Adhesion of wear resistant hard coatings to steel substrates

The scratch-adhesion test has been used to monitor the coating-to-substrate adhesion of hard coatings deposited onto steels by two different techniques. In the first instance TiN, TiC and Al₂O₃ coatings were deposited by chemical vapour deposition (CVD). With the CVD TiC and Al₂O₃ coatings, pre-critical stylus load flaking could be observed at the edges of the scratch channel. In contrast, TiN did not display such flaking. Energy dispersive X-ray analysis (EDXA) and Auger electron spectroscopy (AES) were used to investigate this behaviour. In the second example, the initial stage of a continuous plasma nitride-TiN magnetron deposition process was varied to optimize the adhesion of the subsequently deposited TiN topcoat. A suite of characterization techniques including hardness determination, AES, profilometry, and optical cross-sectioning was used to help explain the scratch-adhesion test results.     

Object detection using hybridization of static and dynamic feature spaces and its exploitation by ensemble classification

This paper presents a learning mechanism based on hybridization of static and dynamic learning. Realizing the detection performances offered by the state-of-the-art deep learning techniques and the competitive performances offered by the conventional static learning techniques, we propose the idea of exploitation of the concatenated (parallel) hybridization of the static and dynamic learning-based feature spaces. This is contrary to the cascaded (series) hybridization topology in which the initial feature space (provided by the conventional, static, and handcrafted feature extraction technique) is explored using deep, dynamic, and automated learning technique. Consequently, the characteristics already suppressed by the conventional representation cannot be explored by the dynamic learning technique. Instead, the proposed technique combines the conventional static and deep dynamic representation in concatenated (parallel) topology to generate an information-rich hybrid feature space. Thus, this hybrid feature space may aggregate the good characteristics of both conventional and deep representations, which are then explored using an appropriate classification technique. We also hypothesize that ensemble classification may better exploit this parallel hybrid perspective of the feature spaces. For this purpose, pyramid histogram of oriented gradients-based static learning has been incorporated in conjunction with convolution neural network-based deep learning to produce concatenated hybrid feature space. This hybrid space is then explored with various state-of-the-art ensemble classification techniques. We have considered the publicly available INRIA person and Caltech pedestrian standard image datasets to assess the performance of the proposed hybrid learning system. Furthermore, McNemar’s test has been used to statistically validate the outperformance of the proposed technique over various contemporary techniques. The validated experimental results show that the employment of the proposed hybrid representation results in effective detection performance (an AUC of 0.9996 for INRIA person and 0.9985 for Caltech pedestrian datasets) as compared to the individual static and dynamic representations.

     

Interconnecting networks with optimized service provisioning

Telecommunication Systems - A recent trend of peering at geo-diversified Internet exchange points (IXPs) has empowered decades-old proposal of inter-networking and opened up new avenues of business...     

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, and COVID-19) collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest (ROIs) and acquire 52 hybrid statistical features for each ROIs. Also, 12 optimized statistical features are selected via the chi-square feature reduction technique. For the classification, five machine learning classifiers named as deep learning J4, multilayer perceptron, support vector machine, random forest, and naive Bayes are deployed to optimize the hybrid statistical features dataset. It is observed that the deep learning J4 has promising results (sensitivity and specificity: 0.987; accuracy: 98.67%) among all the deployed classifiers. As a complementary study, a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.     

Design of a Compact Monopole Antenna for UWB Applications

In this paper, a low cost, highly efficient and low profile monopole antenna for ultra-wideband (UWB) applications is presented. A new inverted triangular-shape structure possessing meander lines is designed to achieve a wideband response and high efficiency. To design the proposed structure, three steps are utilized to achieve an UWB response. The bandwidth of the proposed antenna is improved with changing meander lines parameters, miniaturization of the ground width and optimization of the feeding line. The measured and simulated frequency band ranges from 3.2 to 12 GHz, while the radiation patterns are measured at 4, 5.3, 6 and 8 GHz frequency bands. The overall volume of the proposed antenna is 26 × 25 × 1.6 mm3 ; whereas the FR4 material is used as a substrate with a relative permittivity and loss tangent of 4.3 and 0.025, correspondingly. The peak gain of 4 dB is achieved with a radiation efficiency of 80 to 98% for the entire wideband. Design modelling of proposed antenna is performed in ANSYS HFSS 13 software. A decent consistency between the simulated and measured results is accomplished which shows that the proposed antenna is a potential candidate for the UWB applications.     

Physicochemical investigation of radiation‐grafted poly(acrylic acid)‐graft‐poly(tetrafluoroethylene–ethylene) copolymer membranes and their use in metal recovery from aqueous solution

ET‐g‐PAAc membranes were obtained by radiation grafting of acrylic acid onto poly(tetrafluoroethylene–ethylene) copolymer films using a mutual technique. The ion selectivity of the grafted membranes was determined toward K⁺, Ag⁺, Hg²⁺, Co²⁺, and Cu²⁺ in a mixed aqueous solution. The ion‐exchange capacity of the grafted membranes was measured by back titration and atomic absorption spectroscopy. The Hg²⁺ ion content of the membrane was more than that of either the K⁺ or Ag⁺ ions. The presence of metal ions in the membranes was studied by infrared and energy‐dispersive spectroscopy measurements. Scanning electron microscopy of the grafted and metal‐treated grafted membranes showed modification of the morphology of the surface due to the adsorption of K⁺ and Ag⁺ ions. No change was observed for the surface of the membrane that was treated with Hg²⁺ ions. The thermal stability of different membranes was improved more with Ag⁺ and Hg²⁺ ions than with K⁺ ions. It was found that the modified grafted membranes possessed good hydrophilicity, which may make them promising candidates for practical applications, such as for cation‐exchange membranes in the recovery of metals from an aqueous solution. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 2692–2698, 2002     

Modified stable Euler-number algorithm implementation for real-time image binarization

The stable Euler-number-based image binarization has been shown to give excellent visual results for images containing high amount of image noise. Being computationally expensive, its applications are limited mostly to general-purpose processors and in application specific integrated circuits. In this paper a modified stable Euler-number-based algorithm for image binarization is proposed and its real-time hardware implementation in a Field Programmable Gate Array with a pipelined architecture is presented. The proposed modifications to the algorithm facilitate hardware implementation. The end result is a design that out-performs known software implementations. The amount of noisy pixels introduced during the binarization process is also minimized. Despite the stable Euler-number-based image binarization being computationally expensive, our simulations show that the proposed architecture gives accurate results and this in real time and without consuming all chip resources.     

气提式三重循环生物膜反应器用于制药废水的处理

采用气提式三重循环生物膜反应器（TLABR）处理制药废水，稳定阶段对COD和NH4^＋-N的去除率分别为73％和70％。当温度为27～30℃、pH=8．5时，反应器内形成了稳定的NO2^--N积累。同时，考察了不同HRT下高效反应器的处理效果，探讨了pH对反应器内发生的短程硝化效果的影响以及亚硝化菌对游离氨浓度的适应性。结果表明：在HRT为3、6、9h下，对COD和氨氮的去除率分别为65．3％、73．9％、75．1％和48．9％、68．4％、72．8％；NO2^--N积累率分别为92．7％、76．2％、69．7％；反应器出水水质稳定，体现了高效性和抗冲击负荷能力。