MIRRE approach: nonlinear and multimodal exploration of MIR aggregated search results

Multimedia Tools and Applications - Nowadays, web users frequently explore multimedia contents to satisfy their information needs. The exploration approaches usually provide linear interaction...     

Enhanced halftone-based secure and improved visual cryptography scheme for colour/binary Images

Visual Cryptography (VC) is gaining attraction during the past few years to secure the visual information in the transmission network. It enables the visual data i.e. handwritten notes, photos, printed text, etc. to encrypt in such a way that their decryption can be done through the human visual framework. Hence, no computational assistance is required for the decryption of the secret images they can be seen through naked eye. In this paper, a novel enhanced halftoning-based VC scheme is proposed that works for both binary and color images. Fake share is generated by the combination of random black and white pixels. The proposed algorithm consists of 3 stages i.e., detection, encryption, and decryption. Halftoning, Encryption, (2, 2) visual cryptography and the novel idea of fake share, make it even more secure and improved. As a result, it facilitates the original restored image to the authentic user, however, the one who enters the wrong password gets the combination of fake share with any real share. Both colored and black images can be processed with minimal capacity using the proposed scheme.

     

Mixed-reality environment for frictional parameters identification in servo-pneumatic system

This paper outlines a method to identify the friction parameters for servo-pneumatic systems using a mixed-reality environment. To acquire system friction parameters accurately can be extremely difficult once the servo-system has been assembled because of its highly nonlinear nature, which causes a great difficulty in servo-pneumatic system modelling and control. In this research, a mixed-reality environment has been employed to determine the friction parameters effectively and efficiently through online identification. Traditionally, friction parameters identification can be performed manually or automatically using traditional optimization methods or modern ones such as neural networks. The advantages of the proposed method are the high accuracy in the estimated parameters, its simplicity and its speed. An experimental case study has been conducted and the results showed the accuracy and effectiveness of the proposed method.     

The Role of Deep Learning in Parking Space Identification andPredictionSystems

In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.     

Iterative methods for solving extended general mixed variational inequalities

In this paper, we introduce and consider a new class of mixed variational inequalities involving four operators, which are called extended general mixed variational inequalities. Using the resolvent operator technique, we establish the equivalence between the extended general mixed variational inequalities and fixed point problems as well as resolvent equations. We use this alternative equivalent formulation to suggest and analyze some iterative methods for solving general mixed variational inequalities. We study the convergence criteria for the suggested iterative methods under suitable conditions. Our methods of proof are very simple as compared with other techniques. The results proved in this paper may be viewed as refinements and important generalizations of the previous known results.     

Identifying plant species using mid-wave infrared (2.5–6 μm) and thermal infrared (8–14 μm) emissivity spectra

Plant species discrimination using remote sensing is generally limited by the similarity of their reflectance spectra in the visible, NIR and SWIR domains. Laboratory measured emissivity spectra in the mid infrared (MIR; 2.5 μm–6 μm) and the thermal infrared (TIR; 8 μm–14 μm) domain of different plant species, however, reveal significant differences. It is anticipated that with the advances in airborne and space borne hyperspectral thermal sensors, differentiation between plant species may improve. The laboratory emissivity spectra of thirteen common broad leaved species, comprising 3024 spectral bands in the MIR and TIR, were analyzed. For each wavelength the differences between the species were tested for significance using the one way analysis of variance (ANOVA) with the post-hoc Tukey HSD test. The emissivity spectra of the analyzed species were found to be statistically different at various wavebands. Subsequently, six spectral bands were selected (based on the histogram of separable pairs of species for each waveband) to quantify the separability between each species pair based on the Jefferies Matusita (JM) distance. Out of 78 combinations, 76 pairs had a significantly different JM distance. This means that careful selection of hyperspectral bands in the MIR and TIR (2.5 μm–14 μm) results in reliable species discrimination.     

Absorption and emission properties of Ho doped lead-zinc-borate glasses

This paper reports on the affect of lead content on the absorption and emission spectra of the Ho³⁺ ion doped lead-zinc-borate glasses in the composition (mol%) of (20 − x)PbO-20ZnO-(59 + x)B₂O₃-1.0Ho₂O₃ where x = 0, 5,10,15 of PbO content with λ_exc = 405 nm. The experimental absorption band energies have satisfactorily been correlated with the theoretical results with an r.m.s deviation of zero with the following correction factors obtained by a least square fit analysis: ΔE¹ = 348.495936 cm^− 1, ΔE² = 1.436043 cm^− 1, ΔE³ =  46.481575 cm^− 1, Δξ_4f = − 28.512979 cm^− 1, Δα = 55.508936 cm^− 1, Δβ = − 1394.339908 cm^− 1 and Δγ = 1208.424336 cm^− 1. By applying the Judd-Ofelt intensity parameter Ω₂ has been found to be linearly decreasing with the PbO content from 5 to 10 mol% and then increasing. And also radiative (A, A_T, β, τ_r) characteristic factors of the luminescent transitions (⁵I₈ ← ⁵F_3,4,5 and ⁵S₂) of the glasses have been evaluated. Stimulated emission cross-sections (σ_p^E) of the measured emission transitions of holmium glasses have also been computed.     

Quality of Service Networking for High Performance Grid Applications

This paper reports on different efforts to provide quality of service (QoS) Networking to Grid applications done in the context of the MB-NG, GRS and DataTAG EU projects. These are leading edge network research projects involving more that 50 researchers in the UK, Europe and North America, concerned with the development and testing of protocols and standards for the next generation of high speed networks. We have implemented and tested the Differentiated Services Architecture (DiffServ) in a multi-domain, 2.5 Gbits/s network (the first such deployment) defining appropriate Service Level Agreements (SLAs) to be used between administrative domains to guarantee end-to-end Quality of Service. We characterised several hardware implementations of DiffServ and concluded on their appropriateness for several network scenarios. Since current and future Grid applications will have to use modified mechanisms of congestion control we have evaluated old and new TCP implementations over a Differentiated Services Networks. These quality of service tests have also included innovative MPLS (Multi-Protocol Label Switching) experiments to establish guaranteed bandwidth connections to Grid applications in a fast and efficient way. We have also developed a software based bandwidth broker architecture for Grids based on IETF standards which allows applications to transparently request dynamic and advanced reservations and implemented it in a real experimental network. We finally report on experiences delivering Quality of Service networking to high performance applications like Particle Physics data transfer and High Performance Computation. This includes quantitative results on the performance improvements that QoS brought to real data transfers in the context of High Performance Computing.     

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.