Progressive Bitwidth Assignment Approaches for Efficient Capsule Networks Quantization by Mohsen Raji, Amir Ghazizadeh Ahsaei, Kimia Soroush, Behnam Ghavami

“…Specifically, on the CIFAR-10 dataset using the DeepCaps architecture, we achieved a substantial memory reduction (<inline-formula> <tex-math notation="LaTeX">$7.02\times $ </tex-math></inline-formula> for weights and <inline-formula> <tex-math notation="LaTeX">$3.74\times $ </tex-math></inline-formula> for activations) with a minimal accuracy loss of only 0.09%. …”
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Pac-Man-Shaped Patch-Driven Broadband Circularly Polarized Metasurface Antenna With CMA-Based Quadruple-Mode Excitation by Deepak K. Naik, Dhruba Charan Panda, Rajanikanta Swain, Arjuna Muduli, Sambhudutta Nanda

“…For a quadruple-mode excitation, the antenna uses a unique pac-man-shaped patch (PSP) as the primary CP radiator and above it a <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> sized rectangular MTS, which acts as the secondary radiator. …”
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A Novel Algorithm for Aspect Ratio Estimation in SRAM Design to Achieve High SNM, High Speed, and Low Leakage Power by Sanket M. Mantrashetti, Arunkumar P Chavan, Prakash Pawar, H. V. Ravish Aradhya, Omkar S. Powar

“…The inclusion of precharge and write driver circuits allows for a compact SRAM layout, occupying <inline-formula> <tex-math notation="LaTeX">$9.79~\mu $ </tex-math></inline-formula> m², with the SRAM cell itself occupying <inline-formula> <tex-math notation="LaTeX">$4.1~\mu $ </tex-math></inline-formula> m². …”
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A Readout Scheme for PCM-Based Analog In-Memory Computing With Drift Compensation Through Reference Conductance Tracking by Alessio Antolini, Andrea Lico, Francesco Zavalloni, Eleonora Franchi Scarselli, Antonio Gnudi, Mattia Luigi Torres, Roberto Canegallo, Marco Pasotti

“…Based on several MAC operations, the estimated <inline-formula> <tex-math notation="LaTeX">$512\times 512$ </tex-math></inline-formula> matrix-vector-multiplication (MVM) accuracy is 97.4%, whose decrease in time is less than 3% in the worst case.…”
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A Fourfold Bi-Filter and Permuted Bi-Quad-Wrapper Feature Selection Method for Finding Optimal Moments of Multi-Trajectory Transient Records in Transient Analysis by Seyed Alireza Bashiri Mosavi, Omid Khalaf Beigi

“…Generally, wrapper stages include <inline-formula> <tex-math notation="LaTeX">${ }_{TWSVM}^{SVM} \textrm {IWSS}_{RBF/\,POL}^{RBF/\,DTW\,} $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${ }_{TWSVM}^{SVM} \textrm {IWSSr}_{RBF/\,POL}^{RBF/\,DTW\,}$ </tex-math></inline-formula> in a 24-way permutation. …”
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A Hybrid Retrieval Method for an &#x03A9;-Class Bianisotropic Metamaterial Using Scattering Parameter Method by Gokhan Ozturk, Ugur Cem Hasar, Yunus Kaya, Huseyin Korkmaz, Ivaylo Stoyanov

“…In this study, a hybrid retrieval method is proposed to retrieve all terms in the electric and magnetic tensors (along with coupling tensors) of <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula>-class bianisotropic metamaterial (MM) slabs using scattering (S-) parameters of both normal incidence TE2 mode and oblique incidence TM2 and TE modes. …”
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SRAM and Mixed-Signal Logic With Noise Immunity in 3-nm Nano-Sheet Technology by Rajiv V. Joshi, J. Frougier, Alberto Cestero, Crystal Castellanos, Sudipto Chakraborty, Carl Radens, M. Silvestre, S. Lucarini, I. Ahsan, E. Leobandung

“…Functionality is shown down to a cell supply of 0.45 V with an estimated margin/speed of 6 GHz for SRAM cells (high density&#x2014;<inline-formula> <tex-math notation="LaTeX">$0.026~\mu $ </tex-math></inline-formula>m², and high current&#x2014;<inline-formula> <tex-math notation="LaTeX">$0.032~\mu $ </tex-math></inline-formula>m²).…”
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A Bore-Integrated Patch Antenna Array for Whole-Body Excitation in Ultra-High-Field Magnetic Resonance Imaging by Svetlana S. Egorova, Nikolai A. Lisachenko, Egor I. Kretov, Yang Gao, Xiaotong Zhang, Stanislav B. Glybovski, Georgiy A. Solomakha

“…The human body&#x2019;s ultra-high field magnetic resonance imaging suffers from the inhomogeneity of the radio frequency magnetic field <inline-formula> <tex-math notation="LaTeX">$B_{1}^{+}$ </tex-math></inline-formula> and the high-peak levels of SAR created in body tissues during transmission. …”
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CFO-CR: Carrier Frequency Offset Methodology for High-Rate Common Randomness Generation by Prashanth Kumar Herooru Sheshagiri, Martin Reisslein, Juan A. Cabrera, Frank H. P. Fitzek

“…For generating 2048 bits of CR, other state-of-the-art approaches either require more channel observations (<inline-formula> <tex-math notation="LaTeX">$\ge 2048$ </tex-math></inline-formula>) or incur a higher reconciliation cost (<inline-formula> <tex-math notation="LaTeX">$\ge 450$ </tex-math></inline-formula> bytes).…”
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A Wearable System for Experimental Knee Pain During Real-World Locomotion: Habituation and Motor Adaptation by Jesse M. Charlton, Liam H. Foulger, Calvin Kuo, Jean-Sebastien Blouin

“…A linear model fit the data well for intensities &#x003E;1/10, though a piecewise linear (Adj R<inline-formula> <tex-math notation="LaTeX">$^{{2}} =0.874$ </tex-math></inline-formula>) or exponential model (Adj R<inline-formula> <tex-math notation="LaTeX">$^{{2}} =0.869$ </tex-math></inline-formula>) was required to fit the perception data across the stimulus intensity range (0-5/10). …”
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Fully CMOS Boost Converter Operating at 2.65 GHz for Photovoltaic Energy Harvesting by Pedro Mendonca Dos Santos, Ricardo Alexandre Marques Lameirinhas, Catarina P. Correia V. Bernardo, Joao Paulo Neto Torres, Antonio Serralheiro, Rafael Vieira, Nuno Lourenco

“…The system delivers 1.2 V for a 10 k<inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> load, with an output power of the order of <inline-formula> <tex-math notation="LaTeX">$144~\mu $ </tex-math></inline-formula>W. …”
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Deep Joint Demosaicking and Super-Resolution for Spectral Filter Array Images by Abdelhamid N. Fsian, Jean-Baptiste Thomas, Jon Y. Hardeberg, Pierre Gouton

“…Moreover, for joint demosaicking and super resolution our model averages 35.26 (dB) and 26.29 (dB), respectively for <inline-formula> <tex-math notation="LaTeX">$\times 2$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\times 4$ </tex-math></inline-formula> upscale, outperforming state-of-the-art sequential approach.The codes and datasets are available at <uri>https://github.com/HamidFsian/DRDmSR</uri>.…”
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Study of Highly Stable Nitrogen-Doped a-InGaSnO Thin-Film Transistors by Wenyang Zhang, Li Lu, Chenfei Li, Weijie Jiang, Wenzhao Wang, Xingqiang Liu, Ablat Abliz, Da Wan

“…Compared with undoped a-IGTO TFTs, a-IGTO TFTs with 6 min N plasma treatment exhibit superior bias stress stability and a threshold voltages (<inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {th}}$ </tex-math></inline-formula>) closer to 0 V with almost no decline in mobility. …”
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Adaptive Modification in Agonist Common Drive After Combined Blood Flow Restriction and Neuromuscular Electrical Stimulation by Yi-Ching Chen, Chia-Chan Wu, Yeng-Ting Lin, Yueh Chen, Ing-Shiou Hwang

“…The results showed a significant decrease in MVC after training (<inline-formula> <tex-math notation="LaTeX">$\text {p}\lt 0.001$ </tex-math></inline-formula>). …”
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Amplitude Modulation Depth Coding Method for SSVEP-Based Brain&#x2013;Computer Interfaces by Ruxue Li, Zhenyu Wang, Xi Zhao, Guiying Xu, Honglin Hu, Ting Zhou, Tianheng Xu

“…The results show that the proposed paradigm obtains an average classification accuracy of <inline-formula> <tex-math notation="LaTeX">$81.7~\pm ~12.6$ </tex-math></inline-formula>% with an average information transfer rate (ITR) of <inline-formula> <tex-math notation="LaTeX">$45.4~\pm ~11.5$ </tex-math></inline-formula> bits/min. …”
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An Ultrasound-Based Non-Invasive Blood Pressure Estimation Method Based on Optimal Vascular Wall Tracking Position by Liyuan Liu, Xingguang Geng, Fei Yao, Yitao Zhang, Haiying Zhang, Yunfeng Wang, Zhaoying Zheng

“…The overall mean deviation for systolic blood pressure was <inline-formula> <tex-math notation="LaTeX">$2.2~\pm ~2.1$ </tex-math></inline-formula> mmHg, and for diastolic blood pressure, it was <inline-formula> <tex-math notation="LaTeX">$2.1~\pm ~2.2$ </tex-math></inline-formula> mmHg. …”
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Comparison of Deep-Learning-Based Segmentation Models: Using Top View Person Images by Imran Ahmed, Misbah Ahmad, Fakhri Alam Khan, Muhammad Asif

“…The experimental results reveal the effectiveness and performance of segmentation models by achieving <inline-formula> <tex-math notation="LaTeX">$IoU$ </tex-math></inline-formula> of 83&#x0025;, 84&#x0025;, and 86&#x0025; and <inline-formula> <tex-math notation="LaTeX">$mIoU$ </tex-math></inline-formula> of 80&#x0025; 82&#x0025; and 84&#x0025; for FCN, U-Net, and DeepLabv3 respectively. …”
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Concatenated Vertical Channel Modeling and Performance Analysis for HAP-Based Optical Networks by Neha Tiwari, Swades De, Dharmaraja Selvamuthu

“…For HAP-based optical networks facing weak turbulence, the newly developed expressions provide an accuracy of about 2 dB for a BER of <inline-formula><tex-math notation="LaTeX">$10^{-4}$</tex-math></inline-formula> as compared to the existing competitive models. …”
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Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction by Ivan-Daniel Sievering, Ortal Senouf, Thabo Mahendiran, David Nanchen, Stephane Fournier, Olivier Muller, Pascal Frossard, Emmanuel Abbe, Dorina Thanou

“…<italic>Results:</italic> The results of our framework on a clinical study of 445 patients admitted with acute coronary syndromes confirms that multimodal learning increases the predictive power and achieves good performance (AUC: <inline-formula><tex-math notation="LaTeX">$0.67\pm 0.04$</tex-math></inline-formula> &amp; F1-Score: <inline-formula><tex-math notation="LaTeX">$0.36\pm 0.12$</tex-math></inline-formula>), which outperforms the prediction obtained by each modality independently as well as that of interventional cardiologists (AUC: 0.54 &amp; F1-Score: 0.18). …”
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Ultra-Wideband 4-Bit Distributed Phase Shifters Using Lattice Network at <italic>K/Ka</italic>- and <italic>E/W</italic>-Band by Sungwon Kwon, Minjae Jung, Byung-Wook Min

“…At 28 GHz, the insertion loss was <inline-formula> <tex-math notation="LaTeX">$11.6\pm 0$ </tex-math></inline-formula>.8 dB without dc power consumption. …”
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