Investigating the Possibility of Determining the Sequence Intersecting Lines between LaserJet Printing and Handwriting in Document Examination using Color Measurement Technique

In Questioned Documents Examination, the sequence of crossing lines in the intersection of handwriting and printed area can be important clues for detecting tampered documents. Recognition of such documents is a arduous task and requires people with experience and expertise. In the present work, we investigated the possibility of determining the sequence of intersecting lines between LaserJet printing and handwriting for a series of simulated laboratory specimens in the document examination using color measurement technique. The spectral reflectance curves and color coordinates of some points on and out of the cross lines were compared. Four different commercial ballpoint pens and a black toner LaserJet were used to prepare the specimens. The color change of the intersecting lines was subjectively considered through the captured images and a visual assessment process. It was also objectively determined by determining the color difference values from the colorimetric data in CIELAB and CIELCH color spaces in the visible range. The color change evaluation showed that the order in which printing or handwriting is applied alters colorimetric results. Moreover, the investigations showed small color difference values of less than 2 units between a point of printed area individually, and intersection could be applied as a tolerance limit for pass/fail judgments.

Digital Textile Ink-Jet Printing Innovation: Development and Evaluation of Digital Denim Technology

This research explored the potential for ink-jet printing to replicate the coloration and finishing techniques of traditional denim fabric and standardized the reproduction and evaluation procedure. Although denim fabric is widely consumed and very popular, one drawback to denim is that the finishing and manufacturing processes are energy and water intensive and can cause environmental hazards as well as generation of pollution through water waste, particularly at the finishing stage. Textile ink-jet printing has the potential to replicate some of the coloration and finishing techniques of traditional denim fabric without negative environmental impacts. A two-phase research project was conducted. In Phase I (P1), an optimal standard production workflow for digital denim reproduction (including color and finishing effects) was established, and six different denim samples were reproduced based on the workflow. In Phase II, an expert visual assessment protocol was developed to evaluate the acceptance of the replicated digital denim. Twelve ink-jet printing, color science, and denim industry experts finished the assessment.

Improved Identification of Portraiture of the Julio-Claudian Period with Mobile Apps

We present the results of our image analysis of portrait art from the Roman Empire’s Julio-Claudian dynastic period. Our novel approach involves processing pictures of ancient statues, cameos, altar friezes, bas-reliefs, frescoes, and coins using modern mobile apps, such as Reface and FaceApp, to improve identification of the historical subjects depicted. In particular, we have discovered that the Reface app has limited, but useful capability to restore the approximate appearance of damaged noses of the statues. We confirm many traditional identifications, propose a few identification corrections for items located in museums and private collections around the world, and discuss the advantages and limitations of our approach. For example, Reface may make aquiline noses appear wider or shorter than they should be. This deficiency can be partially corrected if multiple views are available. We demonstrate that our approach can be extended to analyze portraiture from other cultures and historical periods. The article is intended for a broad section of the readers interested in how the modern AI-based solutions for mobile imaging merge with humanities to help improve our understanding of the modern civilization’s ancient past and increase appreciation of our diverse cultural heritage.

On-demand Electrohydrodynamic Jetting of an Ethylene Glycol and Water Mixture—System of Controlled Picoliter Fluid Deposition

On-demand electrohydrodynamic jetting also called electrohydrodynamic atomization (EHDA) is a method to jet small amounts of fluid out of a nozzle with a relatively large diameter by switching on and off an electrical field between the nozzle and the substrate. The total amount of volume deposited is up to 5 pL. The set-up consists of a vertically placed glass pipette with a small nozzle directed downward and a flat substrate placed close to the end of the nozzle. Inside the pipette, an electrode is mounted close to the entrance of the nozzle. The electrode is connected to a high voltage power amplifier. Upon switching on the electrical field, the apparent surface tension drops, the meniscus deforms into a cone and fluid starts to flow toward the nozzle deforming the meniscus. At a certain moment the cone reaches the Taylor cone dimensions and from its tip a jet emerges that decomposes into a stream of charged fL droplets that fly toward the substrate. This process stops when the pulse is switched off. After switching off, the meniscus returns slowly to its equilibrium position. The process is controlled by different time constants, such as the slew rate of the power amplifier and the RC time of the electrical circuit composed of the electrical resistance in the fluid contained in the nozzle between the electrode and the meniscus, and the capacitance of the gap between the meniscus and the flat substrate. Another time constant deals with the fluid flow during the growth of the meniscus, directly after switching on the pulse. This fluid flow is driven by hydrostatic pressure and opposed by a viscous drag in the nozzle. The final fluid flow during droplet formation is governed by the balance between the drag of the charge carriers inside the fluid, caused by the current associated with the charged droplets leaving the meniscus and the viscous drag. These different phenomena will be discussed theoretically and compared to experimental results.

DCNN-based Ship Classification using Enhanced Edge Information and Inception Module

The excellent feature extraction ability of deep convolutional neural networks (DCNNs) has been demonstrated in many image processing tasks, by which image classification can achieve high accuracy with only raw input images. However, the specific image features that influence the classification results are not readily determinable and what lies behind the predictions is unclear. This study proposes a method combining the Sobel and Canny operators and an Inception module for ship classification. The Sobel and Canny operators obtain enhanced edge features from the input images. A convolutional layer is replaced with the Inception module, which can automatically select the proper convolution kernel for ship objects in different image regions. The principle is that the high-level features abstracted by the DCNN, and the features obtained by multi-convolution concatenation of the Inception module must ultimately derive from the edge information of the preprocessing input images. This indicates that the classification results are based on the input edge features, which indirectly interpret the classification results to some extent. Experimental results show that the combination of the edge features and the Inception module improves DCNN ship classification performance. The original model with the raw dataset has an average accuracy of 88.72%, while when using enhanced edge features as input, it achieves the best performance of 90.54% among all models. The model that replaces the fifth convolutional layer with the Inception module has the best performance of 89.50%. It performs close to VGG-16 on the raw dataset and is significantly better than other deep neural networks. The results validate the functionality and feasibility of the idea posited.

Preparation and Properties of Waterborne Varnish for On-Demand Inkjet Printing

A kind of waterborne varnish for inkjet printing was synthesized, and properties of the waterborne varnish were characterized to make it suitable for the glazing requirements of inkjet printing and other printability requirements. The waterborne varnish was synthesized from epoxy resin (E-51), epoxy diluent (ED), acrylic acid, trimellitic anhydride, maleic anhydride and organic amine by three steps of ring opening reaction, esterification reaction, neutralization reaction. The viscosity, film-forming property, water absorption of waterborne varnish and the water resistance, lightness, wear resistance and bonding strength of coated paper were tested. The effects of the ratio of E-51 and ED in the polymerization system on the properties of waterborne varnish were studied. In the test of printability of self-made waterborne varnish, the absorption of digital inkjet paper to varnish and the influence of varnish on color reproduction of printed image were discussed and studied. The results show that when the molar ratio of epoxy group in E-51 and ED is about 1:1, the prepared varnish is suitable for inkjet printing, and its film-forming property, such as water resistance, adhesion and friction resistance are better. At the same time, its printability is also better.

Study on Rapid Archival Technology of Bullets Based on Graph Convolutional Neural Network

Traditional gun archiving methods are mostly carried out through bullets’ physics or photography, which are inefficient and difficult to trace, and cannot meet the needs of large-scale archiving. Aiming at such problems, a rapid archival technology of bullets based on graph convolutional neural network has been studied and developed. First, the spot laser is used to take the circle points of the bullet rifling traces. The obtained data is filtered and noise-reduced to make the corresponding line graph, and then the dynamic time warping (DTW) algorithm convolutional neural network model is used to perform the processing on the processed data. Not only is similarity matched, the rapid matching of the rifling of the bullet is also accomplished. Comparison of experimental results shows that this technology has the advantages of rapid archiving and high accuracy. Furthermore, it can be carried out in large numbers at the same time, and is more suitable for practical promotion and application.

Study on Portrait Tracking Technology of Deep Feature Learning in Monitoring Image Acquisition

In a network information society, there are many occasions where people’s behaviors need to be tracked, photographed, and recognized. Biometric recognition technologies are considered to be one of the most effective solutions. Traditional methods mostly use graph structure and deformed component model to design two-dimensional (2D) human body component detectors, and apply graph models to establish the connectivity of each component. The recognition design process is simple, but the accuracy of recognition and tracking effect applied in monitoring image acquisition is not high. The improved particle swarm optimization algorithm is used to determine the particle structure, and the binary bit string is used to represent the particle structure. The support vector machine (SVM) parameters of discrete particles are optimized, and the synchronous optimization design of feature selection and SVM parameters is carried out to realize the synchronous optimization of portrait feature subset and SVM parameters in discrete space. Through in-depth research, the extracted feature subsets can be effectively optimized and selected, and the parameters of SVM model can be optimized synchronously. The discrete particle structure is associated with the SVM parameters to achieve feature selection and SVM parameter synchronization and optimization. It is not only superior to traditional algorithms in terms of recognition rate, but also reduces the feature dimension and shortens the recognition time. The deep feature recognition built on the learning machine is not easy to diverge and can effectively adjust the particle speed to the global optimal, which is more effective than the particle swarm algorithm to search for the global optimal solution, and has better robustness. In the experiments, the research content of the article is compared with the traditional methods to test and analysis. The results show that the method optimizes the selection of feature subset and eliminates a large number of invalid features. The method not only reduces space complexity and shortens recognition time, but also improves recognition rate. The dimension of feature subset dimensions are superior to those extracted by other algorithms.