Miracle-dz/UniFAR-FTUs huggingface dataset:
Endurance Characteristics of Negative Capacitance FinFETs With Negligible Hysteresis

In this letter, the endurance characteristics of negative capacitance (NC) FinFETs with an ultra-steep subthreshold swing (SS) and negligible hysteresis were investigated by an examination approach to emulate long-term logic operation. Detailed variation characteristics for different electrical parameters are summarized for devices subjected to up to 1010 switching pulse cycles. The average values of the threshold voltages ( ${V}_{t}\text{s}$ ) and hysteresis of the NC-FinFETs exhibited a clear initial increase followed by a decrease as the number of switching cycles was increased; however, the variation of the SS had a different behavior. This result could be attributed to capacitance mismatching in the NC devices induced by a capacitance change of the ferroelectric film. These findings provide an effective method for confirming the capacitance matching model and also offer a guide for design of NC-FET devices and their implementation in future real circuit applications.
Heterogeneous CCN Cache Allocation Strategy With Collaboration Support

Content-Centric Networking (CCN) is regarded as a promising network architecture which has received the distinct interest from many global research communities thanks to its friendly structure, especially its caching technology has attracted the most widespread attention. However, the current caching strategies have some limitations, such as redundant content copies, low cache utilization rate and unbalanced node load. In order to further optimize cache, this paper investigates the classical cache allocation problem, i.e., distributing the cache capacity across some Content Routers (CRs) under a constrained and fixed total cache budget. At first, both topology information and traffic characteristics as two factors to determine the importance of CR, where the evaluation of network topology depends on degree centrality, betweenness centrality and closeness centrality while that of traffic characteristics depends on node load and interest preference. In particular, given the data redundancy due to the high-dimensional feature, increasing the complexity of computation, t-distributed Stochastic Neighbor Embedding (t-SNE) is used to reduce the dimensions of data. Then, for the CR with cache capacity, its Content Store (CS) is divided into collaborative region and non-collaborative region by a heuristic algorithm to facilitate the optimal performance, and the CS with collaboration needs to interact the cached information with its neighborhoods. Finally, the performance evaluation is driven by the real dataset over GTS-CE topology. The experimental results reveal that the proposed collaboration-supported cache allocation strategy is more efficient than two baselines, i.e., increasing cache hit ratio by 7.79%, increasing cache utilization rate by 11.31%, decreasing routing delay by 49.38% and decreasing load balance by 50.98%.
Towards Environment-independent Human Activity Recognition using Deep Learning and Enhanced CSI

Deep learning has shown a strong potential in device-free human activity recognition (HAR). However, a fundamental challenge is ensuring accuracy, without re-training, when exposing a previously trained architecture to a new or unseen environment. To overcome the aforementioned challenge, this paper proposes an environment-robust channel state information (CSI) based HAR by leveraging the properties of a matching network (MatNet) and enhanced features (HAR-MN-EF). To improve the CSI quality, we propose a CSI cleaning and enhancement method (CSI-CE) that includes two key stages: activity-related information extraction (ARIE) and correlation feature extraction based on principal component analysis (CFE-PCA). The ARIE stage is able to effectively enhance the activity-dependent features whilst mitigating behavior-unrelated information. The CFE-PCA stage further improves the extracted features by filtering out the residual activity-unrelated data and the residual noise contained in signals from the former stage. The extracted features are then sequenced into the MatNet to create an environment-robust HAR. Experimental results confirm that an architecture trained by the proposed HAR-MN-EF can be directly adapted to a new environment, achieving reliable sensing accuracies without requiring additional effort.
Analytical Study of SiC MOSFET Based Inverter Output dv/dt Mitigation and Loss Comparison With a Passive dv/dt Filter for High Frequency Motor Drive Applications

Fast switching characteristic of wide bandgap devices enables high switching frequency of power devices and thereby, can facilitate high fundamental frequency operation of electrical machines. However, with the switching transition times in orders of tens of nanoseconds, the high dv/dt is observed across the switching device. The high dv/dt experienced by the switches, and consequently by the machine, can degrade winding insulations or bearings over a period of time. Therefore, it is imperative to maintain the dv/dt below recommended values depending on the machine insulation. The dv/dt across the devices can be adjusted by varying the gate resistance. A high value of gate resistance, however, introduces additional switching losses on the device. Using different dv/dt filtering techniques can also help to control the dv/dt on the machine terminals. These techniques do not increase the switching losses on the device. However, it introduces additional losses in the filter resistors and also increases the cost of the system. In this paper, an analysis based on the impact of gate resistance on the dv/dt across the machine, and the corresponding losses is carried out. An analytical dv/dt filter design strategy is proposed to limit the dv/dt to a particular value. With the proposed design scheme, the value of each filter component can be easily obtained, and filter losses can be estimated accurately. Lastly, a comparison is performed on the basis of efficiency between these two techniques.
Downlink Power Allocation Optimization in Pattern Division Multiple Access

Non-orthogonal multiple access (NOMA), in particular pattern division multiple access (PDMA), has been recently considered as the critical multiple access technologies deployed for 5G applications. However, optimizing power allocation (PA) at the base station (BS) to increase the sum throughput of the downlink (DL) PDMA system still remains a challenge. Although this problem has been explored to some extent by assuming that the BS can obtain perfect channel state information (CSI), this assumption is unfeasible in harsh environments. Therefore, to effectively improve the sum throughput of communications in the real world, we propose using PDMA with optimized PA. With perfect CSI, iterative power allocation (IPA) that can iteratively update the Lagrange multipliers is proposed. Using the Karush-Kuhn-Tucher (KKT) conditions, a PA solution with closed-form expressions is derived from increasing the sum throughput. With imperfect CSI, the outage probability is considered when maximizing the sum throughput. The probabilistic mixture problem can be converted into a non-probability problem, and thus, PA under imperfect CSI can be optimized with the proposed IPA algorithm. The simulation results demonstrate that the proposed scheme outperforms the existing schemes and can be effective in harsh environments.
HRV and Stress: A Mixed-Methods Approach for Comparison of Wearable Heart Rate Sensors for Biofeedback

Stress is one of the most significant health problems in today’s world. Existing work has used heart rate variability (HRV) to detect stress and provide biofeedback in order to regulate it. There has been a growing interest in using wearable biosensors to measure HRV. Each of these sensors acquires heart rate data using different technologies for various bodily locations, therefore posing a challenge for researchers to decide upon a particular device in a research experiment. Previous work has only compared different sensing devices against a gold standard in terms of data quality, thus overlooking qualitative analysis for the usability and acceptability of such devices. This paper introduces a mixed-methods approach to compare the data quality and user acceptance of the six most common wearable heart rate monitoring biosensors. We conducted a 70-minute data collection procedure to obtain HRV data from 32 participants followed by a 10-minute semi-structured interview on sensors’ wearability and comfort, long-term use, aesthetics, and social acceptance. We performed quantitative analysis consisting of correlation and agreement analysis on the HRV data and thematic analysis on qualitative data obtained from interviews. Our results show that the electrocardiography (ECG) chest strap achieved the highest correlation and agreement levels in all sessions and had the lowest amount of artifacts, followed by the photoplethysmography (PPG) wristband, ECG sensor board kit and PPG smartwatch. In all three sessions, wrist-worn devices showed a lower amount of agreement and correlation with the reference device. Qualitative findings from interviews highlight that participants prefer wrist and arm-worn devices in terms of aesthetics, wearability, and comfort, followed by chest-worn devices. Moreover, participants mentioned that the latter are more likely to invite social judgment from others, and they would not want to wear it in public. Participants preferred the chest strap for short-term use and the wrist and arm-worn sensors over long-time.
Time-series Measurement of Parked Domain Names

Domain parking is a monetization mechanism for displaying online advertisements in unused domain names. Some domain names used in cyber attacks are known to leverage domain parking services after the attack. However, the temporal relationships between domain parking services and malicious domain names have not been studied well. In this study, we investigated how malicious domain names using domain parking services change over time. We conducted a large-scale measurement study of more than 66.8 million domain names that have used domain parking services in the past 19 months. We reveal the existence of 3,964 domain names that have been malicious after using domain parking. We also reveal the existence of 3.02 million domain names that utilized multiple parking services simultaneously or while switching between them. Our study can contribute to the efficient analysis of malicious domain names using domain parking services.
Particle Swarm Optimization for Automatically Evolving Convolutional Neural Networks for Image Classification

Designing Convolutional Neural Networks from scratch is a time-consuming process that requires specialist expertise. While automated architecture generation algorithms have been proposed, the underlying search strategies generally are computationally expensive. The existing methods also do not explore the search space efficiently, and often lead to sub-optimal solutions. In this research, we propose a novel Particle Swarm Optimization (PSO)-based model for deep architecture generation to address the above challenges. Our proposed solution incorporates three new components. Firstly, a group-based encoding strategy is devised, which enforces the candidate networks to always follow the best practices. Specifically, it ensures that the number of groups can be adjusted in accordance with the input image size. By restricting the number of groups, we can adapt the frequency of the pooling operations toward the input image size. As such, it ascertains the position and maximum frequency of the pooling operations always result in a valid network architecture without the need for additional complex governing rules. Secondly, a new velocity updating mechanism is devised, which creates new network architectures by identifying the key network configuration differences. Thirdly, a new position updating mechanism using weighted velocity strengths is devised. Both the velocity and position updating mechanisms facilitate the proposed PSO-based model to search the intermediate positions of the particles’ trajectories, allowing a better trade-off between diversification and intensification to be achieved. We employ eight well-known data sets, including Convex, Rectangles, MNIST and its variants, for model evaluation. The proposed PSO-based model achieves up to 7.58% improvement in accuracy and up to 63% reduction in computational cost, in comparison with those from the current state-of-the-art methods.
Mapping the Big Data Landscape: Technologies, Platforms and Paradigms for Real-Time Analytics of Data Streams

The ‘Big Data’ of yesterday is the ‘data’ of today. As technology progresses, new challenges arise and new solutions are developed. Due to the emergence of Internet of Things applications within the last decade, the field of Data Mining has been faced with the challenge of processing and analysing data streams in real-time, and under high data throughput conditions. This is often referred to as the Velocity aspect of Big Data. Whereas there are numerous reviews on Data Stream Mining techniques and applications, there is very little work surveying Data Stream processing paradigms and associated technologies, from data collection through to pre-processing and feature processing, from the perspective of the user, not that of the service provider. In this article, we evaluate a particular type of solution, which focuses on streaming data, and processing pipelines that permit online analysis of data streams that cannot be stored as-is on the computing platform. We review foundational computational concepts such as distributed computation, fault-tolerant computing, and computational paradigms/architectures. We then review the available technological solutions, and applications that pertain to data stream mining as case studies of these theoretical concepts. We conclude with a discussion of the field of data stream processing/analytics, future directions and research challenges.
Maximum Likelihood Time Synchronization for Zero-Padded OFDM

Existing Orthogonal Frequency-Division Multiplexing (OFDM) variants based on cyclic prefix (CP) allow for efficient time synchronization, but suffer from lower power efficiency compared to zero-padded (ZP)-OFDM. Because of its power efficiency, ZP-OFDM is considered as an appealing solution for the emerging low-power wireless systems. However, in the absence of CP, time synchronization in ZP-OFDM is a challenging task. In this paper, the non-data-aided (NDA) maximum-likelihood (ML) time synchronization for ZP-OFDM is analytically derived. We show that the optimal NDA-ML synchronization algorithm offers a high lock-in probability and can be efficiently implemented using Monte Carlo sampling (MCS) technique in combination with golden-section search method in one dimension. To obtain the optimal NDA-ML time synchronization algorithm, we first derive a closed-form expression for the joint probability density function (PDF) of the received ZP-OFDM samples in frequency-selective fading channels. The derived expression is valid for doubly-selective fading channels with mobile users as well. The performance of the proposed synchronization algorithm is evaluated under various practical settings through simulation experiments. It is shown that the proposed optimal NDA-ML synchronization algorithm and its MCS implementation substantially outperforms existing NDA time synchronization algorithms in terms of lock-in probability and mean-squared error (MSE).
Local Augment: Utilizing Local Bias Property of Convolutional Neural Networks for Data Augmentation

Data augmentation is an effective way to increase the diversity of existing training datasets that result in improved generalization ability of convolutional neural networks (CNNs). The augmentation effect is usually global for the existing methods i.e., a single augmentation effect is applied to the whole image, thus limiting the diversity of local characteristics in augmented images. Moreover, the global augmentation effect does not support the most fundamental behavior of CNNs i.e., they focus more on local features (local texture, tiny noise etc.) than global shapes. We refer to this behavior as local bias property. In this paper, we propose a new data augmentation method, called Local Augment (LA), which highly alters the local bias property so that it can generate significantly diverse augmented images and offers the network with a better augmentation effect. First, we select few local patches in an image, then apply different types of augmentation strategies to each local patch. This augmentation process collapses the global structure of the object but creates locally diversified samples, which helps the network to learn the local bias property in a more generalized way. As a result, it increases the generalizability and the prediction accuracy of the network. To verify the effectiveness of the proposed method, we perform comprehensive experiments on image classification with benchmark datasets, where the proposed method outperforms the sate-of-the-art data augmentation techniques on ImageNet and STL10 and shows competitive performance on CIFAR100.
ClusterGrad: Adaptive Gradient Compression by Clustering in Federated Learning

Recently, Federated Learning (FL) has drawn tremendous attentions due to its ability to protect client’s privacy. In FL, clients collaboratively train machine learning models by merely sharing intermediate computations, i.e., gradients of model parameters. However, training a complicated model involves multiple rounds of interactions between clients and the server via the Internet. Consequently, communication is a primary bottleneck of FL attributed to the poor network conditions and the large amount of interchanged computations. To overcome the communication bottleneck, we propose the ClusterGrad algorithm to compress gradients which can considerably reduce the volume of communicated computations. Our design is based on the fact that there is only a small fraction of gradients whose values are far away from the origin in each round of interaction in FL. We first identify these essential gradients that are far away from 0 using the K-means algorithm. These gradient values are approximated by a novel clustering based quantization algorithm. Then, the rest gradients lying close to 0 are approximated with a single value. We can prove that ClusterGrad outperforms the latest FL gradient compression algorithms: Probability Quantization (PQ) and Deep Gradient Compression (DGC). We conduct extensive experiments with the CIFAR-10 datasets which further demonstrate that ClusterGrad can achieve compression ratio (used interchangeably with compression rate) 123 on average in comparison with PQ and DGC with compression ratios 16 and 60 respectively.
Efficient Privacy-Preserving Similarity Range Query based on Pre-computed Distances in eHealthcare

The advance of smart eHealthcare and cloud computing techniques has propelled an increasing number of healthcare centers to outsource their healthcare data to the cloud. Meanwhile, in order to preserve the privacy of the sensitive information, healthcare centers tend to encrypt the data before outsourcing them to the cloud. Although the data encryption technique can preserve the privacy of the data, it inevitably hinders the query functionalities over the outsourced data. Among all practical query functionalities, the similarity range query is one of the most popular ones. However, to our best knowledge, many existing studies on the similarity range query over outsourced data still suffer from the efficiency issue in the query process. Therefore, in this paper, aiming at improving the query efficiency, we propose an efficient privacy-preserving similarity range query scheme based on the precomputed distance technique. In specific, we first introduce a pre-computed distance based similarity range query (PreDSQ) algorithm, which can improve the query efficiency by precomputing some distances. Then, we propose our privacy-preserving similarity query scheme by applying an asymmetric scalar-product-preserving encryption technique to preserve the privacy of the PreDSQ algorithm. Both security analysis and performance evaluation are conducted, and the results show that our proposed scheme is efficient and can well preserve the privacy of data records and query requests.
Iterative Power Allocation and Access Point Scheduling in Uplink Cell-Free Massive MIMO Systems

One of the major bottlenecks that hinders the deployment of Cell-free (CF) massive Multiple-Input MultipleOutput (MIMO) in a cost-effective way is the limited capacity of back/front-haul connections. In this case, the traffic between the large number of distributed access points (APs) and the central processing unit can easily explode. Expensive pointto-point fronthauling technologies are certainly not desirable hence a shared fronthaul medium should be foreseen wherein (a subset of) APs share the same bandwidth-limited medium. However, the point-to-multipoint architectures require proper AP scheduling. In this paper, we propose a novel unified optimization framework for iterative power control and AP scheduling in the uplink of the cell-free massive MIMO system, that offers a systematic user-centric solution towards scalable CF massive MIMO. The experimental results show that power control is not sufficient to reach the best minimum throughput and, therefore, an appropriate association of the users to the APs is crucial to improve the overall system performance.
Linear stability of planar premixed flames: Reactive Navier–Stokes equations with finite activation energy and arbitrary Lewis number

A numerical shooting method for performing linear stability analyses of travelling waves is described and applied to the problem of freely propagating planar premixed flames. Previous linear stability analyses of premixed flames either employ high activation temperature asymptotics or have been performed numerically with finite activation temperature, but either for unit Lewis numbers (which ignores thermal-diffusive effects) or in the limit of small heat release (which ignores hydrodynamic effects). In this paper the full reactive Navier–Stokes equations are used with arbitrary values of the parameters (activation temperature, Lewis number, heat of reaction, Prandtl number), for which both thermal-diffusive and hydrodynamic effects on the instability, and their interactions, are taken into account. Comparisons are made with previous asymptotic and numerical results. For Lewis numbers very close to or above unity, for which hydrodynamic effects caused by thermal expansion are the dominant destablizing mechanism, it is shown that slowly varying flame analyses give qualitatively good but quantitatively poor predictions, and also that the stability is insensitive to the activation temperature. However, for Lewis numbers sufficiently below unity for which thermal-diffusive effects play a major role, the stability of the flame becomes very sensitive to the activation temperature. Indeed, unphysically high activation temperatures are required for the high activation temperature analysis to give quantitatively good predictions at such low Lewis numbers. It is also shown that state-insensitive viscosity has a small destabilizing effect on the cellular instability at low Lewis numbers.