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The past decade has seen the emergence of numerous new wearable devices, including many that have been widely adopted by both physicians and consumers. In this paper, we discuss the design and application of smart insoles to measure gait and plantar pressure. Herein, we investigate the potential applications of insoles with fewer sensing spots and the consequent reduction in the amount of data acquired from both feet. The main purpose is to discuss the influence of the layout of these pressure sensing points of the insole design on the center of pressure (COP) calculation. The insole used in this study has 89 pressure sensing spots, and we used data from 36, 29, 20, and 11 sensing points in simplified calculation types. Among these four simplified calculation types, Type 1 exhibited the best accuracy of the COP calculation, and Type 4 obtained the worst results. Type 2 and Type 3 exhibited inferior accuracy of the COP calculation, but they still sufficed for applications that did not require high accuracy. Aside from the factor of the number of sensing spots used in the calculation, we also demonstrated that the location of selected sensors could influence the accuracy of COP calculation in the analyses by using the different combinations of metatarsal areas and other areas (heel, central, lateral toes, and hallux). The results of this research could be a reference for making a simplified form of pressure sensing Internet-of-Health Things (IoHT) insole with a reduced product cost.
Li-Wei Chou; Jun-Hong Shen; Hui-Ting Lin; Yi-Tung Yang; Wen-Pin Hu. A Study on the Influence of Number/Distribution of Sensing Points of the Smart Insoles on the Center of Pressure Estimation for the Internet of Things Applications. Sustainability 2021, 13, 2934 .
AMA StyleLi-Wei Chou, Jun-Hong Shen, Hui-Ting Lin, Yi-Tung Yang, Wen-Pin Hu. A Study on the Influence of Number/Distribution of Sensing Points of the Smart Insoles on the Center of Pressure Estimation for the Internet of Things Applications. Sustainability. 2021; 13 (5):2934.
Chicago/Turabian StyleLi-Wei Chou; Jun-Hong Shen; Hui-Ting Lin; Yi-Tung Yang; Wen-Pin Hu. 2021. "A Study on the Influence of Number/Distribution of Sensing Points of the Smart Insoles on the Center of Pressure Estimation for the Internet of Things Applications." Sustainability 13, no. 5: 2934.
In IoT environments, geo-tagged data have rapidly been emerging as smart things, e.g., mobile devices or connected cars, are generally equipped with the global positioning system (GPS) module. A large volume of geo-tagged data can be fundamental to providing applications of location-based services (LBSs). One of the important LBS applications is to provide continuous spatial keyword queries. A continuous spatial keyword query monitors a designated region with a set of keywords. In the designated region, if mobile objects contain all the keywords of the query, they are the answer set for the query. The query continuously monitors the spatial region and reports its up-to-date query result. This paper presents a resident-domain-based approach for continuously monitoring spatial keyword queries. The proposed approach shifts the monitoring of tasks of affected queries from the server to mobile objects which have computational and storage abilities. According to their computational ability, the proposed approach assigns as large as possible resident domains to mobile objects. Within the resident domain, the mobile object informs the server about its spatial information only when crossing the boundary of its monitored queries, thereby reducing the communication cost between it and the server. The experimental evaluation has verified that the proposed approach outperforms the existing approach.
Jun-Hong Shen; Mu-Yen Chen; Ching-Ta Lu; Rou-Hua Wang. Monitoring Spatial Keyword Queries Based on Resident Domains of Mobile Objects in IoT Environments. Mobile Networks and Applications 2020, 1 -11.
AMA StyleJun-Hong Shen, Mu-Yen Chen, Ching-Ta Lu, Rou-Hua Wang. Monitoring Spatial Keyword Queries Based on Resident Domains of Mobile Objects in IoT Environments. Mobile Networks and Applications. 2020; ():1-11.
Chicago/Turabian StyleJun-Hong Shen; Mu-Yen Chen; Ching-Ta Lu; Rou-Hua Wang. 2020. "Monitoring Spatial Keyword Queries Based on Resident Domains of Mobile Objects in IoT Environments." Mobile Networks and Applications , no. : 1-11.
For a reverse nearest neighbor (RNN) query, the query object will find the data objects regard it as their nearest neighbor. Over the past few years, the RNN query on the road network database has attracted much attention. In the previous research, a multi-way tree efficiently solves the issue about moving data objects for the RNN query. However, in the scenario that the query object reaches a new location, i.e., the moving query, the multi-way tree needs to be reconstructed, which takes long time. Therefore, in this paper, we propose an adjustable-tree method for solving the above problem and improving the performance efficiency for processing moving queries. Via the performance evaluation, our proposed method performs better than the original multi-way tree method.
Ye-In Chang; Jun-Hong Shen; Che-Min Chu. An Adjustable-Tree Method for Processing Reverse Nearest Neighbor Moving Queries. Lecture Notes in Electrical Engineering 2020, 362 -371.
AMA StyleYe-In Chang, Jun-Hong Shen, Che-Min Chu. An Adjustable-Tree Method for Processing Reverse Nearest Neighbor Moving Queries. Lecture Notes in Electrical Engineering. 2020; ():362-371.
Chicago/Turabian StyleYe-In Chang; Jun-Hong Shen; Che-Min Chu. 2020. "An Adjustable-Tree Method for Processing Reverse Nearest Neighbor Moving Queries." Lecture Notes in Electrical Engineering , no. : 362-371.
The goal of image mining is to find the useful information hidden in image databases. The 9DSPA-Miner approach uses the Apriori strategy to mine the image database, where each image is represented by the 9D-SPA representation. It presents a reasoning method to reason the unknown spatial relation that satisfies the spatial consistency. However, it may generate invalid candidates with the impossible relations that cannot be found in the 2D space or in the input database. Moreover, in this approach, counting the support of the pattern needs to intersect the associated image sets by searching the index structure, taking a long time. Therefore, in this paper, we propose an approach with a frequent pattern list, which generates all valid candidates of frequent patterns. Based on the frequent pattern list, the proposed approach presents two conditions in the candidate generation for finding frequent spatial patterns to avoid generating impossible candidates. Moreover, the proposed approach uses an additional verification step to further avoid generating impossible spatial relations. Therefore, the proposed approach generates fewer candidates than the 9DSPA-Miner approach, reducing the processing time. The experimental results have verified that the proposed approach outperforms the 9DSPA-Miner approach.
Ye-In Chang; Jun-Hong Shen; Chia-En Li; Zih-Siang Chen; Ming-Hsuan Tu. Mining image frequent patterns based on a frequent pattern list in image databases. The Journal of Supercomputing 2019, 76, 2597 -2621.
AMA StyleYe-In Chang, Jun-Hong Shen, Chia-En Li, Zih-Siang Chen, Ming-Hsuan Tu. Mining image frequent patterns based on a frequent pattern list in image databases. The Journal of Supercomputing. 2019; 76 (4):2597-2621.
Chicago/Turabian StyleYe-In Chang; Jun-Hong Shen; Chia-En Li; Zih-Siang Chen; Ming-Hsuan Tu. 2019. "Mining image frequent patterns based on a frequent pattern list in image databases." The Journal of Supercomputing 76, no. 4: 2597-2621.
In the Internet of thing (IoT), with the geographic location of geospatial sensor data and the global positioning systems, location-based services (LBSs) can provide powerful location-aware IoT applications for mobile clients according to their current locations. For LBSs, a k-nearest neighbor (kNN) search can provide a mobile client with geospatial sensor data of k-nearest spatial points of interest (POIs) according to its current location. In this paper, we propose a spatial air index with neighbor information to organize IoT geospatial sensor data for processing kNN searches in the wireless broadcast systems. Since the answered POIs may be neighbors of each other, we add neighbor information to the index structure, which is interleaved with geospatial sensor data, to speed up the query processing. To avoid unnecessary examination of geospatial sensor data from the wireless channel, the proposed method provides the centroid of geospatial data and the corresponding longest distance between the centroid and geospatial data in the region. With this information, the query processing of a kNN search can quickly determine whether to skip examining this region, saving energy consumption of the mobile device. Performance evaluations have verified that the proposed method outperforms the distributed spatial index.
Jun-Hong Shen; Cheng-Jung Yu; Ching-Ta Lu; Wenyen Lin; Neil Y. Yen; Tien-Chi Huang; Hong-Ray Chu. Spatial air index with neighbor information for processing k-nearest neighbor searches in IoT mobile computing. The Journal of Supercomputing 2019, 76, 6177 -6194.
AMA StyleJun-Hong Shen, Cheng-Jung Yu, Ching-Ta Lu, Wenyen Lin, Neil Y. Yen, Tien-Chi Huang, Hong-Ray Chu. Spatial air index with neighbor information for processing k-nearest neighbor searches in IoT mobile computing. The Journal of Supercomputing. 2019; 76 (8):6177-6194.
Chicago/Turabian StyleJun-Hong Shen; Cheng-Jung Yu; Ching-Ta Lu; Wenyen Lin; Neil Y. Yen; Tien-Chi Huang; Hong-Ray Chu. 2019. "Spatial air index with neighbor information for processing k-nearest neighbor searches in IoT mobile computing." The Journal of Supercomputing 76, no. 8: 6177-6194.
Ching-Ta Lu; Mu-Yen Chen; Jun-Hong Shen; Ling-Ling Wang; Chih-Chan Hsu. Removal of salt-and-pepper noise for X-ray bio-images using pixel-variation gain factors. Computers & Electrical Engineering 2018, 71, 862 -876.
AMA StyleChing-Ta Lu, Mu-Yen Chen, Jun-Hong Shen, Ling-Ling Wang, Chih-Chan Hsu. Removal of salt-and-pepper noise for X-ray bio-images using pixel-variation gain factors. Computers & Electrical Engineering. 2018; 71 ():862-876.
Chicago/Turabian StyleChing-Ta Lu; Mu-Yen Chen; Jun-Hong Shen; Ling-Ling Wang; Chih-Chan Hsu. 2018. "Removal of salt-and-pepper noise for X-ray bio-images using pixel-variation gain factors." Computers & Electrical Engineering 71, no. : 862-876.
Impulse noise impacts an image, causing the quality of image to be deteriorated in image transmission or capture. In this paper, we propose a gain factor for the removal of the impulse noise. A 3 × 3 fixed-size local window is employed to analyze each extreme pixel (0 or 255 for an 8-bit gray-level image). All non-extreme pixels are sorted in an ascending order and are grouped according to the variation of pixel levels. If the pixel level between adjacent two sorted pixels varies seriously, a new group is created. Hence, the ratio and median value of each group are computed to determine the values of the gain factors. They are multiplied with the median value of each group to obtain the weighted value which is employed to replace the center pixel with an extreme value, enabling noise-corrupted pixels to be restored. Experimental results show that the proposed method can effectively remove salt-and-pepper noise from a corrupted image for various noise corruption densities (from 10% to 90%); meanwhile, the denoised image is freed from the blurred effect.
Ching-Ta Lu; Jun-Hong Shen; Mu-Yen Chen; Ling-Ling Wang; Chih-Chan Hsu. Removal of Impulse Noise Using Gain Factors Adapted by Noise-Free Pixel Number and Pixel Variation. Lecture Notes in Electrical Engineering 2018, 1 -11.
AMA StyleChing-Ta Lu, Jun-Hong Shen, Mu-Yen Chen, Ling-Ling Wang, Chih-Chan Hsu. Removal of Impulse Noise Using Gain Factors Adapted by Noise-Free Pixel Number and Pixel Variation. Lecture Notes in Electrical Engineering. 2018; ():1-11.
Chicago/Turabian StyleChing-Ta Lu; Jun-Hong Shen; Mu-Yen Chen; Ling-Ling Wang; Chih-Chan Hsu. 2018. "Removal of Impulse Noise Using Gain Factors Adapted by Noise-Free Pixel Number and Pixel Variation." Lecture Notes in Electrical Engineering , no. : 1-11.
An X-ray bio-image might suffer interference from salt-and-pepper (SAP) noise during transmission or capture, thus reducing image quality. This paper proposes a three-stage method to cope with this problem. A directional-weighted-mean (DWM) filter is used to remove the corruption noise in the first stage. In the second stage, extreme pixel (255 or 0 for an 8-bit gray level bio-image) confirmation is performed to restore the X-ray bio-images. In the final stage, block matching identifies blocks with similar textures in a local region. The center pixels of these similar blocks are then averaged to refine the gray value of the restored pixel, thus allowing improvement to the quality of the restored X-ray image through consideration of the texture properties in neighbor pixels over a large size window. Experimental results show that the proposed approach can effectively remove background noise from a SAP noise corrupted bio-image for various noise densities. The reconstructed bio-image does not incur blurring even under heavy noise corruption.
Ching-Ta Lu; Mu-Yen Chen; Jun-Hong Shen; Ling-Ling Wang; Neil Y. Yen; Chia-Hua Liu. X-ray bio-image denoising using directional-weighted-mean filtering and block matching approach. Journal of Ambient Intelligence and Humanized Computing 2018, 1 -18.
AMA StyleChing-Ta Lu, Mu-Yen Chen, Jun-Hong Shen, Ling-Ling Wang, Neil Y. Yen, Chia-Hua Liu. X-ray bio-image denoising using directional-weighted-mean filtering and block matching approach. Journal of Ambient Intelligence and Humanized Computing. 2018; ():1-18.
Chicago/Turabian StyleChing-Ta Lu; Mu-Yen Chen; Jun-Hong Shen; Ling-Ling Wang; Neil Y. Yen; Chia-Hua Liu. 2018. "X-ray bio-image denoising using directional-weighted-mean filtering and block matching approach." Journal of Ambient Intelligence and Humanized Computing , no. : 1-18.
Continuous range queries (CRQs) for moving objects monitor the designated spatial regions and report their up-to-date query results. In such queries, query regions are more static than when compared to moving objects. Therefore, creating an index structure for query regions to process CRQs requires lower maintenance cost of the server than that of moving objects. To relieve the workload of the server, each moving object can be assigned with a resident domain where the object monitors the overlapped query regions and informs the server if any update occurs. In this paper, we propose a grid-based indexing with expansion of resident domains for monitoring CRQs in the mobile/ubiquitous computing environments. The proposed method expands resident domains for moving objects as large as possible so that they have less chance to inform the server about updates. Comprehensive experiments with various settings have verified that our proposed method outperforms the QR*-tree.
Jun-Hong Shen; Ching-Ta Lu; Mu-Yen Chen; Neil Y. Yen. Grid-based indexing with expansion of resident domains for monitoring moving objects. The Journal of Supercomputing 2017, 76, 1482 -1501.
AMA StyleJun-Hong Shen, Ching-Ta Lu, Mu-Yen Chen, Neil Y. Yen. Grid-based indexing with expansion of resident domains for monitoring moving objects. The Journal of Supercomputing. 2017; 76 (3):1482-1501.
Chicago/Turabian StyleJun-Hong Shen; Ching-Ta Lu; Mu-Yen Chen; Neil Y. Yen. 2017. "Grid-based indexing with expansion of resident domains for monitoring moving objects." The Journal of Supercomputing 76, no. 3: 1482-1501.
The power-spectral-subtraction (PSS) algorithm can remove interference noise efficiently by the subtraction of noise power from the power of a noise-interfered signal. However, the performance of this algorithm is not satisfactory for speech communication. This study proposes using an over-subtraction factor adapted by harmonic properties to increase the ability of noise removal. If the value of the over-subtraction factor is large enough, the interference noise can be removed efficiently; meanwhile, denoised speech suffers from serious speech distortion. On the contrary, plenty of residual noise exists when the value of the over-subtraction factor is too small, causing denoised speech to sound annoying to the human ear. How to define the value of this factor is critical to the quality of denoised speech. In addition, musical residual noise can be well reduced by using a spectral reservation factor. We employed the vowel harmonic properties to define the value of over-subtraction and reservation factors of the noisy spectra by using the sigmoid function. This function maps the relation between the values of over-subtraction and reservation factors, as well as the input SNRs. Experiments revealed that the proposed method can improve the performance of the PSS method significantly by increasing the reduction of interference noise and better preservation on weak vowels.
Ching-Ta Lu; Chung-Lin Lei; Jun-Hong Shen; Ling-Ling Wang. Noise reduction using spectral-subtraction algorithm with over-subtraction and spectral-reservation factors adapted by harmonic properties. Noise Control Engineering Journal 2017, 65, 509 -521.
AMA StyleChing-Ta Lu, Chung-Lin Lei, Jun-Hong Shen, Ling-Ling Wang. Noise reduction using spectral-subtraction algorithm with over-subtraction and spectral-reservation factors adapted by harmonic properties. Noise Control Engineering Journal. 2017; 65 (6):509-521.
Chicago/Turabian StyleChing-Ta Lu; Chung-Lin Lei; Jun-Hong Shen; Ling-Ling Wang. 2017. "Noise reduction using spectral-subtraction algorithm with over-subtraction and spectral-reservation factors adapted by harmonic properties." Noise Control Engineering Journal 65, no. 6: 509-521.
Consider skewed access patterns of mobile clients, popular data are broadcast more times than regular ones via non-flat wireless broadcast, resulting in the decrease of the client waiting time for mobile devices to retrieve popular data. Window queries are one of the fundamental spatial queries for location-based services. Such queries retrieve spatial objects in a fixed window region according to clients’ current location. In this paper, considering the skewed access patterns, we propose an efficient processing method for the window queries via non-flat broadcast in the wireless environments. From the experimental results, we have verified that our proposed method performs better than the existing methods.
Jun-Hong Shen; Ching-Ta Lu; Chien-Tang Mai. Efficient Processing of Spatial Window Queries for Non-flat Wireless Broadcast. Lecture Notes in Electrical Engineering 2017, 422, 703 -713.
AMA StyleJun-Hong Shen, Ching-Ta Lu, Chien-Tang Mai. Efficient Processing of Spatial Window Queries for Non-flat Wireless Broadcast. Lecture Notes in Electrical Engineering. 2017; 422 ():703-713.
Chicago/Turabian StyleJun-Hong Shen; Ching-Ta Lu; Chien-Tang Mai. 2017. "Efficient Processing of Spatial Window Queries for Non-flat Wireless Broadcast." Lecture Notes in Electrical Engineering 422, no. : 703-713.
The accuracy of noise estimation is important for the performance of a speech enhancement system. This study proposes using variable segment length for noise tracking and variable thresholds for the determination of speech-presence probability. Initially, the fundamental frequency is estimated to determine whether a frame is a vowel. In the case of a vowel frame, the segment length increases; meanwhile the threshold for speech-presence is decreased. So the noise magnitude is adequately underestimated. The speech distortion is accordingly reduced in enhanced speech. Conversely, the segment length is rapidly decreased during noise-dominant regions. This enables the noise estimate to be updated quickly and the noise variation to be well tracked, yielding background noise being efficiently removed by the process of speech enhancement. Experimental results show that the proposed method can efficiently track the variation of background noise, enabling the performance of speech enhancement to be improved.
Ching-Ta Lu; Yung-Yue Chen; Jun-Hong Shen; Ling-Ling Wang; Chung-Lin Lei. Noise Estimation for Speech Enhancement Using Minimum-Spectral-Average and Vowel-Presence Detection Approach. Lecture Notes in Electrical Engineering 2017, 422, 317 -327.
AMA StyleChing-Ta Lu, Yung-Yue Chen, Jun-Hong Shen, Ling-Ling Wang, Chung-Lin Lei. Noise Estimation for Speech Enhancement Using Minimum-Spectral-Average and Vowel-Presence Detection Approach. Lecture Notes in Electrical Engineering. 2017; 422 ():317-327.
Chicago/Turabian StyleChing-Ta Lu; Yung-Yue Chen; Jun-Hong Shen; Ling-Ling Wang; Chung-Lin Lei. 2017. "Noise Estimation for Speech Enhancement Using Minimum-Spectral-Average and Vowel-Presence Detection Approach." Lecture Notes in Electrical Engineering 422, no. : 317-327.
Although the power-spectral-subtraction (PSS) algorithm is widely used in speech enhancement, this method suffers from musical residual noise. So the enhanced speech sounds annoying to the human ear. This study proposes using the cross term between the spectrum of speech and noise signals to be additionally subtracted from the power spectrum of noisy speech, enabling background noise to be efficiently removed. Experimental results show that the proposed method can significantly improve the performance of the PSS algorithm by the consideration on the cross term. The quantity of musical residual noise can be efficiently removed, while speech components are well preserved in the enhanced speech.
Ching-Ta Lu; Yung-Yue Chen; Jun-Hong Shen; Ling-Ling Wang; Chung-Lin Lei. Improvement of Power-Spectral-Subtraction Algorithm Using Cross-Term Compensation for Speech Enhancement. Lecture Notes in Electrical Engineering 2017, 422, 579 -590.
AMA StyleChing-Ta Lu, Yung-Yue Chen, Jun-Hong Shen, Ling-Ling Wang, Chung-Lin Lei. Improvement of Power-Spectral-Subtraction Algorithm Using Cross-Term Compensation for Speech Enhancement. Lecture Notes in Electrical Engineering. 2017; 422 ():579-590.
Chicago/Turabian StyleChing-Ta Lu; Yung-Yue Chen; Jun-Hong Shen; Ling-Ling Wang; Chung-Lin Lei. 2017. "Improvement of Power-Spectral-Subtraction Algorithm Using Cross-Term Compensation for Speech Enhancement." Lecture Notes in Electrical Engineering 422, no. : 579-590.
The accuracy of noise estimation is important for the performance of a speech denoising system. Most noise estimators suffer from either overestimation or underestimation on the noise level. An overestimate on noise magnitude will cause serious speech distortion for speech denoising. Conversely, a great quantity of residual noise will occur when the noise magnitude is underestimated. Accurately estimating noise magnitude is important for speech denoising. This study proposes employing variable segment length for noise tracking and variable thresholds for the determination of speech presence probability, resulting in the performance improvement for a minima-controlled-recursive-averaging (MCRA) algorithm in noise estimation. Initially, the fundamental frequency was estimated to determine whether a frame is a vowel. In the case of a vowel frame, the increment of segment lengths and the decrement of threshold for speech presence were performed which resulted in underestimating the level of noise magnitude. Accordingly, the speech distortion is reduced in denoised speech. On the contrary, the segment length decreases rapidly in noise-dominant regions. This enables the noise estimate to update quickly and the noise variation to track well, yielding interference noise being removed effectively through the process of speech denoising. Experimental results show that the proposed approach has been effective in improving the performance of the MCRA algorithm by preserving the weak vowels and consonants. The denoising performance is therefore improved.
Ching-Ta Lu; Chung-Lin Lei; Jun-Hong Shen; Ling-Ling Wang; Kun-Fu Tseng. Estimation of Noise Magnitude for Speech Denoising Using Minima-Controlled-Recursive-Averaging Algorithm Adapted by Harmonic Properties. Applied Sciences 2016, 7, 9 .
AMA StyleChing-Ta Lu, Chung-Lin Lei, Jun-Hong Shen, Ling-Ling Wang, Kun-Fu Tseng. Estimation of Noise Magnitude for Speech Denoising Using Minima-Controlled-Recursive-Averaging Algorithm Adapted by Harmonic Properties. Applied Sciences. 2016; 7 (1):9.
Chicago/Turabian StyleChing-Ta Lu; Chung-Lin Lei; Jun-Hong Shen; Ling-Ling Wang; Kun-Fu Tseng. 2016. "Estimation of Noise Magnitude for Speech Denoising Using Minima-Controlled-Recursive-Averaging Algorithm Adapted by Harmonic Properties." Applied Sciences 7, no. 1: 9.
In pervasive computing, location-based services (LBSs) are valuable for mobile clients based on their current locations. LBSs use spatial window queries to enable useful applications for mobile clients. Based on skewed access patterns of mobile clients, non-flat wireless broadcast has been shown to efficiently disseminate spatial objects to mobile clients. In this paper, we consider a scenario in which spatial objects are broadcast to mobile clients over a wireless channel in a non-flat broadcast manner to process window queries. For such a scenario, we propose an efficient spatial air index method to handle window query access in non-flat wireless broadcast environments. The concept of largest empty rectangles is used to avoid unnecessary examination of the broadcast content, thus reducing the processing time for window queries. Simulation results show that the proposed spatial air index method outperforms the existing methods under various settings.
Jun-Hong Shen; Ching-Ta Lu; Mu-Yen Chen; Chien-Tang Mai. Spatial Air Index Based on Largest Empty Rectangles for Non-Flat Wireless Broadcast in Pervasive Computing. ISPRS International Journal of Geo-Information 2016, 5, 211 .
AMA StyleJun-Hong Shen, Ching-Ta Lu, Mu-Yen Chen, Chien-Tang Mai. Spatial Air Index Based on Largest Empty Rectangles for Non-Flat Wireless Broadcast in Pervasive Computing. ISPRS International Journal of Geo-Information. 2016; 5 (11):211.
Chicago/Turabian StyleJun-Hong Shen; Ching-Ta Lu; Mu-Yen Chen; Chien-Tang Mai. 2016. "Spatial Air Index Based on Largest Empty Rectangles for Non-Flat Wireless Broadcast in Pervasive Computing." ISPRS International Journal of Geo-Information 5, no. 11: 211.
Location-based services (LBSs) via wireless data broadcast can provide a huge number of mobile clients for simultaneously accessing spatial data according to their current locations. A continuous window query is one of the important spatial queries for LBSs. It retrieves spatial objects in a fixed window region of every point on a line segment and indicates the valid segments of them. In this paper, we propose a skewed spatial index for continuous window queries considering skewed access patterns in the wireless broadcast environments.
Jun-Hong Shen; Ching-Ta Lu; Ming-Shen Jian; Tien-Chi Huang. A Skewed Spatial Index for Continuous Window Queries in the Wireless Broadcast Environments. Lecture Notes in Electrical Engineering 2013, 253, 707 -715.
AMA StyleJun-Hong Shen, Ching-Ta Lu, Ming-Shen Jian, Tien-Chi Huang. A Skewed Spatial Index for Continuous Window Queries in the Wireless Broadcast Environments. Lecture Notes in Electrical Engineering. 2013; 253 ():707-715.
Chicago/Turabian StyleJun-Hong Shen; Ching-Ta Lu; Ming-Shen Jian; Tien-Chi Huang. 2013. "A Skewed Spatial Index for Continuous Window Queries in the Wireless Broadcast Environments." Lecture Notes in Electrical Engineering 253, no. : 707-715.
A continuous window query is an important class of spatial queries for location-based services. It retrieves spatial objects in a fixed window region of every point on a line segment and indicates the valid segments of them. In this paper, we focus on continuous window queries in wireless data broadcast systems. Since the query result of the continuous window queries has the spatial locality, providing neighbor information of spatial objects can guide clients to efficiently retrieve related objects. Therefore, we propose a neighbor-index method to efficiently support the continuous window queries in wireless data broadcast systems. The proposed method interleaves the neighbor information between spatial objects to guide mobile clients to quickly retrieve the answered objects and save the power consumption of the mobile devices. Experimental results show that our method outperforms the distributed indexing.
Jun Hong Shen; Ching Ta Lu; Ming Shen Jian. Neighbor-Index Method for Continuous Window Queries over Wireless Data Broadcast. Applied Mechanics and Materials 2013, 284-287, 3295 -3299.
AMA StyleJun Hong Shen, Ching Ta Lu, Ming Shen Jian. Neighbor-Index Method for Continuous Window Queries over Wireless Data Broadcast. Applied Mechanics and Materials. 2013; 284-287 ():3295-3299.
Chicago/Turabian StyleJun Hong Shen; Ching Ta Lu; Ming Shen Jian. 2013. "Neighbor-Index Method for Continuous Window Queries over Wireless Data Broadcast." Applied Mechanics and Materials 284-287, no. : 3295-3299.