Named Data Networking (NDN) is one of the most promising proposals for future Industrial Internet of Things (IIoT) architecture, which supports the sharing and distribution of data by providing in-network caching capability via built-in Content Store. In this paper, a smart Content Store with Logical Table (CSLT) is conceived. CSLT not only support the cache replacements efficiently but also line-rate

In this paper, we present an end-to-end approach method using deep learning for grasp detection. Our method is a real-time processing method for discrete depth image sampling and the problems of long calculation times and difficulty in registration caused by object modelling and global searching in traditional methods. The method uses depthwise convolution and pointwise convolution to model the relations

As a long-standing research issue in computer vision and robotics, multi-view registration has attracted much attention in recent years. Most existing works are mainly focus on the estimating the point to point match correspondence, which usually suffers from the poor initial pose and data noise as well as leads to the inaccurate matches. To overcome the aforementioned limitation, we propose a novel

Multi-modal information retrieval is among the prevailing forms of daily human–computer interaction. The recent deep cross-modal hashing methods have received increasing attention because of their superior search performance and efficiency capability. However, effectively exploring the high-ranking semantic correlation and preserving representation consistency are still challengeable due to the heterogeneity

The increasing amount of distributed power generation from rooftop solar panels allows new electricity markets to emerge in which prosumers and consumers can trade locally produced energy. The use of blockchain technology has increasingly emerged in energy markets and shows great potential to facilitate Peer-to-Peer energy trading. However, blockchain technology is still in its infancy meaning it is

The so-called cloud-enabled Internet of Things (CIoT) systems interconnect numerous devices to render robust data storage, efficient analysis, and message exchange features. However, it confronts the confidentiality of data from the owner as the access control of data is performed by honest-but-curious service providers. Although the encryption strategy ensures data security, it has adverse data deliverance

Software-Defined Networking (SDN) makes it easier to manage the network by separating the Data plane and Control plane, but these networks are susceptible to DDoS attacks. Most well-known algorithms for reducing the effects of DDoS attacks are based on SDN traffic analysis and prediction. Their main problems are false positive/negative results. Therefore, we propose PATGEN, a Protocol to reduce the

The education system is one of the mechanisms and aspiration to build the society and contribute to human capital, well-being, and wealth. Security and privacy concerns in the educational organization is always significant due to various violent and terrorist activities. Technologies have been adopted for smart learning systems to improve the learning experience whereas security has been neglected

Lithium-ion batteries (LIBs) are extensively used in many applications; from portable devices to major energy applications such as battery energy storage systems (BESSs). Their packs are usually equipped with accurate battery management systems (BMSs) to maintain the safe operation of the cells. To overcome the drawbacks of BMSs implemented with micro-controllers such as low reliability, low flexibility

Pulmonary Tuberculosis is a plague caused by Mycobacterium tuberculosis or Tubercle bacillus, which kills 1.8 million people worldwide. Tuberculosis is among the top 10 deadly diseases. It can be life-threatening if it does not diagnose at the initial stage. This study identifies Tuberculosis from chest X-ray images, utilizing image preprocessing techniques, deep learning methods and a publicly accessible

The main aim of the study is analyzing of pupils’ knowledge in mathematics by adaptive neuro fuzzy inference system (ANFIS) after implementation of distance learning application or e-learning (electronic learning). Since a large number of faculties and other institutions are increasingly using e-learning, it can be stated that for this purpose the Modular object-oriented dynamic learning environment

Assistive Technology (AT) allows individuals with disabilities to circumvent areas of weakness. Many AT tools have been developed for server-side environments with the assumption that students will have access to sufficiently fast internet when they need it in order to use the tools effectively. However, this is not always the case. In the current study, the impacts of client-side versus server-side

Identifying the core technique in the field of human-computer interaction is beneficial to improve international technical competitiveness of enterprise. By means of patent data of domestic and foreign smart phone, with the adoption of patent portfolio analysis model, it has exploration and research on the core competitiveness of human-computer interaction of smart phone from four aspects: enterprise

This paper proposes a novel framework to design efficient variable-latency speculative adders based on a method that mixes serial/parallel prefix structures. In comparison to the conventional speculative parallel prefix adders, the proposed method eliminates the dependency of error signals, and the corresponding late completion error correction. In comparison to conventional variable latency speculative

An One-Dimension (1D) Cellular Automaton (CA) is studied as a generator for the radix-2 sequence, presenting suitable characteristics for the design of a fast and constant delay synchronous digital counter. The main contribution of this work is the proposal of a new type of synchronous digital counters including a CA-based prescaler and consecutive counting modules of various configurations (in terms

The drastic impact of COVID-19 pandemic is visible in all aspects of our lives including education. With a distinctive rise in e-learning, teaching methods are being undertaken remotely on digital platforms due to COVID-19. To reduce the effect of this pandemic on the education sector, most of the educational institutions are already conducting online classes. However, to make these digital learning

Digital computation using multi-valued logic decreases the requirement of interconnections which leads to a reduction in the power consumption, energy consumption and chip area in digital system design. Among various sequential logic elements, a counter is the main block that is repeatedly used for counting purposes in several processor applications. This work presents the methodology to design asynchronous

Personalized modeling of Peking Opera faces is a challenging question in computer vision due to the artistry of painted faces, which is more challenging on natural human faces. To address this issue, in this paper, we propose a personalized modeling method for Peking Opera faces based on a depth camera device. Firstly, to solve the abnormal deformation of unrelated areas on 3D standard Peking Opera

Understanding the human spoken language recognition process is still a far scientific goal. Nowadays, commercial automatic speech recognisers (ASRs) achieve high performance at recognising clean speech, but their approaches are poorly related to human speech recognition. They commonly process the phonetic structure of speech while neglecting supra-segmental and syllabic tracts integral to human speech

The relevance of research in the field of diagnostics and forecasting the technical condition of critical production facilities at metallurgical works is substantiated. An approach to the analysis of time series of the main parameters of the critical production facilities at the metallurgical works based on a neural network forecasting of the state of diagnostic objects is proposed. The neural network