Compressive sensing is a processing approach aiming to reduce the data stream from the observed o... more Compressive sensing is a processing approach aiming to reduce the data stream from the observed object with the inherent sparsity using the optimal signal models. The compression of the sparse input signal in time or in the transform domain is performed in the transmitter by the Analog to Information Converter (AIC). The recovery of the compressed signal using optimization based on the differential evolution algorithm is presented in the article as an alternative to the faster pseudoinverse algorithm. Pseudoinverse algorithm results in an unambiguous solution associated with lower compression efficiency. The selection of the mathematically appropriate signal model affects significantly the compression efficiency. On the other hand, the signal model influences the complexity of the algorithm in the receiving block. The suitability of both recovery methods is studied on examples of the signal compression from the passive infrared (PIR) motion sensors or the ECG bioelectric signals.
2018 28th International Conference Radioelektronika (RADIOELEKTRONIKA), 2018
This paper presents an approach to QRS complex detection in ECG signals using Hilbert transform a... more This paper presents an approach to QRS complex detection in ECG signals using Hilbert transform and zero-phase filters to locate the R wave peaks. A newly proposed peak equalization and normalization method is used for signal conditioning prior to the fixed threshold peak detector. The performance of this algorithm is tested using standard ECG waveform records from the MIT-BIH arrhythmia database achieving average sensitivity of 99.95%.
Jan Saliga Technical university of Kosice, Slovakia (based on different sources mainly from Natio... more Jan Saliga Technical university of Kosice, Slovakia (based on different sources mainly from National Instruments)  Typical nowadays system:  Low cost instruments (system): microcontroller with fix firmware and fix hardware – no flexibility  Quality instruments: more-less PC with signal inputs and output (signal front – end: analog preprocessing circuit AD and DA convertors, digital/timing IO = fixed hardware)  Flexibility only in software: General purpose OS (e.g., W7) + flexible application software (measurement functions)  Large, high energy consumption, limited flexibility given by software and fixed hardware, problematic in real time signal processing, exact and fast timing (multitask in windows), and similar applications  Easy maintained (software development and installation), multitask, data presentation and archiving.  Typical convenient applications:  Single purpose instrumentation (oscilloscope, basic spectrum analyzers, logic and network analyzers, etc.)  Typical...
The paper presents a new approach to the ADC INL testing methodology by simple monotonic exponent... more The paper presents a new approach to the ADC INL testing methodology by simple monotonic exponential stimulus which data processing algorithm was suggested with targeting simple implementation in ADC on-board self-testing systems. The novelty of the approach is in determination of stimulus parameters by processing of real measured histogram of record instead of direct record processing in time domain that was used in previous papers. The method was verified by simulations and experimental measurements in comparison with standardised sinewave histogram test that confirmed applicability of the method.
A novel method of analog-to-information conversion—the random interval integration—is proposed an... more A novel method of analog-to-information conversion—the random interval integration—is proposed and studied in this paper. This method is intended primarily for compressed sensing of aperiodic or quasiperiodic signals acquired by commonly used sensors such as ECG, environmental, and other sensors, the output of which can be modeled by multi-harmonic signals. The main idea of the method is based on input signal integration by a randomly resettable integrator before the AD conversion. The integrator’s reset is controlled by a random sequence generator. The signal reconstruction employs a commonly used algorithm based on the minimalization of a distance norm between the original measurement vector and vector calculated from the reconstructed signal. The signal reconstruction is performed by solving an overdetermined problem, which is considered a state-of-the-art approach. The notable advantage of random interval integration is simple hardware implementation with commonly used component...
ABSTRACT Sigma delta modulation is useful for high performance analog-to-digital conversion of na... more ABSTRACT Sigma delta modulation is useful for high performance analog-to-digital conversion of narrow band signals. Band-pass sigma delta modulation (BP ΣΔ ADC) is suitable for communication systems such as AM/FM receivers and mobile phones. Application of BP ΣΔ ADC in sensor systems is presented in the paper. BP ΣΔ ADC performs I/Q conversion of the complex impedance from two dimensional sensor into two digital values representing real and imaginary part of sensor output. The BP ΣΔ ADC is useful for outputs processing from the modulating sensors too. Implemented digital filters have important impact on the metrological properties of whole BP ΣΔ ADC. The various types of FIR and IIR filters are studied in the paper and their phase sensitivity and transmission of modulated signal are studied.
Error models of the Analog to Digital Converters describe metrological properties of the signal c... more Error models of the Analog to Digital Converters describe metrological properties of the signal conversion from analog to digital domain in a concise form using few dominant error parameters. Knowledge of the error models allows the end user to provide fast testing in the crucial points of the full input signal range and to use identified error models for post correction in the digital domain. The imperfections of the internal ADC structure determine the error characteristics represented by the nonlinearities as a function of the output code. Progress in the microelectronics and missing information about circuital details together with the lack of knowledge about interfering effects caused by ADC installation prefers another modeling approach based on the input-output behavioral characterization by the input-output error box. Internal links in the ADC structure cause that the input-output error function could be described in a concise form by suitable function. Modeled functional pa...
Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510)
Abstract The integral nonlinearity (INL) of analog to digital converters (ADCs) can be described ... more Abstract The integral nonlinearity (INL) of analog to digital converters (ADCs) can be described by a behavioral error model expressed as one dimensional image in the code domain. This image consists of low and high code frequency components which allow ...
Compressive sensing is a processing approach aiming to reduce the data stream from the observed o... more Compressive sensing is a processing approach aiming to reduce the data stream from the observed object with the inherent sparsity using the optimal signal models. The compression of the sparse input signal in time or in the transform domain is performed in the transmitter by the Analog to Information Converter (AIC). The recovery of the compressed signal using optimization based on the differential evolution algorithm is presented in the article as an alternative to the faster pseudoinverse algorithm. Pseudoinverse algorithm results in an unambiguous solution associated with lower compression efficiency. The selection of the mathematically appropriate signal model affects significantly the compression efficiency. On the other hand, the signal model influences the complexity of the algorithm in the receiving block. The suitability of both recovery methods is studied on examples of the signal compression from the passive infrared (PIR) motion sensors or the ECG bioelectric signals.
2018 28th International Conference Radioelektronika (RADIOELEKTRONIKA), 2018
This paper presents an approach to QRS complex detection in ECG signals using Hilbert transform a... more This paper presents an approach to QRS complex detection in ECG signals using Hilbert transform and zero-phase filters to locate the R wave peaks. A newly proposed peak equalization and normalization method is used for signal conditioning prior to the fixed threshold peak detector. The performance of this algorithm is tested using standard ECG waveform records from the MIT-BIH arrhythmia database achieving average sensitivity of 99.95%.
Jan Saliga Technical university of Kosice, Slovakia (based on different sources mainly from Natio... more Jan Saliga Technical university of Kosice, Slovakia (based on different sources mainly from National Instruments)  Typical nowadays system:  Low cost instruments (system): microcontroller with fix firmware and fix hardware – no flexibility  Quality instruments: more-less PC with signal inputs and output (signal front – end: analog preprocessing circuit AD and DA convertors, digital/timing IO = fixed hardware)  Flexibility only in software: General purpose OS (e.g., W7) + flexible application software (measurement functions)  Large, high energy consumption, limited flexibility given by software and fixed hardware, problematic in real time signal processing, exact and fast timing (multitask in windows), and similar applications  Easy maintained (software development and installation), multitask, data presentation and archiving.  Typical convenient applications:  Single purpose instrumentation (oscilloscope, basic spectrum analyzers, logic and network analyzers, etc.)  Typical...
The paper presents a new approach to the ADC INL testing methodology by simple monotonic exponent... more The paper presents a new approach to the ADC INL testing methodology by simple monotonic exponential stimulus which data processing algorithm was suggested with targeting simple implementation in ADC on-board self-testing systems. The novelty of the approach is in determination of stimulus parameters by processing of real measured histogram of record instead of direct record processing in time domain that was used in previous papers. The method was verified by simulations and experimental measurements in comparison with standardised sinewave histogram test that confirmed applicability of the method.
A novel method of analog-to-information conversion—the random interval integration—is proposed an... more A novel method of analog-to-information conversion—the random interval integration—is proposed and studied in this paper. This method is intended primarily for compressed sensing of aperiodic or quasiperiodic signals acquired by commonly used sensors such as ECG, environmental, and other sensors, the output of which can be modeled by multi-harmonic signals. The main idea of the method is based on input signal integration by a randomly resettable integrator before the AD conversion. The integrator’s reset is controlled by a random sequence generator. The signal reconstruction employs a commonly used algorithm based on the minimalization of a distance norm between the original measurement vector and vector calculated from the reconstructed signal. The signal reconstruction is performed by solving an overdetermined problem, which is considered a state-of-the-art approach. The notable advantage of random interval integration is simple hardware implementation with commonly used component...
ABSTRACT Sigma delta modulation is useful for high performance analog-to-digital conversion of na... more ABSTRACT Sigma delta modulation is useful for high performance analog-to-digital conversion of narrow band signals. Band-pass sigma delta modulation (BP ΣΔ ADC) is suitable for communication systems such as AM/FM receivers and mobile phones. Application of BP ΣΔ ADC in sensor systems is presented in the paper. BP ΣΔ ADC performs I/Q conversion of the complex impedance from two dimensional sensor into two digital values representing real and imaginary part of sensor output. The BP ΣΔ ADC is useful for outputs processing from the modulating sensors too. Implemented digital filters have important impact on the metrological properties of whole BP ΣΔ ADC. The various types of FIR and IIR filters are studied in the paper and their phase sensitivity and transmission of modulated signal are studied.
Error models of the Analog to Digital Converters describe metrological properties of the signal c... more Error models of the Analog to Digital Converters describe metrological properties of the signal conversion from analog to digital domain in a concise form using few dominant error parameters. Knowledge of the error models allows the end user to provide fast testing in the crucial points of the full input signal range and to use identified error models for post correction in the digital domain. The imperfections of the internal ADC structure determine the error characteristics represented by the nonlinearities as a function of the output code. Progress in the microelectronics and missing information about circuital details together with the lack of knowledge about interfering effects caused by ADC installation prefers another modeling approach based on the input-output behavioral characterization by the input-output error box. Internal links in the ADC structure cause that the input-output error function could be described in a concise form by suitable function. Modeled functional pa...
Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510)
Abstract The integral nonlinearity (INL) of analog to digital converters (ADCs) can be described ... more Abstract The integral nonlinearity (INL) of analog to digital converters (ADCs) can be described by a behavioral error model expressed as one dimensional image in the code domain. This image consists of low and high code frequency components which allow ...
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