Daqing Zhang

Bio: Daqing Zhang is an academic researcher from Peking University. The author has contributed to research in topics: Context (language use) & Mobile computing. The author has an hindex of 67, co-authored 331 publications receiving 16675 citations. Previous affiliations of Daqing Zhang include Institut Mines-Télécom & Institute for Infocomm Research Singapore.

A CSI-ratio model based house-level respiration monitoring system using COTS wifi devices

Abstract: The past few years have witnessed the great potential of exploiting channel state information (CSI) retrieved from COTS WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. This sensing range constraint greatly limits the application of the proposed approaches in real life. Different from the existing approaches that apply the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range.1 In this demo, we will demonstrate FarSense - a CSI-ratio model based house-level real-time respiration monitoring system using COTS WiFi devices.

Cyber-Physical-Social-Mediated Communication

Abstract: We introduce the concept of cyber-physical-social-mediated communication (CPS-C) and analyze why CPS-C is better than pure cyber-mediated communication for two popular applications: mobile social networks and mobile crowd sensing. CPS-C utilizes its unique characteristics (i.e., cyber-physical synchronization, human intelligence, and physical displacement) to bring benefits, including natural boundary of information exposure, tangible interaction, targeting receivers on the fly, decentralization, and piggybacking. As a result, energy efficiency and user experience are improved. We highlight the existence of human-machine intelligence in the communication process, which has rarely been addressed.

Polymer/Silica Hybrid Waveguide Thermo-Optic VOA Covering O-Band

Abstract: In this paper, a polymer/silica hybrid waveguide thermo-optic variable optical attenuator (VOA), covering the O-band, is demonstrated. The switch is fabricated by simple and low-cost direct ultraviolet (UV) lithography. The multimode interferences (MMIs) used in the Mach–Zehnder interferometer (MZI)-VOA are well optimized to realize low loss and large bandwidth. The VOA shows an extinction ratio (ER) of 18.64 dB at 1310 nm, with a power consumption of 8.72 mW. The attenuation is larger than 6.99 dB over the O-band. The rise and fall time of the VOA are 184 μs and 180 μs, respectively.

A contactless gesture interaction system using LTE (4G) signals

Abstract: Nowadays, 4G devices are pervasive and most of the homes and offices in modern cities are covered by LTE signals. While it is very attractive to leverage ubiquitous LTE signals and use hand gestures to control the home appliances remotely, there is no work on such contactless gesture interaction systems reported yet. In this work, we present an LTE-based contactless gesture interaction system to recognize various hand gestures around a 4G terminal like mobile phone, which can be used to control the switch, channel and volume of a TV set remotely without holding any devices. The results show that the proposed system can recognize different hand gestures accurately leveraging LTE signals without training, and achieve remote TV control in real time in different settings.

Impromptu Service Discovery and Provision in Heterogeneous Assistive Environments

Abstract: Wireless hotspots are permeating the globe bringing interesting services and spontaneous connectivity to mobile users In order to enable the elderly and disabled to be fully integrated into the society, it's of paramount importance to build a pervasive assistive environment where assistive services can be automatically discovered and easily accessed with the device-to-hand across the physical spaces In this paper, we propose a framework that can support impromptu service discovery and context-aware service provision with mobile devices in heterogeneous smart assistive environments Different from the existing approaches, the framework requires no specialized hardware or software installation in mobile client devices, and it can automatically discover and select appropriate services based on the user profile and situation context, and generate personalized user interfaces according to user preference and device capability To demonstrate the effectiveness of the framework, we prototyped a set of assistive services in public spaces like shopping malls, leveraging the OSGi-based platform accessible from any WLAN enabled mobile devices

Context Aware Computing for The Internet of Things: A Survey

Abstract: As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

Membership Inference Attacks Against Machine Learning Models

Abstract: We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on. We empirically evaluate our inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies.