Cloud RAN (C-RAN) where Base Band Units (BBUs) are collocated in a computing/processing center remotely away from their correspondent Remote Radio Heads (RRHs) for the efficient resource sharing is the prevailing RAN (Radio Access Network) design for next generation mobile networks. However, in C- RAN, the possible high latency from a RRH to the centralized Cloud BBU pool is not desirable for some latency critical applications. Thus, several local and smaller BBU pools are necessary to be deployed close to the RRHs to constrain the latency. This RAN architecture is so-call Fog RAN (F-RAN). A Mobile Edge Computing (MEC) center can be deployed beside or nearby a F-RAN BBU pool for timely processing. In this paper, we tackle the BBU resource allocation problem (a modified bin packing problem) between the set of RRHs and the set of BBU pools in F-RAN so that only a minimal number of BBU pools, i.e., the bins in a bin packing problem, will be turned on to serve all RRHs and so to save the energy consumption. Also, for the stability and fault tolerance, in addition to saving energy, the proposed algorithms also perform load balancing amid serving BBU pools. Our extensive simulation results show that the proposed scheme for energy efficient BBU resource allocation is able to achieve the goal of saving energy consumption and load balancing.

In this paper, a low power and high performance three-dimensional (3-D) stacking multimedia platform called "3D-PAC" is proposed. This platform is a heterogeneous integration composed of a low power design logic layer (2D-PAC) and a reconfigurable memory tier via 3-D technology. After extensive 3-D architecture exploration with Electronic System Level (ESL) simulation, there is a 54% performance speedup compared with the former 2-D architecture for certain multimedia applications. This chip is fabricated in TSMC 90nm generic CMOS technology. The area of 2D-PAC is about 7880 x 7880 μm2 and the SRAM layer is about 3880 x 3880 μm2. Both layers are combined with 1,886 TSVs.

Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in terms of minimizing the cost, but can result in a high error rate as the trade-off. The high error rate holds back the practical use of those algorithms. In this paper, we propose a novel cost-sensitive classification methodology that takes both the cost and the error rate into account. The methodology, called soft cost-sensitive classification, is established from a multicriteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classification with the error rate. The simple methodology allows immediate improvements of existing cost-sensitive classification algorithms. Experiments on the benchmark and the real-world data sets show that our proposed methodology indeed achieves lower test error rates and similar (sometimes lower) test costs than existing cost-sensitive classification algorithms.

We propose a pathogen-classification system using the Surface-Enhanced Raman Scattering (SERS) platform. The system differentiates the pathogens based on their SERS spectra, which are believed to be related to the surface chemical components. The specialty of the system is to not only consider the usual classification accuracy, but also pay attention to the different types of costs during misclassification. For instance, due to the effectiveness of treatments, the cost of classifying a Gram-positive bacterium as another Gram-positive one should be lower than the cost of classifying a Gram-positive bacterium as a Gram-negative one. We express the task as the cost-sensitive classification problem, and take state-of-the-art cost-sensitive classification algorithms from the machine learning community to conquer the task. Our experimental study validates the usefulness of those algorithms on building the system.

Wireless real-time surveillance systems are very valuable for monitoring ecology of wildlife, since the in-situ situation could be grasped immediately. Power source is usually constrained in the wild areas. Thus, in this paper, we developed a low power-consumption image transmission system (called energy-efficient visual eyes system) by integrating a MSP430 micro controller, FPGA circuit, a solar powered battery, a digital camera, and a GPRS (General Packet Radio Service) modem. In order to make a monitoring system suitable for circumstances of wild islands, the proposed system is designed with a packet transmitting strategy capable of enhancing pixel validity while effectively decreasing power consumption. Practically, the proposed system is deployed on a wild island in Taiwan near East China Sea, where is a habitat for migrant seabirds. The experimental results show that the system design is feasible to ecological monitoring and able to effectively maintain the completeness of images with energy efficiency.

In orthogonal frequency division multiplexing (OFDM) systems, high peak-to-average power ratio (PAPR) is one of the major technical challenges, which brings serious impact on the hardware implementation. In literature, many schemes have been proposed for PAPR reduction, wherein the selective mapping (SLM) scheme is the most popular and widely discussed approach. However, SLM suffers from very high computational complexity because of the requirement of a large number of inverse fast Fourier transformation (IFFT) operations. In this work, we propose a modified SLM approach based on time-domain sub-block conversion matrices to reduce the computational complexity due to multiple IFFTs. This scheme is modified from the conversion matrix scheme. By dividing the frequency-domain signals into multiple sub-blocks, the number of the valid conversion matrices can be increased, and thus more candidate signals are available for PAPR reduction. By applying our scheme, the number of candidate signals can be increased from 12 in the original conversion matrix scheme to 28 and 128 for the two-subblock and four-subblock cases, respectively. According to the simulations, the PAPR reduction performance can be improved by 0.2 dB - 0.9 dB.

Bacterial meningitis is still a life-threatening disease, and early diagnosis of pathogen can be crucial to improving survival rate. Using the surface-enhanced Raman scattering (SERS) platform developed by our group, the pathogens can be differentiated on the basis of their SERS spectra which are believed to related to their surface chemical components. We collected the SERS spectra of ten pathogens: Streptococcus pneumoniae(Spn), Streptococcus agalactiae (group B streptococcus, GBS), Staphylococcus aureus (Sa), Pseudomonas aeruginosae (Psa), Acinetobacter baumannii (Ab), Klebsiella pneumoniae (Kp), Neisseria meningitidis (Nm), Listeria monocy-togenes (Lm), Haemophilus influenzae (Hi), and Escherichia coli (E. coli). These samples were obtained from patients in National Taiwan University Hospital, and were believed to represent the real diversity of clinical pathogens. Using the support vector machine (SVM) method, the classification accuracy can achieve around 88%. However, we noted that SVM cannot distinguish between [E. coli, Kp] and [Sa, Hi] due to the fact that the global features of these two groups of pathogens are very similar. We therefore incorporated a classification tree method that can focus on local differences in classification rules. This improved the accuracy to 90%. To get a better understanding of the SERS signals, we also compared several other classification methods. In addition, rule extraction method which attempts to explain why classifier fail or succeed is also discussed. Our preliminary results are interesting, encouraging, and await more thorough investigation.

This paper presents an event-driven adaptive voltage scaling (AVS) system, where a vanguard collaborates with a rearguard to find a minimal supply voltage. While the vanguard is responsible for the slack cycle time estimation, the rearguard allows voltage over-scaling with a variable-latency datapath. An improved vanguard is proposed based on online static timing analysis (STA), which monitors a scalable number of critical path candidates at run time. In our simulations, the delay estimation error is within 10%, which is relatively small compared to critical path variations of prevailing multi-Vt designs in deep-submicron era. A testchip with a 32-bit tiny RISC has been fabricated with the TSMC 65nm LP process technology to demonstrate the effectiveness.

BACKGROUND: Alternative RNA splicing greatly increases proteome diversity and thereby contribute to species- or tissue-specific functions. The possibility to study alternative splicing (AS) events on a genomic scale using splicing-sensitive microarrays, including the Affymetrix GeneChip Exon 1.0 ST microarray (exon array), has appeared very recently. However, the application of this new technology is hindered by the lack of free and user-friendly software devoted to these novel platforms. RESULTS: In this study we present a Java-based freeware, easyExon http://microarray.ym.edu.tw/easyexon, to process, filtrate and visualize exon array data with an analysis pipeline. This tool implements the most commonly used probeset summarization methods as well as AS-orientated filtration algorithms, e.g. MIDAS and PAC, for the detection of alternative splicing events. We include a biological filtration function according to GO terms, and provide a module to visualize and interpret the selected exons and transcripts. Furthermore, easyExon can integrate with other related programs, such as Integrate Genome Browser (IGB) and Affymetrix Power Tools (APT), to make the whole analysis more comprehensive. We applied easyExon on a public accessible colon cancer dataset as an example to illustrate the analysis pipeline of this tool. CONCLUSION: EasyExon can efficiently process and analyze the Affymetrix exon array data. The simplicity, flexibility and brevity of easyExon make it a valuable tool for AS event identification in genomic research.

BACKGROUND: The identification of specific gene expression signature for distinguishing sample groups is a dominant field in cancer research. Although a number of tools have been developed to identify optimal gene expression signatures, the number of signature genes obtained is often overly large to be applied clinically. Furthermore, experimental verification is sometimes limited by the availability of wet-lab materials such as antibodies and reagents. A tool to evaluate the discrimination power of candidate genes is therefore in high demand by clinical researchers. RESULTS: Signature Evaluation Tool (SET) is a Java-based tool adopting the Golub's weighted voting algorithm as well as incorporating the visual presentation of prediction strength for each array sample. SET provides a flexible and easy-to-follow platform to evaluate the discrimination power of a gene signature. Here, we demonstrated the application of SET for several purposes: (1) for signatures consisting of a large number of genes, SET offers the ability to rapidly narrow down the number of genes; (2) for a given signature (from third party analyses or user-defined), SET can re-evaluate and re-adjust its discrimination power by selecting/de-selecting genes repeatedly; (3) for multiple microarray datasets, SET can evaluate the classification capability of a signature among datasets; and (4) by providing a module to visualize the prediction strength for each sample, SET allows users to re-evaluate the discrimination power on mis-grouped or less-certain samples. Information obtained from the above applications could be useful in prognostic analyses or clinical management decisions. CONCLUSION: Here we present SET to evaluate and visualize the sample-discrimination ability of a given gene expression signature. This tool provides a filtration function for signature identification and lies between clinical analyses and class prediction (or feature selection) tools. The simplicity, flexibility and brevity of SET could make it an invaluable tool for marker identification in clinical research.

This paper purpose an embedded AMBA signal tracer for microprocessor-based SoC's. This tracer provides five trace resolution modes that can perform a cycle-accurate or a transaction-based trace collection in an unlimited time. Also this tracer is implemented in a Soft-IP style. It provides four parameters for tracing configuration. The experimental results show that the bus tracer can reach a good compression ratio of 96%.

The bus tracing is used to catch related signals for further investigation and analysis. However, the trace size of cycle-accurate tracing is large and the trace cycle is shallow unless using a proper compression mechanism. In this paper, we propose an embedded multi-resolution AMBA trace analyzer that provides the trade-off between the trace granularity and the trace depth. It consists of two major trace approaches: (1) the signal monitor/tracing which provides the levels of abstraction, and (2) the trace reduction. In the first approach, it allows designers to zoom-in/out to preferred level of abstraction to serve different debugging purposes. In the second approach, the trace analyzer compresses the traced data according to signal characteristics and the cost of on-chip storage is reduced. The trace data will be decompressed on the host for further observation and debugging. The experimental results show that the proposed approach can reach a good compression ratio of 96% and the trace depth is more than two thousand cycles at the higher abstraction level.

UNLABELLED: ArrayFusion annotates conventional CGH results and various types of microarray data from a range of platforms (cDNA, expression, exon, SNP, array-CGH and ChIP-on-chip) and converts them into standard formats which can be visualized in genome browsers (Affymetrix Integrated Genome Browser and GBrowse in the HapMap Project). Converted files can then be imported simultaneously into a single genome browser to benefit a collective interpretation between different array results. ArrayFusion therefore provides a new type of tool facilitating the integration of CGH and array results to provide new experimental directions. AVAILABILITY: http://microarray.ym.edu.tw/tools/arrayfusion

We propose an integrated wavelet-based framework for an active contour model (snake) which is used for the motion tracking of deformable objects in a sequence of images. The input image frame is decomposed into a multiresolution representation using a wavelet transform. First, the wavelet transform coefficients are used in a multiresolution motion estimation process to find the initial contour in the frame. The presented multiresolution motion estimation method allows larger movement of the tracked object than the traditional image-based motion estimation. Secondly, the wavelet transform modulus at each scale is considered in the energy function of the active contour model. The application of biological cell tracking using the proposed method is shown as an example.

A low-voltage (/spl les/3 V) CMOS four quadrant multiple is introduced which has an almost rail-to-rail differential-input-swing with a low signal-distortion (/spl les/1% for 100 kHz signal). The proposed circuit is composed of a pair of rail-to-rail differential-input V-I converters and a pair of voltage-followers. This topology of multiplier results in a high frequency capability with low power consumption. In a 1.2 /spl mu/m n-well CMOS process, the 3 dB frequency of the multiplier is in a range of 103 MHz. Measured total power consumption is around 0.52 mW with supply voltage 2 V. The multiplier can operate at a minimum supply voltage of 1.8 V.