Contrast in Terahertz Images of Archival Documents—Part II: Influence of Topographic Features

We investigate the potential of terahertz time-domain imaging in reflection mode to reveal archival information in documents in a non-invasive way. In particular, this study explores the parameters and signal processing tools that can be used to produce well-contrasted terahertz images of topographic features commonly found in archival documents, such as indentations left by a writing tool, as well as sieve lines. While the amplitude of the waveforms at a specific time delay can provide the most contrasted and legible images of topographic features on flat paper or parchment sheets, this parameter may not be suitable for documents that have a highly irregular surface, such as water- or fire-damaged documents. For analysis of such documents, cross-correlation of the time-domain signals can instead yield images with good contrast. Analysis of the frequency-domain representation of terahertz waveforms can also provide well-contrasted images of topographic features, with improved spatial resolution when utilising high-frequency content. Finally, we point out some of the limitations of these means of analysis for extracting information relating to topographic features of interest from documents.     

Force control for hydraulic load simulator using self-tuning grey predictor – fuzzy PID

Hydraulic systems play an important role in modern industry for the reason that hydraulic actuator systems have many advantages over other technologies with electric motors, as they possess high durability and the ability to produce large forces at high speeds. Therefore, the hydraulic actuator has a wide range of application fields such as hydraulic punching, riveting, pressing machines, and molding technology, where controlled forces or pressures with high accuracy and fast response are the most significant demands. Consequently, many hybrid actuator models have been developed for studying how to control forces or pressures with best results.This paper presents a kind of hydraulic load simulator for conducting performance and stability testing related to the force control problem of hydraulic hybrid systems. In the dynamic loading process, perturbation decreases control performance such as stability, frequency response, and loading sensitivity decreasing or bad. In order to improve the control quality of the loading system while eliminating or reducing the disturbance, a grey prediction model combined with a fuzzy PID controller is suggested. Furthermore, fuzzy controllers and a tuning algorithm are used to change the grey step size in order to improve the control quality. The grey prediction compensator can improve the system settle time and overshoot problems. Simulations and experiments on the hydraulic load simulator are carried out to evaluate the effectiveness of the proposed control method when applied to hydraulic systems with various external disturbances encountered in real working conditions.     

Barrier рBn-Structure Based on GaAsSb/AlAsSb/InAsSb for Detection of IR Radiation in the Spectral Range of 3.1–4.2 µm

Journal of Communications Technology and Electronics - In the study, a new рBn-architecture based on a GaAsSb/AlAsSb/InAsSb heterostructure of III‒V group materials with an...     

FEW-NNN: A Fuzzy Entropy Weighted Natural Nearest Neighbor Method for Flow-Based Network Traffic Attack Detection

Attacks such as APT usually hide communication data in massive legitimate network traffic, and mining structurally complex and latent relationships among flow-based network traffic to detect attacks has become the focus of many initiatives. Effectively analyzing massive network security data with high dimensions for suspicious flow diagnosis is a huge challenge. In addition, the uneven distribution of network traffic does not fully reflect the differences of class sample features, resulting in the low accuracy of attack detection. To solve these problems, a novel approach called the fuzzy entropy weighted natural nearest neighbor(FEW-NNN) method is proposed to enhance the accuracy and efficiency of flowbased network traffic attack detection. First, the FEW-NNN method uses the Fisher score and deep graph feature learning algorithm to remove unimportant features and reduce the data dimension. Then, according to the proposed natural nearest neighbor searching algorithm(NNN_Searching), the density of data points, each class center and the smallest enclosing sphere radius are determined correspondingly. Finally, a fuzzy entropy weighted KNN classification method based on affinity is proposed, which mainly includes the following three steps: 1、 the feature weights of samples are calculated based on fuzzy entropy values, 2、 the fuzzy memberships of samples are determined based on affinity among samples, and 3、 K-neighbors are selected according to the class-conditional weighted Euclidean distance, the fuzzy membership value of the testing sample is calculated based on the membership of k-neighbors, and then all testing samples are classified according to the fuzzy membership value of the samples belonging to each class;that is, the attack type is determined. The method has been applied to the problem of attack detection and validated based on the famous KDD99 and CICIDS-2017 datasets. From the experimental results shown in this paper, it is observed that the FEW-NNN method improves the accuracy and efficiency of flow-based network traffic attack detection.     

The Role of Defects in Metal–Organic Frameworks for Nitrogen Reduction Reaction: When Defects Switch to Features

The electrochemical nitrogen reduction reaction (NRR), a contributor for producing ammonia under mild conditions sustainably, has recently attracted global research attention. Thus far, the design of highly efficient electrocatalysts to enhance NRR efficiency is a specific focus of the research. Among them, defect engineering of electrocatalysts is considered a significant way to improve electrocatalytic efficiency by regulating the electronic state and providing more active sites that can give electrocatalysts better physicochemical properties. Recently, metal–organic frameworks (MOFs), along with their derivatives, have captured immense interest in electrocatalytic reactions owing to not only their large surface area and high porosity but also the ability to create rich defects in their structures. Hence, they can provide plenty of exposed active sites for electron transfer, NN cleavage, and N₂ adsorption to enhance NRR performance. Herein, the concept, the in situ characterizations techniques for defects, and the most common ways to create defects into MOFs have been summarized. Furthermore, the recent advances of MOF-based electrocatalysts towards NRR have been recapitulated. Ultimately, the major challenges and outlook of defects in MOFs for NRR are proposed. This paper is anticipated to provide critical guidelines for optimizing NRR electrocatalysts.     

Reconfigurable intelligent surface: design the channel – a new opportunity for future wireless networks

In this paper, we survey state-of-the-art research outcomes in the burgeoning field of Reconfigurable Intelligent Surface (RIS), given its potential for significant performance enhancement of next-generation wireless communication networks by means of adapting a propagation environment. Emphasis has been placed on several aspects gating the commercial viability of future network deployment. Comprehensive summaries are provided for practical hardware design considerations and broad implications of artificial intelligence techniques, as are in-depth outlooks on the salient aspects of system models, use cases, and physical layer optimization techniques.     

Geometric algebra colour image representations and derived total orderings for morphological operators – Part I: Colour quaternions

The definition of morphological operators for colour images requires a total ordering for colour points. A colour can be represented by different algebraic structures, in this paper we focus on real quaternions. The paper presents two main contributions. On the one hand, we have studied different alternatives to introduce the scalar part to obtain full colour quaternions. On the other hand, several total lexicographic orderings for quaternions have been defined, according to the various quaternion decompositions. The properties of these quaternionic orderings have been characterised to enable the identification of the most useful ones to define colour morphological operators. The theoretical results are illustrated with examples of processed images which show the usefulness of the proposed operators for real life complex problems.     

ICANN – EU can't: Internet governance and Europe's role in the formation of the Internet Corporation for Assigned Names and Numbers (ICANN)

This paper analyzes the policy process that led to the formation of the Internet Corporation for Assigned Names and Numbers (ICANN), focusing on the actions of the European Commission. The analysis of the relevant documents shows the difference between the regulatory ideas in the US and the EU. The European Commission would have preferred an institutional framework with a prominent role for public actors, but had to accept the preference of the US Government, which directed the institutionalization of a private regime for the management of Internet addresses and names. Nevertheless, the Commission managed to establish itself as a major player in the emerging field of Internet governance.     

Une caractérisation non gaussienne et à longue mémoire du trafic Internet et de ses anomalies: validation expérimentale et application à la détection d’attaque de DDoS/Non Gaussian Long Memory Model for Internet Traffic: Experimental Validation and Application to DDoS Detection

Being now a mainstream communication, Internet is subject to many kinds of anomalies (failures, flash-crowds, attacks). In order to compare the statistics of normal traffic with traffic with anomalies, we collect both regular and anomalous traffic. The traffic is collected on the Renater network by the Metrosec project and we produce both Denial of Service (DoS) attacks with real attack softwares (TFN2k, TRIN00) aimed at various services (ICMP, SYN, UDP, TCP), and flash-crowd anomalies. We propose a multiresolution, non-Gaussian model with long memory and the corresponding estimators. It models, jointly at all aggregation levels, normal traffic, and also traffic containing anomalies. We show that the model enables to detect the anomalies in the traffic and distinguish between flash-crowd and Dos types of anomaly.