Search results

The purpose of this paper is to design an intelligent ECG classifier using programmable IC technologies to implement many functional blocks of signal acquisition and processing in one compact device. The main microprocessor also simulates the TSK neuro-fuzzy classifier in testing mode to recognize the ECG beats. The design brings various theoretical solutions into practical applications.

The ECG signals are acquired and pre-processed using the Field-Programmable Analog Array (FPAA) IC due to the ability of precise configuration of analog parameters. The R peak of the QRS complexes and a window of 300 ms of ECG signals around the R peak are detected. In this paper we have proposed a method to extract the signal features using the Hermite decomposition algorithm, which requires only a multiplication of two matrices. Based on the features vectors, the ECG beats are classified using a TSK neuro-fuzzy network, whose parameters are trained earlier on PC and downloaded into the device. The device performance was tested with the ECG signals from the MIT-BIH database to prove the correctness of the hardware implementations.

The FPAA and Programmable System on Chip (PSoC) technologies allow us to integrate many signal processing blocks in a compact device. In this paper the device has the same performance in ECG signal processing and classifying as achieved on PC simulators. This confirms the correctness of the implementation.

The device was fully tested with the signals from the MIT-BIH databases. For new patients, we have tested the device in collecting the ECG signals and QRS detections. We have not created a new database of ECG signals, in which the beats are examined by doctors and annotated the type of the rhythm (normal or abnormal, which type of arrhythmia, etc.) so we have not tested the classification mode of the device on real ECG signals.

The compact design of an intelligent ECG classifier offers a portable solution for patients with heart diseases, which can help them to detect the arrhythmia on time when the doctors are not nearby. This type of device not only may help to improve the patients’ safety but also contribute to the smart, inter-networked life style.

The device integrate a number of solutions including software, hardware and algorithms into a single, compact device. Thank to the advance of programmable ICs such as FPAA and PSoC, the designed device can acquire one channel of ECG signals, extract the features and classify the arrhythmia type (if detected) using the neuro-fuzzy TSK network in online mode.

This study investigates the relationships between environmental performance feedback and green supply chain management (GSCM). It explores how environmental performance above or below aspirations affects the implementation of GSCM practices (specifically sustainable production [SP] and sustainable sourcing [SS]) through the lens of the behavioral theory of the firm (BTOF), which has received scant attention in the operations management literature.

The study used data from the sixth round of the International Manufacturing Strategy Survey (IMSS). It employed hierarchical linear regression to test the proposed hypotheses. Moreover, the study tested an alternate model to rule out the possible role of financial performance aspirations in explaining the implementation of SP and SS.

The results indicate that organizations determine their efforts put into the two GSCM practices according to environmental performance feedback: the greater the aspiration–environmental performance discrepancy, the stronger the efforts put into implementing GSCM practices.

This study contributes to the GSCM literature by revealing the impact of environmental performance aspirations on the implementation of GSCM practices through the lens of the BTOF. It also extends the BTOF by applying it in the GSCM context and indicating that performance feedback is based on environmental performance instead of financial performance in this specific context.

The study aims at providing a reliable system of real-time monitoring for underground mine and tunnels which detects any structural change in the network and reconfigures it for resuming the data delivery process. In high stress environments, e.g. underground mines and tunnels, real-time activity monitoring is an emerging issue. Wireless sensor networks (WSNs) play a key role in ensuring the safety of people working in underground mines and tunnels. WSN not only provide real-time monitoring of underground environment but also detects any structural change in the network itself.

In this paper, results of empirical implementation of a re-configurable WSN, capable of self-healing approach, reconfigure the network connectivity upon failure or addition of nodes in the system. An open-source radio-frequency identification standard for WSN, named as DASH7, is used for practical implementation. The proposed system is capable of determining cluster breakage by sudden disruptions caused by roof falls, explosions and node failures, sensor coverage hole, node re-addition to the network and distress priority signal generation by the miner.

The proposed platform contributes to re-attain network state for establishing a communication link with fusion center in terms of: instant and accurate detection of collapse holes, acceptable error rate, time to re-attain network state, rapid distress signal propagation and low deployment cost. This platform is deployed in four different environments of anechoic chamber, hallway, outdoor and underground mine environment, to test the aforementioned scenarios using DASH7-compatible Bitsense Sensor Motes operating at 433 MHz. The effectiveness of the proposed approach has been experimentally validated for the single and multiple adjacent and disjoint node failures in all the four environments.

The number of monitoring systems was implemented for safety assurance in high stress environments before, but the novelty of our platform is long range, cost effectiveness, quick response to any structural change in the network, rapid and accurate data delivery using WSN operated on DASH7 protocol stack.

This paper attempted a novel approach for system-level modeling and simulation of sigma-delta modulator for low-frequency CMOS integrated analog to digital interfaces. Comparative analysis of various architectures topologies, circuit implementation techniques are described with analytical procedure for effective selection of topologies for targeted specifications.

Virtual instruments are presented in labview environment to analyze the correlation of circuit-level non-ideal effects with key design parameters over sampling ratio, coarse quantizer bits and loop filter order. A fourth-order single-loop sigma-delta modulator is designed and verified in MATLAB simulink environment with careful selection of integrator weights to meet stable desired performance.

The proposed designed achieved SNDR of 122 dB and 20 bit resolution satisfying high-resolution requirements of low-frequency biomedical signal processing applications. Even though the simulation performed at behavioral level, the results obtained are considered as accurate, by including all non-ideal and non-linear circuit errors in simulation process.

Virtual instruments using labview environment used to analyze the correlation of circuit-level non-ideal effects with key design parameters over sampling ratio, coarse quantizer bits and loop filter order for accurate design.