Random Sampling and Signal Bregman Reconstruction Based on Compressed Sensing

2016 ◽  
Vol 4 (2) ◽  
pp. 365
Author(s):  
Guojun Qin ◽  
Jingfang Wang
Keyword(s):  
Compressed Sensing ◽  
Random Sampling ◽  
Sampling Method ◽  
Sampling Rate ◽  
Sparse Sampling ◽  
Sampling Theorem ◽  
Sampling Frequency ◽  
Frequency Resolution ◽  
Observation Matrix ◽  
The Fourier Transform

Compressed sensing (CS) sampling is a sampling method which is based on the signal sparse. Much information can be extracted from as little as possible of the data by applying CS and this method is the idea of great theoretical and applied prospects. In the framework of compressed sensing theory, the sampling rate is no longer decided in the bandwidth of the signal, but it depends on the structure and content of the information in the signal. In this paper, the signal is the sparse in the Fourier transform and random sparse sampling is advanced by programing random observation matrix for peak random base. The signal is successfully restored by the use of Bregman algorithm. The signal is described in the transform space, and a theoretical framework is established with a new signal descriptions and processing. By making the case to ensure that the information loss, signal is sampled at much lower than the Nyquist sampling theorem requiring rate,but also the signal is completely restored in high probability. The random sampling has following advantages: alias-free sampling frequency need not obey the Nyquist limit and higher frequency resolution.

The Random Sampling Method for Gas Sensing Signal Based on Compressed Sensing

2014 ◽  
Vol 496-500 ◽  
pp. 1739-1743
Author(s):  
Qing Luo ◽  
Bao He Yang ◽  
Dong Mei Li
Keyword(s):  
Compressed Sensing ◽  
Sensor Network ◽  
Gas Sensor ◽  
Random Sampling ◽  
Sampling Method ◽  
Gas Sensing ◽  
Low Cost ◽  
Sampling Frequency ◽  
Signal Acquisition ◽  
Recovery Algorithm

This paper presents a random sampling method for gas signal,which is based on the principle called compressed sensing. This method is suitable for gas sensor network, and it is conducive to the realization of low power, low cost of the gas sensor network. According to the characteristics of the output signal of gas sensor, we design the random sampling method which acquire the useful information in signal. Using compressed sensing recovery algorithm to reconstruct signal,we finally fulfill complete signal collection. The simulation results show that the method we proposed can achieve signal acquisition with a sampling frequency less than 1/5 of standard sampling frequency.

Determining the minimum sampling frequency for ground measurements of burn severity

2018 ◽  
Vol 27 (6) ◽  
pp. 387
Author(s):  
Alexander W. Holmes ◽  
Christoph Rüdiger ◽  
Sarah Harris ◽  
Nigel Tapper
Keyword(s):  
Monte Carlo Simulation ◽  
Random Sampling ◽  
Fire Management ◽  
Sampling Methods ◽  
Sampling Rate ◽  
Sampling Frequency ◽  
Burn Severity ◽  
Minimum Sample Size ◽  
Field Sampling ◽  
Transect Sampling

Understanding burn severity is essential to provide an overview of the precursory conditions leading to fires as well as understanding the constraints placed on fire management services when mitigating their effects. Determining the minimum sampling frequency for ground measurements is not only essential for accurately assessing burn severity, but also for fire managers to better allocate resources and reduce the time and costs associated with sampling. In this study, field sampling methods for assessing burn severity are analysed statistically for 10 burn sites across Victoria, Australia, with varying spatial extents, topography and vegetation. Random and transect sampling methods are compared against each other using a Monte Carlo simulation to determine the minimum sample size needed for a difference of 0.02 (2%) in the severity classes proportions relative to the population proportions. We show that, on average, transect sampling requires a sampling rate of 3.16% compared with 0.59% for random sampling. We also find that sites smaller than 400 ha require a sampling rate of between 1.4 and 2.8 times that of sites larger than 400 ha to achieve the same error. The information obtained from this study will assist fire managers to better allocate resources for assessing burn severity.

A New Random Sampling Method based on Compressed Sensing

2018 ◽  
Author(s):  
J.L. Yang ◽  
Y.D. Ni ◽  
X.F. Zou ◽  
Y.J. Lan ◽  
M.J. Yu
Keyword(s):  
Compressed Sensing ◽  
Random Sampling ◽  
Sampling Method

Ultrasonic Phased Array Industrial Imaging with Sub-Nyquist Sampling Rate

2013 ◽  
Vol 347-350 ◽  
pp. 327-331
Author(s):  
Guang Zhi Dai ◽  
Guo Qiang Han ◽  
Xian Yue Ouyang
Keyword(s):  
High Speed ◽  
Phased Array ◽  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Sampling Rate ◽  
Sampling Frequency ◽  
Frequency Resolution ◽  
Signal Frequency ◽  
Beam Focusing ◽  
Ultrasonic Phased Array

this paper uses a new type of FRI (Finite Rate of Innovation) sampling pattern based Sub-Nyquist sampling model breaked through Shannon theorem that it can get accurate signal reconstruction based on signal information rate, which requires the sampling frequency lower than two times the max signal frequency. We apply the new model in the ultrasonic phased array industrial imaging. In the experiment, ultrasonic phased array realized dynamic focusing and the high speed scan by ultrasonic array transducer of various array time delays to get flexible controllable synthesis beam composed signals that received by 32 phased array elements . The results indicate that in the model it greatly reduces the signal sampling frequency and improves the signal-to-noise ratio, frequency resolution at the same of the beam focusing and steering flexible.

Research on Fabric Image Acquisition Based on Compressed Sensing

2014 ◽  
Vol 989-994 ◽  
pp. 3698-3701
Author(s):  
Ji Cheng Dong ◽  
Sheng Qi Guan ◽  
Long Long Chen
Keyword(s):  
Theoretical Analysis ◽  
Compressed Sensing ◽  
Textile Industry ◽  
Image Acquisition ◽  
Sampling Rate ◽  
Image Data ◽  
Sampling Theorem ◽  
High Quality ◽  
Shannon’S Sampling Theorem

Amount of data in collecting data of fabric image in the textile industry put forward a new challenge to sensor end. Compressed Sensing (CS) breaks limit of conventional Shannon’s sampling theorem, so we can reconstruct a signal in Sub-sampling rate. In addition, theoretical analysis tells us that collecting the fabric image data by CS method have a better advantage than collecting the general image data. Having reconstructed three fabric images and one general image by CS method, we can easily find that the former have a high quality of reconstruction.

Faktor-Faktor Yang Mempengaruhi Keputusan Mahasiswa Memilih Program Studi Kebidanan STIKES Muhammadiyah Ciamis Tahun 2016

2018 ◽  
Vol 5 (1) ◽  
pp. 69-82
Author(s):  
Evi Mariana
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Random Sampling ◽  
Sampling Method ◽  
Marketing Mix ◽  
Stratified Random Sampling ◽  
Price Promotion ◽  
F Test ◽  
Physical Evidence

The purpose of this study was to analyze the factors that influence the decisionof the students chose to study in Obstetrics Prodi STIKES Muhammadiyah Ciamis and analyze the factors that most influence the decision of the students chose to study in Obstetrics Prodi STIKES Muhammadiyah Ciamis. Collecting data in this study was conducted using a survey by questionnaire to 114 students by stratified random sampling method. Methods of data analysis using multiple linear regression, F test and test T. The result is a marketing mix that significantly is the product, place, and physical evidence. And that does not affect the marketing mix is price, promotion, place, and processes

BANKACILIKTA MÜŞTERİ İLİŞKİLERİ YÖNETİMİNİN MÜŞTERİ SADAKATİNE ETKİSİ

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Fikret GÜMÜŞBUĞA
Keyword(s):  
Customer Loyalty ◽  
Random Sampling ◽  
Management System ◽  
Care Management ◽  
Sampling Method ◽  
Simple Random Sampling ◽  
Face To Face ◽  
Descriptive Research ◽  
Customer Care ◽  
Research Type

This study mainly focuses on customer care management and customer loyalty. Even though there are many experiential studies about customer care management and customer loyalty system, the lack of studies on customers in Karabük and Safranbolu locally, has leaded to focus on this study. Thus, this study mainly focuses on the influence of customer care treatments of banks in Karabük and Safranbolu on customer loyalty. Descriptive research type was used in the study. In this study simple random sampling method was used which is one of the probability sampling method, face to face surwey to all 726 participants was used for the study. As the result of the experiential study, the attendance and influence of customer care management and loyalty systems have been comparatively low, but it has been figured out that customer care management system influences customer loyalty level.

A Scale Development Approach To Employees’ Perspective Of Knowledge Management In Banks

2019 ◽  
Vol 118 (7) ◽  
pp. 82-94
Author(s):  
DR.C. KATHIRAVAN ◽  
DR.M. MANIVANNAN ◽  
E.CHANDRA MOULI ◽  
A. RAJASEKAR
Keyword(s):  
Knowledge Management ◽  
Random Sampling ◽  
Tamil Nadu ◽  
Sampling Method ◽  
Sampling Technique ◽  
Technology Knowledge ◽  
Interview Method ◽  
Development Approach ◽  
Chennai City ◽  
Future Topics

The data were collected using personal interview method and a total of 455 employees who were in some aspect knowledge management in banks and identified through multistage random sampling method. Multistage random sampling technique is a probability sampling type where available study topics employ future topics from among their contacts. The study was limited to Chennai city of Tamil Nadu. The analysis found that banks employees moderately perceived towards factors of knowledge management such as knowledge utilization, information technology, knowledge motivation, knowledge storage, knowledge sharing enablers and knowledge creation. Hence, it is concluded that training program is important for the survival of knowledge management. It is also imperative for effective of employees’ job performance.

Kapasitas Adaptasi dan Resiliensi Komunitas Menghadapi Bencana Erupsi Gunung Merapi

2019 ◽  
Vol 2 (5) ◽  
pp. 617
Author(s):  
Nyimas Ayu Dillashandy ◽  
Nurmala K Panjaitan
Keyword(s):  
Adaptive Capacity ◽  
Random Sampling ◽  
Qualitative Data ◽  
Sampling Method ◽  
Sampling Technique ◽  
Community Resilience ◽  
Simple Random Sampling ◽  
Quantitative Approach ◽  
Institutional Memory ◽  
Innovative Learning

Mount Merapi eruption has occurred several times in Indonesia and the biggest eruption that last occurred in 2010. The community were suffered losses and were affected by eruptions. The purposes of this research are to analyze community resilience, to analyze the level of vulnerability, and to analize the community adaptive capacity. The research using a quantitative approach supported by qualitative data. Simple random sampling technique is used as the sampling method and the informant was taken purposively. The results of this research showed that when the eruption occurred the community has a high vulnerability. The adaptive capacity is also high with innovative learning based on institutional memory and supported by the connectedness. Communities achieve resilience and can adapt to changes with high adaptive capacity.  Keywords: adaptive capacity, community resilience, eruption, vulnerability ABSTRAK Erupsi Gunung Merapi sudah terjadi beberapa kali di Indonesia dan erupsi terbesar yang terjadi terakhir kalinya yaitu pada tahun 2010. Komunitas mengalami berbagai kerugian dan terkena dampak dari erupsi. Tujuan dari penelitian ini adalah untuk menganalisis resiliensi komunitas, menganalisis tingkat kerentanan komunitas, dan menganalisis kapasitas adaptasi komunitas. Penelitian ini dilaksanakan dengan menggunakan pendekatan kuantitatif yang didukung oleh data kualitatif. Pemilihan responden dilakukan dengan teknik sampel acak sederhana sedangkan pemilihan terhadap informan dilakukan secara sengaja. Hasil penelitian ini menunjukkan bahwa saat erupsi terjadi komunitas memiliki kerentanan yang tinggi. Kapasitas adaptasi komunitas tinggi dengan adanya innovative learning yang didasari oleh pengetahuan dan pengalaman dan didukung oleh jaringan yang dimiliki. Komunitas berhasil mencapai resiliensi dan dapat beradaptasi dengan perubahan-perubahan dengan kapasitas adaptasi yang tinggi.Kata kunci : kapasitas adaptasi, kerentanan, erupsi, resiliensi komunitas

PENGARUH NILAI PELANGGAN, INOVASI PRODUK DAN KUALITAS LAYANAN TERHADAP LOYALITAS NASABAH BANK MUAMALAT INDONESIA CABANG MADIUN

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Binti Mutafarida
Keyword(s):  
Quality Of Service ◽  
Customer Loyalty ◽  
Random Sampling ◽  
Product Innovation ◽  
Customer Value ◽  
Research Method ◽  
Sampling Method ◽  
Descriptive Research ◽  
Independent Variable

The development of sharia banking in Indonesia very rapidly grow up preceded by Bank Muamalat Indonesia and in 2013 ranked as the bank with the highest loyalty and the best among other sharia banking nationally. Bank Muamalat Indonesia branch of Madiun is one of the first branch in Madiun and currently has many competitors from other sharia banking. Based on this background, in this study take what factors affect the size of customer loyalty Bank Muamalat Indonesia branch of Madiun. Based on the result of research, it is found that the level of loyalty of customer of Bank Muamalat of Madiun branch is mostly influenced by product innovation with value of t test value obtained by t-count 2,493, while second factor is influenced by service quality with result of tcount 2,268. So the least influenced factor by the value of the customer is with value of 2.217. This research is a descriptive research method and associative / relationship, this matter to know the value of independent variable. While population of this research is funding customer of Bank Muamalat Indonesia branch of Madiun with amount of 22.196 customer by taking data using random sampling method as much as 108 customer. Keyword: Customer Value, Product Innovation AND Quality Of Service

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