Soft error in FPGA-implemented asynchronous circuits

In this paper, we investigate the mechanism of soft error generation and propagation in asynchronous circuits which are implemented on FPGAs. The effects of the soft errors on Quasi-delay-insensitive (QDI) asynchronous circuits are analyzed. The results show that it is much easier to detect the soft error in asynchronous circuits implemented on FPGAs so that FPGAs can be reprogrammed, compared with traditional synchronous circuits

A Real-time Attendance System Using Deep-learning Face Recognition

A real-time attendance system using deep learning face recognition abstract: Attendance check plays an important role in classroom management. Checking attendance by calling names or passing around a sign-in sheet is time-consuming, and especially the latter is open to easy fraud. This paper presents the detailed implementation of a real-time attendance check system based on face recognition and its results. To recognize a student’s face, the system must first take and save a picture of the student as a reference in a database. During the attendance check, the web camera takes face pictures for a student to be recognized, and then the computer automatically detects the face and identifies a student name who most likely matches the pictures, and finally a excel file will be updated for attendance record based on the face recognition results. In the system, a pre-trained Haar Cascade model is used to detect faces from web camera video. A FaceNet, which has been trained by minimizing the triplet loss, is used to generate a 128-dimensional encoding for a face image. The similarity between the encodings of two face images determines whether the two face images coming from the same students. Novel techniques, including multiple-recognition and distance threshold optimization, have been developed to improve the recognition accuracy. The system has been deployed for several classes at our university (no name provided for blind review requirement). The system can be easily tailored for a different application such as access authentication

The Effect of Training Dataset Size on SAR Automatic Target Recognition Using Deep Learning

Synthetic aperture radar (SAR) is an effective remote sensor for target detection and recognition. Deep learning has a great potential for implementing automatic target recognition based on SAR images. In general, Sufficient labeled data are required to train a deep neural network to avoid overfitting. However, the availability of measured SAR images is usually limited due to high cost and security in practice. In this paper, we will investigate the relationship between the recognition performance and training dataset size. The experiments are performed on three classifiers using MSTAR (Moving and Stationary Target Acquisition and Recognition) dataset. The results show us the minimum size of the training set for a particular classification accuracy

Design of Asynchronous Circuits for High Soft Error Tolerance in Deep Submicron CMOS Circuits

As the devices are scaling down, the combinational logic will become susceptible to soft errors. The conventional soft error tolerant methods for soft errors on combinational logic do not provide enough high soft error tolerant capability with reasonably small performance penalty. This paper investigates the feasibility of designing quasi-delay insensitive (QDI) asynchronous circuits for high soft error tolerance. We analyze the behavior of null convention logic (NCL) circuits in the presence of particle strikes, and propose an asynchronous pipeline for soft-error correction and a novel technique to improve the robustness of threshold gates, which are basic components in NCL, against particle strikes by using Schmitt trigger circuit and resizing the feedback transistor. Experimental results show that the proposed threshold gates do not generate soft errors under the strike of a particle within a certain energy range if a proper transistor size is applied. The penalties, such as delay and power consumption, are also presented

Effectiveness of Using MyFPGA Platform for Teaching Digital Logic

Accompanying electric circuits and computer programming, digital logic is deemed one of the most essential parts of any Electrical and Computer Engineering curriculum, so student success in the course is critical. Furthermore, research shows that the academic performance of students is heavily dependent upon student engagement, which is believed to increase with classroom strategies such as flipped-classrooms, cooperative learning, project-based learning, and virtual labs. The University of Texas Rio Grande Valley (UTRGV) is a Hispanic serving institution with distributive campuses, where many of the students work part-time. With consideration of the special needs of our students and the latest developments in engineering education, this study focuses on our recent experience of teaching digital logical using MyFPGA, online FPGA platform. We first introduce the MyFPGA platform in this paper. Developed by one of the authors of this paper, this web-based design features I/O interfacing circuits with an Intel FPGA hardware board as well as API web services with the Intel Quartus II design software. The platform provides 24/7 real-time hardware design experience at students’ fingertips, requiring only a web browser and internet access. It exposes the students to a complete engineering design cycle that includes problem specification, block diagram design, HDL source code design, simulation and hardware verification, trouble shooting and evaluation, and reporting. We consider different cases of the platform usage in two digital logic courses. To evaluate the effectiveness of the student learning experience, data is collected using outcome assessments, student feedback and self-evaluations, instructor observations, and comparative studies. Preliminary results confirmed the effectiveness of the online digital design platform. We have also identified a few pitfalls, such as instructors’ initial reluctance in adopting the platform and students’ first perception of the platform as a pure simulation tool. Based on the studies, recommendations are made to identify the best practices in the utilization of the platform to better serve Electrical and Computer Engineering majors and secondary school students interested in the general STEM fields

A prognostic signature based on snoRNA predicts the overall survival of lower-grade glioma patients

IntroductionSmall nucleolar RNAs (snoRNAs) are a group of non-coding RNAs enriched in the nucleus which direct post-transcriptional modifications of rRNAs, snRNAs and other molecules. Recent studies have suggested that snoRNAs have a significant role in tumor oncogenesis and can be served as prognostic markers for predicting the overall survival of tumor patients. MethodsWe screened 122 survival-related snoRNAs from public databases and eventually selected 7 snoRNAs that were most relevant to the prognosis of lower-grade glioma (LGG) patients for the establishment of the 7-snoRNA prognostic signature. Further, we combined clinical characteristics related to the prognosis of glioma patients and the 7-snoRNA prognostic signature to construct a nomogram.ResultsThe prognostic model displayed greater predictive power in both validation set and stratification analysis. Results of enrichment analysis revealed that these snoRNAs mainly participated in the post-transcriptional process such as RNA splicing, metabolism and modifications. In addition, 7-snoRNA prognostic signature were positively correlated with immune scores and expression levels of multiple immune checkpoint molecules, which can be used as potential biomarkers for immunotherapy prediction. From the results of bioinformatics analysis, we inferred that SNORD88C has a major role in the development of glioma, and then performed in vitro experiments to validate it. The results revealed that SNORD88C could promote the proliferation, invasion and migration of glioma cells. DiscussionWe established a 7-snoRNA prognostic signature and nomogram that can be applied to evaluate the survival of LGG patients with good sensitivity and specificity. In addition, SNORD88C could promote the proliferation, migration and invasion of glioma cells and is involved in a variety of biological processes related to DNA and RNA

Novel Ionic Liquid with Both Lewis and Brønsted Acid Sites for Michael Addition

Ionic liquid with both Lewis and Brønsted acid sites has been synthesized and its catalytic activities for Michael addition were carefully studied. The novel ionic liquid was stable to water and could be used in aqueous solution. The molar ratio of the Lewis and Brønsted acid sites could be adjusted to match different reactions. The results showed that the novel ionic liquid was very efficient for Michael addition with good to excellent yields within several min. Operational simplicity, high stability to water and air, small amount used, low cost of the catalyst used, high yields, chemoselectivity, applicability to large-scale reactions and reusability are the key features of this methodology, which indicated that this novel ionic liquid also holds great potential for environmentally friendly processes

CANDELS: The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey - The Hubble Space Telescope Observations, Imaging Data Products and Mosaics

This paper describes the Hubble Space Telescope imaging data products and data reduction procedures for the Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS). This survey is designed to document the evolution of galaxies and black holes at $z\sim1.5-8$, and to study Type Ia SNe beyond $z>1.5$. Five premier multi-wavelength sky regions are selected, each with extensive multiwavelength observations. The primary CANDELS data consist of imaging obtained in the Wide Field Camera 3 / infrared channel (WFC3/IR) and UVIS channel, along with the Advanced Camera for Surveys (ACS). The CANDELS/Deep survey covers \sim125 square arcminutes within GOODS-N and GOODS-S, while the remainder consists of the CANDELS/Wide survey, achieving a total of \sim800 square arcminutes across GOODS and three additional fields (EGS, COSMOS, and UDS). We summarize the observational aspects of the survey as motivated by the scientific goals and present a detailed description of the data reduction procedures and products from the survey. Our data reduction methods utilize the most up to date calibration files and image combination procedures. We have paid special attention to correcting a range of instrumental effects, including CTE degradation for ACS, removal of electronic bias-striping present in ACS data after SM4, and persistence effects and other artifacts in WFC3/IR. For each field, we release mosaics for individual epochs and eventual mosaics containing data from all epochs combined, to facilitate photometric variability studies and the deepest possible photometry. A more detailed overview of the science goals and observational design of the survey are presented in a companion paper.Comment: 39 pages, 25 figure

CANDELS: The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey

The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) is designed to document the first third of galactic evolution, over the approximate redshift (z) range 8--1.5. It will image >250,000 distant galaxies using three separate cameras on the Hubble Space Telescope, from the mid-ultraviolet to the near-infrared, and will find and measure Type Ia supernovae at z>1.5 to test their accuracy as standardizable candles for cosmology. Five premier multi-wavelength sky regions are selected, each with extensive ancillary data. The use of five widely separated fields mitigates cosmic variance and yields statistically robust and complete samples of galaxies down to a stellar mass of 10^9 M_\odot to z \approx 2, reaching the knee of the ultraviolet luminosity function (UVLF) of galaxies to z \approx 8. The survey covers approximately 800 arcmin^2 and is divided into two parts. The CANDELS/Deep survey (5\sigma\ point-source limit H=27.7 mag) covers \sim 125 arcmin^2 within GOODS-N and GOODS-S. The CANDELS/Wide survey includes GOODS and three additional fields (EGS, COSMOS, and UDS) and covers the full area to a 5\sigma\ point-source limit of H \gtrsim 27.0 mag. Together with the Hubble Ultra Deep Fields, the strategy creates a three-tiered "wedding cake" approach that has proven efficient for extragalactic surveys. Data from the survey are nonproprietary and are useful for a wide variety of science investigations. In this paper, we describe the basic motivations for the survey, the CANDELS team science goals and the resulting observational requirements, the field selection and geometry, and the observing design. The Hubble data processing and products are described in a companion paper.Comment: Submitted to Astrophysical Journal Supplement Series; Revised version, subsequent to referee repor