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.

Optical Switch Based on Ge2Sb2Se4Te1-Assisted Racetrack Microring

Abstract: In this work, we have proposed and designed a 1 × 1 optical switch based on the optical phase-change material, Ge2Sb2Se4Te1 (GSST), for GSST-assisted silicon racetrack microring. Its optical power can periodically be exchanged between the straight silicon waveguide and the GSST/Si hybrid racetrack waveguide due to the formed directional coupling structure. By changing GSST from the crystalline state to the amorphous state, the switch shifts from the ON state to the OFF state, and vice versa. With finite-difference time-domain method optimization, the proposed switch shows an extinction ratio of 18 dB at 1547.4 nm. The insert losses at the ON and OFF states are both less than 1 dB. The proposed switch unit has the potential to build an N × N switch matrix.

Polarization-Insensitive Graphene Modulator Based on Hybrid Plasmonic Waveguide

Abstract: A polarization-insensitive graphene-assisted electro-optic modulator is proposed. The orthogonal T-shaped metal slot hybrid plasmonic waveguide allows the polarization-independent propagation of transverse electric field mode and complex mode. By the introduction of dual-layer graphene on the ridge waveguide, the polarization-insensitive modulation depths of the TE mode and complex mode are 0.511 dB/µm and 0.502 dB/µm, respectively. The 3 dB bandwidth of the modulator we have proposed is about 127 GHz at the waveguide length of 20 μm. The power consumption of 72 fJ/bit promised potential graphene electro-optic modulator applications for on-chip interconnected information transfer and processing.

TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Trajectory Data

Abstract: To offer personalized, interactive, and traffic-aware trip planning to travellers, in this chapter, we propose a novel framework called TripPlanner leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints. First, based on the information in crowdsourced LBSN and taxi GPS traces, we construct a dynamic point-of-interest network model. Then, a two-phase approach is proposed for personalized trip planning. In the route search phase, candidate routes are generated by interactively working with users. In the route augmentation phase, heuristic algorithms are applied to add user’s preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. We validate the efficiency and effectiveness of TripPlanner by extensive evaluations using large-scale real-world data sets.

Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment

Abstract: The quality of sleep may be a reflection of an elderly individual's health state, and sleep pattern is an important measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novelmulti-parametric sensing system called sleep pattern recognition system (SPRS). This system, equipped with a combination of various non-invasive sensors, can monitor an elderly user's sleep behavior. It accumulates the detecting data from a pressure sensor matrix and ultra wide band (UWB) tags. Based on these two types of complementary sensing data, SPRS can assess the user's sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operateswithout disrupting the users' sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.

AQUEDUC: Improving Quality and Efficiency of Care for Elders in Real Homes

Abstract: Elderly care is facing the challenge of the disequilibrium between the increased number of old people and the low number of personnel in the elderly care. The emerging pervasive technology has revolutionised the way of assistance in elderly care. Current solutions usually focus too much on technology, and fail to address the usability issues. In this paper, we offer a comprehensive system for both elderly care providers and elders. The system enables coordinators to manage care-givers and elders in an efficient way to improve service quality. For instance, care-givers can be scheduled in a real-time manner with mobile phones. We also deploy several sensors in their homes to monitor daily routines to ensure their safety. Alerts will be sent and accessible by coordinators immediately once detected, then elderly care services can be provided accordingly. We test our system in a real home for over 2 months. Finally, we offer a statistical study about the collected data and the reported alerts.

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.