Location Based Sleep Scheduling for Target Tracking Applications in Smart Space Environments

Ubiquitous computing as the integration of sensors, smart devices, and intelligent technologies to form a “smart space” environment relies on the development of both middleware and networking technologies. Several kinds of smart space middleware have been developed and OSGi (Open Service Gateway Initiative) was initiated mainly for interoperability support with service distribution among various middleware environments. In this paper, we analyze the weaknesses in the OSGi service platform such as a non-distributed framework, passive user management, device management, and service bundle management. Moreover, we propose that in a smart space environment, Location-aware SBM (Service Bundle Manager) is most capable of efficiently managing various service bundles. This paper concludes with the implementation results for the SBM.

A two phase hybrid algorithm for estimating the location of a mobile node, which has the capability of measuring signal strength, azimuth, and elevation, in a smart space environment over the visible light channel is proposed. In contrast to conventional triangulation approaches which are performed in a simplified plane, the smart room architecture requires a non-planar solution due to the illumination requirement. Furthermore, conventional triangulation approaches can at times produce numerically ill-defined solutions, thereby prohibiting a notion of target location. Instead of solely relying on triangulation, the mobile node estimate their locations through a two phase approach in which they firstly exploit the signal strength observables with unique IDs to establish a coarse estimate, and secondly use the azimuth and elevation observables to establish a fine estimate. In many cases, the fine estimate will improve upon the coarse estimate; however when triangulation fails, the algorithm yield the coarse estimate rather than a localization failure. Since the environment model relies on the primary requirement of adequate illumination, the number of LED anchors and transmit power for communication functions are determined. Simulation results confirm the effectiveness of the hybrid two phase localization approach in a smart space indoor environment by having a median coarse phase accuracy of 34.88 cm and a fine phase median accuracy of 13.95 cm.

Life cycle management plays an essential role in context-aware systems, it needs to aware surrounding contexts and adapt to changing contexts in highly dynamic environments. Consequently, context management is different from traditional approaches due to the characteristics of contextual information are dynamic, transient, and fallible. Understanding the life cycle of a context is the key issue to maintain consistent contextual information. Accordingly, we propose an ontology-based model for representing, deducing, and managing consistent contextual information. In addition, an agent-based architecture supports the process of context life cycle management.

Limited resource allocation and scheduling are important problems in distributed wireless sensor networks (WSNs). Saving energy and real-time performance deserve research for low-cost sensor nodes in the target tracking application. In this paper, we propose a new dynamic-group idea and apply it to our scheduling scheme. An Extended Kalman Filter (EKF) is employed to predict tracking accuracy, and an energy model is adopted to predict the energy consumption. Simulation results show that, compared with the information-driven sensor quering (IDSQ) and the adaptive sensor scheduling strategy, the proposed scheme can achieve significant improvement on real-time property and tracking accuracy for target tracking in WSN. At the same time, it approaches to the adaptive sensor scheduling strategy in energy consumption.

As an important part of Ideological and political work, mental health education is an effective carrier to promote the implementation of the comprehensive reform of “three complete education”. Fusion of multi-sensor data to achieve higher target tracking accuracy is the current research hotspot, but the research of dynamic level fusion is still a relatively open problem. In target tracking applications, a single sensor may not be able to provide all the information of tracking target. It is easy to lead to inaccuracy of target position estimation by using one of the measured values of sensors for target tracking. Therefore, the fusion of the measured values of sensors in this paper will reduce the uncertainty of tracking target position and improve the estimation accuracy of target position. At the same time, this paper uses brain computer interface technology to study the optimization model of College Students' mental health. After the design of brain computer interface system based on embedded processor, this paper tests the performance of hardware and software of the whole system. It includes the performance test of EEG amplifier, the test of embedded system and brain computer interface application.

Due to their distinctive features, unmanned aerial vehicles (UAVs) have been recently exploited to support a wide range of applications. The features include low maintenance cost, compact size, and excellent capability of maneuvering. In particular, UAVs have the potential capabilities to support different technologies such as Internet-of-things (IoT) devices, sensors, cameras, and systems, thus, performing civilian, target tracking, industrial, and military applications. Specifically, target tracking has been recently configured as one of the most attractive applications of UAVs. With this, UAVs estimate and detect the behavior or locate a moving or a stationary item. Accordingly, several research efforts have been conducted to investigate the promising capabilities of UAVs in target-tracking missions, including indoor and outdoor tracking missions. This paper surveys UAV-based target tracking and monitoring for indoor and outdoor environments, where the deployment scenarios of such UAV-based systems are characterized and investigated. Furthermore, we discuss a set of practical design challenges of UAV-based target tracking systems, and thus, we provide a set of potential solutions to deal with these challenges. Specifically, we present a set of recent enabling technologies that might be integrated into UAV target tracking systems, including machine learning (ML), cloud computing, and emerging fifth-generation (5G) technologies. We also demonstrate a use-case scenario in which ML is used to facilitate indoor target tracking and monitoring. Finally, future research directions are outlined that can help in improving the efficiency of the UAV-based target-tracking systems.

In order to solve the problem that the conventional tracker is not adapted to the abrupt motion, a tracking algorithm based on the improved salp swarm algorithm (ISSA) was proposed. Visual tracking is considered to be a process of locating the optimal position through the interaction between leaders and followers in successive images. Firstly, the adaptive mechanism of leader and follower is introduced into the original salp swarm algorithm (SSA) to balance the exploitation and exploration of the algorithm. This method can improve the accuracy and effect of tracking. Secondly, the golden-sine algorithm was used to update the position of followers, considering that the SSA had a single spatial search mode for followers and was easy to fall into the local optimum. By comparing with 19 classical tracking algorithms, qualitative and quantitative analysis is carried out to verify the tracking effect of the proposed method. A large number of experimental results show that the algorithm proposed here has good performance in visual tracking, especially for mutation motion tracking.

In this paper, a novel tensor operator is introduced that directly solves the prediction step of the Bayes recursion for multi-dimensional discretized probability densities in Canonical Polyadic Decomposition form. The proposed operator combines computational efficiency with the possibility for non-linear and non-Gaussian scenarios. Based on the Continuous White Noise Velocity model, it enables a broad range of application in target tracking.

Real-time localization and classification of fast-moving objects are crucial in various applications. Traditional imaging approaches face significant challenges, including large data requirements, limited update rates, motion blur, and restrictions in non-visible wavelengths. This paper proposes an image-free method based on complementary single-pixel detection and centralized geometric moments, which effectively integrates target localization and classification into a unified framework. By employing only four specific illumination patterns, the method can simultaneously determine the centroid position and shape of the target at an update rate of up to 5.55 kHz. Theoretical simulations verify the robustness of the proposed method under similarity transformations. Experimental results indicate that the proposed system achieves accurate real-time target localization and classification under diverse conditions, with an RMSE for centroid localization below 0.5 pixels and 93.3% classification accuracy for 30 different objects. The proposed method demonstrates strong adaptability to complicated environments. It holds significant potential for applications in target tracking, character recognition, industrial automation, and the development of optoelectronic neural networks for advanced optical computing tasks.

In this paper, various discrete-time estimation problems are studied for a finite and homogeneous Markov chain observed by a marked point process. These problems, which could have significant applications in target tracking, manufacturing or communication theory, have never been studied in the literature. The quantities to be estimated are the state, the number of jumps and the occupation times. The identification of the chain transition matrix is also addressed via an expectation maximization procedure. Solutions, in the sense of the conditional distribution, are obtained by a change of probability measure and are shown to have convenient recursive forms. The efficiency of this new approach for sensor modeling is illustrated by the study of a linear Markovian jump filtering problem where, in addition to a classical state observation, a mode Markov point process observation is assumed. A numerical example is given.

In this paper, a Mean Shift algorithm based on Least Square prediction is proposed. Based on the continuity of target's trace, the Least Square Mean Shift (LSMS) algorithm uses the result of Least Square prediction as the initial search center of Mean Shift algorithm. Then, Mean Shift is applied to get the final target position. The computational complexity is reduced by limiting the number of iterations of Mean shift. Experimental results show that, compared with traditional Mean Shift algorithm, the proposed algorithm improves the real-time realization. Meanwhile, it has better performance on fast moving targets and non-linear moving targets.

Bayesian statistics has attracted people's interest again recently because of the application of Markov Chain Monte Carlo (MCMC) theory. In particle filtering, the diversification of particles disappears in the process of importance sampling. However, this problem can be solved using Metropolis-Hastings (MH) sampling usually used in MCMC theory. As a modification to added MCMC (AMCMC)—an improved MCMC particle filter that can track variable number of targets at the same time, a new approach to optimize those rejected samples in MH sampling process by Mean Shift algorithm is proposed in this paper. Because the operation rate of particles in AMCMC is increased, the circles of sampling needed for the convergence of Markov chain is reduced. It is shown by experiment that, the optimized algorithm has better tracking performance under the condition of fewer particles.

Near‐infrared polarization‐sensitive photodetectors hold the advantages of capturing light signals with high performance while shielding stray light, endowing them the potential applications in target tracking, remote sensing, and computer vision. Here, a 2D polarization‐sensitive, self‐powered, and near‐infrared photodetector is constructed by vertically stacking multilayer p‐type GeSe on n‐type MoTe2. The type‐II energy band alignment and anisotropic crystalline structure of GeSe components allow an effective separation and transmission of polarized light excited carriers, enabling the capability of polarization‐sensitive and self‐powered photodetections. The device exhibits broadband spectral coverage from visible (405 nm) to near‐infrared (1310 nm) wavelength range. At zero bias and 808 nm light, the responsivity (R) and detectivity (D*) can reach 52 mA W–1 and 4.1 × 1011 Jones, respectively. Due to the extremely low dark current of several tens of fA, the photoswitching ratio can reach close to 106. More importantly, because of the strong in‐plane anisotropic orthogonal structure of GeSe, the polarization sensitivity can reach 5.4 under 635 nm polarized light illumination, outperforming the polarization‐sensitive photodetectors based on 2D anisotropic materials and heterostructures. This work provides an effective strategy of using anisotropic/isotropic GeSe/MoTe2 heterojunctions to realize self‐powered, near‐infrared, and polarization‐sensitive photodetectors with integrated angle‐resolved optoelectronic devices.

In this paper, we propose a robust cubature Kalman filter (CKF) for nonlinear state-space models with unknown state and measurement noise covariance matrix. We study situations in which sensors are independent of each other. Therefore, the unknown measurement noise variance is modeled as an unknown inverse gamma (IG) distribution. The Gaussian-Student-t-inverse-Wishart (GSTIW) mixture distribution is used to model the one-step prediction distribution. Modeling generates numerous unknown parameters. Therefore, we adopt the statistical linearization method to linearize the observation function and then estimate the state and parameters separately to reduce the computational burden of estimating unknown parameters, significantly improving the algorithm’s efficiency. Finally, using the variational Bayesian method, a novel and efficient robust CKF based on the IG distribution and GSTIW mixture distributions (IG-GSTIW-CKF) is obtained. Simulation and experimental results show that the proposed method has better estimation accuracy than several advanced algorithms when sensors are independent. In addition, the efficiency of the proposed algorithm is significantly higher than that of other CKF-based methods.

As a sensor that converts angle signal into an electrical signal, the inclination sensor is applied in multiple fields. The photoelectric inclination sensor has the advantages of non-contact and high precision, but it is limited to small angle precision measurement and is expensive. A new photoelectric inclination sensor is obtained by combining photoelectric detection with elastic sensing elements and eliminating vibration or other interference errors based on an electromagnetic feedback system. The structure of the whole prototype is designed, the elastomer is calculated theoretically, the electromagnetic feedback part is designed and selected, the voltage-displacement characteristic test of the photodiode is carried out, and the linear interval is calibrated simultaneously. According to the voltage difference generated by the photodiode, a differential amplification circuit is designed to amplify the small voltage signal. The PID regulating circuit is designed to regulate the current on the electromagnetic coil. The A/D chip is used for analog-to-digital conversion, and the data are collected and uploaded through STM32 and RS485. In the test, the prototype test environment is built. It is proved that 0°∼45° is a linear range of the sensor in the open-loop system without electromagnetic feedback. The nonlinearity is 4.365%, the nonlinearity in the −20°~20° interval is less than 1.2%, and the 45°˜90° is the nonlinear range. In the closed-loop system of electromagnetic feedback, the interval of 0°˜90° is linear, the nonlinearity is 6.861%, and the nonlinearity of 0°˜45° is only 1.06%. This type of sensor is used for multi-station maneuvering target tracking. The aircraft to be tested is simulated and analyzed 100 times. It is found that adding a sensor monitoring station can improve the measurement accuracy and suppress the noise. In the closed-loop system of electromagnetic feedback, 0°˜90° is the linear range, the nonlinearity is 6.861%, and the nonlinearity of 0°˜45° is only 1.06%. Such sensor is used for multi-station maneuvering target tracking, and the aircraft to be tested is simulated and analyzed 100 times. Azimuth, pitch angle and errors from target to the sensor are calculated. The results show that the measurement accuracy can be improved and the noise can be suppressed by adding sensor monitoring stations.