Data clustering is a technique for grouping similar data items together for convenient understanding. Conventional data clustering methods, including agglomerative hierarchical clustering and partitional clustering algorithms, frequently perform unsatisfactorily for large text collections, since the computation complexities of the conventional data clustering methods increase very quickly with the number of data items. Poor clustering results degrade intelligent applications such as event tracking and information extraction. This paper presents an unsupervised document clustering method which identifies topic keyword clusters of the text corpus. The proposed method adopts a multi-stage process. First, an aggressive data cleaning approach is employed to reduce the noise in the free text and further identify the topic keywords in the documents. All extracted keywords are then grouped into topic keyword clusters using the k-nearest neighbor approach and the keyword clustering technique. Finally, all documents in the corpus are clustered based on the topic keyword clusters. The proposed method is assessed against conventional data clustering methods on a web news corpus. The experimental results show that the proposed method is an efficient and effective clustering approach.

Content-based image search has long been considered a difficult task. Making correct conjectures on the user intention (perception) based on the query images is a critical step in the content-based search. One key concept in this paper is how we find the user preferred low-level image characteristics from the multiple positive samples provided by the user. The second key concept is how we generate a set of consistent "pseudo images" when the user does not provide a sufficient number of samples. The notion of image feature stability is thus introduced. The third key concept is how we use negative images as pruning criterion. In realizing the preceding concepts, an image search scheme is developed using the weighted low-level image features. At the end, quantitative simulation results are used to show the effectiveness of these concepts.

For medical imaging, non-rigid motion analysis of the heart deformability is a nontrivial problem. This paper introduces a new method to analyze the gated SPECT (Single Photon Emission Computed Tomography) imges for 3-D motion information of left ventricular. Our motion estimation method is based on a new concept called normal direction constraint" in that the normal of a surface patch of some deforming objects at certain time instant is constant. This paper consists of the following processes: contour extraction, slices interpolation, normal vector field generation, expanding process, motion estimation for producing a 2-D motion vector field, and deprojection for a 3-D motion vector field. In the experiments, we will demonstrate the accuracy of our method in analyzing the 3-D motion field of deforming object.

Most Android applications are written in JAVA and run on a Dalvik virtual machine. For smartphone vendors and users who wish to know the performance of an application on a particular smartphone but cannot obtain the source code, we propose a new technique, Dalvik Profiler for Applications (DPA), to profile an Android application on a Dalvik virtual machine without the support of source code. Within a Dalvik virtual machine, we determine the entry and exit locations of a method, log its execution time, and analyze the log to determine the performance of the application. Our experimental results show an error ratio of less than 5% from the baseline tool Traceview which instruments source code. The results also show some interesting behaviors of applications and smartphones: the performance of some smartphones with higher hardware specifications is 1.5 times less than the phones with lower specifications. DPA is now publicly available as an open source tool.

This letter proposes a nonlinear DCT discriminant feature extraction approach for face recognition. The proposed approach first selects appropriate DCT frequency bands according to their levels of nonlinear discrimination. Then, this approach extracts nonlinear discriminant features from the selected DCT bands by presenting a new kernel discriminant method, i.e. the improved kernel discriminative common vector (KDCV) method. Experiments on the public FERET database show that this new approach is more effective than several related methods.

Natural language (NL) requirements are usually human-centric and therefore error-prone and inaccurate. In order to improve the 3Cs of natural language requirements, namely Consistency, Correctness and Completeness, in this paper we propose a systematic pattern matching approach supporting both NL requirements modeling and inconsistency, incorrectness and incompleteness analysis among requirements. We first use business process modeling language to model NL requirements and then develop a formal language — Workflow Patterns-based Process Language (WPPL) — to formalize NL requirements. We leverage workflow patterns to perform two-level 3Cs checking on the formal representation based on a coherent set of checking rules. Our approach is illustrated through a real world financial service example — Global Equity Trading System (GETS).

Ambient light sensors have been used to reduce power consumption of Active Matrix Liquid Crystal Displays (AMLCD) adjusting display brightness depending on ambient illumination. Discrete sensors have been commonly used for this purpose. They make module design complex. Therefore it has been required to integrate the sensors on the display panels for solving the issue. So far, many kinds of integrated sensors have been developed using Amorphous Silicon (a-Si) technology or Low Temperature Polycrystalline Silicon (LTPS) technology. These conventional integrated sensors have two problems. One is that LTPS sensors have less dynamic range due to the less photosensitivity of LTPS photodiodes. The other is that both the LTPS and a-Si sensors are susceptible to display driving noises. In this paper, we introduce a novel integrated sensor using both LTPS and a-Si technologies, which can solve these problems. It consists of vertical a-Si Schottky photodiodes and an LTPS differential converter circuit. The a-Si photodiodes have much higher photosensitivity than LTPS ones, and this contributes to wide dynamic range and high accuracy. The LTPS differential converter circuit converts photocurrent of the photodiodes to a robust digital signal. In addition it has a function of canceling the influences of the display driving noises. With the circuit, the sensor can stably and accurately work even under the noises. The performance of the sensor introduced in this paper was measured to verify the advantages of the novel design. The measurement result showed that it worked in a wide ambient illuminance range of 5-55,000 lux with small errors of below 5%. It was also verified that it stably and accurately worked even under the display driving noise. Thus the sensor introduced in this paper achieved the wide dynamic range and noise robustness.

The switchable dual-wavelength and wavelength-con-verted nonreturn-to-zero-to-return-to-zero (NRZ-to-RZ) data transformation is demonstrated by externally seeding a synchronously sinusoidal-modulated laser diode with an optical pseudorandom bit sequence data at 1 Gbps. A maximum wavelength tuning range of 30 nm with an SMSR of greater than 36 dB is obtained. 1 Gbps on/off keying of single-mode RZ data pulse-train generated by externally seeding synchronously sinusoidal-modulated laser diode is demonstrated.

This letter presents a global feature-based method for evaluating the no reference quality of scanning electron microscopy (SEM) contrast-distorted images. Based on the characteristics of SEM images and the human visual system, the global features of SEM images are extracted as the score for evaluating image quality. In this letter, the texture information of SEM images is first extracted using a low-pass filter with orientation, and the amount of information in the texture part is calculated based on the entropy reflecting the complexity of the texture. The singular values with four scales of the original image are then calculated, and the amount of structural change between different scales is calculated and averaged. Finally, the amounts of texture information and structural change are pooled to generate the final quality score of the SEM image. Experimental results show that the method can effectively evaluate the quality of SEM contrast-distorted images.