Signal-to-noise improvement by employment of generalized signal detection algorithm

1995 ◽  
Author(s):  
Vyacheslav P. Tuzlukov
Keyword(s):  
Signal Detection ◽  
Detection Algorithm ◽  
Signal To Noise

Head Instability Detection for Testing Process in Perpendicular Magnetic Recording System

2013 ◽  
Vol 770 ◽  
pp. 319-322 ◽  
Author(s):  
Piya Kovintavewat ◽  
Santi Koonkarnkhai ◽  
Aimamorn Suvichakorn
Keyword(s):  
Magnetic Recording ◽  
Signal To Noise Ratio ◽  
Disk Drive ◽  
Detection Algorithm ◽  
Recording System ◽  
High Signal ◽  
Signal To Noise ◽  
Perpendicular Magnetic Recording ◽  
Read Head ◽  
Pass Through

During hard disk drive (HDD) testing process, the magneto-resistive read (MR) head is analyzed and checked if the head is defective or not. Baseline popping (BLP) is one of the crucial problems caused by head instability, whose effect can distort the readback signal to the extent of causing possible sector read failure. Without BLP detection algorithm, the defective read head might pass through HDD assembling process, thus producing an unreliable HDD. This situation must be prevented so as to retain customer satisfaction. This paper proposes a simple (but efficient) BLP detection algorithm for perpendicular magnetic recording systems. Results show that the proposed algorithm outperforms the conventional one in terms of both the percentage of detection and the percentage of false alarm, when operating at high signal-to-noise ratio.

Fiducial visibility on planar images during motion-synchronized tomotherapy treatments

2022 ◽  
Author(s):  
William Ferris ◽  
Larry Albert DeWerd ◽  
Wesley S Culberson
Keyword(s):  
Management System ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Position Error ◽  
Signal To Noise ◽  
Motion Management ◽  
Future Studies ◽  
Planar Images ◽  
Noise Ratio

Abstract Objective: Synchrony® is a motion management system on the Radixact® that uses planar kV radiographs to locate the target during treatment. The purpose of this work is to quantify the visibility of fiducials on these radiographs. Approach: A custom acrylic slab was machined to hold 8 gold fiducials of various lengths, diameters, and orientations with respect to imaging axis. The slab was placed on the couch at the imaging isocenter and planar radiographs were acquired perpendicular to the custom slab with varying thicknesses of acrylic on each side. Fiducial signal to noise ratio (SNR) and detected fiducial position error in millimeters were quantified. Main Results: The minimum output protocol (100 kVp, 0.8 mAs) was sufficient to detect all fiducials on both Radixact configurations when the thickness of the phantom was 20 cm. However, no fiducials for any protocol were detected when the phantom was 50 cm thick. The algorithm accurately detected fiducials on the image when the SNR was larger than 4. The MV beam was observed to cause RFI artifacts on the kV images and to decrease SNR by an average of 10%. Significance: This work provides the first data on fiducial visibility on kV radiographs from Radixact Synchrony treatments. The Synchrony fiducial detection algorithm was determined to be very accurate when sufficient SNR is achieved. However, a higher output protocol may need to be added for use with larger patients. This work provided groundwork for investigating visibility of fiducial-free solid targets in future studies and provided a direct comparison of fiducial visibility on the two Radixact configurations, which will allow for intercomparison of results between configurations.

An automatic seismic signal detection algorithm based on the Walsh transform

1981 ◽  
Vol 71 (4) ◽  
pp. 1351-1360
Author(s):  
Tom Goforth ◽  
Eugene Herrin
Keyword(s):  
Signal Detection ◽  
Real Time ◽  
Seismic Signal ◽  
Detection Algorithm ◽  
Machine Language ◽  
Walsh Transform ◽  
Time Signal ◽  
Data Systems ◽  
Detection Capability ◽  
Short Period

abstract An automatic seismic signal detection algorithm based on the Walsh transform has been developed for short-period data sampled at 20 samples/sec. Since the amplitude of Walsh function is either +1 or −1, the Walsh transform can be accomplished in a computer with a series of shifts and fixed-point additions. The savings in computation time makes it possible to compute the Walsh transform and to perform prewhitening and band-pass filtering in the Walsh domain with a microcomputer for use in real-time signal detection. The algorithm was initially programmed in FORTRAN on a Raytheon Data Systems 500 minicomputer. Tests utilizing seismic data recorded in Dallas, Albuquerque, and Norway indicate that the algorithm has a detection capability comparable to a human analyst. Programming of the detection algorithm in machine language on a Z80 microprocessor-based computer has been accomplished; run time on the microcomputer is approximately 110 real time. The detection capability of the Z80 version of the algorithm is not degraded relative to the FORTRAN version.

Study of BLAST Signal Detection Algorithm in LTE System

2013 ◽  
Vol 756-759 ◽  
pp. 3183-3188
Author(s):  
Tao Lei ◽  
Deng Ping He ◽  
Fang Tang Chen
Keyword(s):  
Signal Detection ◽  
High Speed ◽  
Data Communication ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Detection Algorithms ◽  
High Speed Data ◽  
Simulation Results ◽  
Better Than

BLAST can achieve high speed data communication. Its signal detection directly affects performance of BLAST receiver. This paper introduced several signal detection algorithmsZF algorithm, MMSE algorithm, ZF-SIC algorithm and MMSE-SIC algorithm. The simulation results show that the traditional ZF algorithm has the worst performance, the traditional MMSE algorithm and the ZF-SIC algorithm is similar, but with the increase of the SNR, the performance of ZF-SIC algorithm is better than MMSE algorithm. MMSE-SIC algorithm has the best detection performance in these detection algorithms.

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