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The growing diffusion of pervasive collaboration environments and technical advancement of sensing technologies have fostered the development of a new wave of online services whose functionalities are based on users' physical position. Thanks to the widespread diffusion of mobile devices (e.g. cell phones), many services can be greatly enriched wit...
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... l add =S a (α) represents the additional loss produced by the shape of the antenna a, with α the angle identified by point p and the principal direction of antenna a, and l b is the component of canonical COST231 (i.e., either LOS or NLOS). Fig. 1 shows a comparison between EMF predicted with buildings structure and omni-directional antennas and EMF predicted with buildings structure and directional antennas. accuracy of the prevision. Also, the knowledge of buildings heights and sizes greatly improves the quality of prediction. To assess this improvement, we performed several ...
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... l add = S a ( α ) represents the additional loss produced by the shape of the antenna a, with α the angle identified by point p and the principal direction of antenna a , and l b is the component of canonical COST231 (i.e., either LOS or NLOS ). Fig. 1 shows a comparison between EMF predicted with buildings structure and omni-directional antennas and EMF predicted with buildings structure and directional antennas. The availability of information about the shapes and directions of the antennas, as well as more information on the environment, allows of significantly improving ...
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Citations
... Time-domain and frequency-domain HMMs can be used to identify model parameters prior to filtering and smoothing of noisy speech [13] - [15]. In a position tracking application [16], an HMM is used to estimate a control input for a Kalman filter. In an image target tracking problem [17], an HMM is used to estimate position coordinates which then serve as a measurement input for a Kalman filter. ...
... respectively denote the order-N and order-(N+i) solutions of the ARE (16). Then ...
A linear state-space model is described whose second-order moments match that of a hidden Markov chain. This model enables a modified transition probability matrix to be employed within minimum-variance filters and smoothers. However, the ensuing filter/smoother designs can exhibit suboptimal performance because a previously-reported transition-probability-matrix modification is conservative, and identified models can lack observability and reachability. This paper describes a less-conservative transition-probability-matrix modification and a model-order-reduction procedure to enforce observability and reachability. An optimal minimum-variance predictor, filter and smoother are derived to recover the Markov chain states from noisy measurements. The predictor is asymptotically stable provided that the problem assumptions are correct. It is shown that collapsing the model improves state prediction performance. The filter and smoother recover the Markov states exactly when the measurement noise is negligible. A mining vehicle position tracking application is discussed in which performance benefits are demonstrated.
... Time-domain and frequency-domain HMMs can be used to identify model parameters prior to filtering and smoothing of noisy speech [18] - [20]. In a position tracking application [21], an HMM is used to estimate a control input for a Kalman filter. In an image target tracking problem [22], an HMM is used to estimate position coordinates which then serve as a measurement input for a Kalman filter. ...
The previously-discussed optimal Kalman filter [1] – [3] is routinely used for tracking observed and unobserved states whose second-order statistics change over time. It is often assumed within Kalman filtering applications that one or more random variable sequences are generated by a random walk or an autoregressive process. That is, common Kalman filter parameterisations do not readily exploit knowledge about the random variables’ probability distributions. More precisely, the filter is optimal only for Gaussian variables whose first and second order moments completely specify all relevant probability distributions. For non-Gaussian data, the filter is only optimal over all linear filters [1].
Rather than assuming that random variable sequences are generated by autoregressive processes they may alternatively be modelled as Markov chains. The phrase ‘Markov chain’ was first coined in 1926 by a Russian mathematician S. N. Bernstein to acknowledge previous discoveries made by Andrei Andreevich Markov [4]. Markov was a professor at St Petersburg University and a member of the St Petersburg Academy of Sciences, which was a hub for scientific advances in many fields including probability theory. Indeed, Markov, along with fellow academy members D. Bernoulli, V. Y. Bunyakovsky and P. L. Chebyshev, all wrote textbooks on probability theory. Markov extended the weak law of large numbers and the central limit theorem to certain sequences of dependent random variables forming special classes of what are now known as Markov chains [4].
The basic theory of Hidden Markov models (HMMs) was first published by Baum et al in the 1960s [5]. HMMs were introduced to the speech recognition field in the 1970s by J. Baker at CMU [6], and F. Jelinek and his colleagues at IBM [7]. One of the most influential papers on HMM filtering and smoothing was the tutorial exposition by L. Rabiner [8], which has been accorded a large number of citations. Rabiner explained how to implement the forward-backward algorithm for estimating Markov state probabilities, together with the Baum-Welch algorithm (also known as the Expectation Maximisation algorithm). HMM filters and smoothers can be advantageous in applications where sequences of alphabets occur [8] - [10]. For example, in automatic speech recognition, sentence and language models can be constructed by concatenating phoneme and word-level HMMs. Similarly, stroke, character, word and context HMMs can be used in handwriting recognition. HMMs have been useful in modelling in biological sequences such as proteins and DNA sequences.
The Doob–Meyer decomposition theorem [11] states that a stochastic process may be decomposed into the sum of two parts, namely, a prediction and an input process. The standard Kalman filter [1] makes use of both prediction plus input process assumptions and attains minimum-variance optimality. In contrast, the standard hidden Markov model filter/smoother rely exclusively on (Markov model) prediction and is optimum in a Bayesian sense [8] - [10]. It is shown below that minimum-variance and HMM techniques can be combined for improved state recovery.
The minimum-variance, HMM and combined-minimum-variance-HMM predictions are only calculated from states at the previous time step. Improved predictions can be calculated from states at multiple previous time steps. The desired interdependencies between multiple previous states are conveniently captured by constructing high-order-Kronecker-product state vectors. The theory and implementation of such high-order-minimum-variance-HMM filters is also described below.
The afore-mentioned developments are driven by our rapacious appetites for improved estimator performance. In principle, each additional embellishment, spanning HMM filters, minimum-variance-HMM filters to high-order-minimum-variance-HMM filters, has potential to provide further performance gains, subject to the usual proviso that the underlying modelling assumptions are correct. Needless to say, significantly higher calculation overheads must be reconciled against any performance benefits.
Some prerequisites, namely, some results from probability theory including Markov processes, are introduced in Section 11.2. Bayes’ theorem is judiciously applied in Section 11.3 to derive the HMM filters and smoothers for time-homogenous processes. A state-space model having an output covariance equivalent to an HMM is derived in Section 11.4. This enables transition probability matrices to be employed in optimal filter and smoother constructions that minimise the error variance. Section 11.5 describes high-order-minimum-variance-HMM filters, which employ Kronecker product states.
... We can find the essence of landmarks in recent ambience signature based localization works. Some of the recent localization or place recognition systems have been EZ localization [14], GSM signal fingerprinting [15], Surroundsense [16], RF based techniques [17] or Wi-Fi based schemes [18]. RF based or Wi-Fi based schemes either suffer from infrastructure dependence or high calibration time, while localizing places. ...
Landmarks are signatures of our surroundings
which help us to uniquely identify a location. Recent studies show
that like us humans, it may be possible by the sensors on mobile
devices to identify landmarks [1]. This can open up the possibility
of a lot of applications in the domain of augmented reality,
gaming, retail etc. However, to make such applications a reality,
a particular landmark need to be stable across mobile phones,
persons carrying the mobile phones etc. This paper specifically
builds up a framework to discover such stable landmarks and
demonstrates its utility in the development of next generation
apps. In order to identify such virtual landmarks, we employ a
clustering algorithm to perform non-intuitive feature combination
of sensors like Accelerometer, Gyroscope, Magnetometer, Light,
Sound, Wi-Fi, GSM signal strength etc. Further, we rigorously
test the clusters to ensure that landmarks are stable across
different devices, people, and time. According to our results,
change in device affects the stability of a landmark most.
Finally as a proof of concept, we develop a prototype system
RetailGuide using landmarks to facilitate smart retail analytics
cum recommendation service.
... In this paper, we use Sobel mask on different color spaces and compare the results with other results from color spaces. Again, the edge detection is an important tool in image processing [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. We used three color spaces: RGB, YIQ and HSV, where RGB stands for red, green and blue, YIQ means color space designed for the NTSC color TV system, and HSV is hue, saturation, and value, respectively. ...
Edge detection has been a popular practice in image processing and computer vision applications. Many image processing applications require the discovery of edge details in the gray or color images as a beginning stage of an image processing, vision and understanding. Generally, edge detection on grayscale images is not affluent enough to explain intensity changes. Therefore, we can use color edge information as an important method. Because the result is different when input images are color images or not (grayscale images). The main purpose of the proposed edge detection is to discern significant parts from the normal features in a given image. We assume that intensity varies rapidly in a significant part. There are many color spaces such as RGB, YIQ, and HSV (Hue, Saturation, and Value). In this paper, we conducted edge detection on each color spaces and compared the results. Simulation results show that the HSV color space gives the best detection performance.
... The image resolution of display devices is the number of distinct pixels in horizontal or vertical dimensions which is able to be exhibited [1][2][3][4][5][6]. The image resolution is normally stated as width by height, with the units in pixels. ...
The principle main goal of the image zooming technique is to fit a smaller (or larger) images to larger (or smaller) sized display device. In order to satisfy this operation, image zooming technique must be able to alter the image size to the other one. The proposed method consists of four steps. They are 'obtain original image,' 'image downscaling by image zooming,' 'artifact removal process by circle shaped low pass filtering,' and 'detail preserving process by unsharp masking.' The simulation results approve that the proposed method removed blocking artifact and remained image details in a prosperous manner
... Many geolocation algorithms are currently available and exploit different peculiarities of the cellular network. For instance, many algorithms are based on time measurements (e.g., [13, 19] ), while others are based on signal strength and electromagnetic field prediction (e.g., [2, 3, 12, 25]). An important aspect of the geolocation problem is that it is usually difficult to provide a general solution that works well regardless of the considered environment (e.g., urban, suburban, rural). ...
... In particular, we present how a smart integration can provide benefits for location techniques, providing accurate geolocation also in those areas that are not covered by a reliable signal. We take as a reference our geolocation solution based on Received Signal Strength Indication (RSSI) [3] and on data normally collected and managed by GSM/3G networks, and the landmark infrastructure presented in [11]. Our landmark-based geolocation solution allows high-accurate geolocation and mobility prediction also in critical areas. ...
... In other words, as soon as the landmark infrastructure system identifies a building using an image taken by the integrated camera on the device, such a position identifies a possible location of the user that can be used to correct the geolocation algorithm. Second, we provide an extensive experimentation and a comparison between our solution in [3] and the landmark-based solution in this paper, showing performance improvements. The remainder of this paper is organized as follows. ...
Modern mobile devices integrating sensors, like accelerometers and cameras, are paving the way to the definition of high-quality
and accurate geolocation solutions based on the informations acquired by these sensors, and data collected and managed by
GSM/3G networks. In this paper, we present a technique that provides geolocation and mobility prediction of mobile devices,
mixing the location information acquired with the GSM/3G infrastructure and the results of a landmark matching achieved thanks
to the camera integrated on the mobile devices. Our geolocation approach is based on an advanced Time-Forwarding algorithm
and on database correlation technique over Received Signal Strength Indication (RSSI) data, and integrates information produced
by a landmark recognition infrastructure, to enhance algorithm performances in those areas with poor signal and low accurate
geolocation. Performances of the algorithm are evaluated on real data from a complex urban environment.
KeywordsLandmark–Geolocation–Wireless network
... While the user is mobile it is very important for service providers to know the physical location of its users to provide services according to their location. For instance with the latest regulation by Federal Communications Commission (FCC) 1 , it is required by all network providers to implement the E911 service 2 which will help to get the exact physical location of users when the 911 service is requested. Consequently the physical location data of the user is very important input for Location Base Services (LBS). ...
... In another effort, authors extended the Kalman Filter to merge the time difference of arrival and the received signal strength retrieved from the long and short range [2]. Authors in [1] presented a lookup table correlation technique that applies multiple positioning and locating techniques to be used with advance propagation model in conjunction with Kalman predictive filtering for node localization. Authors in [10] presented a zero-length technique based on received signal strength to compute node localization. ...
Location information is the major component in location based applications.
This information is used in different safety and service oriented applications
to provide users with services according to their Geolocation. There are many
approaches to locate mobile nodes in indoor and outdoor environments. In this
paper, we are interested in outdoor localization particularly in cellular
networks of mobile nodes and presented a localization method based on cell and
user location information. Our localization method is based on hello message
delay (sending and receiving time) and coordinate information of Base
Transceiver Station (BTSs). To validate our method across cellular network, we
implemented and simulated our method in two scenarios i.e. maintaining database
of base stations in centralize and distributed system. Simulation results show
the effectiveness of our approach and its implementation applicability in
telecommunication systems.
... The term "geolocation" denotes a variety of techniques aimed at mobility prediction, that is, computing and tracking the position of mobile terminals, and refers to one of the hottest topics in wireless and mobile computing research. Mobility prediction [1], [2] can be used both at network level to support several crucial tasks for network management (e.g., handoff management [3], efficient code division in 3G networks [4]) and at service level to support -commerce and a number of Location-Based Services (LBSs) (e.g., navigation services, emergency rescue [5]). In both contexts, the geolocation precision and accuracy play a fundamental role. ...
... In this paper, we present a solution based on RSSI and on data normally collected and managed by GSM/3G networks. The main contribution of the paper is developing our previous research [1] towards the definition of a technique that: i) provides high-accurate geolocation and mobility prediction that can be used both at network and service level to provide enhanced functionalities [19], ii) does not require any change to the existing mobile network infrastructure, and iii) is performed at the mobile network side (network-centric), making it more robust against location spoofing and other terminal-based security threats. More specifically, we first propose to apply an improved version of the traditional Database Correlation Method (DCM) [20] to incoming signal strengths to identify a set of candidate positions (Section II). ...
... More specifically, we first propose to apply an improved version of the traditional Database Correlation Method (DCM) [20] to incoming signal strengths to identify a set of candidate positions (Section II). We then describe an Enhanced Time-Forwarding Tracking (ETFT) technique, as an evolution of our previous TFT [1], that exploits GIS map information and a predicted motion model to produce a set of candidate paths (shadow paths in the following) that better suit the motion and map constraints (Section III). Our ETFT technique deals with signal fluctuations, building an extended Time-Forwarding Graph (eTFG) that is used for an initial rough skim of candidate locations, and introduces a Time-Forwarding Filtering (TFF) to perform a high-accurate path selection. ...
Technical enhancements of mobile technologies are paving the way to the definition of high-quality and accurate geolocation solutions based on data collected and managed by GSM/3G networks. We present a technique that provides geolocation and mobility prediction both at network and service level, does not require any change to the existing mobile network infrastructure, and is entirely performed on the mobile network side, making it more robust than other positioning systems with respect to location spoofing and other terminal-based security threats. Our approach is based on a novel database correlation technique over Received Signal Strength Indication (RSSI) data, and provides a geolocation and tracking technique based on advanced map- and mobility-based filtering. The performance of the geolocation algorithm has been carefully validated by an extensive experimentation, carried out on real data collected from the mobile network antennas of a complex urban environment.
... Mobile users are registered with a given mobile network operator to access cellular functionalities and request services from servers accessible via the network. Cellular networks can be enriched with several different positioning systems that measure the physical location of users carrying mobile devices with good accuracy (Anisetti et al. 2008;Gustafsson and Gunnarsson 2005;Munoz et al. 2009;Song 1994). Finally, mobile ad-hoc networks (MANETs) have been recently introduced. ...
Mobile phones are the most ubiquitous communications technology in the world. Besides transforming the way in which we communicate, they can also be used as a powerful tool for conflict prevention and management. This book presents innovative uses of mobile technologies in the areas of early warning, disaster and humanitarian relief, governance, citizens’ participation, etc. and cuts across different regions. The book brings together experts and practitioners from different fields—mobile technologies, information systems, computer sciences, online dispute resolution, law, etc.—to reflect on present experiences and to explore new areas for research on conflict management and online dispute resolution (ODR). It also reflects on the transition from present ODR to future mobile Dispute Resolution and discusses key privacy issues. The book is addressed to anyone involved in conflict prevention and dispute management aiming to learn how mobile technologies can play a disruptive role in the way we deal with conflict.
With the advent of development in LTE system, eNodeB the revolutionised version of BTS is used for a connection between BSCand the users. A better and improved system increases the capacity and the functionality of the mobile wireless communication system. A Base Transceiver System (BTS) is a system in a mobile communication network that houses radio receivers and is used for wireless communication between users and network providers that is under the control of Base Switching Controller (BSC) and then the exchange. This paper deals with the study of a ground based GSM Base Transceiver System (BTS) and it’s installation process, architecture, internal structures, the process involved in the uplink and downlink call procedures and the future works. This study is based on the visit to a ground based BTS at BSNL regional training centre (RTTC), Hyderabad during the EETP course. The observations made are duly recorded, reviewed and presented for a better understanding of a mobile BTS system.
