorithms and present a novel reconfigurable computing platform to accelerate infrared simulation.

Adaptive grid-based confidence assessment for synthetic optoelectronic images by Physical Reasonable Infrared Scene Simulation Engine (PRISSE)

Dynamic infrared scene simulation using grayscale modulation of digital micro-mirror device

We describe the development of a 3-D statistically nonstationary earthlimb stochastic structure representation and its application to earthlimb infrared (IR) background structure simulations. An earthlimb viewing geometry is defined by the location of both observer and source in space. The line-of-sight tangent altitude is the minimum altitude of the trajectory, defined at the earth-centered perpendicular to the line of sight. The stochastic structure overlay is constructed from a 2-D diagonal cut through a 3-D matrix of correlated Gaussian deviates; each plane of the matrix represents a statistically constant representation of the 2-D correlation lengths at a given altitude above the earth. Each matrix plane is generated using successive 2-D fast Fourier transform routines that have empirical values of vertical and horizontal correlation lengths as input. The total deviate variance versus altitude is then scaled from empirical measurements of fluctuations in atmospheric density, temperature, and/or emissivity. These structure generators are used both as perturbations on input atmospheric data to IR radiance codes and as high-resolution overlays to earthlimb lR mean-radiance 2-D scenes. An IR structured scene realization and scene validation analysis is presented.

To retrospectively compare computer-aided mammographic density estimation (MDEST) with radiologist estimates of percentage density and Breast Imaging Reporting and Data System (BI-RADS) density classification. Institutional Review Board approval was obtained for this HIPAA-compliant study; patient informed consent requirements were waived. A fully automated MDEST computer program was used to measure breast density on digitized mammograms in 65 women (mean age, 53 years; range, 24-89 years). Pixel gray levels in detected breast borders were analyzed, and dense areas were segmented. Percentage density was calculated by dividing the number of dense pixels by the total number of pixels within the borders. Seven breast radiologists (five trained with MDEST, two not trained) prospectively assigned qualitative BI-RADS density categories and visually estimated percentage density on 260 mammograms. Qualitative BI-RADS assessments were compared with new quantitative BI-RADS standards. The reference standard density for this study was established by allowing the five trained radiologists to manipulate the MDEST gray-level thresholds, which segmented mammograms into dense and nondense areas. Statistical tests performed include Pearson correlation coefficients, Bland-Altman agreement method, kappa statistics, and unpaired t tests. There was a close correlation between the reference standard and radiologist-estimated density (R = 0.90-0.95) and MDEST density (R = 0.89). Untrained radiologists overestimated percentage density by an average of 37%, versus 6% for trained radiologists (P < .001). MDEST showed better agreement with the reference standard (average overestimate, 1%; range, -15% to +18%). MDEST correlated better with percentage density than with qualitative BI-RADS categories. There were large overlaps and ranges of percentage density in qualitative BI-RADS categories 2-4. Qualitative BI-RADS categories correlated poorly with new quantitative BI-RADS categories, and 16 (6%) of 260 views were erroneously classified by MDEST. MDEST compared favorably with radiologist estimates of percentage density and is more reproducible than radiologist estimates when qualitative BI-RADS density categories are used. Qualitative and quantitative BI-RADS density assessments differed markedly.

The paramount reason for commercial farming is to achieve the economic optimum level of output given the farmers inputs used in the farming operation. To achieve this, different technologies are adopted with a view to discovery the most appropriate technical option to be engaged in the production process. This study evaluates the effect of the different tillage practices on the returns of cowpea production in Obubra Local Government of Cross River State. The goal was to ascertain within the agricultural climatic zone, the best tillage method for cowpea production in Obubra Local government Area of Cross River State, that farmers could adopt in order to ensure optimum productivity. The data was collected from a two- year experiment, using the Randomized Complete Block Design (RCBD) with four treatment, replicated eight times, thus, bringing the total number of sub-plots to 32. Ratio and gross margin analysis was employed to analyse the data. The result showed that cowpea production does best under the bed tillage system. The mean gross income was N780.00 and operating ratio of 0.48 representing the rate of returns per N1.00 used in the production process. The operating ratios for the other tillage systems were as follows: 0.65, 0.58 and 0. 88 for ridges, plough/harrow and zero tillage respectively. From the results, the bed tillage system was recommended for farmers in the study area. Key words: Returns on cowpea production, different tillage method Journal of Agriculture, Forestry and the Social Sciences Vol.2(1) 2004: 24-28

We propose a mathematical model to describe the microstructure of the gas diffusion layer (GDL) in proton exchange membrane fuel cells (PEMFCs) based on tools from stochastic geometry. The GDL is considered as a stack of thin sections. This assumption is motivated by the production process and the visual appearance of relevant microscopic images. The thin sections are modeled as planar [two-dimensional (2D)] random line tessellations which are dilated with respect to three dimensions. Our 3D model for the GDL consists of several layers of these dilated line tessellations. We also describe a method to fit the proposed model to given GDL data provided by scanning electron microscopy images which can be seen as 2D projections of the 3D morphology. In connection with this, we develop an algorithm for the segmentation of such images which is necessary to obtain the required structural information from the given grayscale images.

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard Terra and Aqua satellites provides measurements of several atmospheric parameters.This paper focuses on the cloud fraction data representing the number of cloudy pixels divided by the total number of pixels, and available through 1° x 1° grids spatial resolution with daily or monthly temporal resolution. The aim of the study is to propose a novel method called The Spatial-Temporal Implementation Algorithm (STIA) for analysing satellite daily 1° x 1°grid cloud fraction average values for•Comparing two datasets retrieved by MODIS aboard Aqua and Terra satellites to obtain information on the cloud formation in the afternoon and morning, respectively, thus enhancing the temporal resolution.•Comparing the actual parameter with others retrieved by instruments aboard of different satellites characterized by a better resolution. As an example of STIA application, this study uses the Aerosol Optical Depth (AOD) collected by the Ozone Monitoring Instrument (OMI) on board of Aura satellite for comparison with MODIS instrument to achieve and enhanced spatial resolution of the grid-cell.

Cluster identification using image processing

Medical-image segmentation is of primary importance in the development of computer assisted detection (CAD) of mammographic masses. Image segmentation is used for finding regions of interest (ROI) within mammograms that are further processed to estimate their likelihood of being a potential lesion or mass. This task can be stated as follows: €œA gray-scale image is represented as a 1D array X = (x1, x2,...,xN), where xn is an input feature for pixel n and N is the total number of pixels in the image. The input feature vector xn may be a D dimensional vector or simply the gray-scale value of the pixel n. Let the underlying true segmentation of the image be denoted as Y = (y1, y2,..., yN). It is assumed that the number of classes is predetermined as a set of known class labels I‰l, where l Iµ {1,..., L{ and therefore the class label of pixel n is indicated as y n ∈{I‰ l } L l=1 . The task is to predict the A·n estimate of the segmentation for each pixel. In an automated system, such likelihood estimates are generated using a classifier that takes as input some features that describe region properties, e.g., image texture. The segmentation of digital mammograms is a difficult task. A number of different approaches have been adopted in this area with varying success. Broadly speaking, image segmentation can be carried out either as: (1) image preprocessing step with the main objective of identifying regions of interest (spatially coherent collection of pixels within the image) and a subsequent feature extraction and classification step is required to characterize a given region of interest as either €œnormal€ or €œsuspicious€; or (2) part of the overall classification scheme, where the likelihood of each pixel being €œsuspicious is calculated to generate a probability image using pixel-based features as input to a classifier. The probability image is thereafter thresholded to give both the segmentation and classification output. The main difference between (1) and (2) is that in the first case, image segmentation is needed before classification can be performed; whereas in the second case, classification and segmentation are both based on thresholding probability estimates of pixels and are achieved at the same time. The image segmentation approaches, with the aim of finding regions of interest, were first attempted in digital mammography with varying techniques, including global thresholding, region growing, region clustering, template matching, edge operators and filtering, and edge-based hybrid methods. Image segmentation is developed from pixel-based probability estimation is underpinned by modeling features using parametric methods, Gaussian mixture modeling, Markov random fields, and other methods. A detailed discussion on mammogram-based image segmentation with their algorithms is available in Singh and Bovis. Experience shows that it is quite difficult to select an outright winning image-segmentation method, since different approaches have been shown to be suitable for different data sets and show varied performance under different experimental conditions. However, there is a consensus that image segmentation in medical imaging is most successful when explicitly using a priori information about the problem to be studied.

This paper presents a novel approach for energy optimization in large scale industrial production systems based on an actor-critic reinforcement learning (ACRL) framework. The objective of the on-line capable self-learning algorithm is the optimization of the energy consumption of the whole production process. Our central ACRL framework is realized by artificial neural network (ANN) function approximation using Gaussian radial-basis functions (RBF) for the critic and the actor, respectively, and gives the opportunity to cover not only a discrete but also continuous state and action space, which is necessary for hybrid systems where discrete and continuous actuator behavior is combined. For testing and validation purposes we develop a software model of our bulk good laboratory plant as application example for the developed ACRL algorithm. The model is based on mass-flow equations for a continuous bulk good supply whereas the energy consumption is modeled by functions dependent on the actuators behavior. The capability of our machine learning (ML) approach for energy optimization is underlined by simulation results for the task of supplying bulk good to a subsequent dosing section.

Споживчий сектор економіки є неоднорідним за своїм складом і включає в себе як споживання промислових і непромислових товарів, так і споживання різних видів послуг. За результатами проведеного аналізу зроблено висновок про згортання екстенсивного розвитку споживчого сектору економіки України. Кількість працівників, зайнятих на різних підприємствах споживчого сектору за останні сім років зменшилась на 21,42 %. Зростає важливість розвитку споживчого сектору економіки за рахунок інноваційних чинників. Останні десятиріччя характеризуються тенденцією до динамічних змін споживчих уподобань населення. Споживчий сектор можна вважати індикатором соціально-економічної ефективності інноваційної діяльності підприємств. Питома вага інноваційно активних вітчизняних промислових підприємств склала у 2018 р. 16,1% від їх загальної кількості. Питома вага підприємств, що впроваджували інновації у 2018 р. склала 14,1 % від загальної кількості підприємств і за період 2013-2018 рр. знизилась на 3,67 %. Низький рівень питомої ваги інноваційно активних підприємств є негативним чинником розвитку вітчизняної економіки. Активність українських новаторів за період 2014-2018 рр. зросла за кількістю поданих заявок на реєстрацію прав промислової власності на 24,09 %. Збільшилася кількість заявок за напрямом «Інші споживчі товари» (+35,6 % ). Зважаючи на пріоритетність витрат вітчизняних домогосподарств на продукти харчування та одяг і взуття доцільно стимулювати розвиток інновацій на підприємствах харчової та легкої промисловості. Темпи виробництва продукції у текстильному, швейному та шкіряному виробництвах України зростали в період 2014, 2016-2017 рр., і знижувалися у 2015 р. та 2018 р. Змінюються технології, які використовуються для виготовлення одягу. Використовуються цифрові технології віртуальних моделей одягу, технології 3D-друку моделей одягу і взуття. Це дозволяє задовільнити зростаючі вимоги споживацького попиту та економічно раціонально використовувати матеріальні та трудові резерви виробництва.

Multilayered nanocoatings allow outstanding properties with broad potential for glazing applications. Here, we report on the development of a multilayer nanocoating for zinc oxide (ZnO) and antimony doped tin oxide (ATO). The combination of ZnO and ATO thin films with their promising optical properties is a cost-efficient alternative for the production of energy-efficient glazing. It is an effective modification of the building envelope to reduce current high domestic demand of electrical power for air conditioning, especially in hot climates like Saudi Arabia. In this paper, we report the development of a nanocoating based on the combination of ZnO and ATO. Principle material and film investigations were carried out on lab-scale by dip coating with chemical solution deposition (CSD), while with regard to production processes, chemical vapor deposition (CVD) processes were evaluated in a second stage of the film development. It was found that with both processes, high-quality thin films and multilayer coatings with outstanding optical properties can be prepared. While keeping the optical transmission in the visible range at around 80%, only 10% of the NIR (near infrared) and below 1% of UV (ultraviolet) light passes these coatings. However, in contrast to CSD, the CVD process allows a free combination of the multilayer film sequence, which is of high relevance for production processes. Furthermore, it can be potentially integrated in float glass production lines.