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.

Design of an on-chip ultra-small thermo-optic tunable SOI waveguide microdisk resonator for optical information storage

Abstract: With the development of integrated photonic circuits, optical waveguide microdisk resonators (MRR) devices which can be easily integrated with photonic chips are becoming more and more important in optical communication systems. As the core execution unit to improve response sensitivity, field-programmable gate array, optical waveguide MRR has high applicability in esonators due to its smaller mode volume, and larger free spectral range (FSR). Specially, SOI waveguide fabrication technology is easy to be compatible with CMOS foundry processing and integrated circuit technology with smaller size and lower cost, so it can overcome the shortcomings of micro resonators fabricated by other materials. And SOI waveguide MRR has important research significance and distinguished application prospects, which has considered to be the future large-scale integrated photonic circuit basic devices. Because of its powerful optical signal processing ability, MRR has been widely used in various optical systems. With the advantages of simple manufacturing process, ease integration, and multitudinous functions, the waveguide MRR has become the basic structural unit of integrated photonic system and considered as the basic device of large-scale integrated optical path. Based on the high thermo-optic (TO) coefficient of Si material, the tunable function of the SOI MRR can be realized by TO modulation. A waveguide MRR with large tuning range, low optical transmission loss, simple electrode fabrication method, and high TO efficiency is proposed. In order to enhance the applicability of the designed SOI waveguide MRR at different wavelength bands, a tunable SOI MRR was designed.

GreenPlanner: Planning Fuel-Efficient Driving Routes

Abstract: To save more for taxi drivers, in this chapter, we present a two-phase fuel-efficient path-planning framework called GreenPlanner. In the first phase, we build a personalized fuel consumption model (PFCM) for each driver, based on the individual driving behaviors embedded in the GPS trajectory data and the physical features (e.g., traffic lights, stop signs, road network topology) along the routes provided by road network data. Furthermore, we build a general PFCM which only needs some basic information about the driver, including the category of overall fuel consumption performance in history and the car mode. In the second phase, with the real-time traffic information collected via the mobile crowdsensing manner, we are able to estimate and compare the fuel cost among different routes for a given driver, and recommend him/her with the most fuel-efficient one. We evaluate the two-phase framework using the real-world datasets, and results demonstrate that, compared to the baseline models, the proposed model achieves the best accuracy. Moreover, users could save about 20% fuel consumption on average if driving along the suggested routes in our case studies.

CDTOM: A Context-driven Task-oriented Middleware for Pervasive Homecare Environment

Abstract: With the growing number of the elderly, we see a greater demand for home care, and the vision of pervasive computing is also floating into the domain of the household that aims to build a smart home which can assist inhabitants (users) to live more conveniently and harmoniously. Such health-care pervasive applications in smart home should focus on the inhabitant's goal or task in diverse situations, rather than the various complex devices and services. The core challenge for homecare design is to perceive the environment and assess occurring situations, thus allowing systems to behave intelligently according to the user's intent. Due to the dynamic and heterogeneous nature of pervasive computing environment, it is difficult for an average user to obtain right information and service and in right place at right time. This paper proposes a context-driven task-oriented middleware (CDTOM) to meet the challenge. The most important component is its task model that provides an adequate high-level description of user-oriented tasks and their related contexts. Leveraging the model multiple entities can easily exchange, share and reuse their knowledge. Based on the hierarchy of task ontology, a novel task recognition approach using CBR (case-based reasoning) is presented and the performance of task recognition is evaluated by task number, context size and time costing. Moreover, a dynamic mechanism for mapping the recognized task and services is also discussed. Finally, we present the design and implementation of our task supporting system (TSS) to aid an inhabitant's tasks in light of his lifestyle and environment conditions in pervasive homecare environment, and the results of the prototype system show that our middleware approach achieves good efficiency of context management and good accuracy of user's activity inference, and can improve efficiently quality of user's life.

Trajectory Data Map-matching

Abstract: The task of trajectory data map-matching is important and often a prerequisite step towards the provision of smart urban services. Such task is challenging mainly because of the positioning error caused by GPS devices and the uncertainty between two GPS point samples. Compared to offline methods, online map-matching is more desirable in real cases though it is more challenging due to: (1) only the incomplete trajectory information can be used and (2) the fast response requirement. In this chapter, with the objective of achieving high matching accuracy and efficiency simultaneously, we propose a three-stage online map-matching algorithm, named as SD-Matching algorithm, to fully exploit a new dimension of collected GPS trajectory data (i.e., the vehicle heading direction) in a smart way. The heading direction is commonly collected with the latitude and longitude locations by GPS devices. More specifically, at the first stage, the heading direction is used to facilitate the probability computation when finding true positions for GPS points. At the second stage, it is used to accelerate the path-finding when filling the distance gap between two consecutive GPS points. At the final stage, it is used to refine and select the best path that connects a sequence of GPS points (i.e., trajectory segment). Finally, based on the taxi trajectory and road network datasets collected from the real-world, we conduct a set of experiments to verify the performance in terms of running time and matching accuracy of the proposed SD-Matching algorithm.

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.