Results 61 to 70 of about 9,834 (275)

Full‐Stack Architectures for Intelligent Brain‐Computer Interfaces

Advanced Science, EarlyView.
System‐level overview of brain–computer interfaces (BCIs), illustrating the integration of neural signal acquisition, wireless transmission, and adaptive decoding. Advanced electrode, tissue interfaces, energy‐efficient communication, and robust algorithms collectively enable stable signal quality, real‐time processing, and closed‐loop operation ...
Hee Kyu Lee, Hyun Bin Kim, Sang Uk Park, Janghoon Joo, Jinhong Min, Geumbee Lee, Joohoon Kang, Hyoyoung Jeong, Jae‐Young Yoo, Sang Min Won   +9 more
wiley   +1 more source

Learning a Deep Vector Quantization Network for Image Compression

IEEE Access, 2019
Deep convolutional neural network (DCNN) based image codecs, consisting of encoder, quantizer and decoder, have achieved promising image compression results. The major challenge in learning these DCNN models lies in the joint optimization of the encoder,
Xiaotong Lu, Heng Wang, Weisheng Dong, Fangfang Wu, Zhonglong Zheng, Guangming Shi   +5 more
doaj   +1 more source

On-line trajectory classification [PDF]

, 2003
This study proposes a modular system for clustering on-line motion trajectories obtained while users navigate within a virtual environment. It presents a neural network simulation that gives a set of five clusters which help to differentiate users on the
Gregory O’Hare, O'Hare, Gregory, Sas, Corina, Ronan Reilly, Reilly, R., O'Hare, G., Reilly, Ronan, Corina Sas   +7 more
core   +1 more source

Cursive character recognition by learning vector quantization [PDF]

Pattern Recognition Letters, 2001
Summary: This paper presents a cursive character recognizer embedded in an off-line cursive script recognition system. The recognizer is composed of two modules: the first one is a feature extractor, the second one a learning vector quantizer. The selected feature set was compared to Zernike polynomials using the same classifier.
Francesco Camastra, Alessandro Vinciarelli   +1 more
openaire   +1 more source

Photonic‐Enabled Energy‐Efficient Transparent Neuromorphic Computing Devices: A Review

Advanced Science, EarlyView.
Transparent photonic neuromorphic computing devices merge optics and brain‐inspired computing to overcome von Neumann bottlenecks with ultrafast, low‐energy processing. By exploiting transparent oxides, 2D materials, phase‐change materials, and hybrid heterostructures, these platforms enable photonic synapses, memory, and logic for see‐through edge ...
Shuvaraj Ghosh, Ki‐bum Lee, Junghyeon Lee, Seunghee Cho, Malkeshkumar Patel, Hangfei Li, Yu Wen, Ye Zhou, Joondong Kim   +8 more
wiley   +1 more source

Intelligent design of network multimedia using big data and virtual Artificial Intelligence technology

IET Networks, EarlyView., 2023
The topic discussed was the intelligent design of network multimedia using BD and virtual AI technology. First of all, the authors gave a brief overview of its relevant research background, and then comprehensively analysed the advantages and disadvantages of previous scholars' research on network multimedia.
Xin Zhang
wiley   +1 more source

Klasifikasi Stroke Berdasarkan Kelainan Patologis dengan Learning Vector Quantization

Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems), 2014
Dampak yang ditimbulkan stroke diantaranya kelumpuhan sebagian atau keseluruhan organ tubuh sampai kematian. Tingginya angka kematian akibat stroke disebabkan karena penanganan yang lambat.
Aji Seto Arifianto, Moechammad Sarosa, Onny Setyawati   +2 more
doaj   +1 more source

Divergence-based classification in learning vector quantization [PDF]

Neurocomputing, 2011
We discuss the use of divergences in dissimilarity-based classification. Divergences can be employed whenever vectorial data consists of non-negative, potentially normalized features. This is, for instance, the case in spectral data or histograms. In particular, we introduce and study divergence based learning vector quantization (DLVQ). We derive cost
Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Jennifer R. Aduwo, John A. Quinn, Sven Haase, Thomas Villmann, Michael Biehl   +7 more
openaire   +2 more sources

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Text-Independent Speaker Verification Based on Information Theoretic Learning [PDF]

Mehran University Research Journal of Engineering and Technology, 2011
In this paper VQ (Vector Quantization) based on information theoretic learning is investigated for the task of text-independent speaker verification. A novel VQ method based on the IT (Information Theoretic) principles is used for the task of speaker ...
Sheeraz Memon, Tariq Jameel Saifullah Khanzada, Sania Bhatti   +2 more
doaj