In this paper, we will propose a 3D model retrieval approach using 2D cepstral features. First, six projection planes representing the elevation (depth) value are generated. Then, 2D cepstral features are extracted from each projection plane for searching similar 3D models. Experiments conducted on the Princeton Shape Benchmark (PSB) database have shown that the proposed 2D cepstral features outperforms other state-of-the-art descriptors in terms of the DCG score.

Coverage problems are fundamental and crucial in designing a wireless sensor networks. The target coverage problem is finding an optimal scheduling for sensors such that the time (called lifetime) to monitor every target can be as long as possible. Unfortunately, the target coverage problem has been proved to be NP-complete. Most of previous work only considers one or two factors exclusively and thus fails to prolong the lifetime to near the optimum. The main objective of this work is to design efficient scheduling algorithms to maximize the lifetime of a given whole wireless sensor network by considering adjusting sensing range, locations of target and sensors, residue battery power of sensor nodes, and assignment between sensors and targets simultaneously. A maximum weighted matching algorithm is devised by considering full coverage and the maximum total monitored duration for each target-sensor assignments. We also conduct simulations to demonstrate that the proposed algorithms can achieve very high network lifetime closed to the optimum.

Frequent pattern mining (FPM) is important in data mining field with Apriori algorithm to be one of the commonly used approaches to solve it. However, Aprori algorithm encounters an issue that the computation time increases dramatically when data size increases and when the threshold is small. Many parallel algorithms have been proposed to speed up the computation using computer clusters or grid systems. GPUs have also been applied on FPM with only few adopting OpenCL although OpenCL has the advantage of being platform independent. Thus, the aim of this research is to develop efficient parallel Aprori strategy using GPU and OpenCL. Our novel method, Candidate Slicing Frequent Pattern Mining (CSFPM) algorithm, improves over the previous method by slicing candidate information to better balance the load between processing units. This strategy is proved to be more efficient according to our experiments. For example, CSFPM is at most 2.6 times faster than the previous method. Therefore, CSFPM is an efficient parallel Apriori algorithm which can reduce computation time and improve overall performance.

Mining frequent pattern from transactional database is an important problem in data mining. Many methods have been proposed to solve this problem. However, the computation time still increase significantly while the data size grows. Therefore, parallel computing is a good strategy to solve this problem. Researchers have proposed various parallel and distributed algorithms on cluster system, grid system. However, the construction and maintenance cost is pretty high. In this paper, a multi-core load balancing frequent pattern mining algorithm is presented. The main goal of the proposed algorithm is to reduce the massive duplicated candidates generated in previous method. In order to verify the performance, we also implemented the proposed algorithm as well as previous methods for comparison. The experimental results showed that our method could reduce the computation time dramatically with more threads. Moreover, we could observe that the workload was equally dispatched to each computing unit.

A Virtual Signal Location System (VSLS) is developed to overcome some drawbacks of LANDMARC, a promising indoor-sensing system which implements RFID technology. The concept of VSLS is based on the additional virtual signal tags as well as the characteristics of the normal distribution of signal strength, analysis of sampling rate and equalization to decrease signal intensity error. Some comparisons were made with LANDMARC, VSLS can effectively increase the sampling quality of signal as well as the precision. In addition, the proposed mechanism also improves the tag deployment density along with the accurate enhancement.

The paper employed some independently developed sensors based on the Octopus X platform to build a Zigbee-based Sensor Networks. Also, we implemented a Location-aware living environment by deploying the developed Zigee-based sensor. Besides, through the created window console interface, the sensed data can be transmitted and transformed to the database for further utilized. Moreover, by the devised Web interface, the update request data can be observed efficiently and responded effectively to any situations.

The worldwide Digital Signage market has been getting increasingly popular in recent years. Nevertheless, for service providers, the Digital Signage business is still not easy to manage and time-consuming to operate. In this paper, based on the GPS and wireless infrastructure, we present a mobile location-aware digital signage system (LDSS). Through the centralized system architecture, the applications for the Digital Signage can be expanded to various advertising vehicles including buses and other mobile advertising vehicles. Also, we present an advertisement recommendation algorithm, by which the advertisement can be broadcasted in the right markets more effectively. Evidenced by simulated experiments and analysis, compared with traditional region trigger advertising, sequential advertising and random advertising, our advertisement recommendation algorithm can effectively reach to the key target audiences in planned regions, while show excellent ability to differentiate markets in unplanned regions reaching to the target consumer groups as well.

Extraction of frequent patterns from a transactional database is a fundamental task in data mining. Its applications include association rules, time series, etc. The Apriori approach is a commonly used generate-and-test approach to obtain frequent patterns from a database with a given threshold. Many parallel and distributed methods have been proposed for frequent pattern mining (FPM) to reduce computation time. However, most of them require a Cluster system or Grid system. In this study, a graphic processing unit (GPU) was used to perform FPM with a GPU-FPM to speed-up the process. Because of GPU hardware delimitations, a compact data structure was designed to store an entire database on GPU. In addition, MemPack and CLProgram template classes were also designed. Two datasets with different conditions were used to verify the performance of GPU-FPM. The experimental results showed that the speed-up ratio of GPU-FPM can achieve 14.857 with 16 times of threads.

Application of Automated Material Handling System (AMHS) to the semiconductor manufacturing factories is one of the most important steps in the automation process of semiconductor business. All cassettes in the vehicle of the transportation facility can send their positioning information through this system. Nevertheless, some positioning information may still be interfered or get lost if there is any relocation of the cassette inside the effective ranges of AMHS by operators. In order to retrieve these lost cassettes only manpower is possible, and the relocation process is time wasting and inefficient. In this paper, we have implemented a Clean Room Real-Time Location-aware System (CRRLS) that is based on the technology of RFID (Radio Frequency Identification). The proposed system can reveal the positioning information of the cassette in the clean room by analyzing the received radio frequency energy information. Moreover, we establish a transmitting model of radio signal, and used this model to estimate distance between RFID tag and readers. A triangular positioning algorithm is developed to estimate the location and its 2-dimensional coordinate of the cassette. Furthermore, reusable RFID tags can save additional information sent by the transceivers and data of parameters for the production process to reduce the possibility of severe damage because of wrong parameters setting. As a result, competitive advantage of the semiconductor business can herewith greatly increase.

Recreational cycling is a popular leisure activity in which the cyclist mostly is involved for a whole day at a time. This makes safety and comfort some of the most important factors to be attended to, especially when cycling in group. Group recreational cycling (GRC) is a new kind of tourism involving a courier and a number of tourists. Beside safety and comfort, tracking the cyclists, monitoring their health, and guiding and warning along the way are important in GRC. Information exchanging among cyclists is essential to achieve above goals. In this paper, an event-based wireless navigation and healthcare system for group recreational cycling (EWS) using an ad hoc network with multi-hop protocol to transmit data among devices is proposed. EWS can plan a route, warn about bad road conditions, play multimedia attractions for guiding, track cyclists and monitor their heart rate. Two scenarios were designed to observe and verify the feasibility of EWS.

In this paper, we will propose an automatic music genre classification approach based on long-term modulation spectral analysis of spectral (OSC and MPEG-7 NASE) as well as cepstral (MFCC) features. Modulation spectral analysis of every feature value will generate a corresponding modulation spectrum and all the modulation spectra can be collected to form a modulation spectrogram which exhibits the time-varying or rhythmic information of music signals. Each modulation spectrum is then decomposed into several logarithmically-spaced modulation subbands. The modulation spectral contrast (MSC) and modulation spectral valley (MSV) are then computed from each modulation subband. Effective and compact features are generated from statistical aggregations of the MSCs and MSVs of all modulation subbands. An information fusion approach which integrates both feature level fusion method and decision level combination method is employed to improve the classification accuracy. Experiments conducted on two different music datasets have shown that our proposed approach can achieve higher classification accuracy than other approaches with the same experimental setup.

In this paper, an automatic music genre classification approach which integrates the features derived from static and transitional information of cepstral (MFCC) and spectral (OSC) features will be proposed. MFCC and OSC capture the characteristics of one audio frame. Therefore, the transitional information, including delta-MFCC, delta-OSC, delta-delta-MFCC, and delta-delta-OSC, are then extracted and combined with MFCC and OSC to improve the classification accuracy. Two information fusion techniques, including feature level fusion and decision level fusion, are developed to combine the extracted feature vectors. Experiments conducted on the music database employed in the ISMIR2004 Audio Description Contest have shown that the proposed approach can achieve a classification accuracy of 84.23%, which is better than the winner of the ISMIR2004 Music Genre Classification Contest.

In this paper, we proposed a novel feature, called octave-based modulation spectral contrast (OMSC), for music genre classification. OMSC is extracted from long-term modulation spectrum analysis to represent the time-varying behavior of music signals. Experimental results have shown that OMSC outperforms MFCC and OSC. If OMSC is integrated with MFCC and OSC, the classification accuracy is 84.03% for seven music genre classification.

This paper explores the tracers deployment problem for IP traceback methods how many and where the tracers should be deployed in the network to be effective for locating the attack origins. The minimizing the number of tracers deployment problems depended on locating the attack origins are defined. The problem is proved to be NP-complete. A heuristic method which can guarantee that the distance between any attack origin and its first met tracer be within an assigned distance is proposed. The upper bound for the probability of an undetected attack node can be calculated in advance and used to evaluate the number of tracers needed for the proposed heuristic method. Extended simulations are performed to study the performance of the tracers deployment.