Verification of Identity and Syntax Check of Verilog and LEF Files

The Verilog and LEF files are units of the digital design flow [1][2]. They are being developed in different stages. Before the development of the LEF file, the Verilog file passes through numerous steps during which partial losses of information are possible. The identity check allows to make sure that during the flow the information has not been lost. The syntax accuracy of the Verilog and LEF files is checked as well. nbspnbspnbspnbspnbspnbspnbspnbspnbspnbspnbsp The scripting language Perl is selected for the program. The language is flexible to work with text files [3]. nbspnbspnbspnbspnbspnbspnbspnbspnbspnbspnbsp The method developed in the present paper is substantial as the application of integrated circuits today is actual in different scientific, technical and many other spheres which gradually finds wider application bringing about large demand

Analysis of Cosequences of Faults in General Zero Transmission Lines of Power Supply Stations

Zero transmission line faults in power supply systems in large apartment houses, office blocks, offices and other structures cause voltage excursions,which are in the spotlight of this research. The phenomenon has been commented from the theoretical perspective, and emergency situations, which are likely to arise as a result ofmalfunction of single-phase power supply consumers, as well as the probable dangers of fire occurrences, have been revealed. We offer to install an appropriateprotective device to avoid such emergency situations

Temporal Localization of Fine-Grained Actions in Videos by Domain Transfer from Web Images

We address the problem of fine-grained action localization from temporally untrimmed web videos. We assume that only weak video-level annotations are available for training. The goal is to use these weak labels to identify temporal segments corresponding to the actions, and learn models that generalize to unconstrained web videos. We find that web images queried by action names serve as well-localized highlights for many actions, but are noisily labeled. To solve this problem, we propose a simple yet effective method that takes weak video labels and noisy image labels as input, and generates localized action frames as output. This is achieved by cross-domain transfer between video frames and web images, using pre-trained deep convolutional neural networks. We then use the localized action frames to train action recognition models with long short-term memory networks. We collect a fine-grained sports action data set FGA-240 of more than 130,000 YouTube videos. It has 240 fine-grained actions under 85 sports activities. Convincing results are shown on the FGA-240 data set, as well as the THUMOS 2014 localization data set with untrimmed training videos.Comment: Camera ready version for ACM Multimedia 201

Evaluating Two-Stream CNN for Video Classification

Videos contain very rich semantic information. Traditional hand-crafted features are known to be inadequate in analyzing complex video semantics. Inspired by the huge success of the deep learning methods in analyzing image, audio and text data, significant efforts are recently being devoted to the design of deep nets for video analytics. Among the many practical needs, classifying videos (or video clips) based on their major semantic categories (e.g., "skiing") is useful in many applications. In this paper, we conduct an in-depth study to investigate important implementation options that may affect the performance of deep nets on video classification. Our evaluations are conducted on top of a recent two-stream convolutional neural network (CNN) pipeline, which uses both static frames and motion optical flows, and has demonstrated competitive performance against the state-of-the-art methods. In order to gain insights and to arrive at a practical guideline, many important options are studied, including network architectures, model fusion, learning parameters and the final prediction methods. Based on the evaluations, very competitive results are attained on two popular video classification benchmarks. We hope that the discussions and conclusions from this work can help researchers in related fields to quickly set up a good basis for further investigations along this very promising direction.Comment: ACM ICMR'1

Receptive Field Block Net for Accurate and Fast Object Detection

Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs. Conversely, some lightweight model based detectors fulfil real time processing, while their accuracies are often criticized. In this paper, we explore an alternative to build a fast and accurate detector by strengthening lightweight features using a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness. We further assemble RFB to the top of SSD, constructing the RFB Net detector. To evaluate its effectiveness, experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed. Code is available at https://github.com/ruinmessi/RFBNet.Comment: Accepted by ECCV 201

Model of creation and management of the process of technological projects development

The purpose of the article is to determine and describe main principles and model of development of regional business accelerator on the basis of technical university for development of innovational startups. The research included the work on identification of the problems of the process of commercialization of technological developments, which business accelerator should solve. One of the basic purposes of creation of business accelerator is increase of the flow of prepared projects for consideration by investment committees of funds of early stages. Accelerator can be a full-scale unified mechanism of formation of deals flow from diversified mass of completed R&D. As a result of the research, the authors come to a conclusion that the process of creation of value and movement from prototype to entering a market supposes long chain with formation of various risks at each stage – scientific, financial, administrative, etc. Minimization of such risks requires from partners, involved with the work of business accelerator, solving emerging problems together. Such motivation could be formed by development of business accelerator by the principles of partnership and creation of equal access and rights for basic members.peer-reviewe

Taxes, start-up costs, and innovative entrepreneurship

Prior research investigates the role of start-up costs and taxes with regard to entrepreneurship. Yet, little distinction is made regarding the type of entrepreneurship, particularly innovative versus non-innovative entrepreneurship. We shall argue that start-up costs and taxes are associated in different ways with innovative versus non-innovative entrepreneurship. Taxes being recurring costs should mainly relate to innovative entrepreneurship, whereas start-up costs being one-off costs should mainly relate to non-innovative entrepreneurship. Analyzing a dataset of 632,116 individuals, including 43,223 entrepreneurs from 53 countries, we can partially confirm our predictions. Corporate taxes show a negative relationship with innovative entrepreneurship, whereas income taxes seem to have no relationship. High start-up costs have a positive relationship with innovative entrepreneurship, although this finding only holds true in cross-sectional investigations. Our paper contributes to the discussion on how governmental regulation and taxes relate to entrepreneurship