In this paper a complex filter is presented where the shift in the frequency is obtained using a linear frequency transformation. The linear frequency transformation in the proposed design is ...implemented using a complex impedance. A complex impedance is also proposed in this paper using a Fully Balanced Second Generation Current Conveyor (FBCCII). The FBCCII design used in this paper consumes
84
μ
W
of power and has an open loop gain of 47.83 dB with
62
.
9
∘
of phase margin at 39.8 MHz unity gain bandwidth. A low power 3rd order complex filter is designed by linear frequency transformation at 40 MHz center frequency. The proposed complex filter achieves a bandwidth of 9 MHz with an image rejection ratio of 45 dB. The design consumes 1.5 mW of power and has a group delay of 12.5 ns. The figure of merit of the proposed filter is 0.007 fJ with a SFDR of 63.9 dB. The output noise of the design at 40 MHz center frequency is
45.86
nV
/
Hz
and integrated Input Referred noise is
600
μ
V
. The design is simulated using a 180 nm CMOS technology with a supply voltage of
±
0.5
V
. The circuit’s efficacy is verified and supported by PVT and post layout simulations. The area of the layout of the proposed design is
0.624
mm
2
(i.e.
260
μ
m
×
240
μ
m
).
Preliminary signal processing methods used to create new tools to examine materials and digital sound recording means are described. It is shown that using information redundancy when creating a ...training base for deep learning neural networks used for such examination increases speaker identification efficiency based on voice characteristic parameters by about 15%. It is shown that the proposed processing methods enable speaker identification based on phonograms that are 1 second long.
Amyotrophic Lateral Sclerosis (ALS) is a disorder of the neuromuscular system that causes the impairment of nerve cells from brain to spinal cord and to the voluntary muscles in every part of the ...human physiological system, which totally leads to paralysis. The examination of ALS using Electromyograms (EMG) is a challenging task which requires experts to investigate and diagnose. Hence, the development of an efficient and automated procedure is significant for the analysis of ALS signals. In this work, eighty time-frequency features were extricated from EMG signals transformed into time-frequency images. Further, fifteen highly substantial features were chosen using the firefly algorithm with fractional position update. Further, fractional firefly neural network is introduced and developed to examine the EMG signals. The performance metrics of the fractional firefly based neural network diagnostic system were analyzed with different fractional orders (α) and hidden neurons. Results demonstrated that the proposed technique is highly efficient and yields good statistical significance. Further, the accuracy of the fractional firefly neural network classifier with α = 0.5 and 15 hidden neurons is higher (93.3%) when compared to the accuracy of the classifier with different α values and hidden neurons. The proposed fractional order-based feature selection algorithm and classifier model are highly suitable for development of systems for evaluation of ALS and normal EMG signals, since the proficient discrimination of normal and ALS EMG signals is essential for the identification of neuromuscular disorders.
Heart rate variability (HRV) and electrocardiogram (ECG)-derived respiration (EDR) have been used to detect sleep apnea (SA) for decades. The present study proposes an SA-detection algorithm using a ...machine-learning framework and bag-of-features (BoF) derived from an ECG spectrogram.
This study was verified using overnight ECG recordings from 83 subjects with an average apnea-hypopnea index (AHI) 29.63 (/h) derived from the Physionet Apnea-ECG and National Cheng Kung University Hospital Sleep Center database. The study used signal preprocessing to filter noise and artifacts, ECG time-frequency transformation using continuous wavelet transform (CWT), BoF feature generation, machine-learning classification using support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN) classification, and cross-validation. The time length of the spectrogram was set as 10 and 60 s to examine the required minimum spectrogram window time length to achieve satisfactory accuracy. Specific frequency bands of 0.1-50, 8-50, 0.8-10, and 0-0.8 Hz were also extracted to generate the BoF to determine the band frequency best suited for SA detection.
The five-fold cross-validation accuracy using the BoF derived from the ECG spectrogram with 10 and 60 s time windows were 90.5% and 91.4% for the 0.1-50 Hz and 8-50 Hz frequency bands, respectively.
An SA-detection algorithm utilizing BoF and a machine-learning framework was successfully developed in this study with satisfactory classification accuracy and high temporal resolution.
This paper proposes new state-space formulations of IIR Variable Digital Filters (VDFs) based on frequency transformations. The existing frequency transformation-based state-space VDFs require ...restrictions on the transfer functions, state-space representations, and tuning characteristics. On the other hand, the proposed method is free from such restrictions. We achieve this goal by applying series approximations to the conventional state-space formulations of frequency transformations. This approach also allows us to realize the proposed VDFs without complicated computations such as the inverse matrix and the square root. Furthermore, the proposed VDFs show high accuracy with respect to the finite wordlength effects as well as the approximation errors.
In this study, the authors consider the radar sequence generation problem, where the sequence is required to possess the spectral nulling and low-correlation levels in crowded electromagnetic ...environments. The design problem of the sequence with the desired spectrum and correlation properties is formulated as the minimisation of the difference between the true ones and the desired ones, respectively. Then a spectral fitting method is proposed to solve it, by applying the time-frequency transformation. The proposed method is based solely on fast Fourier transform operations, thus it is computationally efficient. An extension to sequence sets design is also presented. Numerical simulations indicate that, compared with the state-of-the-art algorithms, the proposed method can achieve better or identical results with greatly reduced running time.
This study presents a new compact design technique for dual band stop (DBS) filter. Analytical formulation of DBS frequency transformation/mapping is derived in terms of parametric representation of ...band characteristic of DBS filter. The analytical derivation of the transformation function is based on sequential application of classical low pass to band stop (LP to BS) and low pass to band pass (LP to BP) transformations. Assigned band characteristic of DBS filter can be achieved by applying the proposed transformation function on an LP prototype network. The explicit network topologies for DBS reactance mapping are presented. Several numerical examples are provided to validate the proposed mapping function. An implantation example of DBS filter for GSM900 and GSM1800/UMTS bands is given. Mixed element realisation of the designed filter and measurement results of the design are presented.
Dynamic gesture recognition is a typical human-computer interaction method owing to its great potential in practical applications. Currently, most of research work on gesture recognition has mainly ...focused on vision-based and surface electromyography (sEMG) methods. Compared to vision-based methods, the sequential sEMG signal can directly depict the muscle activity of different gestures which could lead to higher recognition efficiency. However, the effective feature design and selection of sEMG signal is still complicated since muscle fatigue and small electrode displacement will affect the recognition precision of sEMG signals. In this paper, a novel end-to-end dynamic gesture recognition method is developed. The raw sEMG signals are converted into an image form by using the time-frequency transformation method to obtain more comprehensive information for model training and test. And a recognition model based on Convolutional Neural Network (CNN) model is built for high-precision time-frequency image recognition. Experiments indicate that the proposed method could acquire distinguishing features from the pre-prossed images and the overall recognition accuracy on different gestures can reach up to 98.3%.
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