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Sign Out. Sign in Sign in with. All Categories. However, due to the diversity of applications, scenarios, sensors and user requirements, it is difficult to create a universally applicable solution for indoor localization [ 6 ]. Wi-Fi chips have been widely applied in various smartphones and other mobile devices. Wi-Fi networks cover many public places, such as office buildings, airports and shopping malls. Therefore, the use of Wi-Fi signals for indoor localization is a reasonable choice. Wi-Fi fingerprint localization is a widely used indoor localization technique, which usually consists of two phases: an off-line phase and an on-line phase [ 7 ].

During the off-line phase, the known locations of a certain area are selected and the received signal strength indications RSSI from multiple Wi-Fi access points APs are recorded at the selected locations. During the on-line phase, the RSSI from the unknown locations are matched with that from the known locations in the fingerprint database, thus the user location is estimated. However, changes to the space environment result in an inaccurate positioning result, thus the fingerprint database needs to be updated regularly, which is the main challenge of fingerprint location techniques [ 8 ].

The localization techniques based on vision sensors can be divided into two categories. One involves collecting images with a mobile camera and the location of the camera is determined, while the other collects images with a fixed camera and the target location in the image is determined [ 9 ]. Large graphic computation is required in vision localization systems, because of the hardware limitations, and although the systems have a high precision in a specific environment, the real-time performance may not be guaranteed.

With further improvement of smartphone computing capability, this vision localization technique is expected to be further applied and popularized in indoor localization. With the development of micro-electromechanical system MEMS technology, low cost inertial measurement units IMUs such as accelerometers, gyroscopes and magnetometers, etc. These low cost IMUs have the advantages of small size, light weight and low power consumption [ 10 ]. Pedestrian dead reckoning PDR is a localization technique that utilizes IMU data to calculate the pedestrian location.

Compared with the localization techniques based on wireless signals and vision sensors, PDR can give an accurate position in a short period of time, its updating speed of the pedestrian location is faster and the power consumption is lower. More importantly, since additional infrastructure assistance is not required, PDR systems are simpler and more autonomous.

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In mounted-PDR systems, the accuracy of the device is higher, and it is mounted to a certain part of the body, such as feet, legs and waist. It is regarded as a body-fixed system and the location of the pedestrian is obtained by the time integral on the signals of accelerometers and gyroscopes.

Because of the accumulative error, the localization accuracy of mounted-PDR will decrease with time, thus a Zero Velocity Update ZUPT algorithm is used to control the accumulative error [ 11 , 12 , 13 ]. To get rid of the limitation that the device must be mounted to the body, more flexible handheld-PDR systems have been universally used.

Handheld-PDR utilizes handheld mobile devices to obtain the locations and headings of pedestrians, which usually consists of three modules: step detection, step length estimation and heading determination. However, there are still some limitations in the existing techniques, as many localization approaches assume that the heading angle offset remains constant, the heading angle offset is the angle between the direction of smartphone and the direction of pedestrian [ 15 , 16 , 17 ].

The assumption can be satisfied when pedestrians hold smartphones on the front of the body or when pedestrians are making calls. However, during the localization, the phone pose is arbitrary and the heading offset cannot be guaranteed to be constant.

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Thus, this paper mainly addresses the issues of motion mode recognition and indoor localization of pedestrians. Our method improves the accuracy and flexibility of PDR system by solving the issues of pedestrians moving in different states and the smartphone holding in different poses. The main contributions of our work are as follows:.

The motion mode can be divided into two categories: the movement state and the phone pose. The movement state represents the global motion of pedestrian and the phone pose represents the pose of people holding or placing smartphones. The movement state and the phone pose are independent with each other, and they can be combined arbitrarily.

In prior works, only few combination modes are considered. Therefore, in this paper, we adequately consider all 16 combination modes, which are generated by four movement states Walking, Running, Upstairs and Downstairs and four phone poses Holding, Calling, Swinging and Pocket.

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We also analyze the characteristics of accelerometer and gyroscope data in different modes in detail. Their features are extracted at the same time. The classification approach proposed in this paper can accurately recognize any combination mode of the movement state and the phone pose. The accelerometer data are different depending on the various movement states and phone poses.

To adapt to the change, we present an adaptive step detection algorithm. The thresholds of valid peak and minimum step interval are adjusted with the consideration of different movement states and phone poses, and adjacent peaks selection mechanism is added to eliminate the influence of false peaks. To solve the ambiguity problem of the extracted right-vector, we further analyze the signals of accelerometer and gyroscope during pedestrian walking.

The analysis includes the orientations of the smartphone carried in the front pocket of trousers, and pedestrian swings the smartphone with the left-hand or the right-hand. The remainder of this paper is organized as follows: in Section 2 , related works are discussed. Section 3 presents the system overview. In Section 4 , the pre-processing process is introduced. In Section 5 , the movement state and phone pose definition, feature extraction and classification method are illustrated.

The PDR algorithm adapted to different motions is described in Section 6 , including step detection, step length estimation and heading determination. The experimental results of motion recognition and indoor localization are shown in Section 7. Finally, conclusions and future work are discussed in Section 8.

Mobile intelligent platforms have developed rapidly in recent years. Smartphones, smart-glasses, smart-bracelets and smart-watches have become the main terminals of indoor localization systems. Unlike dedicated navigation devices fixed to the body, the users can use these portable devices at will.

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Thus, the attitude of the portable device can change in real-time and the relative position between the user and the device is not so stable, which brings more challenges to PDR systems. There are some restrictions on the current handheld-PDR system in many application scenarios. Some people have classified the possible motion modes during navigation to assist indoor localization.

Ling et al. They compared 27 features extracted from the smartphone sensors and the least squares support vector machine LS-SVM classification algorithm is used to detect eight behavior patterns that commonly occur in indoor navigation. The accuracy range of the classifier was from Susi et al. Shin et al. In [ 25 ], the motions of taking an elevator, and standing or walking on an escalator were taken into consideration.

Elhoushi et al. In [ 27 ], the finite state machine was utilized to conduct practical tracking of pedestrians, and the transition of the smartphone poses can be detected. To relieve the burden of designing and selecting features, Khoshelham et al. The proposed method was independent of the expert knowledge and greatly reduced the work of manual feature design, by the selection and combination of various sensors, the results showed that the classification performance by multiple sensors can be better than that by only accelerometers.

Gu et al. In addition, they added the history information of the motion modes to the classification. However, the movement states and the phone poses can be combined arbitrarily in localization process. In this paper, to achieve a better performance of the combination mode recognition, we select two kinds of classifiers: DT and SVM.

In prior works, accelerometers, gyroscopes and magnetometers are used to obtain the yaw angle of the device, and the yaw angle is regarded as the heading of the pedestrian. Using these methods, the device must always point to the direction of pedestrian movement [ 18 , 19 , 20 , 21 ], but this limitation cannot be feasible all the times. Shen et al. However, when the smartphone is in the dynamic state, such as swinging or placed in the pocket, the prior information of heading deviation will be incorrect.

Kunze [ 35 ] developed a PCA-based method to infer the orientation of mobile device carried in a pocket from the acceleration signal. Steinhoff et al. Deng et al. The architecture of the PDR system based on motion mode recognition, which consists of data pre-processing, motion mode recognition and PDR, is shown in Figure 1. The raw data from smartphone sensors contains random noise, and the magnetometer data can be disturbed by the local magnetic environment. Thus, the raw data must be filtered and calibrated. The time-domain features and the frequency-domain features are extracted from the pre-processed data, which are the input of the classifier.

The parameters of step detection, stride length estimation and heading determination are adjusted based on the results of the classifier, and the locations of the pedestrian are updated by the equation of PDR. The details of the pedestrian localization system will be further discussed in the following sections.

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Because of the low measuring accuracy of the sensors embedded in the smartphone, there is a lot of noise in the raw signals from the sensors. Therefore, a pre-processing process should be adopted to eliminate the noise and errors before processing the motion mode recognition and pedestrian localization.

Based on the analysis of the sensor signal in different motion modes, we found that most energies of the signals are below 15 Hz, so a low-pass filter with a 15 Hz cut-off frequency is adopted to reject the high frequency noise. The signals after the low-pass filter are smoothed again by a moving average filter. As shown in Figure 2 , compared with the raw signal, the filtered signal is less noisy and smoother, thus the motions associated to the pedestrian can be reflected more clearly.

The raw and filtered signal from accelerometer and gyroscope. The smartphone magnetometer has large measurement errors and can be easily disturbed by the local magnetic environment.