Mechanisms of polarization fatigue in ferroelectric PbZr_0.52Ti_0.48O₃epitaxial thin-film capacitors

Polarization fatigue of ferroelectric capacitors has been widely discussed in terms of an interface related effect. However, responsible origin(s) and mechanism(s) behind the development of polarization fatigue have not been fully identified. In this thesis, I pinpoint the fatigue origins and mechanisms of PbZr0.52Ti0.48O3 (PZT) epitaxial thin-film capacitors. It was found that the dominant origin of the early fatigue in PZT capacitors with conventional ex situ metal electrodes is the defective layer of carbon at the capacitor interface. Under field cycling, this defective layer is believed to trap electrons, subsequently hinder domain switching of the PZT layer. To avoid having this defective layer and consequently to improve the fatigue resistance of metal-electrode PZT capacitors, I suggested to use in situ deposited metal layers. The fatigue resistance of the capacitors with in situ metal electrodes is several orders of magnitude higher than those with ex situ metal electrodes. However, the capacitors with in situ metal electrodes still became strongly fatigued under prolonged field cycling. By scanning transmission electron microscopy, we observed that in the fatigued capacitor after prolonged field cycling there is a structurally degraded layer of nm in thickness, containing polycrystalline (Zr,Ti)O2 oxides and diffused Pt grains. Therefore, the development of polarization fatigue in these capacitors was suggested to follow two main stages. First, the metal/ferroelectric interface becomes structurally degraded and decomposed by a high transient depolarization field during the domain switching under repeated field cycles, resulting in an interfacial non-ferroelectric layer in the cycled capacitors. Second, the resulting interfacial non-ferroelectric layer screens the external applied field, leading to a polarization switching suppression in the cycled capacitor. Overall, to improve the fatigue resistance of ferroelectric capacitors with metal electrodes, ones should improve the purity of the capacitor interfaces and enhance the bonding between the metal and the ferroelectric layer

Government Support and Firm Profitability in Vietnam

Existing studies on the linkage between government subsidies and firm financial performance often use a mean regression approach and focus mainly on developed countries. To fill the gap, this study, for the first time, considers the impact of government support activities on the profitability of manufacturing SMEs in a developing country, Vietnam. Using an unbalanced panel dataset covering the period 2009–2015, government financial supports show an insignificant linkage with firm profitability when using OLS. However, a fixed-effect quantile approach reveals that government financial support is negatively related for firms with low profit but is positively related for firms in the high profitability percentile. Our findings also suggest that policymakers should focus on helping start-ups instead of ineffective, informal firms

Enabling non-linear energy harvesting in power domain based multiple access in relaying networks: Outage and ergodic capacity performance analysis

The Power Domain-based Multiple Access (PDMA) scheme is considered as one kind of Non-Orthogonal Multiple Access (NOMA) in green communications and can support energy-limited devices by employing wireless power transfer. Such a technique is known as a lifetime-expanding solution for operations in future access policy, especially in the deployment of power-constrained relays for a three-node dual-hop system. In particular, PDMA and energy harvesting are considered as two communication concepts, which are jointly investigated in this paper. However, the dual-hop relaying network system is a popular model assuming an ideal linear energy harvesting circuit, as in recent works, while the practical system situation motivates us to concentrate on another protocol, namely non-linear energy harvesting. As important results, a closed-form formula of outage probability and ergodic capacity is studied under a practical non-linear energy harvesting model. To explore the optimal system performance in terms of outage probability and ergodic capacity, several main parameters including the energy harvesting coefficients, position allocation of each node, power allocation factors, and transmit signal-to-noise ratio (SNR) are jointly considered. To provide insights into the performance, the approximate expressions for the ergodic capacity are given. By matching analytical and Monte Carlo simulations, the correctness of this framework can be examined. With the observation of the simulation results, the figures also show that the performance of energy harvesting-aware PDMA systems under the proposed model can satisfy the requirements in real PDMA applications.Web of Science87art. no. 81

Joint Fixed Power Allocation and Partial Relay Selection Schemes for Cooperative NOMA

 In the future wireless systems, non-orthogonal multiple-access (NOMA) with partial relay selection scheme is considered as developing research topic. In this paper, dual-hop relaying systems is deployed for NOMA, in which the signal is transfered with the assistance of decode-and-forward (DF) scheme. This paper presents exact expressions for outage probability over independent Rayleigh fading channels, and two partial relay selection schemes are provided. Using matching analytical result and Monte-Carlo method, we introduce forwarding strategy selection for fixed user allocation and exactness of derived formula is checked. The presented simulations confirm the the advantage of such considered NOMA, and the effectiveness of the proposed forwarding strategy

MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation

Few-shot instance segmentation extends the few-shot learning paradigm to the instance segmentation task, which tries to segment instance objects from a query image with a few annotated examples of novel categories. Conventional approaches have attempted to address the task via prototype learning, known as point estimation. However, this mechanism depends on prototypes (\eg mean of $K-$shot) for prediction, leading to performance instability. To overcome the disadvantage of the point estimation mechanism, we propose a novel approach, dubbed MaskDiff, which models the underlying conditional distribution of a binary mask, which is conditioned on an object region and $K-$shot information. Inspired by augmentation approaches that perturb data with Gaussian noise for populating low data density regions, we model the mask distribution with a diffusion probabilistic model. We also propose to utilize classifier-free guided mask sampling to integrate category information into the binary mask generation process. Without bells and whistles, our proposed method consistently outperforms state-of-the-art methods on both base and novel classes of the COCO dataset while simultaneously being more stable than existing methods. The source code is available at: https://github.com/minhquanlecs/MaskDiff.Comment: Accepted at AAAI 2024 (oral presentation

Toward An IoT-based Expert System for Heart Disease Diagnosis

IoT technology has been recently adopted in the healthcare system to collect Electrocardiogram (ECG) signals for heart disease diagnosis and prediction. However, noises in collected ECG signals make the diagnosis and prediction system unreliable and imprecise. In this work, we have proposed a new lightweight approach to removing noises in collected ECG signals to perform precise diagnosis and prediction. First, we have used a revised Sequential Recursive (SR) algorithm to transform the signals into digital format. Then, the digital data is proceeded using a revised Discrete Wavelet Transform (DWT) algorithm to detect peaks in the data to remove noises. Finally, we extract some key features from the data to perform diagnosis and prediction based on a feature dataset. Redundant features are removed by using Fishers Linear Discriminant (FLD). We have used an ECG dataset from MIT-BIH (PhisioNet) to build a knowledge-base diagnosis features. We have implemented a proof-of concept system that collects and processes real ECG signals to perform heart disease diagnosis and prediction based on the built knowledge base

Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models

Recognition across domains has recently become an active topic in the research community. However, it has been largely overlooked in the problem of recognition in new unseen domains. Under this condition, the delivered deep network models are unable to be updated, adapted or fine-tuned. Therefore, recent deep learning techniques, such as: domain adaptation, feature transferring, and fine-tuning, cannot be applied. This paper presents a novel Universal Non-volume Preserving approach to the problem of domain generalization in the context of deep learning. The proposed method can be easily incorporated with any other ConvNet framework within an end-to-end deep network design to improve the performance. On digit recognition, we benchmark on four popular digit recognition databases, i.e. MNIST, USPS, SVHN and MNIST-M. The proposed method is also experimented on face recognition on Extended Yale-B, CMU-PIE and CMU-MPIE databases and compared against other the state-of-the-art methods. In the problem of pedestrian detection, we empirically observe that the proposed method learns models that improve performance across a priori unknown data distributions

Perceptual Learning Style Preference for Medical Terminology: A Case Study of 20 ESP Students

Due to the globalization, there has been a great demand for learning English for Specific Purposes (ESP) in different fields. How to teach medical terminology effectively to pre-service medical professionals is one of the main challenges that ESP instructors encounter in their English in Medicine classrooms. A variety of factors, including but are not limited to student learning style preference, prior knowledge, classroom facilities should be considered. In this study, we conducted a case study of 20 students in an ESP class to explore their learning style preference. This study employed two data collection instruments: questionnaire and interview. Data analysis interestingly revealed that the location of students’ secondary education has an influence on their perceptual learning style preference of medical terminology at tertiary level. Drawing on the results, this study argues for a balance between student learning preference and teaching mythology. Keywords: ESP, perceptual learning style preference, medical terminology DOI: 10.7176/JLLL/66-06 Publication date:March 31st 202