A dual-configuration dual-mode 8-channel high voltage stimulator is proposed and designed for biomedical applications. Dual-configuration indicates the choice between a bipolar or monopolar structural arrangement, while dual- =mode refers to the selection between constant current or constant voltage stimulation. With supply voltages of 1.8 V, ±5 V, ±10 V, and ±15 V, the range of stimulus current is ±0.2 mA to ±10 mA with a 0.2 mA/step increment, and the range of stimulus voltage is ±0.2 V to ±10 V with a 0.2 V/step increment. The 8-channel stimulator chip has been fabricated in a 0.18-µm BCD process with a silicon area of 3570 µm x 1636 µm. Moreover, stimulus functions have been successfully verified through experiments in agar with Pt electrodes and in-vivo animal test on pig with DBS electrode.

Deep convolution Neural Network (DCNN) has been widely used in computer vision tasks. However, for edge device, even then inference has too large computational complexity and data access amount. Due to the mentioned shortcomings, the inference latency of state-of-the-art models are still impractical for real-world applications. In this paper, we proposed a high utilization energy-aware real-time inference deep convolutional neural network accelerator, which outperforms the current accelerators. First, we use 1x1 size convolution kernels as the smallest unit of the computing unit. And we design suitable computing unit for different models based on the requirement of each model. Second, we use Reuse Feature SRAM to store the output of current layer in the chip and use as the input of the next layer. Moreover, we import Output Reuse Strategy and Ring Stream Data flow not only to expand the reuse rate of data in the chip but to reduce the amount of data exchange between chips and DRAM. Finally, we present On-fly Pooling Module to let the calculation of the Pooling layer to be completed directly in the chip. With the aid of the proposed method in this paper, the implemented CNN acceleration chip has extreme high hardware utilization rate. We reduce a generous amount of data transfer on the specific module, ECNN [1]. Compared to the methods without reuse strategy, we can reduce 533 times of data access amount. At the same time, we have enough computing power to perform real-time execution of the existing image classification model, VGG16 [2] and MobileNet [3]. Compared with the design in [4], we can speed up 7.52 times and have 1.92x energy efficiency.

Over the past decade, deep convolutional neural networks (CNN) have been widely embraced in various visual recognition applications owing to their extraordinary accuracy. However, their high computational complexity and excessive data storage present two challenges when designing CNN hardware. In this paper, we propose an energy-aware bit-serial streaming deep CNN accelerator to tackle these challenges. Using ring streaming dataflow and the output reuse strategy to decrease data access, the amount of external DRAM access for the convolutional layers is reduced by 357.26x when compared with that of no output reuse case on AlexNet. We optimize the hardware utilization and avoid unnecessary computations using the loop tiling technique and by mapping the strides of the convolutional layers to unit-ones for computational performance enhancement. In addition, the bit-serial processing element (PE) is designed to use fewer bits in weights, which can reduce both the amount of computation and external memory access. We evaluate our design using the well-known roofline model. The design space is explored to find the solution with the best computational performance and communication to computation (CTC) ratio. We can reach 1.36x speed and reduce energy consumption by 41% for external memory access compared with the design in [1]. The hardware implementation for our PE Array architecture design can reach an operating frequency of 119 MHz and consumes 68 k gates with a power consumption of 10.08 mW using TSMC 90-nm technology. Compared to the 15.4 MB external memory access for Eyeriss [2] on the convolutional layers of AlexNet, our method only requires 4.36 MB of external memory access to dramatically reduce the costliest portion of power consumption.

Over the past decade, deep convolutional neural networks (CNN) have been widely embraced in various visual recognition applications owing to their extraordinary accuracy. However, their high computational complexity and excessive data storage present two challenges when designing CNN hardware. We propose an energy-aware bit-serial streaming deep CNN accelerator to tackle these challenges. With several optimization methods and the proposed ring streaming dataflow, the computational performance is improved and the external memory access is reduced. We evaluate our design with the roofline model by exploring the design space to find the best performance and computation and communication (CTC) ratio solution. We can reach 1.36x speed up and reduce energy consumption by 41% for external memory access compared with the design in [1]. The hardware implementation for our PE Array architecture design can reach the operating frequency of 119 MHz and consumes 68 k gates with a power consumption of 10.08mW using TSMC 90-nm technology.

MOTIVATION: Percent Spliced-In (PSI) values are commonly used to report alternative pre-mRNA splicing (AS) changes. Previous PSI-detection tools were limited to specific AS events and were evaluated by in silico RNA-seq data. We developed PSI-Sigma, which uses a new PSI index, and we employed actual (non-simulated) RNA-seq data from spliced synthetic genes (RNA Sequins) to benchmark its performance (i.e. precision, recall, false positive rate and correlation) in comparison with three leading tools (rMATS, SUPPA2 and Whippet). RESULTS: PSI-Sigma outperformed these tools, especially in the case of AS events with multiple alternative exons and intron-retention events. We also briefly evaluated its performance in long-read RNA-seq analysis, by sequencing a mixture of human RNAs and RNA Sequins with nanopore long-read sequencers. AVAILABILITY AND IMPLEMENTATION: PSI-Sigma is implemented is available at https://github.com/wososa/PSI-Sigma.

This paper describes a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with an energy-efficient trilevel alternate switching capacitive digital-to-analog converter (CDAC). The switching scheme of this CDAC preserves the features of the asymmetric-switching CDAC. By narrowing and smoothing the dynamic variation of DAC voltage, the switching scheme diminishes the dynamic offset effect induced by the asymmetric-switching CDAC. The CDAC reduces the capacitor requirement by almost fourfold and improves the average switching energy efficiency by almost 86.5% when compared with the conventional switching CDACs. This SAR ADC was implemented using the 90-nm CMOS technology, and its measured performances were as follows: 1) spurious free dynamic range of 56.98 dB; 2) signal-to-noise-and-distortion ratio of 68.79 dB; and 3) power dissipation of 3.45 μW at an operation of 0.5 V and 1.28 MS/s. The ADC achieves a figure-of-merit of 4.68-fJ/conversion-step.

Sea level rising, Arctic sea ice melting, Glaciers and permafrost melting, sea-surface and large lakes warming, extreme drought increasing, crops withering, ecosystems changing, hurricanes changed in frequency and strength, more frequent heat waves, warmer temperatures affecting human health, and seawater being becoming more acidic are effects of climate change that we see happening. In recognition of environmental degradation, governments, along with the green movement within the hotel and tourism industry, and travelers, have become increasingly aware of the need for more effective measures to protect the environment. "Green" hotels are environmentally friendly properties that take the initiative and implement very important practices and programs to reduce energy, water, and waste. Although their some green hotels in Taiwan, the percentage is still too low. As a member of the Earth, everyone has the responsibility to put some efforts for our environments. Therefore, we would like to realize people's cognitions of climate change and green hotel in Taiwan to find some strategies to help saving our Earth. The purpose of this paper was to understand the surveyors' cognitions of climate change and green hotel. The study used questionnaires method and convenience sampling. The questionnaire for river recreation tourists sent 330 copies, 304 valid questionnaires, the effective rate was 92.21 %. Sample data were collected by the statistical analysis. The results of this research were as the followings: Less knowledge related to those questions of climate change. The surveyors' knowledge for green hotels and cognitions of environmental protection implementing and climate change was quite well. The percents of correct questions for "methane is not the highest percentage of greenhouse gas", "increased concentrations of carbon dioxide mainly are generated from coal mine power plants", "ozone depletion is not caused by EL Niño ", "La Niño does not cause global warming", and "the reasons of United Nations Framework Convention of Climate Change is not to avoid the destruction of ozone layer" were less than 40 %. Although the more knowledge for climate change was not associated with the surveyors' knowledge of 4 indices for green hotels, 86.5 % of the surveyors can understand 60 % of climate change knowledge, and that the more cognitions of environmental protection implementing were related with the surveyors' knowledge of 4 indices for green hotels, separately. Therefore, we can inference that the surveyors' cognitions of environmental protection implementing had significantly correlations with heir knowledge of 4 indices for green hotels, respectively. It was suggested the green hotel industries to establish a green hotel club for the consumers, the industries can give some discounts or benefits to the members of the green hotel club to attract consumers.

This paper presents a 10-bit successive approximation register (SAR) ADC with an energy-efficient switching approach of capacitive DAC. The proposed switching method lessens the dynamic offset effect coming from the asymmetric capacitive switching. A tri-level algorithm is applied additionally to make the ADC more power-efficient and is implemented with switching-capacitive voltage generator without consuming static power. It consumes 3.87μW at 0.5V supply and 1.28MS/s sampling rate, and achieves ENOB of 9.69-bit and FOM of 3.66 fJ/conversion-step. This SAR ADC has been fabricated by TSMC 90nm CMOS process technology.

A 2-6GHz broadband low-noise amplifier (LNA) with current reuse and noise-shaping technique is proposed for wideband matching, high power gain and flat noise figure (NF) response. The proposed UWB LNA is implemented in TSMC 0.18um CMOS technology. Measured results show that power gain is greater than 15dB and input return loss is larger than lOdB from 2 to 6GHz. The input third-order intercept point (IIP3) is -4dBm. Besides, a good noise figure of 3-3.7dB is obtained over the band of interest with a power dissipation of 15mW under a 1.8V power supply.

BACKGROUND: To automatically process large quantities of biological literature for knowledge discovery and information curation, text mining tools are becoming essential. Abbreviation recognition is related to NER and can be considered as a pair recognition task of a terminology and its corresponding abbreviation from free text. The successful identification of abbreviation and its corresponding definition is not only a prerequisite to index terms of text databases to produce articles of related interests, but also a building block to improve existing gene mention tagging and gene normalization tools. RESULTS: Our approach to abbreviation recognition (AR) is based on machine-learning, which exploits a novel set of rich features to learn rules from training data. Tested on the AB3P corpus, our system demonstrated a F-score of 89.90% with 95.86% precision at 84.64% recall, higher than the result achieved by the existing best AR performance system. We also annotated a new corpus of 1200 PubMed abstracts which was derived from BioCreative II gene normalization corpus. On our annotated corpus, our system achieved a F-score of 86.20% with 93.52% precision at 79.95% recall, which also outperforms all tested systems. CONCLUSION: By applying our system to extract all short form-long form pairs from all available PubMed abstracts, we have constructed BIOADI. Mining BIOADI reveals many interesting trends of bio-medical research. Besides, we also provide an off-line AR software in the download section on http://bioagent.iis.sinica.edu.tw/BIOADI/.

BACKGROUND: The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. RESULTS: Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO http://ehco.iis.sinica.edu.tw, to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. CONCLUSION: This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment.

A three-site rain-fade space-diversity measurements experiment at 20 GHz has been in near continuous operation since September 1, 1994. Two receiver sites (at the Applied Phys. Laboratory ~APL\ of The Johns Hopkins University and COMSAT Laboratories) are located in Central Maryland, and the other is located in Virginia. The APL and COMSAT locations are separated by 33 km. The Virginia site is separated from APL by 44.5 km and from COMSAT by 30.5 km. Receivers at each of the sites measure the 20-GHz CW signal level from the radiating beacon onboard the geostationary Advanced Communications Technology Satellite (ACTS). Because of the lateral variability of rain, the likelihood is diminished that intense rain cells will simultaneously intersect all three Earth-satellite paths. There is also a reduced likelihood that intense rain will simultaneously intersect two Earth-satellite paths associated with different two-site scenarios. Hence, a substantially smaller rain-fade margin is required when the sites operate in a diversity mode (e.g., connected together such that the largest signal is used) vis-a-vis uncoupled single terminal operation. In this effort, we examine the efficacy of employing three-site and two-site space-diversity systems to reduce the required fade margin. Single- and joint-terminal rain-fade distributions are calculated for both the three-site and different combinations of two-site scenarios. Diversity gains, which give a measure of the reduced fade margin, are determined from these distributions. The distributions and diversity gains are also individually determined for two six-month periods of the year for which precipitation is predominantly convective or stratiform, respectively. Comparisons between measured diversity gains and those estimated using the model of the radiocommunications sector of the International Telecommunications Union are made.

This work reviews site diversity concepts and models and presents the results of a site diversity experiment, in which radiometers were used to measure attenuation on Earth-space paths, performed at Columbus, Ohio, for a period of one year. Attenuation statistics are presented first in the form of the joint probability density function of attenuation at the two sites. From these are derived the cumulative two-dimensional distribution of attenuation at both sites, the cumulative distribution of attenuation at each site, the effective cumulative distribution of attenuation for a diversity system that always switches to the better of two signals, and the effective cumulative distribution of attenuation for a system with maximal-ratio signal combining. The switched-system diversity gain is compared with D.B. Hodge's (1982) predictive model, and the diversity improvement is compared with L. Boithias' (Radiowave Propagation, McGraw-Hill, 1987) model. Fade-duration and interfade-interval statistics are presented for each site and for systems that use switching and maximal-ratio combining. Rain-rate and interrainfall interval statistics are included.>