Novel Approach to FDSOI Threshold Voltage Model Validated at Cryogenic Temperatures

The paper presents a novel approach to the modeling of the back-gate dependence of the threshold voltage of Fully Depleted Silicon-On-Insulator (FDSOI) MOSFETs down to cryogenic temperatures by using slope factors with a gate coupling effect. The FDSOI technology is well-known for its capability to modulate the threshold voltage efficiently by the back-gate voltage. The proposed model analytically demonstrates the threshold voltage as a function of the back-gate voltage without the pre-defined threshold condition, and it requires only a calibration point, i.e., a threshold voltage with the corresponding back-gate voltage, front- and back-gate slope factors, and work functions of front and back gates. The model has been validated over a wide range of the back-gate voltages at room temperature and down to 3 K. It is suitable for optimizing low-power circuits at cryogenic temperatures for quantum computing applications.

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Congratulations to our Editor-in-Chief, Prof. Derek Abbott!

We are thrilled to share that the Editor-in-Chief of IEEE Access, Prof. Derek Abbott, along with his colleague, Prof. Alan Collins, have been named the highly prestigious Australian Research Council (ARC) 2024 Australian Laureate Fellows for their work in transforming terahertz biosensing and unearthing the secrets of our planet.

Prof. Abbott will be funded to work on his project, “Advancing the Frontiers of Detection: Ultrasensitive Terahertz Sensing,” which aims to transform terahertz biosensing, creating next-generation sensors for use in security, forensics and space exploration.

Congratulations on this outstanding achievement!

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On the Cyber-Physical Needs of DER-Based Voltage Control/Optimization Algorithms in Active Distribution Network

With the increasing penetration of distributed energy resources (DERs) and extensive usage of information and communications technology (ICT) in decision-making, mechanisms to control/optimize transmission and distribution grid voltage would experience a paradigm shift. Given the introduction of inverter-based DERs with vastly different dynamics, real-world performance characterization of the cyber-physical system (CPS) in terms of dynamical performance, scalability, robustness, and resiliency with the new control algorithms require precise algorithmic classification and suitable metrics. It has been identified that classical controller definitions along with three inter-disciplinary domains, such as (i) power system, (ii) optimization, control, and decision-making, and (iii) networking and cyber-security, would provide a systematic basis for the development of an extended metric for algorithmic performance evaluation; while providing the taxonomy. Furthermore, a majority of these control algorithms operate in multiple time scales, and therefore, algorithmic time decomposition facilitates a new way of performance analysis. Extended discussion on communication requirements while focusing on the architectural subtleties of algorithms is expected to identify the real-world deployment challenges of voltage control/optimization algorithms in the presence of cyber vulnerabilities and associated mitigation mechanisms affecting the controller performance with DERs. Finally, the detailed discussion provided in this paper identifies the modeling requirements of the CPS for real-world deployment, specific to voltage control, facilitating the development of a unified test-bed.

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Published in the IEEE Power & Energy Society Section within IEEE Access

DNN Partitioning for Inference Throughput Acceleration at the Edge

Deep neural network (DNN) inference on streaming data requires computing resources to satisfy inference throughput requirements. However, latency and privacy sensitive deep learning applications cannot afford to offload computation to remote clouds because of the implied transmission cost and lack of trust in third-party cloud providers. Among solutions to increase performance while keeping computation on a constrained environment, hardware acceleration can be onerous, and model optimization requires extensive design efforts while hindering accuracy. DNN partitioning is a third complementary approach, and consists of distributing the inference workload over several available edge devices, taking into account the edge network properties and the DNN structure, with the objective of maximizing the inference throughput (number of inferences per second). This paper introduces a method to predict inference and transmission latencies for multi-threaded distributed DNN deployments, and defines an optimization process to maximize the inference throughput. A branch and bound solver is then presented and analyzed to quantify the achieved performance and complexity. This analysis has led to the definition of the acceleration region, which describes deterministic conditions on the DNN and network properties under which DNN partitioning is beneficial. Finally, experimental results confirm the simulations and show inference throughput improvements in sample edge deployments.

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Effect of Data Characteristics Inconsistency on Medium and Long-Term Runoff Forecasting by Machine Learning

In the application of medium and long-term runoff forecasting, machine learning has some problems, such as high learning cost, limited computing cost, and difficulty in satisfying statistical data assumptions in some regions, leading to difficulty in popularization in the hydrology industry. In the case of a few data, it is one of the ways to solve the problem to analyze the data characteristics consistency. This paper analyzes the statistical hypothesis of machine learning and runoff data characteristics such as periodicity and mutation. Aiming at the effect of data characteristics inconsistency on three representative machine learning models (multiple linear regression, random forest, back propagation neural network), a simple correction/improvement method suitable for engineering was proposed. The model results were verified in the Danjiangkou area, China. The results show that the errors of the three models have the same distribution as the periodic characteristics of the runoff periods, and the correction/improvement based on periodicity and mutation characteristics can improve the forecasting accuracy of the three models. The back propagation neural network model is most sensitive to the data characteristics consistency.

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IEEE Access is a multidisciplinary, online-only, gold fully open access journal, continuously presenting the results of original research or development across all IEEE fields of interest. Supported by article processing charges (APCs), its hallmarks are rapid peer review, a submission-to-publication time of 4 to 6 weeks, and articles that are freely available to all readers.

IEEE Access publishes articles that are of high interest to readers: original, technically correct, and clearly presented. The scope of this journal comprises all IEEE fields of interest, emphasizing applications-oriented and interdisciplinary articles.

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Reducing Losses and Energy Storage Requirements of Modular Multilevel Converters With Optimal Harmonic Injection

Due to the single phase characteristic of the individual arms of the Modular Multilevel Converter (MMC) topology, the difference between the instantaneous AC and DC side power must be buffered in the module capacitors. This results in large module capacitors compromising the power density and cost of the MMC. In this paper, a multi-objective optimization scheme is formulated that aims at reducing the required module capacitance and the semiconductor losses at the same time. Further attention is paid to the maximum AC voltage amplitude or the maximum current. The optimization scheme is based on the injection of circulating current and AC common mode voltage harmonics. Unlike most existing optimization schemes it considers the actual trajectories of capacitor voltage and arm output voltage to maximize the savings in module capacitance and semiconductor losses. For an exemplary medium voltage MMC parameter set, capacitance value reductions of more than 50% are achieved while the semiconductor losses decrease by 8- 18%. Based on a volume estimation for MMC modules, this results a volume reduction of up to 45%.

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Enhancing Accuracy in Actigraphic Measurements: A Lightweight Calibration Method for Triaxial Accelerometers

This paper presents a simple, lightweight, automatic calibration method for low-cost triaxial accelerometers, utilizing the Earth’s gravitational constant in various orientations. It can be easily implemented using only fixed-point arithmetic and can run on low-power microcontrollers for real-time measurements, making it practical for scenarios with limited data storage and computational power, such as actigraphy or IoT applications. The method offers ease of use by automatically detecting motionless intervals, eliminating the need for complex positioning techniques. The procedure detects resting states and calculates the corresponding three-dimensional mean acceleration values during the measurement. After appropriately selecting these mean values, a set of calibration points is formed and passed to a gradient-based optimization algorithm for iterative estimation of the calibration coefficients. Different metrics were used for verification and comparison with other methods, which were calculated through simulations and tests based on real measurements. The results show that, despite its lightweight nature, the method performs equally to more complex solutions. This article provides a thorough explanation of a novel method for collecting calibration points, the optimization algorithm, and the methods used for performance evaluation in a reproducible manner.

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MMNeRF: Multi-Modal and Multi-View Optimized Cross-Scene Neural Radiance Fields

We present MMNeRF, a simple yet powerful learning framework for highly photo-realistic novel view synthesis by learning Multi-modal and Multi-view features to guide neural radiance fields to a generic model. Novel view synthesis has achieved great improvement with the significant success of NeRF-series methods. However, how to make the method generic across scenes has always been a challenging task. A good idea is to introduce 2D image features as prior knowledge for adaptive modeling, yet RGB features lack geometry and 3D spatial information, which causes shape-radiance ambiguity issues and lead to blurry and low-resolution results in the synthesis images. We propose a multi-modal multi-view method to make up for the existing methods. Specifically, we introduce depth features besides RGB features into the model and effectively fuse these multi-modal features by modality-based attention. Furthermore, Our framework innovatively adopts the transformer encoder to fuse multi-view features and uses the transformer decoder to adaptively incorporate the target view with global memory. Extensive experiments are carried out on both categories-specific and category-agnostic benchmarks, and the results demonstrate that our MMNeRF achieves state-of-the-art neural rendering performance.

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Accuracy Enhancement of Hand Gesture Recognition Using CNN

Human gestures are immensely significant in human-machine interactions. Complex hand gesture input and noise caused by the external environment must be addressed in order to improve the accuracy of hand gesture recognition algorithms. To overcome this challenge, we employ a combination of 2D-FFT and convolutional neural networks (CNN) in this research. The accuracy of human-machine interactions is improved by using Ultra Wide Bandwidth (UWB) radar to acquire image data, then transforming it with 2D-FFT and bringing it into CNN for classification. The classification results of the proposed method revealed that it required less time to learn than prominent models and had similar accuracy.

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