This paper investigates a novel set-membership global estimation method for a class of discrete time-varying systems with unknown-but-bounded noises. Firstly, in order to improve the accuracy of the state estimation, a multi-sensor network structure is deployed for the considered system, in which the adjacent sensors can communicate with each other. Considering the different sampling rates of multiple sensors, a resampling strategy is proposed to transform the asynchronous sampling system into a synchronous one. Additionally, the unknown-but-bounded noises and system parameter variations are considered. A novel distributed set-membership filter with parameter variation is designed, and the optimal local state estimation ellipsoid is obtained by developing a convex optimization method. Subsequently, a soft actor-critic algorithm based on reinforcement learning is proposed, which fuses all local estimation ellipsoids from each sensor to obtain global estimation results, providing a solution for the considered system. Finally, a port crane system is employed for performance analysis to verify the feasibility and effectiveness of the proposed method.

Acoustic motion tracking has been viewed as a promising user interaction technique in many scenarios such as Virtual Reality (VR), Smart Appliance, video gaming, etc. Existing acoustic motion tracking approaches, however, suffer from long window of accumulated signal and time-consuming signal processing. They are inherently difficult to achieve both high accuracy and low delay. In this paper, we present Vernier, an efficient and accurate acoustic tracking method based on commodity mobile devices. We design a new approach to efficiently and accurately derive phase change and thus moving distance. Vernier significantly reduces the tracking delay/overhead by removing the complicated frequency analysis and long window of signal accumulation, while keeping a high tracking accuracy. We implement Vernier on Android, and evaluate its performance with COTS mobile devices including Samsung Galaxy S7 and Sony L50t. Experimental results show that Vernier outperforms previous approaches with a tracking error less than 4 mm. The tracking speed achieves 3× improvement to the previous phase based approaches and 10× to Doppler Effect based approaches. Vernier is also validated in applications like controlling and drawing, and we believe it is generally applicable in many real applications.

Acoustic based tracking has been shown promising in many applications like Virtual Reality, smart home, video gaming, etc. Its real life deployments, however, face fundamental limitations. Existing approaches generally need three sound sources, while most COTS devices (e.g., TVs) and speakers have only two sound sources. We present AcouRadar, an acoustic-based localization system with single sound source. In the heart of AcouRadar we adopt a general new model which quantifies signal properties of different frequencies, distances and angles to the source. We verify the model and show that signal from a single source can provide features for localization. To address practical challenges, (1) we design an online model adaption method to address model deviation from real signal, (2) we design pulse modulated signals to alleviate the impact of environment such as multipath effect, and (3) to address signal dynamics over time, we derive relatively stable amplitude ratio between different frequencies, and thus provide a spectrum based localization method. We implement AcouRadar on Android and evaluate its performance for different COTS speakers in different environments. The results show that the model for localization can be generalized to different speakers. AcouRadar can achieve single source localization with average error less than 5 cm and average angle error of 1.76°.

LoRa has been shown as a promising technology for connecting millions for devices in the era of the Internet of Things. LoRa can provide long-distance (up to several kilometers) and low power communication with a modest data rate. However, developing LoRa protocols and testing LoRa systems face practical challenges compared with traditional networks such as Wi-Fi. For example, it requires network deployment in a wide area and even frequently updating the node program in the network for testing. To address those challenges, this paper presents Dandelion, an online testbed for LoRa development. Dandelion provides an easy-to-use interface for developing and testing LoRa applications. Dandelion consists of multiple LoRa nodes, gateways, central controller and web user interfaces. In Dandelion, LoRa nodes are deployed in different environments, each attached to a Raspberry Pi based edge node with out-of-band communication capability to collect system information. Dandelion also provides web based user interface to conveniently program each node, view the status of each node, update the program, etc. This significantly reduces the overhead for LoRa network development. We believe Dandelion can significantly reduce the costs of deployment, maintenance and evaluation of LoRa network. We show two examples of using Dandelion for LoRa network deployment. First, we measure packet reception rate of the LoRa packet with different parameters. Second, we do an RSSI-distance measurement to test and verify existing RSSI-distance LoRa model based on Dandelion.

Video transcoding is an important job in video processing and network service. With the improvement of devices and the Internet, the size of the video increases rapidly so that it takes a lot of resources to transcode. Low efficiency, high cost of upgrading hardware and low capacity of processing failure are problems of the traditional method of serial transcoding. Distributed transcoding can resolve these problems. To reduce the time of serial processing and be able to deal with the fault, this paper means to model a distributed video transcoding system which is based on MapReduce, an open source distribute computing model, and FFmpeg.

This paper proposes a simple approach for client to verify whether his/her data have been modified on the cloud storage server without downloading the data. In our approach, a number of bytes of each data block is collected to compose its metadata and stored in the cloud server along with small extra data. The approach effectively supports dynamic data operations with light overheads, in case of both computation and bandwidth. Our experiment shows that the computation time consumed is much less than the traditional MD5 checksum depending on different variable.

Biological computations like Electrocardiological modeling and simulation usually require high-performance computing environments. This paper introduces an implementation of parallel computation for computer simulation of Electrocardiograms (ECGs). We realized the parallel computation for computer simulation of ECGs on a CPU-GPU cluster using a hybrid parallel algorithm with the parallel program development tools-MPI, OpenMP, and CUDA. Furthermore, we proposed a load-prediction static scheduling and load-prediction dynamic scheduling to achieve efficient process-level and thread-level scheduling, respectively. Compared with traditional static and dynamic scheduling, our scheduling schemes are more efficient. In our research, we achieved a speedup of 55.1 using four PCs for the computer simulation of ECGs. This study demonstrates that the cluster can provide a cheap and efficient environment for parallel computations in biological modeling and simulation studies.

A class of new container cranes with eight-link lifting mechanics which can reduce sway efficiently as a result of improved lifting structure have been put to use recently, and both modeling and swing control of such a crane system attracts a lot attention in the field of control technology development. In this paper, dynamics model of the new container crane is investigated, an anti-swing control scheme with fuzzy uncertainty compensation is proposed to ensure the positioning control as well as overall closed-loop system stability. None of the system parameters is required for the controller design in a priori. In the proposed control laws, the position error can be driven to a bounded area while the swing angle can also be rapidly damped so as to achieve minimal sway of the crane system. Stability analysis of the controller is also given. Finally, simulation results show the performance successfully.

Abstract Queries to Web search engines are usually short and ambiguous, which provides insufficient information needs of users for effectively retrieving relevant Web pages. To address this problem, query suggestion is implemented by most search engines. However, existing methods do not leverage the contradiction between accuracy and computation complexity appropriately (e.g. Google's ‘Search related to’ and Yahoo's ‘Also Try’). In this paper, the recommended words are extracted from the search results of the query, which guarantees the real time of query suggestion properly. A scheme for ranking words based on semantic similarity presents a list of words as the query suggestion results, which ensures the accuracy of query suggestion. Moreover, the experimental results show that the proposed method significantly improves the quality of query suggestion over some popular Web search engines (e.g. Google and Yahoo). Finally, an offline experiment that compares the accuracy of snippets in capturing the number of words in a document is performed, which increases the confidence of the method proposed by the paper. Copyright © 2010 John Wiley & Sons, Ltd.

SUMMARY Semantic similarity measures play important roles in many Web‐related tasks such as Web browsing and query suggestion. Because taxonomy‐based methods can not deal with continually emerging words, recently Web‐based methods have been proposed to solve this problem. Because of the noise and redundancy hidden in the Web data, robustness and accuracy are still challenges. In this paper, we propose a method integrating page counts and snippets returned by Web search engines. Then, the semantic snippets and the number of search results are used to remove noise and redundancy in the Web snippets (‘Web‐snippet’ includes the title, summary, and URL of a Web page returned by a search engine). After that, a method integrating page counts, semantics snippets, and the number of already displayed search results are proposed. The proposed method does not need any human annotated knowledge (e.g., ontologies), and can be applied Web‐related tasks (e.g., query suggestion) easily. A correlation coefficient of 0.851 against Rubenstein–Goodenough benchmark dataset shows that the proposed method outperforms the existing Web‐based methods by a wide margin. Moreover, the proposed semantic similarity measure significantly improves the quality of query suggestion against some page counts based methods. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract Domain keywords of text play a primary role in text classifying, clustering and personalized services. This paper proposes a term frequency inverse document frequency (TFIDF) based method called TDDF (TFIDF direct document frequency of domain) to extract domain keywords from multi‐texts. First, we discuss the optimal parameters of TFIDF, which are used to extract textual keywords and domain keywords. Second, TDDF is proposed to extract domain keywords from multi‐texts, which takes document frequency of domain into account. Finally, the distribution of domain keywords on scientific texts is studied. Experiments and applications show that TDDF is more effective than the optimal TFIDF in the extraction of domain keywords. Domain keywords accord with normal distribution on a single text after deleting the ubiquitous domain keywords. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract Textual knowledge flow (TKF) provides an effective technique and theoretical support for the discovery and cooperation of knowledge innovation, intelligent browsing, and personalized recommendation in Web services and e‐Science Knowledge Grid. For the discovery of TKF, firstly knowledge map (KM) is proposed to represent the textual knowledge; then a hash algorithm is used to code KMs in order to form an Island which contains enormous KMs belonging to a domain. Under the control of the Island, C‐Location and R‐Location are introduced to manage those KMs belonging to an Island. KM‐Chord is proposed to manage the number of Islands, C‐Locations and R‐Locations in Web or a library. With the help of the management of KMs, similar relation and associated relation between KMs are found to build the semantic link network (SLN) between KMs. Based on the SLN and users' profile and input, similar or associated TKF with the user's different demands is activated. Experiments show that the proposed method can effectively discover TKF for Web services and e‐Science Knowledge Grid. Copyright © 2008 John Wiley & Sons, Ltd.