Efficient Registration Research Articles

Point cloud registration method for maize plants based on conical surface fitting\u2014ICP

Reconstructing three-dimensional (3D) point cloud model of maize plants can provide reliable data for its growth observation and agricultural machinery research. The existing data collection systems and registration methods have low collection efficiency and poor registration accuracy. A point cloud registration method for maize plants based on conical surface fitting—iterative closest point (ICP) with automatic point cloud collection platform was proposed in this paper. Firstly, a Kinect V2 was selected to cooperate with an automatic point cloud collection platform to collect multi-angle point clouds. Then, the conical surface fitting algorithm was employed to fit the point clouds of the flowerpot wall to acquire the fitted rotation axis for coarse registration. Finally, the interval ICP registration algorithm was used for precise registration, and the Delaunay triangle meshing algorithm was chosen to triangulate the point clouds of maize plants. The maize plant at the flowering and kernel stage was selected for reconstruction experiments, the results show that: the full-angle registration takes 57.32 s, and the registration mean distance error is 1.98 mm. The measured value’s relative errors between the reconstructed model and the material object of maize plant are controlled within 5%, the reconstructed model can replace maize plants for research.

A Secure and Efficient Cluster-Based Authentication Scheme for Internet of Things (IoTs)

IPv6 over Low Power Wireless Personal Area Network (6LoWPAN) provides IP connectivity to the highly constrained nodes in the Internet of Things (IoTs). 6LoWPAN allows nodes with limited battery power and storage capacity to carry IPv6 datagrams over the lossy and error-prone radio links offered by the IEEE 802.15.4 standard, thus acting as an adoption layer between the IPv6 protocol and IEEE 802.15.4 network. The data link layer of IEEE 802.15.4 in 6LoWPAN is based on AES (Advanced Encryption Standard), but the 6LoWPAN standard lacks and has omitted the security and privacy requirements at higher layers. The sensor nodes in 6LoWPAN can join the network without requiring the authentication procedure. Therefore, from security perspectives, 6LoWPAN is vulnerable to many attacks such as replay attack, Man-in-the-Middle attack, Impersonation attack, and Modification attack. This paper proposes a secure and efficient cluster-based authentication scheme (CBAS) for highly constrained sensor nodes in 6LoWPAN. In this approach, sensor nodes are organized into a cluster and communicate with the central network through a dedicated sensor node. The main objective of CBAS is to provide efficient and authentic communication among the 6LoWPAN nodes. To ensure the low signaling overhead during the registration, authentication, and handover procedures, we also introduce lightweight and efficient registration, de-registration, initial authentication, and handover procedures, when a sensor node or group of sensor nodes join or leave a cluster. Our security analysis shows that the proposed CBAS approach protects against various security attacks, including Identity Confidentiality attack, Modification attack, Replay attack, Man-in-the-middle attack, and Impersonation attack. Our simulation experiments show that CBAS has reduced the registration delay by 11%, handoff authentication delay by 32%, and signaling cost by 37% compared to the SGMS (Secure Group Mobility Scheme) and LAMS (Light-Wight Authentication & Mobility Scheme).

Secured Vehicle Life Cycle Tracking Using Blockchain and Smart Contract

Life Cycle Tracking (LCT) involves continuous monitoring and analysis of various activities associated with a vehicle. The crucial factor in the LCT is to ensure the validity of gathered data as numerous supply chain phases are involved and the data is assessed by multiple stakeholders. Frauds and swindling activities can be prevented if the history of the vehicles is made available to the interested parties. Blockchain provides a way of enforcing trustworthiness to the supply chain participants and the data associated with the various actions performed. Machine learning techniques when combined decentralized nature of blockchains can be used to develop a robust Vehicle LCT model. In the proposed work, Harmonic Optimized Gradient Descent and Łukasiewicz Fuzzy (HOGD-LF) Vehicle Life Cycle Tracking in Cloud Environment is proposed and it involves three stages. First, the Progressive Harmonic Optimized User Registration and Authentication model is designed for computationally efficient registration and authentication. Next, for the authentic user, the Gradient Descent Blockchain-based SVM Data Encryption model is designed with minimum CPU utilization. Finally, Łukasiewicz Fuzzy Smart Contract Verification is performed with encrypted data to ensure accurate and precise fraudulent activity deduction. The experimental analysis shows that the proposed method achieves significant performance in terms of life cycle’s prediction time, overhead, and accuracy for a different number of users.

Rationale for the Choice of the Ellipsoidal Reflector Parameters for Biomedical Photometers

Biomedical photometersʼ information-measuring systems with ellipsoidal reflectors have acceptable results in determining of biological tissues optical properties in the visible and near-infrared spectral range. These photometers make it possible to study the optical radiation propagation in turbid media for direct and inverse problems of light-scattering optics. The purpose of this work is to study the influence of the ellipsoidal reflectors design parameters on the results of biomedical photometry when simulating the optical radiation propagation in a system of biological tissue and reflectors in transmitted and reflected light.The paper substantiates the choice of the ellipsoidal reflectors’ focal parameter for efficient registration of forward and backscattered light. The methodology of the process is illustrated by the results of a model experiment using the Monte Carlo simulation for samples of human brain white and gray matter at the visible range of 405 nm, 532 nm, and 650 nm. The total transmittance, diffuse reflectance, and absorption graphs depending on the sample thickness were obtained. Based on the introduced concepts of the ellipsoidal reflector efficiency index and its efficiency factor, the expediency of choosing the ellipsoidal reflectors focal parameter is analyzed to ensure the registration of the maximum amount of scattered light. The graphs of efficiency index in reflected and transmitted light for different thickness samples of white and gray matter and efficiency factors depending on the sample thickness were obtained.The influence of the reflectors ellipticity on the illuminance of various zones of photometric images using the example of an absorbing biological medium – pig liver tissue – at wavelength of 405 nm with a Monte Carlo simulation was analyzed.The optical properties of biological media (scattering and absorption coefficients, scattering anisotropy factor, refractive index) and the samples’ geometric dimensions, particularly the thickness, are predetermined when choosing the ellipsoidal reflectors parameters for registration of the scattered light. Coordinates of the output of photons and their statistical weight obtained in the Monte Carlo simulation of light propagation in biological tissue have a physical effect on a characteristic scattering spot formation in the receiving plane of a biomedical photometer with ellipsoidal reflectors.

Адаптированный метод расчета функции радиационного отклика при создании высокочувствительных образцовых средств измерений на основе сцинтилляционных блоков детектирования с NaI(Tl) кристаллами для дозиметрии малоинтенсивных и околофоновых уровней

Metrological support of photon radiation fields of low-intensity and near-background levels (0.04–100 μSv/h (μGy/h)) by ambient equivalent dose rate or kerma rate in air using scintillation detectors with NaI(Tl) crystals looks promising and in demand in dosimetry, but nontrivial due to the complex dependence of efficiency registration of gamma quanta from energy. The solution of such problems with the use of these detectors can be based on the use of the radiation response functions, which are functionals of the energy distribution of the radiation field fluence.
 The paper proposes a method for calculating the radiation response function adapted for solving metrological support problems for creating high-precision dosimetric measuring instruments based on scintillation detection units with NaI(Tl) crystals.

GACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene

Point cloud registration is the foundation and key step for many vital applications, such as digital city, autonomous driving, passive positioning, and navigation. The difference of spatial objects and the structure complexity of object surfaces are the main challenges for the registration problem. In this paper, we propose a graph attention capsule model (named as GACM) for the efficient registration of terrestrial laser scanning (TLS) point cloud in the urban scene, which fuses graph attention convolution and a three-dimensional (3D) capsule network to extract local point cloud features and obtain 3D feature descriptors. These descriptors can take into account the differences of spatial structure and point density in objects and make the spatial features of ground objects more prominent. During the training progress, we used both matched points and non-matched points to train the model. In the test process of the registration, the points in the neighborhood of each keypoint were sent to the trained network, in order to obtain feature descriptors and calculate the rotation and translation matrix after constructing a K-dimensional (KD) tree and random sample consensus (RANSAC) algorithm. Experiments show that the proposed method achieves more efficient registration results and higher robustness than other frontier registration methods in the pairwise registration of point clouds.

Conceptual design report of the MPD Cosmic Ray Detector (MCORD)

This report presents a concept of constructing a detector dedicated for detection of muons observed during measurements carried out at the MPD (Multi-Purpose Detector) detector that is currently under construction at the NICA facility, Russia, Dubna. It has been proposed to design and build an additional detector that will complement the current MPD set and increase its measurement capabilities. The main goal of this project is to provide information from cosmic muons that pass the MPD detector in both in-beam and off-beam experiments. Hence, the detector is called the MPD COsmic Ray Detector (MCORD).The conceptual design of MCORD is proposed by a Polish consortium NICA-PL comprising several Polish scientific institutions.The data from cosmic ray muons could be used as a trigger for calibration of other detection systems comprising the MPD detector. Large surface covered by the MCORD offers also possibility for efficient registration of muons generated in expanding atmospheric showers induced by distant sources. Moreover, beyond some energy threshold, observation of muons originating from decays of collision products will also be possible. In this report examples of the MCORD functionality as a part of the MPD detector are presented.The MCORD is designed as a universal, fast triggering system built as a modular reconfigurable construction. The detection system will be based on plastic scintillators equipped with wavelength shifting fibers, and silicon photomultipliers (SiPM) will be used for scintillation readout. The online analysis of received signals will be performed using digital FPGA modules. Due to the modular design, the same system (its small part) can be used for both laboratory testing of other MPD sub-detectors, and the calibration of these detectors after placing them inside the MPD in off-beam mode. The full detector will support these systems as an additional trigger, calibrator, and muon identifier during the normal operation of the MPD detector with the beam. Thanks to its unique construction, it will expand the possibilities of collecting scientific data of the MPD detector with astrophysical observations.The publication will show the assumptions of the mechanical structure and electronic systems of the planned detector. The installation site of the detector as part of the MPD detector will be described in detail. In the following, the results of simulations made in preparation for this project will be presented. In particular, simulations with the CORSIKA code present angular distributions of particles in cosmic showers in the Dubna city region. Since muons dominate the cosmic ray showers, the MPD detector response to expected cosmic muon flux was also simulated. The results provide information about the muon cut-off thresholds depending on the MPD detector composition during the installation campaign. Simulations of muon events that could be used for MPD subsystems calibration were also performed. The results shown for various configuration of MCORD detector modules will enable the estimation of the time necessary to perform such tests in the future.Simulations with UrQMD model shows the muon abundances due to beam-beam collisions. Approximately 90% of muons are created from pions, whereas the number of muons that reach the MCORD detector is 10 times greater than the number of pions. The MPD detector response was also simulated under the influence of a stream of various particles, especially muons. It shows energy dependence of muon transmission coefficient for MPD with and without ECal assembled. Assuming requirement for muon transmission above 95%, the muon cut-off thresholds are 1.6 GeV and 2.0 GeV, respectively.MCORD detector performance evaluation is also reported. In the case when we used scintillators with one fiber with a diameter of 1 mm, the time resolution of about 1.0 ns was recorded, which corresponds to the positional accuracy (σx) of 7.1 cm. The results of laboratory tests show that application of a 2 mm diameter WLS fiber instead of the previously used 1 mm diameter fiber improves the time resolution to 0.80 ns.

Design of Low Power and Area Efficient 8 Bit USR Using mGDI Technology

Technology is growing at a faster rate after the evolution of VLSI, which mainly focuses on three major criteria-speed, area and power. All these criteria determine the compactness of a product in order to produce an efficient output at a higher rate by consuming less power. To achieve the above-mentioned factors, an efficient 8-bit Universal Shift Register (USR) has been designed using modified Gate Diffusion Input (mGDI) technique. The results have been simulated using the TANNER EDA tool and found to contribute for low power and area.

Improvement and Performance Evaluation When Implementing a Distance-Based Registration

An efficient location registration scheme is essential to continuously accommodate the increasing number of mobile subscribers and to offer a variety of multimedia services with good quality. The objective of this study was to analyze the optimal size for the location area of a distance-based registration (DBR) scheme by varying the number of location areas on a cell-by-cell basis, not on a ring-by-ring basis. Using our proposed cell-by-cell distance-based registration scheme with a random walk mobility model, a variety of circumstances were analyzed to obtain the optimal number of cells for location area for minimizing the total signaling cost on radio channels. Analysis results showed that the optimal number of cells for location area was between 4 and 7 in most cases. Our cell-by-cell distance-based location registration scheme had less signaling cost than an optimal ring-by-ring distance-based location registration scheme with an optimal distance threshold of 2 (the optimal number of cells for location area was 7). Therefore, when DBR is adopted, it must be implemented with an LA increasing on a cell-by-cell basis to achieve optimal performance.

Multisensor registration using phase matching for large-scale fringe projection profilometry

Multisensor systems are widely used in large-scale fringe projection profilometry, where the accuracy and efficiency of registration play an important role. However, existing methods using reference targets or auxiliary devices lengthen the coordinate transformation chain and are difficult to achieve high accuracy and efficiency. In this study, we propose a method that uses the absolute phase retrieved from sinusoidal fringe patterns for the registration of different sensors, which can shorten the coordinate transformation chain. First, the corresponding pixels of cameras of different sensors are determined through phase matching and used to calculate the corresponding points on the basis of triangulation, which determines corresponding points between the sensors directly. The coordinate transformation is then calculated. Furthermore, the registration accuracy can be improved by combining the epipolar constraint and bundle adjustment. Experimental results show that the proposed method can be used for accurate and efficient registration without auxiliary devices and reference targets.

An Interband Registration Method for Hyperspectral Images Based on Adaptive Iterative Clustering

In the context of the problem of image blur and nonlinear reflectance difference between bands in the registration of hyperspectral images, the conventional method has a large registration error and is even unable to complete the registration. This paper proposes a robust and efficient registration algorithm based on iterative clustering for interband registration of hyperspectral images. The algorithm starts by extracting feature points using the scale-invariant feature transform (SIFT) to achieve initial putative matching. Subsequently, feature matching is performed using four-dimensional descriptors based on the geometric, radiometric, and feature properties of the data. An efficient iterative clustering method is proposed to perform cluster analysis on the proposed descriptors and extract the correct matching points. In addition, we use an adaptive strategy to analyze the key parameters and extract values automatically during the iterative process. We designed four experiments to prove that our method solves the problem of blurred image registration and multi-modal registration of hyperspectral images. It has high robustness to multiple scenes, multiple satellites, and multiple transformations, and it is better than other similar feature matching algorithms.

SEC adopts offering reforms for BDCs and registered closed-end funds and issues a temporary exemptive order

Purpose To analyze and identify the key findings from the April 8, 2020, U.S. Securities and Exchange Commission’s (the “SEC”) recently approved rule amendments (“Adopted Rules”) extended to business development companies (“BDCs”) and registered closed-end funds and an Exemptive Order providing regulatory flexibility to BDCs. Design/methodology/approach Discusses the key takeaways and implications from the Adopted Rules and Exemptive Order. Findings The Adopted Rules provide BDCs and registered closed-end funds some of the more efficient registration, reporting, offering, and communication requirements currently applicable to operating companies. The Exemptive Order provides BDCs additional flexibility with respect to (1) the issuance and sale of senior securities and (2) the participation in certain joint transactions. Practical implications Firms and their representatives should heed the trends in both the substantial restitution FINRA is ordering and the related enforcement issues in the cases FINRA has brought. Originality/value Expert analysis and guidance from experienced asset management lawyers.

Simple and efficient registration of 3D point cloud and image data for an indoor mobile mapping system.

Registration of 3D lidar point clouds with optical images is critical in the combination of multisource data. Geometric misalignment originally exists in the pose data between lidar point clouds and optical images. To improve the accuracy of the initial pose and the applicability of the integration of 3D points and image data, we develop a simple but efficient registration method. We first extract point features from lidar point clouds and images: point features are extracted from single-frame lidar and point features are extracted from images using a classical Canny operator. The cost map is subsequently built based on Canny image edge detection. The optimization direction is guided by the cost map, where low cost represents the desired direction, and loss function is also considered to improve the robustness of the proposed method. Experiments show positive results.

Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm.

Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.

DDRNet: Fast point cloud registration network for large-scale scenes

Efficient registration for large-scale scene point clouds is a basic capability that is essential for many real-world intelligence applications, such as autonomous driving or simultaneous localization and mapping. Because a large-scale scene point cloud has various characteristics such as large volumes of data, irregular local density, and limited overlapping, most existing approaches can only be trained and operated using small-scale point clouds. In this work, we propose a deep direct registration network, named DDRNet, to efficiently align the point clouds of large-scale scenes. The DDRNet consists of three parts: a local-spatially aware encoder that can efficiently aggregate posture information containing both local and spatial features; an attentional weighting module that allows our network to self-adaptively focus on overlapping areas; and a pyramid transformation decoder used to estimate transformation based on features having different resolutions. Also, we propose a partially-subsampled strategy, which is compatible with any learning-based registration method, to enable the network to be trained and tested in a self-supervised manner. We comprehensively validated our network's efficiency and robustness using four datasets: the ModelNet40, 3Dmatch, S3DIS, and the KITTI odometry datasets. The results demonstrate that our approach is more efficient and performs better than state-of-the-art methods, including both classical and learning-based methods, on large-scale scene data and object point cloud data, but with higher robustness to the variations in point density and overlapping. The efficiency and low registration error will make DDRNet attractive for substantial applications relying on a point cloud registration task.

Factors associated with registration for organ donation among clinical nurses.

Healthcare professionals play an important role in the organ donation process. The aim of this study was to examine the organ donation registration rate and related factors among clinical nurses. In this cross-sectional, correlational study, we used mailed questionnaires to collect data from four geographical areas and three hospital levels in Taiwan from June 6 to August 31, 2018. Two thousand and thirty-three clinical nurses participated in this study. Participants' mean age was 34.47 years, and 95.7% were women. Of them, 78.3% were willing to donate their organs and 20.6% had registered for organ donation after death. The results of logistic regression showed that in the personal domain, higher age (odds ratio (OR) = 1.03, p < 0.001), better knowledge of organ donation (OR = 1.09, p < 0.001), and a positive attitude toward organ donation (OR = 2.91, p < 0.001) were positively associated with organ donation registration, while cultural myths (OR = 0.69, p < 0.001) were negatively correlated. In the policy domain, the convenience of the registration procedure (OR = 1.45, p < 0.001) was positively associated with registration. A gap between willingness to donate and actual registration was observed. Personal factors played an important role in organ donation registration. Therefore, efforts to improve knowledge and inculcate positive cultural beliefs about organ donation among clinical nurses are recommended. There is also a need to cooperate with government policies to provide appropriate in-service training and policy incentives and establish an efficient registration process.

Faster dense deformable image registration by utilizing both CPU and GPU.

.Purpose: Image registration is an important aspect of medical image analysis and a key component in many analysis concepts. Applications include fusion of multimodal images, multi-atlas segmentation, and whole-body analysis. Deformable image registration is often computationally expensive, and the need for efficient registration methods is highlighted by the emergence of large-scale image databases, e.g., the UK Biobank, providing imaging from 100,000 participants.Approach: We present a heterogeneous computing approach, utilizing both the CPU and the graphics processing unit (GPU), to accelerate a previously proposed image registration method. The parallelizable task of computing the matching criterion is offloaded to the GPU, where it can be computed efficiently, while the more complex optimization task is performed on the CPU. To lessen the impact of data synchronization between the CPU and GPU, we propose a pipeline model, effectively overlapping computational tasks with data synchronization. The performance is evaluated on a brain labeling task and compared with a CPU implementation of the same method and the popular advanced normalization tools (ANTs) software.Results: The proposed method presents a speed-up by factors of 4 and 8 against the CPU implementation and the ANTs software, respectively. A significant improvement in labeling quality was also observed, with measured mean Dice overlaps of 0.712 and 0.701 for our method and ANTs, respectively.Conclusions: We showed that the proposed method compares favorably to the ANTs software yielding both a significant speed-up and an improvement in labeling quality. The registration method together with the proposed parallelization strategy is implemented as an open-source software package, deform.

Fast Registration of Multiview Slant-Range SAR Images

Unlike geocoded images, slant-range (SR) synthetic aperture radar (SAR) images vary from imaging resolution to angles, which are difficult to be registered directly using the traditional SAR image registration methods. A possible way is to match their corresponding geocoded images and to project the correspondences to SR images. However, this way is time consuming and suffers from both registration and projection errors. In this letter, an automatic and efficient method is proposed to directly match multiview SR SAR images. We first estimate the scale and rotation differences between two SR images from the metadata delivered by vendors alongside the image file. Specifically, the scale differences of the range and azimuth directions are estimated by transforming the range and azimuth pixel intervals into a uniform geographical resolution, and the rotation differences are estimated by comparing the azimuth angles of an image-pair. A global-to-local framework is then implemented to accelerate the registration process. In the global stage, we fix the scale and rotation parameters in SAR-scale-invariant-feature-transform (SAR-SIFT) method to avoid mismatches. In the local stage, the phase correlation of cropped patches is parallelized to generate accurate matches. Experimental results on 13 multiview SAR images of the Omaha city show that the proposed method can provide accurate and efficient registration results for each pair of the 13 images, and outperforms the state-of-the-art methods both in accuracy and in efficiency.

SAR Image Registration Based on ROEWA-Blocks and Multiscale Circle Descriptor

Given the imaging characteristics of synthetic aperture radar (SAR) images and the inherent speckle noise in them, scale-invariant feature transform based algorithms are unable to perform satisfactorily. To improve registration efficiency between SAR images, we propose a robust and efficient registration method with three main contributions. First, considering sudden dark patches appearing in SAR images, we propose the ratio of exponentially weighted average blocks to suppress the sudden dark patches and better adapt to different test images. This new operator called blocks of the ratio of exponentially weighted averages (ROEWA-B) divides the processing windows of ROEWA into blocks, which can not only reduce speckle noise but also retain more edge details compared to ROEWA when sudden dark patches appear. Second, for outlier removal, we present an approach using the minimum moment map to remove erroneous keypoints. Finally, based on the gradient location orientation histogram descriptor, we propose a novel multiscale circle descriptor, which combines scale change information to give weights to feature points at different scales. Experimental results for various thresholds and evaluations demonstrate the advantage and robustness of our method in registration.

Efficient Implementation of AES and CTR_DRBG on 8-Bit AVR-Based Sensor Nodes

Since Rijndael algorithm was selected as the Advanced Encryption Standard (AES) by NIST, optimization research for the AES has been actively conducted on various IoT-based processors. In an 8-bit AVR environment, LIGHT version of Fast AES CTR-mode Encryption (FACE-LIGHT) was proposed at ICISC'2019 conference. However, in a Wireless Sensor Network environment, where sessions are frequently changed, FACE-LIGHT seems not efficient in terms of available memory and generating a pre-computation table. In this article, we present a new column-wise fashion implementation. Unlike previous best AES implementations, our proposed implementation in an 8-bit AVR microcontroller combines SubBytes, ShiftRows, and MixColums operations and optimizes the operation speed through efficient register scheduling. Our constant-time implementation uses a significantly less table than FACE-LIGHT in an 8-bit AVR microcontroller, achieving 2,251, 2,706, and 3,160 clock cycles when encrypting 128-bit data for each of three security levels. In particular, our 256-bit security level AES implementation is the fastest AES implementation as far as we know in 8-bit AVR microcontroller. Finally, we apply our implementation in CounTeR-mode_Deterministic Random Bit Generator (CTR_DRBG), one of the upper algorithms of a symmetric-key algorithm, to prove the generality of our optimization technology in various operating modes of AES.