scholarly journals On the Analysis of PM/FM Noise Radar Waveforms Considering Modulating Signals with Varied Stochastic Properties

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1727
Author(s):  
Leandro Pralon ◽  
Gabriel Beltrao ◽  
Alisson Barreto ◽  
Bruno Cosenza
Keyword(s):  
Random Phase ◽  
Research Area ◽  
General Term ◽  
Radar Systems ◽  
Noise Radar ◽  
Random Samples ◽  
Spectrum Density ◽  
Important Research Area ◽  
Radar Waveforms ◽  
Stochastic Properties

Noise Radar technology is the general term used to describe radar systems that employ realizations of a given stochastic process as transmit waveforms. Originally, carriers modulated in amplitude by a Gaussian random signal, derived from a hardware noise source, were taken into consideration, justifying the adopted nomenclature. With the advances made in hardware as well as the rise of the software defined noise radar concept, waveform design emerges as an important research area related to such systems. The possibility of generating signals with varied stochastic properties increased the potential in achieving systems with enhanced performances. The characterization of random phase and frequency modulated waveforms (more suitable for several applications) has then gained considerable notoriety within the radar community as well. Several optimization algorithms have been proposed in order to conveniently shape both the autocorrelation function of the random samples that comprise the transmit signal, as well as their power spectrum density. Nevertheless, little attention has been driven to properly characterize the stochastic properties of those signals through closed form expressions, jeopardizing the effectiveness of the aforementioned algorithms as well as their reproducibility. Within this context, this paper investigates the performance of several random phase and frequency modulated waveforms, varying the stochastic properties of their modulating signals.

Research Commentary: Analysis of Curriculum Standards: An Important Research Area

2016 ◽  
Vol 47 (2) ◽  
pp. 118-133 ◽  
Author(s):  
Dung Tran ◽  
Barbara J. Reys ◽  
Dawn Teuscher ◽  
Shannon Dingman ◽  
Lisa Kasmer
Keyword(s):  
Mathematics Education ◽  
Education Reform ◽  
Careful Analysis ◽  
Research Area ◽  
State Standards ◽  
Content Standards ◽  
Curriculum Standards ◽  
Education Professionals ◽  
Important Research Area ◽  
Improving Student Learning

This commentary highlights the contribution that careful and systematic analyses of curriculum or content standards can make to questions and issues important in the mathematics education field. We note the increased role that curriculum standards have played as part of a standards-based education reform strategy. We also review different methods used by researchers to compare and analyze the Common Core State Standards for Mathematics, each method designed for a particular purpose. Finally, we call upon mathematics education researchers to engage in careful analysis of curriculum standards and to share their findings in ways that can inform public debate as well as support education professionals in improving student learning opportunities.

Climate change and employment relations

2017 ◽  
Vol 59 (5) ◽  
pp. 670-679 ◽  
Author(s):  
Caleb Goods
Keyword(s):  
Climate Change ◽  
Research Area ◽  
Research Note ◽  
Employment Relations ◽  
Important Research ◽  
Workplace Relations ◽  
Important Research Area

A central, yet overlooked, aspect of contemporary employment relations is the growing impact climate change is having on workplace relations. This research note outlines how climate change and workplace relations are linked, the minimal academic focus this important research area has received and the limited response from employment relations actors to the climate change challenge. Some examples of ‘climate bargaining’ are given to demonstrate both the connection between employment relations and climate change and to provide possible models for meaningfully advancing climate change actions in the workplace.

MULTI-LEVEL DOCUMENT IMAGE SEGMENTATION USING MULTI-LAYER PERCEPTRON AND SUPPORT VECTOR MACHINE

2012 ◽  
Vol 26 (06) ◽  
pp. 1253002 ◽  
Author(s):  
YAN ZHANG ◽  
BIN YU ◽  
HAI-MING GU
Keyword(s):  
Support Vector Machine ◽  
Image Segmentation ◽  
Research Area ◽  
Document Image ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
Document Image Analysis ◽  
Important Research Area ◽  
Multi Level ◽  
Document Image Segmentation

Document image segmentation is an important research area of document image analysis which classifies the contents of a document image into a set of text and non-text classes. Previous existing methods are often designed to classify text and halftone therefore they perform poorly in classifying graphics, tables and circuit, etc. In this paper, we present a robust multi-level classification method using multi-layer perceptron (MLP) and support vector machine (SVM) to segment the texts from non-texts and thereafter classify them as tables, graphics and halftones. This method outperforms previously existing methods by overcoming various issues associated with the complexity of document images. Experimental results prove the effectiveness of our proposed method. By virtue of our multi-level classification approach, the text components, halftone components, graphic components and table components are accurately classified respectively which would highly improve OCR accuracy to reduce garbage symbols as well as increase compression ratio thereafter simultaneously.

Could the Substance Misuse of a Relative be an Important Factor for Those Seeking Help for Anxiety and Depression? A Preliminary Prevalence Study within Improving Access to Psychological Therapies (IAPT)

2016 ◽  
Vol 44 (6) ◽  
pp. 723-729 ◽  
Author(s):  
Elizabeth Newton ◽  
Nicola Shepherd ◽  
Jim Orford ◽  
Alex Copello
Keyword(s):  
Severe Depression ◽  
Routine Data ◽  
Research Area ◽  
Close Relative ◽  
Service Users ◽  
Depression And Anxiety ◽  
Psychological Therapies ◽  
Important Research Area ◽  
Close Relatives ◽  
Drug Problems

Background: The psychological difficulties and emotional impacts resulting from the substance use of close relatives constitute a large, underestimated and frequently unidentified health burden. The development of primary care mental health services in response to the Improving Access to Psychological Therapies initiative provides an opportunity to investigate this in more depth. Aims: A preliminary exploration of prevalence of IAPT service-users being treated for moderate-severe depression and/or anxiety who report that they have relatives with alcohol and/or drug problems. To explore the characteristics of the sample including comparison with those without a substance misusing relative. Method: One hundred service users completed a brief questionnaire. Routine data on depression and anxiety symptoms were accessed for the full consenting sample. Descriptive statistics were used to explore the family members of substance users and differences to the rest of the sample. Results: Twenty-two of the 100 IAPT service users reported having a close relative whose use of substances was of concern to them. The group with a relative who used substances were more depressed at the beginning of treatment than the rest of the sample. Conclusions: A significant number of people seeking psychological help for depression and anxiety within IAPT services reported being concerned about a close relative who misuses substances. They may be more distressed than those without a relative who misuses substances. Further exploration is warranted but preliminary findings indicate that this is an important research area with significant clinical implications.

scholarly journals The Use of Literary Texts in EFL Coursebooks: An Exploratory Study

2020 ◽  
Vol 10 (3) ◽  
pp. 86
Author(s):  
Shaima Al-Saeed ◽  
Abdullah A. Alenezi
Keyword(s):  
Foreign Language ◽  
Exploratory Study ◽  
Early Period ◽  
Research Area ◽  
Higher Learning ◽  
Literary Texts ◽  
Important Research ◽  
Institutions Of Higher Learning ◽  
Important Research Area ◽  
And Linguistics

This exploratory study investigates the use of literary texts in English as a foreign language (EFL) coursebooks and examines the extent to which literature is used within the coursebooks, the types of texts used as regards authenticity and recency, the criteria for selecting and adapting the texts and the ways of improving the selection and adaptation process. Multiple articles written on this subject show that the evaluation of EFL coursebooks is a relevant and important research area in the study of language and linguistics. This study gives a survey of the extent to which literary texts are used in EFL coursebooks within institutions of higher learning in Kuwait and worldwide. In this study, 44 popular EFL coursebooks (between 2015 and 2019) within higher education institutes, including those in Kuwait, were analysed. The findings demonstrated that literary texts are not included in many of the coursebooks used nowadays and that the literary texts selected were primarily from an early period (more than a century ago). Furthermore, the results revealed that the coursebooks include a large percentage of inauthentic, ill-adapted works. Consequently, this study recommends incorporating authentic literary texts in EFL coursebooks comprising modern literature.

scholarly journals Exploring status consumption in South Africa: a literature review

2016 ◽  
Vol 13 (3) ◽  
pp. 131-136 ◽  
Author(s):  
Nkosivile Welcome Madinga ◽  
Eugine Tafadzwa Maziriri ◽  
Thobekani Lose
Keyword(s):  
South Africa ◽  
Research Area ◽  
Important Research ◽  
Middle Income ◽  
Status Consumption ◽  
Income Groups ◽  
The Status ◽  
Goods Market ◽  
Status Goods ◽  
Important Research Area

South Africa is one of the most important countries in the status goods market. In addition, it has the biggest share from the status consumption market in Africa and it is amongst fastest growing countries worldwide in status consumption. The growth in status consumption in South Africa is attributed to the growth of the high-income and middle-income groups. As the demand for status increases and status goods become more available, the concept of status has become an important research area for academics and marketers. The aim of this study is to explore the concept of status consumption and provide an overview of status consumption. In this study, the literature has been reviewed for the studies on the same subject to make a compilation

scholarly journals LAR: A User Behavior Prediction Model in Server Log Based on LSTM-Attention Network and RSC Algorithm

2020 ◽  
Author(s):  
Yingying Shang
Keyword(s):  
Clustering Algorithm ◽  
Short Term Memory ◽  
User Behavior ◽  
Absolute Error ◽  
Research Area ◽  
Behavior Prediction ◽  
Attention Network ◽  
Important Research Area ◽  
User Access ◽  
Single User

Using server log data to predict the URLs that a user is likely to visit is an important research area in user behavior prediction. In this paper, a predictive model (called LAR) based on the long short-term memory (LSTM) attention network and reciprocal-nearest-neighbors supported clustering algorithm (RSC) for predicting the URL is proposed. First, the LSTM-attention network is used to predict the URL categories a user might visit, and the RSC algorithm is then used to cluster users. Subsequently, the URLs belonging to the same category are determined from the user clusters to predict the URLs that the user might visit. The proposed LAR model considers the time sequence of the user access URL, and the relationship between a single user and group users, which effectively improves the prediction accuracy. The experimental results demonstrate that the LAR model is feasible and effective for user behavior prediction. The accuracy of the mean absolute error and root mean square error of the LAR model are better than those of the other models compared in this study.

Formation of Multiple-Robots Using Vision Based Approach

2013 ◽  
Vol 419 ◽  
pp. 768-773 ◽  
Author(s):  
Bayanjargal Baasandorj ◽  
Aamir Reyaz ◽  
Batmunkh Battulga ◽  
Deok Jin Lee ◽  
Kil To Chong
Keyword(s):  
Artificial Intelligence ◽  
Mobile Robot ◽  
Research Area ◽  
Multiple Robots ◽  
Important Research ◽  
Communication Device ◽  
Important Research Area ◽  
Bluetooth Communication

Multi-robots system has grown enormously with a large variety of topics being addressed. It is an important research area within the robotics and artificial intelligence. By using the vision based approach this paper deals with the formation of multiple-robots. Three NXT robots were used in the experiment and all the three robots work together as one virtual mobile robot. In addition to these things we also used TCP/IP socket, ArToolKit, NXT robot, Bluetooth communication device. And for programming C++ was used. Results achieved from the experiment were highly successful.

Decoding the coupling between the brain and skin reactions in auditory stimulation by information-based analysis of EEG and GSR signals

2021 ◽  
pp. 1-10
Author(s):  
Najmeh Pakniyat ◽  
Hamidreza Namazi
Keyword(s):  
Brain Activity ◽  
Skin Resistance ◽  
Research Area ◽  
Important Research ◽  
Skin Reactions ◽  
Analysis Technique ◽  
Information Contents ◽  
Important Research Area ◽  
Electroencephalogram Eeg ◽  
The Brain

BACKGROUND: The analysis of brain activity in different conditions is an important research area in neuroscience. OBJECTIVE: This paper analyzed the correlation between the brain and skin activities in rest and stimulations by information-based analysis of electroencephalogram (EEG) and galvanic skin resistance (GSR) signals. METHODS: We recorded EEG and GSR signals of eleven subjects during rest and auditory stimulations using three pieces of music that were differentiated based on their complexity. Then, we calculated the Shannon entropy of these signals to quantify their information contents. RESULTS: The results showed that music with greater complexity has a more significant effect on altering the information contents of EEG and GSR signals. We also found a strong correlation (r= 0.9682) among the variations of the information contents of EEG and GSR signals. Therefore, the activities of the skin and brain are correlated in different conditions. CONCLUSION: This analysis technique can be utilized to evaluate the correlation among the activities of various organs versus brain activity in different conditions.

Comparison of Linguistic Summaries and Fuzzy Functional Dependencies Related to Data Mining

2014 ◽  
pp. 174-203 ◽  
Author(s):  
Miroslav Hudec ◽  
Miljan Vučetić ◽  
Mirko Vujošević
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Relational Databases ◽  
Missing Values ◽  
Expert Knowledge ◽  
Real Data ◽  
Research Area ◽  
Functional Dependencies ◽  
Useful Knowledge ◽  
Important Research Area

Data mining methods based on fuzzy logic have been developed recently and have become an increasingly important research area. In this chapter, the authors examine possibilities for discovering potentially useful knowledge from relational database by integrating fuzzy functional dependencies and linguistic summaries. Both methods use fuzzy logic tools for data analysis, acquiring, and representation of expert knowledge. Fuzzy functional dependencies could detect whether dependency between two examined attributes in the whole database exists. If dependency exists only between parts of examined attributes' domains, fuzzy functional dependencies cannot detect its characters. Linguistic summaries are a convenient method for revealing this kind of dependency. Using fuzzy functional dependencies and linguistic summaries in a complementary way could mine valuable information from relational databases. Mining intensities of dependencies between database attributes could support decision making, reduce the number of attributes in databases, and estimate missing values. The proposed approach is evaluated with case studies using real data from the official statistics. Strengths and weaknesses of the described methods are discussed. At the end of the chapter, topics for further research activities are outlined.

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