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The problem of determining total distance ascended during a mountain bike trip is addressed. Altitude measurements are obtained from GPS receivers utilizing both GPS-based and barometric altitude data, with data averaging used to reduce fluctuations. The estimation process is sensitive to the degree of averaging, and is related to the well-known question of determining coastline length. Barometric-based measurements prove more reliable, due to their insensitivity to GPS altitude fluctuations.

We propose a matching method for images captured at different times and under different capturing conditions. Our method is designed for change detection in streetscapes using normal automobiles that have an off-the-shelf car mounted camera and a GPS. Therefore, we are able to analyze low-resolution and low frame-rate images captured asynchronously. To cope with this difficulty, previous and current panoramic images are created from sequential images which are rectified based on the view direction of a camera, and are then compared. In addition, in order to allow the matching method to be applicable to images captured under varying conditions, (1) for different lanes, enlarged/reduced panoramic images are compared with each other, and (2) robustness to noises and changes in illumination is improved by the edge features. To confirm the effectiveness of the proposed method, we conducted experiments matching real images captured under various capturing conditions.

More and more location-based services become relying on the logical notion of a physical location, known as logical location (e.g. Starbucks, KFC). In this paper, we propose a new way to identify logical location using (1) a GPS-enabled mobile phone and (2) a wearable camera embedded in user's glasses. When a user with a wearable camera is detected paying attention to a certain physical location, all the logical locations within the error range of the GPS coordinates are considered as the matched candidates. We select the representative frames in the video stream corresponding to user's interested location in real-time and use multi-view images taken beforehand to represent each logical location. We then extract the Scale Invariant Feature Transform visual features from both the representative video frames and pre-stored images of candidate logical locations for video-image matching, the logical location that the user pays attention to can thus be identified. In order to differentiate the cases where users watch certain objects rather than a logical location in the street, we use Support Vector Machine to classify the two cases so that only the valid logical location is identified. Our proposed approach is proved weather and user independent, and it does not request additional user efforts compared with previous solutions. The results tested using a real-world dataset can achieve an average accuracy of 91.08%.

Over the last few years, research has provided the cellular network system with the capability of fair channel access to multimedia users, while at the same time, insuring a given QoS and optimal utilization of network resources. Scheduling policies play a crucial role in achieving desired QoS goals and optimum utilization of limited resources. Many scheduling approaches have been proposed for the efficient utilization of the network resources. This paper deals with the experimental study of a new proposed scheduling policy for achieving fairness, QoS, and optimal use of resources. The proposed scheduling policy Code Division Multiple Access based on Generalized Processor Sharing with Dynamic Weights (CDMA/GPS-DW) is an improvement of a previous GPS policy. Simulation results show that the proposed policy achieves fairness of the specified QoS and makes efficient use of the network resources.

In this paper, two novel low-profile monopole antennas are presented for simultaneous operation in GPS (Global Positioning System), WLAN (Wireless Local Area Network) and WiMAX (Worldwide Interoperability for Microwave Access) applications. The antennas constitute of a T-shaped microstrip feed line and directly coupled strips to generate multiple bands. The proposed antennas are printed on one side of a low-cost FR4 epoxy substrate and partial ground plane (metal plane is etched partially) are fabricated on the other side of the substrate. The overall dimension of antenna is $50\times 35\times 1.6$mm³. Measured results show that the antenna1 (quad band) covers the four distinct operating bands of 320MHz (2.17–2.49GHz), 190MHz (3.31–3.50GHz), 270MHz (5.18–5.45GHz) and 700MHz (5.5–6.20GHz). Antenna2 (penta band) covers the frequency bands of 1.29–1.98GHz (center frequency 1.61GHz), 2.78–2.91GHz (center frequency 2.83GHz), 3.59–3.94GHz (center frequency 3.75GHz), 5.15–5.33GHz (center frequency 5.24GHz) and 5.39–6.06GHz (center frequency 5.56GHz). The detail antenna design and parametric analyses are discussed in steps. The characteristic of radiation pattern and gain are measured. The measured and simulated results are in good agreement. The antennas are designed using a simulation software HFSS v.15.

Inertial navigation system (INS) is often integrated with satellite navigation systems to achieve the required precision at high-speed applications. In global navigation system (GPS)/INS integration systems, GPS outages are unavoidable and a severe challenge. Moreover, because of the usage of low-cost microelectromechanical sensors (MEMS) with noisy outputs, the INS will get diverged during GPS outages, and that is why navigation precision severely decreases in commercial applications. In this paper, we improve GPS/INS integration system during GPS outages using extended Kalman filter (EKF) and artificial intelligence (AI) together. In this integration algorithm, the AI receives the angular rates and specific forces from the inertial measurement unit (IMU) and velocity from the INS at $t$ and $t-1$. Therefore, the AI has positioning and timing data of the INS. While the GPS signals are available, the output of the AI is compared with the GPS increment; so that the AI is trained. During GPS outages, the AI will practically play the GPS role. Thus, it can prevent the divergence of the GPS/INS integration system in GPS-denied environments. Furthermore, we utilize neural networks (NNs) as an AI module in five different types: multi-layer perceptron (MLP) NN, radial basis function (RBF) NN, wavelet NN, support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed approach, we utilize a real dataset that has been gathered by a mini-airplane. The results demonstrate that the proposed approach outperforms the INS and GPS/INS integration systems with the EKF during GPS outages. Meanwhile, the ANFIS also reached more than 47.77% precision compared to the traditional method.

In Global Positioning System (GPS), receivers use FFT-based convolvers to acquire the signals. This paper shows a robust substitute algorithm for calculating the convolution that is less sensitive to additive noise.

Navigation and guidance of an autonomous vehicle require determination of the position and velocity of the vehicle. Therefore, fusing the Inertial Navigation System (INS) and Global Positioning System (GPS) is important. Various methods have been applied to smooth and predict the INS and GPS errors. Recently, wavelet de-noising methodologies have been applied to improve the accuracy and reliability of the GPS/INS system. In this work, analysis of real data to identify the optimal wavelet filter for each GPS and INS component for high quality error estimation is presented. A comprehensive comparison of various wavelet thresholding selections with different level of decomposition is conducted to study the effect on GPS/INS error estimation while maintaining the original features of the signal. Results show that while some wavelet filters and thresholding selection algorithms perform better than others on each of the GPS and INS components, no specific parameter selection perform uniformly better than others.

The visions of context-sensitive services, ubiquitous computing, and ambient intelligence have driven several aspects of wireless sensor networks research, but perhaps none more so than localization and tracking. Location of a user is one of the most fundamental types of context, and can be applied in a vast number of environments from smart homes to improved security solutions. However, accurate localization of users is not trivial, and most of the individual systems presented suffer from coverage problems. We present a system-level approach to localize and track normal and blind users on the basis of different sources of location information. The system can be applied outdoors and indoors using GPS and Zigbee as the source of location data. The overall system architecture is modular and extendible, allowing for creation of location- and context-aware services for blind child tracking with the inclusion of additional sources of localization data. Extensive performance evaluation presented for blind child navigation shows that the system is both accurate as well as scalable.

Mobile networked digital devices are near ubiquitous, owned by just anyone, in many parts of the world today. Together with positioning techniques this opens for new innovative educational practices through the design of location aware systems. After studying a technology rich university-level discipline, a mobile learning framework, MOTEL, that supports geo-tagging and publishing of tagged areas overlaid on digital maps was designed and implemented. By following an iterative design process it has been possible to answer questions about how it is possible to use locative media to support learning for explorations of sites for learning. The findings from this research and development consist of a set of challenges that need to be solved in current and future mobile learning environment designs. This paper describes the design process that has lead to the MOTEL framework, the framework versions, including a test scenario, envisioned and planned use cases, and the challenges that need to be solved for future designs.

It is generally recognized that permanent displacements estimated by the double integration of acceleration records need a suitable baseline correction. Current baseline correction methods have been validated by comparing the displacements with those from the Global Positioning System (GPS) records nearby, but GPS stations that are sufficiently close to a strong-motion station are scarce. Because the ${\mathrm{\text{M}}}_{\mathrm{\text{w}}}9.0$ Tohoku-Oki earthquake produced geodetic displacements in a wide area and because dense strong-motion and GPS networks are available in Japan, we interpolated the displacements calculated from GPS records to estimate the permanent displacements at 508 strong-motion stations. The estimated results were used to evaluate uncertainties in permanent displacements obtained using two baseline correction methods, and results were found to be reliable only for KiK-net’s borehole acceleration records. A new joint parameter search method for the surface and borehole records was further proposed, and reliable results were obtained for KiK-net’s surface records.

Tsunami waves can induce tsunami traveling ionospheric disturbances (TTIDs) of the total electron content (TEC). In this study, we examine the TEC derived by ground-based receivers of the global positioning system (GPS) and identify TTIDs induced by 2004 Indian Ocean tsunami. Simulations of the COMCOT (Cornell multi-grid coupled tsunami) model and analyses of the circle method, the ray-tracing technique, and the beam-forming technique are used to show that TTIDs can be quickly detected and confirmed after the tsunami occurrence. Finally, the ionospheric TEC derived by existing ground-based GNSS (Global Navigation Satellite Systems) receiving stations is demonstrated to be useful to support the tsunami early warning system.

A set of mobile robots is placed at arbitrary points of an infinite line. The robots are equipped with GPS devices and they may communicate their positions on the line to a central authority. The collection contains an unknown subset of “spies”, i.e., byzantine robots, which are indistinguishable from the non-faulty ones. The set of the non-faulty robots needs to rendezvous in the shortest possible time in order to perform some task, while the byzantine robots may try to delay their rendezvous for as long as possible. The problem facing a central authority is to determine trajectories for all robots so as to minimize the time until all the non-faulty robots have met. The trajectories must be determined without knowledge of which robots are faulty. Our goal is to minimize the competitive ratio between the time required to achieve the first rendezvous of the non-faulty robots and the time required for such a rendezvous to occur under the assumption that the faulty robots are known at the start.

In this paper, we give rendezvous algorithms with bounded competitive ratio, where the central authority is informed only of the set of initial robot positions, without knowing which ones or how many of them are faulty. In general, regardless of the number of faults $f\le n-2$ it can be shown that there is an algorithm with bounded competitive ratio. Further, we are able to give a rendezvous algorithm with optimal competitive ratio provided that the number $f$ of faults is strictly less than $\lceil n/2\rceil$. Note, however, that in general this algorithm does not give an estimate on the actual value of the competitive ratio. However, when an upper bound on the number of byzantine robots is known to the central authority, we can provide algorithms with constant competitive ratios and in some instances we are able to show that these algorithms are optimal. Moreover, in the cases where the number of faults is either $f=1$ or $f=2$ we are able to compute the competitive ratio of an optimal rendezvous algorithm, for a small number of robots.

This paper presents an SMS based design in the GSM system for a portable, light weighted, and small sized TeleAlarm device. The device is composed of a transmitter and a controller. When an emergent situation such as a stroke or a fall occurs, the user only needs to push a button to trigger the controller. The controller automatically sends text messages stored in its database through the transmitter to specified mobile phone numbers for help. The SMS uses only the control channels in the GSM system to transfer the message, which enables the receiving-end user to receive it even during a call. An experimental test shows that a complete message transmission only needs 2.949 seconds in average. The design is convenient to elderly people who may live alone.

The Xidian University is a university in Xian China focuses on the teaching and research in electronic engineering. In 2013, a new e-learning system called “Active Campus” was introduced to facilitate interactive learning, effective school administration and collecting student feedbacks. The system uses the mobile and WiFi network for linking students’ devices such as desktop, laptop and mobile phone for accessing information, conducting two-ways interactive class discussions, collecting survey feedback and course evaluation. It also uses the Global Positioning System (GPS) for student attendance and tutorial registration of which the location of the students is important. Over 90.4% of the students who participated in the pilot agreed that such an interactive learning platform is useful to increase the quality of teaching and learning. This paper describes the system architecture, operations and performance of the project.

In this paper, the quality and safety of aquatic products in our country are analyzed at first. Taking sea cucumber as the research object, this paper describes the whole process of the sea cucumber from the breeding to the sale. By introducing Radio Frequency Identification (RFID), Quick Response Code (QR Code), Global Positioning System (GPS) and other Internet of things technology, this system put forward a kind of product quality traceability system based on the Internet of things. It takes the water supply chain as the angle of view and combines with the different characteristics of each stage to realize the whole process dynamic tracing of the sea cucumber.

As smart phone is becoming more powerful, applications providing location-based service have been increasingly popular. Smart phone is commonly equipped with a variety of sensors, such as GPS, accelerometer, orientation sensor, etc. Although GPS can provide adequate location accuracy, it has limitations such as high energy-consumption. This paper presents an approach based on accelerometer, orientation sensor and GPS. It can reduce energy-consumption without compromising on location accuracy. Evaluation shows energy-saving of smart phone and location accuracy in a typical circumstance.

Driving behavior is reflected by the performance of the actual driver’s behavior in the driving process. It is an important basis for judging whether the driver is safe. Combining factor analysis, K-means algorithm, naive Bayes algorithm and k-fold cross-Validation algorithm, this paper builds a new driving behavior analysis model based on the movement data of the new energy bus. Then, it uses the real CAN and GPS data of the bus to validate the correctness and effectiveness of the model. The experimental results show that the model can classify the driving behavior excellently. It provides a good basis for standardizing driving behavior in the future.

This paper studies a vehicle speed intelligent warning system, designs a vehicle safe running and intelligent control system with dynamic recognition and automatic control functions and establishes the architecture of the intelligent control system. This system judges the safe state of a vehicle automatically by comparing its real-time running speed with safe driving speed limit and then gives the vehicle driver a clue by releasing the vehicle speed warning information and finally controls the running speed by vehicle control module.

Here, we first analyze data from two GPS stations operating in Tashkent and Kitab and new Very Low Frequency (VLF) radio receiver operating in Tashkent for the possible earthquake ionospheric precursors. We find anomalous Total Electron Content (TEC) precursor signals and significant correlation in time between the TEC anomalies and the occurrence of the earthquake in Tashkent on August 22, 2008, M = 4.4. The obtained results have revealed a fine agreement with the TEC anomalies observed during the strong earthquake in Tashkent and we demonstrate the capabilities of the GPS technique to detect ionospheric perturbations caused by the local earthquakes. TEC decrease during the solar eclipse on August 1, 2008 is also obtained from the data at GPS stations in Tashkent and Kitab. We used tweek radio atmospherics (originating from lightning discharges) to estimate electron densities in the D-layer of ionosphere. The propagation characteristics of tweek atmospherics observed at the Tashkent station have been studied near the mode cut-off frequencies. It is shown that the height of the Earth-Ionosphere Wave Guide (EIWG) varies from 84 km to 91 km and nighttime electron density varies from 25 el/cm³ to 27 el/cm³. We have also studied VLF amplitude anomalies related to the earthquakes occurring on the path from the VLF transmitters to the Tashkent station. For analyzing narrowband data we have used the Nighttime Fluctuation method paying attention to the data obtained during the local nighttime (18:00 LT- 06:00 LT). The deathlines for rotating as well as oscillating magnetars are obtained for the different modes of oscillations and it is shown that the oscillations increase the region in the (P is the period) diagram of the magnetars which is allowed for the radio emission.