Window Scaling Research Articles

Plasma window performance and scaling for an accelerator-based neutron source.

A "plasma window" was developed for use with a SHINE accelerator-based neutron source. In this work, the design of the plasma window is presented along with results demonstrating successful operation in deuterium over a range of aperture diameters (5-12mm), gas flow rates (3.5 to 12.5 slm), and input powers (10-55 kW). An empirically determined relationship between the aperture diameter, deuterium gas flow rate, input power, and plasma window pressure differential is also presented.

Transfer Learning Approach for Indoor Localization with Small Datasets

Indoor pedestrian localization has been the subject of a great deal of recent research. Various studies have employed pedestrian dead reckoning, which determines pedestrian positions by transforming data collected through sensors into pedestrian gait information. Although several studies have recently applied deep learning to moving object distance estimations using naturally collected everyday life data, this data collection approach requires a long time, resulting in a lack of data for specific labels or a significant data imbalance problem for specific labels. In this study, to compensate for the problems of the existing PDR, a method based on transfer learning and data augmentation is proposed for estimating moving object distances for pedestrians. Consistent high-performance moving object distance estimation is achieved using only a small training dataset, and the problem of the concentration of training data only on labels within a certain range is solved using window warping and scaling methods. The training dataset consists of the three-axes values of the accelerometer sensor and the pedestrian’s movement speed calculated based on GPS coordinates. All data and GPS coordinates are collected through the smartphone. A performance evaluation of the proposed moving pedestrian distance estimation system shows a high distance error performance of 3.59 m with only approximately 17% training data compared to other moving object distance estimation techniques.

Extensive performance analysis of OpenDayLight (ODL) and Open Network Operating System (ONOS) SDN controllers

The term Software Defined Network or SDN is one of the many emerging technologies that has been revolutionizing the concept of networking. By decoupling the Data and Control plane, it has lot to offer in the field of information technology. The Control Plane is regarded as the brain of the network, which is a centralized unit of SDN network along with the controller. Selection of SDN controller is one of the most important task in designing of SDN based Network. Through this paper, we intend to compare two eminent controllers: OpenDayLight and ONOS controllers, according to their efficacy. Mininet, which is meant for demonstrating the simulation of switching and end-devices, has been taken into account. Apart from this, we have used Wireshark Packet Analyser and iperf, which provides real-time traffic flow between Mininet and the Controller. Performance of the controller plays an important role in the network and answers question like how it is going to perform. There are the various factors that helps in evaluating the performance of SDN controller. After analysing, it has been found that ONOS has better performance, with respect to throughput, TCP Window Scaling, TCP/UDP Bandwidth, Burst Rate, Jitter, Goodput, TCP Stevens Graph, Round Trip Time (RTT), and its usability. This paper helps us to understand performance abilities between ONOS and ODL in medium-to-large scale SDN Networks.

STOWP: A light-weight deep residual network integrated windowing strategy for storage workload prediction in cloud systems

Accurate storage workload forecasting of big data applications is a constructive approach to improve the job scheduling and fine-grained load balancing in real-time cluster systems. However, despite the recent advances of deep learning architectures, demands for more accurate workload time series forecasting algorithm exist. Therefore, we propose a light-weight STOrage Workload time series Prediction method named as ‘STOWP’ integrating Neural Basis Expansion Analysis (N-BEATS) deep model with windowing strategy. The STOWP approach implements a multi-input–multi-output (MIMO) window strategy for capturing the historical storage variation patterns of the workload data. Furthermore, a within window scaling strategy is adopted to effectively estimate the diversity of the workload requests during different time horizons. For experimental evaluation, we used Web-Search dataset containing Search Engine’s I/O real-time workload traces. To improve the performance of STOWP, the hyper-parameters’ sensitivity is well investigated. Through results, we observed that the ’STOWP’ improves the RMSE by 3.33% and MAE by 3.44% atleast in comparison with the existing benchmarks storage workload forecasting techniques.

Exploring the use of water resource recovery facility instrument data to visualise dynamic resilience to environmental stressors

Water resource recovery facilities (WRRF) face increasingly dynamic stressors, such as higher rainfall intensity and extended dry periods, which can exert stress on ageing water infrastructure and processes. These events can generate process stresses, which lead to wastewater process failures which result in pollution events that could be identified from instrument data used for operational/compliance monitoring. This extraction can be performed on two levels (1) for discrete processes that generate data to monitor process control variables and (2) at the WRRF process boundary (global), which is mainly used for compliance. Both levels of data hold valuable information on the dynamic influence of environmental stressors (cause) and the resulting process stress or resilience (effect) as ‘dynamic resilience’. This paper proposes a novel methodology that uses actual water company instrument data to evaluate the ‘discrete’ (unit processes) and ‘global’ (WRRF boundary) dynamic resilience of a WRRF in the south of the UK. Dynamic resilience is presented as a four-stage methodology, which; (1) cleans WRRF data and extracts a standard operating condition; (2) identifies dynamic high and low flow environmental stressor events (one in five years); (3) models the process stresses and resilience generated by the imposed dynamic stressor before; (4) generating a contoured heat map of process-related stresses or resilience as a self ordering window. These methods demonstrate the possibility of visualising the dynamics of WRRF resilience (dynamic stressors and process stresses/resilience) resulting from high and low flow dynamic environmental stressors. Despite some challenges experienced with self ordering window scaling, the results demonstrate the possibility of identifying zones of process stress and resilience. It may also be possible to expand the methods developed to incorporate storm flows and combined sewer discharges.

Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province, China as an example

A method is proposed for the prospecting prediction of subsurface mineral deposits based on soil geochemistry data and a deep convolutional neural network model. This method uses three techniques (window offset, scaling, and rotation) to enhance the number of training data for the model. A window area is used to extract the spatial distribution characteristics of soil geochemistry and measure their correspondence with the occurrence of known subsurface deposits. Prospecting prediction is achieved by matching the characteristics of the window area of an unknown area with the relationships established in the known area. This method can efficiently predict mineral prospective areas where there are few ore deposits used for generating the training dataset, meaning that the deep-learning method can be effectively used for deposit prospecting prediction. Using soil active geochemical measurement data, this method was applied in the Daqiao area, Gansu Province, for which seven favorable gold prospecting target areas were predicted. The Daqiao orogenic gold deposit of latest Jurassic and Early Jurassic age in the southern domain has more than 105 t of gold resources at an average grade of 3–4 g/t. In 2020, the project team drilled and verified the K prediction area, and found 66 m gold mineralized bodies. The new method should be applicable to prospecting prediction using conventional geochemical data in other areas.© 2022 China Geology Editorial Office.

Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province, China as an example

A method is proposed for the prospecting prediction of subsurface mineral deposits based on soil geochemistry data and a deep convolutional neural network model. This method uses three techniques (window offset, scaling, and rotation) to enhance the number of training data for the model. A window area is used to extract the spatial distribution characteristics of soil geochemistry and measure their correspondence with the occurrence of known subsurface deposits. Prospecting prediction is achieved by matching the characteristics of the window area of an unknown area with the relationships established in the known area. This method can efficiently predict mineral prospective areas where there are few ore deposits used for generating the training dataset, meaning that the deep-learning method can be effectively used for deposit prospecting prediction. Using soil active geochemical measurement data, this method was applied in the Daqiao area, Gansu Province, for which seven favorable gold prospecting target areas were predicted. The Daqiao orogenic gold deposit of latest Jurassic and Early Jurassic age in the southern domain has more than 105 t of gold resources at an average grade of 3–4 g/t. In 2020, the project team drilled and verified the K prediction area, and found 66 m gold mineralized bodies. The new method should be applicable to prospecting prediction using conventional geochemical data in other areas.© 2022 China Geology Editorial Office.

BBR-CWS: Improving the Inter-Protocol Fairness of BBR

TCP congestion control adjusts the sending rate in order to protect Internet from the continuous traffic and ensure fair coexistence among multiple flows. Especially, loss-based congestion control algorithms were mainly used, which worked relatively well for past Internet with low bandwidth and small bottleneck buffer size. However, the modern Internet uses considerably more sophisticated network equipment and advanced transmission technologies, and loss-based congestion control can cause performance degradation due to excessive queueing delay and packet loss. Therefore, Google introduced a new congestion control in 2016, Bottleneck Bandwidth Round-trip propagation time (BBR). In contrast with traditional congestion control, BBR tries to operate at the Kleinrock’s optimal operating point, where delivery rate is maximized and latency is minimized. However, when BBR and loss-based congestion control algorithms coexist on the same bottleneck link, most of bottleneck bandwidth is occupied by flows that use a particular algorithm, and excessive packet retransmission can occur. Therefore, this paper proposes a BBR congestion window scaling (BBR-CWS) scheme to improve BBR’s inter-protocol fairness with a loss-based congestion control algorithm. Through Mininet experiment results, we confirmed that fairness between BBR-CWS and CUBIC improved up to 73% and has the value of 0.9 or higher in most bottleneck buffer environments. Moreover, the number of packet retransmissions was reduced by up to 96%, compared to the original BBR.

Congestion Window Scaling Method to Optimize Delay in TCP/IP

TCP (Transmission Control Protocol) is a protocol of layer four (Transport Layer), and IP (Internet Protocol) is in the Network Layer (Layer 3) of the OSI Model. The TCP protocol is the most used of the network applications on the Internet. TCP performance is strongly influenced by its congestion control algorithms that limit the amount of transmitted traffic based on the estimated network capacity and utilization. TCP is a connection-oriented protocol which guarantees the sent packets to the destination in accurate order whereas UDP is an unreliable/connectionless protocol which does not guarantee that the sent messages would reach the destination. A TCP connection is used to control packet sizes, the rate of data exchange, flow control and network congestion. Unfortunately, congestion/network overload happens in the TCP connection which affects the rate of data flow, loss in packets transmitted, packet transmission delay and delayed acknowledgment. In this paper, a method to scale the congestion window on the sender and receiver to optimize the delay of transmission in TCP in order to reduce the congestion rate is proposed. The proposed congestion control algorithm enables a sender to optimize and initialize the congestion window before transmitting packets. The proposed method is evaluated by simulating the transmission in Network Simulation 3 (NS3).

Moving object classification using local shape and HOG features in wavelet-transformed space with hierarchical SVM classifiers

This paper proposes an integrated system for the segmentation and classification of four moving objects, including pedestrians, cars, motorcycles, and bicycles, from their side-views in a video sequence. Based on the use of an adaptive background in the red–green–blue (RGB) color model, each moving object is segmented with its minimum enclosing rectangle (MER) window by using a histogram-based projection approach or a tracking-based approach. Additionally, a shadow removal technique is applied to the segmented objects to improve the classification performance. For the MER windows with different sizes, a window scaling operation followed by an adaptive block-shifting operation is applied to obtain a fixed feature dimension. A weight mask, which is constructed according to the frequency of occurrence of an object in each position within a square window, is proposed to enhance the distinguishing pixels in the rescaled MER window. To extract classification features, a two-level Haar wavelet transform is applied to the rescaled MER window. The local shape features and the modified histogram of oriented gradients (HOG) are extracted from the level-two and level-one sub-bands, respectively, of the wavelet-transformed space. A hierarchical linear support vector machine classification configuration is proposed to classify the four classes of objects. Six video sequences are used to test the classification performance of the proposed method. The computer processing times of the object segmentation, object tracking, and feature extraction and classification approaches are 79ms, 211ms, and 0.01ms, respectively. Comparisons with different well-known classification approaches verify the superiority of the proposed classification method.

The Recognition of Handwritten Digital Based on BP Neural Network

The article put forward to new recognition method of handwritten digital based on BP neural network. Its recognition process mainly includes ten aspect: incline correction of handwritten number, edge detection and separation of a set number, binarization, denoising, extraction of numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. The test results show that the recognition rate of this method can be over 92 percent. The recognition time of characters for character is less than 1.1 second, which means that the method is more effective recognition ability and can better satisfy the real system requirements.It should be widely applied practical significance for Book Number Recognition, zip code recognition sorting.

A bit error rate analysis for TCP traffic over parallel free space photonics

Inter-satellite links (ISL) are a useful technology to transmit data to space stations and to communicate between satellites. However, there are serious limitations due to long delays and poor channel performance, resulting in high bit error rates (BER). In this paper, parallel transmission and the scaling of the Transport Control Protocol (TCP) window in free space optics (FSO) communications are analyzed in order to overcome these disadvantages in optical inter-satellite links. Latency and BER are the dominant effects that determine link performance. Thus, a physical, link, network and transport cross-layer analysis for FSO over ISL is presented in this paper. This analysis shows the advantages and disadvantages of using optical parallel transmission and TCP window scaling for free space optical links between stations and satellite constellations. The key contribution of this work is to simulate the effects of the BER and to link the results to packet error rate (PER) to determine the goodput for TCP transmissions by using a cross-layering approach. The results give evidence that wavelength division multiplexing (WDM) can mitigate the effects of long delay and high BER for a FSO communication using TCP.

Finding the functional grain: comparing methods for scaling resistance surfaces

The influence of landscape features on the movement of an organism between two point locations is often measured as an effective distance. Typically, raster models of landscape resistance are used to calculate effective distance. Because organisms may experience landscape heterogeneity at different scales (i.e. functional grains), using a raster with too fine or too coarse a spatial grain (i.e. analysis grain) may lead to inaccurate estimates of effective distance. We adopted a simulation approach where the true functional grain and effective distance for a theoretical organism were defined and the analysis grains of landscape connectivity models were systematically changed. We used moving windows and grains of connectivity, a recently introduced landscape graph method that uses an irregular tessellation of the resistance surface to coarsen the landscape data. We then used least-cost path metrics to measure effective distance and found that matching the functional and analysis grain sizes was most accurate at recovering the expected effective distance, affirming the importance of multi-scale analysis. Moving window scaling with a maximum function (win.max) performed well when the majority of landscape structure influencing connectivity consisted of high resistance features. Moving window scaling with a minimum function (win.min) performed well when the relevant landscape structure consisted of low resistance regions. The grains of connectivity method performed well under all scenarios, avoiding an a priori choice of window function, which may be challenging in complex landscapes. Appendices are provided that demonstrate the use of grains of connectivity models.

Remote access of radiological images using android

Aims: To develop a Graphical User Interface (GUI) system for mobile application to provide secured remote access to medical images for faster diagnostic and therapeutic use. Settings and Design: We have designed an android application using the android operating system. The application is compatible to DICOM images. The application has two layouts and with tools such as window scaling, bone edge detection and RGB colouring. We have also designed a database server for storing patients medical images were a physician can store patient's medical image taken through one of the imaging modality. Methods and Material: The system consists of two separate modules one is at the hospital called Base unit and the other at doctor's site called Mobile unit. At the base unit we have developed a database server for storing the medical images of the patient. We have developed the database using the PHP and my SQL programming languages. The software design and implementation is similar to the client server model. Communication between the two parts is achieved using TCP/IP as network protocol, which ensures safe data transmission over different telecommunication means such as GSM, Satellite, and POTS. The Mobile unit mainly consists of a software module which is built into an android application used for receiving the image data (DICOM Images) of the patient, viewing the image and sending back the report of the patient to the base unit using a communication module (GSM, Satellite or POTS modem). Here the Mobile unit is the mobile phone supporting android applications (smart phones, tablet computers) [5] . An android application viewer is created in the mobile phone of the doctor's through which he can access the medical images stored in the base unit. Results: Proposed work is capable of accessing the medical images from the remote area by the doctor's using their android smart phones. At the mobile unit doctors can use the image enhancement techniques provided for better visualization of the medical images. Also doctor can send the report back to the base unit. Conclusions: As wireless technology is becoming an important part of health care centre's, proposed work intended to use small and portable mobile devices for the assistance of healthcare personnel. It enables the remote access of patient information and images through his wireless handheld devices. Development of such applications will enable to provide better healthcare facilities in time to patients.

Machine-Printed Invoice Number Based on Fuzzy Recognition

A new fuzzy recognition method of machine-printed invoice number based on neural network is presented. This method includes ten links: invoice number detection and separation of right on top of invoice, binarization, denoising, incline correction, extraction of invoice code numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. Through testing, the recognition rate of this method can be over 99%.The recognition time of characters for character is less than 1 second, which means that the method is of more effective recognition ability and can better satisfy the real system requirements.

WE‐E‐AUD B‐03: Analysis of Dose Perturbation Factors for An NACP‐02 Parallel‐Plate Ionization Chamber in Clinical Electron Beams

Purpose: To investigate the dose perturbation factors pwall, pcav, pQ in water of an NACP‐02 parallel‐plate ionization chamber in clinical electron beams (4–18 MeV). These perturbations factors are assumed unity in current dosimetry protocols, but recent publications have cast doubts on this. Method and Materials: An EGSnrc Monte Carlo model was developed for the NACP‐02 parallel‐plate ionization chamber. Perturbation factors for the presence of the non‐water wall pwall, the presence of an air cavity pcav and the total perturbation factor pQ were calculated in water. The influence of the front and back walls were investigated. The influence of electron beam energy and depth in water were investigated. Results: pwall is significantly different from unity for low energy electron beams, and decreases with energy. pcav is close to unity for all beam energies. Both pwall and pcav increase significantly with depth in water, leading to an increasing pQ, which reaches a value of 1.23 at a depth of R50 for 4 MeV electrons. This was, however, found to depend strongly on whether scaling of the non‐water equivalent front wall of the chamber was performed; pQ decreased from 1.23 to 1.02 for 4 MeV electrons at R50 when scaling is done. At the reference depth zref, little effect of the window scaling was noted. The back and front wall of the ion chamber were found to have opposite influences on pwall. The effect of in‐scattering and track obliquity on the depth dependence of pcav was investigated. Conclusion: The findings of an increased pQ with depth in water could have important consequences for clinical depth dose measurements. However, the issue of front window scaling needs further study, and recommendations need to be issued. The behaviour of pcav and pwall with depth and energy were explained. Studies like these may allow improved chamber design.

Signature image stability and metal transfer in gas metal arc welding

This work presents a method for determining a stability index during welding by comparing signature images calculated successively from the welding voltage and current data. It is shown that, with a reasonable choice for the signature image window scaling, the stability index agrees with welding experience in the common gas metal arc welding transfer modes of short circuiting and spray transfer and reflects the decrease in stability inherent in the globular transfer mode. The effect of changing welding consumables, wire and shielding gas, is illustrated. The connection between the stability index and metal transfer phenomena is investigated, and it is shown that very detailed aspects of metal transfer are reflected in the stability index. It is concluded that the signature image stability index not only provides valuable data on the operating regions of welding processes, but also gives information which otherwise would only be available from cumbersome optical diagnostics.

Global scaling behaviors and chaotic measure characterized by the convergent rates of period-p-tupling bifurcations.

The Derrida-Gervois-Pomeau composition of the sequences Q* is a topological conjugate compression operation. On the basis of the numerical calculation for the quadratic map f(lambda)(x) = 1 - lambda x(2), it is found that Q* has the uniform compression ratio delta(-1)(Q), i.e., the reciprocal of the universal convergent rate. A series of global scaling behaviors relating to the periodic windows, the window bands, and the steps of equal topological entropy class (ETEC) are revealed to be independent of the sequences. In particular, the geometric interpretation for the Yorke-Grebogi-Ott-Tedeschini-Lalli normalized crisis value of ''windows'' mu(c) = 9/4 is given clearly through the scaling for the ETEC steps. The universal positions of superstable points in all the periodic windows are determined by the window scaling ratios gamma(PDB)=1 for the period-doubling bifurcation (PDB) sequences and gamma(N)=1/3 for the non-PDB sequences. The approximate analytic formula of the chaotic measure is obtained by employing the convergent rates delta of periodic sequences. The singularity spectrum and the generalized fractal dimension of the chaotic set are also calculated. These results imply that the metric universality of the periodic sequences may be a very good complete description for the topological space of two symbols.

Window scaling in one-dimensional maps

We describe both the internal structure and the width of the periodic windows in one-dimensional maps, by considering a universal local submap. Both features are found to depend only on the order of the extremum of this submap. Moreover, we discuss how the windows are grouped in accumulating families, and we calculate the scaling of the widths within these families.

Scaling behaviour of windows and intermittency in one-dimensional maps

We prove that, for one-dimensional maps, the approximate window scaling proposed by Grebogi et al. can be derived from rigorous results on type I intermittency. We give estimates for the size Δμ of the windows and the width Δ x i of the chaotic bands in terms of intermittency parameters. Finally, we find that the ratio Δx 0/Δμ 1 2 , where Δ x 0 is the central chaotic band width, becomes asymtotically a window independent constant.