Search results for: electrocardiogram signals

Authors: Andrey Kupriyanov

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In recent years, much attention has been paid to the problems of ionospheric disturbances and their influence on the signals of global navigation satellite systems (GNSS) around the world. This is due to the increase in solar activity, the expansion of the scope of GNSS, the emergence of new satellite systems, the introduction of new frequencies and many others. The influence of the Earth's ionosphere on the propagation of radio signals is an important factor in many applied fields of science and technology. The paper considers the application of the method of transionospheric sounding using measurements from signals from Global Navigation Satellite Systems to determine the TEC distribution and scintillations of the ionospheric layers. To calculate these parameters, the International Reference Ionosphere (IRI) model of the ionosphere, refined in the local area, is used. The organization of operational monitoring of ionospheric parameters is analyzed using several NovAtel GPStation6 base stations. It allows performing primary processing of GNSS measurement data, calculating TEC and fixing scintillation moments, modeling the ionosphere using the obtained data, storing data and performing ionospheric correction in measurements. As a result of the study, it was proved that the use of the transionospheric sounding method for reconstructing the altitude distribution of electron concentration in different altitude range and would provide operational information about the ionosphere, which is necessary for solving a number of practical problems in the field of many applications. Also, the use of multi-frequency multisystem GNSS equipment and special software will allow achieving the specified accuracy and volume of measurements.

Keywords: global navigation satellite systems (GNSS), GPstation6, international reference ionosphere (IRI), ionosphere, scintillations, total electron content (TEC)

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Authors: B. Siva Kumar Reddy, B. Lakshmi

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Cognitive Radio is a turning out technology that empowers viable usage of the spectrum. Energy Detector-based Sensing is the most broadly utilized spectrum sensing strategy. Besides, it is a lot of generic as receivers does not like any information on the primary user's signals, channel data, of even the sort of modulation. This paper puts forth the execution of energy detection sensing for AM (Amplitude Modulated) signal at 710 KHz, FM (Frequency Modulated) signal at 103.45 MHz (local station frequency), Wi-Fi signal at 2.4 GHz and WiMAX signals at 6 GHz. The OFDM/OFDMA based WiMAX physical layer with convolutional channel coding is actualized utilizing USRP N210 (Universal Software Radio Peripheral) and GNU Radio based Software Defined Radio (SDR). Test outcomes demonstrated the BER (Bit Error Rate) augmentation with channel noise and BER execution is dissected for different Eb/N0 (the energy per bit to noise power spectral density ratio) values.

Keywords: BER, Cognitive Radio, GNU Radio, OFDM, SDR, WiMAX

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Authors: Yaacoub Tina, Youssef Roua, Radoi Emanuel, Burel Gilles

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Recently, low-complexity sub-Nyquist sampling schemes based on the Finite Rate of Innovation (FRI) theory have been introduced to sample parametric signals at minimum rates. The multichannel modulating waveforms (MCMW) is such an efficient scheme, where the received signal is mixed with an appropriate set of arbitrary waveforms, integrated and sampled at rates far below the Nyquist rate. In this paper, the MCMW scheme is adapted to the special case of ultra wideband (UWB) channel estimation, characterized by dense multipaths. First, an appropriate structure, which accounts for the bandpass spectrum feature of UWB signals, is defined. Then, a novel approach to decrease the number of processing channels and reduce the complexity of this sampling scheme is presented. Finally, the proposed concepts are validated by simulation results, obtained with real filters, in the framework of a coherent Rake receiver.

Keywords: coherent rake receiver, finite rate of innovation, sub-nyquist sampling, ultra wideband

Procedia PDF Downloads 256

Authors: Shiuh-Kuang Yang, Sheam-Chyun Lin, Jyin-Wen Cheng, Deng-Guei Hsu

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The torsional mode of guided wave, T(0,1), has been applied to detect characteristics and defects in pipelines, especially in the cases of coated, elevated and buried pipes. The signals of minor corrosions would be covered by the noise, unfortunately, because the coated material and buried medium always induce a strong attenuation of the guided wave. Furthermore, the guided wave would be attenuated more seriously and make the signals hard to be identified when setting the array ring of the transducers on a general corrosion area of the pipe. The objective of this study is then to discuss the effects of the above-mentioned general corrosion on guided wave tests by experiments and signal processing techniques, based on the use of the finite element method, the two-dimensional Fourier transform and the continuous wavelet transform. Results show that the excitation energy would be reduced when the array ring set on the pipe surface having general corrosion. The non-uniformed contact surface also produces the unwanted asymmetric modes of the propagating guided wave. Some of them are even mixing together with T(0,1) mode and increase the difficulty of measurements, especially when a defect or local corrosion merged in the general corrosion area. It is also showed that the guided waves attenuation are increasing with the increasing corrosion depth or the rising inspection frequency. However, the coherent signals caused by the general corrosion would be decayed with increasing frequency. The results obtained from this research should be able to provide detectors to understand the impact when the array ring set on the area of general corrosion and the way to distinguish the localized corrosion which is inside the area of general corrosion.

Keywords: guided wave, finite element method, two-dimensional fourier transform, wavelet transform, general corrosion, localized corrosion

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Authors: Leonardo C. Pacheco-Londoño, Nataly J Galan-Freyle, Lisandro Pacheco-Lugo, Antonio Acosta-Hoyos, Elkin Navarro, Gustavo Aroca-Martinez, Karin Rondón-Payares, Alberto C. Espinosa-Garavito, Samuel P. Hernández-Rivera

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At the Life Science Research Center at Simon Bolivar University, a primary focus is the diagnosis of various diseases, and the use of gold nanoparticles (Au-NPs) in diverse biomedical applications is continually expanding. In the present study, Au-NPs were employed as substrates for Surface-Enhanced Raman Spectroscopy (SERS) aimed at diagnosing kidney diseases arising from Lupus Nephritis (LN), preeclampsia (PC), and Hypertension (H). Discrimination models were developed for distinguishing patients with and without kidney diseases based on the SERS signals from urine samples by partial least squares-discriminant analysis (PLS-DA). A comparative study of the Raman signals across the three conditions was conducted, leading to the identification of potential metabolite signals. Model performance was assessed through cross-validation and external validation, determining parameters like sensitivity and specificity. Additionally, a secondary analysis was performed using machine learning (ML) models, wherein different ML algorithms were evaluated for their efficiency. Models’ validation was carried out using cross-validation and external validation, and other parameters were determined, such as sensitivity and specificity; the models showed average values of 0.9 for both parameters. Additionally, it is not possible to highlight this collaborative effort involved two university research centers and two healthcare institutions, ensuring ethical treatment and informed consent of patient samples.

Keywords: SERS, Raman, PLS-DA, kidney diseases

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Authors: Faruk Ortes, Derya Karabulut, Yunus Ziya Arslan

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Myoelectric features gathering from musculature environment are considered on a preferential basis to perceive muscle activation and control human arm prostheses according to recent experimental researches. EMG (electromyography) signal based human arm prostheses have shown a promising performance in terms of providing basic functional requirements of motions for the amputated people in recent years. However, these assistive devices for neurorehabilitation still have important limitations in enabling amputated people to perform rather sophisticated or functional movements. Surface electromyogram (EMG) is used as the control signal to command such devices. This kind of control consists of activating a motion in prosthetic arm using muscle activation for the same particular motion. Extraction of clear and certain neural information from EMG signals plays a major role especially in fine control of hand prosthesis movements. Many signal processing methods have been utilized for feature extraction from EMG signals. The specific objective of this study was to compare widely used time domain features of EMG signal including integrated EMG(IEMG), root mean square (RMS) and waveform length(WL) for prediction of externally applied forces to human hands. Obtained features were classified using artificial neural networks (ANN) to predict the forces. EMG signals supplied to process were recorded during only type of muscle contraction which is isometric and isotonic one. Experiments were performed by three healthy subjects who are right-handed and in a range of 25-35 year-old aging. EMG signals were collected from muscles of the proximal part of the upper body consisting of: biceps brachii, triceps brachii, pectorialis major and trapezius. The force prediction results obtained from the ANN were statistically analyzed and merits and pitfalls of the extracted features were discussed with detail. The obtained results are anticipated to contribute classification process of EMG signal and motion control of powered human arm prosthetics control.

Keywords: assistive devices for neurorehabilitation, electromyography, feature extraction, force estimation, human arm prosthesis

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Authors: Umberto Cerasani, Luca Cerasani

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Methods for earthquakes forecasting have been intensively investigated in the last decades, but there is still no universal solution agreed by seismologists. Rock failure is most often preceded by a tiny elastic movement in the failure area and by the appearance of micro-cracks. These micro-cracks could be detected at the soil surface and represent useful earth-quakes precursors. The aim of this study was to verify whether tiny raw acceleration signals (in the 10⁻¹ to 10⁻⁴ cm/s² range) prior to the arrival of main primary-waves could be exploitable and related to earthquakes magnitude. Mathematical tools such as Fast Fourier Transform (FFT), moving average and wavelets have been applied on raw acceleration data available on the ITACA web site, and the study focused on one of the most unpredictable earth-quakes, i.e., the August 24th, 2016 at 01H36 one that occurred in the central Italy area. It appeared that these tiny acceleration signals preceding main P-waves have different patterns both on frequency and time domains for high magnitude earthquakes compared to lower ones.

Keywords: earthquake, accelerometer, earthquake forecasting, seism

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Authors: Shweta Singh, Dipali Bansal, Rashima Mahajan

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Psychoacoustics has become a potential area of research due to the growing interest of both laypersons and medical and mental health professionals. Non-invasive brain computer interface like Electroencephalography (EEG) is widely being used in this field. An attempt has been made in this paper to examine the response of EEG signals to acoustic stimuli further analysing the brain electrical activity. The real time EEG is acquired for 6 participants using a cost effective and portable EMOTIV EEG neuron headset. EEG data analysis is further done using EMOTIV test bench, EDF browser and EEGLAB (MATLAB Tool) application software platforms. Spectral analysis of acquired neural signals (AF3 channel) using these software platforms are clearly indicative of increased brain activity in various bands. The inferences drawn from such an analysis have significant correlation with subject’s subjective reporting of the experiences. The results suggest that the methodology adopted can further be used to assist patients with sleeping and depressive disorders.

Keywords: OM chant, spectral analysis, EDF browser, EEGLAB, EMOTIV, real time acquisition

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Authors: Bright Emeni

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The project is a system that allows users to locate their misplaced or lost devices by using Bluetooth technology. It utilizes the DFRobot Bettle BLE Arduino microcontroller as its main component for communication and control. By interfacing it with an LED and a buzzer, the system provides visual and auditory signals to assist in locating the target device. The search process can be initiated through an Android application, by which the system creates a Bluetooth connection between the microcontroller and the target device, permitting the exchange of signals for tracking purposes. When the device is within range, the LED indicator illuminates, and the buzzer produces audible alerts, guiding the user to the device's location. The application also provides an estimated distance of the object using Bluetooth signal strength. The project’s goal is to offer a practical and efficient solution for finding misplaced devices, leveraging the capabilities of Bluetooth technology and microcontroller-based control systems.

Keywords: Bluetooth finder, object finder, Bluetooth tracking, tracker

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Authors: Mahdi Shamsi, Tohid Yousefi Rezaii, Siavash Eftekharifar

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Compressed sensing (CS) is a new powerful mathematical theory concentrating on sparse signals which is widely used in signal processing. The main idea is to sense sparse signals by far fewer measurements than the Nyquist sampling rate, but the reconstruction process becomes nonlinear and more complicated. Common dilemma in sparse signal recovery in CS is the lack of knowledge about sparsity order of the signal, which can be viewed as model order selection procedure. In this paper, we address the problem of sparsity order estimation in sparse signal recovery. This is of main interest in situations where the signal sparsity is unknown or the signal to be recovered is approximately sparse. It is shown that the proposed method also leads to some kind of signal denoising, where the observations are contaminated with noise. Finally, the performance of the proposed approach is evaluated in different scenarios and compared to an existing method, which shows the effectiveness of the proposed method in terms of order selection as well as denoising.

Keywords: compressed sensing, data denoising, model order selection, sparse representation

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Authors: Salah Al-Din I. Badran, Samad Ahmadi, Ismail Shahin

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A blind source separation method is proposed; in this method we use a non-uniform filter bank and a novel normalisation. This method provides a reduced computational complexity and increased convergence speed comparing to the full-band algorithm. Recently, adaptive sub-band scheme has been recommended to solve two problems: reduction of computational complexity and increase the convergence speed of the adaptive algorithm for correlated input signals. In this work the reduction in computational complexity is achieved with the use of adaptive filters of orders less than the full-band adaptive filters, 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 bandwidth, and can promote better rates of convergence.

Keywords: blind source separation, computational complexity, subband, convergence speed, mixture

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Authors: S. Hosseini, M. Ahmadi Najafabadi, M. Akhlaghi

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The health of the journal bearings is very important in preventing unforeseen breakdowns in rotary machines, and poor lubrication is one of the most important factors for producing the bearing failures. Hydrodynamic lubrication (HL), mixed lubrication (ML), and boundary lubrication (BL) are three regimes of a journal bearing lubrication. This paper uses acoustic emission (AE) measurement technique to correlate features of the AE signals to the three lubrication regimes. The transitions from HL to ML based on operating factors such as rotating speed, load, inlet oil pressure by time domain and time-frequency domain signal analysis techniques are detected, and then metal-to-metal contacts between sliding surfaces of the journal and bearing are identified. It is found that there is a significant difference between theoretical and experimental operating values that are obtained for defining the lubrication regions.

Keywords: acoustic emission technique, pressure fed journal bearing, time and frequency signal analysis, metal-to-metal contact

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Authors: Somayeh Komeylian

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Array signal processing techniques have been recently developing in a variety application of the performance enhancement of receivers by refraining the power of jamming and interference signals. In this scenario, biases induced to the antenna array receiver degrade significantly the accurate estimation of the carrier phase. Owing to the integration of frequency becomes the carrier phase, we have obtained the unbiased doppler frequency for the high precision estimation of carrier phase. The unbiased characteristic of Doppler frequency to the power jamming and the other interference signals allows achieving the highly accurate estimation of phase carrier. In this study, we have rigorously investigated the unbiased characteristic of Doppler frequency to the variation of the antenna array geometries. The simulation results have efficiently verified that the Doppler frequency remains also unbiased and accurate to the variation of antenna array geometries.

Keywords: array signal processing, unbiased doppler frequency, GNSS, carrier phase, and slowly fluctuating point target

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Authors: James Rigor Camacho, Wansu Lim

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Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use.

Keywords: edge AI device, EEG, emotion recognition system, supervised learning algorithm, sensors

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Authors: Bogusław Schreyer

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The paper enquires on the two methods of the wheeled robot braking torque control. Those two methods are applied when the adhesion coefficient under left side wheels is different from the adhesion coefficient under the right side wheels. In case of the select-low (SL) method the braking torque on both wheels is controlled by the signals originating from the wheels on the side of the lower adhesion. In the select-high (SH) method the torque is controlled by the signals originating from the wheels on the side of the higher adhesion. The SL method is securing stable and secure robot behaviors during the braking process. However, the efficiency of this method is relatively low. The SH method is more efficient in terms of time and braking distance but in some situations may cause wheels blocking. It is important to monitor the velocity of all wheels and then take a decision about the braking torque distribution accordingly. In case of the SH method the braking torque slope may require significant decrease in order to avoid wheel blocking.

Keywords: select-high, select-low, torque distribution, wheeled robots

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Authors: Taslim Khan, Ray Hua Horng, Rajendra Singh

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This study presents a comprehensive analysis of the design, fabrication, and performance evaluation of a solar-blind Schottky photodetector based on ZnGa₂O₄ grown via MOCVD, utilizing Ni/Au as the Schottky electrode. ZnGa₂O₄, with its wide bandgap of 5.2 eV, is well-suited for high-performance solar-blind photodetection applications. The photodetector demonstrates an impressive responsivity of 280 A/W, indicating its exceptional sensitivity within the solar-blind ultraviolet band. One of the device's notable attributes is its high rejection ratio of 10⁵, which effectively filters out unwanted background signals, enhancing its reliability in various environments. The photodetector also boasts a photodetector responsivity contrast ratio (PDCR) of 10⁷, showcasing its ability to detect even minor changes in incident UV light. Additionally, the device features an outstanding detective of 10¹⁸ Jones, underscoring its capability to precisely detect faint UV signals. It exhibits a fast response time of 80 ms and an ON/OFF ratio of 10⁵, making it suitable for real-time UV sensing applications. The noise-equivalent power (NEP) of 10^-17 W/Hz further highlights its efficiency in detecting low-intensity UV signals. The photodetector also achieves a high forward-to-backward current rejection ratio of 10⁶, ensuring high selectivity. Furthermore, the device maintains an extremely low dark current of approximately 0.1 pA. These findings position the ZnGa₂O₄-based Schottky photodetector as a leading candidate for solar-blind UV detection applications. It offers a compelling combination of sensitivity, selectivity, and operational efficiency, making it a highly promising tool for environments requiring precise and reliable UV detection.

Keywords: wideband gap, solar blind photodetector, MOCVD, zinc gallate

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Authors: John Lachapelle

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Introduction: Neural stimulation is increasingly targeted toward treatment of back pain, PTSD, Parkinson’s disease, and for sensory perception. Sensory recording during stimulation is important in order to examine neural response to stimulation. Most neural amplifiers (headstages) focus on noise efficiency factor (NEF). Conversely, neural headstages need to handle artifacts from several sources including power lines, movement (EMG), and neural stimulation itself. In this work a layered approach to artifact rejection is used to reduce corruption of the neural ENG signal by 60dBv, resulting in recovery of sensory signals in rats and primates that would previously not be possible. Methods: The approach combines analog techniques to reduce and handle unwanted signal amplitudes. The methods include optimized (1) sensory electrode placement, (2) amplifier configuration, and (3) artifact blanking when necessary. The techniques together are like concentric moats protecting a castle; only the wanted neural signal can penetrate. There are two conditions in which the headstage operates: unwanted artifact < 50mV, linear operation, and artifact > 50mV, fast-settle gain reduction signal limiting (covered in more detail in a separate paper). Unwanted Signals at the headstage input: Consider: (a) EMG signals are by nature < 10mV. (b) 60 Hz power line signals may be > 50mV with poor electrode cable conditions; with careful routing much of the signal is common to both reference and active electrode and rejected in the differential amplifier with <50mV remaining. (c) An unwanted (to the neural recorder) stimulation signal is attenuated from stimulation to sensory electrode. The voltage seen at the sensory electrode can be modeled Φ_m=I_o/4πσr. For a 1 mA stimulation signal, with 1 cm spacing between electrodes, the signal is <20mV at the headstage. Headstage ASIC design: The front end ASIC design is designed to produce < 1% THD at 50mV input; 50 times higher than typical headstage ASICs, with no increase in noise floor. This requires careful balance of amplifier stages in the headstage ASIC, as well as consideration of the electrodes effect on noise. The ASIC is designed to allow extremely small signal extraction on low impedance (< 10kohm) electrodes with configuration of the headstage ASIC noise floor to < 700nV/rt-Hz. Smaller high impedance electrodes (> 100kohm) are typically located closer to neural sources and transduce higher amplitude signals (> 10uV); the ASIC low-power mode conserves power with 2uV/rt-Hz noise. Findings: The enhanced neural processing ASIC has been compared with a commercial neural recording amplifier IC. Chronically implanted primates at MGH demonstrated the presence of commercial neural amplifier saturation as a result of large environmental artifacts. The enhanced artifact suppression headstage ASIC, in the same setup, was able to recover and process the wanted neural signal separately from the suppressed unwanted artifacts. Separately, the enhanced artifact suppression headstage ASIC was able to separate sensory neural signals from unwanted artifacts in mouse-implanted peripheral intrafascicular electrodes. Conclusion: Optimizing headstage ASICs allow observation of neural signals in the presence of large artifacts that will be present in real-life implanted applications, and are targeted toward human implantation in the DARPA HAPTIX program.

Keywords: ASIC, biosensors, biomedical signal processing, biomedical sensors

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Authors: Priyanka Bhatnagar, Malkeshkumar Patel, Joondong Kim, Joonpyo Hong

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Artificial skin and sensory memory platforms are produced using a flexible, transparent photovoltaic (TPV) device. The TPV device is composed of a metal oxide heterojunction (nZnO/p-NiO) and transmits visible light (> 50%) while producing substantial electric power (0.5 V and 200 μA cm-2 ). This TPV device is a transparent energy interface that can be used to detect signals and propagate information without an external energy supply. The TPV artificial skin offers a temperature detection range (0 C75 C) that is wider than that of natural skin (5 C48 °C) due to the temperature-sensitive pyrocurrent from the ZnO layer. Moreover, the TPV thermoreceptor offers sensory memory of extreme thermal stimuli. Much like natural skin, artificial skin uses the nociceptor mechanism to protect tissue from harmful damage via signal amplification (hyperalgesia) and early adaption (allodynia). This demonstrates the many features of TPV artificial skin, which can sense and transmit signals and memorize information under self-operation mode. This transparent photovoltaic skin can provide sustainable energy for use in human electronics.

Keywords: transparent, photovoltaics, thermal memory, artificial skin, thermoreceptor

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Authors: H. Alheeh, M. Alameri, A. Al Tarabsheh

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There has been a growing interest in CMOS-based sensors technology in cameras as they afford low-power, small-size, and cost-effective imaging systems. This article describes the CMOS image sensor pixel categories and presents the design and the simulation of the 3-Transistor (3T) Active Pixel Sensor (APS) in MATLAB/Simulink tool. The analysis investigates the conversion of the light into an electrical signal for a single pixel sensing circuit, which consists of a photodiode and three NMOS transistors. The paper also proposes three modes for the pixel operation; reset, integration, and readout modes. The simulations of the electrical signals for each of the studied modes of operation show how the output electrical signals are correlated to the input light intensities. The charging/discharging speed for the photodiodes is also investigated. The output voltage for different light intensities, including in dark case, is calculated and showed its inverse proportionality with the light intensity.

Keywords: APS, CMOS image sensor, light intensities photodiode, simulation

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Authors: Lusungu Ndovi

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Radio propagation from point-to-point is affected by the physical channel in many ways. A signal arriving at a destination travels through a number of different paths which are referred to as multi-paths. Research in this area of wireless communications has progressed well over the years with the research taking different angles of focus. By this is meant that some researchers focus on ways of reducing or eluding Multipath effects whilst others focus on ways of mitigating the effects of Multipath through compensation schemes. Baseband processing is seen as one field of signal processing that is cardinal to the advancement of software-defined radio technology. This has led to wide research into the carrying out certain algorithms at baseband. This paper considers compensating for Multipath for Frequency Modulated signals. The compensation process is carried out at Radio frequency (RF) and at Quadrature baseband (QBB) and the results are compared. Simulations are carried out using MatLab so as to show the benefits of working at lower QBB frequencies than at RF.

Keywords: quadrature baseband, qadio frequency, qultipath compensation, frequency qodulation, signal processing

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Authors: Minjin Kim, Jihwan Kim, Bongsoo Kim, Junho Suh

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We demonstrate nanomechanical resonators constructed with synthetic nanowires (NWs) and study their electro-mechanical properties at millikelvin temperatures. Nanomechanical resonators are fabricated using single-crystalline Au NWs and InAs NWs. The mechanical resonance signals are acquired by either magnetomotive or capacitive detection methods. The Au NWs are synthesized by chemical vapor transport method at 1100 °C, and they exhibit clean surface and single-crystallinity with little defects. Due to pristine surface quality, these Au NW mechanical resonators could provide an ideal model system for studying surface-related effects on the mechanical systems. The InAs NWs are synthesized by molecular beam epitaxy or metal organic chemical vapor deposition method. The InAs NWs show electronic conductance modulation resembling Coulomb blockade, which also manifests in the mechanical resonance signals in the form of damping and resonance frequency shift. Our result provides an evidence of strong electro-mechanical coupling in synthetic NW nanomechanical resonators.

Keywords: Au nanowire, InAs nanowire, nanomechanical resonator, synthetic nanowires

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Authors: Seyed Fakhroddin Hejazi, Mohammad Saleh Sadeghi, Leili Iranirad

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Silent myocardial infarction is defined as the appearance of pathological Q waves in the electrocardiogram, without objective signs of myocardial infarction and any minimal or atypical symptoms. Although this condition has been known for a long time, but little is known about its phenomenon and the mechanisms of it remain unclear. Its coincidence with stroke is also still controversial. This case report introduces a middle-aged man with silent myocardial infarction presented with homonymous hemianopia, which except stage 1 hypertension, had no other major cardiovascular risk factors including diabetes mellitus, hypercholesterolemia, family history of cardiac diseases and smoking. In conclusion, this case report indicated that existence of only one cardiovascular risk factor would lead to the development of MI or stroke.

Keywords: silent myocardial infarction, homonymous hemianopia, stroke, hypertension

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Authors: Ning Xue, Srinivas Merugu, Ignacio Delgado Martinez, Tao Sun, John Tsang, Shih-Cheng Yen

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We have developed a polyimide based neural interface electrode to record nerve signals from the sciatic nerve of a rat. The neural interface electrode has a split-ring shape, with four protruding gold electrodes for recording, and two reference gold electrodes around the split-ring. The split-ring electrode can be opened up to encircle the sciatic nerve. The four electrodes can be bent to sit on top of the nerve and hold the device in position, while the split-ring frame remains flat. In comparison, while traditional cuff electrodes can only fit certain sizes of the nerve, the developed device can fit a variety of rat sciatic nerve dimensions from 0.6 mm to 1.0 mm, and adapt to the chronic changes in the nerve as the electrode tips are bendable. The electrochemical impedance spectroscopy measurement was conducted. The gold electrode impedance is on the order of 10 kΩ, showing excellent charge injection capacity to record neural signals.

Keywords: impedance, neural interface, split-ring electrode, neural signal recording

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Authors: Haruo Okabayashi

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Human biological signals (pulse wave and brain wave, etc.) have a rhythm which shows fluctuations. This study investigates the relationship between fluctuations of biological signals which are shown by a finger plethysmogram (i.e., finger pulse wave) in conversation and anthropophobic tendency, and identifies whether the fluctuation could be an index of mental health. 32 college students participated in the experiment. The finger plethysmogram of each subject was measured in the following conversation situations: Fun memory talking/listening situation and regrettable memory talking/ listening situation for three minutes each. Lyspect 3.5 was used to collect the data of the finger plethysmogram. Since Lyspect calculates the Lyapunov spectrum, it is possible to obtain the largest Lyapunov exponent (LLE). LLE is an indicator of the fluctuation and shows the degree to which a measure is going away from close proximity to the track in a dynamical system. Before the finger plethysmogram experiment, each participant took the psychological test questionnaire “Anthropophobic Scale.” The scale measures the social phobia trend close to the consciousness of social phobia. It is revealed that there is a remarkable relationship between the fluctuation of the finger plethysmography and anthropophobic tendency scale in talking about a regrettable story in conversation: The participants (N=15) who have a low anthropophobic tendency show significantly more fluctuation of finger pulse waves than the participants (N=17) who have a high anthropophobic tendency (F (1, 31) =5.66, p<0.05). That is, the participants who have a low anthropophobic tendency make conversation flexibly using large fluctuation of biological signal; on the other hand, the participants who have a high anthropophobic tendency constrain a conversation because of small fluctuation. Therefore, fluctuation is not an error but an important drive to make better relationships with others and go towards the development of interaction. In considering mental health, the fluctuation of biological signals would be an important indicator.

Keywords: anthropophobic tendency, finger plethymogram, fluctuation of biological signal, LLE

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Authors: Pooja S. Billade, Sanjay S. Chopade

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This paper focuses on the design of three phase induction motor control using single phase input and GSM.The controller used in this work is a wireless speed control using a GSM technique that proves to be very efficient and reliable in applications.The most common principle is the constant V/Hz principle which requires that the magnitude and frequency of the voltage applied to the stator of a motor maintain a constant ratio. By doing this, the magnitude of the magnetic field in the stator is kept at an approximately constant level throughout the operating range. Thus, maximum constant torque producing capability is maintained. The energy that a switching power converter delivers to a motor is controlled by Pulse Width Modulated signals applied to the gates of the power transistors in H-bridge configuration. PWM signals are pulse trains with fixed frequency and magnitude and variable pulse width. When a PWM signal is applied to the gate of a power transistor, it causes the turn on and turns off intervals of the transistor to change from one PWM period.

Keywords: index terms— PIC, GSM (global system for mobile), LCD (Liquid Crystal Display), IM (Induction Motor)

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Authors: Salah Al-Din I. Badran, Samad Ahmadi, Ismail Shahin

Abstract:

A blind source separation method is proposed; in this method, we use a non-uniform filter bank and a novel normalisation. This method provides a reduced computational complexity and increased convergence speed comparing to the full-band algorithm. Recently, adaptive sub-band scheme has been recommended to solve two problems: reduction of computational complexity and increase the convergence speed of the adaptive algorithm for correlated input signals. In this work, the reduction in computational complexity is achieved with the use of adaptive filters of orders less than the full-band adaptive filters, 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 subband than the input signal at full bandwidth, and can promote better rates of convergence.

Keywords: blind source separation, computational complexity, subband, convergence speed, mixture

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Authors: Onder Yakut, Serdar Solak, Emine Dogru Bolat

Abstract:

Measuring the Electrocardiogram (ECG) signal is an essential process for the diagnosis of the heart diseases. The ECG signal has the information of the degree of how much the heart performs its functions. In medical diagnosis and treatment systems, Decision Support Systems processing the ECG signal are being developed for the use of clinicians while medical examination. In this study, a modular wireless ECG (WECG) measuring and recording system using a single board computer and e-Health sensor platform is developed. In this designed modular system, after the ECG signal is taken from the body surface by the electrodes first, it is filtered and converted to digital form. Then, it is recorded to the health database using Wi-Fi communication technology. The real time access of the ECG data is provided through the internet utilizing the developed web interface.

Keywords: ECG, e-health sensor shield, Raspberry Pi, wiFi technology

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Authors: José Luis Carrillo-Medina, Roberto Latorre

Abstract:

Experimental evidence has revealed that different living neural systems can sign their output signals with some specific neural signature. Although experimental and modeling results suggest that neural signatures can have an important role in the activity of neural networks in order to identify the source of the information or to contextualize a message, the functional meaning of these neural fingerprints is still unclear. The existence of cellular mechanisms to identify the origin of individual neural signals can be a powerful information processing strategy for the nervous system. We have recently built different models to study the ability of a neural network to process information based on the emission and recognition of specific neural fingerprints. In this paper we further analyze the features that can influence on the information processing ability of this kind of networks. In particular, we focus on the role that the duration of a refractory period in each neuron after emitting a signed message can play in the network collective dynamics.

Keywords: neural signature, neural fingerprint, processing based on signal identification, self-organizing neural network

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Authors: Akshay B. Pawar, Rohit Y. Parasnis

Abstract:

Heart Rate Variability (HRV) is a widely used indicator of the regulation between the autonomic nervous system (ANS) and the cardiovascular system. Besides being non-invasive, it also has the potential to predict mortality in cases involving critical injuries. The gold standard method for determining HRV is based on the analysis of RR interval time series extracted from ECG signals. However, because it is much more convenient to obtain photoplethysmogramic (PPG) signals as compared to ECG signals (which require the attachment of several electrodes to the body), many researchers have used pulse cycle intervals instead of RR intervals to estimate HRV. They have also compared this method with the gold standard technique. Though most of their observations indicate a strong correlation between the two methods, recent studies show that in healthy subjects, except for a few parameters, the pulse-based method cannot be a surrogate for the standard RR interval- based method. Moreover, the former tends to overestimate short-term variability in heart rate. This calls for improvements in or alternatives to the pulse-cycle interval method. In this study, besides the systolic peak-peak interval method (PP method) that has been studied several times, four recent PPG-based techniques, namely the first derivative peak-peak interval method (P1D method), the second derivative peak-peak interval method (P2D method), the valley-valley interval method (VV method) and the tangent-intersection interval method (TI method) were compared with the gold standard technique. ECG and PPG signals were obtained from 10 young and healthy adults (consisting of both males and females) seated in the armchair position. In order to de-noise these signals and eliminate baseline drift, they were passed through certain digital filters. After filtering, the following HRV parameters were computed from PPG using each of the five methods and also from ECG using the gold standard method: time domain parameters (SDNN, pNN50 and RMSSD), frequency domain parameters (Very low-frequency power (VLF), Low-frequency power (LF), High-frequency power (HF) and Total power or “TP”). Besides, Poincaré plots were also plotted and their SD1/SD2 ratios determined. The resulting sets of parameters were compared with those yielded by the standard method using measures of statistical correlation (correlation coefficient) as well as statistical agreement (Bland-Altman plots). From the viewpoint of correlation, our results show that the best PPG-based methods for the determination of most parameters and Poincaré plots are the P2D method (shows more than 93% correlation with the standard method) and the PP method (mean correlation: 88%) whereas the TI, VV and P1D methods perform poorly (<70% correlation in most cases). However, our evaluation of statistical agreement using Bland-Altman plots shows that none of the five techniques agrees satisfactorily well with the gold standard method as far as time-domain parameters are concerned. In conclusion, excellent statistical correlation implies that certain PPG-based methods provide a good amount of information on the pattern of heart rate variation, whereas poor statistical agreement implies that PPG cannot completely replace ECG in the determination of HRV.

Keywords: photoplethysmography, heart rate variability, correlation coefficient, Bland-Altman plot

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Authors: Karan Sharma, Ajay Kumar

Abstract:

Epileptic seizure is a type of disease due to which electrical charge in the brain flows abruptly resulting in abnormal activity by the subject. One percent of total world population gets epileptic seizure attacks.Due to abrupt flow of charge, EEG (Electroencephalogram) waveforms change. On the display appear a lot of spikes and sharp waves in the EEG signals. Detection of epileptic seizure by using conventional methods is time-consuming. Many methods have been evolved that detect it automatically. The initial part of this paper provides the review of techniques used to detect epileptic seizure automatically. The automatic detection is based on the feature extraction and classification patterns. For better accuracy decomposition of the signal is required before feature extraction. A number of parameters are calculated by the researchers using different techniques e.g. approximate entropy, sample entropy, Fuzzy approximate entropy, intrinsic mode function, cross-correlation etc. to discriminate between a normal signal & an epileptic seizure signal.The main objective of this review paper is to present the variations in the EEG signals at both stages (i) Interictal (recording between the epileptic seizure attacks). (ii) Ictal (recording during the epileptic seizure), using most appropriate methods of analysis to provide better healthcare diagnosis. This research paper then investigates the effects of a noninvasive healing therapy on the subjects by studying the EEG signals using latest signal processing techniques. The study has been conducted with Reiki as a healing technique, beneficial for restoring balance in cases of body mind alterations associated with an epileptic seizure. Reiki is practiced around the world and is recommended for different health services as a treatment approach. Reiki is an energy medicine, specifically a biofield therapy developed in Japan in the early 20th century. It is a system involving the laying on of hands, to stimulate the body’s natural energetic system. Earlier studies have shown an apparent connection between Reiki and the autonomous nervous system. The Reiki sessions are applied by an experienced therapist. EEG signals are measured at baseline, during session and post intervention to bring about effective epileptic seizure control or its elimination altogether.

Keywords: EEG signal, Reiki, time consuming, epileptic seizure

Procedia PDF Downloads 406