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小特集　そのとき研究の歴史が動いた　――画像認識の発展の歴史を振り返って――
　
小特集編集にあたって
Editorial Preface

編集チームリーダー　黒川茂莉

特集　耐量子計算機暗号の最新動向
　
ハッシュ関数を用いた署名方式について
Hash-based Signature Scheme

廣瀬勝一

ハッシュ関数を用いた署名方式は，RSA署名方式と同様，1970年代に基礎が確立され，その後の研究で改良が行われてきた．ハッシュ関数を用いた署名方式は，安全性の観点から最も信頼性の高い耐量子計算機暗号の一つと考えられており，現在，実用のための方式として，米国国立標準技術研究所（NIST）の二種類の推奨方式とNISTによる耐量子計算機暗号標準化プロセスで標準化候補アルゴリズムとなった一つの方式が存在する．本稿では，それらに共通する基本構造を中心に紹介し，それぞれの特徴について概観する．

特集　耐量子計算機暗号の最新動向
　
NIST標準化の格子暗号方式の紹介
Introduction to NIST-standardized Cryptography Based on Lattices

安田雅哉

量子計算機の実用化に向けた研究開発が加速化する一方，量子計算機を用いた解読でも困難な耐量子計算機暗号の研究開発が活発化している．2022年7月に，米国標準技術研究所（NIST:National Institute of Standards and Technology）は耐量子計算機暗号の次世代標準方式を発表した．本稿では，標準化が決定した格子に基づく耐量子計算機暗号方式であるCRYSTALS-Kyber，CRYSTALS-Dilithium，FALCONの三つの方式の基本構成を概説する．

特集　耐量子計算機暗号の最新動向
　
現行の公開鍵暗号方式に対するShorのアルゴリズムの脅威
Threat of Shor’s Algorithm to Current Public Key Cryptography

國廣　昇　高安　敦

RSA暗号やだ円曲線暗号は現在広く利用されている公開鍵暗号方式であり，情報社会の安全性の根幹を支える基盤技術となっている．これらの暗号方式が安全であるためには，それぞれ素因数分解やだ円曲線上の離散対数が効率的に計算できないことが必要となるが，Shorの量子アルゴリズムはこれらを多項式時間で計算できることが分かっている．問題の簡潔さから，素因数分解に対するShorのアルゴリズムは活発に議論されているが，それと比べてだ円曲線上の離散対数計算に関する研究はまだ発展途上である．そのため本稿では，だ円曲線上の離散対数計算を中心に，現行の公開鍵暗号方式に対するShorのアルゴリズムの脅威に関する結果を紹介する．

特別小特集　Artificial Intelligence of Things (AIoT) for Smart Farming 4
　
Wireless Sensor Network Optimization in Agriculture:Brief Review and Case Study Using 2.4GHz Transceivers
電力消費を考慮して広域な農場をカバーするセンサ配置の求め方

Wei Kitt WONG, Saaveethya SIVAKUMAR, Filbert H. JUWONO, and Ing Ming CHEW

Internet of things (IoT) has been widely applied in various sectors, including in agricultural sector for crop monitoring. To cover a wide area, the deployment of a wireless sensor network (WSN) is required to transmit data to a single access point. The sensors, however, have a few constraints, including energy consumption and service coverage (range). As a result, it is critical to optimize the configuration of the sensors to obtain the desired performance. With this in mind, the objective of this paper is threefold. First, the state-of-the-art of the WSN-IoT in agricultural sector is presented. subsequently, some of the optimization techniques corresponding to the WSN are discussed along with some areas that need to be considered in the optimization technique. Lastly, to solidy the the entire discussion, a case study of optimizing WSN topology parameters in order to minimize the power consumption of the NRF24L01＋transceivers is presented. In particular, Adaptive Differential Evolution (ADE) algorithm with normalization-binarization approach is proposed to encode the adjacency matrix for optimizing the power consumption.

特別小特集　Artificial Intelligence of Things (AIoT) for Smart Farming 3
　
Mm-wave and THz Scanning for Non-invasive Farm Product Safety
ミリ波/THz 波を利用した異物混入のリアルタイム検出技術

Nguyen NGOC MAI-KHANH, Shintaro TAKADA, Keizo INAGAKI, Tran NGOC LE, Tran THI MY HANH, Hinano SUGIMOTO, Akio HIGO, Hitoshi ABATA, Makoto IKEDA, Bich-Yen NGUYEN, and Tetsuya KAWANISHI

Our project is to develop a portable and cost-effective scanner for real-time detection of foreign contaminants in agricultural staples and/or animal feed with a unique and novel mm-wave/THz sensing capability. This paper presents both qualitative and quantitative detection methods for the harmful substance melamine in a wide frequency range, from mm-wave to the THz range. Melamine has been illegally forcibly added to meals, including powdered milk or pet feed, in order to raise the protein level, one of the most crucial quality indicators. A method for quantitative detection of melamine is presented in this paper. Validation in both mm-wave and THz ranges is performed using experiments. The measurement results show that the estimation of melamine content in mixtures can be performed in several mm-wave and THz frequency bands, including 220-330GHz, 2THz, 2.26THz, and 4THz. Especially the peak around 4THz of melamine was found and to our knowledge this is the first study and THz measurement. To further evaluate the properties and response of melamine and its mixtures in the infrared spectrum and other chromatography, the quantitative determination of melamine was performed by both Fourier-transform infrared spectroscopy (FTIR) method and ultra-performance liquid chromatography (HPLC). These frequency bands of melamine property are useful for implementing a portable detection system with an integrated antenna array or THz pulse transceiver since the size of the components and the sampling resolution essentially depend on the operating frequency band or wavelength.

特別小特集　Artificial Intelligence of Things (AIoT) for Smart Farming 2
　
Oil Palm Fresh Fruit Bunch Detection and Ripeness Classification Using YOLOv5
モバイル端末で自動診断スマート農業技術の実用例

Mohamed Yasser Mohamed Ahmed Mansour, Katrina D. Dambul, and Kan Yeep Choo

In oil palm estates, smart farming technology such as visual-based mobile applications can improve the harvesting process by assisting in the oil palm fresh fruit bunch (FFB) ripeness classification process and allow the monitoring of the harvesting process remotely. In this paper, oil palm FFB detection and ripeness classification is developed using a YOLOv5s model. Augmentation and label smoothing techniques were applied to improve the performance of the model. The results after applying label smoothing techniques showed that the mean average precision of the YOLOv5s model is 87.85%(0.5:0.95) with a precision of 96.19% and a recall of 95.19%. The results after image augmentation (Mosaic and Cut-Out) showed that the mean average precision of the YOLOv5s model is 86.67%(0.5:0.95) with a precision of 95.78% and a recall of 98.00%. This model can potentially be implemented in a mobile device to be used in a real time harvesting process.

特別小特集　Artificial Intelligence of Things (AIoT) for Smart Farming 1
　
Image-based Classification of Leaf Diseases Using Convolutional Neural Networks
葉病害を早期検知畳込みニューラルネットワークによる葉病分類

Muhammad Umair, Wooi-Haw Tan, and Yee-Loo Foo

Smart farming is on the uprising demand all over the world. Artificial Intelligence (AI) is considered as one of the latest tools to be utilized for smart farming. However, the practical implementation of AI for smart farming is often a challenge. One of the challenges is to optimize the algorithms for accurate classification of plant diseases. In this study, we have proposed a Convolutional Neural Network (CNN) for the classification of leaf diseases. The framework of the proposed CNN is designed using the Depthwise Separable Convolution (DWS) technique that consists of two stages, i. e., depthwise and pointwise feature extractions. We have compared the model with the classical convolutional approach. Results show that the proposed model outperformed the conventional CNN model with a precision of 0.932, recall 0.992, F1 score of 0.961 and a test accuracy of 95.25% whereas the conventional model achieved precision 0.941, recall 0.961, F1 score 0.951 and 93.76% of test accuracy.

特別小特集　Artificial Intelligence of Things (AIoT) for Smart Farming
　
Editorial Preface

Editorial Team Leader Yee-Loo Foo

小特集　セルフリー通信技術の最新動向
　
小特集編集にあたって
Editorial Preface

編集チームリーダー　吉井一駿