Journal articles: 'Windowing environment'

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Montaniz, Frank, and Robert Mack. "A Comparison of Touch Interface Techniques for a Graphical Windowing Software Environment." Proceedings of the Human Factors Society Annual Meeting 35, no. 5 (September 1991): 290–94. http://dx.doi.org/10.1177/154193129103500511.

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Park, Seongho, and JaeWon Kang. "An Investigative Study on the Paradigm Shift of Windowing of Korean Broadcasting Programs: Segmented Window Types and Windowing Features in a Convergence Environment." Journal of Cybercommunication Academic Society 38, no. 1 (March 31, 2021): 5–57. http://dx.doi.org/10.36494/jcas.2021.03.38.1.5.

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Kataoka, Yutaka, Masato Morisaki, Hiroshi Kuribayashi, and Hiroyoshi Ohara. "A model for input and output of multilingual text in a windowing environment." ACM Transactions on Information Systems 10, no. 4 (October 1992): 438–51. http://dx.doi.org/10.1145/146486.146558.

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Mack, Robert, and Frank Montaniz. "A Comparison of Touch and Mouse Interaction Techniques for a Graphical Windowing Software Environment." Proceedings of the Human Factors Society Annual Meeting 35, no. 5 (September 1991): 286–89. http://dx.doi.org/10.1177/154193129103500510.

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Popieul, Jean-Christophe, Pierre Loslever, Alexis Todoskoff, Philippe Simon, and Matthias Rötting. "Multivariate analysis of human behavior data using fuzzy windowing: Example with driver–car–environment system." Engineering Applications of Artificial Intelligence 25, no. 5 (August 2012): 989–96. http://dx.doi.org/10.1016/j.engappai.2011.11.011.

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Mack, Robert, and Kathy Lang. "A Benchmark Comparison of Mouse and Touch Interface Techniques for an Intelligent Workstation Windowing Environment." Proceedings of the Human Factors Society Annual Meeting 33, no. 5 (October 1989): 325–29. http://dx.doi.org/10.1177/154193128903300520.

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This study presents evidence that a prototype touch interface technology emulating basic interaction techniques of a mouse pointing device is comparable in overall usability to a conventional mouse for a direct manipulation, graphical windowing software environment. The touch technology prototype involves using either a stylus or finger, with an overlay sensitive to changes in capacitance. Users practiced each technique (mouse, stylus, finger, keyboard with no mouse), in the context of carrying out office-related tasks on the first of a two day study, and then eight similar test tasks on the second day, in a completely within-subject design. Significant effects for time on task were found for Techniques and Tasks for five practice tasks on the second day of the study. The clearest significant effect was that the stylus technique was faster than the keyboard. A qualitative analysis of errors indicates that there were problems with the precision of pointing using the finger, and to a lesser extent the stylus and mouse. User comments and ratings indicate that the stylus and mouse were preferred comparably, and were preferred to the finger and keyboard techniques.

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Bahoura, Mohammed. "Efficient FPGA-Based Architecture of the Overlap-Add Method for Short-Time Fourier Analysis/Synthesis." Electronics 8, no. 12 (December 12, 2019): 1533. http://dx.doi.org/10.3390/electronics8121533.

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This paper proposes a simple and efficient FPGA-based architecture of the overlapping/windowing and overlap-add methods for real-time FFT/IFFT-based signal processing algorithms. The analyzed signal is divided into short-time overlapping frames that are windowed before applying Fourier analysis/synthesis. Then, the original signal is reconstructed from the windowed (modified) frames using the overlap-add (OLA) technique. The proposed architecture was implemented on Field Programmable Gate Array (FPGA) using a high-level programming tool in MATLAB/SIMULINK environment. Its performance was evaluated on artificial and actual signals using objective metrics.

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King, Franklin, Jagadeesan Jayender, Sharath K. Bhagavatula, Paul B. Shyn, Steve Pieper, Tina Kapur, Andras Lasso, and Gabor Fichtinger. "An Immersive Virtual Reality Environment for Diagnostic Imaging." Journal of Medical Robotics Research 01, no. 01 (March 2016): 1640003. http://dx.doi.org/10.1142/s2424905x16400031.

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Purpose: Advancements in and adoption of consumer virtual reality (VR) are currently being propelled by numerous upcoming devices such as the Oculus Rift. Although applications are currently growing around the entertainment field, wide-spread adoption of VR devices opens up the potential for other applications that may have been unfeasible with past implementations of VR. A VR environment may provide an equal or larger screen area than what is provided with the use of multiple conventional displays while remaining comparatively cheaper and more portable making it an attractive option for diagnostic radiology applications. Methods A VR application for the viewing of multiple image slices was designed using: the Oculus Rift head-mounted display (HMD), Unity, and 3D Slicer. Volumes loaded within 3D Slicer are sent to a Unity application that proceeds to render a scene for the Oculus Rift HMD. Users may interact with the images adjusting windowing and leveling using a handheld gamepad controller. Multiple images may be brought closer to the user for detailed inspection. Results Application usage was demonstrated with the simultaneous visualization of longitudinal slices of a serial CT scan of a patient with a lung nodule. Pilot studies for validating usage of the VR system for differential diagnosis and remote collaboration were performed. Initial results suggest that using the VR system increased both task load and time taken to complete tasks, however, the resulting accuracy in assessing nodule growth of nodules was not significantly different than that achieved using a DICOM viewer application on a traditional display.

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Remy, Philippe, Bernard Devloo, and JoséSergio Rodrigues Alves Filho. "An object oriented approach to finite element programming (Phase I): a system independent windowing environment for developing interactive scientific programs." Advances in Engineering Software 14, no. 1 (January 1992): 41–46. http://dx.doi.org/10.1016/0965-9978(92)90082-q.

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Shih, Timothy K., Yule-Chyun Lin, Wen C. Pai, and Chun-Chia Wang. "An Object-Oriented Design Complexity Metric Based on Inheritance Relationships." International Journal of Software Engineering and Knowledge Engineering 08, no. 04 (December 1998): 541–66. http://dx.doi.org/10.1142/s0218194098000297.

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Software metrics serve as a significant issue to improve software quality. It is an important research of software engineering. In line with the methodologies of object-oriented analysis and design widely developed, many software metrics techniques have been proposed. However, not many focus on the metrics evaluation of an inheritance hierarchy. In this paper, we propose a novel concept named unit repeated inheritance (URI) in Z notation to realize object-oriented software metrics. The approach describes an inheritance level technique (ILT) method as a guide to measure the software complexity of an inheritance hierarchy. The measures of inheritance metrics are formed based on the proposed mechanism. Also, we use Lex and Yacc to construct a windowing tool which is used in conjunction with a conventional C++ programming environment to assist a programmer to analyze and measure his/her C++ programs.

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Schmalstieg, Dieter, Anton Fuhrmann, Gerd Hesina, Zsolt Szalavári, L. Miguel Encarnação, Michael Gervautz, and Werner Purgathofer. "The Studierstube Augmented Reality Project." Presence: Teleoperators and Virtual Environments 11, no. 1 (February 2002): 33–54. http://dx.doi.org/10.1162/105474602317343640.

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Our starting point for developing the Studierstube system was the belief that augmented reality, the less obtrusive cousin of virtual reality, has a better chance of becoming a viable user interface for applications requiring manipulation of complex three-dimensional information as a daily routine. In essence, we are searching for a 3-D user interface metaphor as powerful as the desktop metaphor for 2-D. At the heart of the Studierstube system, collaborative augmented reality is used to embed computer-generated images into the real work environment. In the first part of this paper, we review the user interface of the initial Studierstube system, in particular the implementation of collaborative augmented reality, and the Personal Interaction Panel, a two-handed interface for interaction with the system. In the second part, an extended Studierstube system based on a heterogeneous distributed architecture is presented. This system allows the user to combine multiple approaches— augmented reality, projection displays, and ubiquitous computing—to the interface as needed. The environment is controlled by the Personal Interaction Panel, a twohanded, pen-and-pad interface that has versatile uses for interacting with the virtual environment. Studierstube also borrows elements from the desktop, such as multitasking and multi-windowing. The resulting software architecture is a user interface management system for complex augmented reality applications. The presentation is complemented by selected application examples.

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Nunes, Luana F., and Kevin Gurney. "Multi-alternative decision-making with non-stationary inputs." Royal Society Open Science 3, no. 8 (August 2016): 160376. http://dx.doi.org/10.1098/rsos.160376.

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One of the most widely implemented models for multi-alternative decision-making is the multihypothesis sequential probability ratio test (MSPRT). It is asymptotically optimal, straightforward to implement, and has found application in modelling biological decision-making. However, the MSPRT is limited in application to discrete (‘trial-based’), non-time-varying scenarios. By contrast, real world situations will be continuous and entail stimulus non-stationarity. In these circumstances, decision-making mechanisms (like the MSPRT) which work by accumulating evidence, must be able to discard outdated evidence which becomes progressively irrelevant. To address this issue, we introduce a new decision mechanism by augmenting the MSPRT with a rectangular integration window and a transparent decision boundary. This allows selection and de-selection of options as their evidence changes dynamically. Performance was enhanced by adapting the window size to problem difficulty. Further, we present an alternative windowing method which exponentially decays evidence and does not significantly degrade performance, while greatly reducing the memory resources necessary. The methods presented have proven successful at allowing for the MSPRT algorithm to function in a non-stationary environment.

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Quigley, Bronagh, Mark Donnelly, George Moore, and Leo Galway. "A Comparative Analysis of Windowing Approaches in Dense Sensing Environments." Proceedings 2, no. 19 (October 17, 2018): 1245. http://dx.doi.org/10.3390/proceedings2191245.

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Windowing is an established technique employed within dense sensing environments to extract relevant features from sensor data streams. Among the established approaches of Explicit, Time-based and Sensor-Event based windowing, Dynamic windowing approaches are beginning to emerge. These dynamic approaches claim to address the inherent shortcomings of the aforementioned established approaches by determining the appropriate window length for live sensor data streams in real-time, thereby offering the potential to optimize and increase the recognition of these sensor represented activities. Beyond these potential benefits, dynamic approaches can also support anomaly detection by actively uncovering new, unknown window patterns within a trained model. This paper presents findings from a study which utilizes data from a single source dataset, towards benchmarking and comparing more traditional windowing approaches against a dynamic windowing approach. The experiments conducted on a real-world smart home dataset suggest Time-based windowing is the best approach. Through evaluation of results, Dynamic windowing approaches may benefit from carefully annotated datasets.

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Collins, Bradley Dean, Stephan Heyns, Schalk Kok, and Daniel Nico Wilke. "Application of Anti-Diagonal Averaging in Response Reconstruction." Symmetry 13, no. 7 (June 28, 2021): 1165. http://dx.doi.org/10.3390/sym13071165.

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Response reconstruction is used to obtain accurate replication of vehicle structural responses of field recorded measurements in a laboratory environment, a crucial step in the process of Accelerated Destructive Testing (ADA). Response Reconstruction is cast as an inverse problem whereby an input signal is inferred to generate the desired outputs of a system. By casting the problem as an inverse problem we veer away from the familiarity of symmetry in physical systems since multiple inputs may generate the same output. We differ in our approach from standard force reconstruction problems in that the optimisation goal is the recreated output of the system. This alleviates the need for highly accurate inputs. We focus on offline non-causal linear regression methods to obtain input signals. A new windowing method called AntiDiagonal Averaging (ADA) is proposed to improve the regression techniques’ performance. ADA introduces overlaps within the predicted time signal windows and averages them. The newly proposed method is tested on a numerical quarter car model and shown to accurately reproduce the system’s outputs, which outperform related Finite Impulse Response (FIR) methods. In the nonlinear configuration of the numerical quarter car, ADA achieved a recreated output Mean Fit Function Error (MFFE) score of 0.40% compared to the next best performing FIR method, which generated a score of 4.89%. Similar performance was shown for the linear case.

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Andò, Bruno, Salvatore Baglio, Adi R. Bulsara, and Vincenzo Marletta. "Toward a Self-Powered Vibration Sensor: The Signal Processing Strategy." Energies 14, no. 3 (February 1, 2021): 754. http://dx.doi.org/10.3390/en14030754.

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This paper, for the first time, investigates the possibility of exploiting a nonlinear bistable snap-through buckling structure employing piezoelectric transducers, to implement an autonomous sensor of mechanical vibrations, with an embedded energy harvesting functionality. The device is operated in the presence of noisy vibrations superimposed on a subthreshold deterministic (sinusoidal) input signal. While the capability of the device to harvest a significant amount of energy has been demonstrated in previous works, here, we focus on the signal processing methodology aimed to extract from the sensor output the information about the noise level (in terms of the standard deviation) and the root mean square amplitude of the deterministic component. The developed methodology, supported by experimental evidence, removes the contribution to the overall piezoelectric output voltage ascribable to the deterministic component using a thresholding and windowing algorithm. The contribution to the output voltage due to the noise can be used to unambiguously estimate the noise level. Moreover, an analytical model to estimate, from the measurement of the output voltage, the RMS amplitude of the deterministic input and the noise-related component is proposed.

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Yonto, Daniel, L. Michele Issel, and Jean-Claude Thill. "Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments." Urban Science 3, no. 3 (July 22, 2019): 75. http://dx.doi.org/10.3390/urbansci3030075.

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Spatial data analytics can detect patterns of clustering of events in small geographies across an urban region. This study presents and demonstrates a robust research design to study the longitudinal stability of spatial clustering with small case numbers per census tract and assess the clustering changes over time across the urban environment to better inform public health policy making at the community level. We argue this analysis enables the greater efficiency of public health departments, while leveraging existing data and preserving citizen personal privacy. Analysis at the census tract level is conducted in Mecklenburg County, North Carolina, on hypertension during pregnancy compiled from 2011–2014 birth certificates. Data were derived from per year and per multi-year moving counts by aggregating spatially to census tracts and then assessed for clustering using global Moran’s I. With evidence of clustering, local indicators of spatial association are calculated to pinpoint hot spots, while time series data identified hot spot changes. Knowledge regarding the geographical distribution of diseases is essential in public health to define strategies that improve the health of populations and quality of life. Our findings support that spatial aggregation at the census tract level contributes to identifying the location of at-risk “hot spot” communities to refine health programs, while temporal windowing reduces random noise effects on spatial clustering patterns. With tight state budgets limiting health departments’ funds, using geographic analytics provides for a targeted and efficient approach to health resource planning.

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Joly, Daniel, and Thierry Brossard. "Contribution of environmental factors to temperature distribution at different resolution levels on the forefield of the Loven Glaciers, Svalbard." Polar Record 43, no. 4 (October 2007): 353–59. http://dx.doi.org/10.1017/s003224740700678x.

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ABSTRACTThe climate and its components (temperature and precipitation) are organised according to different spatial scales that are structured hierarchically. The aim of this paper is to explore the dependence between temperature and deterministic factors at different scales on a 10 km2 study area on the northwestern coast of Svalbard. A GIS was developed which contained three sources of information: temperature, remotely sensed imagery and digital elevation models (DEM), and derived raster data layers. The first layer, temperatures, was acquired at regularly observed temporal intervals from 53 stations. The second layer comprised remotely sensed images (aerial photography and SPOT imagery) and DEM data at 2 m and 20 m resolution, respectively. From these, a windowing procedure was applied to derive several spatial subsets of different spatial resolutions (6, 14, 30, 60, 140, and 300 m). The third layer comprised slope, aspect, and a theoretical solar radiation value derived from the DEM, and a vegetation index derived from the remotely sensed imagery. Linear regressions were then systematically conducted on the datasets, with temperature as the dependent variable, and each of the other data layers as the independent variables. By using graphical analysis, we link the correlation coefficients obtained for each factor, from the smallest spatial resolution (6 m) to the largest resolution (300 m). The results indicated that each explanatory variable and scale brings a specific contribution to changes in temperature. For example, the effect of elevation remains constant for all spatial resolutions, reflecting a quasi ‘non-scalar’ pattern of this variable. For other variables however, the effect of spatial scale can have a strong effect. In the case of solar radiation, a maximum of explanation was obtained for spatial resolutions of 14 m and 60 m; for vegetation index the optimum contribution was related to the 300 m resolution. Thus, different environment characteristics may have significant effects on changes in temperature when differences in spatial scale are taken into account.

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Catak, Ferhat Ozgur, Javed Ahmed, Kevser Sahinbas, and Zahid Hussain Khand. "Data augmentation based malware detection using convolutional neural networks." PeerJ Computer Science 7 (January 22, 2021): e346. http://dx.doi.org/10.7717/peerj-cs.346.

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Due to advancements in malware competencies, cyber-attacks have been broadly observed in the digital world. Cyber-attacks can hit an organization hard by causing several damages such as data breach, financial loss, and reputation loss. Some of the most prominent examples of ransomware attacks in history are WannaCry and Petya, which impacted companies’ finances throughout the globe. Both WannaCry and Petya caused operational processes inoperable by targeting critical infrastructure. It is quite impossible for anti-virus applications using traditional signature-based methods to detect this type of malware because they have different characteristics on each contaminated computer. The most important feature of this type of malware is that they change their contents using their mutation engines to create another hash representation of the executable file as they propagate from one computer to another. To overcome this method that attackers use to camouflage malware, we have created three-channel image files of malicious software. Attackers make different variants of the same software because they modify the contents of the malware. In the solution to this problem, we created variants of the images by applying data augmentation methods. This article aims to provide an image augmentation enhanced deep convolutional neural network (CNN) models for detecting malware families in a metamorphic malware environment. The main contributions of the article consist of three components, including image generation from malware samples, image augmentation, and the last one is classifying the malware families by using a CNN model. In the first component, the collected malware samples are converted into binary file to 3-channel images using the windowing technique. The second component of the system create the augmented version of the images, and the last part builds a classification model. This study uses five different deep CNN model for malware family detection. The results obtained by the classifier demonstrate accuracy up to 98%, which is quite satisfactory.

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M. Al-Sammna, Ahmed, Marwan Hadri Azmi, and Tharek Abd Rahman. "Time-Varying Ultra-Wideband Channel Modeling and Prediction." Symmetry 10, no. 11 (November 12, 2018): 631. http://dx.doi.org/10.3390/sym10110631.

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This paper considers the channel modeling and prediction for ultra-wideband (UWB) channels. The sparse property of UWB channels is exploited, and an efficient prediction framework is developed by introducing two simplified UWB channel impulse response (CIR) models, namely, the windowing-based on window delay (WB-WD) and the windowing-based on bin delay (WB-BD). By adopting our proposed UWB windowing-based CIR models, the recursive least square (RLS) algorithm is used to predict the channel coefficients. By using real CIR coefficients generated from measurement campaign data conducted in outdoor environments, the modeling and prediction performance results and the statistical properties of the root mean square (RMS) delay spread values are presented. Our proposed framework improves the prediction performances with lower computational complexity compared with the performance of the recommended ITU-R UWB-CIR model. It is shown that our proposed framework can achieved 15% lower prediction error with a complexity reduction by a factor of 12.

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Tan, Kah-Chye, Hock Lim, and B. T. G. Tan. "Windowing techniques for image restoration." CVGIP: Graphical Models and Image Processing 53, no. 5 (September 1991): 491–500. http://dx.doi.org/10.1016/1049-9652(91)90033-g.

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Bergamo, Paolo, Daniele Boiero, and Laura Valentina Socco. "Retrieving 2D structures from surface-wave data by means of space-varying spatial windowing." GEOPHYSICS 77, no. 4 (July 1, 2012): EN39—EN51. http://dx.doi.org/10.1190/geo2012-0031.1.

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Surface-wave techniques are mainly used to retrieve 1D subsurface models. However, in 2D environments, the 1D approach usually neglects the presence of lateral variations and because the surface-wave path crosses different materials, the resulting model is a simplified or misleading description of the site. We tested a processing technique to retrieve 2D structures from surface-wave data acquired with a limited number of receivers. Our technique was based on a two-step process. First, we extracted several local dispersion curves along the survey line using a spatial windowing based on a set of Gaussian windows with different shapes; the window maxima span the survey line so that we were able to extract a dispersion curve from the seismic record for every window. This provided a set of local dispersion curves each of them referring to a different subsurface portion. This space varying spatial windowing provided a good compromise between wavenumber resolution and the lateral resolution of the obtained local dispersion curves. In the second step, we inverted the retrieved set of dispersion curves using a laterally constrained inversion scheme. We applied this procedure to the processing of synthetic and real data sets and the method proved to be successful in reconstructing even complex 2D structures in the subsurface.

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Rahman, Md Saifur, Yosuke Sugiura, and Tetsuya Shimamura. "Utilization of windowing effect and accumulated autocorrelation function and power spectrum for pitch detection in noisy environments." IEEJ Transactions on Electrical and Electronic Engineering 15, no. 11 (September 7, 2020): 1681–90. http://dx.doi.org/10.1002/tee.23238.

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Aucott, Lorna S., Stephen T. Buckland, Paul H. Garthwaite, Ian M. Nevison, and Ian Murray. "Windowing technique for determining the composition of organic samples by near-infrared reflectance." Analyst 117, no. 6 (1992): 947. http://dx.doi.org/10.1039/an9921700947.

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Pita, Antonio, Francisco J. Rodriguez, and Juan M. Navarro. "Cluster Analysis of Urban Acoustic Environments on Barcelona Sensor Network Data." International Journal of Environmental Research and Public Health 18, no. 16 (August 4, 2021): 8271. http://dx.doi.org/10.3390/ijerph18168271.

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As cities grow in size and number of inhabitants, continuous monitoring of the environmental impact of sound sources becomes essential for the assessment of the urban acoustic environments. This requires the use of management systems that should be fed with large amounts of data captured by acoustic sensors, mostly remote nodes that belong to a wireless acoustic sensor network. These systems help city managers to conduct data-driven analysis and propose action plans in different areas of the city, for instance, to reduce citizens’ exposure to noise. In this paper, unsupervised learning techniques are applied to discover different behavior patterns, both time and space, of sound pressure levels captured by acoustic sensors and to cluster them allowing the identification of various urban acoustic environments. In this approach, the categorization of urban acoustic environments is based on a clustering algorithm using yearly acoustic indexes, such as Lday, Levening, Lnight and standard deviation of Lden. Data collected over three years by a network of acoustic sensors deployed in the city of Barcelona, Spain, are used to train several clustering methods. Comparison between methods concludes that the k-means algorithm has the best performance for these data. After an analysis of several solutions, an optimal clustering of four groups of nodes is chosen. Geographical analysis of the clusters shows insights about the relation between nodes and areas of the city, detecting clusters that are close to urban roads, residential areas and leisure areas mostly. Moreover, temporal analysis of the clusters gives information about their stability. Using one-year size of the sliding window, changes in the membership of nodes in the clusters regarding tendency of the acoustic environments are discovered. In contrast, using one-month windowing, changes due to seasonality and special events, such as COVID-19 lockdown, are recognized. Finally, the sensor clusters obtained by the algorithm are compared with the areas defined in the strategic noise map, previously created by the Barcelona city council. The developed k-means model identified most of the locations found on the overcoming map and also discovered a new area.

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Lim, Hock, Kah-Chye Tan, and B. T. G. Tan. "Edge errors in inverse and Wiener filter restorations of motion-blurred images and their windowing treatment." CVGIP: Graphical Models and Image Processing 53, no. 2 (March 1991): 186–95. http://dx.doi.org/10.1016/1049-9652(91)90060-w.

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Lois, A., F. Kopsaftopoulos, D. Giannopoulos, K. Polychronopoulou, and N. Martakis. "A two-stage procedure for the automated identification of micro-earthquakes: implementation on single-station 3C passive seismic data." Geophysical Journal International 224, no. 3 (November 24, 2020): 1705–23. http://dx.doi.org/10.1093/gji/ggaa558.

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SUMMARY In this paper, we propose a two-step procedure for the automated detection of micro-earthquakes, using single-station, three-component passive seismic data. The first step consists of the computation of an appropriate characteristic function, along with an energy-based thresholding scheme, in order to attain an initial discrimination of the seismic noise from the ‘useful’ information. The three-component data matrix is factorized via the singular value decomposition by means of a properly selected moving window and for each step of the windowing procedure a diagonal matrix containing the estimated singular values is formed. The ${L_2}$-norm of the singular values resulting from the above-mentioned windowing process defines the time series which serves as a characteristic function. The extraction of the seismic signals from the initial record is achieved by following a histogram-based thresholding scheme. The histogram of the characteristic function, which constitutes its empirical probability density function, is estimated and the optimum threshold value is chosen corresponds to the bin that separates the above-mentioned histogram in two different areas delineating the background noise and the outliers. Since detection algorithms often suffer from false alarms, which increase in extremely noisy environments, as a second stage, we propose a new ‘decision-making’ scenario to be applied on the extracted intervals, for the purpose of decreasing the probability of false alarms. In this context, we propose a methodology, based on comparing among autoregressive models estimated both on isolated seismic noise, in addition to the detections resulting from the first stage. The performance and efficiency of the proposed technique is supported by its application to a series of experiments that were based on both synthetic and real data sets. In particular, we investigate the effectiveness of the characteristic function, along with the thresholding scheme by subjecting them to noise robustness tests using synthetic seismic noise, with different statistical characteristics and at noise levels varying from 5 down to –5 dB. Results are compared with those obtained by the implementation of a three-component version of the well-known STA/LTA algorithm to the same data set. Moreover, the proposed technique and its potential to distinguish seismic noise from the useful information through the proposed decision making scheme is evaluated, by its application to real data sets, acquired by three-component short-period recorders that were installed for monitoring the microseismic activity in areas characterized by different noise attributes.

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Vedala, Krishnatej, S. M. Amin Motahari, Mohammed Goryawala, Mercedes Cabrerizo, Ilker Yaylali, and Malek Adjouadi. "Quasi-Stationarity of EEG for Intraoperative Monitoring during Spinal Surgeries." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/468269.

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We present a study and application of quasi-stationarity of electroencephalogram for intraoperative neurophysiological monitoring (IONM) and an application of Chebyshev time windowing for preconditioning SSEP trials to retain the morphological characteristics of somatosensory evoked potentials (SSEP). This preconditioning was followed by the application of a principal component analysis (PCA)-based algorithm utilizing quasi-stationarity of EEG on 12 preconditioned trials. This method is shown empirically to be more clinically viable than present day approaches. In all twelve cases, the algorithm takes 4 sec to extract an SSEP signal, as compared to conventional methods, which take several minutes. The monitoring process using the algorithm was successful and proved conclusive under the clinical constraints throughout the different surgical procedures with an accuracy of 91.5%. Higher accuracy and faster execution time, observed in the present study, in determining the SSEP signals provide a much improved and effective neurophysiological monitoring process.

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McKay, Helen, Nathan Griffiths, Phillip Taylor, Theo Damoulas, and Zhou Xu. "Bi-directional online transfer learning: a framework." Annals of Telecommunications 75, no. 9-10 (October 2020): 523–47. http://dx.doi.org/10.1007/s12243-020-00776-1.

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Abstract Transfer learning uses knowledge learnt in source domains to aid predictions in a target domain. When source and target domains are online, they are susceptible to concept drift, which may alter the mapping of knowledge between them. Drifts in online environments can make additional information available in each domain, necessitating continuing knowledge transfer both from source to target and vice versa. To address this, we introduce the Bi-directional Online Transfer Learning (BOTL) framework, which uses knowledge learnt in each online domain to aid predictions in others. We introduce two variants of BOTL that incorporate model culling to minimise negative transfer in frameworks with high volumes of model transfer. We consider the theoretical loss of BOTL, which indicates that BOTL achieves a loss no worse than the underlying concept drift detection algorithm. We evaluate BOTL using two existing concept drift detection algorithms: RePro and ADWIN. Additionally, we present a concept drift detection algorithm, Adaptive Windowing with Proactive drift detection (AWPro), which reduces the computation and communication demands of BOTL. Empirical results are presented using two data stream generators: the drifting hyperplane emulator and the smart home heating simulator, and real-world data predicting Time To Collision (TTC) from vehicle telemetry. The evaluation shows BOTL and its variants outperform the concept drift detection strategies and the existing state-of-the-art online transfer learning technique.

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Mahajan, Ajay, and Fernando Figueroa. "Intelligent seam tracking using ultrasonic sensors for robotic welding." Robotica 15, no. 3 (May 1997): 275–81. http://dx.doi.org/10.1017/s0263574797000313.

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This paper presents a novel approach for seam tracking using ultrasonics. An ultrasonic seam tracking system has been developed for robotic welding which tracks a seam that curves freely on a two-dimensional surface. The seam is detected by scanning the area ahead of the torch and monitoring the amplitude of the waves received after reflection from the workpiece surface. Scanning is accomplished by using two ultrasonic sensors (a transmitter and a receiver) mounted on a stepper motor such that the transmitter angle is the same as the receiver angle. The motor is mounted on the end-effector just ahead of the welding torch and covers a ninety degree arc in front of the torch. If there is no seam then the receiver receives most of the transmitted waves after reflection, but if there is a seam then most of the transmitted waves are dispersed in directions other than that of the receiver. The system has been tested and is very robust in the harsh environments generated by the arc welding process. The robustness of the system stems from using various schemes such as time windowing, a waveguide, air and metal shields, and an intelligent sensor manager. This ultrasonic system offers some distinct advantages over traditional systems using vision and other sensing techniques. It can be used to weld very shiny surfaces, and is a very economical method in terms of cost as well as computational intensity. The system can be used to detect seams less than 0.5 mm wide and 0.5 mm deep.

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Spinsante, Susanna, Alberto Angelici, Jens Lundström, Macarena Espinilla, Ian Cleland, and Christopher Nugent. "A Mobile Application for Easy Design and Testing of Algorithms to Monitor Physical Activity in the Workplace." Mobile Information Systems 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/5126816.

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This paper addresses approaches to Human Activity Recognition (HAR) with the aim of monitoring the physical activity of people in the workplace, by means of a smartphone application exploiting the available on-board accelerometer sensor. In fact, HAR via a smartphone or wearable sensor can provide important information regarding the level of daily physical activity, especially in situations where a sedentary behavior usually occurs, like in modern workplace environments. Increased sitting time is significantly associated with severe health diseases, and the workplace is an appropriate intervention setting, due to the sedentary behavior typical of modern jobs. Within this paper, the state-of-the-art components of HAR are analyzed, in order to identify and select the most effective signal filtering and windowing solutions for physical activity monitoring. The classifier development process is based upon three phases; a feature extraction phase, a feature selection phase, and a training phase. In the training phase, a publicly available dataset is used to test among different classifier types and learning methods. A user-friendly Android-based smartphone application with low computational requirements has been developed to run field tests, which allows to easily change the classifier under test, and to collect new datasets ready for use with machine learning APIs. The newly created datasets may include additional information, like the smartphone position, its orientation, and the user’s physical characteristics. Using the mobile tool, a classifier based on a decision tree is finally set up and enriched with the introduction of some robustness improvements. The developed approach is capable of classifying six activities, and to distinguish between not active (sitting) and active states, with an accuracy near to 99%. The mobile tool, which is going to be further extended and enriched, will allow for rapid and easy benchmarking of new algorithms based on previously generated data, and on future collected datasets.

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Nafisah, Syifaun, Oyas Wahyunggoro, and Lukito Edi Nugroho. "THE COMPARATIVE PERFORMANCE EVALUATION OF WINDOW FUNCTIONS UNDER NOISY ENVIRONMENT FOR SPEECH RECOGNITION." Jurnal Teknologi 78, no. 5-7 (May 19, 2016). http://dx.doi.org/10.11113/jt.v78.8690.

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The accuracy and user acceptance of speech recognition systems is increasing in the last few years especially for automated identification and biomedical applications. In implementation, it works based on the feature of utterance that will be recognized through a feature extraction process. One process in feature extraction is windowing that is done for minimizing the disruptions at the first and last of the frame. Basically, many window functions exist such as rectangular window, flat top window, hamming window, etc, but in the real application only hamming or Hanning function that are usually used as a function in the windowing. This article will analyzed the performance of all of window functions to prove the performance of those function. The method that was used are mel-frequencies cepstral coefficients (MFCCs) as feature extractor technique and back propagation neural networks (BPNNs) as classifier. The result shows that it can produce an accuracy at least 99%. The optimal accuracy up to 99.86% is achieved using rectangle window with the duration of process is 15.47 msec. This results show the superior performance of rectangle window as reference to recognize an isolated word based on speech.

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Hare, Brian M., Harald Edens, Paul Krehbiel, William Rison, O. Scholten, S. Buitink, A. Corstanje, et al. "Timing Calibration and Windowing Technique Comparison for Lightning Mapping Arrays." Earth and Space Science 8, no. 7 (July 2021). http://dx.doi.org/10.1029/2020ea001523.

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Bhandari, Ashish Kumar, Deepak Kumar, and Anil Kumar. "Intrascale windowing-based cuckoo search–optimized sub-band thresholding for satellite image denoising." Arabian Journal of Geosciences 13, no. 16 (August 2020). http://dx.doi.org/10.1007/s12517-020-05798-6.

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Pondicherry, Rasik, Marc C. Besch, Arvind Thiruvengadam, and Daniel Carder. "A Vehicle Activity-Based Windowing Approach to Evaluate Real-World NOx Emissions from Modern Heavy-Duty Diesel Trucks." Atmospheric Environment, December 2020, 118169. http://dx.doi.org/10.1016/j.atmosenv.2020.118169.

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Colquhoun, Douglas A., Ryan P. Davis, Theodore T. Tremper, Jenny J. Mace, Jan M. Gombert, William D. Sheldon, Joseph J. Connolly, Justin F. Adams, and Kevin K. Tremper. "Design of a novel multifunction decision support/alerting system for in-patient acute care, ICU and floor (AlertWatch AC)." BMC Anesthesiology 21, no. 1 (July 24, 2021). http://dx.doi.org/10.1186/s12871-021-01411-9.

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Abstract Background Multifunction surveillance alerting systems have been found to be beneficial for the operating room and labor and delivery. This paper describes a similar system developed for in-hospital acute care environments, AlertWatch Acute Care (AWAC). Results A decision support surveillance system has been developed which extracts comprehensive electronic health record (EHR) data including live data from physiologic monitors and ventilators and incorporates them into an integrated organ icon-based patient display. Live data retrieved from the hospitals network are processed by presenting scrolling median values to reduce artifacts. A total of 48 possible alerts are generated covering a broad range of critical patient care concerns. Notification is achieved by paging or texting the appropriated member of the critical care team. Alerts range from simple out of range values to more complex programing of impending Ventilator Associated Events, SOFA, qSOFA, SIRS scores and process of care reminders for the management of glucose and sepsis. As with similar systems developed for the operating room and labor and delivery, there are green, yellow, and red configurable ranges for all parameters. A census view allows surveillance of an entire unit with flashing or text to voice alerting and enables detailed information by windowing into an individual patient view including live physiologic waveforms. The system runs via web interface on desktop as well as mobile devices, with iOS native app available, for ease of communication from any location. The goal is to improve safety and adherence to standard management protocols. Conclusions AWAC is designed to provide a high level surveillance view for multi-bed hospital units with varying acuity from standard floor patients to complex ICU care. Alerts are generated by algorithms running in the background and automatically notify the selected member of the patients care team. Its value has been demonstrated for low acuity patients, further study is required to determine its effectiveness in high acuity patients.

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Journal articles on the topic 'Windowing environment'

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