Zooming Techniques Research Articles

Periodic boundary conditions and G 2 cosmology

In the standard concordance cosmology the spatial curvature is assumed to be constant and zero (or at least very small). In particular, in numerical computations of the structure of the Universe using N-body simulations, exact periodic boundary conditions are assumed which constrains the spatial curvature. In order to confirm this qualitatively, we numerically evolve a special class of spatially inhomogeneous G 2 models with both periodic initial data and non periodic initial data using zooming techniques. We consequently demonstrate that in these models periodic initial conditions do indeed suppress the growth of the spatial curvature as the models evolve away from their initial isotropic and spatially homogeneous state, thereby verifying that the spatial curvature is necessarily very small in standard cosmology.

Evaluation of deep learning models for detecting breast cancer using histopathological mammograms Images

Breast cancer detection based on the deep learning approach has gained much interest among other conventional-based CAD systems as the conventional based CAD system's accuracy results seems to be inadequate. The convolution neural network, a deep learning approach, has emerged as the most promising technique for detecting cancer in mammograms. In this paper we delve into some of the CNN classifiers used to detect breast cancer by classifying mammogram images into benign, cancer, or normal class. Our study evaluated the performance of various CNN architectures such as AlexNet, VGG16, and ResNet50 by training some of them from scratch and some using transfer learning with pre-trained weights. The above model classifiers are trained and tested using mammogram images from the mini-DDSM dataset which is publicly available. The medical dataset contains limited samples of data due to low patient volume; this can lead to overfitting issue, so to overcome this limitation data augmentation process is applied. Rotation and zooming techniques are applied to increase the data volume. The validation strategy used here is 90:10 ratio. AlexNet showed an accuracy of 65 percent, whereas VGG16 and ResNet50 showed an accuracy of 65% and 61%, respectively when fine-tuned with pre-trained weights. VGG16 performed significantly worse when trained from scratch, whereas AlexNet outperformed others. VGG16 and ResNet50 performed well when transfer learning was applied.

Please, Help Me! I Am Lost in Zoom

Abstract. LostInZoom is a new research project that will seek to design novel ways of zooming into multi-scale maps, to overcome the desert fog effect that occurs with current multi-scale cartography techniques. The desert fog effect makes you feel lost for a few seconds after a zoom in or out, because the map has changed. The idea developed in this project is to propose anchor-based zooming techniques, where salient landmarks salient at multiple scales serve as anchors between maps during the zoom. This paper discusses the main challenges that will be addressed in this project: (1) better understand and measure the desert fog effect with maps; (2) defining and modelling the best anchors for anchor-based zooming; (3) designing more progressive multi-scale maps to host these anchors; (4) designing staged animations based on anchors between maps at different scales.

Semantic segmentation of tea geometrid in natural scene images using discriminative pyramid network

A discriminative pyramid (DP) network-based method is presented in this paper, which aims to perform semantic segmentation on tea geometrid in natural scene images. The method uses image flipping, translation, mirroring, random zooming, and other techniques for training sample augmentation and adopts local histogram equalization to reduce nonuniform light influence on segmentation. Moreover, this method constructs a DP network to improve segmentation accuracy. The DP network contains two sub networks: pyramid attention and border networks. The pyramid attention network can capture the global context information of targets at different scales, increase the receptive fields to focus on small targets, and solve the problems of large shape change, small size, and difficult detection of tea geometrids. The border network can increase the differences between tea geometrids and tea tree stalks, diseased leaves, and other types of backgrounds with similar appearances by extracting and supervising semantic boundaries to help the network learn additional discriminative features. Experiment results show that the proposed method has outstanding accuracy for semantic segmentation of tea geometrid in natural scene images.

A Green Traffic Steering Solution for Next Generation Communication Networks

In recent years, green communications and networks have drawn increasing attention from researchers and relevant industries owing to their economic and environmental benefits. In mobile networks, base stations (BSs) account for 56% of network power consumption. Hence, using green technologies in base stations would have a considerable impact on service providers' energy bills. Consequently, researchers have investigated various green communication techniques. In this paper, we introduce a new traffic steering technique for energy conservation in next generation communication networks. By leveraging spectrum-based cell zooming techniques, the long-term evolution licensed assisted access (LTE-LAA) concept and software defined radio technology, we introduce a green traffic-steering framework that endeavors to minimize overall network on-grid power consumption and maximize aggregated throughput. Considering the time complexity of the optimum solution, we also propose a heuristic solution. We examine the proposed solution against existing solutions, and our obtained results show that the proposed framework saves up to 15% of on-grid power and increases aggregated throughput by 23%.

The Influence of Quizizz Learning Media Assisted Lecture Learning Methods on Cognitive and Psychomotor Learning Outcomes on Panning and Zooming Techniques in Printing Graphic Design Subjects

The aim of this study was to determine whether lecturing teaching method that enhanced by Quizziz has impact on student cognitive and psychomotor learning outcomes. The benefit of research is to prove that although the learning method implemented is lecture-based, it can have an impact on students' cognitive or psychomotoric learning outcomes. This research was conducted using a quasi-experimental method in the Printing Graphic Design subject and limited on panning and zooming technique section. The results showed that there was an effect of the implementation of lecturing teaching method that enhanced by Quizizz on cognitive learning outcomes. However, the implementation of lecturing teaching method that enhanced by Quizizz did not have effect on psychomotoric learning outcomes. It can be interpreted that the lecturing teaching method that enhanced by Quizizz is relevant for conceptual teaching material, but less relevant for procedural teaching material.

Cell Zooming Techniques and Optimization Function in Cellular Based Networks with Energy Efficient and Consumption

Today, the major mobile network utilizes energy in the range of giga watts per year. Nowadays, in European markets energy prices increased around 18% of mobile network operational cost. The design methodology is provisioned with tradeoff between maintenance cost, energy consumption, QOS assurance and deployment. In the existing work, an optimization function for the network design and management has been formulated and its validity has been verified using results of simulation The overall objective of this work is to evolve a strategy to operate a cellular network in an energy efficient manner, thereby reducing energy consumption and electromagnetic pollution. Range of each sector corresponding to a particular cell differs from each other. This allows the sectors to be treated as individual cells and hence, sector zooming can be done.

Waveform quick positioning and zooming techniques for a faster response time mass storage data acquisition system.

Memory depth represents an oscilloscope's capability to continuously acquire and store sampling points at the highest real-time sampling rate. Improving the memory depth is conducive to improving the system's capability to analyze waveform details, but it will slow down the response time of the system. This paper proposes a high-speed deep memory data acquisition system with a real-time sampling rate of up to 10 GSPS and a memory depth up to 1 Gpts based on ultra-high-speed parallel sampling, waveform quick positioning and zooming, and waveform 3D mapping and display. This study focuses on the use of waveform quick positioning and zooming techniques under deep memory conditions to solve the slow response time and low waveform capture rate that are the result of improving the memory depth. This paper gives the experimental results for real-time signal data acquisition. The results show that adopting waveform quick positioning and zooming techniques achieved the following aims: significantly improved system response time, rapid positioning and display waveform details available to users, fast waveform capture rate and improved oscilloscope performance.

Antenna array techniques for nonuniform cell zooming and adaptive frequency planning

SummaryThis paper proposes the antenna array system and the techniques for the nonuniform cell zooming and adaptive frequency planning which is suitable for the energy‐efficient LTE network and multitier 5G heterogeneous network. The antenna array system is designed for the beam width control and scan control in the azimuth plane. The beam tilt is controlled in the elevation plane to control the cell radius. The first feature of the antenna array system is to perform nonuniform sector size control in order to deal with the nonuniform traffic distribution. The second feature of the antenna array system is to control the beam scan in the azimuth plane for the adaptive frequency planning. The footprint control of the current work is evaluated considering the antenna array's radiation pattern and all the physical parameters. Adaptive frequency planning can mitigate cochannel interference (CCI), which may be caused due to the cell zooming techniques proposed in this work. The footprint simulations confirm that the antenna array system can control the sector size with the highest possible flexibility. The antenna array system due to these features can effectively mitigate the interference. The results also confirm that the current antenna array system outperforms the existing antenna array systems.

Energy Harvesting and Cell Zooming in $K-$ Tier Heterogeneous Random Cellular Networks

In this paper, we study the performance of ${K}$ –tier heterogeneous random cellular networks (HetNets) in terms of energy efficiency (EE) and spectral efficiency (SE). In order to improve efficiency, two cell zooming (CZ) techniques, namely telescopic and ON/OFF schemes, are applied to these networks. Accordingly, we investigate two scenarios in this paper. First, we focus on a single tier scenario and derive a new expression for the ergodic capacity of a Poisson–Voronoi tessellation random cellular network using stochastic geometry. Besides, the EE and SE of this model are depicted for several user densities and CZ parameters. In the second scenario, we investigate multitier HetNets in which the base stations gather their required energy from the environment. Therefore, two important concepts in green cellular networks, namely CZ and energy harvesting, are addressed in this paper. Subsequently, numerical results show the performance improvement in terms of the coverage and blocking probabilities in addition to EE and SE.

Principles for Designing Large-Format Refreshable Haptic Graphics Using Touchscreen Devices

Touchscreen devices, such as smartphones and tablets, represent a modern solution for providing graphical access to people with blindness and visual impairment (BVI). However, a significant problem with these solutions is their limited screen real estate, which necessitates panning or zooming operations for accessing large-format graphical materials such as maps. Non-visual interfaces cannot directly employ traditional panning or zooming techniques due to various perceptual and cognitive limitations (e.g., constraints of the haptic field of view and disorientation due to loss of one's reference point after performing these operations). This article describes the development of four novel non-visual panning methods designed from the onset with consideration of these perceptual and cognitive constraints. Two studies evaluated the usability of these panning methods in comparison with a non-panning control condition. Results demonstrated that the exploration, learning, and subsequent spatial behaviors were similar between panning and non-panning conditions, with one panning mode, based on a two-finger drag technique, revealing the overall best performance. Findings provide compelling evidence that incorporating panning operations on touchscreen devices -- the fastest growing computational platform among the BVI demographic -- is a viable, low-cost, and immediate solution for providing BVI people with access to a broad range of large-format digital graphical information.

ContextZoom: A Single-Handed Partial Zooming Technique for Touch-Screen Mobile Devices

ABSTRACTDespite its ubiquitous use, the pinch zooming technique is not effective for single-handed interaction with touch-screen mobile devices. A novel technique for single-handed zooming on touch-screen mobile devices, ContextZoom, is proposed in this paper. It allows users to specify any location on a device screen as the zooming center and ensures that this intended zooming center remains at the original location after zooming. ContextZoom works as an add-on feature for existing zooming techniques by supporting zooming in/out a portion of a viewport and provides a quick switch between partial and whole viewports, which can be operated with a single hand only. An empirical evaluation of ContextZoom through a controlled laboratory experiment was conducted to compare participants’ performance and perceptions while using the Google Maps’ single-handed (GMS) zooming technique and a button-based (BB) zooming technique with and without ContextZoom. Results show that equipped with ContextZoom, users’ performances with the GMS zooming technique and the BB zooming technique in partial viewport zooming were improved significantly in terms of task completion time and number of discrete actions. Participants also reported higher levels of perceived effectiveness and overall satisfaction with ContextZoom than without ContextZoom while using the GMS zooming technique and reported a similar level of perceived ease of use. On the other hand, ContextZoom increased the completion time but reduced the number of discrete actions in whole viewport zooming, and users did not perceive ContextZoom as a whole viewport zooming tool as well as its counterparts.

IJIREEICE - Electrical, Electronics, Instrumentation and Control

This paper investigate the problem regarding the minimization of the power of the base station by using the cell zooming technique. In this paper, we mainly focused on reducing the power consumed at the base station end, whereas most of the power minimization related work in the literature today is focused on the mobile end only. Cell zooming is a technology that promises to minimize the resource utilization. This technique is designed to save the energy consumed by the base station by maintaining the quality of services. To understand the working of the cell zooming techniques centralized algorithm is a very basic way. Our simulations shows that the proposed technique can achieve upto 25% reduction in the power consumption over the base station.

A fuzzy-rule-based approach for single frame super resolution.

In this paper, a novel fuzzy rule-based prediction framework is developed for high-quality image zooming. In classical interpolation-based image zooming, resolution is increased by inserting pixels using certain interpolation techniques. Here, we propose a patch-based image zooming technique, where each low-resolution (LR) image patch is replaced by an estimated high-resolution (HR) patch. Since an LR patch can be generated from any of the many possible HR patches, it would be natural to develop rules to find different possible HR patches and then to combine them according to rule strength to get the estimated HR patch. Here, we generate a large number of LR–HR patch pairs from a collection of natural images, group them into different clusters, and then generate a fuzzy rule for each of these clusters. The rule parameters are also learned from these LR-HR patch pairs. As a result, an efficient mapping from LR patch space to HR patch space can be formulated. The performance of the proposed method is tested on different images, and is also compared with other representative as well as state-of-the-art image zooming techniques. Experimental results show that the proposed method is better than the competing methods and is capable of reconstructing thin lines, edges, fine details, and textures in the image efficiently.

Image Zooming using Non-linear Partial Differential Equation

The main issue in any image zooming techniques is to preserve the structure of the zoomed image. The zoomed image may suffer from the discontinuities in the soft regions and edges; it may contain artifacts, such as image blurring and blocky, and staircase effects. This paper presents a novel image zooming technique using Partial Differential Equations (PDEs). It combines a non-linear Fourth-order PDE method with the Locally Adaptive Zooming (LAZ) algorithm. The proposed method uses high-resolution image obtained from LAZ algorithm to construct zoomed image by Fourth-order PDE. This proposed method preserves edges and minimizes blurring and staircase effects in the zoomed image. In order to evaluate image quality obtained from the proposed method, this paper focuses on both subjective and objective assessments. The results of these measures on a variety of images show that the proposed method is superior over the other image zooming methods.

Design and evaluation of 3D selection techniques based on progressive refinement

Issues such as hand and tracker jitter negatively affect user performance with 3D selection techniques based on the ray-casting metaphor. This makes it difficult for users to select objects that have a small visible area, since small targets require high levels of precision. We introduce an approach to address this issue that uses progressive refinement of the set of selectable objects to reduce the required precision of the task. We present three exemplar techniques (sphere-casting refined by QUAD menu (SQUAD), discrete zoom, and continuous zoom) and derive a preliminary design space for progressive refinement from their characteristics. We explore the trade-offs between progressive refinement and immediate selection techniques in two studies: first comparing SQUAD to ray-casting; and second comparing the zooming techniques to ray-casting. In both studies, an analytical evaluation based on a distal pointing model and an empirical evaluation demonstrates that progressive refinement selection can provide significant benefits compared to immediate techniques. In the first study, SQUAD was much more accurate than ray-casting, and SQUAD was faster than ray-casting with small targets and less cluttered environments. The issue with SQUAD, however, is that it requires all selectable objects to be visually distinct. The zooming techniques address this issue by exploring other areas of the progressive refinement design space. They allow users to use the spatial relationships among objects as criteria for selection and to increase precision without requiring precision in pointing. The results of the second study show that while the zooming techniques were significantly slower than ray-casting, accuracy was much higher. Additionally, depending on the size of the target, users chose not to use zoom and, therefore, performed as fast as with ray-casting.

Intuitive Tactile Zooming for Graphics Accessed by Individuals Who are Blind and Visually Impaired

One possibility of providing access to visual graphics for those who are visually impaired is to present them tactually: unfortunately, details easily available to vision need to be magnified to be accessible through touch. For this, we propose an "intuitive" zooming algorithm to solve potential problems with directly applying visual zooming techniques to haptic displays that sense the current location of a user on a virtual diagram with a position sensor and, then, provide the appropriate local information either through force or tactile feedback. Our technique works by determining and then traversing the levels of an object tree hierarchy of a diagram. In this manner, the zoom steps adjust to the content to be viewed, avoid clipping and do not zoom when no object is present. The algorithm was tested using a small, "mouse-like" display with tactile feedback on pictures representing houses in a community and boats on a lake. We asked the users to answer questions related to details in the pictures. Comparing our technique to linear and logarithmic step zooming, we found a significant increase in the correctness of the responses (odds ratios of 2.64:1 and 2.31:1, respectively) and usability (differences of 36% and 19%, respectively) using our "intuitive" zooming technique.

Improving the controllability of tilt interaction for mobile map-based applications

This article proposes enhanced techniques for improving the controllability of tilt interaction. Tilt interaction offers an intuitive, one-handed form of interaction for mobile applications. Tilt interaction is particularly well-suited to mobile map-based applications, where traditional mobile interaction techniques suffer from several shortcomings. Current implementations of tilt interaction, however, suffer from several shortcomings. Existing zooming techniques used in conjunction with tilt interaction are often difficult to control. Tilt-controlled panning can also be difficult to control in a mobile context of use, where the user's walking motion affects the accuracy of tilt interaction. The use of rate-controlled tilt interaction to perform zooming is proposed. Two different approaches to sensitivity adaptation are investigated, where the sensitivity of tilt-controlled panning is automatically adjusted to compensate for the user's current context of use. A usability study was conducted with thirty participants to evaluate the proposed techniques. The results of the usability study showed that tilt zooming offered better efficiency, user satisfaction and perceived workload than gesture zooming. The use of a static dampening factor to compensate for walking motion was found to provide actual and perceived controllability improvements.

A postprocessing method based on chirp Z transform for FDTD calculation of point defect states in two-dimensional phononic crystals

In this paper, a new postprocessing method for the finite difference time domain (FDTD) calculation of the point defect states in two-dimensional (2D) phononic crystals (PNCs) is developed based on the chirp Z transform (CZT), one of the frequency zooming techniques. The numerical results for the defect states in 2D solid/liquid PNCs with single or double point defects show that compared with the fast Fourier transform (FFT)-based postprocessing method, the method can improve the estimation accuracy of the eigenfrequencies of the point defect states significantly when the FDTD calculation is run with relatively few iterations; and furthermore it can yield the point defect bands without calculating all eigenfrequencies outside the band gaps. The efficiency and accuracy of the FDTD method can be improved significantly with this new postprocessing method.

Zooming and Scanning Gregorian Confocal Dual Reflector Antennas

The zooming and scanning capabilities of a Gregorian confocal dual reflector antenna are described. The basic antenna configuration consists of two oppositely facing paraboloidal reflectors sharing a common focal point. A planar feed array is used to illuminate the subreflector allowing the antenna to scan its beam. The resulting quadratic aberrations can be compensated by active mechanical deformation of the subreflector surface, which is based on translation, rotation and focal length adjustment. In order to reduce the complexity of the mechanical deformation, least squares fit paraboloids are defined to approximate the optimal correction surface. These best fit paraboloids considerably reduce scanning losses and pattern degradation. This work also introduces two different zooming techniques for the Gregorian confocal dual reflector antenna: the first consists of introducing a controlled quadratic path error to the main reflector aperture; and the second is based on reducing the size of the radiating aperture of the feeding array.