Space Cost Research Articles

Location based Continuous Query Processing over Geo-streaming Data

In recent years, many data-intensive and location based applications have emerged that need to process stream data in applications such as network monitoring, telecommunications data management, and sensor networks. Unlike regular queries, a continuous query exists for certain period of time and need to be continuously processed during this time. The algorithms used for data processing for the traditional database systems are not suited to tackle complex and various continuous queries over dynamic streaming data. The indexing for finite queries is preferred to indexing on infinite data to avoid expensive operations of index maintenance. Previous related work focused on moving queries on static objects or static queries on moving object. But now-a-days queries as well as objects are dynamic. So, hybrid indexing for queries significantly reduces the space costs and scales well with the increasing data. To deal with the speed of unbounded data, it is necessary to use data parallelism in query processing. The data parallelism in query processing offers better performance, availability and scalability.

Refinement of matching costs for stereo disparities using recurrent neural networks

Depth is essential information for autonomous robotics applications that need environmental depth values. The depth could be acquired by finding the matching pixels between stereo image pairs. Depth information is an inference from a matching cost volume that is composed of the distances between the possible pixel points on the pre-aligned horizontal axis of stereo images. Most approaches use matching costs to identify matches between stereo images and obtain depth information. Recently, researchers have been using convolutional neural network-based solutions to handle this matching problem. In this paper, a novel method has been proposed for the refinement of matching costs by using recurrent neural networks. Our motivation is to enhance the depth values obtained from matching costs. For this purpose, to attain an enhanced disparity map by utilizing the sequential information of matching costs in the horizontal space, recurrent neural networks are used. Exploiting this sequential information, we aimed to determine the position of the correct matching point by using recurrent neural networks, as in the case of speech processing problems. We used existing stereo algorithms to obtain the initial matching costs and then improved the results by utilizing recurrent neural networks. The results are evaluated on the KITTI 2012 and KITTI 2015 datasets. The results show that the matching cost three-pixel error is decreased by an average of 14.5% in both datasets.

Resonant Power Frequency Converter and Application in High-Voltage and Partial Discharge Test of a Voltage Transformer

This paper presents application of a resonant power frequency converter for high-voltage (HV) and partial discharge (PD) test of a voltage transformer. The rating voltage, power, and frequency of the system are 70 kVrms, 40 kVA, and 200 Hz, respectively. The testing system utilized the converter feeding to an HV testing transformer connected to a conventional partial discharge detection system. The converter system comprising a rectifier and insulated-gate bipolar (IGBT) switches with the H-bridge configuration was applied as a low-voltage source instead of a conventional motor-generator test set which requires large space and high cost. The requirements of the test according to the standards are quality of the test voltage and the background noise level. The required voltage must have the different voltage (DV) and total harmonic distortion (THDv) in the acceptable values of less than 5%. The DV is defined as the difference of the root mean square and peak voltages in percent. The required background noise level must be lower than 2.5 pC. Simulations and experiments were performed for verification of the developed system performance in comparison with those of the previously developed system based on the pulse width modulation converter. It is found that the developed system can provide the testing voltage with the DV and the THDv of lower than 1% and the background noise level of lower than 1 pC. Considering this achievement of promising performance, the developed system is an attractive choice for the HV and PD testing of voltage transformers in real practice.

A Design of Intelligent Wardrobe Imitating Labyrinth

This paper presents a new type of intelligent wardrobe based on the application of family daily life. Through the innovation of mechanism combination and electronic control, the device realizes the imitation labyrinth design of the wardrobe, and realizes the user’s intelligent access to clothes. The upper cabinet lattice is lowered to a position suitable for users to access clothes through a series of movable pulley structure in coordination with the horizontal movement of the lower cabinet lattice, so as to make rational use of the high space. The addition of intelligent electronic control module makes it easier for users to get the clothes they want. The device has excellent performance, simple operation process, low cost in space and economy, wide target audience and good market prospect.

An Approach for Retrieving Faster Query Results From Data Warehouse Using Synonymous Materialized Queries

The enterprise data warehouse stores an enormous amount of data collected from multiple sources for analytical processing and strategic decision making. The analytical processing is done using online analytical processing (OLAP) queries where the performance in terms of result retrieval time is an important factor. The major existing approaches for retrieving results from a data warehouse are multidimensional data cubes and materialized views that incur more storage, processing, and maintenance costs. The present study strives to achieve a simpler and faster query result retrieval approach from data warehouse with reduced storage space and minimal maintenance cost. The execution time of frequent queries is saved in the present approach by storing their results for reuse when the query is fired next time. The executed OLAP queries are stored along with the query results and necessary metadata information in a relational database is referred as materialized query database (MQDB). The tables, fields, functions, relational operators, and criteria used in the input query are matched with those of stored query, and if they are found to be same, then the input query and the stored query are considered as a synonymous query. Further, the stored query is checked for incremental updates, and if no incremental updates are required, then the existing stored results are fetched from MQDB. On the other hand, if the stored query requires an incremental update of results, then the processing of only incremental result is considered from data marts. The performance of MQDB model is evaluated by comparing with the developed novel approach, and it is observed that, using MQDB, a significant reduction in query processing time is achieved as compared to the major existing approaches. The developed model will be useful for the organizations keeping their historical records in the data warehouse.

Extended File Hierarchy Access Control Scheme with Attribute-Based Encryption in Cloud Computing

In cloud computing, attribute based encryption (ABE) is often used to solve the challenging issue in secure data storage. In order to lighten the burden of authority center, hierarchical ABE schemes is a very effective way. File hierarchy attribute based encryption (FH-CP-ABE) is a scheme, which both saves storage space of ciphertext and reduces the computation overhead of encryption. However, it's impossible to encrypt multiple files on the same access level in existing FH-CP-ABE scheme. The scheme is obviously not practical. In this paper, an efficient extended file hierarchy CP-ABE scheme (EFH-CP-ABE) is proposed, which can encrypt multiple files on the same access level. Our scheme is very practical especially for those big institutions or companies which have many hierarchical sectors, since it greatly saves storage space and computation cost for them on the cloud servers. Furthermore, our solution also achieves secure and flexible access control for users in cloud storage. We formally prove the security for our new scheme under the standard model. Finally, we implement the corresponding experiment for EFH-CP-ABE scheme and achieve desirable experimental results.

Testing self-healing cyber-physical systems under uncertainty with reinforcement learning: an empirical study

Self-healing is becoming an essential feature of Cyber-Physical Systems (CPSs). CPSs with this feature are named Self-Healing CPSs (SH-CPSs). SH-CPSs detect and recover from errors caused by hardware or software faults at runtime and handle uncertainties arising from their interactions with environments. Therefore, it is critical to test if SH-CPSs can still behave as expected under uncertainties. By testing an SH-CPS in various conditions and learning from testing results, reinforcement learning algorithms can gradually optimize their testing policies and apply the policies to detect failures, i.e., cases that the SH-CPS fails to behave as expected. However, there is insufficient evidence to know which reinforcement learning algorithms perform the best in terms of testing SH-CPSs behaviors including their self-healing behaviors under uncertainties. To this end, we conducted an empirical study to evaluate the performance of 14 combinations of reinforcement learning algorithms, with two value function learning based methods for operation invocations and seven policy optimization based algorithms for introducing uncertainties. Experimental results reveal that the 14 combinations of the algorithms achieved similar coverage of system states and transitions, and the combination of Q-learning and Uncertainty Policy Optimization (UPO) detected the most failures among the 14 combinations. On average, the Q-Learning and UPO combination managed to discover two times more failures than the others. Meanwhile, the combination took 52% less time to find a failure. Regarding scalability, the time and space costs of the value function learning based methods grow, as the number of states and transitions of the system under test increases. In contrast, increasing the system’s complexity has little impact on policy optimization based algorithms.

SymPas: Symbolic Program Slicing

Program slicing is a technique for simplifying programs by focusing on selected aspects of their behavior. Current mainstream static slicing methods operate on dependence graph PDG (program dependence graph) or SDG (system dependence graph), but these friendly graph representations may be a bit expensive for some users. In this paper we attempt to study a light-weight approach of static program slicing, called Symbolic Program Slicing (SymPas), which works as a dataflow analysis on LLVM (low-level virtual machine). In our SymPas approach, slices are stored in symbolic forms, not in procedures being re-analyzed (cf. procedure summaries). Instead of re-analyzing a procedure multiple times to find its slices for each callling context, we calculate a single symbolic slice which can be instantiated at call sites avoiding re-analysis; SymPas is implemented with LLVM to perform slicing on LLVM intermediate representation (IR). For comparison, we systematically adapt IFDS (interprocedural finite distributive subset) analysis and the SDG-based slicing method (SDG-IFDS) to statically slice IR programs. Evaluated on open-source and benchmark programs, our backward SymPas shows a factor-of-6 reduction in time cost and a factor-of-4 reduction in space cost, compared with backward SDG-IFDS, thus being more efficient. In addition, the result shows that after studying slices from 66 programs, ranging up to 336 800 IR instructions in size, SymPas is highly size-scalable.

Offices scarce but housing scarcer: Estimating the premium for London office conversions

AbstractBritish planning is among the most restrictive in the world and has been restrictive for longer than elsewhere. Restrictive regulation substantially increases the costs of office space and, especially in London, increases the house prices. Partly in response to the crisis in housing supply an automatic right to convert offices to residential use was introduced in 2013. Some major office locations in central London and Manchester were excluded from this relaxation. We exploit the resulting boundary discontinuities to estimate the impact on prices of the new right to convert offices to housing. Using a panel data set of some 2,000 office transactions between 2009 and 2016, we find a significant increase in the price of offices eligible for this automatic conversion: our central estimate is a premium of 50%. This result demonstrates that London's restrictions on the supply of housing were substantially more severe than those on offices–already estimated to have been tighter than anywhere else in Europe. This article contributes to the small literature analyzing the restrictive effect of regulation on offices and is, as far as we are aware, the first analyzing regulatory restriction on offices relative to housing.

PCSP: A Protected Cloud Storage Provider employing light weight techniques

ABSTRACT One of the major practices of cloud computing is the storage service that it offers. In spite of its many creditable advantages, it also has some disadvantages like data security and data availability. These two are the main issues that a user face. Many models have been proposed to solve these issues. These models use cryptographic methods to secure the data and data redundancy method to ensure data availability. Both these methods solved the issues at the cost of extra storage space and increased time consumption both at the user and at the server side. This paper recommends a model PCSP (Protected Cloud Service Provider) which solves these issues in a novel way. The model uses light weight techniques which does not employ cryptographic methods. PCSP uses a layered approach, with three entities – the user, the PCSP, and the vendor. Due to the use of light weight techniques, the execution time is reduced by 80% and the storage needed is also reduced by 60%. Thus, there is still more reduction in the storage space. The implementation and analysis serve as the proof of concept

The learning behaviours of dropouts in MOOCs: A collective attention network perspective

Understanding the dropout phenomenon has advanced from viewing it as a sign of deficient quality to viewing it as an explicit sign of individual choice, which highlights the importance of investigating how dropouts learn in massive open online courses (MOOCs). Nevertheless, the short, limited and heterogeneous behaviours of individual dropouts create challenges for understanding how dropouts learn over time. Taking a systematic network perspective, this study used clickstream data to build a flow network model of collective attention to investigate how dropouts learn in XuetangX's Introduction to Psychology (2018 autumn). The results showed that the quantification of behavioural data presented a stereotypical image of dropouts, but the network analytics presented a rather different picture of how dropouts learn. Recognising the distinct roles of introductory learning resources could prevent dropping out and improve the accuracy of prediction models. Interestingly, the assessments embedded in the MOOCs performed a scaffolding role in guiding dropouts to learn. Thus, redesigning quizzes or examinations in open and flexible learning environments to construct a minimum cost network of collective attention is vital to making this online space cost effective for learners at risk.

A Future without Human Driving

Cities, especially those lacking a well-functioning public transport system, have long had a love-hate relationship with privately owned cars. On the one hand, cars allow a massive influx of people into cities from the surrounding areas. This inflow helps cities maintain their economic vitality and social relevance. On the other hand, there are massive prices to pay. Congested traffic means a slow commute, which is both unpleasant and wasteful. Furthermore, car parking is enormously expensive in terms of physical space and financial cost. Worse still is the cost in life and limb that car accidents cause. Small wonder that commentators have called for the complete removal of cars from cities or that many cities have taken up the initiative of banning cars from their centers. Autonomous Vehicles (AV) hold the promise of a “gentler, kinder” car, which will allow more individual passengers to get to their desired destinations faster, more comfortably, and more safely than they do now. We have two goals in this paper: first, to present the technology in its transition from the drawing board into mainstream use while outlining current trends in emerging technologies regulation (AVs included); and second, to suggest that AVs hold particular promise in the urban context. Here, we look into AV regulation in more detail, discussing the social impact and legal considerations that should be addressed as we move forward to the future with (or without) human driving.

Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network

Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, redundant and low-utility stations waste public urban space and maintenance costs of DL-PBS vendors. In this article, we propose a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the optimal bicycle station layout for the DL-PBS network. The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation. In the bicycle drop-off location clustering module, candidate bicycle stations are clustered from each spatio-temporal subset of the large-scale cycling trajectory records. In the bicycle-station graph modeling module, a weighted digraph model is built based on the clustering results and inferior stations with low station revenue and utility are filtered. Then, graph models across time periods are combined to create a graph sequence model. In the bicycle-station location prediction module, the GGNN model is used to train the graph sequence data and dynamically predict bicycle stations in the next period. In the bicycle-station layout recommendation module, the predicted bicycle stations are fine-tuned according to the government urban management plan, which ensures that the recommended station layout is conducive to city management, vendor revenue, and user convenience. Experiments on actual DL-PBS networks verify the effectiveness, accuracy, and feasibility of the proposed BSDP system.

Light-weight network for real-time adaptive stereo depth estimation

Self-supervised learning methods have been proved effective in the task of real-time stereo depth estimation with the requirement of lower memory space and less computational cost. In this paper, a light-weight adaptive network (LWANet) is proposed by combining the self-supervised learning method to perform online adaptive stereo depth estimation for low computation cost and low GPU memory space. Instead of a regular 3D convolution, the pseudo 3D convolution is employed in the proposed light-weight network to aggregate the cost volume for achieving a better balance between the accuracy and the computational cost. Moreover, based on U-Net architecture, the downsample feature extractor is combined with a refined convolutional spatial propagation network (CSPN) to further refine the estimation accuracy with little memory space and computational cost. Extensive experiments demonstrate that the proposed LWANet effectively alleviates the domain shift problem by online updating the neural network, which is suitable for embedded devices such as NVIDIA Jetson TX2. The relevant codes are available at https://github.com/GANWANSHUI/LWANet

Effect of precooling with transcritical CO2 cycle on two types of LNG boil-off gas reliquefaction systems

Precooling improves the performance of boil-off gas reliquefiers in LNG carrier ships. However, net savings of power, additional space and capital cost and fire-safety are some of the factors that decide the choice of precooling system. Carbon dioxide (CO2) may be a preferred refrigerant for offshore systems as it is non-toxic, non-flammable and has lower global warming and zero ozone depleting potential. In this paper, CO2 refrigeration cycle is integrated with reverse Brayton cycle based indirect reliqufier and also with Claude cycle based direct reliquefier. Performances of these systems are compared with reliquefaction systems integrated with vapour compression cycles working on flammable refrigerants. All options of precooling are evaluated using exergy analysis after simulating them in Aspen HYSYS V8.6®. Upon precooling with transcritical CO2 cycle, reverse Brayton and Claude systems show improvements in exergy efficiency by 7.5% and 7.0% respectively. A low precooling temperature suits reverse Brayton system. However, Claude system demands an optimum precooling temperature (280 K) to maximise the exergy efficiency that suits the use of a transcritical CO2 precooler. Though precooling with ethylene-propylene cascaded vapour compression cycles may further improve the performance of reliquefiers, the flammability factor may stand on the way of its selection for onboard systems.

Estimating the social cost of on-street parking space for residents considering the opportunity cost of the road

Estimating the social cost of on-street parking space for residents considering the opportunity cost of the road

An Efficient Computational Model for Large-Scale Prediction of Protein–Protein Interactions Based on Accurate and Scalable Graph Embedding

Protein–protein interaction (PPI) is the basis of the whole molecular mechanisms of living cells. Although traditional experiments are able to detect PPIs accurately, they often encounter high cost and require more time. As a result, computational methods have been used to predict PPIs to avoid these problems. Graph structure, as the important and pervasive data carriers, is considered as the most suitable structure to present biomedical entities and relationships. Although graph embedding is the most popular approach for graph representation learning, it usually suffers from high computational and space cost, especially in large-scale graphs. Therefore, developing a framework, which can accelerate graph embedding and improve the accuracy of embedding results, is important to large-scale PPIs prediction. In this paper, we propose a multi-level model LPPI to improve both the quality and speed of large-scale PPIs prediction. Firstly, protein basic information is collected as its attribute, including positional gene sets, motif gene sets, and immunological signatures. Secondly, we construct a weighted graph by using protein attributes to calculate node similarity. Then GraphZoom is used to accelerate the embedding process by reducing the size of the weighted graph. Next, graph embedding methods are used to learn graph topology features from the reconstructed graph. Finally, the linear Logistic Regression (LR) model is used to predict the probability of interactions of two proteins. LPPI achieved a high accuracy of 0.99997 and 0.9979 on the PPI network dataset and GraphSAGE-PPI dataset, respectively. Our further results show that the LPPI is promising for large-scale PPI prediction in both accuracy and efficiency, which is beneficial to other large-scale biomedical molecules interactions detection.

The Use of a Vascular Surgery Operative Cost Sheet to Increase Surgeon Financial Awareness and Decrease Operative Cost

Operative expenses compose one-third of the rising health care costs in the United States. Therefore, novel strategies for mitigating cost in the vascular surgery space are warranted. Our institution implemented a cost reduction strategy in 2017 by simply showing surgeons the costs of their implants and disposable supplies at the end of each case in order to increase surgeon financial awareness and reduce costs. We performed a retrospective analysis of recorded supply and implant costs for vascular procedures from 2015 to 2018 at a single institution. Total operative cost was defined as the accumulated implant and supply cost during a procedure. We assessed total implant and disposable supply cost, supply cost alone, and cost per time. Cost was analyzed over two periods: the 24 months prior (PRE) to the tool implementation and 24 months following (POST) with a 1-month washout. Medians and interquartile ranges were calculated for each parameter and compared using the Mann-Whitney U test. We analyzed a total of 1372 procedures performed over the time period studied. Total supply and implant cost (–17.5%; P = .008), supply cost (–1.99%; P = .049) and cost per minute (–28.24%; P = .015) all saw significant reductions across the composite of open procedures from the PRE to POST time point (Table). Largest supply cost reductions were seen in common femoral artery endarterectomy (–42%; P < .001). The total operative cost/time was most significantly reduced for carotid endarterectomy (–8.89%; P = .001), abdominal aortic aneurysm repair (–16.21; P = .019), carotid-subclavian bypass (–9.94%; P = .032), and common femoral endarterectomy (–41.16%; P = .06), with nonsignificant cost reductions in arteriovenous grafting and rib resection. Relative price increases of 31%, 8%, and 3% were calculated for temporal artery biopsy, vein transposition, and distal bypass, respectively. The composite of all endovascular procedures saw large implant cost increases with minimal change in supply cost. We demonstrate that a low-cost, highly implemenTable cost sheet is effective at reducing cost in vascular surgery. Further investigations should assess how and why surgeons choose supplies and implants in order to target further intraoperative cost savings.TableCost trends before and after implementation of a cost sheet across multiple vascular surgery proceduresProcedureTotal operative cost (supply + implant), $Supply cost, $Total cost/time (supply + implant), $/minutePREPOST% ΔP valuePREPOST% ΔP valuePREPOST% ΔP valueCarotid endarterectomy504.3 (411.5-664)452.6 (409.5-525.7)–10.25.012250.3 (238.5-277)249.1 (235-276.1)–0.45.35344.86 (4.05-6.09)4.43 (3.77-5.25)–8.89.001AAA repair1686 (1268-2041)1399 (1257-1717)–17.01.037542.5 (432-602.5)536.8 (456.6-621.8)–1.052.81857.59 (5.67-9.49)6.36 (1.65-8.13)–16.21.019Carotid-subclavian bypass681 (609.1-805.4)682.6 (641-771.6)0.23.953272.3 (245.8-385.7)259.9 (245.1-290)–4.54.36155.49 (4.52-6.36)4.85 (1.39-5.76)–9.94.032Common femoral endarterectomy3102 (1107-5232)971.3 (532.5-4309)–68.69.0107662.4 (441.3-886.1)382.8 (237.8-566.8)–42.21<.000114.35 (6.32-14.35)8.45 (3.81-8.45)–41.16.061Arteriovenous graft181.8 (130.8-189.2)183.3 (123.8-223.1)0.822.5425113.8 (108.3-132.4)112.5 (106.7-125.9)1.0865.54112.07 (1.39-10.57)1.70 (1.48-2.67)–17.65.14Rib resection119.7 (115.8-124.6)116.6 (112.2-149.2)–2.577.8662120.3 (117.8-123)114.6 (109.7-138.3)–4.738.05621.46 (1.25-2.06)1.39 (1.02-2.11)–4.46.56Temporal artery biopsy45.25 ( 34.41-73.4)90.26 (47.84-121.4)99.49.000345.25 (34.41-73.4)90.55 (48.17-121.4)100.01.00041.38 (0.79-1.79)1.81 (1.25-3.05)30.92.0099Upper extremity vein transposition181.9 (125.1-249)187.7 (132.2-269.2)3.16.5116127.2 (113-145.7)114.9 (101.3-121.1)–9.669.00081.89 (1.02-2.84)2.05 (1.39-2.56)8.68.53Distal bypass831.7 (491.8-2315)767.6 (480-2987)–7.69.8428308.8 (244.9-382.6)377.9 (242.4-572)22.36.08172.64 (1.87-7.98)2.72 (1.59-8.29)3.26.94All endovascular (EVAR + TEVAR)17653 (12890-24376)22524 (15366-29092)27.59.00022031 (1581-2588)2022 (1559-2597)0.437.8103180.3 (11.4-262.1)163.2 (97.84-271.4)–9.48.6396All open549 (275.1-1115)452.7 (209.3-782.4)–17.54.0078250.3 (141.1-411.1)245.3 (121.5-343.2)1.99.04935.237 (2.039-11.03)3.758 (1.86-7.241)–28.24.015AAA, Abdominal aortic aneurysm; EVAR, endovascular aneurysm repair; POST, the 24 months following tool implementation; PRE, The 24 months prior to the tool implementation; TEVAR, thoracic endovascular aneurysm repair.Values are median (interquartile range). Open table in a new tab

Optimization of Inventory Holding Cost Due to Price, Weight, and Volume of Items

The inventory carrying cost has been assumed uniform for all products in an organization or a warehouse. This assumption is not valid for a diversified range of items in an organization or warehouse. This paper tested this hypothesis of variations in inventory holding costs in a warehouse in two industries based on the physical nature and the price of products. It is found that organizations with a wide variety of products need to calculate the inventory holding cost for each item (SKU) rather than using an average percentage cost of inventory. Inventory holding costs of items in two different organizations were calculated based on the various factors, including the actual cost of space due to the voluminous nature of the items with their existing inventory policies. A variation in inventory holding costs for each item was observed. The variation was small for an organization with homogeneous input costs, and it was large for a multi-product organization. The overall savings in the inventory holding cost due to adjusting the inventory policies through this methodology was found to be about 3%, which is significant for a big organization. This analysis will affect the decision the determining inventory carrying cost, inventory policies (e.g., stocking levels), and pricing policies (e.g., quantity discounts) for retail organizations.

Array Integration and Far-Field Detection of Biocompatible Wireless LC Pressure Sensors.

In this paper, three configurations of LC (inductor-capacitor) pressure sensors are developed, namely series LC pressure sensors, compact LC pressure sensors, and far-field LC pressure sensor tags. The modified silk protein films have been chosen as substrates due to their good biocompatibility and air/water permeability, which is suitable for continuously pasting such substrates on skin. For series LC pressure sensors, conducting wire is used to connect the flexible capacitor and spiral inductor. It exhibits good cycling stability and high sensitivities, suitable for electronic skin. For compact LC pressure sensors, the spiral coil functions as inductor, antenna, and capacitor electrode simultaneously, minimizing the space cost and is suitable for array integration, while the sensitivities remain the same. By tailoring the turn of the spiral coil, the resonate frequency can be regulated continuously. An annular array of compact LC sensors with ten distinct resonate frequencies ranged from 400 to 1000MHz is developed to remotely monitor the press of number 0-9. Finally, far-field LC pressure sensor tags with elongated detection distances are developed in which each compact LC sensor acts as a filter. A wireless in-shoe plantar to detect the sole pressure distribution using the far-field LC sensor configuration is developed.