Towards Inferring Mechanical Lock Combinations using Wrist-Wearables as a Side-Channel

Wrist-wearables such as smartwatches and fitness bands are equipped with a variety of high-precision sensors that support novel contextual and activity-based applications. The presence of a diverse set of on-board sensors, however, also expose an additional attack surface which, if not adequately protected, could be potentially exploited to leak private user information. In this paper, we investigate the feasibility of a new attack that takes advantage of a wrist-wearable's motion sensors to infer input on mechanical devices typically used to secure physical access, for example, combination locks. We outline an inference framework that attempts to infer a lock's unlock combination from the wrist motion captured by a smartwatch's gyroscope sensor, and uses a probabilistic model to produce a ranked list of likely unlock combinations. We conduct a thorough empirical evaluation of the proposed framework by employing unlocking-related motion data collected from human subject participants in a variety of controlled and realistic settings. Evaluation results from these experiments demonstrate that motion data from wrist-wearables can be effectively employed as a side-channel to significantly reduce the unlock combination search-space of commonly found combination locks, thus compromising the physical security provided by these locks

Bathymetric Artifacts in Sea Beam Data: How to Recognize Them and What Causes Them

Sea Beam multibeam bathymetric data have greatly advanced understanding of the deep seafloor. However, several types of bathymetric artifacts have been identified in Sea Beam\u27s contoured output. Surveys with many overlapping swaths and digital recording on magnetic tape of Sea Beam\u27s 16 acoustic returns made it possible to evaluate actual system performance. The artifacts are not due to the contouring algorithm used. Rather, they result from errors in echo detection and processing. These errors are due to internal factors such as side lobe interference, bottom-tracking gate malfunctions, or external interference from other sound sources (e.g., 3.5 kHz echo sounders or seismic sound sources). Although many artifacts are obviously spurious and would be disregarded, some (particularly the omega effects described in this paper) are more subtle and could mislead the unwary observer. Artifacts observed could be mistaken for volcanic constructs, abyssal hill trends, hydrothermal mounds, slump blocks, or channels and could seriously affect volcanic, tectonic, or sedimentological interpretations. Misinterpretation of these artifacts may result in positioning errors when seafloor bathymetry is used to navigate the ship. Considering these possible geological misinterpretations, a clear understanding of the Sea Beam system\u27s capabilities and limitations is deemed essential

Current methods for characterising mixing and flow in microchannels

This article reviews existing methods for the characterisation of mixing and flow in microchannels, micromixers and microreactors. In particular, it analyses the current experimental techniques and methods available for characterising mixing and the associated phenomena in single and multiphase flow. The review shows that the majority of the experimental techniques used for characterising mixing and two-phase flow in microchannels employ optical methods, which require optical access to the flow, or off-line measurements. Indeed visual measurements are very important for the fundamental understanding of the physics of these flows and the rapid advances in optical measurement techniques, like confocal scanning laser microscopy and high resolution stereo micro particle image velocimetry, are now making full field data retrieval possible. However, integration of microchannel devices in industrial processes will require on-line measurements for process control that do not necessarily rely on optical techniques. Developments are being made in the areas of non-intrusive sensors, magnetic resonance techniques, ultrasonic spectroscopy and on-line flow through measurement cells. The advances made in these areas will certainly be of increasing interest in the future as microchannels are more frequently employed in continuous flow equipment for industrial applications

Fingerprinting Smart Devices Through Embedded Acoustic Components

The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart devices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote identification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an individual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic features and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even possible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high success rate and examines the effect of background noise and other variables on fingerprinting accuracy

Acoustical Ranging Techniques in Embedded Wireless Sensor Networked Devices

Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments; where, typically, there is a need for higher degree of accuracy. In this article, we focus on robust range estimation, an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in delivering high ranging accuracy, we present the design, implementation and evaluation of acoustic (both ultrasound and audible) ranging systems.We distill the limitations of acoustic ranging; and present efficient signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution, tolerance to Doppler’s effect, and audible intensity. We evaluate our proposed techniques experimentally on TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate an operational range of 20 m (outdoor) and an average accuracy 2 cm in the ultrasound domain. Finally, we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance

Development of a continuous flow ultrasonic harvesting system for microalgae

2014 Fall.Microalgae have vast potential as a sustainable source of biofuel. However, numerous technoeconomic analyses have indicated that microalgae harvesting represents a critical bottleneck in the microalgae value chain in terms of energy requirements, capital cost and operating cost. This dissertation presents an approach that uses a combination of acoustophoretic, fluid mechanical, and gravitational forces toward the development of a continuous flow microalgae harvesting system. Ultrasonic Standing Waves have been widely reported in the literature as an approach to manipulate particles in a fluid, a phenomena known as acoustophoresis. These waves exert an acoustic force that agglomerate the cells in the wave nodes or antinodes and the force is directly proportional to the cell acoustic contrast factor. Ultrasonic microalgae harvesting is a promising low cost and low energy approach. However, a better understanding of the acoustic properties of microalgae is essential for the development of this technology. Accordingly, a major component of this work focused on accurately quantifying the acoustic contrast factor of microalgae cells of Nannochloropsis oculata, Nannochloropsis gaditana, Phaeodactylum tricornutum and Chlamydomonas reinhardtii by measuring the average cell density and speed of sound using a vibrating tube densitometer. The results indicate a linear correlation of density and speed of sound as a function of cell concentration. Using this correlation, non-scattering volume average relationships were used to compute density and speed of sound for the average algal cell. The acoustic contrast factor was estimated to be between 0.04 - 0.06 for microalgae cells in their corresponding growth media. Second, particle tracking velocimetry was used to determine the magnitude of the acoustophoretic force. In these studies, in addition to microalgae cells, polyamide seeding particles were used as a surrogate. The results obtained conclude that the maximum acoustophoretic forces are approximately 5 pN for Chlamydomonas reinhardtii cells and the results also show that there is change in the acoustic contrast factor from positive to negative with lipid accumulation. This dissertation also presents a novel device for the acoustic harvesting of microalgae. The design is based on using the acoustophoretic force, acoustic transparent materials and inclined settling (Boycott effect). A filtration efficiency of 70% ± 5% and a concentration factor of 11.6 ± 2.2 were achieved at a flow rate of 25 mL • min-1 and an energy consumption of 3.6 ± 0.9 kWh • m-3. The effects of the applied power, flow rate, inlet cell concentration and inclination were explored. It was found that the filtration efficiency of the device is proportional to the power applied. However, the filtration efficiency experienced a plateau at a 100 W • L-1 of power density applied. The filtration efficiency also increased with increasing inlet cell concentration and was inversely proportional to the throughput of the device as measured flow rate. It was also found that the optimum settling angle for maximum concentration factor occurred at an angle of 50° ± 5°. At these optimum conditions, the device had higher filtration efficiency in comparison to other similar devices reported in the previous literature

Predicting Audio Advertisement Quality

Online audio advertising is a particular form of advertising used abundantly in online music streaming services. In these platforms, which tend to host tens of thousands of unique audio advertisements (ads), providing high quality ads ensures a better user experience and results in longer user engagement. Therefore, the automatic assessment of these ads is an important step toward audio ads ranking and better audio ads creation. In this paper we propose one way to measure the quality of the audio ads using a proxy metric called Long Click Rate (LCR), which is defined by the amount of time a user engages with the follow-up display ad (that is shown while the audio ad is playing) divided by the impressions. We later focus on predicting the audio ad quality using only acoustic features such as harmony, rhythm, and timbre of the audio, extracted from the raw waveform. We discuss how the characteristics of the sound can be connected to concepts such as the clarity of the audio ad message, its trustworthiness, etc. Finally, we propose a new deep learning model for audio ad quality prediction, which outperforms the other discussed models trained on hand-crafted features. To the best of our knowledge, this is the first large-scale audio ad quality prediction study.Comment: WSDM '18 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 9 page

Deep Room Recognition Using Inaudible Echos

Recent years have seen the increasing need of location awareness by mobile applications. This paper presents a room-level indoor localization approach based on the measured room's echos in response to a two-millisecond single-tone inaudible chirp emitted by a smartphone's loudspeaker. Different from other acoustics-based room recognition systems that record full-spectrum audio for up to ten seconds, our approach records audio in a narrow inaudible band for 0.1 seconds only to preserve the user's privacy. However, the short-time and narrowband audio signal carries limited information about the room's characteristics, presenting challenges to accurate room recognition. This paper applies deep learning to effectively capture the subtle fingerprints in the rooms' acoustic responses. Our extensive experiments show that a two-layer convolutional neural network fed with the spectrogram of the inaudible echos achieve the best performance, compared with alternative designs using other raw data formats and deep models. Based on this result, we design a RoomRecognize cloud service and its mobile client library that enable the mobile application developers to readily implement the room recognition functionality without resorting to any existing infrastructures and add-on hardware. Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and 89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in a quiet museum, and 15 spots in a crowded museum, respectively. Compared with the state-of-the-art approaches based on support vector machine, RoomRecognize significantly improves the Pareto frontier of recognition accuracy versus robustness against interfering sounds (e.g., ambient music).Comment: 29 page