A New Method for Designing Lightweight S-boxes with High Differential and Linear Branch Numbers, and Its Application

Bit permutations are efficient linear functions often used for lightweight cipher designs. However, they have low diffusion effects, compared to word-oriented binary and MDS matrices. Thus, the security of bit permutation-based ciphers is significantly affected by differential and linear branch numbers (DBN and LBN) of nonlinear functions. In this paper, we introduce a widely applicable method for constructing S-boxes with high DBN and LBN. Our method exploits constructions of S-boxes from smaller S-boxes and it derives/proves the required conditions for smaller S-boxes so that the DBN and LBN of the constructed S-boxes are at least 3. These conditions enable us to significantly reduce the search space required to create such S-boxes. In order to make cryptographically good and efficient S-boxes, we propose a unbalanced-Bridge structure that accepts one 3-bit and two 5-bit S-boxes, and produces 8-bit S-boxes. Using the proposed structure, we develop a variety of new lightweight S-boxes that provide not only both DBN and LBN of at least 3 but also efficient bitsliced implementations including at most 11 nonlinear bitwise operations. The new S-boxes are the first that exhibit these characteristics. Moreover, we propose a block cipher PIPO based on one of the new S-boxes, which supports a 64-bit plaintext and a 128 or 256-bit key. Our implementations demonstrate that PIPO outperforms existing block ciphers (for the same block and key lengths) in both side-channel protected and unprotected environments, on an 8-bit AVR. The security of PIPO has been scrutinized with regards to state-of-the-art cryptanalysis

Generating Cryptographic S-Boxes Using the Reinforcement Learning

Substitution boxes (S-boxes) are essential components of many cryptographic primitives. The Dijkstra algorithm, SAT solvers, and heuristic methods have been used to find bitsliced implementations of S-boxes. However, it is difficult to apply these methods for 8-bit S-boxes because of their size. Therefore, to implement these S-boxes so that the countermeasure of side-channel attack can be applied efficiently, using structures such as Feistel, Lai-Massey, and MISTY that can be bitsliced implemented with a small number of nonlinear operations has been widely used. Since S-boxes constructed with structures consist of small S-boxes and have specific designs, there are limitations to their cryptographic security and efficiency. In this paper, we propose a new method for generating S-boxes by stacking bitwise operations from the identity function, an approach that is different from existing methods. This method can be expressed in Markov decision process, and reinforcement learning is a suitable solver for Markov decision process. Our goal is to train this method to an agent through reinforcement learning to generate S-boxes to which the masking scheme, which is a countermeasure of side-channel attack, can be efficiently applied. In particular, our method provided various S-boxes superior or comparable to existing S-boxes. We produced 8-bit S-boxes with differential uniformity 16 (resp. 32) and linearity 128 (resp. 128), generated with nine (resp. eight) nonlinear operations, for the first time. To our best knowledge, this is the first study to construct cryptographic S-Box by incorporating reinforcement learning

Deep Learning-Based Image Classification for Major Mosquito Species Inhabiting Korea

Mosquitoes are one of the deadliest insects, causing harm to humans worldwide. Preemptive prevention and forecasting are important to prevent mosquito-borne diseases. However, current mosquito identification is mostly conducted manually, which consumes time, wastes labor, and causes human error. In this study, we developed an automatic image analysis method to identify mosquito species using a deep learning-based object detection technique. Color and fluorescence images of live mosquitoes were acquired using a mosquito capture device and were used to develop a deep learning-based object detection model. Among the deep learning-based object identification models, the combination of a swine transformer and a faster region-convolutional neural network model demonstrated the best performance, with a 91.7% F1-score. This indicates that the proposed automatic identification method can be rapidly applied for efficient analysis of species and populations of vector-borne mosquitoes with reduced labor in the field

A 0.1-1.5-GHz Wide Harmonic-Locking-Free Delay-Locked Loop Using an Exponential DAC

This letter presents a delay-locked loop (DLL) that can have a wide harmonic-locking-free frequency range, by using a digital-to-analog converter-based (DAC-based) band-selection circuit (BSC). The proposed exponential DAC (EDAC) used for the BSC generates a set of initial control voltages that follow a geometric sequence while satisfying the condition for avoiding harmonic locking. Thus, the BSC can cover a much wider range of frequencies free from harmonic locking than it could cover when it used a conventional, linear DAC that generated a set of control voltages following an arithmetic sequence. In this letter, the DLL was fabricated in a 65-nm CMOS and it had a measured harmonic-locking-free range from 0.1 to 1.5 GHz. The measured 1-MHz phase noise and rms jitter at 1.0 GHz were -128 dBc/Hz and 1.99 ps, respectively. The active area was 0.052 mm(2), and the power consumption was 5.5 mW

Non-destructive prediction of protein contents of soybean seeds using near-infrared hyperspectral imaging

Protein content is one of the most crucial factors in soybean quality. However, the breeding procedure necessitates the time-consuming and costly selection of elite genotypes from many experimental lines in a destructive manner. The present work aims to predict protein content in single soybean seeds non-destructively using Near-Infrared (NIR) Hyperspectral Imaging (HSI). 1491 seed samples from 3 varieties of the low, medium, and high protein content (consisting of 371, 560, and 560 samples, respectively) were measured using the NIR-HSI system with a range of 900–1800 nm. The spectral data extracted from the HSI 3D hypercube were synchronised to the reference values examined from chemical analysis. The calibration model was constructed using partial least square regression (PLSR) methods based on the 70% spectral data and then validated using the remaining 30% of data. The result showed that the NIR-HSI technique is a promising method to predict protein content in soybean seeds, as shown by an R2 of 0.92 and a root mean square error (RMSE) of 1.08% . In addition, the chemical images visualised the distribution of protein content for the multiple soybean seed showed the possibility of the developed technique for the use of rapid evaluation of massive samples in the processing line

An Ultra-Low-Jitter, mmW-Band Frequency Synthesizer Based on Digital Subsampling PLL Using Optimally Spaced Voltage Comparators

This article presents a cascaded architecture of a frequency synthesizer to generate ultra-low-jitter output signals in a millimeter-wave (mmW) frequency band from 28 to 31 GHz. The mmW-band injection-locked frequency multiplier (ILFM) placed at the second stage has a wide bandwidth so that the performance of the jitter of this frequency synthesizer is determined by the GHz-band, digital subsampling phase-locked loop (SSPLL) at the first stage. To suppress the quantization noise of the digital SSPLL while using a small amount of power, the optimally spaced voltage comparators (OSVCs) are presented as a voltage quantizer. This article was designed and fabricated using 65-nm CMOS technology. In measurements, this prototype frequency synthesizer generated output signals in the range of 28-31 GHz, with an rms jitter of less than 80 fs and an integrated phase noise (IPN) of less than -40 dBc. The active silicon area was 0.32 mm², and the total power consumption was 41.8 mW

Deep Learning-Based Plant Organ Segmentation and Phenotyping of Sorghum Plants Using LiDAR Point Cloud

Increasing food demands, global climatic variations, and population growth have spurred the growth of crop yield driven by plant phenotyping in the age of Big Data. High-throughput phenotyping of sorghum at each plant and organ level is vital in molecular plant breeding to increase crop yield. LiDAR (light detection and ranging) sensor provides 3-D point clouds of plants with the advantages of high precision, high resolution, and rapid measurement. However, need to develop robust algorithms for extracting the phenotypic traits of sorghum plants using LiDAR 3-D point cloud. This study utilized four 3-D point cloud-based deep learning models named PointNet, PointNet++, PointCNN, and dynamic graph CNN for the specific objective of the segmentation of sorghum plants. Subsequently, phenotypic traits were extracted using the segmentation results. Study plants sample were grown under controlled conditions at various developmental stages. The extracted phenotypic traits outcome has been validated through the manually measured phenotypic traits of the sorghum plant. PointNet++ outperformed the other three deep learning models and provided the best segmentation result with a mean accuracy of 91.5%. The correlations of the six phenotypic traits, such as plant height, plant crown diameter, plant compactness, stem diameter, panicle length, and panicle width were calculated from the segmentation results of the PointNet++ model and the measured coefficient of determination (R2) were 0.97, 0.96, 0.94, 0.90, 0.95, and 0.88, respectively. The obtained results showed that LiDAR 3-D point cloud have good potential to measure the sorghum plant phenotype traits rapidly and accurately using deep learning techniques