The Role of Cloud Computing in a Modern Reverse Engineering Workflow at the 5nm Node and Beyond

A modern reverse engineering (RE) workflow contains many challenges, especially as process nodes drop below the 5nm node. With increased complexity, more circuitry is packed into a smaller area, requiring large quantities of raw data collected and subsequently processed to help reconstruct the original schematics. By leveraging inexpensive cloud computing, orders of magnitude improvement in throughput were achieved for 2D image registration and high quality image segmentation was achieved using machine learning.

A High Fidelity Authentication Scheme for AMBTC Compressed Image Using Reference Table Encoding

In this paper, we propose a high-quality image authentication method based on absolute moment block truncation coding (AMBTC) compressed images. The existing AMBTC authentication methods may not be able to detect certain malicious tampering due to the way that the authentication codes are generated. In addition, these methods also suffer from their embedding technique, which limits the improvement of marked image quality. In our method, each block is classified as either a smooth block or a complex one based on its smoothness. To enhance the image quality, we toggle bits in bitmap of smooth block to generate a set of authentication codes. The pixel pair matching (PPM) technique is used to embed the code that causes the least error into the quantization values. To reduce the computation cost, we only use the original and flipped bitmaps to generate authentication codes for complex blocks, and select the one that causes the least error for embedment. The experimental results show that the proposed method not only obtains higher marked image quality but also achieves better detection performance compared with prior works.

GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit

3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines. Like many computer vision challenges, the 3D reconstruction task suffers from a lack of readily available training data in most domains, with methods typically depending on large datasets of high-quality image-model pairs. In this paper, we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images only exist in our source synthetic domain, and training is supplemented with different unlabelled datasets from the target real domain. We approach the problem of 3D reconstruction using volumetric regression and produce a training set of 25,000 pairs of images and volumes using hand-crafted 3D models of bananas rendered in a 3D modelling environment (Blender). Each image is then enhanced by a GAN to more closely match the domain of photographs of real images by introducing a volumetric consistency loss, improving performance of 3D reconstruction on real images. Our solution harnesses the cost benefits of synthetic data while still maintaining good performance on real world images. We focus this work on the task of 3D banana reconstruction from a single image, representing a common task in plant phenotyping, but this approach is general and may be adapted to any 3D reconstruction task including other plant species and organs.

Optimisasi Dosis dan Kualitas Citra CT-Scan untuk Variasi Pitch sebagai Upaya Proteksi Radiasi di Rumah Sakit Umum Pusat Adam Malik Medan

Optimization is an effort to ensure patient radiation safety and is the main action in overcoming concerns about CT-Scan radiation exposure. This led to the emergence of various measures to reduce the dose. This study aims to obtain a minimal dose with a high-quality image. Optimization efforts were carried out by the radiology team at Adam Malik Hospital Medan using a 16 slice GE CT-Scan and a water phantom with a diameter of 16 and 32 cm and an image quality questionnaire form. Collected data by observing the head, chest, and abdomen CT-Scan in adult patients (≥15 years). The data taken is the value of CTDI vol and DLP for a year. Then a water phantom scan was carried out with the head protocol using pitch parameters 0.562 and 0.938. The chest and abdomen use pitches of 1.375 and 1.75. The results obtained were evaluated and applied to patients, then filled in the image quality questionnaire scores. The results of CTDI_vol and DLP values with 16 and 32 cm water phantom scans showed a decrease in the dose value; for pitch 0.938, it was 1.6% lower than pitch 0.562, and pitch 1.75 was 1.2% lower, compared to pitch 1.375. For CT head examination using a pitch of 0.963, the CTDI_vol value was 1.5%, and DLP was 2%. For chest using a pitch of 1.75, CTDI_vol values were 1.3% and DLP 2%, while abdominal examination with a pitch of 1.75 obtained CTDI_vol values 1.8% and DLP 1.4%. From these three results, the CTDI_vol and DLP values were higher than the national DRL values. The value obtained is higher than the national DRL due to differences in the phantom test protocol with clinical implementation and the lack of accuracy in using other parameters. Changes in scan parameters are not comprehensive. Obtained a score of 3 in the questionnaire form stating that the radiology doctor can still interpret the image. This study concluded that it could make optimization efforts by changing the pitch parameter by paying attention to other parameters without reducing the quality of the image interpreted by the radiologist.

PARAMETRIC BLIND-DECONVOLUTION METHOD TO REMOVE IMAGE ARTIFACTS IN WAVEFRONT CODING IMAGING SYSTEMS

Wavefront coding technique includes a phase mask of asymmetric phase mask kind in the pupil plane to extend the depth of field of an imaging system and the digital processing step to obtain the restored final high-quality image. However, the main drawback of wavefront coding technique is image artifacts on the restored final images. In this paper, we proposed a parameter blind-deconvolution method based on maximizing of the variance of the histogram of restored final images that enables to obtain the restored final image with artifact-free over a large range of defocus.

A Compact High-Quality Image Demosaicking Neural Network for Edge-Computing Devices

Image demosaicking has been an essential and challenging problem among the most crucial steps of image processing behind image sensors. Due to the rapid development of intelligent processors based on deep learning, several demosaicking methods based on a convolutional neural network (CNN) have been proposed. However, it is difficult for their networks to run in real-time on edge computing devices with a large number of model parameters. This paper presents a compact demosaicking neural network based on the UNet++ structure. The network inserts densely connected layer blocks and adopts Gaussian smoothing layers instead of down-sampling operations before the backbone network. The densely connected blocks can extract mosaic image features efficiently by utilizing the correlation between feature maps. Furthermore, the block adopts depthwise separable convolutions to reduce the model parameters; the Gaussian smoothing layer can expand the receptive fields without down-sampling image size and discarding image information. The size constraints on the input and output images can also be relaxed, and the quality of demosaicked images is improved. Experiment results show that the proposed network can improve the running speed by 42% compared with the fastest CNN-based method and achieve comparable reconstruction quality as it on four mainstream datasets. Besides, when we carry out the inference processing on the demosaicked images on typical deep CNN networks, Mobilenet v1 and SSD, the accuracy can also achieve 85.83% (top 5) and 75.44% (mAP), which performs comparably to the existing methods. The proposed network has the highest computing efficiency and lowest parameter number through all methods, demonstrating that it is well suitable for applications on modern edge computing devices.

Slope Mapping using Unmanned Aerial Vehicle (UAV)

This paper discusses the applications of unmanned aerial vehicle (UAV) for slope mapping and also its important parameters including perimeter, area and also volume of certain selected area. With the development of modern technology, the utilization of UAV to gather data for slope mapping becoming easier as it is quick, reliable, precise, cost-effective and also easily to operate. Modern UAV able to take high quality image which essential for the effectiveness and nature of normal mapping output such as Digital Surface Model (DSM) and Digital Orthophoto. This photo captured by UAV will later transfer to commercial software to generate full map of study area. With the help of established software, the measurement of selected study areas can be determined easily which can be considered as the main interest in this study. In addition, another outcome of this study is, this modern method of mapping will be compare to traditional method of mapping which proven to be more effective in term of low costing, low time consuming, can gather huge amount of data within short period of time, low man power needed and almost no potential risk of hazardous effect to man.

Getting science priorities straight: how to increase the reliability of specimen identification?

‘We advise the authors to find a native English speaker to proofread the manuscript’. This is a standard feedback journals give to non-native English speakers. Journals are justifiably concerned with grammar but do not show the same rigour about another step crucial to biological research: specimen identification. Surveying the author guidelines of 100 journals, we found that only 6% of them request explicitly citation of the literature used in specimen identification. Authors hamper readers from contesting specimen identification whenever vouchers, identification methods, and taxon concepts are not provided. However, unclear taxonomic procedures violate the basic scientific principle of reproducibility. The scientific community must continuously look for practical alternatives to improve taxonomic identification and taxonomic verification. We argue that voucher pictures are an accessible, cheap and time-effective alternative to mitigate (not abolish) bad taxonomy by exposing preventable misidentifications. Voucher pictures allow scientists to judge specimen identification actively, based on available data. The popularization of high-quality image devices, photo-identification technologies and computer vision algorithms yield accurate scientific photo-documentation, improving taxonomic procedures. Taxonomy is timeless, transversal and essential to most scientific disciplines in biological sciences. It is time to demand rigour in taxonomic identifications.