The smart speakers benefit from the ability to localize and identify users. Specifically, we can analyze the user's habits from the historical trajectory to provide better voice-based services. However, current voice localization method requires the user to actively issue voice commands, which makes smart speakers unable to track and identify silent users most of the time. This paper introducesWSTrack+, a system that combinesWi-Fi andSound to track human movement and recognize gait patterns. In particular, current smart speakers naturally support both Wi-Fi and acoustic functions. As a result, we are able to construct the system using just one router and a smart speaker, which is a more promising approach compared to existing systems that rely on multiple routers for sensing. To track and identify the silent user, our insights are twofold: 1) the smart speakers can hear the sound of the user's footstep, and then extract which direction the user is in; 2) we can extract the reflected path change rate from the Wi-Fi signals, and the acoustic signal can help us convert the path change rate into the actual user's velocity. Our implementation and evaluation on commodity devices demonstrate thatWSTrack+can realize simultaneous tracking and gait recognition, where the median tracking error is$0.34m$and the recognition accuracy is 88.6% for 12 users.

The evolution of Wi-Fi to next-generation 802.11bf demonstrates the potential of device-free Wi-Fi sensing applications, where we can remotely infer the behaviors of users without bringing into physical contact with them. Among these sensing applications, Wi-Fi tracking is critical to provide location based services. Recent Wi-Fi tracking systems can be cataloged into model-based and data-based approaches: (1) the model-based approach is to build the mathematical tracking model. However, this method is sensitive to environmental noise, and spends more execution time; (2) the data-based approach is to train a neural network. However, this method requires a lot of efforts to collect training dataset, and cannot handle all types of trajectories well. To resolve these issues, we propose theNNE-Tracking, a Neural Network Enhanced tracking framework. The core design principle ofNNE-Trackingis as follows: we improve the tracking accuracy based on the data-based approach, and utilize the model-based approach to supervise whether the neural network is already working well. Moreover, we also design a framework to estimate unknown parameters of the tracking model, so that the system can automatically generate the Wi-Fi map. We take the Wi-Fi passive tracking as a specific example to explain how to applyNNE-Trackingin practical applications. Experimental results demonstrate that our design can reduce 59.4% ∼ 85.3% tracking errors while significantly saving execution time. As for deployment costs, we can automatically infer the Wi-Fi map without manual calibration; As for stability, when we repeat the training process with different hidden layers and random seeds, the tracking standard deviation of these neural networks is only 1.4cm.

Device-free Wi-Fi tracking has become essential for ubiquitous wireless sensing. However, current device-free Wi-Fi tracking systems suffer from two limitations: First, abnormal signals interfere with tracking performance when the user walks across the direct link of the transceivers and second, the tracking error based on the velocity integral accumulates over time. This article proposes CrossTrack, the first device-free cross-link tracking system with commodity Wi-Fi. Our inspiration is to regard the cross-link behavior as an opportunity to correct the trajectory instead of disturbing noise like previous work. Our approach involves three main steps. First, we devise a metric that is capable of detecting cross-link behavior. Second, we propose a new theoretical model that identifies the cross-link position as a landmark. Third, we develop a path revision technique that utilizes this landmark to optimize the trajectory. The technique innovation of this article is to reveal the theoretical approach to transform cross-link interference into optimization in device-free tracking for the first time. We implement CrossTrack based on commercial Wi-Fi devices and conduct comprehensive experiments. Our results show that CrossTrack can reduce tracking errors by 48.75%, and the median tracking error is 0.41 m.

A positioning method based on magnetic gradient tensor was proposed for UXO detection on movable platforms. A multi-resolution linear regression algorithm was developed to select measurement points collected when the instrument was relatively stable. In this algorithm, Pearson Correlation Coefficient (PCC) was adopted as a quality factor for characterizing the stability of instrument. Then, selected measurement points were processed in a modified Euler method for UXO position inversion. The results from the modified Euler method were selected again based on normalized magnetic source strength (NSS) and clustered by the DBSCAN algorithm. Validity of the method was tested with both simulation data and field experiment data collected by a backpack gradiometer. The test result showed that all 4 targets were detected and positioning errors were all lower than 0.15m.

Opium is a narcotic obtained from opium poppy and is the raw material of heroin for the illegal drug trade. Monitoring the illegal concentrated cultivation of opium poppy in major regions is critical for the understanding by governments and international communities of the scale of illegal drug trade. This paper investigates whether opium poppy can be discriminated from its coexisting plants using analytical-spectral-device field spectrometer data in the visible to short-wave infrared spectral range. Canopy spectral measurements were conducted during three different growth periods of opium poppy. A synthetic method with three analysis levels was applied to discriminate opium poppy from other species and to select optimal bands for opium poppy discrimination. First, the Mann-WhitneyU-test method was used to test the spectral reflectance difference between opium poppy and coexisting crops at each wavelength. Then, the Jeffries-Matusita distance and band correlation analysis were conducted to select the optimal wavebands for discriminating opium poppy using the significant wavebands from the test results. Finally, classification and regression tree analysis was employed to validate the classification accuracy based on the selected optimal wavebands. The results indicated that the spectral reflectance of opium poppy was significantly different from that of coexisting crops in many surveyed wavebands, and opium poppy could be discriminated using a field survey spectrum at canopy level. The best time for discriminating opium poppy from coexisting crops was around flowering time. This paper provided the prerequisite for monitoring opium poppy using satellite remote sensing data in some regions of concern.

Crop growth monitoring is very important in agriculture resource management. Crop growth monitoring could provide crop state information for the decisive maker and reflect variety information of crop yield in time. The index of crop growth monitoring has closely relation with crop yield, which could as early as forecast large-scale food state that possibility missing or surplus. Therefore, there are important meanings to the macro control for food. Traditionally, the monitoring of crop growth and yield forecasts are made on the basis of samples by field visits or written inquiries. On national scale, the processing of these sample data is an expensive and time-consuming procedure. Recent developments in remote sensing technologies have created promising opportunities for monitoring agricultural crop growth. The moderate resolution imaging spectroradiometer (MODIS) is one detector board on Terra's (EOS-AMI), which was lunched on December 18, 1999 by NASA. It offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop growth monitoring. The paper studied the method of crop growth monitoring based on data of MODIS/TERRA vegetation indices. Results from the study not only monitor the crop growth in investigation area, but also illustrate the powerful potential to provide information about crop growth based MODIS VI data.

Crop acreage monitoring is basic information necessary for wise management of plant natural resources. Recent developments in remote sensing technologies have created promising opportunities for improving agricultural statistics systems. The Moderate Resolution Imaging Spectroradiometer (MODIS) is one detector board on Terra's (EOS-AM1), which was lunched on December 18, 1999 by NASA. It offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop acreage estimating. However, because of subpixel heterogeneity, the application of traditional hard classification approaches to MODIS data may result in significant errors in crop area estimation, especially in China. This paper developed and tested an unmixing approach with MODIS data that estimates subpixel fractions of crop area based on the temporal signature of reflectance throughout the growing season. A zone that can get LANDSAT/TM data was chosen to be train dataset in this method. The paper assumes that the crop area estimating from LANDSAT/TM data is correct; in the training zone the crop area based on MODIS data can get from the classification result of LANDSAT/TM data. Then we can extend the result to a large-scale; finally we compare the result to national statistic data. The results of this study demonstrate the importance of subpixel heterogeneity in cropland systems, and the potential of temporal unmixing to provide accurate and rapid assessments of crop distributions using MODIS data. I INTRODUCTION

Micro-watershed or sub watershed is the unit of soil and water conservation practices in China. Every year the Chinese government devotes fund and manpower in regional soil and water conservation practices. How to get the information about regional devotion objectively becomes a problem. The management organization needs a new tool to learn the spatial pattern of certain conservation technique and ecological change. High spatial resolution image data is very expensive in cost to monitor every Micro-watershed. So depending on the exiting Micro-watershed distribution map, the attribute information in tables about soil and water conservation practices, road information in GIS, Landsat TM data and GPS, a field survey and sampling framework strategy of soil and water conservation practices on regional scale was developed in this paper and applied to the upstream Guan Ting reservoir.

Miyun reservoir is an important water supplier of Beijing, China, therefore soil erosion of this area is very critical and must be paid sufficient attention. To monitor and manage soil erosion information in Miyun reservoir area, a monitoring information system that is dynamic, interactive and Internet-based was developed. The paper describes how the WebGIS application was developed, implemented and used. The overall monitoring information system is multi-scale, multi-source, flexible and geographically organized. It uses an Internet-based GIS ("WebGIS") technology, and has obtained information about soil erosion in Miyun reservoir area through four methods: RUSLE model, TM images visual interpretation, model based on vegetation cover and slope, and data fusion of three results. The decision-making officers can access and analyze these data more effectively and conveniently via the Internet.

Drought is one of the major environmental disasters in north China, and it is very important to detect and monitor drought periodically at large scale for decision making. The Normalized Difference Vegetation Index (NDVI) has been widely used to monitor moisture-related vegetation condition. To better understand the relationship between vegetation vigor and moisture availability, the Normalized Difference Water Index (NDWI) was calculated in addition to the NDVI. In this study, the analysis was conducted on time series compositing NDVI and NDWI of the period of ten days. With the support of land use map and soil humidity of crop for the growing season, we build the simple model of northern China for crop drought monitoring. The results of July 2002 show that the large scale temporal and spatial characteristics of drought in Northern China can be effectively detected by this way. Based on this method, we have developed a operational crop drought monitoring system for whole China land areas.

With the great agriculture population and limited cropland, it is very important to estimate the output of grain produce in China by remote sensing technology. However, the smallholders of cropland can plant what they like, thus it is difficult to monitor the crop planting proportion with only RS images, even with IKONOS/QUICKBIRD data. In GVG agro-status sampling system, the video camera connected with a notebook by a video capture card and GPS receive card are integrated into the GIS environment. GVG is fixed on a motor and restore the crop pictures and their GPS data when the car is moving along the country road derived from the linear sampling frame in a plantation division unit. A great many of crop pictures along the sample lines are obtained on the field in a limited time, and then all pictures with geographical data are interpreted to calculate the ratio of each type of crop plantation. The crop proportion of plantation division unit is estimated by all picture's plantation ratio of sample line due to this unit. This literature review has demonstrated the GVG systems hardware's constitute, working principle and the case studies. GVG agro-status sampling system not only can be acquire every crop's planting proportion of large areas in short time, but also can check up the results, which get from remote sensing crop classification.

Miyun Reservoir is located in the northeast of Beijing and it is the most important drinking water resource of the city. Soil and water loss in this area directly affects local eco-environment and people's life. The soil and water conservation project has been launched out to combat the degrading environment in the upper reach of Miyun Reservoir basin. A ruled-based approach based on the objective of catchments, which is the unit of most soil conservation project, was applied to evaluate soil loss. For each heterogeneous hilly valley in the study area, a set of knowledge-based rules was formulated with remotely sensed images, land use map, DEM and ground investigated data. The relevant parameters, such as slope, vegetation fraction, ravine density, and rainfall distribution, which are also the input parameters of the widely applied universal soil loss equation (USLE), were scaling to the object properties to count this ruled-based model. Finally, all catchments were grouped into four grades according to the soil loss intensity, namely very severe, severe, moderate and slight, the result of the study was practicable to support to make the soil conservation planning.

The China Brazil Earth Resources Satellite (CBERS)-2 CCD has 4 bands of 19.5m spatial resolutions in the visible/near-infrared wavelength regions and 1 pan band of 19.5m spatial resolutions. These bands (except pan) have the same spectral zones as Landsat7 ETM+ multispectral bands. We compared the performance of CCD image with ETM image from 4 key aspects in order to accelerate its application for agriculture monitoring. These 4 key aspects are geometric correction, typical surface features identification, land target area measurement, image classification and interpretation. The results show that CCD image can be geometrically corrected with high accuracy, is better than ETM+ image for typical surface features identification, records the small land targets in detail, can be more suit for recognition by eyes, can be used for measuring land targets area with high accuracy, has the same good performance as ETM image for image classification and interpretation. CBERS-2 CCD shows great potential for the applications of agricultural monitoring.

By interactive interpretation method with the assistance of GIS and RS, soil and water loss classification information in the upper basin of Miyun reservoir was derived from Landsat Enhanced Thematic Mapper (ETM) images and other relevant datum. The ecological and environmental background database was built in the region, which consists of the controlling factors, such as vegetation fraction, ravine density, isohyet map, to the intensity of soil and water loss. Although these factors, directly or indirectly influencing the process of soil erosion, are the key parameters in the widely applied revised universal soil loss equation (RUSLE), they may have the different contribution to the soil erosion at the diverse spatial scale. The correlation between these environmental factors and soil erosion intensity was researched at a regional scale in This work. The purpose of the paper is to bring the reference of the case study for the application of the revised universal soil loss equation in the same region, as well as to verify these factors data, and the veracity of the result of RUSLE application.

The ability to map and monitor the spatial extent of the built environment, and associated temporal changes, has important societal and economic meaning. In This work, the high spatial resolution of the image - Quick Bird was used to create a detailed land cover maps of Taigu region, Shanxi province, China. Adopting object-oriented image segmentation and classification which is based on fuzzy logic allows the integration of a broad spectrum of different object features, such as spectral values, shape and texture. In this study we use not only image object's attributes, but also the relationship between networked image objects, it can perform sophisticated classification and get satisfied classification result. The aim of this work was to develop an object-oriented segmentation and classification approach for operational land cover mapping.

In China, crop structure adjustment policy has brought great change of different breed's planting area in different years. Government managers of agricultural industry need timely crop structure information to monitor the performance of the crop structure adjustment policy. The objective of this research was to evaluate the synergistic effects of multitemporal RADAR SAT synthetic aperture radar (SAR) and Land sat ETM+ data for extracting agricultural crops structure using an object-oriented classification approach. This work instructs and analyses the crop structure near the Kaifeng city area in 2002. Four crop types were extracted: corn, soybean, cotton, and peanut. With the object-oriented classification approach, the overall accuracy of crop structure extracting from two-date F5 mode's SAR data (mid- to last-season) and two-date Land sat ETM+ is over 90%.

There is an urgent need to monitor and evaluate the forest ecosystem that supports richer assemblages of biological species in order to preserve the habitats and protect the greatest number of species. Remotely sensed data hold tremendous potential for mapping species habitats and indicators of biological diversity, such as species richness. The objective of this research was to develop the empirical relationships between Landsat-5 TM spectral response and broad-leaved forest inventory parameter of species in the Longmenhe preserve of China. The regression analysis had been used to link the field-measured species richness index and remote sensing data. The accuracy of the resulting mapping was assessed by the field inventory. The results demonstrated that plant diversity can be predicted from satellite remotely sensed data, and the species richness index was highly correlated to TM Tasseled Cap wetness