Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Statistics for Spatial Data.

Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

Survey of Wireless Indoor Positioning Techniques and Systems

Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then forms a map of the network. Various application requirements influence the design of sensor localization systems. In this article, the authors describe the measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. The article briefly surveys a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.

Locating the nodes: cooperative localization in wireless sensor networks

With digital equipment becoming increasingly networked, either on wired or wireless networks, for personal and professional use alike, distributed software systems have become a crucial element in information and communications technologies. The study of these systems forms the core of the ARLES' work, which is specifically concerned with defining new system software architectures, based on the use of emerging networking technologies. In this context, we concentrate on the study of distributed systems which bring to life the vision of ubiquitous computing systems, also known as ambient intelligence.

반도체 공정 overview

Localization of users is an important part of location aware systems and smart environments. It forms a major data source for superimposed intention recognition systems. In RF device-free localization (DFL), the person being tracked does not need to wear a RF transmitter or receiver in order to be located. Instead, they are tracked using the changes in signal strength measured on static links in a wireless network. This work presents a new algorithm for RF DFL using passive RFID networks. We formulate and show how a tomographic imaging algorithm provides both low computational complexity and highly accurate position estimates. Using measurements conducted in an indoor environment with various human positions, we find the algorithm can locate the human with as low as 30 cm mean location error.

Passive RFID tomographic imaging for device-free user localization

In the study of the multipoint-to-multipoint (M2M) radio channel, the physical backbone of wireless ad hoc networks, has direct application in the simulation and design of multi-hop routing protocols. The ad hoc network radio channel differs from the point-to-point or point-to-multipoint channels previously investigated, since each device may communicate with any other device. First, this paper presents a M2M measurement campaign conducted in an open-plan office area at 925 MHz. The measurements are analyzed to demonstrate spatial correlation between neighboring hops in the network. Then, the measurements are used to numerically characterize the effectiveness of a minimum-energy routing scheme. These measurements show that using existing models in the simulation of ad hoc networks can result in inaccurate results. Observations are made about the performance of a minimum-energy routing protocol in a real M2M radio channel. Finally, a channel model is suggested to more accurately represent the M2M radio channel.

/pdf/the-importance-of-the-multipoint-to-multipoint-indoor-radio-2mbsgit4ln.pdf

The importance of the multipoint-to-multipoint indoor radio channel in ad hoc networks

This paper addresses the performance of systems which use commercial wireless devices to make bistatic RF channel measurements for non-contact respiration sensing. Published research has typically presented results from short controlled experiments on one system. In this paper, we deploy an extensive real-world comparative human subject study. We observe twenty patients during their overnight sleep (a total of 160 hours), during which contact sensors record ground-truth breathing data, patient position is recorded, and four different RF breathing monitoring systems simultaneously record measurements. We evaluate published methods and algorithms. We find that WiFi channel state information measurements provide the most robust respiratory rate estimates of the four RF systems tested. However, all four RF systems have periods during which RF-based breathing estimates are not reliable.

https://dl.acm.org/doi/pdf/10.1145/3241539.3241560

Experience: Cross-Technology Radio Respiratory Monitoring Performance Study

New techniques in cross-layer wireless networks are building demand for ubiquitous channel sounding, that is, the capability to measure channel impulse response (CIR) with any standard wireless network and node. Towards that goal, we present a software-defined IEEE 802.11b receiver and CIR measurement system with little additional computational complexity compared to 802.11b reception alone. The system implementation, using the universal software radio peripheral (USRP) and GNU Radio, is described and compared to previous work. We validate the CIR measurement system and present the results of a measurement campaign which measures millions of CIRs between WiFi access points and a mobile receiver in urban and suburban areas.

/pdf/channel-sounding-for-the-masses-low-complexity-gnu-802-11b-3hp8fjkai2.pdf

Channel Sounding for the Masses: Low Complexity GNU 802.11b Channel Impulse Response Estimation

In energy-limited wireless sensor networks, detection using 'censoring sensors' reduces the probability that a sensor must transmit, thereby saving energy. We introduce a hierarchical distributed detection scheme designed specifically for multihop networks. If a sensor's local likelihood ratio (LLR) crosses a threshold, it is sent to the next higher level sensor. A simple feedback scheme is also considered. We study the performance of a Gaussian change-of-mean detection system using this hierarchical censoring scheme, with and without feedback. We show that good detection performance can be achieved while significantly reducing sensor transmissions compared to the optimal detection system.

/pdf/hierarchical-censoring-for-distributed-detection-in-wireless-3ppwuctfc7.pdf

Neal Patwari

Papers

Passive RFID tomographic imaging for device-free user localization

The importance of the multipoint-to-multipoint indoor radio channel in ad hoc networks

Experience: Cross-Technology Radio Respiratory Monitoring Performance Study

Channel Sounding for the Masses: Low Complexity GNU 802.11b Channel Impulse Response Estimation

Hierarchical censoring for distributed detection in wireless sensor networks