Collaborating Authors

CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning Artificial Intelligence

GPS technology has revolutionized the way we localize and navigate outdoors. However, the poor reception of GPS signals in buildings makes it unsuitable for indoor localization. WiFi fingerprinting-based indoor localization is one of the most promising ways to meet this demand. Unfortunately, most work in the domain fails to resolve challenges associated with deployability on resource-limited embedded devices. In this work, we propose a compression-aware and high-accuracy deep learning framework called CHISEL that outperforms the best-known works in the area while maintaining localization robustness on embedded devices.

A CNN-LSTM Quantifier for Single Access Point CSI Indoor Localization Machine Learning

This paper proposes a combined network structure between convolutional neural network (CNN) and long-short term memory (LSTM) quantifier for WiFi fingerprinting indoor localization. In contrast to conventional methods that utilize only spatial data with classification models, our CNN-LSTM network extracts both space and time features of the received channel state information (CSI) from a single router. Furthermore, the proposed network builds a quantification model rather than a limited classification model as in most of the literature work, which enables the estimation of testing points that are not identical to the reference points. We analyze the instability of CSI and demonstrate a mitigation solution using a comprehensive filter and normalization scheme. The localization accuracy is investigated through extensive on-site experiments with several mobile devices including mobile phone (Nexus 5) and laptop (Intel 5300 NIC) on hundreds of testing locations. Using only a single WiFi router, our structure achieves an average localization error of 2.5~m with $\mathrm{80\%}$ of the errors under 4~m, which outperforms the other reported algorithms by approximately $\mathrm{50\%}$ under the same test environment.

Automated analysis of High‐content Microscopy data with Deep Learning


Advances in automated image acquisition and analysis, coupled with the availability of reagents for genome‐scale perturbation, have enabled systematic analyses of cellular and subcellular phenotypes (Mattiazzi Usaj et al, 2016). One powerful application of microscopy‐based assays involves assessment of changes in the subcellular localization or abundance of fluorescently labeled proteins in response to various genetic lesions or environmental insults (Laufer et al, 2013; Ljosa et al, 2013; Chong et al, 2015). Proteins localize to regions of the cell where they are required to carry out specific functions, and a change in protein localization following a genetic or environmental perturbation often reflects a critical role of the protein in a biological response of interest. High‐throughput (HTP) microscopy enables analysis of proteome‐wide changes in protein localization in different conditions, providing data with the spatiotemporal resolution that is needed to understand the dynamics of biological systems. The budding yeast, Saccharomyces cerevisiae, remains a premiere model system for the development of experimental and computational pipelines for HTP phenotypic analysis.

Observation of Anderson localization in disordered nanophotonic structures


Anderson localization is an interference effect crucial to the understanding of waves in disordered media. However, localization is expected to become negligible when the features of the disordered structure are much smaller than the wavelength. Here we experimentally demonstrate the localization of light in a disordered dielectric multilayer with an average layer thickness of 15 nanometers, deep into the subwavelength regime. We observe strong disorder-induced reflections that show that the interplay of localization and evanescence can lead to a substantial decrease in transmission, or the opposite feature of enhanced transmission. This deep-subwavelength Anderson localization exhibits extreme sensitivity: Varying the thickness of a single layer by 2 nanometers changes the reflection appreciably.

'Zelda: Breath Of The Wild' Is Increasingly Looking Like It Will Be Out This Spring

Forbes - Tech

After all the rumors, it seems that according to a recent issue of CoroCoro Comic, it looks like Zelda: Breath of the Wild will be coming out for the Wii U and Nintendo Switch this Spring. Coupled with reports from elsewhere, as covered by Paul Tassi and myself, it is clear that this March release date seems far more credible and lends credence to the launch window of the Nintendo Switch itself. To be honest, without Breath of the Wild it is hard to think of a game that could initially push the Nintendo Switch in terms of its launch. Maybe the rumored update to Pokémon Sun and Moon could do it, but a new mainline Zelda game is definitely a much stronger contender. After all, the launch of the Wii had Twilight Princess and that clearly played a key role in helping the console find its feet.