187,284 research outputs found

    BigDataBench: a Big Data Benchmark Suite from Internet Services

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    As architecture, systems, and data management communities pay greater attention to innovative big data systems and architectures, the pressure of benchmarking and evaluating these systems rises. Considering the broad use of big data systems, big data benchmarks must include diversity of data and workloads. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above. This paper presents our joint research efforts on this issue with several industrial partners. Our big data benchmark suite BigDataBench not only covers broad application scenarios, but also includes diverse and representative data sets. BigDataBench is publicly available from http://prof.ict.ac.cn/BigDataBench . Also, we comprehensively characterize 19 big data workloads included in BigDataBench with varying data inputs. On a typical state-of-practice processor, Intel Xeon E5645, we have the following observations: First, in comparison with the traditional benchmarks: including PARSEC, HPCC, and SPECCPU, big data applications have very low operation intensity; Second, the volume of data input has non-negligible impact on micro-architecture characteristics, which may impose challenges for simulation-based big data architecture research; Last but not least, corroborating the observations in CloudSuite and DCBench (which use smaller data inputs), we find that the numbers of L1 instruction cache misses per 1000 instructions of the big data applications are higher than in the traditional benchmarks; also, we find that L3 caches are effective for the big data applications, corroborating the observation in DCBench.Comment: 12 pages, 6 figures, The 20th IEEE International Symposium On High Performance Computer Architecture (HPCA-2014), February 15-19, 2014, Orlando, Florida, US

    Distinguishing Posed and Spontaneous Smiles by Facial Dynamics

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    Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features, local phase quantization (LPQ), dense optical flow and histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for micro-expression smile amplification along with three normalization procedures for distinguishing posed and spontaneous smiles. Although the deep CNN face model is trained with large number of face images, HOG features outperforms this model for overall face smile classification task. Using EVM to amplify micro-expressions did not have a significant impact on classification accuracy, while the normalizing facial features improved classification accuracy. Unlike many manual or semi-automatic methodologies, our approach aims to automatically classify all smiles into either `spontaneous' or `posed' categories, by using support vector machines (SVM). Experimental results on large UvA-NEMO smile database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial Behavior Analysi

    Microsystems technology: objectives

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    This contribution focuses on the objectives of microsystems technology (MST). The reason for this is two fold. First of all, it should explain what MST actually is. This question is often posed and a simple answer is lacking, as a consequence of the diversity of subjects that are perceived as MST. The second reason is that a map of the somewhat chaotic field of MST is needed to identify sub-territories, for which standardization in terms of system modules an interconnections is feasible. To define the objectives a pragmatic approach has been followed. From the literature a selection of topics has been chosen and collected that are perceived as belonging to the field of MST by a large community of workers in the field (more than 250 references). In this way an overview has been created with `applications¿ and `generic issues¿ as the main characteristics

    A compact targeted drug delivery mechanism for a next generation wireless capsule endoscope

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    This paper reports a novel medication release and delivery mechanism as part of a next generation wireless capsule endoscope (WCE) for targeted drug delivery. This subsystem occupies a volume of only 17.9mm3 for the purpose of delivering a 1 ml payload to a target site of interest in the small intestinal tract. An in-depth analysis of the method employed to release and deliver the medication is described and a series of experiments is presented which validates the drug delivery system. The results show that a variable pitch conical compression spring manufactured from stainless steel can deliver 0.59 N when it is fully compressed and that this would be sufficient force to deliver the onboard medication

    A handheld high-sensitivity micro-NMR CMOS platform with B-field stabilization for multi-type biological/chemical assays

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    We report a micro-nuclear magnetic resonance (NMR) system compatible with multi-type biological/chemical lab-on-a-chip assays. Unified in a handheld scale (dimension: 14 x 6 x 11 cm³, weight: 1.4 kg), the system is capable to detect<100 pM of Enterococcus faecalis derived DNA from a 2.5 μL sample. The key components are a portable magnet (0.46 T, 1.25 kg) for nucleus magnetization, a system PCB for I/O interface, an FPGA for system control, a current driver for trimming the magnetic (B) field, and a silicon chip fabricated in 0.18 μm CMOS. The latter, integrated with a current-mode vertical Hall sensor and a low-noise readout circuit, facilitates closed-loop B-field stabilization (2 mT → 0.15 mT), which otherwise fluctuates with temperature or sample displacement. Together with a dynamic-B-field transceiver with a planar coil for micro-NMR assay and thermal control, the system demonstrates: 1) selective biological target pinpointing; 2) protein state analysis; and 3) solvent-polymer dynamics, suitable for healthcare, food and colloidal applications, respectively. Compared to a commercial NMR-assay product (Bruker mq-20), this platform greatly reduces the sample consumption (120x), hardware volume (175x), and weight (96x)
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