Low-Cost Methods for Generating Panoramic Views for a Mobile Virtual Heritage Application and its Application to the Heritage Zone of George Town Malaysia

With rapid advancement of technology, people can roam around the virtual world through the aid of the Internet. One of these advances is a photographic technique called panoramic view where the images are captured with elongated field of view using specialized software or equipments. One popular software for generating panoramic views is Apple Inc.’s QuickTime VR (QTVR). However, iphone Operating System (iOS) does not support the existing QTVR software. Therefore, a low-cost method for generating panoramic views on mobile platform is proposed. The proposed method is to store finite images in an array in order to generate a 360o panoramic view from different angles of the heritage sites. This method can be supported various platforms and can be installed in any mobile device without using intermediate software to convert the image file format. The key aspects of the iOS User Experience (UX) are also explored from the perspectives of Model-View-Control (MVC) strategies. The outcome is 360o cylindrical panoramic views that allow the user to gain a clear vision around historical monuments with standardize iOS interface design on a mobile platform using lower computational cost but with similar quality of production. The results of the evaluation have shown that the application is successfully implemented in George Town, Malaysia.

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Tile-Based Panoramic View in Mobile Devices

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.3487 ◽

2013 ◽

Vol 284-287 ◽

pp. 3487-3491

Author(s):

Kian Lam Tan ◽

Chen Kim Lim ◽

Abdullah Zawawi Talib

Keyword(s):

High Resolution ◽

Mobile Devices ◽

Mobile Device ◽

Mobile Platform ◽

High Definition ◽

Panoramic View ◽

Limited Bandwidth ◽

Panoramic Views

Cylindrical panoramic view is a 360-degrees horizontal representation of a certain scene. The user can navigate interactively through the scene and change their view angles. Basically, panoramic views are often high-resolution, high-definition and consume a significant amount of bandwidth for transmission through Internet in mobile platform. In this paper, a method is proposed by combining a proposed tile-based algorithm with scrollViewDidScroll protocol of the iOS platform. With the tile-based algorithm, the panoramic views are divided into tiles and only tiles of interest are viewed on the mobile screen. Besides, the scrollViewDidScroll protocol of the iOS platform is used to link back the end of panoramic view with the start of the panoramic view to produce a cylindrical panoramic view. From here, the user can view the same output as panoramic views on a mobile device but using limited bandwidth, memory, and number of processors. Based on the evaluation, the respondents gave favorable response to the method.

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An Investigation of Workability Experiment for Crowd in 3600 Panoramic View on Mobile Platform

10.15405/epsbs.2016.08.15 ◽

2016 ◽

Author(s):

Chen Kim Lim

Keyword(s):

Mobile Platform ◽

Panoramic View

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Status Estimation and In-Process Connection of Kanbans Using BLE Beacons and LPWA Network to Implement Intra-Traceability for the Kanban System

Sensors ◽

10.3390/s21155038 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5038

Author(s):

Kosuke Shima ◽

Masahiro Yamaguchi ◽

Takumi Yoshida ◽

Takanobu Otsuka

Keyword(s):

Inventory Management ◽

Low Cost ◽

Computational Cost ◽

Low Energy ◽

Manufacturing Strategy ◽

Implementation Cost ◽

Measurement Systems ◽

Process Route ◽

Kanban System ◽

Low Computational Cost

IoT-based measurement systems for manufacturing have been widely implemented. As components that can be implemented at low cost, BLE beacons have been used in several systems developed in previous research. In this work, we focus on the Kanban system, which is a measure used in manufacturing strategy. The Kanban system emphasizes inventory management and is used to produce only required amounts. In the Kanban system, the Kanban cards are rotated through the factory along with the products, and when the products change to a different process route, the Kanban card is removed from the products and the products are assigned to another Kanban. For this reason, a single Kanban cannot trace products from plan to completion. In this work, we propose a system that uses a Bluetooth low energy (BLE) beacon to connect Kanbans in different routes but assigned to the same products. The proposed method estimates the beacon status of whether the Kanban is inside or outside a postbox, which can then be computed by a micro controller at low computational cost. In addition, the system connects the Kanbans using the beacons as paired connection targets. In an experiment, we confirmed that the system connected 70% of the beacons accurately. We also confirmed that the system could connect the Kanbans at a small implementation cost.

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Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network

Inventions ◽

10.3390/inventions6040070 ◽

2021 ◽

Vol 6 (4) ◽

pp. 70

Author(s):

Elena Solovyeva ◽

Ali Abdullah

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Recurrent Neural Networks ◽

Low Cost ◽

Computational Cost ◽

High Accuracy ◽

Activation Functions ◽

Fully Connected ◽

Fully Connected Networks

In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications. The advantage of the proposed structure is the absence of multiple fully connected layers, which is used to increase the classification accuracy but raises the computational cost. The combination of low-cost separable convolutional layers and a convolutional layer is proposed to gain high accuracy and, simultaneously, to reduce the complexity of neural classifiers. Advantages are demonstrated at binary and multiclass classifications of written texts by means of the proposed networks under the sigmoid and Softmax activation functions in convolutional layer. At binary and multiclass classifications, the accuracy obtained by separable convolutional neural networks is higher in comparison with some investigated types of recurrent neural networks and fully connected networks.

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More and Faster: Simultaneously Improving Reaction Coverage and Computational Cost in Automated Reaction Prediction Tasks

10.26434/chemrxiv.13076087 ◽

2020 ◽

Author(s):

Qiyuan Zhao ◽

Brett Savoie

Keyword(s):

Transition State ◽

Domain Knowledge ◽

Low Cost ◽

Computational Cost ◽

Chemical Degradation ◽

Reaction Pathways ◽

Reaction Networks ◽

Complex Reaction ◽

Success Rates ◽

Reaction Prediction

<div> <div> <div> Automated reaction prediction has the potential to elucidate complex reaction networks for applications ranging from combustion to materials degradation. Although substantial progress has been made in predicting specific reaction pathways and resolving mechanisms, the computational cost and inconsistent reaction coverage of automated prediction are still obstacles to exploring deep reaction networks without using heuristics. Here we show that cost can be reduced and reaction coverage can be increased simultaneously by relatively straight- forward modifications of the reaction enumeration, geometry initialization, and transition state convergence algorithms that are common to many emerging prediction methodologies. These changes are implemented in the context of Yet Another Reaction Program (YARP), our reaction prediction package, for which we report a head-to-head comparison with prevailing methods for two benchmark reaction prediction tasks. In all cases, we observe near perfect recapitulation of established reaction pathways and products by YARP, without the use of heuristics or other domain knowledge to guide reaction selection. In addition, YARP also discovers many new kinetically relevant pathways and products reported here for the first time. This is achieved while simultaneously reducing the cost of reaction characterization nearly 100-fold and increasing transition state success rates and intended rates over 2-fold and 10-fold, respectively, compared with recent benchmarks. This combination of ultra-low cost and high reaction-coverage creates opportunities to explore the reactivity of larger sys- tems and more complex reaction networks for applications like chemical degradation, where approaches based on domain heuristics fail. </div> </div> </div>

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Wind Turbine Noise Reduction from Seismological Data

10.5194/egusphere-egu21-4907 ◽

2021 ◽

Author(s):

Janis Heuel ◽

Wolfgang Friederich

Keyword(s):

Signal To Noise Ratio ◽

Low Cost ◽

Computational Cost ◽

Noise Signal ◽

Noise Model ◽

Threshold Function ◽

High Signal ◽

Time Data ◽

Negative Effects ◽

Before And After

Over the last years, installations of wind turbines (WTs) increased worldwide. Owing to negative effects on humans, WTs are often installed in areas with low population density. Because of low anthropogenic noise, these areas are also well suited for sites of seismological stations. As a consequence, WTs are often installed in the same areas as seismological stations. By comparing the noise in recorded data before and after installation of WTs, seismologists noticed a substantial worsening of station quality leading to conflicts between the operators of WTs and earthquake services.In this study, we compare different techniques to reduce or eliminate the disturbing signal from WTs at seismological stations. For this purpose, we selected a seismological station that shows a significant correlation between the power spectral density and the hourly windspeed measurements. Usually, spectral filtering is used to suppress noise in seismic data processing. However, this approach is not effective when noise and signal have overlapping frequency bands which is the case for WT noise. As a first method, we applied the continuous wavelet transform (CWT) on our data to obtain a time-scale representation. From this representation, we estimated a noise threshold function (Langston & Mousavi, 2019) either from noise before the theoretical P-arrival (pre-noise) or using a noise signal from the past with similar ground velocity conditions at the surrounding WTs. Therefore, we installed low cost seismometers at the surrounding WTs to find similar signals at each WT. From these similar signals, we obtain a noise model at the seismological station, which is used to estimate the threshold function. As a second method, we used a denoising autoencoder (DAE) that learns mapping functions to distinguish between noise and signal (Zhu et al., 2019).In our tests, the threshold function performs well when the event is visible in the raw or spectral filtered data, but it fails when WT noise dominates and the event is hidden. In these cases, the DAE removes the WT noise from the data. However, the DAE must be trained with typical noise samples and high signal-to-noise ratio events to distinguish between signal and interfering noise. Using the threshold function and pre-noise can be applied immediately on real-time data and has a low computational cost. Using a noise model from our prerecorded database at the seismological station does not improve the result and it is more time consuming to find similar ground velocity conditions at the surrounding WTs.

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Low-Cost Molecular Excited States from a State-Averaged Resonating Hartree-Fock Approach

10.26434/chemrxiv.7871504 ◽

2019 ◽

Author(s):

Jacob Nite ◽

Carlos A. Jimenez-Hoyos

Keyword(s):

Excited States ◽

Configuration Interaction ◽

Low Cost ◽

Computational Cost ◽

Mean Field ◽

Chem Phys ◽

Spin Projection ◽

Excitation Energies ◽

Hartree Fock ◽

Orbital Relaxation

Quantum chemistry methods that describe excited states on the same footing as the ground state are generally scarce. In previous work, Gill et al. (J. Phys. Chem. A 112, 13164 (2008)) and later Sundstrom and Head-Gordon (J. Chem. Phys. 140, 114103 (2014)) considered excited states resulting from a non-orthogonal configuration interaction (NOCI) on stationary solutions of the Hartree–Fock equations. We build upon those contributions and present the state-averaged resonating Hartree–Fock (sa-ResHF) method, which differs from NOCI in that spin-projection and orbital relaxation effects are incorporated from the onset. Our results in a set of small molecules (alanine, formaldehyde, acetaldehyde, acetone, formamide, and ethylene) suggest that sa-ResHF excitation energies are a notable improvement over configuration interaction singles (CIS), at a mean-field computational cost. The orbital relaxation in sa-ResHF, in the presence of a spin-projection operator, generally results in excitation energies that are closer to the experimental values than the corresponding NOCI ones.

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Low-Cost Molecular Excited States from a State-Averaged Resonating Hartree-Fock Approach

10.26434/chemrxiv.7871504.v1 ◽

2019 ◽

Author(s):

Jacob Nite ◽

Carlos A. Jimenez-Hoyos

Keyword(s):

Excited States ◽

Configuration Interaction ◽

Low Cost ◽

Computational Cost ◽

Mean Field ◽

Chem Phys ◽

Spin Projection ◽

Excitation Energies ◽

Hartree Fock ◽

Orbital Relaxation

Quantum chemistry methods that describe excited states on the same footing as the ground state are generally scarce. In previous work, Gill et al. (J. Phys. Chem. A 112, 13164 (2008)) and later Sundstrom and Head-Gordon (J. Chem. Phys. 140, 114103 (2014)) considered excited states resulting from a non-orthogonal configuration interaction (NOCI) on stationary solutions of the Hartree–Fock equations. We build upon those contributions and present the state-averaged resonating Hartree–Fock (sa-ResHF) method, which differs from NOCI in that spin-projection and orbital relaxation effects are incorporated from the onset. Our results in a set of small molecules (alanine, formaldehyde, acetaldehyde, acetone, formamide, and ethylene) suggest that sa-ResHF excitation energies are a notable improvement over configuration interaction singles (CIS), at a mean-field computational cost. The orbital relaxation in sa-ResHF, in the presence of a spin-projection operator, generally results in excitation energies that are closer to the experimental values than the corresponding NOCI ones.

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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

Sensors ◽

10.3390/s21227716 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7716

Author(s):

Krzysztof K. Cwalina ◽

Piotr Rajchowski ◽

Alicja Olejniczak ◽

Olga Błaszkiewicz ◽

Robert Burczyk

Keyword(s):

Deep Learning ◽

Interface Design ◽

Mean Squared Error ◽

Radio Channel ◽

Computational Cost ◽

Urban Environments ◽

Data Allocation ◽

Urban Networks ◽

Radio Interface ◽

Channel State Estimation

Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost.

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