In this paper, we proposed an area-efficient hardware implementation of modular inversion/division, which is a complex and crucial component in elliptic curve cryptography (ECC). Our modular inversion/division is based on our modified binary inversion algorithm. The proposed hardware implementation of modular inversion/division improves the area efficiency and was designed and implemented on Xilinx Spartan-6 and Virtex-7 field-programmable gate array (FPGA) platforms and simulated with TSMC 90nm and 180nm technology nodes. Our proposed modular inversion/division is suitable for prime fields used in public key cryptography, including the NIST-recommended elliptic curves. It occupies 618 slices and 607 slices in Xilinx Spartan-6 and Virtex-7 FPGA platform, computes in$10.6\ \mu \mathrm{s}$and 6.45 over the prime filed P-256, at a maximum operating frequency of 33.76 MHz and 55.49 MHz. It occupies 23997 GE and 28471 GE, computes in$1.25\ \mu \mathrm{s}$and$2.43\ \mu \mathrm{s}$over the prime fields P-256 at a maximum operating frequency of 285.71 MHz and 147.06 MHZ, respectively for TSMC 90nm and 180nm technology node implementation.

An adaptive PUF source stabilization digital engine is proposed, applied to a symmetrical SR-latch structure for metastable PUF source, and implemented on TSMC 90nm process. In a global PUF system design, the stability of the PUF source directly affects the burden of error correction coding, as well as the required time and space resource consumption. According to the measurement and stability performance analysis of the PUF challenge and response results from 16 chips, although most of the PUF cells were having stable states, a non-negligible number of cells remained in oscillating states, in addition to major inter-chips differences for stability performances. Dark bit masking and Temporal Majority Voting(TMV) is a common and effective method to stabilize a PUF source but it involves a trade-off between stability and entropy loss. Stabilization with a fixed process design cannot achieve the best performance considering the large stability variations across different chips. We proposed an adaptive PUF stabilization processing engine that provides an enhanced TMV approach with the ability to stabilize the PUF source without reducing the expected number of PUF entropy. The PUF source raw bit error rate(BER) improved from 1.6e-2 to 1.7e-6 after stabilization.

This paper presents a wearable system able to transmit a real-time electrocardiogram (ECG) from a chest device to a photoplethysmogram (PPG) device on a wristband using human body communication (HBC). The proposed communication system was designed based on wet electrodes body channel measurements and was implemented using off-the-shelf components and conductive fabric rather than the original wet electrodes. This work attempts to build a bridge between standard surface potential monitoring and smart clothing technologies. The transmission of the ECG using the body as a transmission media from the chest to wrist reached an average accuracy of 97.1% tested for three typical cardiovascular monitoring positions. This work demonstrates a fully wearable HBC system that can be considered for smart clothing technology. The effective data transfer rate was 468kbps using on-off-keying (OOK) centered at 30MHz.

A human body communication system connecting a wearable electrocardiogram device and photoplethysmogram device system will be demonstrated. This live demonstration system is composed of two wearable sensors and real-time signals display on a laptop. During the live demonstration, the system will demonstrate the effective transmission of ECG data via a body communication link with a parallel Bluetooth comparison as reference. The PPG device system acts as body communication receiver and the final display will present sample-synchronized ECG and PPG signals creating a on-body sensors sub-network.

In this paper, we developed and integrated an AI-edge emotion recognition platform using multiple wearable physiological signals sensors: Electroencephalogram (EEG), electrocardiogram (ECG), and photoplethysmogram (PPG) sensors. The emotion recognition platform used two combined machine learning approaches based on two systems input and preprocessing: An EEG-based emotion recognition system and an ECG/PPG-based system. The EEG-based system is a convolution neural network (CNN) that classifies three emotions, happiness, anger and sadness. The inputs of the CNN are extracted from the EEG signals using short-time Fourier transform (STFT), and the average accuracy for a subject-independent classification reached 76.94%. The ECG/PPG-based system used a similar CNN with an extracted features vector as input. The subject-dependent ECG/PPG classification system reached an average accuracy of 76.8%. The proposed system was integrated using the RISC-V processor and FPGA platforms to implement realtime monitoring and classification on edge. A 3-to-1 Bluetooth piconet was deployed to transmit all physiological signals on a single platform access point and to make use of low power wireless technologies.