East China Jiaotong University

Nowadays, the main communication form on the Internet is human-human. But it is foreseeable that in a near soon that any object will have a unique way of identification and can be addressed so that every object can be connected. The Internet will become to the Internet of Things. The communicate forms will expand from human-human to human-human, human-thing and thing-thing (also called M2M).This will bring a new ubiquitous computing and communication era and change people's life extremely. Radio Frequency Identification techniques (RFID) and related identification technologies will be the cornerstones of the upcoming Internet of Things (IOT).This paper aims to show a skeleton of the Internet of Things and we try to address some essential issues of the Internet of Things like its architecture and the interoperability, etc. At the beginning we describe an overview of the Internet of Things. Then we give our architecture design proposal of the Internet of Things and then we design a specific the Internet of Things application model which can apply to automatic facilities management in the smart campus. At last, we discuss some open questions about the Internet of Things.

The paper describes the process of building data warehouses. It discusses the functions and main points of data warehouse, and the affections on decision support system.

Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision tasks that only examine the spatial context, our proposed method can exploit both spatial context and spectral correlation to enhance hyperspectral image classification. In particular, we advocate four new deep learning models, namely, 2-D convolutional neural network (2-D-CNN), 3-D-CNN, recurrent 2-D CNN (R-2-D-CNN), and recurrent 3-D-CNN (R-3-D-CNN) for hyperspectral image classification. We conducted rigorous experiments based on six publicly available data sets. Through a comparative evaluation with other state-of-the-art methods, our experimental results confirm the superiority of the proposed deep learning models, especially the R-3-D-CNN and the R-2-D-CNN deep learning models.

In vector space model (VSM), text representation is the task of transforming the content of a textual document into a vector in the term space so that the document could be recognized and classified by a computer or a classifier. Different terms (i.e. words, phrases, or any other indexing units used to identify the contents of a text) have different importance in a text. The term weighting methods assign appropriate weights to the terms to improve the performance of text categorization. In this study, we investigate several widely-used unsupervised (traditional) and supervised term weighting methods on benchmark data collections in combination with SVM and kappa NN algorithms. In consideration of the distribution of relevant documents in the collection, we propose a new simple supervised term weighting method, i.e. tf.rf, to improve the terms' discriminating power for text categorization task. From the controlled experimental results, these supervised term weighting methods have mixed performance. Specifically, our proposed supervised term weighting method, tf.rf, has a consistently better performance than other term weighting methods while other supervised term weighting methods based on information theory or statistical metric perform the worst in all experiments. On the other hand, the popularly used tf.idf method has not shown a uniformly good performance in terms of different data sets.

In this paper, we propose a wireless sensor network paradigm for real-time forest fire detection. The wireless sensor network can detect and forecast forest fire more promptly than the traditional satellite-based detection approach. This paper mainly describes the data collecting and processing in wireless sensor networks for real-time forest fire detection. A neural network method is applied to in-network data processing. We evaluate the performance of our approach by simulations.

Cloud Computing is becoming a well-known buzzword nowadays. Many companies, such as Amazon, Google, Microsoft and so on, accelerate their paces in developing Cloud Computing systems and enhancing their services to provide for a larger amount of users. However, security and privacy issues present a strong barrier for users to adapt into Cloud Computing systems. In this paper, we investigate several Cloud Computing system providers about their concerns on security and privacy issues. We find those concerns are not adequate and more should be added in terms of five aspects (i.e., availability, confidentiality, data integrity, control, audit) for security. Moreover, released acts on privacy are out of date to protect users' private information in the new environment (i.e., Cloud Computing system environment) since they are no longer applicable to the new relationship between users and providers, which contains three parties (i.e., Cloud service user, Cloud service provider/Cloud user, Cloud provider). Multi located data storage and services (i.e., applications) in the Cloud make privacy issues even worse. Hence, adapting released acts for new scenarios in the Cloud, it will result in more users to step into Cloud. We claim that the prosperity in Cloud Computing literature is to be coming after those security and privacy issues having be resolved.

The application of visible (Vis; 400–750 nm) and near infrared red (NIR; 750–2500 nm) region spectroscopy to assess fruit and vegetables is reviewed in context of ‘point’ spectroscopy, as opposed to multi- or hyperspectral imaging. Vis spectroscopy targets colour assessment and pigment analysis, while NIR spectroscopy has been applied to assessment of macro constituents (principally water) in fresh produce in commercial practice, and a wide range of attributes in the scientific literature. This review focusses to key issues relevant to the widespread implementation of Vis-NIR technology in the fruit sector. A background to the concepts and technology involved in the use of Vis-NIR spectroscopy is provided and instrumentation for in-field and in-line applications, which has been available for two and three decades, respectively, is described. A review of scientific effort is made for the period 2015 - February 2020, in terms of the application areas, instrumentation, chemometric methods and validation procedures, and this work is critiqued through comparison to techniques in commercial use, with focus to wavelength region, optical geometry, experimental design, and validation procedures. Recommendations for future research activity in this area are made, e.g., application development with consideration of the distribution of the attribute of interest in the product and the matching of optically sampled and reference method sampled volume; instrumentation comparisons with consideration of repeatability, optimum optical geometry and wavelength range). Recommendations are also made for reporting requirements, viz. description of the application, the reference method, the composition of calibration and test populations, chemometric reporting and benchmarking to a known instrument/method, with the aim of maximising useful conclusions from the extensive work being done around the world.

Design methods and applications of nanocomposite hydrogel-based strain and pressure sensors have been summarized and classified in this review.

Conductive hydrogels have become one of the most promising materials for skin-like sensors because of their excellent biocompatibility and mechanical flexibility. However, the limited stretchability, low toughness, and fatigue resistance lead to a narrow sensing region and insufficient durability of the hydrogel-based sensors. In this work, an extremely stretchable, highly tough, and anti-fatigue conductive nanocomposite hydrogel is prepared by integrating hydrophobic carbon nanotubes (CNTs) into hydrophobically associated polyacrylamide (HAPAAm) hydrogel. In this conductive hydrogel, amphiphilic sodium dodecyl sulfate was used to ensure uniform dispersion of CNTs in the hydrogel network, and hydrophobic interactions between the hydrogel matrix and the CNT surface formed, greatly improving the mechanical properties of the hydrogel. The obtained CNTs/HAPAAm hydrogel showed excellent stretchability (ca. 3000%), toughness (3.42 MJ m –3 ), and great anti-fatigue property. Moreover, it exhibits both high tensile strain sensitivity in the wide strain ranges (gauge factor = 4.32, up to 1000%) and high linear sensitivity (0.127 kPa –1 ) in a large-pressure region within 0–50 kPa. The CNTs/HAPAAm hydrogel-based sensors can sensitively and stably detect full-range human activities (e.g., elbow rotation, finger bending, swallowing motion, and pronouncing) and handwriting, demonstrating the CNTs/HAPAAm hydrogel’s potential as the wearable strain and pressure sensors for flexible devices.

Most existing multiobjective evolutionary algorithms aim at approximating the Pareto front (PF), which is the distribution of the Pareto-optimal solutions in the objective space. In many real-life applications, however, a good approximation to the Pareto set (PS), which is the distribution of the Pareto-optimal solutions in the decision space, is also required by a decision maker. This paper considers a class of multiobjective optimization problems (MOPs), in which the dimensionalities of the PS and the PF manifolds are different so that a good approximation to the PF might not approximate the PS very well. It proposes a probabilistic model-based multiobjective evolutionary algorithm, called MMEA, for approximating the PS and the PF simultaneously for an MOP in this class. In the modeling phase of MMEA, the population is clustered into a number of subpopulations based on their distribution in the objective space, the principal component analysis technique is used to estimate the dimensionality of the PS manifold in each subpopulation, and then a probabilistic model is built for modeling the distribution of the Pareto-optimal solutions in the decision space. Such a modeling procedure could promote the population diversity in both the decision and objective spaces. MMEA is compared with three other methods, KP1, Omni-Optimizer and RM-MEDA, on a set of test instances, five of which are proposed in this paper. The experimental results clearly suggest that, overall, MMEA performs significantly better than the three compared algorithms in approximating both the PS and the PF.

The fabrication of ultrathin films that are electrically conductive and mechanically strong for electromagnetic interference (EMI) shielding applications is challenging. Herein, ultrathin, strong, and highly flexible Ti 3 C 2 T x MXene/bacterial cellulose (BC) composite films are fabricated by a scalable in situ biosynthesis method. The Ti 3 C 2 T x MXene nanosheets are uniformly dispersed in the three-dimensional BC network to form a mechanically entangled structure that endows the MXene/BC composite films with excellent mechanical properties (tensile strength of 297.5 MPa at 25.7 wt % Ti 3 C 2 T x ) and flexibility. Importantly, a 4 μm thick Ti 3 C 2 T x /BC composite film with 76.9 wt % Ti 3 C 2 T x content demonstrates a specific EMI shielding efficiency of 29141 dB cm 2 g –1, which surpasses those of most previously reported MXene-based polymer composites with similar MXene contents and carbon-based polymer composites. Our findings show that the facile, environmentally friendly, and scalable fabrication method is a promising strategy for producing ultrathin, strong, and highly flexible EMI shielding materials such as the freestanding Ti 3 C 2 T x /BC composite films for efficient EMI shielding to address EMI problems of a fast-developing modern society.

The plasmon-induced transparency (PIT), which is destructive interference between the superradiation mode and the subradiation mode, is studied in patterned graphene-based terahertz metasurface composed of graphene ribbons and graphene strips. As the results of finite-difference time-domain (FDTD) simulation and coupled-mode theory (CMT) fitting, the PIT can be dynamically modulated by the dual-mode. The left (right) transmission dip is mainly tailored by the gate voltage applied to graphene ribbons (stripes), respectively, meaning a dual-mode on-to-off modulator is realized. Surprisingly, an absorbance of 50% and slow-light property of 0.7 ps are also achieved, demonstrating the proposed PIT metasurface has important applications in absorption and slow-light. In addition, coupling effects between the graphene ribbons and the graphene strips in PIT metasurface with different structural parameters also are studied in detail. Thus, the proposed structure provides a new basis for the dual-mode on-to-off multi-function modulators.

Complicated weather conditions lead to intermittent, random and volatility in photovoltaic (PV) systems, which makes PV predictions difficult. A recurrent neural network (RNN) is considered to be an effective tool for time-series data prediction. However, when the weather changes intensely, the long-term sequence of multivariate may cause gradient vanishing (exploding) during the training of RNN, leading the prediction results to local optimum. Long short-term memory (LSTM) network is the deep structure of RNN. Due to its special hidden layer unit structure, it can preserve the trend information contained in the long-term sequence, which is allowed to solve the problems of RNN and improve performance. An LSTM-based approach is applied for short-term predictions in this study based on a timescale that encompasses global horizontal irradiance (GHI) one hour in advance and one day in advance. Inaccurate forecasts usually occur on cloudy days, and the results of ANN and SVR in the literature prove this. To improve prediction accuracy on cloudy days, the clearness-index was introduced as an input data for the LSTM model and to classify the type of weather by k-means during the data processing, where cloudy days are classified as the cloudy and the mixed(partially cloudy). NN models are established to compare the accuracy of different approaches and the cross-regional study is to prove whether the method can be generalizable. From the results of hourly forecast, the R2coefficient of LSTM on cloudy days and mixed days is exceeding 0.9, while the R2of RNN is only 0.70 and 0.79 in Atlanta and Hawaii. From the results of daily forecast, All R2on cloudy days is about 0.85. However, the LSTM is still very effective in improving of RNN and more accurate than other models.

Zwitterionic hydrogels exhibit eminent nonfouling and hemocompatibility. Several key challenges hinder their application as coating materials for blood-contacting biomedical devices, including weak mechanical strength and low adhesion to the substrate. Here, we report a poly(carboxybetaine) microgel reinforced poly(sulfobetaine) (pCBM/pSB) pure zwitterionic hydrogel with excellent mechanical robustness and anti-swelling properties. The pCBM/pSB hydrogel coating was bonded to the PVC substrate via the entanglement network between the pSB and PVC chain. Moreover, the pCBM/pSB hydrogel coating can maintain favorable stability even after 21 d PBS shearing, 0.5 h strong water flushing, 1000 underwater bends, and 100 sandpaper abrasions. Notably, the pCBM/pSB hydrogel coated PVC tubing can not only mitigate the foreign body response but also prevent thrombus formation ex vivo in rats and rabbits blood circulation without anticoagulants. This work provides new insights to guide the design of pure zwitterionic hydrogel coatings for biomedical devices.

As a legacy from conventional wireless services, illegal eavesdropping is regarded as one of the critical security challenges in Connected and Autonomous Vehicles (CAVs) network. Our work considers the use of Distributed Kalman Filtering (DKF) and Deep Reinforcement Learning (DRL) techniques to improve anti-eavesdropping communication capacity and mitigate jamming interference. Aiming to improve the security performance against smart eavesdropper and jammer, we first develop a DKF algorithm that is capable of tracking the attacker more accurately by sharing state estimates among adjacent nodes. Then, a design problem for controlling transmission power and selecting communication channel is established while ensuring communication quality requirements of the authorized vehicular user. Since the eavesdropping and jamming model is uncertain and dynamic, a hierarchical Deep Q-Network (DQN)-based architecture is developed to design the anti-eavesdropping power control and possibly channel selection policy. Specifically, the optimal power control scheme without prior information of the eavesdropping behavior can be quickly achieved first. Based on the system secrecy rate assessment, the channel selection process is then performed when necessary. Simulation results confirm that our jamming and eavesdropping defense technique enhances the secrecy rate as well as achievable communication rate compared with currently available techniques.

Memristor is an ideal electronic device used as an artificial nerve synapse due to its unique memory function. This article presents a design of a new Hopfield neural network (HNN) that can generate multiscroll attractors by utilizing a new memristor as a synapse in the HNN. Differing from the others, this memristor is constructed with hyperbolic tangent functions. Taking the memristor as a self-feedback synapse of a neuron in the HNN, the memristive HNN can yield multidouble-scroll attractors, and its parameters can be used to effectively control the number of double scrolls contained in an attractor. Interestingly, the generation of multidouble-scroll attractors is independent of the memductance function but depends only on the internal state equation. Thus, the memductance function can be adjusted to yield various complex dynamical behaviors. Moreover, amplitude control effects and quantitatively controllable multistability are revealed by numerical analysis. The accurate reproduction of some dynamical behaviors by a designed circuit verifies the correctness of the numerical analysis. Finally, based on the proposed memristive HNN, a novel image encryption scheme in the 3-D setting is designed and evaluated, demonstrating its good encryption performances.

Solar energy absorption is a very important field in photonics. The successful development of an efficient, wide-band solar absorber is an extremely powerful driver in this field. We propose an ultra-wideband (UWB) solar energy absorber composed of a Ti ring and SiO2-Si3N4-Ti thin films. In the range of 300-4000 nm, the wide band has an absorption efficiency of more than 90% and can reach 3683 nm, and it has four absorption peaks with a high absorptivity. Moreover, the weighted average absorption efficiency of the solar absorber under AM 1.5 is maintained above 97.03%, which indicates it has great potential for use in the field of solar energy absorption. Moreover, we proved that the polarization is insensitive by analyzing the absorption characteristics at arbitrary polarization angles. For both the transverse electric (TE) and transverse magnetic (TM) modes, the UWB absorption is maintained at more than 90% in the wide incidence angle range of 60°. The UWB solar energy absorber has great potential for use in a variety of applications, such as converting solar light and heat into electricity for public use and reducing the side effects of coal-fired power generation. It can also be used in information detection and infrared thermal imaging owing to its UWB characteristics.

Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, potential eavesdropping is one of the main threats to V2V communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multiple armed bandit problem is designed to implement the secret key switching procedure when it is needed. Through constant perception-execution loops (PELs), the security and confidentiality is improved for the authorized vehicles in their behavioral interactions with the illegal eavesdropper. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.

This note addresses the stability problem of continuous-time positive systems with time-varying delays. It is shown that such a system is asymptotically stable for any continuous and bounded delay if and only if the sum of all the system matrices is a Hurwitz matrix. The result is a time-varying version of the widely-known asymptotic stability criterion for constant-delay positive systems. A numerical example illustrates the correctness of our result.

The insulator is an important catenary component that maintains the insulation between the catenary and earth. Due to the long-term impact of railway vehicles and the environment, defects in the insulator are inevitable. Recently, automatic catenary inspection using computer vision and pattern recognition has been introduced to improve the safety of railway operation. However, achieving full automation of insulator defect detection is still very challenging due to the visual complexity of defects and the small number of defective insulators. To overcome these problems, this paper proposes a novel insulator surface defect detection system using a deep convolutional neural network (CNN). The proposed system consists of two stages. First, a Faster R-CNN network is adopted to localize the key catenary components, and the image areas that contain the insulators are obtained. Then, the classification score and anomaly score are determined from a deep multitask neural network that is composed of a deep material classifier and a deep denoising autoencoder. The defect state is determined by analyzing the classification score and anomaly score. Experiments of the catenary insulator defect detection along the Hefei-Fuzhou high-speed railway line indicate that the system can achieve high detection accuracy.