scholarly journals Image Speckle Denoising for Securing Internet of Smart Sensors

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wei Ma ◽  
Zhihui Xin ◽  
Licun Sun ◽  
Jun Zhang
Keyword(s):  
Target Recognition ◽  
Great Influence ◽  
Speckle Noise ◽  
Research Problem ◽  
Smart Sensors ◽  
Hole Filling ◽  
Sensitive Data ◽  
Malicious Attack ◽  
Important Research Problem ◽  
Detection And Recognition

How to improve utility performance when securing sensitive data is an important research problem in Internet of smart sensors. In this paper, we study secured image speckle denoising for networked synthetic aperture radar (SAR). Speckle noise of SAR affects image quality and has a great influence on target detection and recognition. MSTAR dataset is often used in image target recognition. In this paper, a subregion-based method is proposed in order to improve the accuracy of target recognition and better retain target information while filtering and denoising the image. The new method applies advanced encryption techniques to protect sensitive data against malicious attack. Firstly, the image is divided into marked areas and unmarked areas through edge extraction and hole filling. Secondly, we use different size windows and filtering methods to filter the image in different areas. The experimental results show that the proposed algorithm has obvious advantages over MR-NLM, SSIM-NLM, Frost, and BM3D filtering in terms of equivalent view number and preserving edge and structure.

Invariant Probabilities with Geometric Tail

1996 ◽  
Vol 10 (2) ◽  
pp. 213-221 ◽  
Author(s):  
Jean B. Lasserre ◽  
Henk Tijms
Keyword(s):  
Markov Chain ◽  
Linear Algebra ◽  
Research Problem ◽  
Important Research ◽  
Countable State ◽  
Important Research Problem

We present necessary and suffi2ient Foster-type conditions for a countable state Markov chain to have an invariant probability with at least a geometric tail. These conditions are obtained by using a generalized Farkas Theorem in Linear Algebra. The purpose of this note is also to pose an interesting and important research problem that is still largely open.

scholarly journals TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1724
Author(s):  
Zilu Ying ◽  
Chen Xuan ◽  
Yikui Zhai ◽  
Bing Sun ◽  
Jingwen Li ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Network Architecture ◽  
Learning Strategy ◽  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Speckle Noise ◽  
Small Sample ◽  
Operating Conditions ◽  
Small Samples

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model’s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.

scholarly journals Advanced exact synthesis of Clifford+T circuits

2020 ◽  
Vol 19 (9) ◽  
Author(s):  
Philipp Niemann ◽  
Robert Wille ◽  
Rolf Drechsler
Keyword(s):  
Large Scale ◽  
Fault Tolerant ◽  
Logic Synthesis ◽  
Research Problem ◽  
Quantum Systems ◽  
Quantum Circuits ◽  
Research Attention ◽  
Small Quantum ◽  
Significant Research ◽  
Important Research Problem

Abstract Quantum systems provide a new way of conducting computations based on the so-called qubits. Due to the potential for significant speed-ups, this field received significant research attention in recent years. The Clifford+T library is a very promising and popular gate library for these kinds of computations. Unlike other libraries considered so far, it consists of only a small number of gates for all of which robust, fault-tolerant realizations are known for many technologies that seem to be promising for large-scale quantum computing. As a consequence, (logic) synthesis of Clifford+T quantum circuits became an important research problem. However, previous work in this area has several drawbacks: Corresponding approaches are either only applicable to very small quantum systems or lead to circuits that are far from being optimal. The latter is mainly caused by the fact that current synthesis realizes the desired circuit by a local, i.e., column-wise, consideration of the underlying unitary transformation matrix to be synthesized. In this paper, we analyze the conceptual drawbacks of this approach and propose to overcome them by taking a global view of the matrices and perform a separation of concerns regarding individual synthesis steps. We precisely describe a corresponding algorithm as well as its efficient implementation on top of decision diagrams. Experimental results confirm the resulting benefits and show improvements of up to several orders of magnitudes in costs compared to previous work.

scholarly journals A Reinforcement Learning Based Dirt-Exploration for Cleaning-Auditing Robot

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8331
Author(s):  
Thejus Pathmakumar ◽  
Mohan Rajesh Elara ◽  
Braulio Félix Gómez ◽  
Balakrishnan Ramalingam
Keyword(s):  
Reinforcement Learning ◽  
Research Work ◽  
Leading Edge ◽  
Research Problem ◽  
Fundamental Question ◽  
Policy Learning ◽  
Policy Network ◽  
Important Research ◽  
Important Research Problem

Cleaning is one of the fundamental tasks with prime importance given in our day-to-day life. Moreover, the importance of cleaning drives the research efforts towards bringing leading edge technologies, including robotics, into the cleaning domain. However, an effective method to assess the quality of cleaning is an equally important research problem to be addressed. The primary footstep towards addressing the fundamental question of “How clean is clean” is addressed using an autonomous cleaning-auditing robot that audits the cleanliness of a given area. This research work focuses on a novel reinforcement learning-based experience-driven dirt exploration strategy for a cleaning-auditing robot. The proposed approach uses proximal policy approximation (PPO) based on-policy learning method to generate waypoints and sampling decisions to explore the probable dirt accumulation regions in a given area. The policy network is trained in multiple environments with simulated dirt patterns. Experiment trials have been conducted to validate the trained policy in both simulated and real-world environments using an in-house developed cleaning audit robot called BELUGA.

scholarly journals A System for Outlier Detection of High Dimensional Data

2012 ◽  
pp. 197-201
Author(s):  
Bharat Gupta ◽  
Durga Toshniwal
Keyword(s):  
Outlier Detection ◽  
High Dimensional Data ◽  
Research Problem ◽  
High Dimensional ◽  
Full Data ◽  
Data Set ◽  
Detection Techniques ◽  
New Concepts ◽  
Low Dimensional ◽  
Important Research Problem

In high dimensional data large no of outliers are embedded in low dimensional subspaces known as projected outliers, but most of existing outlier detection techniques are unable to find these projected outliers, because these methods perform detection of abnormal patterns in full data space. So, outlier detection in high dimensional data becomes an important research problem. In this paper we are proposing an approach for outlier detection of high dimensional data. Here we are modifying the existing SPOT approach by adding three new concepts namely Adaption of Sparse Sub-Space Template (SST), Different combination of PCS parameters and set of non outlying cells for testing data set.

scholarly journals Topic Jerk Detector: Detection of Tweet Bursts Related to the Fukushima Daiichi Nuclear Disaster

Information ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 368 ◽  
Author(s):  
Hiroshi Nagaya ◽  
Teruaki Hayashi ◽  
Hiroyuki A. Torii ◽  
Yukio Ohsawa
Keyword(s):  
Social Media ◽  
Information Sharing ◽  
Social Issues ◽  
Research Problem ◽  
Nuclear Disaster ◽  
Social Media Platforms ◽  
Efficient Information ◽  
Fukushima Disaster ◽  
Important Research Problem

In recent disaster situations, social media platforms, such as Twitter, played a major role in information sharing and widespread communication. These situations require efficient information sharing; therefore, it is important to understand the trends in popular topics and the underlying dynamics of information flow on social media better. Developing new methods to help us in these situations, and testing their effectiveness so that they can be used in future disasters is an important research problem. In this study, we proposed a new model, “topic jerk detector.” This model is ideal for identifying topic bursts. The main advantage of this method is that it is better fitted to sudden bursts, and accurately detects the timing of the bursts of topics compared to the existing method, topic dynamics. Our model helps capture important topics that have rapidly risen to the top of the agenda in respect of time in the study of specific social issues. It is also useful to track the transition of topics more effectively and to monitor tweets related to specific events, such as disasters. We attempted three experiments that verified its effectiveness. First, we presented a case study applied to the tweet dataset related to the Fukushima disaster to show the outcomes of the proposed method. Next, we performed a comparison experiment with the existing method. We showed that the proposed method is better fitted to sudden burst accurately detects the timing of the bursts of the topic. Finally, we received expert feedback on the validity of the results and the practicality of the methodology.

Variants of the organization in transport in the distribution system

2018 ◽  
Vol 120 ◽  
pp. 319-330
Author(s):  
Dariusz Pyza ◽  
Monika Miętus
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Direct Consequence ◽  
Research Problem ◽  
Economic Effects ◽  
Main Task ◽  
Specific Group ◽  
Common Criterion ◽  
Important Research Problem ◽  
Made In

Distribution occupies a significant place in the elements of the logistics chain, because its main task is to meet the expectations set by the customer. The decisions regarding the method of selling goods made in enterprises can be classified as strategic. Their direct consequence is the company's economic effects. The article analyzed the popularity of transport in the distribution system and developed variant ways of delivering goods for a specific group of goods. An important research problem is the identification of distribution channels, which gather dependent and interacting organizations involved in the process of meeting the requirements of the buyer. An unambiguous assessment of the choice of distribution system depends on many criteria, which depending on the demand may be different. The most common criterion used to select a carriage is the time and cost of the task. The specificity of distribution systems shows that full-truck and groupage systems are the most often chosen.

scholarly journals PRIPRO—Privacy Profiles: User Profiling Management for Smart Environments

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1519 ◽  
Author(s):  
Jonas Cesconetto ◽  
Luís Augusto Silva ◽  
Fabricio Bortoluzzi ◽  
María Navarro-Cáceres ◽  
Cesar A. Zeferino ◽  
...  
Keyword(s):  
Pervasive Computing ◽  
User Profiling ◽  
Management Tool ◽  
Smart Sensors ◽  
Smart Environments ◽  
Android Application ◽  
Sensitive Data ◽  
Computing Systems ◽  
Daily Routines ◽  
Comfort Levels

Smart environments are pervasive computing systems that provide higher comfort levels on daily routines throughout interactions among smart sensors and embedded computers. The lack of privacy within these interactions can lead to the exposure of sensitive data. We present PRIPRO (PRIvacy PROfiles), a management tool that includes an Android application that acts on the user’s smartphone by allowing or blocking resources according to the context, in order to address this issue. Back-end web server processes and imposes a protocol according to the conditions that the user selected beforehand. The experimental results show that the proposed solution successfully communicates with the Android Device Administration framework, and the device appropriately reacts to the expected set of permissions imposed according to the user’s profile with low response time and resource usage.

scholarly journals Notes from the Editor

2017 ◽  
Vol 111 (2) ◽  
pp. iii-x
Keyword(s):  
Political Science ◽  
American Political Science Review ◽  
Research Problem ◽  
General Interest ◽  
Original Research ◽  
Political Issues ◽  
Theoretical Perspectives ◽  
Research Problems ◽  
American Political Science ◽  
Important Research Problem

In this issue's Notes from the Editors, we are excited to be able to present not only our first big innovation for theAmerican Political Science Review, ourletterformat, but also articles that are concurrent with present political affairs, a difficult task due to the intricacies of peer reviewed science. We would first like to draw attention to our new publication format,letters. We hope to further the idea of publishing important insights to research problems in political science and encourage scholarly debate in the discipline. Some of these insights, however, might not fit in the traditional, longerarticleformat, which is tailored to original work advancing the understanding of political issues that are of general interest to the field of political science. Instead, letters provide an opportunity to report about original research that moves the subfields of political science forward as they develop alongside their counterparts in related disciplines, such as new theoretical perspectives, methodological progress, alternative empirical findings, as well as comments on and extensions of existing work. Moreover, our letter format attempts to increase inter-disciplinary recognition by broadening readership and eventually authorship from scholars of other disciplines that address an important research problem in political science.

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