scholarly journals A Novel Approach to Evaluate Reduced Inter Symbol Interference in UFMC Systems

2021 ◽  
Author(s):  
Elavel Visuvanathan. G ◽  
Jaya. T
Keyword(s):  
Original Data ◽  
Adaptive Equalization ◽  
Modulation Scheme ◽  
Fifth Generation ◽  
Novel Approach ◽  
Adaptive Equalizers ◽  
The Ideal ◽  
Equalization Method ◽  
Inter Symbol Interference

The UFMC modulation scheme has been proposed as a solid competitive framework for future portable fifth generation communication. UFMC can be considered as a candidate waveform for 5G communications since it gives strength against Inter Symbol Interference (ISI) [1]. Inter-symbol interference prompted error can make the receiver neglect to reproduce the original data. Equalizers in the receivers, which are extraordinary sorts of filters, moderate the direct twisting created by the channel [2]. On the off chance that the channel’s time-fluctuating qualities are known from the earlier, at that point, the ideal setting for equalizers can be worked out. But in practical systems the channel’s time-changing attributes are not known from the earlier, so adaptive equalization method is applied in this paper based on the LMS algorithms. Adaptive equalizers are adjusted, or change the estimation of its taps as time advances [3].

scholarly journals COMPARATIVE ANALYSIS OF MODULATION SCHEMES USING ADAPTIVE EQUALIZATION AS FADING MITIGATION TECHNIQUE

2012 ◽  
pp. 25-31
Author(s):  
SABITA NAHATA ◽  
SUBRATA BHATTACHARYA
Keyword(s):  
Comparative Analysis ◽  
Data Transmission ◽  
High Speed ◽  
Signal To Noise Ratio ◽  
Multipath Fading ◽  
Symbol Error Rate ◽  
Adaptive Equalization ◽  
Modulation Scheme ◽  
Modulation Schemes ◽  
Mitigation Technique

Inter-symbol interference (ISI) due to multipath fading is a vital problem in high-speed wireless communication which restricts communication quality and capacity. Therefore, in addition to choosing a fading mitigation technique, it is also important to strategically select a modulation scheme for effective data transmission. Recent literature review on wireless standards, such as 3G and 4G indicates that QAM and QPSK are suitable choices for data transmission. In this paper, a comparative analysis on selected modulation schemes is performed in a fading environment. The mitigation of fading is done using adaptive equalization technique. Also, we show that the signal to noise ratio (SNR) is an important parameter to choose. It is observed that, even when an adaptive equalizer is used at the receiver, a very low SNR gives very high symbol error rate (SER). We derive some important conclusions from our simulation result: QPSK shows minimum SER, whereas 256-PSK and 256-PAM perform worse. Given its spectral efficiency and a low SER, the best choice is 256- QAM.

scholarly journals Opposition Intensity-Based Cuckoo Search Algorithm for Data Privacy Preservation

2019 ◽  
Vol 29 (1) ◽  
pp. 1441-1452 ◽  
Author(s):  
G.K. Shailaja ◽  
C.V. Guru Rao
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Original Data ◽  
Cuckoo Search Algorithm ◽  
Research Issues ◽  
Novel Approach ◽  
Two Phases

Abstract Privacy-preserving data mining (PPDM) is a novel approach that has emerged in the market to take care of privacy issues. The intention of PPDM is to build up data-mining techniques without raising the risk of mishandling of the data exploited to generate those schemes. The conventional works include numerous techniques, most of which employ some form of transformation on the original data to guarantee privacy preservation. However, these schemes are quite multifaceted and memory intensive, thus leading to restricted exploitation of these methods. Hence, this paper intends to develop a novel PPDM technique, which involves two phases, namely, data sanitization and data restoration. Initially, the association rules are extracted from the database before proceeding with the two phases. In both the sanitization and restoration processes, key extraction plays a major role, which is selected optimally using Opposition Intensity-based Cuckoo Search Algorithm, which is the modified format of Cuckoo Search Algorithm. Here, four research issues, such as hiding failure rate, information preservation rate, and false rule generation, and degree of modification are minimized using the adopted sanitization and restoration processes.

scholarly journals Ellipse Coefficient Map-Based Geomagnetic Fingerprint Considering Azimuth Angles

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 708
Author(s):  
Ju-Hyeon Seong ◽  
Seung-Hyun Lee ◽  
Kyoung-Kuk Yoon ◽  
Dong-Hoan Seo
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Azimuth Angle ◽  
High Signal ◽  
Linear Least Squares ◽  
Positioning Accuracy ◽  
Signal Stability ◽  
Novel Approach ◽  
Magnetic Vectors ◽  
Equalization Method

Geomagnetic fingerprint has been actively studied because of the high signal stability and positioning resolution even when the time has elapsed. However, since the measured three-axis geomagnetism signals at one position are irregular according to the change of the azimuth angle, a large-sized database which is stored magnitudes per angles is required for robust and accurate positioning against the change of the azimuth angle. To solve this problem, this paper proposes a novel approach, an elliptic coefficient map based geomagnetic fingerprint. Unlike the general fingerprint, which stores strength or magnitude of the geomagnetism signals depending on the position, the proposed algorithm minimized the size of databased by storing the Ellipse coefficient map through the ellipse equation derived from the characteristics of 2-D magnetic vectors depending on the position. In addition, the curvature bias of ellipse was reduced by applying the normalized linear least-squares method to 2-D geomagnetic characteristics and the positioning accuracy was improved by applying the weighted geomagnetic signal equalization method.

scholarly journals Privacy Protection Method for Vehicle Trajectory Based on VLPR Data

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Hua Chen ◽  
Chen Xiong ◽  
Jia-meng Xie ◽  
Ming Cai
Keyword(s):  
Mathematical Model ◽  
Data Acquisition ◽  
Privacy Protection ◽  
Rapid Development ◽  
Original Data ◽  
License Plate ◽  
Model Based ◽  
Protection Method ◽  
Novel Approach ◽  
Vehicle Trajectory

With the rapid development of data acquisition technology, data acquisition departments can collect increasingly more data. Various data from government agencies are gradually becoming available to the public, including license plate recognition (VLPR) data. As a result, privacy protection is becoming increasingly significant. In this paper, an adversary model based on passing time, color, type, and brand of VLPR data is proposed. Through experimental analysis, the tracking probability of a vehicle’s trajectory can be more than 94% if utilizing the original data. To decrease the tracking probability, a novel approach called the (m, n)-bucket model based on time series is proposed since previous works, such as those using generalization and bucketization models, cannot deal with data with multiple sensitive attributes (SAs) or data with time correlations. Meanwhile, a mathematical model is established to expound the privacy protection principle of the (m, n)-bucket model. By comparing the average calculated linking probability of all individuals and the actual linking probability, it is shown that the mathematical model that is proposed can well expound the privacy protection principle of the (m, n)-bucket model. Extensive experiments confirm that our technique can effectively prevent trajectory privacy disclosures.

Improvement of Amplitude-Shift-Keying Signal Quality by Employing an Effective Spectrum Equalization Method in a Combined FSK/ASK Modulation Scheme

2005 ◽  
Vol 22 (8) ◽  
pp. 1948-1950 ◽  
Author(s):  
Xin Xiang-Jun ◽  
P. S André ◽  
A. L. J Teixeira ◽  
Yu Chong-Xiu ◽  
Ana Ferreira ◽  
...  
Keyword(s):  
Modulation Scheme ◽  
Signal Quality ◽  
Amplitude Shift Keying ◽  
Shift Keying ◽  
Equalization Method

A Winch Fault Classification Algorithm Based on Cluster Kernel Semi-Supervised Support Vector Machine

2012 ◽  
Vol 220-223 ◽  
pp. 452-458
Author(s):  
Xian Xin Shi ◽  
Zhong Xiang Zhao ◽  
Chang Jian Zhu ◽  
Xiao Xiao Kong ◽  
Jun Fei Chai ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Spectral Clustering ◽  
Cluster Algorithm ◽  
Original Data ◽  
Fault Classification ◽  
Support Vector ◽  
The Novel ◽  
High Classification Accuracy ◽  
Novel Approach ◽  
Cluster Assumption

A cluster kernel semi-supervised support vector machine (CKS3VM) based on spectral cluster algorithm is proposed and applied in winch fault classification in this paper. The spectral clustering method is used to re-represent original data samples in an eigenvector space so as to make the data samples in the same cluster gather together much better. Then, a cluster kernel function is constructed upon the eigenvector space. Finally, a cluster kernel S3VM is designed which can satisfy the cluster assumption of semi-supervised study. The experiments on winch fault classification show that the novel approach has high classification accuracy.

scholarly journals Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources (Preprint)

2017 ◽  
Author(s):  
Yingxiang Huang ◽  
Junghye Lee ◽  
Shuang Wang ◽  
Jimeng Sun ◽  
Hongfang Liu ◽  
...  
Keyword(s):  
Medical Information ◽  
Original Data ◽  
Global Model ◽  
Local Models ◽  
Multiple Sources ◽  
Model Combining ◽  
Novel Approach ◽  
Private Data ◽  
Wide Range ◽  
Medical Concepts

BACKGROUND Data sharing has been a big challenge in biomedical informatics because of privacy concerns. Contextual embedding models have demonstrated a very strong representative capability to describe medical concepts (and their context), and they have shown promise as an alternative way to support deep-learning applications without the need to disclose original data. However, contextual embedding models acquired from individual hospitals cannot be directly combined because their embedding spaces are different, and naive pooling renders combined embeddings useless. OBJECTIVE The aim of this study was to present a novel approach to address these issues and to promote sharing representation without sharing data. Without sacrificing privacy, we also aimed to build a global model from representations learned from local private data and synchronize information from multiple sources. METHODS We propose a methodology that harmonizes different local contextual embeddings into a global model. We used Word2Vec to generate contextual embeddings from each source and Procrustes to fuse different vector models into one common space by using a list of corresponding pairs as anchor points. We performed prediction analysis with harmonized embeddings. RESULTS We used sequential medical events extracted from the Medical Information Mart for Intensive Care III database to evaluate the proposed methodology in predicting the next likely diagnosis of a new patient using either structured data or unstructured data. Under different experimental scenarios, we confirmed that the global model built from harmonized local models achieves a more accurate prediction than local models and global models built from naive pooling. CONCLUSIONS Such aggregation of local models using our unique harmonization can serve as the proxy for a global model, combining information from a wide range of institutions and information sources. It allows information unique to a certain hospital to become available to other sites, increasing the fluidity of information flow in health care.

scholarly journals Linear Manifold Regularization with Adaptive Graph for Semi-supervised Dimensionality Reduction

2017 ◽  
Author(s):  
Kai Xiong ◽  
Feiping Nie ◽  
Junwei Han
Keyword(s):  
Dimensionality Reduction ◽  
Objective Function ◽  
Linear Manifold ◽  
Original Data ◽  
Manifold Regularization ◽  
Redundant Information ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Optimal Graph ◽  
Subsequent Task

Many previous graph-based methods perform dimensionality reduction on a pre-defined graph. However, due to the noise and redundant information in the original data, the pre-defined graph has no clear structure and may not be appropriate for the subsequent task. To overcome the drawbacks, in this paper, we propose a novel approach called linear manifold regularization with adaptive graph (LMRAG) for semi-supervised dimensionality reduction. LMRAG directly incorporates the graph construction into the objective function, thus the projection matrix and the optimal graph can be simultaneously optimized. Due to the structure constraint, the learned graph is sparse and has clear structure. Extensive experiments on several benchmark datasets demonstrate the effectiveness of the proposed method.

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