Application of Deep Learning-Based Methods to the Single Image Non-Uniform Blind Motion Deblurring Problem

In this paper, we present deep learning-based blind image deblurring methods for estimating and removing a non-uniform motion blur from a single blurry image. We propose two fully convolutional neural networks (CNN) for solving the problem. The networks are trained end-to-end to reconstruct the latent sharp image directly from the given single blurry image without estimating and making any assumptions on the blur kernel, its uniformity, and noise. We demonstrate the performance of the proposed models and show that our approaches can effectively estimate and remove complex non-uniform motion blur from a single blurry image.

The Role of Information Theory in the Field of Big Data Privacy

Protecting privacy in Big Data is a rapidly growing research area. The first approach towards privacy assurance was the anonymity method. However, recent research indicated that simply anonymized data sets can be easily attacked. Later, differential privacy was proposed, which proved to be the most promising approach. The trade-off between privacy and the usefulness of published data, as well as other problems, such as the availability of metrics to compare different ways of achieving anonymity, are in the realm of Information Theory. Although a number of review articles are available in literature, the information - theoretic methods capacities haven’t been paid due attention. In the current article an overview of state-of-the-art methods from Information Theory to ensure privacy are provided.

Education Through Wikipedia

Wikipedia belongs to education in various ways. One gains knowledge by reading Wikipedia, the other obtains profound knowledge by contributing to Wikipedia. It is the reason why educators in many countries include Wikipedia editing into their curriculum. The article is dedicated to the ecosystem of education through Wikipedia and other Wiki projects which were created by the author, developed by Wikimedia Armenia and settled in Armenia. For seven years Wikimedia Armenia has been implementing Wikipedia Educational projects in different rural regions of Armenia and hopefully will continue its development. The system offers permanent creative learning for teachers, as well as deep and interdisciplinary education on their future field of engagement with students. It revolutionarily changes the attitude of teachers and students towards education. It facilitates the teacher and student relationships. It also changes students' interrelation from contest to cooperation. It shifts the attention of educational players from marks to topics’ perception. Of course, the most valuable advantage of this approach is that teachers improve their knowledge continuously and students, even not the smart ones gain comprehensive knowledge. This ecosystem is constantly improved based on statistical surveys. The components of the ecosystem were honored as the coolest Wikimedia projects and registered as trademarks: Wikicamp, Wikiclub. In the current article the full overview of education through wiki projects is given. The detailed description and innovative solutions on the challenges of today’s education will be introduced in the upcoming articles of the publication issue.

Designing and Implementing a Method of Data Augmentation Using Machine Learning

Efficiency of neural network (NN) models depend on the parameters given and the input data. Due to the complexity of environmental conditions and limitations the data for NN models, especially for the case of images, can be insufficient. To overcome this problem data augmentation has been used to enlarge the dataset. The task is to generate diverse set of images from a small set of images for NN training. Due to data augmentation transformation, 3105 new images out of 345 input data were created for classification, detection and image segmentation.

BIST Architecture for Magnetic Memories

Magnetic random-access memory (MRAM) is one of the emerging memory technologies, which can be considered as the next universal memory because of its good parameters. Nevertheless, this type of memory is not guaranteed from defects and it is very important to understand the fault typology and develop a test solution that addresses these faults. In this paper a Built-in Self-Test (BIST) solution is presented, which is specifically tailored for MRAMs and efficiently deals with MRAM specific faults.

A Note on Hamiltonian Bypasses in Digraphs with Large Degrees

Let D be a 2-strongly connected directed graph of order p ≥ 3. Suppose that d(x) ≥ p for every vertex x ∈ V (D) \ {x0}, where x0 is a vertex of D. In this paper, we show that if D is Hamiltonian or d(x0) > 2(p − 1)/5, then D contains a Hamiltonian path, in which the initial vertex dominates the terminal vertex.

Automated Linescan Analysis for CMP Modeling

Many of the process steps used in semiconductor chip manufacturing require planar (smooth) surfaces on the wafer to ensure correct pattern printing and generation of multilevel interconnections in the chips during manufacturing. Chemical-mechanical polishing/planarization (CMP) is the primary process used to achieve these surface planarity requirements. Modeling of CMP processes allows users to detect and fix large surface planarity variations (hotspots) in the layout prior to manufacturing. Fixing hotspots before tape-out may significantly reduce turnaround time and the cost of manufacturing. Creating an accurate CMP model that takes into account complicated chemical and mechanical polishing mechanisms is challenging. Measured data analysis and extraction of erosion and dishing data from profile linescans from test chips are important steps in CMP model building. Measured linescans are often tilted and noisy, which makes the extraction of erosion and dishing data more difficult. The development and implementation of algorithms used to perform automated linescan analysis may significantly reduce CMP model building time and improve the accuracy of the models. In this work, an automated linescan analysis (ALSA) tool is presented that performs automated linescan delineation, test pattern separation, and automatic extraction of erosion and dishing values from linescan data.

A Built-in Self-Test System for External DRAM

In the fast-growing Integrated Circuits (IC) industry, memory is one of the few keys to have systems with improved and fast performance. Only one transistor and a capacitor are required for Dynamic Random-Access Memory (DRAM) bit. It is widely used for mass storage. Although the high-efficiency tests are performed to provide the reliability of the memories, maintaining acceptable yield and quality is still the most critical task. To perform a high-speed effective test of DRAM memories, a built-in self-test (BIST) mechanism is proposed.

BIST Architecture for Magnetic Memories

Magnetic random-access memory (MRAM) is one of the emerging memory technologies, which can be considered as the next universal memory because of its good parameters. Nevertheless, this type of memory is not guaranteed from defects and it is very important to understand the fault typology and develop a test solution that addresses these faults. In this paper a Built-in Self-Test (BIST) solution is presented,which is specifically tailored for MRAMs and efficiently deals with MRAM specific faults.

An Approximate Method for Calculating the Distance Between Regular Languages for Multitape Finite Automata

Sets of word tuples, accepted by multitape finite automata and a metric space for languages accepted by these automata, are considered. These languages are represented using the same notation as the known notation of regular expressions for languages accepted by one-tape automata. The only difference is the interpretation of the ”concatenation” operation in the notation. An algorithm is proposed for calculating the introduced distance between regular languages accepted by multitape finite automata.