Results 111 to 120 of about 5,787,304 (258)

Investigation of QAM based on mean square error (MSE) channel estimation (CE) for MIMO-OFDM pilot based system / Mohd Ariff Ibrahim [PDF]

, 2013
Channel Estimation (CE) is functioning as a medium to reduce the diversity and error in MIMO-OFDM system which containing few other techniques which are Least Square Error (LSE), Mean Square Error (MSE) and Discrete Fourier Transform (DFT). The diversity
Ibrahim, Mohd Ariff
core  

Standardized mean square error (MSE) and standardized bias for corrected (Corr.) and uncorrected (Unc.) variance estimates from the bias-correction simulations.

, 2015
Standardized mean square error (MSE) and standardized bias for corrected (Corr.) and uncorrected (Unc.) variance estimates from the bias-correction simulations.
Patrick J. Monnahan (735483), John K. Kelly (117806)   +1 more
core   +1 more source

Uncoupling Type I Interferon Benefits From Inflammatory Toxicity: Transformer‐Prioritized Precision Agonists for Potent and Safer Cancer Immunotherapy

Advanced Science, EarlyView.
A Transformer‐based AI framework, DLINP, screens millions of compounds to identify Co68, a cobalt‐pincer organometallic complex that biases TLR4‐MD2 signaling toward antitumor interferon activation while suppressing inflammatory toxicity through an early TLR4‐SYK‐STAT1 axis.
Xuefei Guo, Yang Zhao, Xianle Rong, Xingyu Chen, Xiao Wang, Tianyi Liu, Yunfei Xie, Yushu Zou, Pingsen Zhao, Qiang Liu, Fuping You   +10 more
wiley   +1 more source

Performance analysis of multi-ary systems with iterative linear minimum-mean-square-error detection

, 2008
This paper is concerned with coded multi-ary systems over linear channels. Based on a semi-analytical evolution technique, the impact of signaling schemes on the performance of low-cost iterative linear minimum-mean-square-error (LMMSE) detection is ...
Jun Tong (19588204), Li Ping (427468), Xiaojun Yuan (410299), Qinghua Guo (19588207)   +3 more
core  

A Unified Hierarchical Multiscale Fusion Framework for Drug–Target Affinity Prediction: From Benchmark Performance to Nanomolar Inhibitor Discovery

Advanced Science, EarlyView.
A multimodal fusion framework integrating sequence, atomic, and fragment representations captures drug–target interactions across multiple scales. The model delivers strong predictive performance and enables efficient virtual screening. Applied to hematopoietic progenitor kinase 1 (HPK1), it identifies structurally diverse inhibitors with nanomolar ...
Shuo Liu, Xiang Zhang, Haixia Feng, Yuquan Li, Xiaoqing Gong, Yong Liang, Xiaojun Yao, Huanxiang Liu   +7 more
wiley   +1 more source

A Reconfigurable Memristive Spiking Neuron Enabling Advanced Neuromorphic Computing

Advanced Science, EarlyView.
Pek Jun Tiw and co‐workers develop a reconfigurable memristive neuron that can exhibit four spiking behaviors with freely tunable spike train parameters. The neuron supports multiplexed spike encoding of multiple pieces of information. It also supports advanced neural network strategies, such as neural heterogeneity and dynamic mode switching ...
Pek Jun Tiw, Yuqi Li, Zhongyuan Li, Qihang Ding, Yuzhe Wang, Jiarong Wang, Yuchao Yang   +6 more
wiley   +1 more source

Quantum‐Trained AI Enables Inverse Design of Organic Frontier Orbitals at Billion‐Scale

Advanced Science, EarlyView.
Quantum‐trained AI links molecular substructures to frontier‐orbital energetics and enables interpretable screening across nearly one billion GDB‐13 molecules. By combining fragment‐ and ring‐level insights with donor–acceptor energy alignment against ITIC, the framework narrows an immense chemical space to a small set of promising candidates and ...
Yeongnam Ko, Se Jin Kim, Ki Chul Kim
wiley   +1 more source

The ε-normalized sign regressor least mean square (NSRLMS) adaptive algorithm

, 2011
In this paper, expressions are derived for the steady-state and tracking excess-mean-square error (EMSE) of the ε-normalized sign regressor least mean square (NSRLMS) adaptive algorithm.
Zerguine, A., Ulla Faiz, M.
core   +1 more source

Similarity‐Enhanced Representation Learning of Non‐Canonical Amino Acids for Therapeutic Peptide Modeling

Advanced Science, EarlyView.
Non‐canonical amino acids (ncAAs) enhance peptide therapeutics but remain difficult to model computationally. SinCAA, a similarity‐enhanced pretraining framework, jointly optimizes contrastive learning guided by a novel conformational similarity metric with masked node reconstruction, capturing both functional relationships and chemical identity of ...
Chencheng Xu, Lesong Wei, Jianmin Wang, Yuanpeng Xiong, Ruochi Zhang, Yu Wang, Chao Zha, Qiangcheng Zeng, Xin Gao   +8 more
wiley   +1 more source