Sampling Rate Of Signal Research Articles

VLSI Implementation of an Efficient Digital Up Converter based FIR Filter Architecture

An optimization technique for an efficient design of a Multi-Standard Digital Up Converter (DUC) based FIR filter Architecture to minimize area and power consumption. Digital Up Converter will converts low signal sample rate into high signal sample rate. The existing design of DUC has internal shift add logic block which is used to partial product generation but it was complex and consumes more power. This drawback overcomes shift add logic block is replaced into Hybrid Full Adder logic. This Hybrid Full Adder has Gate Diffusion Input (GDI) logic which reduces transistor counts and minimizes addition and multiplication operation compared to shift and add logic. This proposed designed architecture has achieved in reducing the power and area along with the improvement of operating frequency.

FPGA realization of Farrow structure for sampling rate change

In numerous implementations of modern telecommunications and digital audio systems there is a need for sampling rate change of the system input signal. When the relation between signal input and output sampling frequencies is a fraction of two large integer numbers, Lagrange interpolation based on Farrow structure can be used for the efficient realization of the resample block. This paper highlights efficient realization and estimation of necessary resources for polynomial cubic Lagrange interpolation in the case of the demand for the signal sampling rate change with the factor 160/147 on Field-Programmable Gate Array architecture (FPGA).

Miniaturized Human Insertable Cardiac Monitoring System with Wireless Power Transmission Technique

Prolonged monitoring is more likely to diagnose atrial fibrillation accurately than intermittent or short-term monitoring. In this study, an implantable electrocardiograph (ECG) sensor to monitor atrial fibrillation patients in real time was developed. The implantable sensor is composed of a micro controller unit, an analog-to-digital converter, a signal transmitter, an antenna, and two electrodes. The sensor detects ECG signals from the two electrodes and transmits these to an external receiver carried by the patient. Because the sensor continuously transmits signals, its battery consumption rate is extremely high; therefore, the sensor includes a wireless power transmission module that allows it to charge wirelessly from an external power source. The integrated sensor has the approximate dimensions 3 mm × 4 mm × 14 mm, which is small enough to be inserted into a patient without the need for major surgery. The signal and power transmission data sampling rate and frequency of the unit are 300 samples/s and 430 Hz, respectively. To validate the developed sensor, experiments were conducted on small animals.

Is 50 Hz high enough ECG sampling frequency for accurate HRV analysis?

With the worldwide growth of mobile wireless technologies, healthcare services can be provided at anytime and anywhere. Usage of wearable wireless physiological monitoring system has been extensively increasing during the last decade. These mobile devices can continuously measure e.g. the heart activity and wirelessly transfer the data to the mobile phone of the patient. One of the significant restrictions for these devices is usage of energy, which leads to requiring low sampling rate. This article is presented in order to investigate the lowest adequate sampling frequency of ECG signal, for achieving accurate enough time domain heart rate variability (HRV) parameters. For this purpose the ECG signals originally measured with high 5 kHz sampling rate were down-sampled to simulate the measurement with lower sampling rate. Down-sampling loses information, decreases temporal accuracy, which was then restored by interpolating the signals to their original sampling rates. The HRV parameters obtained from the ECG signals with lower sampling rates were compared. The results represent that even when the sampling rate of ECG signal is equal to 50 Hz, the HRV parameters are almost accurate with a reasonable error.

A sampled-data approach to optimal non-causal downsampling

Downsampling is the process of reducing the sampling rate of a discrete signal. This paper describes how sampled data system theory can be used to design an L2 or L∞ optimal downsampler which reduces the sampling rate by a positive integer factor from a given fast sampler. This paper also describes the effect of noise on the optimal downsampling.

A Real Time Secured Voice Framework Using the Totally Reconstructed Discrete Wavelet Transformation

In our life today and with the upraise of the mobile computation and mobile phones we are using more voice applications. The voice application could then send voice information with different information over different communication channels, with an increasing need to protect this information without any delay when applying any modification on it. To protect this voice information, we encrypt it. In this paper, we are proposing a Framework using the totally reconstructed Discrete Wavelet Transformation (DWT) to encrypt the voice information in real time, where the sample rate of the encrypted voice signal is equal to the original voice signal sample rate. The framework is designed and tested with sample tests to measure the performance of this Framework, showing the speed and complexity of this Framework.

An adaptive transpose measurement matrix algorithm for signal reconstruction in compressed sensing

Compressed sensing is a new signal sampling theory put forward in recent years. It can obtain a signal's discrete sample in the condition that the sampling rate of signal is far smaller than the Nyquist sampling rate, and then make the original signal perfectly reconstructed with nonlinear reconstruction algorithm. In this paper, three typical greedy reconstruction algorithms, i.e., orthogonal matching pursuit, regularised orthogonal matching pursuit, and subspace tracking algorithm are compared in terms of reconstruction accuracy, error rate and time of reconstruction. Furthermore, a new transpose measurement matrix reconstruction algorithm is proposed and tested through simulation. The experiments on both simulation and calculation showed that this new algorithm can effectively improve the optimisation of the signal transmission.

Influence of sampling on the tuning of PID controller parameters

The paper deals with an analysis of automatic control system with continuous and discrete PID controllers. A method of tuning the parameters of the continuous controller is presented, which is optimal according to the ITAE criterion. The behavior of control systems with discrete controllers whose parameters were tuned using the mentioned method are described. The impact of changes in the sampling period of controlled signal on the control quality is shown. Changes of the values of optimal parameters of discrete PID controllers in relation to changes of the sampling rate of controlled signal are characterized.

Analysis of Dynamic Performance of a Kalman Filter for Combining Multiple MEMS Gyroscopes

In this paper, the dynamic performance of a Kalman filter (KF) was analyzed, which is used to combine multiple measurements of a gyroscopes array to reduce the noise and improve the accuracy of the individual sensors. A principle for accuracy improvement by the KF was briefly presented to obtain an optimal estimate of input rate signal. In particular, the influences of some crucial factors on the KF dynamic performance were analyzed by simulations such as the factors input signal frequency, signal sampling, and KF filtering rate. Finally, a system that was comprised of a six-gyroscope array was designed and implemented to test the dynamic performance. Experimental results indicated that the 1σ error for the combined rate signal was reduced to about 0.2°/s in the constant rate test, which was a reduction by a factor of more than eight compared to the single gyroscope. The 1σ error was also reduced from 1.6°/s to 0.48°/s in the swing test. It showed that the estimated angular rate signal could well reflect the dynamic characteristic of the input signal in dynamic conditions.

DDC Design for Multi-Frequency Receiver Based on RF Sampling

Multi-frequency receivers rely on GNSS to achieve high-precision positioning, high sensitivity and other functions. GNSS signal frequencies are widely distributed around 1.1GHz to 1.6GHz. Good performance RF front-end receiver will provide high-quality acquisition and tracking environment, reducing the SER to ensure CNR. RF sampling technique is widely used in software radio for its simple structure, flexible configuration, and other advantages, but rarely used in satellite navigation, because the high signal carrier frequency and sampling rate, down-conversion based on RF sampling becomes a big challenge. So, to solve the problems of widely distributed we design three signal bands, using different local oscillator to implement signal down-conversion; to solve the problems of high sampling rate, we propose a down-conversion program of using a cascade of CIC filter’s ployphase and FIR compensation filter. Through the simulation of MATLAB after tracking tests, analysis of CNR and other indicators of programs. Ultimately, we implement the program with Verilog language on the multi-frequency GNSS receiver. The results show that this scheme can achieve digital down-conversion and stable performance of tracking loop with less consumption of resources.

Downsampling Direct Sequence Spread Spectrum Signal Using Orthogonal Pretreatment

The Direct Sequence Spread Spectrum (DSSS) signal is widely used because of its good concealment and anti-jamming performance. The Compressive Sensing (CS) theory reduced the sampling rate of the DSSS signal effectively compared with the traditional Nyquist-rate sampling theory. While in the process of CS sampling the sensing matrix and the sparse basis generally have a strong correlation when the DSSS signal is decomposed with a complete dictionary. This paper presents a novel orthogonal pretreatment method with which the incoherence between sensing matrix and sparse basis can be improved. As a result, the reconstructed signal is more accurate. Simulation results demonstrate that this method is effective and efficient.

An Efficient Separation for Convolutive Mixtures

This paper describes a new efficient blind source separation method; in this method we uses a non-uniform filter bank and a new structure with different sub-bands. This method provides a reduced permutation and increased convergence speed comparing to the full-band algorithm. Recently, some structures have been suggested to deal with two problems: reducing permutation and increasing the speed of convergence of the adaptive algorithm for correlated input signals. The permutation problem is avoided with the use of adaptive filters of orders less than the full-band adaptive filter, which operate at a sampling rate lower than the sampling rate of the input signal. The decomposed signals by analysis bank filter are less correlated in each sub-band than the input signal at full-band, and can promote better rates of convergence.

NOVEL FPGA DESIGN AND IMPLEMENTATION OF DIGITAL UP CONVERTER

In our world, communication systems play an importa nt role in day to day life. In wireless and wired c ommunication systems, signals are to be upsampled at the transmitter. Digital up converter (DUC) is a sample rate conversion techniq ue which is widely used to increase the sampling rate of an input signal. The digital up converter converts low sampled digital b aseband signal to a pass band signal. In this paper, we are going to design and i mplement a low noise digital up converter on a FPGA (Field Programmable Gate Array). In digital up converter, the input signal i s filtered and converted to higher sampling rate an d then it is modulated with the carrier signal generated from the direct digital sy nthesizer (DDS). This system consists of a cascaded integrator comb (CIC) interpolation filter, cascaded integrator comb comp ensation filter, multiplier and a direct digital synthesizer. The cascaded integrator comb interpolation filter performs upsampling of th e input signal and the cascaded integrator comb com pensation filter is used to compensate the losses of CIC filter by filtering th e input signal. The multiplier is used for multiply ing the upsampled signal from CIC filter with the carrier signal generated from DDS a nd gives the DUC output. In this DUC, the input si gnal is upsampled at the rate of eight. Here, two digital up converters are used and connected with an adder in order to obtain a low n oise output signal. The coding of this work is done in VHDL. The simulation and fu nctional verification is carried out using Xilinx I SE and FPGA implementation is carried out using Virtex 5.

Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems

Physical activity has a positive impact on people's well-being and it had been shown to decrease the occurrence of chronic diseases in the older adult population. To date, a substantial amount of research studies exist, which focus on activity recognition using inertial sensors. Many of these studies adopt a single sensor approach and focus on proposing novel features combined with complex classifiers to improve the overall recognition accuracy. In addition, the implementation of the advanced feature extraction algorithms and the complex classifiers exceed the computing ability of most current wearable sensor platforms. This paper proposes a method to adopt multiple sensors on distributed body locations to overcome this problem. The objective of the proposed system is to achieve higher recognition accuracy with “light-weight” signal processing algorithms, which run on a distributed computing based sensor system comprised of computationally efficient nodes. For analysing and evaluating the multi-sensor system, eight subjects were recruited to perform eight normal scripted activities in different life scenarios, each repeated three times. Thus a total of 192 activities were recorded resulting in 864 separate annotated activity states. The methods for designing such a multi-sensor system required consideration of the following: signal pre-processing algorithms, sampling rate, feature selection and classifier selection. Each has been investigated and the most appropriate approach is selected to achieve a trade-off between recognition accuracy and computing execution time. A comparison of six different systems, which employ single or multiple sensors, is presented. The experimental results illustrate that the proposed multi-sensor system can achieve an overall recognition accuracy of 96.4% by adopting the mean and variance features, using the Decision Tree classifier. The results demonstrate that elaborate classifiers and feature sets are not required to achieve high recognition accuracies on a multi-sensor system.

Performance Analysis and Image Processing of Phase-modulated Signal on Airborne Synthetic Aperture Ladar

In this study, the performance of three phase-modulated signals used in airborne Synthetic Aperture Ladar (SAL) is investigated. The transmitting and receiving modes and imaging processing methods for these signals are studied. Considering that the swath of the airborne SAL is very narrow, in order to reduce the AD sampling rate of the wideband signal, a phase-dechirping receiving mode is proposed for the phase-modulated signal based on the Linear Frequency Modulation (LFM) signal. The imaging simulation results for these three phase-modulated signals validate the effectiveness of the proposed method.

Analyzing the Unscented Kalman Filter Robustness for Orbit Determination through Global Positioning System Signals

The nonlinear unscented Kalman filter (UKF) is evaluated for the satellite orbit determination problem, using Global Positioning System (GPS) measurements. The assessment is based on the robustness of the filter. The main subjects for the evaluation are convergence speed and dynamical model complexity. Such assessment is based on comparing the UKF results with the extended Kalman filter (EKF) results for the solution of the same problem. Based on the analysis of such criteria, the advantages and drawbacks of the implementations are presented. In this orbit determination problem, the focus is to analyze UKF convergence behavior using different sampling rates for the GPS signals, where scattering of measurements will be taken into account. A second aim is to evaluate how the dynamical model complexity affects the performance of the estimators in such adverse situation. After solving the real-time satellite orbit determination problem using actual GPS measurements, through EKF and UKF algorithms, the results obtained are compared in computational terms such as complexity, convergence, and accuracy.

Design of New Transformer Protection Device Based on Wavelet Energy Entropy-Neural Network Theory and FPGA

Transformer differential protection may be of malfunction at the emergence of inrush current, and it will affect the normal operation of the transformer. So the paper puts forward a new application of wavelet energy spectrum entropy-neural network theory in transformer microcomputer protection, in which the multi-resolution analysis of wavelet transform and information entropy technology are combined firstly and forms a new conception named wavelet energy spectrum entropy, and it will be put into neural network theory as the feature vector, forms the new algorithm in the end. This method decomposes signal through wavelet transform, and extracts the high frequency part of energy in each scale of wavelet transform from inrush current signal and the short circuit current signal, and calculates the wavelet energy entropy value, which will be as the input feature vector of modified BP neural network. And this feature vector is used as training characteristic value for training in BP neural network. According to the measured data of the system, it has achieved good effect. At the same time, for the large amount of calculation and the high requirements of signal sampling rate in wavelet energy entropy - neural network algorithm, a new idea which uses the high-speed hardware platform of FPGA to realize the algorithm application is put forward, and it will break the bottleneck of traditional microcomputer protection that the MCU would not give consideration to both the speed and the accuracy of the protection at the same time. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.3524

A FRACTIONAL DELAY FIR FILTER BASED ON LAGRANGE INTERPOLATION OF FARROW STRUCTURE

An efficient implementation technique for the Lagrange interpolation is derived. This formulation called the Farrow structure leads to a version of Lagrange interpolation that is well suited to time varying FD filtering. Lagrange interpolation is mostly used for fractional delay approximation as it can be used for increasing the sampling rate of signals and systems. Lagrange interpolation is one of the representatives for a class of polynomial interpolation techniques. The computational cost of this structure is reduced as the number of multiplications are minimised in the new structure when compared with the conventional structure.

Design of PR NMDFB based up and down converters for transmit downlink and receive uplink of combined 3GPP LTE and UMTS radios

In this paper we present a novel application of perfect reconstruction (PR), non-maximally decimated (NMD) filter banks whose assembly gives life to compact digital up converter and digital down converter for combined Third Generation Partnership Protocol, Long Term Evolution radios and Universal Mobile Telecommunications System radios, which are Wideband Code Division Multiple Access based. The design of the PR NMD analysis---synthesis chains in both transmit downlink and receive uplink has been optimized to maximize the performance according to the specific application scenario. In the receive uplink an 80-path, 40-to-1, PR NMD polyphase down converter channelizer accomplishes the pre-processing task of decomposing the whole input frequency range and of aliasing all the spectral fragments to base-band while reducing the signal sample rate. A selector block is in charge of connecting the ports, which contain the fragments belonging to the same band, to a proper PR 2-to-N up converter channelizer, which synthesizes them in base-band. The dual architecture, composed of small N-to-2 polyphase down converters followed by an 80-path, 2-to-40, up converter, has been designed for the transmit downlink. The down converters accomplish the pre-processing task of decomposing the input spectra and of simultaneously aliasing their fragments to base-band while reducing the input sample rate to 7.68 Msps. The 80-path, 1-to-40 up converter, accomplishes the task of recombining the spectral fragments at the desired center frequencies while increasing the signal sample rate to 307.2 Msps.

Removing bleaching artifacts from voltage sensitive dye recordings with ICA

Voltage sensitive dye (VSD) imaging of multiple neurons becomes one of the most promising up-to-date methods to investigate neuronal network activity. However, optical imaging signals are often superimposed by noise and artifacts. Hence, post-processing methods are needed to overcome this corruption and separate neuronal activity from other signals. One of the significant artifacts in VSD imaging is bleaching, a decrease of the optical signal while the recorded signal of the local field potentials remains unchanged [1]. In this study we used independent component analysis (ICA) in comparison to principal component analysis (PCA) and detrend method to separate the neuronal VSD signals from bleaching artifacts. ICA is a blind source separation method that has been used in many different approaches such as to recover action potentials of neurons in multiple-detector optical recordings [3]. We used the ICA-DTU Toolbox [http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=4043] with maximum likelihood formulation to identify neuronal signals and the effect of bleaching. Experimentally, we recorded from Retzius cells of an isolated midbody ganglion of the electrophysiologically well- characterized leech nervous system. VSD imaging (Figure A) based on a new VSD dye with a very good signal-to-noise ratio [2] was combined with simultaneous intracellular recording of the membrane potential (Figure B) and electrical stimulation. VSD signals obtained from the region of the Retzius cell body (Figure ​(Figure1A1A and ​and1C,1C, red) and from a slightly larger region (Figure ​(Figure1A1A and ​and1C,1C, blue) were used as input signals to ICA. Of the two components returned by the algorithm one decreased exponentially, while the other resembled neuronal activity. Figure ​Figure1D1D shows an exponential fit of the component representing bleaching (green), and the estimated neuronal activity (red). Taking the lower sampling rate of the VSD signal (100 Hz vs 10,000 Hz) into account, this estimate reflects the dynamics of the intracellular recording (1B) well. While PCA was not able to separate bleaching from neuronal signals (not shown), the detrend method also yielded good results (Figure ​(Figure1E).1E). This method fits a piecewise linear line (1E, green) to the VSD signal, representing bleaching effect. However, the difference (1E, red) between the original trace and this line represents the graded de-and hyperpolarisations of the membrane potential seen in the intracellular recording (1B) less well than the ICA result (1D, red). Figure 1 VSD and intracellular recording of leech ganglion and analysis results.