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Fitness tracking, fall detection, indoor navigation, and visual aid applications for smart glasses are rapidly emerging. The performance of these applications heavily relies on the accuracy of step detection, which has rarely been studied for smart glasses. In this paper, we develop an accelerometer-based algorithm for step calculation on smart glasses. Designed based on a salience-analysis approach, the algorithm provides a highly accurate step calculation. An activity monitoring application for a commercial Android-based smart glasses (Vuzix M100) is designed and realized for algorithm evaluation. Experimental results from 10 participants wearing the smart glasses running our application achieved average step detection error of 2.6% demonstrating the feasibility of our salience-based algorithm for performing pedometry on smart glasses.

Without any preinstalled infrastructure, pedestrian dead reckoning (PDR) is a promising indoor positioning technology for pedestrians carrying portable devices to navigate. Step detection and step length estimation (SLE) are two essential components for the pedestrian navigation based on PDR. To solve the overcounting problem, this study proposes a peak-valley detection method, which can remove the abnormal values effectively. The current step length models mostly depend on individual parameters that need to be predetermined for different users. Based on fuzzy logic (FL), we establish a rule base that can adjust the coefficient in the Weinberg model adaptively for every detected step of various human shapes walking. Specifically, to determine the FL rule base, we collect user acceleration data from 10 volunteers walking under the combination of diverse step length and stride frequency, and each one walks 49 times at all. The experimental results demonstrate that our proposed method adapts to different kinds of persons walking at various step velocities. Peak-valley detection can achieve an average accuracy of 99.77% during 500 steps of free walking. Besides, the average errors of 5 testers are all less than 4 m per 100 m and the smallest one is 1.74 m per 100 m using our coefficient self-determined step length estimation model.

Statistical Process Control (SPC) is widely used in practice, and the control chart is a main tool in SPC. The control chart monitors process quality and detects process variations in real time to reduce the defective products. Regulatory maps are used in clinical trials and agronomic industries, and the mean and variance are not constants, and the coefficient of variation is an indicator of the reliability of chemical tests. However, the coefficient of variation control chart (CV Chart) monitors the coefficient of variation, and the ability to detect small shift sizes is not good. This study uses a double sampling plan combined with a coefficient of variation control chart (DS CV chart) to improve the performance of CV Chart. A design model of DS CV chart is also created to optimize its parameters. The results of the study show that the good performance of DS CV chart in small shift detection.

User-generated data over the internet is highly opinionated. Sentiment analysis is a task to extract opinions without losing any crucial information. Polarity classification is most researched from the last two decades under sentiment analysis. However, Polarity classification is a tedious job in natural language processing. There are certain circumstances where the polarity of the phrase differs from the polarity of words into it. This inconsistency is due to some trigger words that make the word polarity to get reversed. One of the main reasons for the performance degrade of the classification algorithm is the polarity shift. Polarity shift detection and handling approaches are applied to detect and handle polarity shifts, which can enhance the accuracy and effectiveness in classification. This paper gives a better understanding of the issue of polarity shift and surveys various methods of polarity shift detection and handling.

In view of the fact that most current cycle clip detection methods do not take into account the clock jump of receivers, which results in the reduction of PPP data processing accuracy and efficiency, this paper introduce the GNSS receiver clock jump theory systematically, to analyze its impact on the observed data and the cycle clip detection principle of interference, and improvement based on domain using doppler observations of clock jump detection method. The clock jump in the simulation data and the original BDS data was successfully detected without the first time excluding the cycle jump.

It is a well-known fact that the exponentially weighted moving average (EWMA) type control chart is very effective in detecting small and moderate shifts. To further enhance the sensitivity of the EWMA control chart in shift detection, this paper proposes the variable sampling interval EWMA chart by using a single auxiliary variable (AI) (abbreviated as VSI EWMA-AI) to monitor the process mean, where the statistic of the proposed chart is based on the information of the study and auxiliary variables. The performance of the proposed chart is evaluated by using the average time to signal (ATS) criterion, where the derivation of ATS formula is presented in this paper. Subsequently, the performance of the proposed chart is compared with the existing EWMA-AI chart in literature. The comparison shows that the VSI EWMA-AI chart has better detection ability in mean shifts than the EWMA-AI chart.

We develop a novel, option-based approach for detecting intraday jumps in stock prices. One of the components involved in intraday jump detection is instantaneous volatility, by which intraday returns are scaled. The existing intraday jump detection approaches assume that volatility does not change drastically over a short period, which, however, is in conflict with empirical evidence that volatility can exhibit large movements. We tackle this problem by introducing a method with a completely new proxy for instantaneous volatility, extracted from the option-implied volatility index. This allows us to detect jumps while there are large movements in volatility. We verify our approach with extensive ex-post Monte Carlo experiments. The results show that our approach is of high statistical power, is more robust to variation in volatility, and outperforms the baseline approaches from the literature in terms of both spurious and actual jumps. In the empirical part, we use eight years of high-frequency data on S&P 500 index options. We find that, in comparison with the conventional baseline approaches, our approach identifies fewer jumps, suggesting that true price variation coming from jumps is overstated. Moreover, our method identifies different locations for a large portion of jumps, which emphasizes the important role played by the volatility proxy.

Today wearable devices can be used to monitor the health of patients, among other uses. This chapter proposes an intelligent wearable device (IWD) with a pedometer algorithm that detects and records physical movement such as walking. This device uses a smart algorithm for IWDs for step detection that analyzes the instantaneous acceleration versus time waveform and can detect the specific movements involved in stepping. The results of the algorithm are discussed with different cases and threshold values considered. This algorithm uses a mobile app using the Internet of Things to keep track of steps, and the Gaussian noise is removed from the accelerometer data. This power-efficient pedometer algorithm can be used for IWDs.

Today, in order to overcome the low flexibility and low openness of traditional hardware communication systems, software-based software radio platforms have emerged. In order to more flexibly test the design of 3-bit tag passive wireless RFID strain patch antenna, this paper chooses to use energy detection algorithm on USRP to monitor the change of its resonant frequency. Second-order variable step detection is proposed to balance the sampling resolution and time cost, making spectrum sensing more intelligent. Good experimental results have been obtained, which provides a way to broaden the scope of application and reduce costs.

To ensure effective medical follow-up, it is important to observe the physical aspects of a patient. With limited human resources and a large number of patients, the physical space of hospitals are only available for patients on serious health conditions. However medical follow-up is still necessary in some cases. To ease the medical follow-up, wearable technology can be used to allow for a remote monitoring of patients. A microelectronic system is able to verify and monitor parameters related to the health status of a patient. In this work we measure and analyze the angular acceleration and velocity of the three axes. We also consider the sensor position, ankle or wrist, and its impact on the step detection performance. Results are compared for various algorithms such as fast Fourier transform (FFT) analysis for acceleration, FFT analysis for angular velocity and the estimation of signal parameters by rotational invariance techniques (ESPRIT) are proposed for step detection with reduced error when compared to the state of the art.

The correctness of athlete’s behavior can be controlled by health care cyber-physical system containing distributed (mobile) sensors and intelligent data processing. Such cyber-physical systems determine a concrete set of events, such as jumps or falls and identify events parameters, e.g. height and duration. The proposed accelerometer data based cyber-physical system differs from existed ones by an original method for detection of various types of athlete’s behavior. A proposed cyber-physical system contains on three modules: the data acquisition module, the data processing module and the processed data visualization module. A method for jump recognition is based on high frequency accelerometer data. The system is developed using Android Studio, R Studio development environments. The results provided by accelerometer data based cyber-physical system might be used for coaches and doctor in sports medicine for decisions regarding the optimal load in future training sessions. Use cases including different experimental setup shows the efficiency of the proposed system.

In view of user's requirements of high performance, easy operation and low cost with indoor location, data acquisition software based on Android platform utilize sensors built-in cellphone is used to study the pedestrian dead reckoning (PDR). Step detection and step length estimation are carried out by collecting leg swing angle while walking, and the classical step length estimation algorithm is improved. At the same time, the improved heuristic drift elimination algorithm is used to calculate the direction. Finally, the experiment results show that the PDR technology can better satisfy the requirements of indoor pedestrian positioning, and as for the low-cost multi-sensor. compared with the existing algorithm, the improved algorithm with higher positional accuracy.

Background: Objective gait assessment is key for the follow-up of patients with progressive multiple sclerosis (pMS). Inertial measurement units (IMUs) provide reliable and yet easy quantitative gait assessment in routine clinical settings. However, to the best of our knowledge, no automated step-detection algorithm performs well in detecting severely altered pMS gait. Method: This article elaborates on a step-detection method based on personalized templates tested against a gold standard. Twenty-two individuals with pMS and 10 young healthy subjects (HSs) were instructed to walk on an electronic walkway wearing synchronized IMUs. Templates were derived from the IMU signals by using Initial and Final Contact times given by the walkway. These were used to detect steps from other gait trials of the same individual (intra-individual template-based detection, IITD) or another participant from the same group (pMS or HS) (intra-group template-based detection, IGTD). All participants were seen twice with a 6-month interval, with two measurements performed at each visit. Performance and accuracy metrics were computed, along with a similarity index (SId), which was computed as the mean distance between detected steps and their respective closest template. Results: For HS participants, both the IITD and the IGTD algorithms had precision and recall of 1.00 for detecting steps. For pMS participants, precision and recall ranged from 0.94 to 1.00 for IITD and 0.85 to 0.95 for IGTD depending on the level of disability. The SId was correlated with performance and the accuracy of the result. An SId threshold of 0.957 (IITD) and 0.963 (IGTD) could rule out decreased performance (F-measure ≤ 0.95), with negative predictive values of 0.99 and 0.96 with the IITD and IGTD algorithms. Also, the SId computed with the IITD and IGTD algorithms could distinguish individuals showing changes at 6-month follow-up. Conclusion: This personalized step-detection method has high performance for detecting steps in pMS individuals with severely altered gait. The algorithm can be self-evaluating with the SI, which gives a measure of the confidence the clinician can have in the detection. What is more, the SId can be used as a biomarker of change in disease severity occurring between the two measurement times.

The use of marijuana is now legal for medical purposes in 39 of the 50 United States. Eleven of these 39 states have also legalized marijuana for non-medical usage. Marijuana impairs the motor skills of users, making Driving Under the Influence of Marijuana (DUIM) a growing public health concern. There are currently few accessible and accurate methods to assess the impairment levels of drivers who have used marijuana. Current assessment methods include self-reports and testing urine, oral fluid, and blood. However, self-reports are often biased and biological tests are cumbersome to perform in situ. In this paper, we investigate whether dose-dependent changes in participants gait (walk) can be detected using data gathered from their smartphone motion sensors (accelerometer and gyroscope). We envision WeedGait, a smartphone sensing system that will assess the gait of marijuana users passively and warn them when they are too impaired to drive safely. To the best of our knowledge, this is the first study on using smartphones to assess marijuana-induced gait impairment. Gait data was collected from 10 subjects and pre-processing steps included low pass filtering, step cycle detection and segmentation, and normalization. We present a novel gait analysis approach that analyzes normalized, single-step segments to achieve higher accuracy than prior approaches. We compared the classification results of various machine and deep learning models, and found that Long Short Time Memory (LSTM) and Support Vector Machines performed best, discriminating the gait of subjects after smoking either marijuana with 3% or 7.2% THC versus smoking a placebo marijuana cigarette with an accuracy of 92.1%. These results suggest that smartphone-based marijuana testing is more accurate than urine-based tests but slightly less accurate than oral fluid based testing. Moreover, smartphone sensing of marijuana is completely passive and hence more convenient, which facilitates pervasive testing in natural settings and could have massive impact due to the near-ubiquity of smartphones.

In the indoor environment, the Global Positioning System (GPS) cannot provide accurate location information due to the complexity of the internal structure of buildings and the interference of indoor equipments. Pedestrian Dead Reckoning (PDR) is an indoor positioning method that relies on smartphone carrying sensors (such as accelerometer, gyroscope, and magnetometer) without requiring additional equipments. The intense electromagnetic interferences caused by indoor electronic equipments have serious influences on the accuracies of indoor location. This paper presents an improved step detection and counting method based on PDR. There is continuity in the walking process. In order to make full use of this feature, this paper firstly filters and differential processes the acceleration data obtained from the smartphone, then uses the improved peak detection algorithm to detect and count the steps. This paper compares two algorithms of step length estimation, and presents an improved step length estimation algorithm based on differential acceleration data. As a result, this paper improves the accuracies of both step number detection and step length estimation.

When assessing change in patient-reported outcomes, the meaning in patients’ self-evaluations of the target construct is likely to change over time. Therefore, methods evaluating longitudinal measurement non-invariance or response shift at item-level were proposed, based on structural equation modelling or on item response theory. Methods coming from Rasch measurement theory could also be valuable. The lack of evaluation of these approaches prevents determining the best strategy to adopt. A simulation study was performed to compare and evaluate the performance of structural equation modelling, item response theory and Rasch measurement theory approaches for item-level response shift detection. Performances of these three methods in different situations were evaluated with the rate of false detection of response shift (when response shift was not simulated) and the rate of correct response shift detection (when response shift was simulated). The Rasch measurement theory-based method performs better than the structural equation modelling and item response theory-based methods when recalibration was simulated. Consequently, the Rasch measurement theory-based approach should be preferred for studies investigating only recalibration response shift at item-level. For structural equation modelling and item response theory, the low rates of reprioritization detection raise issues on the potential different meaning and interpretation of reprioritization at item-level.

The frequency estimation solutions based on the discrete phase locked loop (PLL) technology are widely applied in the intelligence electronic devices for power system. The PLL method cannot provide both the fast response and the good reliability under the system disturbance. In this paper, an improved frequency estimation solution is presented. Based on the angle characteristic of the positive sequence voltage, this method directly calculates the frequency deviation from angle difference of the continuous power cycles, without any iteration calculation. Unlike the discrete phase locked loop, which is a close-loop structure, the new method is based on an open-loop structure with the fixed sampling frequency. There is no interdependent between the calculation loops. Hence, the response time is shorter. Moreover, the 2 cycles hamming window is applied to reduce the interfaces and the angle jump detection based on the abnormal rate of change of frequency is also applied to improve the reliability of the solution. The theoretical analysis and Matlab simulations are performed. The results show the frequency estimation performance can meet the application requirements.

The proliferation of smartphones has made positioning technologies available to a wide range of users. For outdoor localization, global navigation satellite systems (GNSSs) are the most elaborated and widely used technologies for positioning. In open sky conditions, GNSSs provide sufficient position accuracies for most mass market applications. However, for GNSS-denied environments, such as indoors and urban canyons, GNSS positioning accuracies are drastically reduced. In these environments, the received GNSS signals can be blocked, affected by multipath effects or received with low power. To enhance the positioning performance, alternative methods and sensor systems provide position information to support or even replace GNSSs. Most of the indoor positioning systems use local infrastructure like positioning with radio frequency identification (RFID), mobile communication base-stations, wireless local area network (WLAN) and ultra-wideband (UWB) systems. Since WLAN infrastructure is widely deployed, WLAN-based indoor positioning approaches are most common. Today, the majority of smartphones feature a micro-electro-mechanical system (MEMS) inertial measurement unit (IMU). An IMU is an electronic device that provides inertial measurements by using a combination of accelerometers and gyroscopes. The accelerometers measure the linear accelerations and the gyroscopes measure the turn rates. Numerical integrations of the inertial measurements allow to continuously compute position and attitude for inertial navigation: double integrating the acceleration yields to the position change; integrating the turn rate yields to the attitude change. IMUs are classically built as mechanical devices which consist of precision mechanical gyroscopes and accelerometers used in e.g. airplanes. In recent years, MEMS IMUs became popular with sub-cm size. However, compared to the mechanical IMUs, MEMS IMUs are not able to provide the same precision or long term stability. Because the position calculation involves double integrations, even small measurement errors quickly cause drifts, which lead to an unreliable position solution over time. Pedestrian dead reckoning (PDR) refers to the process of estimating the pose (3D position and heading) of a pedestrian at each step by integrating the cumulative change in pose since the last step. In other words, PDR is the process of estimating the individual steps of the pedestrian while moving. To estimate steps, PDR primarily relies on inertial sensors and also on additional sensors, e.g. the magnetometer which is typically used to correct the biases of the gyroscope. The basic pattern of human walking is cyclical. Hence, the acceleration data measured by the accelerometers carried by pedestrians are cyclical, too. Making use of the cyclical characteristics of human walking, PDR calculates the traveled distance by counting the steps and estimating each step length through inertial sensors. Heading can be estimated with magnetometers together with accelerometers and/or gyroscopes. The disadvantage of PDR systems is that the measurement error (drift) grows unboundedly over time. Processing the measurements in a way that additional information such as zero velocity updates (ZUPTs) from stance detection can be used, the error growth of inertial navigation can be reduced from cubic to linear. However, this typically requires mounting the IMU close to the foot. Many researchers are working on the difficult problem of calculating PDR from sensors at different body placements, such as pocket, wrist, and hand-held. Additionally, in order to calculate a global position, PDR relies on a known initial position, like outside buildings typically obtained with GNSS. However, if the positioning mode is switched on in GNSS-denied environments, e.g. inside a building, where no accurate global position is available, it is not possible to obtain an accurate position by PDR. Therefore, this paper addresses four aspects: 1. We present a novel step length estimation algorithm for smartphones which are carried in the hand or located in the pocket. The step detection algorithm incorporates walking direction changes for the step length estimation. Hence, in addition to the acceleration measurement, the proposed algorithm uses gyroscope measurements. Measurements showed that pedestrians are slowing down during walking curves. Thus, the step length estimation algorithm incorporates the heading changes. We compare the novel algorithm to state-of-the-art algorithms. Most other publications determine the accuracy of the PDR model by evaluating a whole walk of a pedestrian. Hence, the error is determined based on the whole walked distance. Contrarily, we evaluate the different algorithms by determining the accuracy of each step. 2. As mentioned before, in PDR the positioning error grows unbounded. In order to decrease the error we show a novel PDR which is able to compensate the error by detecting different courses of movement. By nature, human take the shortest possible path to get over a certain distance, which implies that pedestrian are mainly walking straight. By recognizing this human behavior, the error drift of the gyroscope and accelerometer can be corrected similarly to ZUPT. 3. Maps of building further allow reducing the positioning error. In order to obtain a map of an indoor area, we propose a simultaneous localization and mapping (SLAM) algorithm. The SLAM algorithm addresses the estimation of the pose of the moving pedestrian within an unknown environment by simultaneously estimating the map of the environment as the pedestrian moves. The estimated map is based on previously visited locations of the pedestrian (or other pedestrians). Hence, as soon as the pedestrian returns to an already mapped position, information of the previous movements at this position can be reused to obtain better predictions for the further movement. Similarly to previous publications, we propose a map that represents visited receiver positions by a two-dimensional hexagonal grid. 4. By generating a map of the pedestrian movement, we additionally map information of WLAN stations (RSS, IDs), Bluetooth anchor points, and mobile communication stations. This allows building an accurate database of the building which can be used for fingerprinting. Thereby, the database can help to improve the overall positioning accuracy for each pedestrian. Additionally, based on the database an accurate initial position estimate can be obtained, when the positioning device is switched on indoors. To verify the refined algorithm, the evaluations are based on measurement data of moving pedestrians. A Google Pixel 3 is used for recording the sensor data and used for all walks/pedestrians. The measurements are carried out inside and outside of an office building. The proposed algorithm estimates the position of the pedestrians in a local coordinate system. For the measurements we assume that at least one pedestrian is starting outdoors before entering the building. Hence, the GNSS receiver is used to initialize the position of the pedestrian outdoors in order to fix the coordinate system. The evaluations show that the horizontal positioning error of the developed positioning algorithm is around 2m for the first pedestrian. After multiple pedestrian shared there obtained map, the position accuracy of the pedestrian decreases below one meter.

Many multiscale segmentation methods have been proven to work successfully for detecting multiple change-points, mainly because they provide faithful statistical statements, while at the same time allowing for efficient computation. Underpinning theory has been studied exclusively for models which assume that the signal is an unknown step function. However, when the signal is only approximately piecewise constant, which often occurs in practical applications, the behavior of multiscale segmentation methods is still not well studied. To narrow this gap, we investigate the asymptotic properties of a certain class of \emph{multiscale change-point segmentation} methods in a general nonparametric regression setting. The main contribution of this work is the adaptation property of these methods over a wide range of function classes, although they are designed for step functions. On the one hand, this includes the optimal convergence rates (up to log-factor) for step functions with bounded or even increasing to infinite number of jumps. On the other hand, for models beyond step functions, which are characterized by certain approximation spaces, we show the optimal rates (up to log-factor) as well. This includes bounded variation functions and (piecewise) H\"{o}lder functions of smoothness order $ 0 < \alpha \leq 1$. All results are formulated in terms of $L^p$-loss, $0 < p < \infty$, both almost surely and in expectation. In addition, we show that the convergence rates readily imply accuracy of feature detection, such as change-points, modes, troughs, etc. The practical performance is examined by various numerical simulations.

An algorithm for measurement of the cutting blade of the ultrasonic vibration cutting machine used in an automation machine is proposed in this paper. The proposed algorithm, which is based upon a multi-step detection method, is developed for the accurate measurement of the cutting blade by exactly determining its rotation angle, length, and thickness. Instead of the commonly used Otsu method, we propose a new curvature-based adaptive binarization method, which provides more accurate details about the dimensions of the cutting blade. A region of interest containing the cutting blade from the acquired image is first extracted in the multi-step detection method, which is further processed to remove the noise, which increases the measurement reliability. An important feature of the proposed process is the restoration of the cutting blade&rsquo, s tip data, which used to be lost during the fine noise-filtering process. The rotation angle and length are measured using the minimum rotated rectangle while the line fitting based upon the least square method is applied to increase the reliability of the thickness measurement. Experimental results validate the superiority of the proposed method over the conventional Otsu method.

In this article method of estimating the landmark azimuth for the tasks of mobile autonomous robot’s navigation in the miss of GPS has been described. There are the several methods for measuring azimuth of targets in traditional radars. All of them assume, that the echo signals from the target differ by some parameters from other signals, for example, by the Doppler shift frequency. The case of a fixed landmark on the background of stationary objects of environment has been analyzed in the article. Wherein, difference determination between the echo signals from the landmarks and the signals reflected from the environment without a landmark will find difficulty. The differences appear when space is scanning by the robot’s antenna. As a result, the random process of signal amplitude is appeared at the input of the receiver, in which possible jumps of the amplitude can arise due to the presence of a landmark in the angular range of the antenna directivity pattern. The system of stochastic differential equations has been developed. It allows to determine the probability of the amplitude jump detection, the speed of system operation, the estimates of amplitude and its variance. The landmark azimuth has been determined as the average time of estimating the maximum probability of detecting the amplitude jump multiplied by the scanning speed of the antenna. If the amplitude of the echo signal from the landmark is commensurate with the mean amplitude of the signals reflected from the surrounding area, the errors of measuring azimuth at the selected threshold may be acceptable for practice.

The last years have seen great growth in wearable devices for activity monitoring. Fueled by miniaturization and market demand, many devices can accurately estimate physical activity. However, and despite the variety of solutions, existing wearables are tied to classifying activity from a single body position. Once users move their wearable, accuracy is affected. This paper describes the development and validation of a position-independent activity monitoring solution, the GoLiveClip. The performance of GoLiveClip is comparable to position-dependent devices, such as Fitbit Flex 2, Mi Band 2, Garmin Vivoactive HR, and Jawbone Up 24, for activity recognition, step detection, and distance estimation. These results motivate the development of position-independent activity monitoring solutions.

Real-time orbit and clock products are crucial for real-time precise point positioning (PPP) applications. The International Global Navigation Satellite System (GNSS) Service (IGS) launched the Real-Time Service (RTS) in 2013 to provide real-time satellite orbit and clock products which have since found wide applications. Currently, the IGS RTS provides two different types of satellite orbit and clock products: the products generated from individual Analysis Centers such as CLK01, CLK22 and CLK90 and the combination products such as IGS01 and IGS03. These IGS RTS products are different in terms of precision and reliability. First, we verify that the phenomenon of clock datum jumps is obvious in IGS01, which has not been considered by the GNSS community when applying the IGS01 product to the real-time PPP. Afterward, the causes of the clock datum jump are investigated. The results indicate that a switchover of the reference product or a datum jump in the reference product will cause a clock datum jump. In order to solve the clock datum jumps issue, a new clock datum jump detection and mitigation algorithm is proposed. Real-time kinematic PPP experiments are then carried out to validate the proposed satellite clock detection and mitigation algorithm. The positioning results with 27 globally distributed IGS stations show that the root mean square improvements in the north, east and up components are 61.5%, 36.5% and 55.1% after detecting and mitigating the datum jumps.

With rapid development of Internet of Things (IoT) and pervasive computing, indoor localization and tracking has attracted considerable attentions. This work aims at designing an effective and scalable indoor tracking system based on smart phones embedded with Wi-Fi interfaces and inertial sensors. Specifically, we first propose a zero site-survey overhead algorithm (ZSSO), which includes a step detection mechanism, a map constraint construction method and a customized particle filter. The step detection mechanism is used to count user steps based on raw data extracted from inertial sensors. The map constraint construction method is adopted to generate obstacle constraints of the indoor environment based on a two-step conversion method designed for indoor map. Finally, a customized particle filter is proposed to track user's positions continuously. Further, we propose an enhanced version of ZSSO (i.e., E-ZSSO) to improve tracking performance by incorporating with Wi-Fi fingerprint based localization technique. First, an automatic Wi-Fi fingerprint collection mechanism is developed for building the fingerprint database without extra site-survey overhead. Then, the Wi-Fi fingerprint based localization results are further adopted to speed up the convergence of the particle filter as well as to better calibrate the localization results. We have implemented the indoor tracking system in real-world environments and conducted comprehensive performance evaluation. The field testing results conclusively demonstrate the scalability and effectiveness of our proposed algorithms.

Global Positioning System (GPS) is frequently used for location determination in military and civil aviation sector, vehicles, smartphones etc. However, when satellite signal is weak or there is not any signal to connect and also for indoor navigation problems, an alternative solution is needed for location estimation. This study contains Pedestrian Dead-Reckoning (PDR) by aid of smartphone body-mounted Micro Electro-Mechanic (MEMS) without GPS usage. Quaternion based attitude estimation is used to avoid Euler integration singularity problem. Furthermore, attitude estimation with accelerometer/magnetometer and the quaternion attitude outputs is fused with Kalman Filter (KF) to get rid of gyroscope drift error. Steps are detected by using accelerometer magnitude and then step frequencies are calculated for the adaptive step length estimation. Thereafter, pedestrian position is obtained by using the estimated attitude and step length. Indoor and outdoor test scenarios are specified and with gathering data, PDR is performed to designate the accuracy of this study.

Navigation is an emerging field and in the recent past extensive research is being carried out. Navigation can be broadly differentiated into Indoor Navigation and Outdoor Navigation[1]. Outdoor Navigation immediately recollects of the one with GPS which is most popularly used these days. Absence of such a technology would probably lose the entire world as its importance has been world widely proved by lot many. GPS doesn’t function appropriately indoor because of which an alternate solution is necessary. The key idea of this step counting assistant which works on accelerometer and gyroscopic sensor is to track the path been walked upon which is further projected to a robot that can help in household managements i.e., cleaning the room, watering in the garden, accessing the household appliances and controlling them. It can also be used outdoor where GPS footprint is quite less like in deep jungles, uneven lands, too many of irregular steps, etc. Many people these days use handy objects such as wristbands, step counting apps, etc. This assistant can also serve the same purpose. Existing step detection techniques, both hardware and software, does not satisfactorily cater the accuracies demanded by localization systems especially at low walking speeds observed in natural walking.

Despite the accessibility of several step count measurement systems, count accuracy in real environments remains a major challenge. Microelectromechanical systems and pressure sensors seem to present a potential solution for step count accuracy. The purpose of this study was to equip an insole with pressure sensors and to test a novel and potentially more accurate method of detecting steps. Methods: Five force-sensitive resistors (FSR) were integrated under the heel, the first, third, and fifth metatarsal heads and the great toe. This system was tested with twelve healthy participants at self-selected and maximal walking speeds in indoor and outdoor settings. Step counts were computed based on previously reported calculation methods, individual and averaged FSR-signals, and a new method: cumulative sum of all FSR-signals. These data were compared to a direct visual step count for accuracy analysis. Results: This system accurately detected steps with success rates ranging from 95.5 &plusmn, 3.5% to 98.5 &plusmn, 2.1% (indoor) and from 96.5 &plusmn, 3.9% to 98.0 &plusmn, 2.3% (outdoor) for self-selected walking speeds and from 98.1 &plusmn, 2.7% to 99.0 &plusmn, 0.7% (indoor) and 97.0 &plusmn, 6.2% to 99.4 &plusmn, 0.7% (outdoor) for maximal walking speeds. Cumulative sum of pressure signals during the stance phase showed high step detection accuracy (99.5 &plusmn, 0.7%&ndash, 99.6 &plusmn, 0.4%) and appeared to be a valid method of step counting. Conclusions: The accuracy of step counts varied according to the calculation methods, with cumulative sum-based method being highly accurate.

Molecular dynamics is a versatile and powerful method to study diffusion in solid-state ionic conductors, requiring minimal prior knowledge of equilibrium or transition states of the system's free energy surface. However, the analysis of trajectories for relevant but rare events, such as a jump of the diffusing mobile ion, is still rather cumbersome, requiring prior knowledge of the diffusive process in order to get meaningful results. In this work, we present a novel approach to detect the relevant events in a diffusive system without assuming prior information regarding the underlying process. We start from a projection of the atomic coordinates into a landmark basis to identify the dominant features in a mobile ion's environment. Subsequent clustering in landmark space enables a discretization of any trajectory into a sequence of distinct states. As a final step, the use of the smooth overlap of atomic positions descriptor allows distinguishing between different environments in a straightforward way. We apply this algorithm to ten Li-ionic systems and conduct in-depth analyses of cubic Li$_{7}$La$_{3}$Zr$_{2}$O$_{12}$, tetragonal Li$_{10}$GeP$_{2}$S$_{12}$, and the $\beta$-eucryptite LiAlSiO$_{4}$. We compare our results to existing methods, underscoring strong points, weaknesses, and insights into the diffusive behavior of the ionic conduction in the materials investigated.

With the rapid development of smartphone technology, pedestrian navigation based on built-in inertial sensors in smartphones shows great application prospects. Currently, most smartphone-based pedestrian dead reckoning (PDR) algorithms normally require a user to hold the phone in a fixed mode and, thus, need to correct the gyroscope heading with inputs from other sensors, which restricts the viability of pedestrian navigation significantly. In this paper, in order to improve the accuracy of the traditional step detection and step length estimation method for different users, a state transition-based step detection method and a step length estimation method using a neural network are proposed. In order to decrease the heading errors and inertial sensor errors in multi-mode system, a multi-mode intelligent recognition method based on a neural network was constructed. On this basis, we propose a heading correction method based on zero angular velocity and an overall correction method based on lateral velocity limitation (LV). Experimental results show that the maximum positioning errors obtained by the proposed algorithm are about 0.9% of the total path length. The proposed novel PDR algorithm dramatically enhances the user experience and, thus, has high value in real applications.

Noise interference and the need to process massive image data present challenges to change detection in synthetic aperture radar (SAR) images. In order to improve the change detection accuracy and decrease the processing time, this paper proposes a novel unsupervised change detection algorithm for SAR images. The logarithmic transformation is applied to transform images into the logarithmic domain, while the multiplicative noise in images is transformed into additive noise. The total variation (TV) denoising algorithm is then used to reduce image noise, and the difference operator in the logarithmic domain is employed to provide the difference image. The k-means clustering algorithm, which does not require consideration of the statistical properties of an image, is employed to cluster the difference image into two disjointed classes: changed and unchanged. The experimental results demonstrate that change detection results achieved by the proposed algorithm offer great improvement over existing alg...

Wearable miniature inertial sensors have been widely used for pedestrian dead reckoning (PDR). Typical low-cost PDR systems use sensors attached to either the human trunk or feet. The recent emergence of smartglasses and smart watches provides an opportunity to use both types of wearable devices in position tracking. This paper proposes a novel method of utilizing both a smartwatch and smartglasses for PDR. The general idea is to use the relative angle between arm swing direction and head direction to detect any head-turn motion that would otherwise skew the position dead reckoning propagation. A complete PDR solution that includes step detection, step length estimation, head-rotation detection, and dead reckoning using a smartwatch and smartglasses that are currently available in the market is presented. Using the smartglasses, step detection with an error rate less than 0.4% and a cumulative distance error of less than 2.4% on 800 m walks and runs is achieved. In the dead reckoning field experiments, the proposed algorithm produces result that closely track the actual path when plotted on Google Maps, outperforming solutions that only use the smartwatch or smartglasses alone.

There has been an increasing demand for infrastructureless localization. Current approaches involving inertial measurement unit (IMU) generally utilize step detection and step counting to estimate the displacement. However, the accuracy is affected, because the step sizes are neglected. Some groups have proposed algorithms that involve placing the IMU on the foot to estimate the step size, but users have commented that it affects their walking. Hence, this paper presents a new method to estimate both the forward displacement and orientation with the IMU placed at the upper torso. Placing IMU at the upper torso to estimate horizontal displacement has been challenging, as the accuracy of the inertial sensors is greatly handicapped by the notorious integration drift when performing integration in the travel direction with the lack of opportunity for zero velocity update. Thus, a novel method is proposed in this paper by exploiting the vertical component of the accelerometer reading. An inverted pendulum model is proposed with a step detector and a step length estimation method. The system is implemented, and two sets of experiments are conducted to demonstrate the capability. The experiment sets include straight lines and rectangular shape path, and in each set, four step sizes of small, normal, large, and mixture are conducted for each test and each test is performed four times. The experimental results show an average displacement error of 1% for straight line paths and 2% for the rectangular paths.

A foot-mounted pedestrian dead reckoning system is a self-contained technique for indoor localization. An inertial pedestrian navigation system includes wearable MEMS inertial sensors, such as an accelerometer, gyroscope, or digital compass, which enable the measurement of the step length and the heading direction. Therefore, the use of zero velocity updates is necessary to minimize the inertial drift accumulation of the sensors. The aim of this paper is to develop a foot-mounted pedestrian dead reckoning system based on an inertial measurement unit and a permanent magnet. Our approach enables the stance phase and the step duration detection based on the measurements of the permanent magnet field during each gait cycle. The proposed system involves several parts: inertial state estimation, stance phase detection, altitude measurement, and error state Kalman Filter with zero velocity update and altitude measurement update. Real indoor experiments demonstrate that the proposed algorithm is capable of estimating the trajectory accurately with low estimation error.

The Global Positioning System (GPS) can be readily used for outdoor localization, but GPS signals are degraded in indoor environments. How to develop a robust and accurate indoor localization system is an emergent task. In this paper, we propose a smartphone inertial sensor-based indoor localization and tracking system with occasional iBeacon corrections. Some important issues in a smartphone-based pedestrian dead reckoning (PDR) approach, i.e., step detection, walking direction estimation, and initial point estimation, are studied. One problem of the PDR approach is the drift with walking distance. We apply a recent technology, iBeacon, to occasionally calibrate the drift of the PDR approach. By analyzing iBeacon measurements, we define an efficient calibration range where an extended Kalman filter is utilized. The proposed localization and tracking system can be implemented in resource-limited smartphones. To evaluate the performance of the proposed approach, real experiments under two different environments have been conducted. The experimental results demonstrated the effectiveness of the proposed approach. We also tested the localization accuracy with respect to the number of iBeacons.

Image denoising has been a well-studied problem for imaging systems, especially imaging sensors. Despite remarkable progress in the quality of denoising algorithms, persistent challenges remain for a wide class of general images. In this paper, we present a new concept of sequence-to-sequence similarity (SSS). This similarity measure is an efficient method to evaluate the content similarity for images, especially for edge information. The approach differs from the traditional image processing techniques, which rely on pixel and block similarity. Based on this new concept, we introduce a new SSS-based filter for image denoising. The new SSS-based filter utilizes the edge information in the corrupted image to address image denoising problems. We demonstrate the filter by incorporating it into a new SSS-based image denoising algorithm to remove Gaussian noise. Experiments are performed over synthetic and experimental data. The performance of our methodology is experimentally verified on a variety of images and Gaussian noise levels. The results demonstrate that the proposed method’s performance exceeds several current state-of-the-art works, which are evaluated both visually and quantitatively. The presented framework opens up new perspectives in the use of SSS methodologies for image processing applications to replace the traditional pixel-to-pixel similarity or block-to-block similarity.

One of the major reasons why the elderly lose their ability to live independently at home is the decline in gait performance. A measure to assess gait performance using accelerometers is step counting. The main problem with most step detection algorithms is the loss of accuracy at low speeds ( $ 0.8 m/s) which limits their use in frail elderly populations. In this paper, a step detection algorithm was developed and validated using data from 10 healthy adults and 21 institutionalized seniors, predominantly frail older adults. Data were recorded using a single waist-worn triaxial accelerometer as each of the subjects performed one 10-m-walk trial. The algorithm demonstrated high mean sensitivity (99 $\pm$ 1%) for gait speeds between 0.2–1.5 m/s. False positives were evaluated with a series of motion activities performed by one subject. These activities simulate acceleration patterns similar to those generated near the body's center of mass while walking in terms of amplitude signal and periodicity. Cycling was the activity which led to a higher number of false positives. By applying template matching, we reduced by $73\%$ the number of false positives in the cycling activity and eliminated all false positives in the rest of activities. Using K-means clustering, we obtained two different characteristic step patterns, one for normal and one for frail walking, where particular gait events related to limb impacts and muscle flexions were recognized. The proposed system can help to identify seniors at high risk of functional decline and monitor the progress of patients undergoing exercise therapy interventions.

Activity levels can be used as a predictor of health status, physical condition, feed efficiency, and coping style in animals. Small, portable data loggers have been validated as an inexpensive and effective alternative to video or live observation for automated activity detection in several livestock species. Our study aimed to validate the use of Onset HOBO Pendant (R) G acceleration data loggers for automated step detection in two age classes of male turkeys: growers and finishers. The loggers were attached to one leg in an elastic harness and programmed to record each turkey's leg accelerations every 0.1 s. For the grower turkeys, the activities of 60 males were recorded in a runway twice per week from 9-11 weeks of age. In the finisher trial, the loggers were attached to eight 14 week old male turkeys to record their stepping activities in a trial pen over 54 min. Simultaneous video observation allowed for logger suitability to be determined for reporting walking activity. The 0.1 s data points were identified as either: true positives, false positives, false negatives, or true negatives then the logger sensitivity, false discovery rate, specificity, precision, and accuracy were calculated for each turkey run. Over the 309 grower turkey runs, 4364 steps were observed in the video while the loggers detected 4865 steps. The grower logger results showed a mean sensitivity of 96.02 +/- 6.82%, specificity of 99.10 +/- 0.86%, and an average false discovery rate of 12.41 +/- 110.13% for step detection (mean standard deviation). For the grower turkey loggers, the mean precision and accuracy for step detection were 87.59 +/- 10.13% and 98.95 +/- 0.77%. For the eight finisher turkeys, a total of 2420 steps were seen by the video and the loggers recorded 2691 data points as steps. The finisher turkey logger data showed a mean sensitivity of 89.58 +/- 11.75%, mean false discovery rate of 14.73 +/- 10.66%, and average specificity of 99.81 +/- 0.19%. The finisher logger results had a mean precision of 85.27 +/- 10.66% and a mean accuracy of 99.73 +/- 0.13%. Although the loggers showed high sensitivity for detecting turkey steps under research settings, future research should try to reduce the loggers' susceptibility to background noise, which would improve their performance for application in commercial conditions. (C) 2016 Elsevier B.V. All rights reserved.

Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil‐Sen median trend estimator, for which the ordinary version is the median of slopes vij = (xj–xi)/(tj–ti) computed between all data pairs i > j. For normally distributed data, Theil‐Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil‐Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one‐sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root‐mean‐square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences., Key Points MIDAS is a robust estimator of time series trendMIDAS estimates of GPS velocities are resistant to outliers, steps, and seasonalityMIDAS velocities are as accurate as the best methods involving step detection

Background:There is a need to examine step-counting accuracy of activity monitors during different types of movements. The purpose of this study was to compare activity monitor and manually counted steps during treadmill and simulated free-living activities and to compare the activity monitor steps to the StepWatch (SW) in a natural setting.Methods:Fifteen participants performed laboratory-based treadmill (2.4, 4.8, 7.2 and 9.7 km/h) and simulated free-living activities (eg, cleaning room) while wearing an activPAL, Omron HJ720-ITC, Yamax Digi-Walker SW-200, 2 ActiGraph GT3Xs (1 in “low-frequency extension” [AGLFE] and 1 in “normal-frequency” mode), an ActiGraph 7164, and a SW. Participants also wore monitors for 1-day in their free-living environment. Linear mixed models identified differences between activity monitor steps and the criterion in the laboratory/free-living settings.Results:Most monitors performed poorly during treadmill walking at 2.4 km/h. Cleaning a room had the largest errors of all simulated free-living activities. The accuracy was highest for forward/rhythmic movements for all monitors. In the free-living environment, the AGLFE had the largest discrepancy with the SW.Conclusion:This study highlights the need to verify step-counting accuracy of activity monitors with activities that include different movement types/directions. This is important to understand the origin of errors in step-counting during free-living conditions.

Frequency jumps are common in rubidium frequency sources. They affect the estimation of user position in navigational satellite systems. These jumps must be detected and corrected immediately as they have direct impact on the navigation system integrity. A novel weak frequency jump detector is proposed based on a Kalman filter with a multi-interval approach. This detector can be applied for both "sudden" and "slow" frequency transitions. In this detection method, noises of clock data are reduced by Kalman filtering, for accurate estimation of jump size with less latency. Analysis on in-orbit rubidium atomic frequency standard (RAFS) phase telemetry data shows that the detector can be used for fast detection and correction of weak frequency jumps. Furthermore, performance comparison of different existing frequency jump detection techniques with the proposed detector is discussed. A multialgorithm-based strategy is proposed depending on the jump size and latency for onboard navigation satellites having RAFS as the primary frequency source.

The paper is devoted to forecasting hourly day-ahead electricity prices from the perspective of the existence of jumps. We compare the results of different jump detection techniques and identify common features of electricity price jumps. We apply the jump-diffusion model with a double exponential distribution of jump sizes and explanatory variables. In order to improve the accuracy of electricity price forecasts, we take into account the time-varying intensity of price jump occurrences. We forecast moments of jump occurrences depending on several factors, including seasonality and weather conditions, by means of the generalised ordered logit model. The study is conducted on the basis of data from the Nord Pool power market. The empirical results indicate that the model with the time-varying intensity of jumps and a mechanism of jump prediction is useful in forecasting electricity prices for peak hours, i.e., including the probabilities of downward, no or upward jump occurrences into the model improves the forecasts of electricity prices.

Power quality and stability have become the most important issues in power system operations, Micro Grids, and Smart Grids. Sensitive equipment can be seriously damaged when exposed to unstable power swing conditions. An unstable system may cause serious damage to Micro Grid System elements such as generators, transformers, transmission lines, and so forth. Therefore, out-of-step detection is essential for the safe operation of a Micro Grid system. In general, Equal Area Criterion (EAC) is a method for evaluating the stability of Smart Grid systems. However, EAC can be performed only if it is possible to analyze the active power and generator angle. This paper presents an analysis of the trajectory of complex power using a mathematical model. The variation of complex power is analyzed using a mathematical method, and then the relationship between complex power and EAC is presented, and a simulation performed. Later, in part II, a novel out-of-step detection algorithm based on part I will be presented and tested.

We present an efficient computational approach composed of forward and inverse analyses that can neatly capture multiple flaw clusters with high accuracy yet low computational effort. The inverse analysis consists of three continuous steps with distinct targets. Firstly, a combination of improved discrete artificial bee colony (IDABC) algorithm and hierarchical clustering analysis (HCA) is applied to seize sub-domains that contain flaw clusters with a conservative number of sensors. Secondly, additional sensors are introduced into the captured sub-domains so that flaw numbers and their rough geometries can be achieved by the IDABC-HCA optimizer. Eventually, the initial guesses of approximate geometries are turned towards the true geometries by using Broyden-Fletcher-Goldfarb-Shanno approach. In addition, a “Queue and Kill” method is used to determine and eliminate incorrect candidate flaws and/or clusters of flaws in the first two steps. The forward analysis is solved through a combination of dynamic extended finite element method (XFEM) enhanced by local mesh refinement, which highly matches the three-step strategy. The local mesh refinement not only improves the computational accuracy of concerning areas, i.e., sub-domains, but also controls the computation amount. Numerical examples demonstrate the effectiveness and robustness of the developed method in detecting multiple flaw clusters.

Background Physical therapy interventions for ambulatory youth with cerebral palsy (CP) often focus on activity-based strategies to promote functional mobility and participation in physical activity. The use of activity monitors validated for this population could help to design effective personalized interventions by providing reliable outcome measures. The objective of this study was to devise a single-sensor based algorithm for locomotion and cadence detection, robust to atypical gait patterns of children with CP in the real-life like monitoring conditions. Methods Study included 15 children with CP, classified according to Gross Motor Function Classification System (GMFCS) between levels I and III, and 11 age-matched typically developing (TD). Six IMU devices were fixed on participant’s trunk (chest and low back/L5), thighs, and shanks. IMUs on trunk were independently used for development of algorithm, whereas the ensemble of devices on lower limbs were used as reference system. Data was collected according to a semi-structured protocol, and included typical daily-life activities performed indoor and outdoor. The algorithm was based on detection of peaks associated to heel-strike events, identified from the norm of trunk acceleration signals, and included several processing stages such as peak enhancement and selection of the steps-related peaks using heuristic decision rules. Cadence was estimated using time- and frequency–domain approaches. Performance metrics were sensitivity, specificity, precision, error, intra-class correlation coefficient, and Bland-Altman analysis. Results According to GMFCS, CP children were classified as GMFCS I (n = 7), GMFCS II (n = 3) and GMFCS III (n = 5). Mean values of sensitivity, specificity and precision for locomotion detection ranged between 0.93–0.98, 0.92–0.97 and 0.86–0.98 for TD, CP-GMFCS I and CP-GMFCS II-III groups, respectively. Mean values of absolute error for cadence estimation (steps/min) were similar for both methods, and ranged between 0.51–0.88, 1.18–1.33 and 1.94–2.3 for TD, CP-GMFCS I and CP-GMFCS II-III groups, respectively. The standard deviation was higher in CP-GMFCS II-III group, the lower performances being explained by the high variability of atypical gait patterns. Conclusions The algorithm demonstrated good performance when applied to a wide range of gait patterns, from normal to the pathological gait of highly affected children with CP using walking aids.

BackgroundMobile accelerometry is a powerful and promising option to capture long-term changes in gait in both clinical and real-world scenarios. Increasingly, gait parameters have demonstrated their value as clinical outcome parameters, but validation of these parameters in elderly patients is still limited.ObjectiveThe aim of this study was to implement a validation framework appropriate for elderly patients and representative of real-world settings, and to use this framework to test and improve algorithms for mobile accelerometry data in an orthogeriatric population.MethodsTwenty elderly subjects wearing a 3D-accelerometer completed a parcours imitating a real-world scenario. High-definition video and mobile reference speed capture served to validate different algorithms.ResultsParticularly at slow gait speeds, relevant improvements in accuracy have been achieved. Compared to the reference the deviation was less than 1% in step detection and less than 0.05 m/s in gait speed measurements, even for slow walking subjects (< 0.8 m/s).ConclusionWith the described setup, algorithms for step and gait speed detection have successfully been validated in an elderly population and demonstrated to have improved performance versus previously published algorithms. These results are promising that long-term and/or real-world measurements are possible with an acceptable accuracy even in elderly frail patients with slow gait speeds.

Atomic clocks are fundamental elements of a Satellite Navigation system due to its high accuracy and stability. Different clock technologies are used in satellites and in satellite navigation ground stations. The improvised clock technologies help in acquiring the position and timing with greater accuracy. To ensure the timing capabilities and position accuracy, the analysis of atomic clock performances is essential. The fundamental SI unit of time namely one Second or Second is derived from the caesium clock and hence caesium clocks serve as the primary frequency standards. caesium clocks are used in Satellite Navigation systems to obtain the accuracy and long term stability. In this work results of long term monitoring of caesium clock parameters during a clock failure conditions are presented and a new method is proposed for monitoring caesium frequency standard. The clock jumps may not be detected only by observing health parameters, hence in this paper a method is proposed for clock jump detection. The proposed method helps to detect the jumps in the clock and also helps in preventive maintenance of caesium clocks including the future failure predictions and hence helps to avoid sudden operations failure in a mission critical operational network.

Pedestrian dead reckoning (PDR) is an essential technology for positioning and navigation in complex indoor environments. In the process of PDR positioning and navigation using mobile phones, gait information acquired by inertial sensors under various carrying positions differs from noise contained in the heading information, resulting in excessive gait detection deviation and greatly reducing the positioning accuracy of PDR. Using data from mobile phone accelerometer and gyroscope signals, this paper examined various phone carrying positions and switching positions as the research objective and analysed the time domain characteristics of the three-axis accelerometer and gyroscope signals. A principal component analysis algorithm was used to reduce the dimension of the extracted multidimensional gait feature, and the extracted features were random forest modelled to distinguish the phone carrying positions. The results show that the step detection and distance estimation accuracy in the gait detection process greatly improved after recognition of the phone carrying position, which enhanced the robustness of the PDR algorithm.