Sequential pattern mining can be applied to various fields such as disease prediction and stock analysis. Many algorithms have been proposed for sequential pattern mining, together with acceleration methods. In this paper, we show that a heterogeneous platform with CPU and GPU is more suitable for sequential pattern mining than traditional CPU-based approaches since the support counting process is inherently succinct and repetitive. Therefore, we propose the PArallel SequenTial pAttern mining algorithm, referred to as PASTA, to accelerate sequential pattern mining by combining the merits of CPU and GPU computing. Explicitly, PASTA adopts the vertical bitmap representation of database to exploits the GPU parallelism. In addition, a pipeline strategy is proposed to ensure that both CPU and GPU on the heterogeneous platform operate concurrently to fully utilize the computing power of the platform. Furthermore, we develop a swapping scheme to mitigate the limited memory problem of the GPU hardware without decreasing the performance. Finally, comprehensive experiments are conducted to analyze PASTA with different baselines. The experiments show that PASTA outperforms the state-of-the-art algorithms by orders of magnitude on both real and synthetic datasets.

In recent years, a large volume of natural signal data has become available for scientists because of the maturity of sensor techniques. However, the sensor data can form huge data streams that are non-linear and non-stationary. Existing methods cannot process such a large volume of data efficiently with a single CPU because of the high complexity of the algorithms. In this paper, we present Massive Parallelism GPU-Optimized Adaptive Data Analysis (MG-ADA), a new parallel signal data analysis algorithm that utilizes General-Purpose Graphics Programming Unit (GPGPU) to improve data scalability and reduce computation time for large non-linear and non-stationary datasets. We propose effective strategies to significantly improve the efficiency and scalability of MG-ADA. Our experimental results show that MG-ADA provides high scalability and significantly reduces the processing time in large datasets compared to other baseline algorithms.

Mining big graph data is an important problem in the graph mining research area. Although cloud computing is effective at solving traditional algorithm problems, mining frequent patterns of a massive graph with cloud computing still faces the three challenges: 1) the graph partition problem, 2) asymmetry of information, and 3) pattern-preservation merging. Therefore, this paper presents a new approach, the cloud-based SpiderMine (c-SpiderMine), which exploits cloud computing to process the mining of large patterns on big graph data. The proposed method addresses the above issues for implementing a big graph data mining algorithm in the cloud. We conduct the experiments with three real data sets, and the experimental results demonstrate that c-SpiderMine can significantly reduce execution time with high scalability in dealing with big data in the cloud.

This paper presents a theoretical nonlinearity analysis of R-2R ladder-based current-steering digital to analog converter (DAC). Besides the current mismatches, this paper analyzes static nonlinearity caused by resistor mismatches in the R-2R ladder. An elliptic equation is then developed capable of deriving the required current and resistor matching of N-bits R-2R ladder-based current-steering DAC simultaneously to achieve the static nonlinearity specifications and required yield. Monte-Carlo simulation results agree well with the theoretical equation and confirm its accuracy.

This study has developed an object detection and segmentation technique for processing cytoplasm and cell nucleus on ThinPrep-cervical smear images at various magnifications. Both edge detection techniques and region growing for adaptive threshold were applied to a segment cell nucleus, a cytoplasm, and backgrounds using a cervical cell image. To validate the accuracy and feasibility of the proposed method, we took a variety of cervical cell images to perform a series of experiments. The images were of superficial cells, intermediate cells, and abnormal cells, with each taken from ThinPrep smears at various magnifications. The results indicate that the proposed method can automatically segment cell nucleus and cytoplasm regions while accurately extracting object contours. These results can serve as a reference for examiners of cell pathologies.

In this paper, a low power low-noise amplifier (LNA) using inductor-coupling resonated technique is designed for ultra-wideband (UWB) wireless system. The design consists of a wideband input impedance matching network, one stage cascode amplifier with inductor-coupling resonated load, and an output buffer; it was fabricated in TSMC 0.18 um standard RF CMOS process. The UWB LNA gives 10.8 dB power gain and 9.4 GHz 3 dB bandwidth (1.2 GHz - 10.6 GHz) while consuming only 6.2 mW through a 1.2 V supply, including output buffer. Over the 3.1 GHz - 10.6 GHz frequency band, a minimum noise figure of 3.9 dB and input return loss lower than -5.7 dB have been achieved.

Radio-frequency identification (RFID) technologies allow remote identification as well as generic data access using radio waves. It is also commonly used in transportation and other payment systems, e.g., the MIFAREof NXP Semiconductors, one of the most widely deployed contactless smart card standards. Recently, the interest in using RFID for micro payment grows rapidly as users get used to the convenience brought by RFID,and corporations discover that RFID can significantly lower the cost of operation. However, there are security concerns, as many passive RFID technologies do not have adequate cryptographic protection. Furthermore, thecommunication can be eavesdropped by a third party, making RFID particularly vulnerable to all sorts of attacks.In this work, we examine the EasyCard of the Taipei Metro Rapid Transit (MRT) Corporation, a transportation ticketing system based on the MIFARE Classic technology. We capture and analyze the communication between a legitimate reader and an EasyCard using GNURadio, an open-source software-defined radio running on PC. We will share our experiences with EasyCard security and hopefully provide some insights into RFID security inpractice.

A content authentication scheme for digital videos is proposed in this paper. In order to prevent the content from being unnoticeably tampered with by using digital editing facilities, we employ the approach of scalar/vector quantization on the reliable features extracted from video frame blocks to form the authentication code, which is transmitted along with the video. The resulting authentication code is sensitive to malicious modifications of video data but resilient to allowed lossy compression processes, such as H.264/advanced video coding (AVC). The integrity of video content can thus be guaranteed if the extracted feature matches the transmitted authentication code. Experimental results will show the feasibility of the proposed scheme.

Interactivity is a critical issue in designing a good game or similar virtual environments. As the computer hardware is continuously improved, more computing power can be invested in interactivity in addition to graphics rendering. For games with virtual characters, how a character interact with a user is usually specified at design time according to a given scene. Consequently, the characters in a game usually can only display canned motions at designated locations of a scene. After several runs of practice, the user may easily get bored because these actions become predictable. Therefore, it is highly desirable to have a smarter character that can plan its motions according to the inputs from the user as well as other constraints from the environments.