A digital pre-distortion waveform shaping scheme combined with a blue filter is proposed to optimize both the rise and fall times of a light-emitting diode (LED) and the optical receiver current of the signal of the real-time visible light communication (VLC) system. The proposed scheme is implemented on a field-programmable gate array (FPGA) and a digital-to-analog converter-based testbed, which is flexible and reconfigurable by programming the FPGA to match different LED characteristics and varied data rates. A 262-Mbps non-return-to-zero on-off keying modulation-based real-time VLC-link with a bit error rate of less than 1.0$\times$10$^{-6}$ is achieved over a transmission distance of 5.0 m, which uses a single white phosphorous LED with a limited power of 0.1-W.

In wireless communication, unmanned-aerial-vehicle (UAV) relay seems to be an available paradigm to combat path-loss and expand signal coverage. Due to the openness of wireless channel and the inconvenient recharging, transmission security and energy efficiency are the most concerned issues for UAV-enabled communication. In this paper, for an UAV-enabled two-way relay system, where two wireless terminals exchange their information assisted by a flying UAV and a malicious user tries to eavesdrop the transmitted information, the achievable secrecy energy-efficiency is formulated when the UAV speed, trajectory, allowable power and the time scheduling in the uplink and downlink transmission are considered in a comprehensive manner. To solve this non-convex problem, we decompose it into three subproblems based on the block coordinate descent (BCD) method. Then, the successive convex approximation (SCA) technique and the Dinkelbach algorithm are combined to obtain the global solution in an iterative manner. Simulation results show that the proposed schemes have higher secrecy energy-efficiency compared to the existing baseline schemes.

To combat the severe path fading between two users/access points and obtain enough signal gain for demodulation, a three-terminal cooperative communication system with a UAV operating as a two-way relay is presented in this paper, and then its optimal energy efficiency is investigated. Specifically, considering the time slot pairing between multiple access (MA) and broadcast (BC) phases, the energy efficiency of the UAV is maximized by optimizing the transmit power of the three terminals and the UAV trajectory jointly. For such a non-convex problem, to obtain its optimal solution, it is decomposed into three subproblems based on the block coordinate descent (BCD) method. Then, after the optimal solutions of the subproblems have been derived based on the successive convex approximation (SCA) techniques, the overall solution of the original optimization problem is obtained in an alternative manner. Simulation results show that the proposed optimization scheme can enhance energy efficiency significantly compared to other existing schemes while considering the reduction system power consumption.

In recent years, the field of emotion recognition has witnessed significant advancements, with applications spanning from healthcare monitoring to human-computer interaction. However, deploying such systems on resource-constrained end devices, such as wearable sensors or mobile phones, remains a challenging task due to limitations in computational power, memory, and battery life. This paper proposes a novel approach to address these challenges by utilizing lightweight deep learning models that are specifically designed to operate efficiently on such devices. The proposed system integrates multi-physiological signals, including Electrocardiogram (ECG), Photoplethysmographic (PPG) readings, Heartbeat Rate (HR), Skin Temperature measurements (SKT), and Respiratory patterns (RSP) to provide a comprehensive assessment of emotional states. We evaluate the performance of our system on a dataset of physiological signals collected from subjects undergoing video emotional stimuli. Experimental results demonstrate that our lightweight models achieve comparable accuracy to heavier models while significantly reducing computational overhead and power consumption. The findings of this study pave the way for the development of practical, real-time emotion recognition systems that can be deployed on a wide range of resource-constrained end devices.

Ensuring secrecy information transmission with enhanced energy efficiency is one of the critical issues in unmanned aerial vehicles (UAVs)-assisted wireless systems owing to the open nature of wireless channels and the limited battery power. In this paper, the secrecy energy efficiency (SEE) is investigated for a more critical scenario, where a UAV acts as a bidirectional relay exchanging information between two ground users and a malicious terminal attempts to implement eavesdropping and interfering on the confidential information simultaneously. Multiple parameters, such as time scheduling, power allocation, UAV trajectory within a given flight cycle, and both multiple access (MA) and broadcast (BC) phases are optimized jointly to maximize the system SEE. To address this non-convex optimization problem involved with coupled variables tightly, we employ the block coordinate descent (BCD) method and develop an iterative algorithm that leverages successive convex approximation (SCA) and Dinkelbach algorithm to obtain the optimal solution. Meanwhile, simulation results are presented to demonstrate the superiority of our proposed scheme in terms of SEE.