Integration Concepts for the Fabrication of LTCC Structures

At the Keck Smart Materials Integration Laboratory at Penn State University, low-temperature co-fired ceramic (LTCC) material systems have been used to fabricate a number of devices for a variety of applications. This article presents an overview of the integration of the concepts and materials that we have used to achieve miniaturization and unique device function. Examples of microwave filters, metamaterial antennas, and a dielectrophoretic cell sorter will be presented, with emphasis on device modeling and design, prototype construction methods, and test results.     

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...     

Cryptanalysis of an embedded systems’ image encryption

Multimedia Tools and Applications - Recently, a new image encryption-scheme for embedded systems based on continuous third-order hyperbolic sine chaotic system, has been proposed. The...     

Iris-based human identity recognition with machine learning methods and discrete fast Fourier transform

Innovations in Systems and Software Engineering - One of the most important modules of computer systems is the one that is responsible for user safety. It was proven that simple passwords and...     

Automatic Quality Inspection of Bakery Products Based on Shape and Color Information

基于形状和颜色信息的烘焙产品的自动质量检测

Babar Khan,韩芳*,王直杰,Ather Iqbal

（东华大学 信息科学与技术学院,上海 201620）

创新点说明：

利用支持向量机和基于生物启发式HMAX模型的形状辨识器与RGB对抗颜色通道辨识器集成而形成一种有效的智能分类系统,并将其用于烘焙产品的质量检测。

研究目的：

本文要对烘焙产品进行更有效的质量检测,需要提出一种智能方法。

研究方法：

本文采用了支持向量机、基于生物启发式HMAX模型的形状辨识器与RGB对抗颜色通道辨识器集成,形成一种高级自动分类系统,对烘焙产品根据其形状和颜色（烘焙时间）进行对应分类。

结果：

利用本文方法对烘焙产品进行质量检测的结果与其它方法相比较,发现本文提出的基于生物启发的机器视觉系统运行更加精确和有效,同时发现采用One Versus One支持向量机和Directed Acyclic Graph支持向量机可以得到最大的分类精确率。本文方法对饼干形状和颜色的辨识分别达到了95%和100%的分类精确率。同时,算法稳定。

结论：

本文方法因其更加可靠和精确可用于代替现有的烘焙产品检测方法。

关键词：烘焙产品质量检测,计算机视觉,HMAX, 对抗颜色通道,RGB颜色辨识器,支持向量机

     

Progress and challenges in sustainability,compatibility, and production of eco-composites: A state-of-art review

Owing to economic and environmental benefits, new generations of materials/commodities follow “from waste to wealth” strategy. Recently, there has been a huge upsurge in research on the development of eco-composites using recycled plastic polymers and agro-residues because the eco-composites satisfy the stringent environment regulations and are cost-effective. Herein, we present a detailed review on the potential use of several types of natural fillers as an efficient reinforcement for recycled plastic polymers. In particular, the characterization of different categories of eco-composites according to their morphological, physical, thermal, and mechanical properties is extensively reviewed and their results are analyzed, compared, and highlighted. Furthermore, a framework to produce functional eco-composites, which includes functionalization of ingredients, critical issues on microstructural parameters, processing, and fabrication methods, is outlined and supported with sufficient data from the literature. Finally, the review outlines the emerging challenges and future prospects of eco-composites to be addressed by interested researchers to bridge the gap between research and commercialization of such a class of material. Overall, the acquired knowledge will guide researchers, scientists, and manufacturers to plan, select, and develop various forms of eco-composites with enhanced properties and optimized production processes.     

Refrigerated cooling air cutting of difficult-to-cut materials

One approach to enhance machining performance is to apply cutting fluids during cutting process. However, the use of cutting fluids in machining process has caused some problems such as high cost, pollution, and hazards to operator's health. All the problems related to the use of cutting fluids have urged researchers to search for some alternatives to minimize or even avoid the use of cutting fluids in machining operations. Cooling gas cutting is one of these alternatives. This paper investigates the effect of cooling air cutting on tool wear, surface finish and chip shape in finish turning of Inconel 718 nickel-base super alloy and high-speed milling of AISI D2 cold work tool steel. Comparative experiments were conducted under different cooling/lubrication conditions, i.e. dry cutting, minimal quantity lubrication (MQL), cooling air, and cooling air and minimal quantity lubrication (CAMQL). For this research, composite refrigeration method was adopted to develop a new cooling gas equipment which was used to lower the temperature of compressed gas. The significant experimental results were: (i) application of cooing air and CAMQL resulted in drastic reduction in tool wear and surface roughness, and significant improvement in chip shape in finish turning of Inconel 718, (ii) in the high-speed milling of AISI D2, cooling air cutting presented longer tool life and slightly higher surface roughness than dry cutting and MQL. Therefore, it appears that cooling air cutting can provide not only environment friendliness but also great improvement in machinability of difficult-to-cut materials.     

Corrosion behavior of HVOF coated sheets

High velocity oxygen-fuel (HVOF) thermal spray coating finds application in industry due to its superior resistance to corrosion and thermal loading. In the HVOF process, the metallic powders at elevated temperature are sprayed at supersonic speed onto a substrate material. The powder granules sprayed impact onto each other, forming a mechanical bonding across the coating layer. In most of the cases, the distances among the particles (powder granules sprayed) are not the same, which in turn results in inhomogeneous structure across the coating layer. Moreover, the rate of oxidation of the powder granules during the spraying process varies. Consequently, the electrochemical response of the coating layer surfaces next to the base material and free to atmosphere differs. In the current study, the electrochemical response of a coating sheet formed during HVOF thermal spraying was investigated. NiCrMoNb alloy (similar to Inconel 625) wass used for the powder granules. Thermal spraying was carried out onto a smooth surface of stainless steel workpiece (without grid blasting), and later the coating layer was removed from the surface to obtain the coating sheet for the electrochemical tests. It was found that the corrosion rate of the smooth surface (surface next to the stainless steel surface before its removal) is considerably larger than that corresponding to the rough surface (free surface) of the coating sheet, and no specific patterns were observed for the pit sites.     

DCT statistics-based digital dropout detection in degraded archived media

With the rapid development of visual digital media, the demand for better quality of service has increased the pressure on broadcasters to automate their error detection and restoration activities for preserving their archives. Digital dropout is one of the defects that affect archived visual materials and tends to occur in block by block basis (size of 8 × 8). It is well established that human visual system (HVS) is highly adapted to the statistics of its visual natural environment. Consequently, in this paper, we have formulated digital dropout detection as a classification problem which predicts block label based on statistical features. These statistical features are indicative of perceptual quality relevant to human visual perception, and allow pristine images to be distinguished from distorted ones. Here, the idea is to extract discriminant block statistical features based on discrete cosine transform (DCT) coefficients and determine an optimal neighborhood sampling strategy to enhance the discrimination ability of block representation. Since this spatial frame based approach is free from any motion computation dependency, it works perfectly in the presence of fast moving objects. Experiments are performed on video archives to evaluate the efficacy of the proposed technique.     

Siamese multi-layer perceptrons for dimensionality reduction and face identification

This paper presents a framework using siamese Multi-layer Perceptrons (MLP) for supervised dimensionality reduction and face identification. Compared with the classical MLP that trains on fully labeled data, the siamese MLP learns on side information only, i.e., how similar of data examples are to each other. In this study, we compare it with the classical MLP on the problem of face identification. Experimental results on the Extended Yale B database demonstrate that the siamese MLP training with side information achieves comparable classification performance with the classical MLP training on fully labeled data. Besides, while the classical MLP fixes the dimension of the output space, the siamese MLP allows flexible output dimension, hence we also apply the siamese MLP for visualization of the dimensionality reduction to the 2-d and 3-d spaces.