Results 141 to 150 of about 4,861 (197)

Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model. [PDF]

Sci Rep
Alotaibi M, Alshardan A, Maashi M, Asiri MM, Alotaibi SR, Yafoz A, Alsini R, Khadidos AO.   +7 more
europepmc   +1 more source

Real-time, neural signal processing for high-density brain-implantable devices. [PDF]

Bioelectron Med
Sodagar AM, Khazaei Y, Nekoui M, Shaeri M.   +3 more
europepmc   +1 more source

A novel scrolling text reading paradigm for improving the performance of multiclass and hybrid brain computer interface systems. [PDF]

PLoS One
Ergün E, Aydemir Ö, Korkmaz OE.
europepmc   +1 more source

Roadmap on computational methods in optical imaging and holography [invited]. [PDF]

Appl Phys B
Rosen J, Alford S, Allan B, Anand V, Arnon S, Arockiaraj FG, Art J, Bai B, Balasubramaniam GM, Birnbaum T, Bisht NS, Blinder D, Cao L, Chen Q, Chen Z, Dubey V, Egiazarian K, Ercan M, Forbes A, Gopakumar G, Gao Y, Gigan S, Gocłowski P, Gopinath S, Greenbaum A, Horisaki R, Ierodiaconou D, Juodkazis S, Karmakar T, Katkovnik V, Khonina SN, Kner P, Kravets V, Kumar R, Lai Y, Li C, Li J, Li S, Li Y, Liang J, Manavalan G, Mandal AC, Manisha M, Mann C, Marzejon MJ, Moodley C, Morikawa J, Muniraj I, Narbutis D, Ng SH, Nothlawala F, Oh J, Ozcan A, Park Y, Porfirev AP, Potcoava M, Prabhakar S, Pu J, Rai MR, Rogalski M, Ryu M, Choudhary S, Salla GR, Schelkens P, Şener SF, Shevkunov I, Shimobaba T, Singh RK, Singh RP, Stern A, Sun J, Zhou S, Zuo C, Zurawski Z, Tahara T, Tiwari V, Trusiak M, Vinu RV, Volotovskiy SG, Yılmaz H, De Aguiar HB, Ahluwalia BS, Ahmad A.   +82 more
europepmc   +1 more source

Exact Non-Markovian Quantum Dynamics on the NISQ Device Using Kraus Operators. [PDF]

ACS Omega
Seneviratne A, Walters PL, Wang F.
europepmc   +1 more source

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Fast Algorithm for Walsh Hadamard Transform on Sliding Windows

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
This paper proposes a fast algorithm for Walsh Hadamard Transform on sliding windows which can be used to implement pattern matching most efficiently. The computational requirement of the proposed algorithm is about 1.5 additions per projection vector per sample, which is the lowest among existing fast algorithms for Walsh Hadamard Transform on sliding
Wanli, Ouyang, Wai-Kuen, Cham
openaire   +4 more sources

Unified Matrix Treatment of the Fast Walsh-Hadamard Transform

IEEE Transactions on Computers, 1976
A unified matrix treatment is presented for the various orderings of the Walsh-Hadamard (WH) functions using a general framework. This approach clarifies the different definitions of the WH matrix, the various fast algorithms and the reorderings of the WH functions.
Fino, Bernard J., Algazi, V. Ralph
openaire   +4 more sources

Recursive Algorithm for Sliding Walsh Hadamard Transform

IEEE Transactions on Signal Processing, 2014
In this paper, a recursive sliding transform (RST) algorithm is proposed for the fast implementation of the Walsh Hadamard Transform (WHT) on sliding windows. The proposed RST algorithm accelerates the sliding WHT process by recursively reducing the order of WHT.
Chun-Su Park
openaire   +3 more sources

Real-Time Walsh-Hadamard Transformation

IEEE Transactions on Computers, 1972
A technique for obtaining the digital Walsh-Hadamard transform of two-dimensional discrete pictures in real time is presented. A considerable reduction in total time for the transformation of a picture results since the process of scanning and digitizing one picture line overlaps with computations on the previous line. This is accomplished by operating
Alexandridis, Nikitas A., Klinger, Allen
openaire   +1 more source