Search results for: Signal

Authors: Shalini Rankawat, Mansi Rankawat, Rahul Dubey, Mazad Zaveri

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Physiological signals such as electrocardiogram (ECG) and arterial blood pressure (ABP) in the intensive care unit (ICU) are often seriously corrupted by noise, artifacts, and missing data, which lead to errors in the estimation of heart rate (HR) and incidences of false alarm from ICU monitors. Clinical support in ICU requires most reliable heart rate estimation. Cardiac activity, because of its relatively high electrical energy, may introduce artifacts in Electroencephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG) recordings. This paper presents a robust heart rate estimation method by detection of R-peaks of ECG artifacts in EEG, EMG & EOG signals, using energy-based function and a novel Signal Quality Index (SQI) assessment technique. SQIs of physiological signals (EEG, EMG, & EOG) were obtained by correlation of nonlinear energy operator (teager energy) of these signals with either ECG or ABP signal. HR is estimated from ECG, ABP, EEG, EMG, and EOG signals from separate Kalman filter based upon individual SQIs. Data fusion of each HR estimate was then performed by weighing each estimate by the Kalman filters’ SQI modified innovations. The fused signal HR estimate is more accurate and robust than any of the individual HR estimate. This method was evaluated on MIMIC II data base of PhysioNet from bedside monitors of ICU patients. The method provides an accurate HR estimate even in the presence of noise and artifacts.

Keywords: ECG, ABP, EEG, EMG, EOG, ECG artifacts, Teager-Kaiser energy, heart rate, signal quality index, Kalman filter, data fusion

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Authors: Valentina Narvaez Gaitan, Laura Valentina Rodriguez Leguizamon, Ruben Dario Hernandez B.

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This work describes a physiological signal emulation system that uses electromyography (EMG) signals obtained from muscle sensors in the first instance. These signals are used to extract their characteristics to model and emulate specific arm movements. The main objective of this effort is to develop a new biomedical software system capable of generating physiological signals through the use of embedded systems by establishing the characteristics of the acquired signals. The acquisition system used was Biosignals, which contains two EMG electrodes used to acquire signals from the forearm muscles placed on the extensor and flexor muscles. Processing algorithms were implemented to classify the signals generated by the arm muscles when performing specific movements such as wrist flexion extension, palmar grip, and wrist pronation-supination. Matlab software was used to condition and preprocess the signals for subsequent classification. Subsequently, the mathematical modeling of each signal is performed to be generated by the embedded system, with a validation of the accuracy of the obtained signal using the percentage of cross-correlation, obtaining a precision of 96%. The equations are then discretized to be emulated in the embedded system, obtaining a system capable of generating physiological signals according to the characteristics of medical analysis.

Keywords: classification, electromyography, embedded system, emulation, physiological signals

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Authors: Mohamed O. Babana

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In wireless channel multipath is the propagation phenomena where the transmitted signal arrive at the receiver side with many of paths, the signal at these paths arrive with different time delay the results is random signal fading due to intersymbols interference(ISI). This paper deals with identification of orthogonal frequency division multiplexing (OFDM) technology, and how it is used to overcome intersymbol interference due to multipath. Also investigates the effect of Additive White Gaussian Noise Channel (AWGN) on OFDM using multi-level modulation of Phase Shift Keying (PSK), computer simulation to calculate the bit error rate (BER) under AWGN channel is applied. A comparison study is carried out to obtain the Bit Error Rate performance for OFDM to identify the best multi-level modulation of PSK.

Keywords: intersymbol interference(ISI), bit error rate(BER), modulation, multiplexing, simulation

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Authors: Noriyoshi Yamauchi, Etsuo Horikawa, Takunori Tsuji

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Training by exoskeleton robot is drawing attention as a rehabilitation method for body paralysis seen in many cases, and there are many forms that assist with the myoelectric signal generated by exercise commands from the brain. Rehabilitation requires more frequent training, but it is one of the reasons that the technology is required for the identification of the myoelectric potential derivation site and attachment of the device is preventing the spread of paralysis. In this research, we focus on improving the efficiency of gait training by exoskeleton type robots, improvement of myoelectric acquisition and analysis method using active matrix sensing method, and improvement of walking rehabilitation and walking by optimization of robot control.

Keywords: active matrix sensing, brain machine interface (BMI), the central pattern generator (CPG), myoelectric signal processing, robot rehabilitation

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Authors: Hailang Yang

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To ensure the confidentiality and integrity of speech signals in communication transmission, this paper proposes a voice signal encryption scheme based on chaotic theory. Firstly, the scheme utilizes chaotic mapping to generate a key stream and then employs the key stream to perform bitwise exclusive OR (XOR) operations for encrypting the speech signal. Additionally, the scheme utilizes a chaotic hash function to generate a Message Authentication Code (MAC), which is appended to the encrypted data to verify the integrity of the data. Subsequently, we analyze the security performance and encryption efficiency of the scheme, comparing and optimizing it against existing solutions. Finally, experimental results demonstrate that the proposed scheme can resist common attacks, achieving high-quality encryption and speed.

Keywords: chaotic theory, XOR encryption, chaotic hash function, Message Authentication Code (MAC)

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Authors: Mehdi Shahbazian, Alireza Aarabi, Mohsen Hadiyan

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Proportional-Integral-Derivative (PID) controllers are the most widely used controllers in industry because of its simplicity and robustness. Different values of PID parameters make different step response, so an increasing amount of literature is devoted to proper tuning of PID controllers. The problem merits further investigation as traditional tuning methods make large control signal that can damages the system but using evolutionary algorithms based tuning methods improve the control signal and closed loop performance. In this paper three tuning methods for PID controllers have been studied namely Ziegler and Nichols, which is traditional tuning method and evolutionary algorithms based tuning methods, that are, Genetic algorithm and particle swarm optimization. To examine the validity of PSO and GA tuning methods a comparative analysis of DC motor plant is studied. Simulation results reveal that evolutionary algorithms based tuning method have improved control signal amplitude and quality factors of the closed loop system such as rise time, integral absolute error (IAE) and maximum overshoot.

Keywords: evolutionary algorithm, genetic algorithm, particle swarm optimization, PID controller

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Authors: Mohamed R. Al-Mulla, Bader Al-Bader, Firouz K. Ghaaedi, Francisco Sepulveda

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Surface electromyography (sEMG) is utilised in numerous studies on muscle activity. In the beginning, single electrodes were utilised; however, the newest approach is to use an array of electrodes or a grid of electrodes to improve the accuracy of the recorded reading. This research focuses on electrode placement on the biceps brachii, using an array of electrodes placed longitudinal and diagonally on the muscle belly. Trials were conducted on four healthy males, with sEMG signal acquisition from fatiguing isometric contractions. The signal was analysed using the power spectrum density. The separation between the two classes of fatigue (non-fatigue and fatigue) was calculated using the Davies-Bouldin Index (DBI). Results show that higher separability between the fatigue content of the sEMG signal when placed longitudinally, in the same direction as the muscle fibers.

Keywords: array electrodes, biceps brachii, electrode placement, EMG, isometric contractions, muscle fatigue

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Authors: Hiroyuki Aoki

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Molecular imaging has attracted much attention recently, which visualizes biological molecules, cells, tissue, and so on. Among various in vivo imaging techniques, the fluorescence imaging method has been widely employed as a useful modality for small animals in pre-clinical researches. However, the higher signal intensity is needed for highly sensitive in vivo imaging. The objective of the current study is the development of a fluorescent imaging agent with high brightness for the tumor imaging of a mouse. The strategy to enhance the fluorescence signal of a bio-imaging agent is the increase of the absorption of the excitation light and the fluorescence conversion efficiency. We developed a nano-particle fluorescence imaging agent consisting of a π-conjugated polymer emitting a fluorescence signal in a near infrared region. A large absorption coefficient and high emission intensity at a near infrared optical window for biological tissue enabled highly sensitive in vivo imaging with a tumor-targeting ability by an EPR (enhanced permeation and retention) effect. The signal intensity from the π-conjugated fluorescence imaging agent is larger by two orders of magnitude compared to a quantum dot, which has been known as the brightest imaging agent. The π-conjugated polymer nano-particle would be a promising candidate in the in vivo imaging of small animals.

Keywords: fluorescence, conjugated polymer, in vivo imaging, nano-particle, near-infrared

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Authors: Maryam Abata, Moulhime El Bekkali, Said Mazer, Catherine Algani, Mahmoud Mehdi

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One major problem of most electronic systems operating in the millimeter wave band is the signal generation with a high purity and a stable carrier frequency. This problem is overcome by using the combination of a signal with a low frequency local oscillator (LO) and several stages of frequency multipliers. The use of these frequency multipliers to create millimeter-wave signals is an attractive alternative to direct generation signal. Therefore, the isolation problem of the local oscillator from the other stages is always present, which leads to have various mechanisms that can disturb the oscillator performance, thus a buffer amplifier is often included in oscillator outputs. In this paper, we present the study and design of a buffer amplifier in the mm-wave band using a 0.15μm pHEMT from UMS foundry. This amplifier will be used as a part of a frequency quadrupler at 60 GHz.

Keywords: Mm-wave band, local oscillator, frequency quadrupler, buffer amplifier

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Authors: Ajish K. Abraham

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Hearing aid is the most fundamental technology employed towards rehabilitation of persons with sensory neural hearing impairment. Hearing in noise is still a matter of major concern for many hearing aid users and thus continues to be a challenging issue for the hearing aid designers. Several techniques are being currently used to enhance the speech at the hearing aid output. Most of these techniques, when implemented, result in reduction of intelligibility of the speech signal. Thus the dissatisfaction of the hearing aid user towards comprehending the desired speech amidst noise is prevailing. Multichannel Wiener Filter is widely implemented in binaural hearing aid technology for noise reduction. In this study, Wiener filter based noise reduction approach is experimented for a single microphone based hearing aid set up. This method checks the status of the input speech signal in each frequency band and then selects the relevant noise reduction procedure. Results showed that the Wiener filter based algorithm is capable of enhancing speech even when the input acoustic signal has a very low Signal to Noise Ratio (SNR). Performance of the algorithm was compared with other similar algorithms on the basis of improvement in intelligibility and SNR of the output, at different SNR levels of the input speech. Wiener filter based algorithm provided significant improvement in SNR and intelligibility compared to other techniques.

Keywords: hearing aid output speech, noise reduction, SNR improvement, Wiener filter, speech enhancement

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Authors: Toufik Bensana, Medkour Mihoub, Slimane Mekhilef

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The use of vibration analysis has been established as the most common and reliable method of analysis in the field of condition monitoring and diagnostics of rotating machinery. Rolling bearings cover a broad range of rotary machines and plays a crucial role in the modern manufacturing industry. Unfortunately, the vibration signals collected from a faulty bearing are generally nonstationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA), and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that, the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. The results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing.

Keywords: fault diagnosis, rolling element bearing, local mean decomposition, condition monitoring

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Authors: L. Della Ragione, G. Meccariello

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In recent years the progress of the automobile industry in Italy in the field of reduction of emissions values is very remarkable. Nevertheless, their evaluation and reduction is a key problem, especially in the cities, which account for more than 50% of world population. In this paper we dealt with the problem of describing a quantitative approach for the reconstruction of GPS coordinates and altitude, in the context of correlation study between driving cycles / emission / geographical location, during an experimental campaign realized with some instrumented cars.

Keywords: air pollution, driving cycles, GPS signal, vehicle location

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Authors: Amanpreet Kaur, Ravinder Agarwal, Amod Kumar

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Surface electromyography is a strategy to measure the muscle activity of the skin. Sensors placed on the skin recognize the electrical current or signal generated by active muscles. A lot of the research has focussed on the detection of signal from upper limb amputee with activity of triceps and biceps muscles. The purpose of this study was to correlate phantom movement and sEMG activity in residual stump muscles of transhumeral amputee from the shoulder muscles. Eight non- amputee and seven right hand amputees were recruited for this study. sEMG data were collected for the trapezius, pectoralis and teres muscles for elevation, protraction and retraction of shoulder. Contrast between the amputees and non-amputees muscles action have been investigated. Subsequently, to investigate the impact of class separability for different motions of shoulder, analysis of variance for experimental recorded data was carried out. Results were analyzed to recognize different shoulder movements and represent a step towards the surface electromyography controlled system for amputees. Difference in F ratio (p < 0.05) values indicates the distinction in mean therefore these analysis helps to determine the independent motion. The identified signal would be used to design more accurate and efficient controllers for the upper-limb amputee for researchers.

Keywords: around shoulder amputation, surface electromyography, analysis of variance, features

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Authors: Zerihun Ketema Tadesse, Dabbu Suman Reddy

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Brain-Computer Interfaces (BCIs) give the possibility for disabled people to communicate and control devices. This work aims at developing steady-state visual evoked potential (SSVEP)-based BCI for patients with limb disabilities. In hospitals, devices like nurse emergency call devices, lights, and TV sets are what patients use most frequently, but these devices are operated manually or using the remote control. Thus, disabled patients are not able to operate these devices by themselves. Hence, SSVEP-based BCI system that can allow disabled patients to control nurse calling device and other devices is proposed in this work. Portable LED visual stimulator that flickers at specific frequencies of 7Hz, 8Hz, 9Hz and 10Hz were developed as part of this project. Disabled patients can stare at specific flickering LED of visual stimulator and Emotiv EPOC used to acquire EEG signal in a non-invasive way. The acquired EEG signal can be processed to generate various control signals depending upon the amplitude and duration of signal components. MATLAB software is used for signal processing and analysis and also for command generation. Arduino is used as a hardware interface device to receive and transmit command signals to the experimental setup. Therefore, this study is focused on the design and development of Steady-state visually evoked potential (SSVEP)-based BCI for limb disabled patients, which helps them to operate and control devices in the hospital room/wards.

Keywords: SSVEP-BCI, Limb Disabled Patients, LED Visual Stimulator, EEG signal, control devices, hospital room/wards

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

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Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods.

Keywords: EEG, epilepsy, phase correlation, seizure

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Authors: Lorico D. S. Lapitan Jr., Yihan Xu, Yuan Guo, Dejian Zhou

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The ability of ultrasensitive detection of specific genes and discrimination of single nucleotide polymorphisms is important for clinical diagnosis and biomedical research. Herein, we report the development of a new ultrasensitive approach for label-free DNA detection using magnetic nanoparticle (MNP) assisted rapid target capture/separation in combination with signal amplification using poly-enzyme tagged polymer nanobead. The sensor uses an MNP linked capture DNA and a biotin modified signal DNA to sandwich bind the target followed by ligation to provide high single-nucleotide polymorphism discrimination. Only the presence of a perfect match target DNA yields a covalent linkage between the capture and signal DNAs for subsequent conjugation of a neutravidin-modified horseradish peroxidase (HRP) enzyme through the strong biotin-nuetravidin interaction. This converts each captured DNA target into an HRP which can convert millions of copies of a non-fluorescent substrate (amplex red) to a highly fluorescent product (resorufin), for great signal amplification. The use of polymer nanobead each tagged with thousands of copies of HRPs as the signal amplifier greatly improves the signal amplification power, leading to greatly improved sensitivity. We show our biosensing approach can specifically detect an unlabeled DNA target down to 10 aM with a wide dynamic range of 5 orders of magnitude (from 0.001 fM to 100.0 fM). Furthermore, our approach has a high discrimination between a perfectly matched gene and its cancer-related single-base mismatch targets (SNPs): It can positively detect the perfect match DNA target even in the presence of 100 fold excess of co-existing SNPs. This sensing approach also works robustly in clinical relevant media (e.g. 10% human serum) and gives almost the same SNP discrimination ratio as that in clean buffers. Therefore, this ultrasensitive SNP biosensor appears to be well-suited for potential diagnostic applications of genetic diseases.

Keywords: DNA detection, polymer beads, signal amplification, single nucleotide polymorphisms

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Authors: A. Azari, S. S. K. Seyyedi

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One of the unaddressed and open challenges in the nano-networking is the characteristics of noise. The previous analysis, however, has concentrated on end-to-end communication model with no separate modelings for propagation channel and noise. By considering a separate signal propagation and noise model, the design and implementation of an optimum receiver will be much easier. In this paper, we justify consideration of a separate additive Gaussian noise model of a nano-communication system based on the molecular communication channel for which are applicable for MSK and MOSK modulation schemes. The presented noise analysis is based on the Brownian motion process, and advection molecular statistics, where the received random signal has a probability density function whose mean is equal to the mean number of the received molecules. Finally, the justification of received signal magnitude being uncorrelated with additive non-stationary white noise is provided.

Keywords: molecular, noise, diffusion, channel

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Authors: R. J. Chang

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A cyclostationary Gaussian linearization method is formulated for investigating the time average response of nonlinear system under sinusoidal signal and white noise excitation. The quantitative measure of cyclostationary mean, variance, spectrum of mean amplitude, and mean power spectral density of noise is analyzed. The qualitative response behavior of stochastic jump and bifurcation are investigated. The validity of the present approach in predicting the quantitative and qualitative statistical responses is supported by utilizing Monte Carlo simulations. The present analysis without imposing restrictive analytical conditions can be directly derived by solving non-linear algebraic equations. The analytical solution gives reliable quantitative and qualitative prediction of mean and noise response for the Duffing system subjected to both sinusoidal signal and white noise excitation.

Keywords: cyclostationary, duffing system, Gaussian linearization, sinusoidal, white noise

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Authors: Somayeh Komeylian

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The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE).

Keywords: DoA estimation, Adaptive antenna array, Deep Neural Network, LS-SVM optimization model, Radial basis function, and MSE

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Authors: Yan-Ren Chen, Jenn-Kaie Lain

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This paper proposes a novel indoor positioning algorithm based on visible light communications, implemented by light-emitting diode fixtures. In the proposed positioning algorithm, distances between light-emitting diode fixtures and mobile terminal are derived from the assumption of ideal Lambertian optic radiation model, and Trilateration positioning method is proceeded immediately to get the coordinates of mobile terminal. The proposed positioning algorithm directly obtains distance information from the optical signal modeling, and therefore, statistical distribution of received signal strength at different positions in interior space has no need to be pre-established. Numerically, simulation results have shown that the proposed indoor positioning algorithm can provide accurate location coordinates estimation.

Keywords: indoor positioning, received signal strength, trilateration, visible light communications

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Authors: Beata Zima, Octavio A. Márquez Reyes, Masoud Mohammadgholiha, Jochen Moll, Luca de Marchi

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This document presents the approach of using compressed sensing in signal encoding and information transferring within a guided wave sensor network, comprised of specially designed frequency steerable acoustic transducers (FSATs). Wave propagation in a damaged plate was simulated using commercial FEM-based software COMSOL. Guided waves were excited by means of FSATs, characterized by the special shape of its electrodes, and modeled using PIC255 piezoelectric material. The special shape of the FSAT, allows for focusing wave energy in a certain direction, accordingly to the frequency components of its actuation signal, which makes available a larger monitored area. The process begins when a FSAT detects and records reflection from damage in the structure, this signal is then encoded and prepared for transmission, using a combined approach, based on Compressed Sensing Matching Pursuit and Quadrature Amplitude Modulation (QAM). After codification of the signal is in binary chars the information is transmitted between the nodes in the network. The message reaches the last node, where it is finally decoded and processed, to be used for damage detection and localization purposes. The main aim of the investigation is to determine the location of detected damage using reconstructed signals. The study demonstrates that the special steerable capabilities of FSATs, not only facilitate the detection of damage but also permit transmitting the damage information to a chosen area in a specific direction of the investigated structure.

Keywords: data compression, ultrasonic communication, guided waves, FEM analysis

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Authors: Junfeng Yan, Wenkui Hou

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Prognostics and Health Management (PHM) is a technology to monitor the equipment status and predict impending faults. It is used to predict the potential fault and provide fault information and track trends of system degradation by capturing characteristics signals. So how to detect characteristics signals is very important. The select of test point plays a very important role in detecting characteristics signal. Traditionally, we use dependency model to select the test point containing the most detecting information. But, facing the large complicated system, the dependency model is not built so easily sometimes and the greater trouble is how to calculate the matrix. Rely on this premise, the paper provide a highly effective method to select test point without dependency model. Because signal flow model is a diagnosis model based on failure mode, which focuses on system’s failure mode and the dependency relationship between the test points and faults. In the signal flow model, a fault information can flow from the beginning to the end. According to the signal flow model, we can find out location and structure information of every test point and module. We break the signal flow model up into serial and parallel parts to obtain the final relationship function between the system’s testability or prediction metrics and test points. Further, through the partial derivatives operation, we can obtain every test point’s importance degree in determining the testability metrics, such as undetected rate, false alarm rate, untrusted rate. This contributes to installing the test point according to the real requirement and also provides a solid foundation for the Prognostics and Health Management. According to the real effect of the practical engineering application, the method is very efficient.

Keywords: false alarm rate, importance degree, signal flow model, undetected rate, untrusted rate

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Authors: Farhan Beg

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A space vector based pulse width modulation control technique for the three-phase PWM converter is proposed in this paper. The proposed control scheme is based on a synchronous reference frame model. High performance and efficiency is obtained with regards to the DC bus voltage and the power factor considerations of the PWM rectifier thus leading to low losses. MATLAB/SIMULINK are used as a platform for the simulations and a SIMULINK model is presented in the paper. The results show that the proposed model demonstrates better performance and properties compared to the traditional SPWM method and the method improves the dynamic performance of the closed loop drastically. For the space vector based pulse width modulation, sine signal is the reference waveform and triangle waveform is the carrier waveform. When the value of sine signal is larger than triangle signal, the pulse will start producing to high; and then when the triangular signals higher than sine signal, the pulse will come to low. SPWM output will change by changing the value of the modulation index and frequency used in this system to produce more pulse width. When more pulse width is produced, the output voltage will have lower harmonics contents and the resolution will increase.

Keywords: power factor, SVPWM, PWM rectifier, SPWM

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Authors: Sachin Singh, Thomas Penzel, Dinesh Nandan

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Accurate identification of Sleep Apnea Hypopnea Syndrom Diagnosis is difficult problem for human expert because of variability among persons and unwanted noise. This paper proposes the diagonosis of Sleep Apnea Hypopnea Syndrome (SAHS) using airflow, ECG, Pulse and SaO2 signals. The features of each type of these signals are extracted using statistical methods and ANN learning methods. These extracted features are used to approximate the patient's Apnea Hypopnea Index(AHI) using sample signals in model. Advance signal processing is also applied to snore sound signal to locate snore event and SaO2 signal is used to support whether determined snore event is true or noise. Finally, Apnea Hypopnea Index (AHI) event is calculated as per true snore event detected. Experiment results shows that the sensitivity can reach up to 96% and specificity to 96% as AHI greater than equal to 5.

Keywords: neural network, AHI, statistical methods, autoregressive models

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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: Yo-Sheng Lin, Run-Chi Liu, Chien-Chu Ji, Chih-Chung Chen, Chien-Chin Wang

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A W-band double-balanced mixer for direct up-conversion using standard 90 nm CMOS technology is reported. The mixer comprises an enhanced double-balanced Gilbert cell with PMOS negative resistance compensation for conversion gain (CG) enhancement and current injection for power consumption reduction and linearity improvement, a Marchand balun for converting the single LO input signal to differential signal, another Marchand balun for converting the differential RF output signal to single signal, and an output buffer amplifier for loading effect suppression, power consumption reduction and CG enhancement. The mixer consumes low power of 6.9 mW and achieves LO-port input reflection coefficient of -17.8~ -38.7 dB and RF-port input reflection coefficient of -16.8~ -27.9 dB for frequencies of 90~100 GHz. The mixer achieves maximum CG of 3.6 dB at 95 GHz, and CG of 2.1±1.5 dB for frequencies of 91.9~99.4 GHz. That is, the corresponding 3 dB CG bandwidth is 7.5 GHz. In addition, the mixer achieves LO-RF isolation of 36.8 dB at 94 GHz. To the authors’ knowledge, the CG, LO-RF isolation and power dissipation results are the best data ever reported for a 94 GHz CMOS/BiCMOS up-conversion mixer.

Keywords: CMOS, W-band, up-conversion mixer, conversion gain, negative resistance compensation, output buffer amplifier

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Authors: Bowei Yuan, Shi Li, Liuyang Song, Huaqing Wang, Lingli Cui

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Convolutional neural networks (CNN) have received extensive attention in the field of fault diagnosis. Many fault diagnosis methods use CNN for fault type identification. However, when the amount of raw data collected by sensors is massive, the neural network needs to perform a time-consuming classification task. In this paper, a mechanical fault diagnosis method based on vibration signal down-sampling and the improved one-dimensional convolutional neural network is proposed. Through the robust principal component analysis, the low-rank feature matrix of a large amount of raw data can be separated, and then down-sampling is realized to reduce the subsequent calculation amount. In the improved one-dimensional CNN, a smaller convolution kernel is used to reduce the number of parameters and computational complexity, and regularization is introduced before the fully connected layer to prevent overfitting. In addition, the multi-connected layers can better generalize classification results without cumbersome parameter adjustments. The effectiveness of the method is verified by monitoring the signal of the centrifugal pump test bench, and the average test accuracy is above 98%. When compared with the traditional deep belief network (DBN) and support vector machine (SVM) methods, this method has better performance.

Keywords: fault diagnosis, vibration signal down-sampling, 1D-CNN

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Authors: Naila Sher, Mushtaq Ahmed, Nadia Mushtaq, Rahmat Ali Khan

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In cancer therapy, nanoparticles (NPs) shall be applied possibly by inoculation in the veins of humans. This action will connect them with white (WBCs) and red blood cells (RBCs) in the bloodstream before they reach their main targeted cancer cells. However, possible effects of silver, gold, and silver/gold bimetallic NPs (Ag, Au, and Ag/Au BNPs) on baby hamster kidney-21 (BHK-21) cells were studied by MTT assay. Here, Ag, Au, and their Ag/Au BNPs (bimetallic nanoparticles) were synthesized by using Hippeastrum hybridum (HH) extract. These NPs were characterized by UV-visible spectroscopy, FT-IR, XRD, and EDX, and SEM analysis. XRD analysis conferring the crystal structure with an average size of 13.3, 10.72, and 8.34nm of Ag, Au, and Ag/Au BNPs, respectively. SEM showed that Ag, Au, and Ag/Au BNPs had irregular morphologies, with nano measures calculated sizes of 40, 30, and 20 nm respectively. EDX spectrometers confirmed the presence of elemental Ag signal of the AgNPs with 22.75%, Au signal of the AuNPs with 48.08%, Ag signal with 12%, and Au signal with 38.26% of the Ag/Au BNPs. The BHK-21cells were incubated in the existence of doxorubicin, plant extract, Ag, Au, and Ag/Au BNPs. The cytotoxic effects could be observed in a dose-dependent mode; doxorubicin and Ag/Au BNPs were more toxic than plant extract, Ag, and Au NPs. It is demonstrated that NPs interact with BHK-21cells and significantly reduce cell viability in a concentration-dependent manner. However, to reduce the potential threats of NPs further studies are recommended.

Keywords: hippeastrum hybridum, nanoparticle, BHK-21cells

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Authors: Adeniran K. Ademuwagun, Alastair Allen

Abstract:

The relationship dependence between RSS and distance in an enclosed environment is an important consideration because it is a factor that can influence the reliability of any localization algorithm founded on RSS. Several algorithms effectively reduce the variance of RSS to improve localization or accuracy performance. Our proposed algorithm essentially avoids this pitfall and consequently, its high adaptability in the face of erratic radio signal. Using 3 anchors in close proximity of each other, we are able to establish that RSS can be used as reliable indicator for localization with an acceptable degree of accuracy. Inherent in this concept, is the ability for each prospective anchor to validate (guarantee) the position or the proximity of the other 2 anchors involved in the localization and vice versa. This procedure ensures that the uncertainties of radio signals due to multipath effects in enclosed environments are minimized. A major driver of this idea is the implicit topological relationship among sensors due to raw radio signal strength. The algorithm is an area based algorithm; however, it does not trade accuracy for precision (i.e the size of the returned area).

Keywords: anchor nodes, centroid algorithm, communication graph, radio signal strength

Procedia PDF Downloads 507

Authors: Daniel F. Bohorquez, Luis M. Agudelo, Henry H. León

Abstract:

Heart rate variability (HRV) is defined as the temporary variation between heartbeats or RR intervals (distance between R waves in an electrocardiographic signal). This distance is currently a recognized biomarker. With the analysis of the distance, it is possible to assess the sympathetic and parasympathetic nervous systems. These systems are responsible for the regulation of the cardiac muscle. The analysis allows health specialists and researchers to diagnose various pathologies based on this variation. For the acquisition and analysis of HRV taken from a cardiac electrical signal, electronic equipment and analysis software that work independently are currently used. This complicates and delays the process of interpretation and diagnosis. With this delay, the health condition of patients can be put at greater risk. This can lead to an untimely treatment. This document presents a single portable device capable of acquiring electrocardiographic signals and calculating a total of 19 HRV metrics. This reduces the time required, resulting in a timelier intervention. The device has an electrocardiographic signal acquisition card attached to a microcontroller capable of transmitting the cardiac signal wirelessly to a mobile device. In addition, a mobile application was designed to analyze the cardiac waveform. The device calculates the RR and different metrics. The application allows a user to visualize in real-time the cardiac signal and the 19 metrics. The information is exported to a cloud database for remote analysis. The study was performed under controlled conditions in the simulated hospital of the Universidad de la Sabana, Colombia. A total of 60 signals were acquired and analyzed. The device was compared against two reference systems. The results show a strong level of correlation (r > 0.95, p < 0.05) between the 19 metrics compared. Therefore, the use of the portable system evaluated in clinical scenarios controlled by medical specialists and researchers is recommended for the evaluation of the condition of the cardiac system.

Keywords: biological signal análisis, heart rate variability (HRV), HRV metrics, mobile app, portable device.

Procedia PDF Downloads 182