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

As demand for radio spectrum increases with the widespread use of wireless devices, effective spectrum allocation requires more flexibility in terms of time, space, and frequency. In order to protect users in next-generation wireless networks from interference, spectrum managers must have the ability to efficiently and accurately locate transmitters. We present TL;DL, a practical deep-learning based technique for multiple transmitter localization on crowdsourced data where all sensors and transmitters may be mobile and transmit with unknown power. We map sensor readings to an image representing the sensor location, then use a convolutional neural network to learn to generate a target image of transmitter locations. We also introduce a novel data-augmentation technique to drastically improve generalization and enable accurate localization on limited data. In our evaluation, TL;DL outperforms previous approaches on small real-world datasets with low sensor density, in terms of both accuracy and detection.

/pdf/deep-learning-based-localization-in-limited-data-regimes-2ybsrhle.pdf

Deep Learning-based Localization in Limited Data Regimes

We introduce an platform architecture and algorithm to frequency synchronize multiple devices. The platform allows clock unification among oscillator, microcontroller, and radio. The platform accesses complex baseband samples from the radio, estimates the carrier frequency offset, and iteratively drives the main local oscillator (LO) frequency difference between two devices to zero.

https://dl.acm.org/doi/pdf/10.1145/2994551.2996532

A Platform Enabling Local Oscillator Frequency Synchronization: Demo Abstract

Variance-based Radio Tomographic Imaging (VRTI) is an emerging technology that locates moving objects in areas surrounded by simple and inexpensive wireless sensor nodes. VRTI uses human motion induced variation in RSS and spatial correlation between link variations to locate and track people. An artificially induced power variations in the deployed network by an adversary can introduce unprecedented errors in localization process of VRTI and, given the critical applications of VRTI, can potentially lead to serious consequences including loss of human lives. In this paper, we tackle the problem of detecting malicious receivers that report false RSS values to induce artificial power variations in a VRTI system. We use the term “Receiver Attack” to refer to such malicious power changes. We use a combination of statistical hypothesis testing and heuristics to develop real-time methods to detect receiver attack in a VRTI system. Our results show that we can detect receiver attacks of reasonable intensity and identify the source(s) of malicious activity with very high accuracy.

Detecting receiver attacks in VRTI-based device free localization

A DS-CDMA receiver requires accurate timing information to synchronize the receiver PN code correlator with the received signal. When utilized for position location applications, the DS-CDMA receiver must further provide accurate timing information to the location algorithm. Typically, DS-CDMA timing estimation algorithms require the transmission of a training sequence to establish timing lock. In addition, the carrier frequency drift encountered in many wireless communication applications adversely effects the performance of these algorithms. This paper introduces a new DS-CDMA code-timing estimation algorithm that operates without a training sequence and provides robustness to carrier frequency drift.

Neal Patwari

Papers

Deep Learning-based Localization in Limited Data Regimes

A Platform Enabling Local Oscillator Frequency Synchronization: Demo Abstract

Detecting receiver attacks in VRTI-based device free localization

A new code-timing estimation algorithm for DS-CDMA

Evaluating AAL Systems Through Competitive Benchmarking - Communications in Computer and Information Science Volume 362