Stability and control of nonlinear systems described by retarded functional equations: a review of recent results

This paper reports on recent results in a series of the work of the authors on the stability and nonlinear control for general dynamical systems described by retarded functional differential and difference equations. Both internal and external stability properties are studied. The corresponding Lyapunov and Razuminkhin characterizations for input-to-state and input-to-output stabilities are proposed. Necessary and sufficient Lyapunov-like conditions are derived for robust nonlinear stabilization. In particular, an explicit controller design procedure is developed for a new class of nonlinear time-delay systems. Lastly, sufficient assumptions, including a small-gain condition, are presented for guaranteeing the input-to-output stability of coupled systems comprised of retarded functional differential and difference equations.     

Learning Image-Text Associations

Web information fusion can be defined as the problem of collating and tracking information related to specific topics on the World Wide Web. Whereas most existing work on Web information fusion has focused on text-based multidocument summarization, this paper concerns the topic of image and text association, a cornerstone of cross-media Web information fusion. Specifically, we present two learning methods for discovering the underlying associations between images and texts based on small training data sets. The first method based on vague transformation measures the information similarity between the visual features and the textual features through a set of predefined domain-specific information categories. Another method uses a neural network to learn direct mapping between the visual and textual features by automatically and incrementally summarizing the associated features into a set of information templates. Despite their distinct approaches, our experimental results on a terrorist domain document set show that both methods are capable of learning associations between images and texts from a small training data set.     

Exponential stability for uncertain neutral systems with Markov jumps

This paper deals with the global exponential stability problems for stochastic neutral Markov jump systems （MJSs） with uncertain parameters and multiple time-delays. The delays are respectively considered as constant and time varying cases, and the uncertainties are assumed to be norm bounded. By selecting appropriate Lyapunov-Krasovskii functions, it gives the sufficient condition such that the uncertain neutral MJSs are globally exponentially stochastically stable for all admissible uncertainties. The stability criteria are formulated in the form of linear matrix inequalities （LMIs）, which can be easily checked in practice. Finally, two numerical examples are exploited to illustrate the effectiveness of the developed techniques.     

云计算技术在区域安全通信中的应用

随着互联网技术的不断发展,网络已成为社会经济发展的重要支撑工具。然而,受限于传统通信技术的约束,网络通信信息存在被共享、泄露等风险。基于云计算技术的区域安全通信技术可以有效解决无线通信技术按身份分配不同通信权限的问题,用户拥有“普通域”与“安全域”功能,以保证通信网络的安全。文中以云计算技术的概念为切入点,阐述了云计算技术在区域通信中的应用价值,最后提出了构建基于云计算区域安全通信系统的具体方案,并通过实验验证了该系统具有良好的应用性能。     

2D Bi2O2Te Semiconductor with Single-Crystal Native Oxide Layer

Following logic in the silicon semiconductor industry, the existence of native oxide and suitable fabrication technology is essential for 2D semiconductors in planar integronics, which are surface-sensitive to typical coating technologies. To date, very few types of integronics are found to possess this feature. Herein, the 2D Bi₂O₂Te developed recently is reported to possess large-area synthesis and controllable thermal oxidation behavior toward single-crystal native oxides. This shows that surface-adsorbed oxygen atoms are inclined to penetrate across [Bi₂O₂]_n²ⁿ⁺ layers and bond with the underlying [Te]_n²ⁿ⁻ at elevated temperatures, transforming directly into [TeO₄]_n²ⁿ⁻ with the basic architecture remaining stable. The oxide can be adjusted to form in an accurate layer-by-layer manner with a low-stress sharp interface. The native oxide Bi₂TeO₆ layer (bandgap of ≈2.9 eV) exhibits visible-light transparency and is compatible with wet-chemical selective etching technology. These advances demonstrate the potential of Bi₂O₂Te in planar-integrated functional nanoelectronics such as tunnel junction devices, field-effect transistors, and memristors.     

A 2.20 eV Bandgap Polymer Donor for Efficient Colorful Semitransparent Organic Solar Cells

Semitransparent organic solar cells (ST-OSCs) have attracted increasing attention due to their promising prospect in building-integrated photovoltaics. Generally, efficient ST-OSCs with good average visible transmittance (AVT) can be realized by developing active layer materials with light absorption far from the visible light range. Herein, the development of ultrawide bandgap polymer donors with near-ultraviolet absorption, paired with near-infrared acceptors, is proposed to achieve high-performance ST-OSCs. The key points for the design of ultrawide bandgap polymers include constructing donor–donor type conjugated skeleton, suppressing the quinoidal resonance effect, and minimizing the twist of conjugated skeleton via noncovalent conformational locks. As a proof of concept, a polymer named PBOF with an optical bandgap of 2.20 eV is synthesized, which exhibited largely reduced overlap with the human eye photopic response spectrum and afforded a power conversion efficiency (PCE) of 16.40% in opaque device. As a result, ST-OSCs with a PCE over 10% and an AVT over 30% are achieved without optical modulation. Moreover, colorful ST-OSCs with visual aesthetics can be achieved by tuning the donor/acceptor weight ratio in active layer benefiting from the ultrawide bandgap nature of PBOF. This study demonstrates the great potential of ultrawide bandgap polymers for efficient colorful ST-OSCs.     

Design techniques for voltage-to-time converters with nonlinearity emphasis

This paper provides an in-depth treatment of voltage-to-time converters (VTCs) for time-based signal processing with a nonlinearity emphasis. The need for VTCs in deployment of time-based techniques for high-speed or high-resolution analog-to-digital converters is investigated. It is followed with the classification of VTCs. A detailed treatment of the principle, topology, operation, and design consideration of variable-slope (VS) and constant-slope (CS) VTCs is provided. The nonlinearity of VS-VTCs and that of CS-VTCs are analyzed in detail analytically. It is shown that VS-VTCs is inherently nonlinear while CS-VTCs is intrinsically linear. Factors contributing to the nonlinearity of these VTCs are investigated. VS-VTCs and CS-VTCs studied are designed in TSMC 130 nm 1.2 V CMOS and analyzed using Spectre from Cadence Design Systems with BSIM3.3 device models. A good agreement between simulation and analytical results is obtained. The average gain of the VS-VTC is 4.4 times that of the CS-VTC. The 2nd and 3rd harmonics of the CS-VTC are significantly smaller as compared with those of the VS-VTC at the price of more power consumption.

     

Friendly spectrum jamming against MIMO eavesdropping

Wireless Networks - Friendly spectrum jamming is a flexible scheme to establish secure communications among heterogeneous wireless devices without the need of encryption. Previous works have...     

A Survey on Long-Tailed Visual Recognition

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.