APPLICATION OF HOUSE OF QUALITY IN THE CONCEPTUAL DESIGN OF BATIK WAX EXTRUDER AND PRINTER

Malaysian batik production is dominated by two techniques known as hand-drawn batik, or batik tjanting, and stamp batik, or batik block. In comparison to batik block, the more popular batik tjanting takes a longer time to produce. A Standardized Nordic Questionnaire (SNQ) for musculoskeletal symptom examination involving batik artisans in Kelantan and Terengganu identified high rates of musculoskeletal disorders in respondents due to their working posture during the batik tjanting process. It was also observed that the number of workers and artisans willing to participate in the traditional batik industry is on the decline. These problems have led to a systematic Quality Functional Deployment approach to facilitate the decision-making process for the conceptual design of an automatic batik printer. In this study, house of quality (HOQ) was applied to identify the critical features for a batik printer based on the voice of the customer (VOC). A survey done to rate the importance of VOC using an 8-point Likert scale revealed that the batik practitioners topmost priority for the batik printer feature is the 'ability to adjust and maintain the temperature of wax' (17.54%) while the non-batik practitioners chose 'ability to deliver a variety of complex designs' (15.94%). The least required feature for the batik printer was related to the size of the batik printer. The mapping between customer requirements (VOC) and technical requirements identified that the extruder design (21.3%), the heating element (18%), and nozzle diameter (17.8%) were the most critical components for the batik printer. Several conceptual designs of the extrusion unit, cartesian-based batik printer, and 2D image conversion using open-sourced software were proposed at the end of this work. ABSTRAK: Pengeluaran batik Malaysia telah didominasi oleh dua teknik yang dikenali sebagai batik lukisan-tangan (batik canting) dan batik cap (batik blok). Sebagai perbandingan, batik canting yang popular mengambil masa lebih lama bagi dihasilkan. Soal Selidik Nordic Standad (SNQ) bagi meneliti gejala muskuloskeletal melibatkan tukang batik di Kelantan dan Terengganu telah menunjukkan persamaan kadar muskuloskeletal yang tinggi pada postur badan semasa bekerja canting batik. Bilangan pekerja yang terlibat dalam industri tradisional batik ini turut terjejas. Masalah-masalah ini telah mengarah kepada kaedah Pengerahan Fungsi Kualiti bagi membantu proses membuat keputusan dalam rekaan konsep pencetak batik automatik. Kajian ini telah mengadaptasi Kualiti Rumah (HOQ) bagi mengesan ciri-ciri kritikal pada pencetak batik berdasarkan suara pelanggan (VOC). Kaji selidik telah dilakukan bagi menilai kepentingan VOC menggunakan skala Likert 8-poin. Didapati keutamaan yang diperlukan oleh 17.54% ahli batik adalah; ciri pencetak batik ini perlu mempunyai ‘keupayaan dalam menyelaras dan menetapkan suhu lilin’, manakala sebanyak 15.94% bukan ahli batik memilih ‘keupayaan pencetak ini harus berjaya menghasilkan pelbagai rekaan yang kompleks’. Ciri yang kurang diberi tumpuan adalah berkaitan saiz pencetak batik. Persamaan antara kehendak pelanggan (VOC) dan kehendak teknikal dalam mengenal pasti komponen-komponen penting bagi pencetak batik adalah rekaan penyemperit (21.3%), elemen pemanas (18%), dan diameter nozel (17.8%). Pelbagai rekaan konsep bagi unit penyemperit, pencetak batik canting, dan imej konversi 2D menggunakan perisian sumber terbuka telah dicadangkan di bahagian akhir kajian ini.

Segmentation of Tomato Growing Truss a Depth Image Conversion Model Based on CycleGAN

Background: The truss on tomato plants is a group or cluster of smaller stems where flowers and fruit develop, while a growing truss is the most extended part of the stem. Because the state of the growing truss reacts sensitively to the surrounding environment, it is essential to control the growth in the early stages. With the recent development of IT and artificial intelligence technology in agriculture, a previous study developed a real-time acquisition and evaluation method for images using robots. Further, we used image processing to locate the growing truss and flowering rooms to extract growth information such as the height of the flower room and hard crab. Among the different vision algorithms, the CycleGAN algorithm was used to generate and transform unpaired images using generatively learning images. In this study, we developed a robot-based system for simultaneously acquiring RGB and depth images of the tomato growing truss and flower room groups.Results: The segmentation performance for approximately 35 samples was compared through the false negative (FN) and false positive (FP) indicators. For the depth camera image, we obtained FN as 17.55±3.01% and FP as 17.76±3.55%. Similarly, for CycleGAN, we obtained FN as approximately 19.24±1.45% and FP as 18.24±1.54%. As a result of image processing through depth image, IoU was 63.56 ± 8.44%, and when segmentation was performed through CycelGAN, IoU was 69.25 ± 4.42%, indicating that CycleGAN is advantageous in extracting the desired growing truss. Conclusions: The scannability was confirmed when the image scanning robot drove in a straight line through the plantation in the tomato greenhouse, which confirmed the on-site possibility of the image extraction technique using CycleGAN. In the future, the proposed approach is expected to be used in vision technology to scan the tomato growth indicators in greenhouses using an unmanned robot platform.

AutoMicroED: A semi-automated MicroED processing pipeline

Despite rapid adaptation of micro-electron diffraction (MicroED) for protein and small molecule structure determination to sub-angstrom resolution, the lack of automation tools for easy MicroED data processing remains a challenge for expanding to the broader scientific community. In particular, automation tools, which are novice user friendly, compatible with heterogenous datasets and can be run in unison with data collection to judge the quality of incoming data (similar to cryosparc LIVE for single particle cryoEM) do not exist. Here, we present AutoMicroED, a cohesive and semi-automatic MicroED data processing pipeline that runs through image conversion, indexing, integration and scaling of data, followed by merging of successful datasets that are pushed through phasing and final structure determination. AutoMicroED is compatible with both small molecule and protein datasets and creates a straightforward and reproducible method to solve single structures from pure samples, or multiple structures from mixed populations. The immediate feedback on data quality, data completeness and more parameters, aids users to identify whether they have collected enough data for their needs. Overall, AutoMicroED permits efficient structure elucidation for both novice and experienced users with comparable results to more laborious manual processing.

IMAGE CONVERSION OF ϭ- AND π-COMPONENTS OF RADIATION OF RELATIVISTIC ELECTRON AND METROLOGICAL CHARACTERISTICS OF THE PHOTON FLUX IN THE SYNCHROTRON RADIATION OUTLET

The paper deals with the efficiency of the capture of a photon flux of the synchrotron radiation (SR) σ- and π-components by the optical window in the SR quantum extraction channel of the NESTOR generator. It also anal-yses the dependence between the capture quality and different radiation wavelengths. Consideration has been giv-en to the beam size effect on the shape and dimensions of the angular distribution of the photon flux. A model has been constructed to describe the optical imaging in the registration plane. Expressions are given for estimating the efficiency of the capture of SR quanta into the optical window of the extraction channel. The factors that exert influence on the efficiency of capturing through the window are analyzed. Examples of numerical calculations are provided for formation of the final SR spectral density of the 225 MeV relativistic electrons at the output of the optical channel. The dimensions of the optical window have been determined, which ensure the reliable registration of the total flux of SR quanta for the chosen spectral range of SR quanta wavelengths.

Detection Method for Classifying Malicious Firmware

A malicious firmware update may prove devastating to the embedded devices both that make up the Internet of Things (IoT) and that typically lack the same security verifications now applied to full operating systems. This work converts the binary headers of 40,000 firmware examples from bytes into 1024-pixel thumbnail images to train a deep neural network. The aim is to distinguish benign and malicious variants using modern deep learning methods without needing detailed functional or forensic analysis tools. One outcome of this image conversion enables contact with the vast machine learning literature already applied to handle digit recognition (MNIST). Another result indicates that greater than 90% accurate classifications prove possible using image-based convolutional neural networks (CNN) when combined with transfer learning methods. The envisioned CNN application would intercept firmware updates before their distribution to IoT networks and score their likelihood of containing malicious variants. To explain how the model makes classification decisions, the research applies traditional statistical methods such as both single and ensembles of decision trees with identifiable pixel or byte values that contribute the malicious or benign determination.

GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement

Traditional time-series clustering methods usually perform poorly on high-dimensional data. However, image clustering using deep learning methods can complete image annotation and searches in large image databases well. Therefore, this study aimed to propose a deep clustering model named GW_DC to convert one-dimensional time-series into two-dimensional images and improve cluster performance for algorithm users. The proposed GW_DC consisted of three processing stages: the image conversion stage, image enhancement stage, and image clustering stage. In the image conversion stage, the time series were converted into four kinds of two-dimensional images by different algorithms, including grayscale images, recurrence plot images, Markov transition field images, and Gramian Angular Difference Field images; this last one was considered to be the best by comparison. In the image enhancement stage, the signal components of two-dimensional images were extracted and processed by wavelet transform to denoise and enhance texture features. Meanwhile, a deep clustering network, combining convolutional neural networks with K-Means, was designed for well-learning characteristics and clustering according to the aforementioned enhanced images. Finally, six UCR datasets were adopted to assess the performance of models. The results showed that the proposed GW_DC model provided better results.

Developing an engaging whiteboard animation video for vitamins

Whiteboard animation videos are engaging learning media for Generation Z. This study aimed to develop and assess the feasibility of a whiteboard animation video for vitamin in the Nutrition Science subject for class X culinary vocational school. This study was research and development using a 4D approach (define, design, develop, disseminate). The define stage analyzed the curriculum, material, student character, learning media, and school facilities. The design stage included the writing and assessing the material, the writing and assessing of storyboards, and production team selection. The develop stage was making a video and assessing the feasibility. Animation images were created using Paint Tool Sai. Image conversion from JPEG to SVG was performed with Inkscape. The dubber’s voice was recorded with Universal Audio using the Apollo Twin audio interface and Avantone CV12 microphone. The audio was edited in Pro Tools 2020.9. To produce a whiteboard animation video, animation images, captions, dubber’s voice, and back sound were combined with VideoScribe. Videos used mpg format. In order to maintain video duration below 10 minutes, the video was divided into two parts. The assessment of video feasibility was carried out by one media expert and two content experts. The assessment of feasibility at the disseminate stage was carried out to 30 users. Based on the feasibility assessment, this video was very suitable to be applied as a learning medium.

An Effective Denoising and Enhancement Strategy for Medical Image Using Rl-Gl-Caputo Method

At present, fractional differential is the effective mathematical approach which deals with the factual problems. This projected technique employs the fractional derivatives definitions Riemann-Liouville (R-L), Grunwald-Letnikov (G-L) and the caputo technique for denoising medical image. The presented method based on fractional derivative which in turn improves the quality of image. The input image is processed on integer order method such as pre-processing operation, image conversion and noise image. The fractional differential mask method is to be applied with the help of Riemann Liouville, and Caputo algorithm. After denoising the medical image enhanced using Anisotropic diffusion process and the result is analyzed to finally get denoised and predicted image.