scholarly journals Intelligent LED Certification System in Mass Production

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2891
Author(s):  
Galina Malykhina ◽  
Dmitry Tarkhov ◽  
Viacheslav Shkodyrev ◽  
Tatiana Lazovskaya
Keyword(s):  
Neural Network ◽  
Impulse Response ◽  
Mass Production ◽  
Adaptive Filters ◽  
Finite Impulse Response ◽  
Statistical Moments ◽  
Signal Extraction ◽  
Infinite Impulse Response ◽  
Cooling Curves ◽  
Generalized Moments

It is impossible to effectively use light-emitting diodes (LEDs) in medicine and telecommunication systems without knowing their main characteristics, the most important of them being efficiency. Reliable measurement of LED efficiency holds particular significance for mass production automation. The method for measuring LED efficiency consists in comparing two cooling curves of the LED crystal obtained after exposure to short current pulses of positive and negative polarities. The measurement results are adversely affected by noise in the electrical measuring circuit. The widely used instrumental noise suppression filters, as well as classical digital infinite impulse response (IIR), finite impulse response (FIR) filters, and adaptive filters fail to yield satisfactory results. Unlike adaptive filters, blind methods do not require a special reference signal, which makes them more promising for removing noise and reconstructing the waveform when measuring the efficiency of LEDs. The article suggests a method for sequential blind signal extraction based on a cascading neural network. Statistical analysis of signal and noise values has revealed that the signal and the noise have different forms of the probability density function (PDF). Therefore, it is preferable to use high-order statistical moments characterizing the shape of the PDF for signal extraction. Generalized statistical moments were used as an objective function for optimization of neural network parameters, namely, generalized skewness and generalized kurtosis. The order of the generalized moments was chosen according to the criterion of the maximum Mahalanobis distance. The proposed method has made it possible to implement a multi-temporal comparison of the crystal cooling curves for measuring LED efficiency.

scholarly journals Comprehensive Optimal Fir Filter Design Procedures With Various Impacts

2019 ◽  
Vol 8 (3) ◽  
pp. 1562-1566
Keyword(s):  
Signal Processing ◽  
Impulse Response ◽  
Adaptive Filters ◽  
Filter Design ◽  
Finite Impulse Response ◽  
Digital Signal ◽  
Infinite Impulse Response ◽  
Processing Unit ◽  
Filter Noise ◽  
Fir Filter Design

Digital-signal-processing (DSP) is one of the recent emerging techniques contain more filtering operations. It may an image type or audio/ video signal processing. Each processing unit has filtering sections to filter noise elements. Hence, there is a need for efficient and secure algorithmic scheme. Here, a exhaustive scrutiny use of complex optimization algorithms towards the digital-filter construction is conferred. In appropriate, the scrutiny target on the identification of various suggestions and limitations in FIR system design. For exact representations, the infinite impulse response adaptive filters and finite impulse response models are considered for estimation. It is designed to review a various swarm and evolutionary computing structures employed for filter design schemes. Some popular computing algorithms are noticed to recover characteristics of percolate design approach. Further, compared with recent research for identifying the updating features in optimization schemes. Finally, this review suggested that the swarm intelligence based researchers improved the constraints and its attributes.

On a Real-Time First-Order Difference Evaluation

2012 ◽  
Vol 17 (4) ◽  
pp. 167-171
Author(s):  
Piotr Ostalczyk ◽  
Piotr Duch
Keyword(s):  
Real Time ◽  
Impulse Response ◽  
Adaptive Filters ◽  
Finite Impulse Response ◽  
Short Review ◽  
Infinite Impulse Response ◽  
Measured Signal ◽  
Linear Filters ◽  
First Order ◽  
Non Linear

Abstract A subject of this paper is a real-time evaluation of the first-order backward difference of a measured signal with noise. It is wellknown that a difference evaluation strongly increase a noise amplitude so the signal prefiltering is necessary. A very short review of practically used filters is given. To perform the pre-filtering operation one mainly apply linear filters characterized by their impulse response shape: finite impulse response (FIR) and infinite impulse response (IIR). To enhance the filtering operation one successfully apply so called adaptive filters and these described by non-linear characteristics. In this paper a simple non-linear filtering algorithm is proposed and examined in the first-order backward difference of a measured signal.

scholarly journals Dual channel speech enhancement using particle swarm optimization

2021 ◽  
Vol 23 (2) ◽  
pp. 821
Author(s):  
Dalal Hamza ◽  
Tariq Tashan
Keyword(s):  
Particle Swarm Optimization ◽  
Impulse Response ◽  
Adaptive Filters ◽  
Finite Impulse Response ◽  
Signal To Noise Ratio ◽  
Particle Swarm ◽  
Infinite Impulse Response ◽  
Swarm Optimization ◽  
Dual Channel ◽  
Coefficient Optimization

Adaptive processing for canceling noise is a powerful technology for signal processing that can completely remove background noise. In general, various adaptive filter algorithms are used, many of which can lack the stability to handle the convergence rate, the number of filter coefficient variations, and error accuracy within tolerances. Unlike traditional methods, to accomplish these desirable characteristics as well as to efficiently cancel noise, in this paper, the cancelation of noise is formulated as a problem of coefficient optimization, where the particle swarm optimization (PSO) is employed. The PSO is structured to minimize the error by using a very short segment of the corrupted speech. In contrast to the recent and conventional adaptive noise cancellation methods, the simulation results indicate that the proposed algorithm has better capability of noise cancelation. The results show great improvement in signal to noise ratio (SNR) of 96.07 dB and 124.54 dB for finite impulse response (FIR) and infinite impulse response (IIR) adaptive filters respectively.

scholarly journals NANO-studio, the design environment of filter banks implemented in standard CMOS technology

2021 ◽  
Author(s):  
Andrzej Handkiewicz ◽  
Mariusz Naumowicz
Keyword(s):  
Impulse Response ◽  
Filter Banks ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Cmos Process ◽  
Digital Cmos ◽  
Additional Advantage ◽  
Analysis And Synthesis ◽  
Optimization Parameters ◽  
Synthesis Filter

AbstractThe paper presents a method of optimizing frequency characteristics of filter banks in terms of their implementation in digital CMOS technologies in nanoscale. Usability of such filters is demonstrated by frequency-interleaved (FI) analog-to-digital converters (ADC). An analysis filter present in these converters was designed in switched-current technique. However, due to huge technological pitch of standard digital CMOS process in nanoscale, its characteristics substantially deviate from the required ones. NANO-studio environment presented in the paper allows adjustment, with transistor channel sizes as optimization parameters. The same environment is used at designing a digital synthesis filter, whereas optimization parameters are input and output conductances, gyration transconductances and capacitances of a prototype circuit. Transition between analog s and digital z domains is done by means of bilinear transformation. Assuming a lossless gyrator-capacitor (gC) multiport network as a prototype circuit, both for analysis and synthesis filter banks in FI ADC, is an implementation of the strategy to design filters with low sensitivity to parameter changes. An additional advantage is designing the synthesis filter as stable infinite impulse response (IIR) instead of commonly used finite impulse response (FIR) filters. It provides several dozen-fold saving in the number of applied multipliers.. The analysis and synthesis filters in FI ADC are implemented as filter pairs. An additional example of three-filter bank demonstrates versatility of NANO-studio software.

Comparison of Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) Filters in Cardiac Optical Mapping Records

2021 ◽  
pp. 207-224
Author(s):  
David Rivas-Lalaleo ◽  
Sergio Muñoz-Romero ◽  
Monica Huerta ◽  
Víctor Bautista-Naranjo ◽  
Jorge García-Quintanilla ◽  
...  
Keyword(s):  
Impulse Response ◽  
Optical Mapping ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Fir Filters

The Spectra of Filters

2021 ◽  
pp. 204-268
Author(s):  
Victor Lazzarini
Keyword(s):  
Fourier Transform ◽  
Impulse Response ◽  
Discrete Time ◽  
Filter Design ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Main Body ◽  
Analysis Tool ◽  
The Fourier Transform ◽  
Definition Of

This chapter now turns to the discussion of filters, which extend the notion of spectrum beyond signals into the processes themselves. A gentle introduction to the concept of delaying signals, aided by yet another variant of the Fourier transform, the discrete-time Fourier transform, allows the operation of filters to be dissected. Another analysis tool, in the form of the z-transform, is brought to the fore as a complex-valued version of the discrete-time Fourier transform. A study of the characteristics of filters, introducing the notion of zeros and poles, as well as finite impulse response (FIR) and infinite impulse response (IIR) forms, composes the main body of the text. This is complemented by a discussion of filter design and applications, including ideas related to time-varying filters. The chapter conclusion expands once more the definition of spectrum.

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