Bashima Islam

SoundSifter: Mitigating Overhearing of Continuous Listening Devices

In this paper, we study the overhearing problem of continuous acoustic sensing devices such as Amazon Echo, Google Home, or such voice-enabled home hubs, and develop a system called SoundSifter that mitigates personal or contextual information leakage due to the presence of unwanted sound sources in the acoustic environment. Instead of proposing modifications to existing home hubs, we build an independent embedded system that connects to a home hub via its audio input. Considering the aesthetics of home hubs, we envision SoundSifter as a smart sleeve or a cover for these devices. SoundSifter has hardware and software to capture the audio, isolate signals from distinct sound sources, filter out signals that are from unwanted sources, and process the signals to enforce policies such as personalization before the signals enter into an untrusted system like Amazon Echo or Google Home. We conduct empirical and real-world experiments to demonstrate that SoundSifter runs in real-time, is noise resilient, and supports selective and personalized voice commands that commercial voice-enabled home hubs do not.

Demo Abstract: A Motion-Triggered Stereo Camera for 3D Experience Capture

This demo is an implementation of our motion-triggered camera system that captures, processes, stores, and transmits 3D visual information of a real-world environment using a low-cost camera-based sensor system that is constrained by its limited processing capability, storage, and battery life. This system can be used in applications such as capturing and sharing 3D content in the social media, training people in different professions, and post-facto analysis of an event. This system uses off-the-shelf hardware and standard computer vision algorithms. Its novelty lies in the ability to optimally control camera data acquisition and processing stages to guarantee the desired quality of captured information and battery life. The design of the controller is based on extensive measurements and modeling of the relationships between the linear and angular motion of a camera and the quality of generated 3D point clouds as well as the battery life of the system. To achieve this, we 1) devise a new metric to quantify the quality of generated 3D point clouds, 2) formulate an optimization problem to find an optimal trigger point for the camera system and prolongs its battery life while maximizing the quality of captured 3D environment, and 3) make the model adaptive so that the system evolves and its performance improves over time.

A motion-triggered stereo camera for 3D experience capture: demo abstract

This demo is an implementation of our motion-triggered camera system that captures, processes, stores, and transmits 3D visual information of a real-world environment using a low-cost camera-based sensor system that is constrained by its limited processing capability, storage, and battery life. This system can be used in applications such as capturing and sharing 3D content in the social media, training people in different professions, and post-facto analysis of an event. This system uses off-the-shelf hardware and standard computer vision algorithms. Its novelty lies in the ability to optimally control camera data acquisition and processing stages to guarantee the desired quality of captured information and battery life. The design of the controller is based on extensive measurements and modeling of the relationships between the linear and angular motion of a camera and the quality of generated 3D point clouds as well as the battery life of the system. To achieve this, we 1) devise a new metric to quantify the quality of generated 3D point clouds, 2) formulate an optimization problem to find an optimal trigger point for the camera system and prolongs its battery life while maximizing the quality of captured 3D environment, and 3) make the model adaptive so that the system evolves and its performance improves over time.

Glimpse.3D: a motion-triggered stereo body camera for 3D experience capture and preview

The Glimpse.3D is a body-worn camera that captures, processes, stores, and transmits 3D visual information of a real-world environment using a low-cost camera-based sensor system that is constrained by its limited processing capability, storage, and battery life. The 3D content is viewed on a mobile device such as a smartphone or a virtual reality headset. This system can be used in applications such as capturing and sharing 3D content in the social media, training people in different professions, and post-facto analysis of an event. Glimpse.3D uses off-the-shelf hardware and standard computer vision algorithms. Its novelty lies in the ability to optimally control camera data acquisition and processing stages to guarantee the desired quality of captured information and battery life. The design of the controller is based on extensive measurements and modeling of the relationships between the linear and angular motion of a body-worn camera and the quality of generated 3D point clouds as well as the battery life of the system. To achieve this, we 1) devise a new metric to quantify the quality of generated 3D point clouds, 2) formulate an optimization problem to find an optimal trigger point for the camera system that prolongs its battery life while maximizing the quality of captured 3D environment, and 3) make the model adaptive so that the system evolves and its performance improves over time.

Rethinking ranging of unmodified BLE peripherals in smart city infrastructure

Mobility tracking of IoT devices in smart city infrastructures such as smart buildings, hospitals, shopping centers, warehouses, smart streets, and outdoor spaces has many applications. Since Bluetooth Low Energy (BLE) is available in almost every IoT device in the market nowadays, a key to localizing and tracking IoT devices is to develop an accurate ranging technique for BLE-enabled IoT devices. This is, however, a challenging feat as billions of these devices are already in use, and for pragmatic reasons, we cannot propose to modify the IoT device (a BLE peripheral) itself. Furthermore, unlike WiFi ranging - where the channel state information (CSI) is readily available and the bandwidth can be increased by stitching 2.4GHz and 5GHz bands together to achieve a high-precision ranging, an unmodified BLE peripheral provides us with only the RSSI information over a very limited bandwidth. Accurately ranging a BLE device is therefore far more challenging than other wireless standards. In this paper, we exploit characteristics of BLE protocol (e.g. frequency hopping and empty control packet transmissions) and propose a technique to directly estimate the range of a BLE peripheral from a BLE access point by multipath profiling. We discuss the theoretical foundation and conduct experiments to show that the technique achieves a 2.44m absolute range estimation error on average.

Trusted Worrier: A low-cost and high-accuracy user authentication system for firearm exploiting dynamic hand pressure biometrics

Usage of firearm by only original users is one of the prime concerns of the research community considering limitless damage and even lethal consequences in case of having the usage in any other way. However, a low-cost, limited-resources, and high-accuracy solution for performing real-time user identification of firearm is yet to be proposed in the literature. As a remedy to this situation, in this paper, we propose a novel solution named Trusted Worrier that can identify users of a firearm in real time using only a small number of low-cost and low-power COTS pressure sensors. Here, we propose judicious positioning of the sensors such that the number of required sensors can retain a small value (five in our case). Besides, we develop a novel machine learning technique that exhibits high accuracy in user authentication demanding small amount of resource and execution time. We evaluate the approach using real data collected from twenty nine users. Our rigorous analysis over the data confirms effectiveness of Trusted Worrier in identifying users of a firearm.

SEUS: A Wearable Multi-Channel Acoustic Headset Platform to Improve Pedestrian Safety: Demo Abstract

With the prevalence of smartphones, pedestrians and joggers today often walk or run while listening to music. Since they are deprived of their auditory senses that would have provided important cues to dangers, they are at a much greater risk of being hit by cars or other vehicles. In this demonstration we present SEUS, a wearable system aimed at Sense Enhancement for Urban Safety. SEUS uses a three-stage architecture, consisting of headset mounted audio sensors, an embedded front-end for signal processing and feature extraction, and machine learning based classification on a smartphone, to provide early danger detection for pedestrians in real-time.

High Performance Approximate Computing by Adaptive Relaxed Synchronization

Approximate computing has the potential to provide approximate results with user defined error bound faster than conventional computing. Relaxed synchronization is one of the many ways to achieve approximate computation. Researchers in this area primarily focus on programming languages like C/C++, but languages like Java are still largely overlooked. In Java, generally full synchronization can be achieved by using synchronized keyword for method and block level or by using various locks of Java concurrency utilities framework. We provide a detailed performance evaluation of these different mechanisms to achieve full synchronization in Java. We introduce an adaptive locking mechanism using existing locks of Java concurrency utilities framework to provide relaxed synchronization for Java to be used for approximate computing. Our novel relaxed synchronization based framework achieved one of the important outcomes of approximate computing, better performance.