A Framework to Measure Reliance of Acoustic Latency on Smartphone Status

Audio latency, defined as the time duration when an audio signal travels from the microphone to an app or from an app to the speakers, significantly influences the performance of many mobile sensing applications including acoustic based localization and speech recognition. It is well known within the mobile app development community that audio latencies can be significant (up to hundreds of milliseconds) and vary from smartphone to smartphone and from time to time. Therefore, it is essential to study the causes and effects of the audio latency in smartphones. To the best of our knowledge, there exist mobile apps that can measure audio latency but not the corresponding status of smartphones such as available RAM, CPU loads, battery level, and number of files and folders. In this paper, we are the first to propose a framework that can simultaneously log both the audio latency and the status of smartphones. The proposed framework does not require time synchronization or firmware reprogramming and can run on a standalone device. Since the framework is designed to study the latency causality, the status of smartphones are deliberately and randomly varied as maximum as possible. To evaluate the framework, we present a case study with Android devices. We design and implement a latency app that simultaneously measures the latency and the status of smartphones. The preliminary results show that the latency values have large means (50 - 150 ms) and variances (4-40 ms). The effect of latency can be considerably reduced by just simply subtracting the offset. In order to achieve improved latency prediction that can cope with the variances an advanced regression model would be preferred

Ambient Sound-Based Collaborative Localization of Indeterministic Devices

Localization is essential in wireless sensor networks. To our knowledge, no prior work has utilized low-cost devices for collaborative localization based on only ambient sound, without the support of local infrastructure. The reason may be the fact that most low-cost devices are indeterministic and suffer from uncertain input latencies. This uncertainty makes accurate localization challenging. Therefore, we present a collaborative localization algorithm (Cooperative Localization on Android with ambient Sound Sources (CLASS)) that simultaneously localizes the position of indeterministic devices and ambient sound sources without local infrastructure. The CLASS algorithm deals with the uncertainty by splitting the devices into subsets so that outliers can be removed from the time difference of arrival values and localization results. Since Android is indeterministic, we select Android devices to evaluate our approach. The algorithm is evaluated with an outdoor experiment and achieves a mean Root Mean Square Error (RMSE) of 2.18 m with a standard deviation of 0.22 m. Estimated directions towards the sound sources have a mean RMSE of 17.5 ° and a standard deviation of 2.3 °. These results show that it is feasible to simultaneously achieve a relative positioning of both devices and sound sources with sufficient accuracy, even when using non-deterministic devices and platforms, such as Android

COOD:Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification

High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper, we focus on the species recognition task in images concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality. We propose a framework for combining individual OOD measures into one combined OOD (COOD) measure using a supervised model. The individual measures are several existing state-of-the-art measures and several novel OOD measures developed with novel class detection and hierarchical class structure in mind. COOD was extensively evaluated on three large-scale (500k+ images) biodiversity datasets in the context of anomaly and novel class detection. We show that COOD outperforms individual, including state-of-the-art, OOD measures by a large margin in terms of TPR@1% FPR in the majority of experiments, e.g., improving detecting ImageNet images (OOD) from 54.3% to 85.4% for the iNaturalist 2018 dataset. SHAP (feature contribution) analysis shows that different individual OOD measures are essential for various tasks, indicating that multiple OOD measures and combinations are needed to generalize. Additionally, we show that explicitly considering ID images that are incorrectly classified for the original (species) recognition task is important for constructing high-performing OOD detection methods and for practical applicability. The framework can easily be extended or adapted to other tasks and media modalities

執筆者紹介・専修大学法学会評議員・奥付

This file includes 932 DICOM files of a male and female Hemiscyllium trispeculare specimen housed in the spirit collection of Naturalis Biodiversity Center, Leiden, the Netherlands with registrationnumber RMNH.PISC.35295.a and RMNH.PISC.35295.b respectively. The specimens are scanned in a medical CT scanner (Toshiba Aquilion 64) at the Leiden University Medical Center, the Netherlands at 100 kV and 150 mAs with a slice thickness of 0.5 mm

Iowa Aviation System Plan 2010-2030: Individual Airport Report; Tipton Municipal - Mathews Memorial Airport, June 8, 2011

The Iowa Aviation System Plan Individual Airport Report provides an overview of the aviation system in Iowa, as well as specific information related to the local Airport. Iowa’s air transportation system plays a critical role in the economic development of the state and quality of life for Iowans. The Iowa Aviation System Plan evaluates existing conditions and makes recommendations for future development of the air transportation system to meet the needs of users over the next 20 years. Airport sponsors and airport management can use the Individual Airport Report to better understand the role their airport plays in the state and use it as a guide to improve facilities and services for their aviation users

Biology Seminar held at Des Moines, IA, January 10, 1950, Vol. 1, no.1

Seminar reporting on animal and bird populations in Iowa. Held in Des Moines, Iowa