Small Window Size Research Articles

Host-Guest Chemistry of Truncated Tetrahedral Imine Cages with Ammonium Ions.

Three shape‐persistent [4+4] imine cages with truncated tetrahedral geometry with different window sizes were studied as hosts for the encapsulation of tetra‐n‐alkylammonium salts of various bulkiness. In various solvents the cages behave differently. For instance, in dichloromethane the cage with smallest window size takes up NEt4 + but not NMe4 +, which is in contrast to the two cages with larger windows hosting both ions. To find out the reason for this, kinetic experiments were carried out to determine the velocity of uptake but also to deduce the activation barriers for these processes. To support the experimental results, calculations for the guest uptakes have been performed by molecular mechanics’ simulations. Finally, the complexation of pharmaceutical interested compounds, such as acetylcholine, muscarine or denatonium have been determined by NMR experiments.

Aircraft Inflight Icing Detection Based on Statistical Decision Theory

Aircraft inflight icing detection is very important for aviation flight safety. The ice alarm signal given by detectors can help the pilot to actuate the ice protection system. The application of a Neyman Pearson (NP) based statistical decision approach in icing detection is discussed in this paper. First, the flight dynamics for icing aircraft is modelled, the dynamic behaviour of an airbus influenced by ice accreting on the leading edge of wings is analysed. Then the residuals which are directly affected by the aerodynamic parameters are presented and their generation way is given. The Generalized Likelihood Ratio Test (GLRT) method is utilized in detecting the changes of residuals inflight, its performance is assessed by a flight scenario with wing ice. The results show that the fault of wing icing can be detected by every residual under different window sizes. By comparing the results of the residuals, it can be concluded that the small window size will degrade the detection performance with the longer alarming time delay and the lower probability of detection; appropriately increasing the window size can reduce the alarming time delay; diagnosing by residual of normal overload is more reliable than by the other two residuals.

Time-invariant restrictions of volatility functionals: Efficient estimation and specification tests

This paper studies the estimation and inference problems of time-invariant restrictions on certain known functions of the stochastic volatility process. We first develop a more efficient GMM estimator and derive the efficiency bound under such restrictions. Then we construct an integrated Hausman-type test by summing up the squared differences between this more efficient estimator and the unrestricted estimator computed at different time points. Although less efficient under the null, the latter estimator is consistent under both the null and the alternative. The efficient GMM estimator can also be used to update an existing Bierens-type test and simplify the calculation of the asymptotic variance. Since the quadratic function puts more weight on large values, the Hausman-type test can have superior power than the Bierens-type test, which is based on a linear function of the differences. The simulation study shows that except for very small local window sizes, the Hausman-type test has good size and superior power. We finally apply these tests to studying the constant beta hypothesis using empirical data and find substantial evidence against this hypothesis.

Time-Invariant Specification Test for Volatility Functionals

This paper studies the estimation and inference problems for time-invariant restrictions on certain functions of the stochastic volatility process. We first develop a more efficient GMM estimator and derive the efficiency bound under such restrictions. Then we construct an integrated Hausman-type test by summing up the standardized squared differences between this more efficient estimator and the unrestricted estimator, which is less efficient under the null but consistent under both the null and the alternative, at different time points. This efficient GMM estimator can also be used to update an existing Bierens-type test and simplifies the calculation of the asymptotic variance. Since quadratic function puts more weights on large values, the Hausman-type test can have superior power than the Bierens-type test, which is based on a linear function of the differences. Simulation study shows that except for very small local window size, the Hausman-type test has good size and superior power. We finally apply these tests to studying the constant beta hypothesis using empirical data and find substantial evidence against this hypothesis.

LEAST SQUARE MATCHING 1 TO COMBINE THE TWO IMAGES OF NCU AREA

Least square matching technique is included in area-based digital matching method. Conceptually, least square matching is closely related to the correlation method, with the added advantage of being able to obtain the match location to a fraction of a pixel. Least square matching (LS1)1 has merit to minimize the sum of squares for grayscale differences, so the result will be more accurate. The images covering the National Central University (Taiwan) area are aerial images taken from digital camera with sensor ultracam-D. Interior orientation parameter consist of focal length in 101.400000mm, principal point offset (0.000000e+000, 0.000000e+000)mm, and principal point symmetry (-2.110000e-001, 0.000000e+000)mm. The experimental result shows that the best accuracy of x direction is reached when the rotation angle is 9 degree, then those of y direction is reached when the rotation angle is 3 degree. The accuracy of both directions is getting worse when the scale of image is less than 0.8. The success rate 100% is reached in all of window size except 51 and 101. Then, the best accuracy of x direction is showed in 3x3 window size, those of y direction is employed when the work used the window size 11x11. Based on the experimental result, it can be concluded that using different rotation and scale can get the different result that it will be worse or better. Thus, to get the better result in matching image and better accuracy, the work should use the orthorectified image as base image to do rotation scheme and use small window size to minimize the number iteration, but it will be not significant with RMSe.

An effective back‐off selection technique for reliable beacon reception in VANETs

SummaryIn safety‐critical scenarios, reliable reception of beacons transmitted by a subject vehicle is critical to avoid vehicle collision. According to the employed contention window sizes in IEEE 802.11p, beacons are transmitted with a small contention window size. As a result, multiple vehicles contend for the shared channel access by selecting the same back‐off slot. This is a perfect recipe for synchronous collisions wherein reliable beacon delivery cannot be guaranteed for any vehicle. We consider the problem of selecting the back‐off slots from the current contention window to provide reliable delivery of beacons transmitted by a subject vehicle to its neighbors. Given a safety scenario, we propose a Pseudo‐Random Number Generator (PRNG)‐inspired back‐off selection (PBS) technique. The proposed technique works on the hypothesis that synchronous collisions of beacons transmitted by a subject vehicle can be reduced if all its neighbors select different back‐off slots (ie, not the back‐off slot selected by the subject vehicle). The discrete‐event simulations demonstrate that PBS can increase the overall message reception from a subject vehicle, in comparison with the uniform random probability back‐off selection in IEEE 802.11p.

Effect of distance between ignition location and window on indoor gas explosion development

Introduction. It has been previously known that for gas explosions in an unconfined chamber the following rule applies: the larger the distance between gas ignition location and relief opening (window), the higher the explosion pressure. This statement is based on results obtained by a number of researchers, including ourselves. However, as demonstrated by recent physical experiments, it is valid only for window sizes comparable to those recommended by guidelines to ensure certain safety conditions. For smaller window sizes, this relationship is leveled out or even changes its sign. Research objective is to determine the cause of inversed relationship between distance from the window to ignition location and explosion pressure. Tackling this objective is of scientific and practical importance. Research methods and tools. Two mathematical model variants for gas explosion development in an unconfined chamber were employed to study the revealed phenomenon, i. e. simplified model and numerical model. The first one, i. e. simplified model, is based on chamber representation as lumped volume, and using the Clapeyron equation in differential form. It was obtained that besides known factors, such as window size, properties of outflowing gases, etc., explosion development is influenced by the area of flame front and the time when it approaches the window. Unfortunately, this model does not take into account the dynamics of last factors development altogether. This task can be handled by the other model, numerical, implemented in Vulkan-M software. It is based on solving the gas dynamics equation system using large-particle method in Eulerian representation with added flame propagation conditions. Besides, Vulkan-M can visualize the physical process evolution, as well as record how its parameters and indicators develop. Research results. It was found that if the window size is comparable to regulatory values, such a strong influence of window position on pressure is due not only to the difference of out flowing gas properties (initial mixture and combustion products), but also dueto the factthatin the initial period of explosion development the flame front area is much larger for a further removed window than in case of a small distance between the window and ignition location. For a smaller window, the pressure increase rate in the initial period is high and almost identical for both explosion scenarios. Therefore, combustion time becomes decisive for the maximum pressure value. If the window is located far from the ignition, combustion time is shorter than in case of a smaller distance. As a result, maximum pressure in the second case is higher than in the first case. This explains the revealed phenomenon. Conclusion. The larger the window size, the stronger it affects the explosion pressure. This influence is determined not only by gas outflow, but it intensifies, sometimes significantly, due to the influence on flame front development. If the window size is decreased, its influence on flame front development is weakened and becomes negligible. In this case, the explosion pressure is affected by combustion time, besides window size.

Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

There is a growing demand for detailed and accurate landslide maps and inventories around the globe, but particularly in hazard-prone regions such as the Himalayas. Most standard mapping methods require expert knowledge, supervision and fieldwork. In this study, we use optical data from the Rapid Eye satellite and topographic factors to analyze the potential of machine learning methods, i.e., artificial neural network (ANN), support vector machines (SVM) and random forest (RF), and different deep-learning convolution neural networks (CNNs) for landslide detection. We use two training zones and one test zone to independently evaluate the performance of different methods in the highly landslide-prone Rasuwa district in Nepal. Twenty different maps are created using ANN, SVM and RF and different CNN instantiations and are compared against the results of extensive fieldwork through a mean intersection-over-union (mIOU) and other common metrics. This accuracy assessment yields the best result of 78.26% mIOU for a small window size CNN, which uses spectral information only. The additional information from a 5 m digital elevation model helps to discriminate between human settlements and landslides but does not improve the overall classification accuracy. CNNs do not automatically outperform ANN, SVM and RF, although this is sometimes claimed. Rather, the performance of CNNs strongly depends on their design, i.e., layer depth, input window sizes and training strategies. Here, we conclude that the CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner. Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the effects of augmentation strategies to artificially increase the number of existing samples are better understood.

A high fidelity haze removal algorithm for optical satellite images using progressive transmission estimation based on the dark channel prior

ABSTRACTIn this paper, a high fidelity haze removal algorithm is proposed for optical satellite images by using progressive transmission estimation based on the dark channel prior (DCP). The transmission is estimated adaptively according to the histogram of the dark channel image and the constraint of maximum transmission. Then, the guided filter is used to refine the transmission to obtain a continuous transmission map in which the clean areas are retained as much as possible. The refined transmission is applied to each visual band to obtain the initial de-hazing image. Then, the transmission is re-evaluated for the initial de-hazing image, and a guided filter with a small window size is used to refine the re-evaluated transmission. Furthermore, the transmission is stretched with the power-law transformation (PLT). To ensure fidelity in hazy areas, the optimal stretched transmission is estimated according to the artificially selected samples, from which the final haze removal results can be achieved. Several optical satellite images are collected and tested to validate the effectiveness of the proposed method. The evaluation results demonstrate that the proposed method is superior to the traditional methods and can recover a haze-free image with high fidelity.

Analysing the Dynamics of Interbeat Interval Time Series Using Grouped Horizontal Visibility Graph

Horizontal visibility graph (HVG) motifs have been recently introduced to analyze the dynamical information encoded by biological signals. However, the result of the analysis strongly depends on the selected window size of the motifs. Different sizes ranging from 3 to 5 have been previously used, but such small window sizes are insufficient to cope with the complexity of biological systems and often fail to extract salient features of the encoded information. It is known that larger window size increases the total number of possible motifs, and it leads to the distribution of the statistics into too many motifs, which causes each individual motif to contain too little information and make it even more difficult to reliably detect system dynamics. To resolve this problem, we group the motifs based on the number of edges. Using the grouped motifs, we propose grouped horizontal visibility entropy (GHVE) to quantify the complexity based on the probability distribution of the observations within these groups. We apply GHVE to quantify the complexity of simulated white and 1/f noise. The results reveal that the 1/f noise time series exhibits a higher complexity than white noise time series, which indicates that the 1/f noise is structurally more complex than white Gaussian noise. We apply the method for analyzing interbeat intervals time series. The results show that the proposed GHVE measure is more accurate in distinguishing healthy and pathological subjects than its non-grouped counter-part HVG. It is, therefore, better suited to detect changes in aging, disease severity, and activity levels (sleep and wake period).

Creation and Analysis of Earth’s Surface Roughness Maps from Airborne LiDAR Measurements in Downtown Urban Landscape

The Earth’s surface roughness constitutes an important parameter in terrain analysis for studying different environmental and engineering problems. Authors gave different definitions and measures for the earth’s surface roughness that usually depend on exploitation of digital elevation data for its reliable determination. This research aimed at exploring the different approaches for defining and extraction of the Earth’s surface roughness from Airborne LiDAR Measurements. It also aimed at evaluating the effects of the window size of the standard deviation filter on the created roughness maps in downtown landscapes using three known approaches namely; standard deviation filtering of the Digital Elevation Model (DEM), standard deviation filtering of the slope gradient model and standard deviation filtering of the profile curvature model. In this context, different roughness maps have been created from Airborne LiDAR measurements of the City of Toronto, Canada using the three filtering approaches with varying window sizes. Visual analysis has shown color tones of small roughness values with smooth textures dominate the roughness maps from small window sizes of the standard deviation filter, however, increasing the window sizes has produced wider variations of the color tones and rougher texture roughness maps. The standard deviations and ranges of the roughness maps from LiDAR DEM have increased due to increasing the filter window size while the skewness and kurtosis have decreased due to increasing the window size, indicating that the roughness maps from larger window sizes are statistically more symmetrical and more consistent. Thus, kurtosis has decreased by 53% and 82% due to increasing the window size to 7 × 7 and 15 × 15 respectively. The standard deviations of the roughness maps from the slope gradient model have increased due to increasing the window size till 15 × 15 while they have decreased with more increases. However, skewness has decreased due to increasing the window size till 15 × 15 and the kurtosis has decreased with higher rate till window size of 11 × 11. In the roughness maps from the profile curvature model, the ranges and skewness have decreased by 93.6% and 82.6% respectively due to increasing the window size to 15 × 15 while, kurtosis has decreased by 58.6%, 76.3% and 93.76% due to increases in the filter window size to 5 × 5, 7 × 7 and 15 × 15 respectively.

Small moving window calibration models for soft sensing processes with limited history

While many soft-sensing strategies have been developed for monitoring chemical processes, most require extensive historical data to train the soft sensor calibration. Many of these strategies involve very complex models to account for drift and other effects in the process. When a process changes, perhaps because of some upset or a change in process parameters, it is often necessary to rebuild the soft-sensing model, a process that can take considerable time. This paper focuses on use of simple models that can be used to provide soft-sensing with minimal calibration data, and with minimal training of hyperparameters to permit process monitoring while a more complex model is rebuilt. The goal of this study was to explore simple soft sensing strategies for the case where a new process has been put online with limited history. Five soft sensor methodologies with two update conditions were compared on two experimentally-obtained datasets and one simulated dataset. The soft sensors investigated were moving window partial least squares regression (and a recursive variant), moving window random forest regression, the mean moving window of the property y, and a novel random forest partial least squares regression ensemble (RF-PLS), all of which can be used with small sample sizes so that they can be rapidly placed online. It was found that, on two of the datasets studied, small window sizes led to the lowest prediction errors for all of the moving window methods studied. On the majority of datasets studied, the RF-PLS calibration method offered the lowest one-step-ahead prediction errors compared to those of the other methods, and it demonstrated greater predictive stability at larger time delays than moving window PLS alone. Both the random forest and RF-PLS methods most adequately modeled datasets that did not feature purely monotonic increases in property values, but both methods performed more poorly than moving window PLS models on one dataset with purely monotonic property values. Other data dependent findings are presented and discussed.

Mobile edge computing based QoS optimization in medical healthcare applications

Emerging trends in mobile edge computing for developing the efficient healthcare application such as, remote monitoring of the patients with central electronics clouds (e-Clouds) and their increasing voluminous multimedia have caught the attention of everyone in industry and academia. So, clear visualization, big sensing level, and better quality of service (QoS) is the foremost priority. This paper proposes the window-based Rate Control Algorithm (w-RCA) to optimize the medical quality of service (m-QoS) in the mobile edge computing based healthcare by considering the network parameters for instance, peak-to-mean ratio (PMR), standard deviation (Std.dev), delay and jitter during 8 min medical video stream named “Navigation to the Uterine Horn, transection of the horn and re-anastomosis’ transmission over 5 G networks. The performance of the proposed w-RCA is evaluated and compared with the conventional battery smoothing algorithm (BSA) and Baseline by using MPEG-4 encoder for optimizing m-QoS at the source or the server side. The experimental results demonstrate that the w-RCA outperforms the BSA and Baseline by optimizing QoS in remote healthcare application i.e., Tele-surgery. Besides, it is observed and analyzed that w-RCA produces better and effective results at small buffer and window sizes unlike BSA and Baseline by adopting large buffer size during QoS optimization.

An object-based convolutional neural network (OCNN) for urban land use classification

Urban land use information is essential for a variety of urban-related applications such as urban planning and regional administration. The extraction of urban land use from very fine spatial resolution (VFSR) remotely sensed imagery has, therefore, drawn much attention in the remote sensing community. Nevertheless, classifying urban land use from VFSR images remains a challenging task, due to the extreme difficulties in differentiating complex spatial patterns to derive high-level semantic labels. Deep convolutional neural networks (CNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction. However, blurred object boundaries and geometric distortion, as well as huge computational redundancy, severely restrict the potential application of CNN for the classification of urban land use. In this paper, a novel object-based convolutional neural network (OCNN) is proposed for urban land use classification using VFSR images. Rather than Pixel-wise convolutional processes, the OCNN relies on segmented objects as its functional units, and CNN networks are used to analyse and label objects such as to partition within-object and between-object variation. Two CNN networks with different model structures and window sizes are developed to predict linearly shaped objects (e.g. Highway, Canal) and general (other non-linearly shaped) objects. Then a rule-based decision fusion is performed to integrate the class-specific classification results. The effectiveness of the proposed OCNN method was tested on aerial photography of two large urban scenes in Southampton and Manchester in Great Britain. The OCNN combined with large and small window sizes achieved excellent classification accuracy and computational efficiency, consistently outperforming its sub-modules, as well as other benchmark comparators, including the Pixel-wise CNN, contextual-based MRF and object-based OBIA-SVM methods. The proposed method provides the first object-based CNN framework to effectively and efficiently address the complicated problem of urban land use classification from VFSR images.

Displacement field determination using an iterative optical flow strategy

This study presents a new strategy of retrieving displacement fields from two different images or two video frames obtained at different times using an iterative optical flow method. First, illumination modeling and image preprocessing are required before the displacement calculations can be performed. Due to the displacement continuity assumption, the displacement differences between the neighboring points are small. Then, the initial displacements of the unprocessed points are determined by the displacements of the processed neighboring points. Next, an iterative optical flow method is performed with a small window size to obtain the displacement offsets between the matching point pairs. The calculation is performed at the subpixel level in each iteration, and thus image interpolation is necessary. In this paper, image upsampling of the deformed image is applied using bi-cubic spline interpolation to achieve the subpixel intensity before all the calculations. This improvement avoids the requirement for image interpolation to be in all iterations. Several numerical tests and real image datasets were used to validate the performance of the proposed method. The results were compared with those obtained by a traditional digital image correlation method using the traditional forward additive Newton–Raphson algorithm and the iterative least square algorithm. Both tests showed that the proposed algorithm exhibits excellent performance regarding computational accuracy and speed.

Performance estimation of Adaptive threshold median filter design in image Denoising

Picture denoising is utilized as a part of numerous fields like PC vision, remote detecting, medicinal imaging, apply autonomy and so forth. In a significant number of these applications the presence of rash clamour in the procured pictures is a standout amongst the most widely recognized issues. The concept of this method is to provide simple but efficient method of image de-noising using filter to improve the performance and reduce the complexity of implementation. This method use the combination of average filtering and median filtering to remove the noise and produce better results with small window size 3x3. So the image details preservation is also better with small window. Mathematical results show that quality of image is better than the other filtering methods. Hardware implementation of this method is also very easy; because less number of calculations required removing the noise. Reconfigurable hardware filters may be embedded with photo acquirements provision to gain that goal. Field programmable doorway order (FPGA) is appropriate because pipelining or parallelism facts processing. What’s more, though the filtering algorithm techniques huge amount over data, however such does no longer require to shops a cluster regarding intermediate data and has the consequent properties: easy of computing or reproducible, for this reason it is suitable to be applied the usage of FPGA.

Spectral analysis of time-dependent market-adjusted return correlation matrix

We present an adjusted method for calculating the eigenvalues of a time-dependent return correlation matrix in a moving window. First, we compare the normalized maximum eigenvalue time series of the market-adjusted return correlation matrix to that of the logarithmic return correlation matrix on an 18-year dataset of 310 S&P 500-listed stocks for small and large window or memory sizes. We observe that the resulting new eigenvalue time series is more stationary than the time series obtained without the adjustment. Second, we perform this analysis while sweeping the window size τ∈{5,…,100}∪{500} in order to examine the dependence on the choice of window size. This approach demonstrates the multi-modality of the eigenvalue distributions. We find that the three dimensional distribution of the eigenvalue time series for our market-adjusted return is significantly more stationary than that produced by classic method. Finally, our model offers an approximate polarization domain characterized by a smooth L-shaped strip. The polarization with large amplitude is revealed, while there is persistence in agreement of individual stock returns’ movement with market with small amplitude most of the time.

Uncertainty propagation of material properties in energy simulation of existing residential buildings: The role of buildings features

Although energy simulation can provide valuable information about building energy behavior, the inaccuracy of input data can undermine the reliability of the results. Despite the vast literature about uncertainty analysis, little is known about the influence of building characteristics on the propagation of uncertainty through the energy simulation models. This study investigates the extent to which uncertain thermal conductivity and specific heat of structural layers affect the annual heating and cooling needs for a set of 144 simplified reference building configurations in three European climates. The analysis is carried out by means of a Monte Carlo technique coupled with TRNSYS hourly simulations. This study points out that the uninsulated residential buildings with a high aspect ratio, a small transparent surface with south exposure and low SHGC are more sensitive in the cooling needs estimation to the propagation of uncertainty in material properties. Similarly, the heating needs precision is greatly reduced when the uncertainty affects the thermal conductivity in uninsulated buildings with a low aspect ratio, a high SHGC and small window size. On the contrary, the uncertainty in specific heat is emphasized in buildings with external insulation having large windows oriented to either east or south and with glazing characterized by high SHGC.

Boosting Oxidative Desulfurization of Model and Real Gasoline over Phosphotungstic Acid Encapsulated in Metal–Organic Frameworks: The Window Size Matters

AbstractIt is desirable to develop new materials that can efficiently lower sulfur content in fossil fuels, such as gasoline and diesel oil. Polyoxometallic acids supported on metal–organic frameworks (MOFs) are an important class of heterogeneous catalysts for oxidative desulfurization. However, there has been no comprehensive study on the correlation between desulfurization activity and the window size of MOFs. A series of robust MOFs, which include MIL‐100(Fe), UiO‐66, and ZIF‐8, with different window sizes were exploited as hosts to encapsulate phosphotungstic acid by the “bottle around the ship” method and utilized for the ultra‐deep oxidative desulfurization (ODS) of model and real gasoline. Compared with UiO‐66 and ZIF‐8, which have very small window sizes, mesoporous MIL‐100(Fe), which has a large window size, exhibited the best catalytic performance in the ODS of refractory sulfur compounds (benzothiophene, dibenzothiophene, and 4,6‐dimethyl‐dibenzothiophene) and in recycling experiments. The correlations between the desulfurization activity and the window size of the corresponding MOFs could provide insights for the design of new porous catalysts for ODS and other size‐selective catalysis reactions in the future.

Using object-based analysis to derive surface complexity information for improved filtering of airborne laser scanning data

Airborne laser scanning (ALS) is a technique used to obtain Digital Surface Models (DSM) and Digital Terrain Models (DTM) efficiently, and filtering is the key procedure used to derive DTM from point clouds. Generating seed points is an initial step for most filtering algorithms, whereas existing algorithms usually define a regular window size to generate seed points. This may lead to an inadequate density of seed points, and further introduce error type I, especially in steep terrain and forested areas. In this study, we propose the use of objectbased analysis to derive surface complexity information from ALS datasets, which can then be used to improve seed point generation.We assume that an area is complex if it is composed of many small objects, with no buildings within the area. Using these assumptions, we propose and implement a new segmentation algorithm based on a grid index, which we call the Edge and Slope Restricted Region Growing (ESRGG) algorithm. Surface complexity information is obtained by statistical analysis of the number of objects derived by segmentation in each area. Then, for complex areas, a smaller window size is defined to generate seed points. Experimental results show that the proposed algorithm could greatly improve the filtering results in complex areas, especially in steep terrain and forested areas.