Results 71 to 80 of about 9,834 (275)

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

Advanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez, Niladri Bhattacharjee, Yuxuan He, Giulio Galderisi, Brojogopal Sapui, Sara Mannaa, Ian O'Connor, Violetta Sessi, Peter Javorka, Thomas Mikolajick, Jens Trommer   +10 more
wiley   +1 more source

Pengenalan Bahasa Isyarat Huruf Abjad Menggunakan Metode Learning Vector Quantization (LVQ)

Jurnal Masyarakat Informatika, 2017
Komunikasi paling efektif bagi mereka yang kurang beruntung (dalam hal ini penderita tuna rungu) adalah komunikasi non verbal. Komunikasi non verbal menggunakan gerakan tangan maupun gerakan tubuh dalam komunikasinya.
Sulistia Rauf Yulian, Suhartono Suhartono   +1 more
doaj   +1 more source

Combined compression and classification with learning vector quantization [PDF]

IEEE Transactions on Information Theory, 1999
Summary: Combined compression and classification problems are becoming increasingly important in many applications with large amounts of sensory data and large sets of classes. These applications range from automatic target recognition (ATR) to medical diagnosis, speech recognition, and fault detection and identification in manufacturing systems.
John S. Baras, Subhrakanti Dey
openaire   +4 more sources

On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification

Advanced Electronic Materials, EarlyView.
ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky   +4 more
wiley   +1 more source

SISTEM VERIFIKASI WAJAH MENGGUNAKAN JARINGAN SARAF TIRUAN LEARNING VECTOR QUANTIZATION

Jurnal Informatika, 2010
Verifikasi wajah merupakan salah satu teknologi biometrika yang menjadi perhatian para peneliti. Banyak sekali sistem aplikasi yang berbasis kepada verifikasi wajah misalnya: akses pintu, akses mesin ATM, sistem presensi kehadiran, dll.
Abdul Fadlil, Surya Yeki
doaj   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Toward Capacitive In‐Memory‐Computing: A Device to Systems Level Perspective on the Future of Artificial Intelligence Hardware

Advanced Intelligent Discovery, EarlyView.
Capacitive, charge‐domain compute‐in‐memory (CIM) stores weights as capacitance,eliminating DC sneak paths and IR‐drop, yielding near‐zero standbypower. In this perspective, we present a device to systems level performance analysis of most promising architectures and predict apathway for upscaling capacitive CIM for sustainable edge computing ...
Kapil Bhardwaj, Ella Paasio, Sayani Majumdar   +2 more
wiley   +1 more source

Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks

IEEE Open Journal of the Communications Society, 2020
Sparse signals, encountered in many wireless and signal acquisition applications, can be acquired via compressed sensing (CS) to reduce computations and transmissions, crucial for resource-limited devices, e.g., wireless sensors.
Markus Leinonen, Marian Codreanu
doaj   +1 more source

Consideration of Different Variants of Large Margin Learning Vector Quantization [PDF]

, 2021
In machine learning, Learning Vector Quantization (LVQ) is well known as supervised vector quantization. LVQ has been studied to generate optimal reference vectors because of its simple and fast learning algorithm [2].
Maheshwari, Avinash
core  

Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Advanced Intelligent Discovery, EarlyView.
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto, Yuxin Jin, Nicholas Roy, Sarthak Misra   +3 more
wiley   +1 more source