Results 101 to 110 of about 7,265 (245)
Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation
Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison +4 more
wiley +1 more source
This study introduces a subthreshold in‐memory anomaly detection architecture for wearable ECG monitoring using floating‐gate IGZO content‐addressable memories on a flexible substrate. Area‐engineered coupling provides a high subthreshold slope, while subthreshold operation realizes intrinsic exponential distance evaluation and linear voltage sensing ...
Hyung‐Jun Noh +4 more
wiley +1 more source
Energy‐Aware Perturbation Optimization for Memristor‐Array Convolutional Neural Networks
Memristor‐array inference becomes more energy efficient when layer inputs are reshaped before computation. Sinusoidal perturbation encoding with dual‐threshold screening reduces active voltage pulses and contracts ADC input‐current ranges, jointly lowering crossbar and peripheral energy while preserving accuracy across hardware MNIST validation, deep ...
Ao Xu +6 more
wiley +1 more source
Single‐Step Analog In‐Memory Matrix Computation in Three‐Dimensional Circuits
A wooden model illustrates a 3D memristive array designed for single‐step kernel convolutions. It shows how two‐dimensional input data are mapped into the 3D array and convolved in parallel with stored kernels. Color‐coded connection points represent programmed memory‐element values, with each color corresponding to a specific kernel weight.
Alireza Jaberi Rad +8 more
wiley +1 more source
ABSTRACT The rapid evolution of the Internet of Things (IoT) has significantly advanced the field of electrocardiogram (ECG) monitoring, enabling real‐time, remote, and patient‐centric cardiac care. This paper presents a comprehensive survey of AI assisted IoT‐based ECG monitoring systems, focusing on the integration of emerging technologies such as ...
Amrita Choudhury +2 more
wiley +1 more source
Using Synthetic Glycans to Investigate Anti‐Glycan Antibodies and Explore Their Medical Potential
Anti‐glycan antibodies are essential in health and disease. Access to novel glycan structures paves the way for progress in antibody profiling for biomarker discovery, antibody development, and vaccine design. We summarize the strategies to synthesize and utilize synthetic glycans for the development and application of anti‐glycan antibodies in basic ...
Fabienne Weber +4 more
wiley +1 more source
This comprehensive review presents a progressive roadmap for perovskite vision detectors. Centered on perovskite‐based artificial perception, the graphic illustrates a systematic evolution: starting with fundamental material engineering and device architectures, advancing toward complex functional strategies such as flexible neuromorphic imaging ...
Chenglong Li +14 more
wiley +1 more source
Stochastic geometry provides a powerful analytical framework for evaluating interference-limited cellular networks with randomly deployed base stations (BSs).
Moonsik Min, Sungmin Lee, Tae-Kyoung Kim
doaj +1 more source
An Ultra‐Low‐Power Biopotential Acquisition Analog Front‐End Chip for Wearable Devices
A 0.8‐V, 1.2‐μW biopotential AFE combines a chopper‐stabilized CCIA with dual‐time‐scale EDO management. The slow DSL preserves > 1 GΩ input impedance, whereas an event‐triggered fast‐recovery path shortens return to the linear range after abrupt electrode‐offset disturbances. ABSTRACT A 0.8‐V, 1.2‐μW biopotential analog front‐end (AFE) IC is presented
Zhang Xin +4 more
wiley +1 more source
ABSTRACT We present a hybrid interpretable Physics‐Informed Neural Network Long‐Short Term Memory (Hybrid PINN LSTM) framework for predicting the seismic response of rocking blocks. Existing analytical models rely on uncertain idealizations, while purely data‐driven and machine‐learning approaches lack physical consistency and interpretability.
Shirley Shen +1 more
wiley +1 more source

