Modular addition is a fundamental operation in public-key cryptographic algorithms operating in finite fields, such as elliptic curve cryptography (ECC), Chebyshev polynomials, and post-quantum cryptography (PQC). The performance of these cryptographic algorithms is limited by the conventional modular adder approach, which incorporates two cascaded adders in series. This approach leads to a doubled critical path delay, ultimately causing a decrease in frequency despite utilizing a high-performance adder. This research presents a high-performance, low-area architecture for a modular adder, employing a novel approach. Specifically designed for various prime fields recommended in public key cryptography, the architecture optimally utilizes the carry chain and exploits the structural advantages of the 7-series field programmable gate array and series beyond. Implementation results demonstrate superior performance, achieving operating frequencies of 290.0 MHz for 192 bits and 205.5 MHz for 1024 bits. Notably, the proposed design performs modular addition in a single clock cycle, resulting in an approximate 57% frequency enhancement compared to the conventional approach. Consequently, this architecture stands as an optimal solution for systems demanding high-speed operations.

Abstract The aim of this article is to design a new telehealth system with secured wireless transmission and classification of multiple biosignals using e‐Health sensors platform and Xbee modules with Arduino Uno and Raspberry Pi as acquisition and processing units, respectively. The collected data, such as temperature, airflow, position, Galvanic skin response and oxygen in the blood can be evaluated in order to monitor patient health state using threshold detection. The prediction of the cardiac state based on automatic identification of arrhythmias is validated by the classification of ElectroCardioGram (ECG) signals using Artificial Intelligence (AI) by exploiting TensorFlow and Keras tools. Different AI algorithms and a combination with different Machine Learning (ML) basing to transfer learning approach are tested. These algorithms include Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Support Vector Machine (SVM), K‐Nearest Neighbour (KNN) and Random Forest (RF). At first, ANN and CNN are used to classify ECG‐scalogram images using softmax, then the used CNN model (VGG16) is employed to extract features and pass them to other traditional classifiers (SVM, KNN and RF) allowing to evaluate and select the best classifier, such that the ECG signal can be classified into four categories namely Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Congestive Heart Failure (CHF) and other cardiac arrhythmia (ARR). The proposed method has been evaluated using real recorded signals and four PhysioNet databases. A Graphical User Interface (GUI) has been designed with C# under Visual Studio IDE allowing to display the results using personal computer (PC) or a network linked phone, which makes it possible to transfer the diagnosis with the prediction results to a remote clinic control room as Internet of Things (IoT) system application. The best classification accuracy of 99.56% is attained, confirming that the designed system allows a good trade‐off between low cost and performances in addition, it is easy to use with quick access to multiple biosignals. It has improved vital characteristics monitoring and diagnosis services quality under a robust secured wireless transmission using lightweight chaos‐based algorithm, thus preventing loss of life during critical health situations.