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The power consumption of high-speed, high-resolution analog to digital converters (ADCs) is a limiting factor in implementing large-bandwidth mm-wave communication systems. A mitigating solution, which has drawn considerable recent…

We propose a decentralized spatial soft-core cache placement (SSCC) policy for wireless networks. SSCC yields a spatially balanced sampling via negative dependence across caches, and can be tuned to satisfy cache size constraints with high…

Multi-port DC-DC converters are gaining more significance in modern power system environments by enabling the connection of multiple renewable energy sources, so the efficient operation of these converters is paramount. Soft switching…

In this article, we propose a nonlinear voltage control to ensure power exchange in a multiport interconnected system, which consists of a bidirectional DC-DC converter and generating-storing devices. The converter topology under…

A sensitivity conditioning control strategy is analyzed in this paper and it is applied to power electronic converters. The term is used to improve closed loop systems, such as power converters with cascaded inner and outer loop…

Modern dense Flash memory devices operate at very low error rates, which require powerful error correcting coding (ECC) techniques. An emerging class of graph-based ECC techniques that has broad applications is the class of…

We propose an autonomous quantum error correction scheme using squeezed cat (SC) code against the dominant error source, excitation loss, in continuous-variable systems. Through reservoir engineering, we show that a structured dissipation…

Conventional boost converters are essential to connect low-voltage energy source such as battery with high voltage DC bus in Electric Vehicles due to its simple construction and high conversion efficiency. However, large output capacitor…

The interest in polar codes has been increasing significantly since their adoption for use in the 5$^{\rm th}$ generation wireless systems standard. Successive cancellation (SC) decoding algorithm has low implementation complexity, but…

Stochastic computing (SC) offers significant reductions in hardware complexity for traditional convolutional neural networks(CNNs). However, despite its advantages, stochastic computing neural networks (SCNNs) often suffer from high…

Rapid developments in power distribution systems and renewable energy have widened the applications of dc--dc buck--boost converters in dc voltage regulation. Applications include vehicular power systems, renewable energy sources that…

Spin Transfer Torque MRAMs are attractive due to their non-volatility, high density and zero leakage. However, STT-MRAMs suffer from poor reliability due to shared read and write paths. Additionally, conflicting requirements for data…

The paper provides a new perspective on peak- and average-constrained Gaussian channels. Such channels model optical wireless communication (OWC) systems which employ intensity-modulation with direct detection (IM/DD). First, the paper…

Spiking neural networks (SNNs) offer an inherent ability to process spatial-temporal data, or in other words, realworld sensory data, but suffer from the difficulty of training high accuracy models. A major thread of research on SNNs is on…

In this paper, we provide a performance analysis of a new class of serial concatenated convolutional codes (SCCC) where the inner encoder can be punctured beyond the unitary rate. The puncturing of the inner encoder is not limited to inner…

In this paper, a full-bridge boost power converter topology is studied for power factor control, using output high order sliding mode control. The AC/DC converters are used for charging the battery and super-capacitor in hybrid electric…

In this work, the problem of switching control design for DC-DC boost converter is considered, in the case of operation under uncertain equilibrium condition arising due to perturbations in the input and load parameters. Assuming that these…

Superconducting (SC) diodes are key passive building blocks for future SC electronics. However, realizing their active counterparts is essential for functional logic. Here, we demonstrate deterministic nonvolatile electrical switching of…

Spiking Neural Networks (SNNs) are seen as an energy-efficient alternative to traditional Artificial Neural Networks (ANNs), but the performance gap remains a challenge. While this gap is narrowing through ANN-to-SNN conversion, substantial…

Spiking neural networks (SNNs) have garnered interest due to their energy efficiency and superior effectiveness on neuromorphic chips compared with traditional artificial neural networks (ANNs). One of the mainstream approaches to…