Related papers: Exploiting Dual-Gate Ambipolar CNFETs for Scalable…

We present a spin Hall magnetoresistance sensor based on a NiFe/Pt multiring bridge structure, which exhibits high sensitivity and good linearity in two perpendicular directions within the sensor plane. Under DC excitation, it responds…

User-centric cell-free (UCCF) massive multiple-input multiple-output (MIMO) systems are considered a viable solution to realize the advantages offered by cell-free (CF) networks, including reduced interference and consistent quality of…

Mixed-signal artificial neural networks (ANNs) that employ analog matrix-multiplication accelerators can achieve higher speed and improved power efficiency. Though analog computing is known to be susceptible to noise and device…

Optical and hybrid convolutional neural networks (CNNs) recently have become of increasing interest to achieve low-latency, low-power image classification and computer vision tasks. However, implementing optical nonlinearity is challenging,…

Due to the over-fitting problem caused by imbalance samples, there is still room to improve the performance of data-driven automatic modulation classification (AMC) in noisy scenarios. By fully considering the signal characteristics, an AMC…

This paper describes a machine learning method to automate reading of cockpit gauges, using a CNN to invert affine transformations and deduce aircraft states from instrument images. Validated with synthetic images of a turn-and-bank…

Neural networks for visual content understanding have recently evolved from convolutional ones (CNNs) to transformers. The prior (CNN) relies on small-windowed kernels to capture the regional clues, demonstrating solid local expressiveness.…

The rapidly growing computational demands of deep neural networks require novel hardware designs. Recently, tunable nanoelectronic devices were developed based on hopping electrons through a network of dopant atoms in silicon. These "Dopant…

Positive-Unlabeled (PU) learning aims to train a binary classifier (positive vs. negative) where only limited positive data and abundant unlabeled data are available. While widely applicable, state-of-the-art PU learning methods…

Most carbon nanotube field-effect transistors (CNTFETs) directly attach metal source/drain contacts to an intrinsic nanotube channel. When the gate oxide thickness is reduced, such transistors display strong ambipolar conduction, even when…

We develop theoretical arguments that demonstrate the possibility of metallic field-effect transistors (METFET's) in one-dimensional systems and particularly in armchair carbon nanotubes. A very inhomogeneous electric field, such as the…

Real-world multimodal systems routinely face missing-input scenarios, and in reality, robots lose audio in a factory or a clinical record omits lab tests at inference time. Standard fusion layers either preserve robustness or calibration…

Despite their appeal as physics-inspired, energy-based and generative nature, general Boltzmann Machines (BM) are considered intractable to train. This belief led to simplified models of BMs with restricted intralayer connections or…

This paper presents a semi-supervised learning framework that is new in being designed for automatic modulation classification (AMC). By carefully utilizing unlabeled signal data with a self-supervised contrastive-learning pre-training…

We introduce a novel architecture and computational framework for formal, automated analysis of systems with a broad set of nonlinearities in the feedback loop, such as neural networks, vision controllers, switched systems, and even simple…

Emerging non-volatile memory (NVM), or memristive, devices promise energy-efficient realization of deep learning, when efficiently integrated with mixed-signal integrated circuits on a CMOS substrate. Even though several algorithmic…

The development of computer vision and in-situ monitoring using visual sensors allows the collection of large datasets from the additive manufacturing (AM) process. Such datasets could be used with machine learning techniques to improve the…

Multi-bridge channel field effect transistor (MBCFET) provides several advantages over FinFET technology and is an attractive solution for sub-5 nm technology nodes. MBCFET is a natural choice for devices that use semiconducting layered…

Automatic Modulation Classification (AMC) is a signal processing technique widely used at the physical layer of wireless systems to enhance spectrum utilization efficiency. In this work, we propose a fast and accurate AMC system, termed…

A vertical partial gate carbon nanotube (CNT) field-effect transistor (FET), which is amenable to the vertical CNT growth process and offers the potential for a parallel CNT array channel, is simulated using a self-consistent atomistic…