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

Pulse oximeters play a critical role in health monitoring. Pulse ox measurements have statistical bias that is a function of race, which results in higher rates of occult hypoxemia, i.e., missed detection of dangerously low oxygenation, in patients of color. This paper further characterizes the statistical distribution of pulse ox measurements, showing they also have a higher variance for patients racialized as Black, compared to those racialized as white. By analyzing the performance of hypoxemia detection as a detector, we show that no single race-based correction factor will provide equal performance by race. As a result, for racially equitable pulse oximetry, the pulse oximeter itself must be fixed, not just the hypoxemia thresholds.

Short: Racial Disparities in Pulse Oximetry Cannot Be Fixed With Race-Based Correction

Future virtualized radio access network (vRAN) infrastructure providers (and today’s experimental wireless testbed providers) may be simultaneously uncertain what signals are being transmitted by their base stations and legally responsible for their violations. These providers must monitor the spectrum of transmissions and external signals without access to the radio itself. In this paper, we propose FDMonitor, a full-duplex monitoring system attached between a transmitter and its antenna to achieve this goal. Measuring the signal at this point on the RF path is necessary but insufficient since the antenna is a bidirectional device. FDMonitor thus uses a bidirectional coupler, a two-channel receiver, and a new source separation algorithm to simultaneously estimate the transmitted signal and the signal incident on the antenna. Rather than requiring an offline calibration, we also adaptively estimate the linear model for the system on the fly. FDMonitor has been running on a real-world open wireless testbed, monitoring 19 SDR platforms controlled (with bare metal access) by outside experimenters over a seven month period, sending alerts whenever a violation is observed. Our experimental results show that FDMonitor accurately separates signals across a range of signal param-eters. Over more than 7 months of observation, it achieves a positive predictive value of 97%, with a total of 20 false alerts

Two Measure is Two Know: Calibration-free Full Duplex Monitoring for Software Radio Platforms

Past research has identified discriminatory automatic speech recognition (ASR) performance as a function of the racial group and nationality of the speaker. In this paper, we expand the discussion beyond bias as a function of the individual national origin of the speaker to look for bias as a function of the geopolitical orientation of their nation of origin. We audit some of the most popular English language ASR services using a large and global data set of speech from The Speech Accent Archive, which includes over 2,700 speakers of English born in 171 different countries. We show that, even when controlling for multiple linguistic covariates, ASR service performance has a statistically significant relationship to the political alignment of the speaker's birth country with respect to the United States' geopolitical power. This holds for all ASR services tested. We discuss this bias in the context of the historical use of language to maintain global and political power.

Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition

Orthogonal frequency-division multiplexing (OFDM), widely recommended for sharing the spectrum among different nodes in a dynamic spectrum access network, imposes tight timing and frequency synchronization requirements. We examine the use of filterbank multicarrier (FBMC), a some-what lesser known and understood alternative, for dynamic spectrum access in vehicular networks. FBMC promises very low out-of-band energy of each subcarrier signal when compared to OFDM. In order to fully understand and evaluate the promise of FBMC in mobile, outdoor settings, we first examine the use of special pulse shaping filters of the FBMC PHY layer in reliably transmitting data packets at a very high rate. Next, to gain an understanding of the cross-layer performance of FBMC, as well as to understand its impact beyond the PHY layer, we build a discrete event simulator using realistic models. Using extensive simulations, we show that FBMC consistently achieves an order of magnitude performance improvement over OFDM in terms of packet transmission delays and effective data transmission rate available to each node, over large distances in comparison to OFDM. Finally, our analysis in the case of multi-hop networks shows that FBMC can achieve about 20x smaller end-to-end data packet delivery delays, and relatively low packet drop probabilities in comparison to OFDM. In summary, our results can serve as guidelines for designing ad hoc, dynamic spectrum access communication standards for future vehicular networks.

Neal Patwari

Papers

Short: Racial Disparities in Pulse Oximetry Cannot Be Fixed With Race-Based Correction

Two Measure is Two Know: Calibration-free Full Duplex Monitoring for Software Radio Platforms

Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition

Efficient dynamic spectrum access in vehicular networks using filterbank multicarrier