The term photonic crystals appears because of the analogy between electron waves in crystals and the light waves in artificial periodic dielectric structures. During the recent years the investigation of one-, two-and three-dimensional periodic structures has attracted a widespread attention of the world optics community because of great potentiality of such structures in advanced applied optical fields. The interest in periodic structures has been stimulated by the fast development of semiconductor technology that now allows the fabrication of artificial structures, whose period is comparable with the wavelength of light in the visible and infrared ranges.

http://ieeexplore.ieee.org/iel5/6354/16969/00781867.pdf

Photonic crystals

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Advances in soft robotics, materials science, and stretchable electronics have enabled rapid progress in soft grippers. Here, a critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures. Soft gripping can be categorized into three technologies, enabling grasping by: a) actuation, b) controlled stiffness, and c) controlled adhesion. A comprehensive review of each type is presented. Compared to rigid grippers, end-effectors fabricated from flexible and soft components can often grasp or manipulate a larger variety of objects. Such grippers are an example of morphological computation, where control complexity is greatly reduced by material softness and mechanical compliance. Advanced materials and soft components, in particular silicone elastomers, shape memory materials, and active polymers and gels, are increasingly investigated for the design of lighter, simpler, and more universal grippers, using the inherent functionality of the materials. Embedding stretchable distributed sensors in or on soft grippers greatly enhances the ways in which the grippers interact with objects. Challenges for soft grippers include miniaturization, robustness, speed, integration of sensing, and control. Improved materials, processing methods, and sensing play an important role in future research.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.201707035

Soft Robotic Grippers.

Finite element methods for Maxwell's equations.

Driven by functionality and purity demand for applications of inorganic nanoparticle colloids in optics, biology, and energy, their surface chemistry has become a topic of intensive research interest. Consequently, ligand-free colloids are ideal reference materials for evaluating the effects of surface adsorbates from the initial state for application-oriented nanointegration purposes. After two decades of development, laser synthesis and processing of colloids (LSPC) has emerged as a convenient and scalable technique for the synthesis of ligand-free nanomaterials in sealed environments. In addition to the high-purity surface of LSPC-generated nanoparticles, other strengths of LSPC include its high throughput, convenience for preparing alloys or series of doped nanomaterials, and its continuous operation mode, suitable for downstream processing. Unscreened surface charge of LSPC-synthesized colloids is the key to achieving colloidal stability and high affinity to biomolecules as well as support materials,...

Laser Synthesis and Processing of Colloids: Fundamentals and Applications

Method for the production of high efficiency photocathodes for ultraviolet based on nanodiamonds, comprising providing a support (10; 20, 21) capable of conducting electrons, and producing a photosensitive layer of nanodiamonds (30) on the support (10; 20, 21). Production of the photosensitive layer includes providing nanodiamond particles in the form of powder, hydrogenating the nanoparticles in a H2 plasma, preparing a dispersion of the hydrogenated particles in a solvent, and spraying the dispersion onto the support and waiting for the solvent to evaporate from the support, the spray and waiting cycle being repeated several times in order to obtain a continuous photosensitive layer.

High-efficiency nanodiamond-based ultraviolet photocathodes

The proposed study deals with the design and the development of a Decision Support System (DSS) platform suitable for the global distribution system (GDS). Precisely, the prototype platform combines artificial intelligence and data mining algorithms to process data collected into a Cassandra Big Data system. In the first part of the paper platform architectures together with all the adopted frameworks including Key Performance Indicators (KPIs) definitions and risk mapping design have been discussed. In the second part data mining algorithms have been applied in order to predict main KPIs. The adopted artificial neural networks architectures are Long Short-Term Memory (LSTM), standard Recurrent Neural Network (RNN) and Gated Recurrent Units (GRU). A dataset with KPIs has been generated in order to test the algorithms. All performed algorithms show a good matching with the generated dataset, thus proving to be the correct approach to predict KPIs. The best performances in terms of Accuracy and Loss are reached by using the standard RNN. The proposed platform represents a solution to increase the Knowledge Base (KB) for a strategic marketing and advanced business intelligence operations.

/pdf/data-mining-applied-in-food-trade-network-2ry26wvb7e.pdf

Data Mining Applied in Food Trade Network

This work discusses smart building applications involving the Internet of Things (IoT) which are focused on energy consumption monitoring and forecasting systems, as well as indoor air quality (IAQ) control. Low-cost hardware integrating sensors and open source platforms are implemented for cloud data transmission, data storage and data processing. Advanced data analytics is performed by the seasonal autoregressive integrated moving average (SARIMA) method and a long short-term memory (LSTM) neural network with an accurate calculation performance about energy predictions. The proposed results are developed within the framework of the R&D project Data System Platform for Smart Communities (D-SySCOM), which is oriented to a smart public building application. The main goal of the work was to define a guideline-matching energy efficiency with wellness in public indoor environments, by providing modular low-cost solutions which are easily implementable for advanced data processing. The implemented technologies are suitable to define an efficient organizational user protocol based on energy efficiency and worker wellness. The estimated performance of mean square error (MSE) of 0.01 of the adopted algorithms proves the efficiency of the implemented building monitoring system in terms of energy consumption forecasting. In addition, the possibility of designing and implementing a modular low-cost hardware–software system was demonstrated utilizing open source tools in a way that was oriented to smart buildings approaches.

/pdf/advanced-data-systems-for-energy-consumption-optimization-27z364h0.pdf

Advanced Data Systems for Energy Consumption Optimization and Air Quality Control in Smart Public Buildings Using a Versatile Open Source Approach

In this paper is analyzed a case study of an industry project concerning irrigation decision support system (DSS) suitable for precision agriculture applications. In particular, a first prototype irrigation module has been developed by testing different components. The prototypal system concerns the irrigation management by reading field and weather values and, by enabling electrovalves through cloud control. A web panel will monitor in real time all sensors data, besides the DSS will activate or disactivate the irrigation pipelines. The irrigation decision is performed by comparing the measurements with pre-set threshold limits of sensor values and by analyzing predicted weather data. The paper describes in details the network design and implementation by discussing the sequence diagram describing the DSS data flow. Finally is proposed the DSS algorithm by discussing the DSS logic and its first implementation. The proposed DSS behaves as an engine processing simultaneously multiple parameters. The goal of the paper is to prove how potentially a microcontroller can perform a DSS which can be customized for different cultivations.

/pdf/a-precision-agriculture-dss-based-on-sensor-threshold-bgrp5bpfvd.pdf

A precision agriculture dss based on sensor threshold management for irrigation field

An effective method of managing water pipelines is essential for maximizing water efficiency and preserving water quality for both human and agricultural use. The aim of this study is to determine and test such a method for large-scale water piping networks intended for irrigation. Despite the considerable efforts being made worldwide to reduce the waste of potable water, nowadays there is still no widely used technique for this purpose. The most common methodologies for the detection of leaks use acoustic emission, gas detection, various types of sensors, such as hydraulic pressure and temperature. Unfortunately, these methods present critical issues and are difficult to apply on a large scale. This paper proposes an approach based on infrared thermography that can be associated with the use of a drone in order to reach and monitor large areas in a short time. A real time monitoring and decision support technological platform oriented towards water efficiency and the computation of hydrogeological risks has been implemented. In fact, the system integrates different detection technologies, as the Infrared Thermographic (IRT) technique is supported by the Ground Penetrating Radar (GPR) for the inspection of buried pipelines. Artificial Intelligence (AI) algorithms have been used for radiometric image processing with the aim of identifying water leaks even when these are latent. In particular, a Convolutional Neural Network (CNN) was implemented to identify leakages. As regards the calculation of hydrogeological risk, algorithms based on logical conditions have been implemented that allow to match the meteorological data and the hydrogeological risk maps of the places of interest, in order to predict the hydrogeological risk of the territory.

Alessandro Massaro

Papers

High-efficiency nanodiamond-based ultraviolet photocathodes

Data Mining Applied in Food Trade Network

Advanced Data Systems for Energy Consumption Optimization and Air Quality Control in Smart Public Buildings Using a Versatile Open Source Approach

A precision agriculture dss based on sensor threshold management for irrigation field

Technological Platform for Hydrogeological Risk Computation and Water Leakage Detection based on a Convolutional Neural Network