Search results for: Noise analysis

Authors: S. Rozhkova, O. Rozhkova, A. Harlova, V. Lasukov

Abstract:

We have conducted the optimal synthesis of rootmean- squared objective filter to estimate the state vector in the case if within the observation channel with memory the anomalous noises with unknown mathematical expectation are complement in the function of the regular noises. The synthesis has been carried out for linear stochastic systems of continuous - time.

Keywords: Mathematical expectation, filtration, anomalous noise, memory.

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Authors: Hyun-Woo Cho

Abstract:

Real-time or in-line process monitoring frameworks are designed to give early warnings for a fault along with meaningful identification of its assignable causes. In artificial intelligence and machine learning fields of pattern recognition various promising approaches have been proposed such as kernel-based nonlinear machine learning techniques. This work presents a kernel-based empirical monitoring scheme for batch type production processes with small sample size problem of partially unbalanced data. Measurement data of normal operations are easy to collect whilst special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing process monitoring performance. Furthermore, preprocessing of raw process data is used to get rid of unwanted variation of data. The performance of the monitoring scheme was demonstrated using three-dimensional batch data. The results showed that the monitoring performance was improved significantly in terms of detection success rate of process fault.

Keywords: Process Monitoring, kernel methods, multivariate filtering, data-driven techniques, quality improvement.

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Authors: Leszek Chybowski, Artur Bejger, Katarzyna Gawdzińska

Abstract:

Subversion analysis is a tool used in the TRIZ (Theory of Inventive Problem Solving) methodology. This article introduces the history and describes the process of subversion analysis, as well as function analysis and analysis of the resources, used at the design stage when generating possible undesirable situations. The article charts the course of subversion analysis when applied to a fuel injection nozzle of a marine engine. The work describes the fuel injector nozzle as a technological system and presents principles of analysis for the causes of a cracked tip of the nozzle body. The system is modelled with functional analysis. A search for potential causes of the damage is undertaken and a cause-and-effect analysis for various hypotheses concerning the damage is drawn up. The importance of particular hypotheses is evaluated and the most likely causes of damage identified.

Keywords: Complex technical system, fuel injector, function analysis, importance analysis, resource analysis, sabotage analysis, subversion analysis, TRIZ.

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Authors: B. L. Ho, L. Shi, D. F. Wang, V. C. T. Mok

Abstract:

The brain’s functional connectivity while temporally non-stationary does express consistency at a macro spatial level. The study of stable resting state connectivity patterns hence provides opportunities for identification of diseases if such stability is severely perturbed. A mathematical model replicating the brain’s spatial connections will be useful for understanding brain’s representative geometry and complements the empirical model where it falls short. Empirical computations tend to involve large matrices and become infeasible with fine parcellation. However, the proposed analytical model has no such computational problems. To improve replicability, 92 subject data are obtained from two open sources. The proposed methodology, inspired by financial theory, uses multivariate regression to find relationships of every cortical region of interest (ROI) with some pre-identified hubs. These hubs acted as representatives for the entire cortical surface. A variance-covariance framework of all ROIs is then built based on these relationships to link up all the ROIs. The result is a high level of match between model and empirical correlations in the range of 0.59 to 0.66 after adjusting for sample size; an increase of almost forty percent. More significantly, the model framework provides an intuitive way to delineate between systemic drivers and idiosyncratic noise while reducing dimensions by more than 30 folds, hence, providing a way to conduct attribution analysis. Due to its analytical nature and simple structure, the model is useful as a standalone toolkit for network dependency analysis or as a module for other mathematical models.

Keywords: Functional magnetic resonance imaging, multivariate regression, network hubs, resting state functional connectivity.

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Authors: Young Kuen Cho, Dae Won Lee, Young-Jin Jung, Sung Kuk Kim, Dong-Hyun Seo, Chang-Yong Ko, Han Sung Kim

Abstract:

Many medical devices are driven by motor pumps. Some researchers reported that the vibration mainly affected medical devices using a motor pump. The purpose of this study was to examine the effect of stiffness and damping coefficient in a 3-dimensional (3D) model of a motor pump and spring. In the present paper, experimental and mathematical tests for the moments of inertia of the 3D model and the material properties were investigated by an INSTRON machine. The response surfaces could be generated by using 3D multi-body analysis and the design of experiment method. It showed that differences in contours of the response surface were clearly found for the particular area. Displacement of the center of the motor pump was decreased at K≈2000 N/M, C≈12.5 N-sec/M. However, the frequency was increased at K≈2000 N/M, C≈15 N-sec/M. In this study, this study suggested experimental technique for vibration reduction for a motor pump in medical device. The combined method suggested in this study will greatly contribute to design of medical devices concerning vibration and noise intervention.

Keywords: Motor pump, Spring, Vibration reduction, Medicaldevices, Moment of Inertia

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Authors: Samir Khrais, Adel Mahammod Hassan , Amro Gazawi

Abstract:

The aim of this research is to evaluate surface roughness and develop a multiple regression model for surface roughness as a function of cutting parameters during the turning of flame hardened medium carbon steel with TiN-Al2O3-TiCN coated inserts. An experimental plan of work and signal-to-noise ratio (S/N) were used to relate the influence of turning parameters to the workpiece surface finish utilizing Taguchi methodology. The effects of turning parameters were studied by using the analysis of variance (ANOVA) method. Evaluated parameters were feed, cutting speed, and depth of cut. It was found that the most significant interaction among the considered turning parameters was between depth of cut and feed. The average surface roughness (Ra) resulted by TiN-Al2O3- TiCN coated inserts was about 2.44 μm and minimum value was 0.74 μm. In addition, the regression model was able to predict values for surface roughness in comparison with experimental values within reasonable limit.

Keywords: Medium carbon steel, Prediction, Surface roughness, Taguchi method

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Authors: Samar Krimi, Kaïs Ouni, Noureddine Ellouze

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The method described in this paper deals with the problems of T-wave detection in an ECG. Determining the position of a T-wave is complicated due to the low amplitude, the ambiguous and changing form of the complex. A wavelet transform approach handles these complications therefore a method based on this concept was developed. In this way we developed a detection method that is able to detect T-waves with a sensitivity of 93% and a correct-detection ratio of 93% even with a serious amount of baseline drift and noise.

Keywords: ECG, Modulus Maxima Wavelet Transform, Performance, T-wave detection

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Authors: Sotiris B. Kotsiantis

Abstract:

Bagging and boosting are among the most popular re-sampling ensemble methods that generate and combine a diversity of regression models using the same learning algorithm as base-learner. Boosting algorithms are considered stronger than bagging on noise-free data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using an averaging methodology of bagging and boosting ensembles with 10 sub-learners in each one. We performed a comparison with simple bagging and boosting ensembles with 25 sub-learners on standard benchmark datasets and the proposed ensemble gave better accuracy.

Keywords: Regressors, statistical learning.

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Authors: V. R. Asghari, M. Ardebilipour

Abstract:

In the context of spectrum surveillance, a method to recover the code of spread spectrum signal is presented, whereas the receiver has no knowledge of the transmitter-s spreading sequence. The approach is based on a genetic algorithm (GA), which is forced to model the received signal. Genetic algorithms (GAs) are well known for their robustness in solving complex optimization problems. Experimental results show that the method provides a good estimation, even when the signal power is below the noise power.

Keywords: Code estimation, genetic algorithms, spread spectrum.

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Authors: Seyed-Yaser Nabavi-Chashmi, Davood Asadi, Karim Ahmadi, Eren Demir

Abstract:

The landing phase of a UAV is very critical as there are many uncertainties in this phase, which can easily entail a hard landing or even a crash. In this paper, the estimation of relative distance and velocity to the ground, as one of the most important processes during the landing phase, is studied. Using accurate measurement sensors as an alternative approach can be very expensive for sensors like LIDAR, or with a limited operational range, for sensors like ultrasonic sensors. Additionally, absolute positioning systems like GPS or IMU cannot provide distance to the ground independently. The focus of this paper is to determine whether we can measure the relative distance and velocity of UAV and ground in the landing phase using just low-resolution images taken by a monocular camera. The Lucas-Konda feature detection technique is employed to extract the most suitable feature in a series of images taken during the UAV landing. Two different approaches based on Extended Kalman Filters (EKF) have been proposed, and their performance in estimation of the relative distance and velocity are compared. The first approach uses the kinematics of the UAV as the process and the calculated optical flow as the measurement. On the other hand, the second approach uses the feature’s projection on the camera plane (pixel position) as the measurement while employing both the kinematics of the UAV and the dynamics of variation of projected point as the process to estimate both relative distance and relative velocity. To verify the results, a sequence of low-quality images taken by a camera that is moving on a specifically developed testbed has been used to compare the performance of the proposed algorithm. The case studies show that the quality of images results in considerable noise, which reduces the performance of the first approach. On the other hand, using the projected feature position is much less sensitive to the noise and estimates the distance and velocity with relatively high accuracy. This approach also can be used to predict the future projected feature position, which can drastically decrease the computational workload, as an important criterion for real-time applications.

Keywords: Automatic landing, multirotor, nonlinear control, parameters estimation, optical flow.

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Authors: Victor Maldonado

Abstract:

Recent concerns of the growing impact of aviation on climate change has prompted the emergence of a field referred to as Sustainable or “Green” Aviation dedicated to mitigating the harmful impact of aviation related CO2 emissions and noise pollution on the environment. In the current paper, a unique “green” business jet aircraft called the TransAtlantic was designed (using analytical formulation common in conceptual design) in order to show the feasibility for transatlantic passenger air travel with an aircraft weighing less than 10,000 pounds takeoff weight. Such an advance in fuel efficiency will require development and integration of advanced and emerging aerospace technologies. The TransAtlantic design is intended to serve as a research platform for the development of technologies such as active flow control. Recent advances in the field of active flow control and how this technology can be integrated on a sub-scale flight demonstrator are discussed in this paper. Flow control is a technique to modify the behavior of coherent structures in wall-bounded flows (over aerodynamic surfaces such as wings and turbine nozzles) resulting in improved aerodynamic cruise and flight control efficiency. One of the key challenges to application in manned aircraft is development of a robust high-momentum actuator that can penetrate the boundary layer flowing over aerodynamic surfaces. These deficiencies may be overcome in the current development and testing of a novel electromagnetic synthetic jet actuator which replaces piezoelectric materials as the driving diaphragm. One of the overarching goals of the TranAtlantic research platform include fostering national and international collaboration to demonstrate (in numerical and experimental models) reduced CO2/ noise pollution via development and integration of technologies and methodologies in design optimization, fluid dynamics, structures/ composites, propulsion, and controls.

Keywords: Aircraft Design, Sustainable “Green” Aviation, Active Flow Control, Aerodynamics.

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Authors: Kamran Hameed, Adeel Mumtaz, S.A.M. Gilani

Abstract:

In this paper, we start by first characterizing the most important and distinguishing features of wavelet-based watermarking schemes. We studied the overwhelming amount of algorithms proposed in the literature. Application scenario, copyright protection is considered and building on the experience that was gained, implemented two distinguishing watermarking schemes. Detailed comparison and obtained results are presented and discussed. We concluded that Joo-s [1] technique is more robust for standard noise attacks than Dote-s [2] technique.

Keywords: Digital image, Copyright protection, Watermarking, Wavelet transform.

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Authors: Gökhan Silahtaroğlu

Abstract:

Clustering large populations is an important problem when the data contain noise and different shapes. A good clustering algorithm or approach should be efficient enough to detect clusters sensitively. Besides space complexity, time complexity also gains importance as the size grows. Using hierarchies we developed a new algorithm to split attributes according to the values they have and choosing the dimension for splitting so as to divide the database roughly into equal parts as much as possible. At each node we calculate some certain descriptive statistical features of the data which reside and by pruning we generate the natural clusters with a complexity of O(n).

Keywords: Clustering, tree, split, pruning, entropy, gini.

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Authors: M. Bouri, S. Bourennane

Abstract:

In this paper, we propose a novel method for subspace estimation used high resolution method without eigendecomposition where the sample Cross-Spectral Matrix (CSM) is replaced by upper triangular matrix obtained from LU factorization. This novel method decreases the computational complexity. The method relies on a recently published result on Rank-Revealing LU (RRLU) factorization. Simulation results demonstrates that the new algorithm outperform the Householder rank-revealing QR (RRQR) factorization method and the MUSIC in the low Signal to Noise Ratio (SNR) scenarios.

Keywords: Factorization, Localization, Matrix, Signalsubspace.

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Authors: M. Ketcham, S. Vongpradhip

Abstract:

In this paper, an innovative watermarking scheme for audio signal based on genetic algorithms (GA) in the discrete wavelet transforms is proposed. It is robust against watermarking attacks, which are commonly employed in literature. In addition, the watermarked image quality is also considered. We employ GA for the optimal localization and intensity of watermark. The watermark detection process can be performed without using the original audio signal. The experimental results demonstrate that watermark is inaudible and robust to many digital signal processing, such as cropping, low pass filter, additive noise.

Keywords: Intelligent Audio Watermarking, GeneticAlgorithm, DWT Domain.

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Authors: Rohan Putatunda, Aryya Gangopadhyay

Abstract:

Acoustic sensors, if embedded in smart street lights, can help in capturing the activities (car honking, sirens, events, traffic, etc.) in cities. Needless to say, the acoustic data from such scenarios are complex due to multiple audio streams originating from different events, and when decomposed to independent signals, the amount of retrieved data volume is small in quantity which is inadequate to train deep neural networks. So, in this paper, we address the two challenges: a) separating the mixed signals, and b) developing an efficient acoustic classifier under data paucity. So, to address these challenges, we propose an architecture with supervised deep learning, where the initial captured mixed acoustics data are analyzed with Fast Fourier Transformation (FFT), followed by filtering the noise from the signal, and then decomposed to independent signals by fast independent component analysis (Fast ICA). To address the challenge of data paucity, we propose a multi feature-based deep neural network with high performance that is reflected in our experiments when compared to the conventional convolutional neural network (CNN) and multi-layer perceptron (MLP).

Keywords: FFT, ICA, vehicle classification, multi-feature DNN, CNN, MLP.

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Authors: J. P. Dubois, O. M. Abdul-Latif

Abstract:

Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of medical ultrasound images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to clinical ultrasound images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected ultrasound images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (detection hypotheses) in the original images.

Keywords: LS-SVM, medical ultrasound imaging, partially developed speckle, multi-look model.

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Authors: Farhad Kolahan, Mohsen Manoochehri, Abbas Hosseini

Abstract:

Machining is an important manufacturing process used to produce a wide variety of metallic parts. Among various machining processes, turning is one of the most important one which is employed to shape cylindrical parts. In turning, the quality of finished product is measured in terms of surface roughness. In turn, surface quality is determined by machining parameters and tool geometry specifications. The main objective of this study is to simultaneously model and optimize machining parameters and tool geometry in order to improve the surface roughness for AISI1045 steel. Several levels of machining parameters and tool geometry specifications are considered as input parameters. The surface roughness is selected as process output measure of performance. A Taguchi approach is employed to gather experimental data. Then, based on signal-to-noise (S/N) ratio, the best sets of cutting parameters and tool geometry specifications have been determined. Using these parameters values, the surface roughness of AISI1045 steel parts may be minimized. Experimental results are provided to illustrate the effectiveness of the proposed approach.

Keywords: Taguchi method, turning parameters, tool geometry specifications, S/N ratio, statistical analysis.

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Authors: G. Sadashivappa, K. V. S. AnandaBabu

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The aim of this paper to characterize a larger set of wavelet functions for implementation in a still image compression system using SPIHT algorithm. This paper discusses important features of wavelet functions and filters used in sub band coding to convert image into wavelet coefficients in MATLAB. Image quality is measured objectively using peak signal to noise ratio (PSNR) and its variation with bit rate (bpp). The effect of different parameters is studied on different wavelet functions. Our results provide a good reference for application designers of wavelet based coder.

Keywords: Wavelet, image compression, sub band, SPIHT, PSNR.

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Authors: Mazdak Zamani , Azizah A. Manaf , Rabiah B. Ahmad , Akram M. Zeki , Shahidan Abdullah

Abstract:

In this paper, we present a novel, principled approach to resolve the remained problems of substitution technique of audio steganography. Using the proposed genetic algorithm, message bits are embedded into multiple, vague and higher LSB layers, resulting in increased robustness. The robustness specially would be increased against those intentional attacks which try to reveal the hidden message and also some unintentional attacks like noise addition as well.

Keywords: Artificial Intelligence, Audio Steganography, DataHiding, Genetic Algorithm, Substitution Techniques.

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Authors: Fruzsina Pataki-Bittó, Edit Németh

Abstract:

On the one hand, new technologies and communication tools improve employee productivity and accelerate information and knowledge transfer, while on the other hand, information overload and continuous interruptions make it even harder to concentrate at work. It is a great challenge for companies to find the right balance, while there is also an ongoing demand to recruit and retain the talented employees who are able to adopt the modern work style and effectively use modern communication tools. For this reason, this research does not focus on the objective measures of office interruptions, but aims to find those disruption factors which influence the comfort and job satisfaction of employees, and the way how they feel generally at work. The focus of this research is on how employees feel about the different types of interruptions, which are those they themselves identify as hindering factors, and those they feel as stress factors. By identifying and then reducing these destructive factors, job satisfaction can reach a higher level and employee turnover can be reduced. During the research, we collected information from depth interviews and questionnaires asking about work environment, communication channels used in the workplace, individual communication preferences, factors considered as disruptions, and individual steps taken to avoid interruptions. The questionnaire was completed by 141 office workers from several types of workplaces based in Hungary. Even though 66 respondents are working at Hungarian offices of multinational companies, the research is about the characteristics of the Hungarian labor force. The most important result of the research shows that while more than one third of the respondents consider office noise as a disturbing factor, personal inquiries are welcome and considered useful, even if in such cases the work environment will not be convenient to solve tasks requiring concentration. Analyzing the sizes of the offices, in an open-space environment, the rate of those who consider office noise as a disturbing factor is surprisingly lower than in smaller office rooms. Opinions are more diverse regarding information communication technologies. In addition to the interruption factors affecting the employees' job satisfaction, the research also focuses on the role of the offices in the 21^st century.

Keywords: Information overload, interruption, job satisfaction, office environment, work efficiency.

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Authors: M. R. Homaeinezhad, E. Tavakkoli, M. Habibi, S. A. Atyabi, A. Ghaffari

Abstract:

This paper describes a new supervised fusion (hybrid) electrocardiogram (ECG) classification solution consisting of a new QRS complex geometrical feature extraction as well as a new version of the learning vector quantization (LVQ) classification algorithm aimed for overcoming the stability-plasticity dilemma. Toward this objective, after detection and delineation of the major events of ECG signal via an appropriate algorithm, each QRS region and also its corresponding discrete wavelet transform (DWT) are supposed as virtual images and each of them is divided into eight polar sectors. Then, the curve length of each excerpted segment is calculated and is used as the element of the feature space. To increase the robustness of the proposed classification algorithm versus noise, artifacts and arrhythmic outliers, a fusion structure consisting of five different classifiers namely as Support Vector Machine (SVM), Modified Learning Vector Quantization (MLVQ) and three Multi Layer Perceptron-Back Propagation (MLP–BP) neural networks with different topologies were designed and implemented. The new proposed algorithm was applied to all 48 MIT–BIH Arrhythmia Database records (within–record analysis) and the discrimination power of the classifier in isolation of different beat types of each record was assessed and as the result, the average accuracy value Acc=98.51% was obtained. Also, the proposed method was applied to 6 number of arrhythmias (Normal, LBBB, RBBB, PVC, APB, PB) belonging to 20 different records of the aforementioned database (between– record analysis) and the average value of Acc=95.6% was achieved. To evaluate performance quality of the new proposed hybrid learning machine, the obtained results were compared with similar peer– reviewed studies in this area.

Keywords: Feature Extraction, Curve Length Method, SupportVector Machine, Learning Vector Quantization, Multi Layer Perceptron, Fusion (Hybrid) Classification, Arrhythmia Classification, Supervised Learning Machine.

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Authors: Anil Kumar, K. Negrat, A. M. Negrat, Abdelsalam Almarimi

Abstract:

In this paper, we present a robust and secure algorithm for watermarking, the watermark is first transformed into the frequency domain using the discrete wavelet transform (DWT). Then the entire DWT coefficient except the LL (Band) discarded, these coefficients are permuted and encrypted by specific mixing. The encrypted coefficients are inserted into the most significant spectral components of the stego-image using a chaotic system. This technique makes our watermark non-vulnerable to the attack (like compression, and geometric distortion) of an active intruder, or due to noise in the transmission link.

Keywords: Blind source separation (BSS), Chaotic system, Watermarking, DWT.

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Authors: József Valyon, Gábor Horváth

Abstract:

In comparison to the original SVM, which involves a quadratic programming task; LS–SVM simplifies the required computation, but unfortunately the sparseness of standard SVM is lost. Another problem is that LS-SVM is only optimal if the training samples are corrupted by Gaussian noise. In Least Squares SVM (LS–SVM), the nonlinear solution is obtained, by first mapping the input vector to a high dimensional kernel space in a nonlinear fashion, where the solution is calculated from a linear equation set. In this paper a geometric view of the kernel space is introduced, which enables us to develop a new formulation to achieve a sparse and robust estimate.

Keywords: Support Vector Machines, Least Squares SupportVector Machines, Regression, Sparse approximation.

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Authors: Wen-Yen Wu, Tsung-Cheng Hsieh, Ching-Sung Lai

Abstract:

In this paper, we propose a method to extract the road signs. Firstly, the grabbed image is converted into the HSV color space to detect the road signs. Secondly, the morphological operations are used to reduce noise. Finally, extract the road sign using the geometric property. The feature extraction of road sign is done by using the color information. The proposed method has been tested for the real situations. From the experimental results, it is seen that the proposed method can extract the road sign features effectively.

Keywords: Color information, image processing, road sign.

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Authors: M. I. Youssef, A. E. Emam, M. Abd Elghany

Abstract:

In this paper, a residue number arithmetic is used in direct sequence spread spectrum system, this system is evaluated and the bit error probability of this system is compared to that of non residue number system. The effect of channel bandwidth, PN sequences, multipath effect and modulation scheme are studied. A Matlab program is developed to measure the signal-to-noise ratio (SNR), and the bit error probability for the various schemes.

Keywords: Spread Spectrum, Direct sequence, Bit errorprobability and Residue number system.

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Authors: Jamal R. Elbergali

Abstract:

Multiple Input Multiple Output (MIMO) systems are wireless systems with multiple antenna elements at both ends of the link. Wireless communication systems demand high data rate and spectral efficiency with increased reliability. MIMO systems have been popular techniques to achieve these goals because increased data rate is possible through spatial multiplexing scheme and diversity. Spatial Multiplexing (SM) is used to achieve higher possible throughput than diversity. In this paper, we propose a Zero- Forcing (ZF) detection using a combination of Ordered Successive Interference Cancellation (OSIC) and Zero Forcing using Interference Cancellation (ZF-IC). The proposed method used an OSIC based on Signal to Noise Ratio (SNR) ordering to get the estimation of last symbol, then the estimated last symbol is considered to be an input to the ZF-IC. We analyze the Bit Error Rate (BER) performance of the proposed MIMO system over Rayleigh Fading Channel, using Binary Phase Shift Keying (BPSK) modulation scheme. The results show better performance than the previous methods.

Keywords: SNR, BER, BPSK, MIMO, Modulation, Zero forcing (ZF), OSIC, ZF-IC, Spatial Multiplexing (SM).

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Authors: J. P. Dubois

Abstract:

Single side band modulation is a widespread technique in communication with significant impact on communication technologies such as DSL modems and ATSC TV. Its widespread utilization is due to its bandwidth and power saving characteristics. In this paper, we present a new scheme for SSB signal generation which is cost efficient and enjoys superior characteristics in terms of frequency stability, selectivity, and robustness to noise. In the process, we develop novel Hilbert transform properties.

Keywords: Crystal filter, frequency drift, frequency mixing, Hilbert transform, phasing, selectivity, single side band AM.

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Authors: Youngpo Lee, Seokho Yoon

Abstract:

In this paper, we propose a distance estimation scheme for radar systems using direct sequence ultra wideband (DS-UWB) signals. The proposed distance estimation scheme averages out the noise by accumulating the correlator outputs of the radar, and thus, helps the radar to employ a short-length DS-UWB signal reducing the correlation processing time. Numerical results confirm that the proposed distance estimation scheme provides a better estimation performance and a reduced correlation processing time compared with those of the conventional DS-UWB radars.

Keywords: Radar, DS-UWB, distance estimation, correlation accumulation.

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Authors: Fu-Hsuan Huang, Po-Jung Yuan, Kang-Ping Lin, Hen-Hong Chang, Cheng-Lun Tsai

Abstract:

Photoplethysmography is a simple measurement of the variation in blood volume in tissue. It detects the pulse signal of heart beat as well as the low frequency signal of vasoconstriction and vasodilation. The transmission type measurement is limited to only a few specific positions for example the index finger that have a short path length for light. The reflectance type measurement can be conveniently applied on most parts of the body surface. This study analyzed the factors that determine the quality of reflectance photoplethysmograph signal including the emitter-detector distance, wavelength, light intensity, and optical properties of skin tissue. Light emitting diodes (LEDs) with four different visible wavelengths were used as the light emitters. A phototransistor was used as the light detector. A micro translation stage adjusts the emitter-detector distance from 2 mm to 15 mm. The reflective photoplethysmograph signals were measured on different sites. The optimal emitter-detector distance was chosen to have a large dynamic range for low frequency drifting without signal saturation and a high perfusion index. Among these four wavelengths, a yellowish green (571nm) light with a proper emitter-detection distance of 2mm is the most suitable for obtaining a steady and reliable reflectance photoplethysmograph signal

Keywords: Reflectance photoplethysmograph, Perfusion index, Signal-to-noise ratio

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