AN ADAPTIVE NOISE CANCELER IMPLEMENTED WITH CMOS ANALOG TECHNOLOGY

1996 ◽  
Vol 06 (02) ◽  
pp. 139-154
Author(s):  
GABRIEL GOMEZ ◽  
RAYMOND SIFERD
Keyword(s):  
Finite Impulse Response ◽  
Learning Algorithm ◽  
Test Results ◽  
Static Power ◽  
Analog Implementation ◽  
Adaptive Noise ◽  
On Chip ◽  
Processing Techniques ◽  
Noise Canceler ◽  
Signal Processing Techniques

A fully analog implementation of an adaptive noise canceler is presented, including design, simulation, and test results of the fabricated chip. The prototype chip was fabricated using 2-µ CMOS P-Well technology on a 4.0 mm2 die and uses ±5 V power supplies. The static power dissipation is 276 milliwatts. Analog signal processing techniques are used to realize an adaptive system based upon a finite impulse response (FIR) filter and least mean squares (LMS) adaptive algorithm. The circuit is tested as an adaptive noise canceler, where a signal corrupted by noise is the input. The circuit adaptively converges to cancel the noise to produce an output that is the best LMS estimate of the signal. The circuit could be used for other real-time adaptive filter applications or for realizing an on-chip learning algorithm. The implementation illustrates the advantages of an analog system with no requirements for A/D and D/A converters, reduced size of circuit subsystems (e.g. multipliers), and the relatively fast convergence.

scholarly journals Investigating an Optimal Signal Epoch Length for Cardiotocographic Classification

2020 ◽  
Vol 6 (3) ◽  
pp. 514-517
Author(s):  
Patricio Fuentealba ◽  
Alfredo Illanes ◽  
Frank Ortmeier ◽  
Prabal Poudel
Keyword(s):  
Main Idea ◽  
Ensemble Empirical Mode Decomposition ◽  
Classification Performance ◽  
Autoregressive Modelling ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Signal Segment ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Optimal Signal

AbstractThis work focuses on investigating an optimal foetal heart rate (FHR) signal segment to be considered for automatic cardiotocographic (CTG) classification. The main idea is to evaluate a set of signal segments of different length and location based on their classification performance. For this purpose, we employ a feature extraction operation based on two signal processing techniques, such as the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and time-varying autoregressive modelling. For each studied segment, the features are extracted and evaluated based on their performance in CTG classification. For the proposed evaluation, we make use of real CTG data extracted from the CTU-UHB database. Results show that the classification performance depends considerably on the selected FHR segment. Likewise, we have found that an optimal FHR segment for foetal welfare assessment during labour corresponds to a segment of 30 minutes long.

Detecting faults in VSC-HVDC systems by deep learning and K-means

2020 ◽  
Vol 13 ◽  
Author(s):  
Roohollah Sadeghi Goughari ◽  
Mehdi Jafari Shahbazzadeh ◽  
Mahdiyeh Eslami
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Transmission Lines ◽  
Fault Location ◽  
Learning Algorithm ◽  
Hvdc Transmission ◽  
Deep Learning Algorithm ◽  
Processing Techniques ◽  
Signal Processing Techniques

Background: In this paper, two methods and their comparison used to determine the fault locaton in VSC-HVDC transmission lines. Fast and reliable control are features of these systems. Methods: Additionally, wavelet transform from advanced techniques of signal processing is employed for the purpose of extracting important characteristics of fault signal from both sides of the line by PMU. To do so, Deep learning is used to identify the relation between the extracted features from wavelet analysis of the fault current and variations under fault conditions. As such, wavelet transform and advanced signal processing techniques are used to extract important features of fault signal from both sides of the line by the PMU. Results: The results show the high accuracy of finding fault location by the deep learning algorithm method compared to the k-means algorithm with an error rate of <1%. Conclusion: Studies on the 50 kV VSC-HVDC transmission line with a length of 25 km in MATLAB have been simulated.

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

scholarly journals Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1127
Author(s):  
Ji Hyung Nam ◽  
Dong Jun Oh ◽  
Sumin Lee ◽  
Hyun Joo Song ◽  
Yun Jeong Lim
Keyword(s):  
Deep Learning ◽  
Small Bowel ◽  
Scoring System ◽  
Operating Characteristic ◽  
Clinical Evidence ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
External Validation ◽  
Test Results ◽  
Deep Learning Algorithm

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

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