This systematic review examines harassment in virtual reality (VR), synthesizing findings from 85 studies published between 2017 and 2025. We propose a nuanced typology of harassment, encompassing spatial intrusion, sexual and verbal abuse, identity-based discrimination, group-targeted harassment, and systemic harms, and demonstrate how VR’s immersive and embodied features amplify risk and impact. Marginalized users, such as women, LGBTQ+ individuals, children, and people with disabilities, face disproportionate harm. We further analyze the psychological and behavioral consequences of harassment, as well as the effectiveness and limitations of current governance, design, and AI-driven interventions. Our review identifies persistent research gaps in theory, measurement, and inclusive protection, and advocates for ethical, participatory, and preventive approaches to platform safety. This work aims to guide researchers and designers in building more equitable and safe VR environments.

Traditional Internet of Things (IoT) technologies in complex pipeline networking systems of industrial automation face limitations in sensor data acquisition, making it challenging to comprehensively capture the complex distributions of multi-physical fields, such as pressure, temperature, and velocity. This restricts holistic system analysis and optimal decision-making capabilities. However, accurate and efficient prediction of these multi-physical fields is essential for intelligent decision-making and optimization in IoT-enabled industrial systems. Traditional Computational Fluid Dynamics (CFD) methods deliver high fidelity but are computationally expensive and unsuitable for real-time monitoring and control scenarios characteristic of IoT environments. Recent AI-based generative methods, including Transformers and generative adversarial networks (GANs), improve computational efficiency but often suffer from overly smooth predictions and training instability, limiting their effectiveness for precise industrial IoT applications. Motivated by the outstanding performance of diffusion models in generative tasks, we propose CFDiff, a diffusion-based architecture designed explicitly for predicting complex multi-physical fields in IoT-based industrial pipeline applications. By introducing the simulation-free flow-map, a single-channel orientation field that encodes the inlet-to-outlet direction, CFDiff leverages a generative diffusion process combined with a lightweight Cross-Attention Fusion (CAF) module, effectively integrating sparse and multimodal data to generate high-fidelity field distributions. Experimental results demonstrate that CFDiff significantly outperforms existing state-of-the-art methods, reducing prediction errors by approximately 41.6% across various scenarios relevant to industrial IoT. Comprehensive ablation studies further confirm the efficacy of each proposed component, positioning CFDiff as a robust and practical solution to enhance the accuracy, safety, and efficiency of IoT-based monitoring and predictive maintenance systems.

This paper presents a Deep Q-Network (DQN)-based algorithm for NOMA-aided resource allocation in smart factories, addressing the stringent requirements of Ultra-Reliable Low-Latency Communication (URLLC). The proposed algorithm dynamically allocates sub-channels and optimizes power levels to maximize throughput while meeting strict latency constraints. By incorporating a tunable parameter$\lambda$, the algorithm balances the trade-off between throughput and latency, making it suitable for various devices, including robots, sensors, and controllers, each with distinct communication needs. Simulation results show that robots achieve higher throughput, while sensors and controllers meet the low-latency requirements of URLLC, ensuring reliable communication for real-time industrial applications.

In most Internet of Vehicles (IoV) scenarios, intelligent vehicle terminals are required to cope with a multitude of heterogeneous tasks, each of which is subject to increasingly strict constraints on delay and energy consumption. Task offloading is an efficient way to tackle this issue. However, due to performance constraints, a single or two-tier offloading strategy can not enable fine-grained task allocation and flexible service deployment. To address the above problems, we propose a collaborative cloud-edge-end task offloading scheme for IoV scenarios. Since traditional single-agent Deep Reinforcement Learning (DRL) makes it difficult to coordinate multiple objectives of dynamic services simultaneously, we propose a task offloading strategy based on multiagent Deep Deterministic Policy Gradient (DDPG), to jointly consider service delay and energy consumption. We further introduce attentive experience replay (AER) to mitigate the issue of insufficient experience sampling in the DDPG algorithm caused by the catastrophic forgetting problem. Through simulation of IoV scenarios, our proposed model significantly enhances task offloading effectiveness and concurrently reduces delay by 10.6% and energy consumption by 8.1% compared to other state-of-the-art baseline algorithms.

According to the World Health Organization, an estimated 1.28 billion adults aged 30–79 years worldwide have hypertension. 46% of adults with hypertension are unaware that they have the condition. The only way to know is to get your blood pressure checked. Typically, we use a blood pressure monitor (cuff) at home. However, this method can be uncomfortable and stressful. This paper proposes a non-contact method for estimating blood pressure using 2.4GHz microwave signals. We conduct experiments aimed at capturing the pulse waveform through the reflection of microwave signals. In our previous paper, we only estimated the SBP and focused on increasing the number of features value. In this paper, we estimate both SBP and DBP, and clarify which feature values are important to estimate blood pressure. Also, we attempt the real-time detection of blood pressure. Our studies show that both SBP and DBP can be estimated at a chest. This paper is to achieve predicted blood pressure values that meet the accuracy standards defined by the Japanese Industrial Standards (JIS).

Cardiovascular diseases (CVDs) constitute the primary cause of human mortality globally in recent decades. To effectively detect CVDs, heart auscultation plays an important role in early diagnosis. With the development of artificial intelligence (AI), many studies have designed varying AI-assisted diagnosis systems helping people discriminate abnormal heart sounds. Yet, a robust system usually requires a noise-less input signal, which is critical as heart sounds are often affected by some unavoidable noise. Therefore, many heart sound classification models use filters or other methods to obtain the clean signals. However, these classic techniques are not adaptable enough to distinguish the meaningful murmurs and real noises. Thus, we propose a novel approach to transfer an audio source separation model to denoise the heart sound. In this paper, we test different denoisers on synthesis heart sound with additive white Gaussian noises. Our method performs well on the noise reduction metrics. Meanwhile, we evaluate the classification performance of each denoiser with some classifiers on the PhysioNet dataset. Experimental results demonstrate that our method can outperform other denoising techniques by achieving the highest unweighted average recall (UAR) at 95.7% with the smallest standard deviation. The results confirm that our method is robust and adaptable in improving audio's denoising.

Inthis study, we proposed a closed-loop transformer neural network decoder-based signal generation method to record, decode, and stimulate the brain to intercept a previously functional brain area. The dataset consists of the auditory cortex output of computational modeling of the human auditory system, following voice input. The system is comprised of three phases. In Phase I artificial brain signal was generated based on a generative sequence-to-sequence machine learning model. In Phase II the cortical area downstream was stimulated with the generated brain signal, effectively replacing the upstream cortical area on which the machine learning model is based. Finally, in Phase III, incremental learning is applied to fine-tune the model to unlock differences between what is being recorded by our electrodes and the hidden neural patterns of the brain. As an evaluation, we computed RMSE and similarity index between the generated stimulation output and inputted original computational model output without any interception. The generative regression model using our proposed method shows an average RMSE = 0.009845, and similarity index (SI) = 0.9416 across the models without an word embedding layer, and SI = 0.855283 and RMSE = 0.033068 for the model with a word embedding layer. This could be evidence to support that the Transformer-Decoder model has extracted distinguishable features during heard speech.

Mobile healthcare has attracted significant attention in recent years as people have become more health -conscious. Furthermore, there is an increasing demand for non-contact and non-invasive diagnostic methods to reduce the risk of infection and pain. This study proposes a healthcare system that detects pathological symptoms by utilizing speech audio. Existing methods for the detection of COVID-19 have relied on cough recordings. To investigate the potential application of this method in the real use case, this study utilizes speech audio instead of cough recording. Our target is to alleviate the burden on the medical field and help medical workers by implementing this mobile healthcare system. This method uses transfer learning to address the problem of limited data. We evaluate the accuracy with transfer learning and random forest classification utilizing the specific features extracted in such a spectrum and spectrogram.

Atherosclerosis is an extremely common and serious health problem that is often asymptomatic. Typically, symptoms of atherosclerosis do not appear in the early stages, making timely diagnosis difficult. This paper proposes a non-contact method for estimating arteriosclerosis using 2.4GHz microwave signals. From the gained waveforms, we calculated the pulse wave velocity (PWV) as an index of arteriosclerosis. As an evaluation method for the experiment, we conducted a Bland-Altman and Four-Quadrant analysis and a T-test to determine if there was a significant difference between the data of healthy subjects and hypertensive subjects and evaluated it using the p-value. It was found that the speed of the PWV of hypertensive subjects was higher than the speed of the PWV of healthy subjects. The p-value was less than 0.05 when using a pulse waveform. These results indicate the potential to detect arteriosclerosis using microwaves. Traditionally, detecting arterial stiffness has required undergoing tests at hospitals. By measuring at the same frequency as Wi-Fi, in the future, it may be possible to measure blood pressure with a smartphone and easily assess the risk of arteriosclerosis, potentially preventing serious illnesses.

Breast tumor is one of the most common malignant cancers in women. Precise classification of breast tumors is pivotal for clinical treatment. Recently, deep learning, especially the Convolutional Neural Network (CNN), has been commonly used to address this problem. However, CNN models have been observed to inadequately capture global information. Hence, to further improve the accuracy, the Vision Transformer method was utilized in this study for breast tumor classification based on magnetic resonance imaging (MRI). Additionally, a majority decision fusion strategy was incorporated to enhance classification performance. Initially, the lesion part was manually segmented and extracted from MRI images. Subsequently, three grayscale images from the same individual were combined into a single RGB image, allowing it to be input into the ViT. Once the training phase was completed, a voting mechanism was applied. Given that MRI images for each patient present a series to depict the three-dimensional structure of the breast, a majority-based voting approach was adopted to refine accuracy. As a result, an accuracy of 91.89% was achieved by the model, surpassing VGG16, ResNet50, and other models. With the inclusion of voting, the accuracy of the ViT was observed to reach 98.2%. As the quality of medical data evolves and the benefits of sizable models in addressing image challenges become more evident, it is anticipated that more such models will be integrated into the healthcare domain. Consequently, this study may provide invaluable insights for researchers aiming to enhance performance in medical imaging, especially in breast tumor classification.

The typical measurement method using a cuff is physiologically stressful for many, especially the elderly people. In order to eliminate the burden and realize comfortable measurements, a non-contact method for monitoring blood pressure is necessary. This paper proposes a non-contact method to detect the human pulse, acceleration pulse waveform and in turn, predict blood pressure. In the experiment, microwave signals were transmitted against, and reflected from the chest and wrist, upon which the time-varying Insertion loss of Scattering parameter(S21) was acquired. Pulse, respiration rate and Augmentation Index(AIx) are first estimated via postprocessing of acquired raw data. Further signal processing also detects the acceleration pulse waveform. Blood pressure is then predicted via Machine Learning (ML) methods with parameter values derived from the detected pulse waveform, respiration waveform and acceleration waveform. Our works indicate that our proposed non-contact method is practical and has great potential for future smart health solutions.

This study proposes a salvage transmission (ST) scheme for 5G networks using device-to-device (D2D) communication. In mobile networks, communication is disabled when issues emerge in the core network or base station of the operator. To address this problem, the proposed scheme provides mobile communication services via D2D communication to user equipment (UE) that cannot use cellular networks. The users of an operator that is unable to provide service due to communication failure transmit signal using D2D communication with the closest UE of another operator. This ST scheme enables the mitigation of communication failures. In addition, this study introduced the basic protocols of ST with Wi-Fi Direct (IEEE 802.11) and 5G networks. Furthermore, we verified the effectiveness of the ST scheme in the case of communication failures via a simulation study.

With the increasing number of 4K/8K channels and the development of broadcasting technology, the frequency used for the intermediate frequency (IF) signal keeps increasing. Using fiber to replace cable is proved to achieve lower signal loss and reduces the noise sensitivity in 4k/8k broadcasting systems. Power-over-fiber (PoF) is chosen for the power supply. In this research, a 4K/8K broadcasting system using double-clad fiber (DCF) to maintain power and signal transfer simultaneously is proposed. DCF has three layers and can achieve Single-Mode and Multi-Mode transmission simultaneously. The breakthrough we make is using HPLD for energy supply to combine RoF and PoF together.

In recent years, hypertension has become a leading cause of diseases worldwide. Despite the recommendation from medical experts to measure blood pressure on a daily basis, only a small few do so. The main reason for this is that the typical measurement method using a cuff is physiologically stressful for many, especially for physically handicapped people, and the elderly. In order to eliminate the burden during measurement, a non-contact method for monitoring blood pressure is necessary. This paper proposes a non-contact method to detect the human pulse and in turn, estimate blood pressure. In the experiment, 2.4GHz microwave signals were transmitted against, and reflected from the body, upon which the time-varying reflection intensity was acquired. Pulse rates are first estimated via post-processing of acquired raw data. Blood pressure is then estimated via Machine Learning (ML) methods with parameter values derived from the detected pulse waveform. Experiments indicate that our proposed method is practical and has great potential for future smart health solutions.

Wireless power transmission via radio frequency (RF) attracts attention as a power source for small devices such as wireless sensor networks (WSN). Among these, the method using RF waves have the advantage that power can be transmitted over long distances about several meters. Because RF waves can transmit power from a single power supply over a wide area simultaneously. RF can also drive a wireless sensor network semi-permanently. However, simply transmitting radio waves will cause problems such as interference and reflection, then it will reduce efficiency. In this paper, we propose an efficient wireless power transmission scheme by controlling the phase using an all-pass filter (APF). APF is a filter that acts only on the phase of the signal and realizes interference mitigation by controlling the phase difference. Experimental results show that the application of APF to wireless power transfer increased the voltage across WSN devices.

Gesture recognition has been a popular research field under the trend of IoT and intelligent devices. Air-writing is the most challenging and crucial topic in the gesture recognition field. In this paper, we propose a wearable air-writing system that makes users can write the English alphabet in the three-dimensional space without any write rules. The proposed system is based on the Inertial Measurement Unit (IMU), and it uses dynamic time warping (DTW) as the main recognition algorithm. In addition, to improve the recognition accuracy and take a better advantage of the DTW algorithm, we present an adjustment system that gives some new optimization methods to the application of IMU and DTW. In the experiment, the accuracy of recognition is 84.6% for the uppercase alphabet (from `A' to `Z') in user-dependent case. And we also confirmed that the recognition method only based on the DTW algorithm is one kind of user-dependent methods, which means this method is heavily dependent on personalization.

Non-invasive Blood Glucose Measurement (NGM) technology is a crucial challenge for not only academic communities but also industrial communities. Therefore, finding an applicable and accurate NGM technology is supportive and desired for patients with hyperglycemia or hypoglycemia. Among various NGM technologies, infrared is considered as the most popular and prospective method. In this paper, the mid-infrared spectroscope is used in the experiment, there are significant differences in transmittance in the range of 3000 ~ 3500 cm−1(3333 ~ 2857 nm). The association between mid-infrared spectroscopy transmittance and blood glucose concentration is presented. The infrared with a peak wavelength of 3000 ~ 3500 cm−1could be a good solution to measuring the physical signals in the experiment. Besides, an NGM prototype that aims to improve self-management motivation is designed and presented. Mid-infrared is very potential and prospective in NGM research. Also, further investigation and consideration ear needed in future work.

Currently, non-invasive blood pressure monitoring with using a cuff is commonly used. However, this monitoring method is not suitable for some people who cannot wear the cuff and who might feel uncomfortable and troublesome. Non-contact measurement method provides a safer and more comfortable way to measure blood pressure. This paper describes the research on a non-contact pulse and blood pressure monitoring system. The frequencies of millimeter waves used in this experiment are 28GHz and 32GHz, which are the same as 5G millimeter wave signal. In this experiment, the millimeter waves are transmitted and reflected to the body and measure the reception intensity. As the results, we can detect pulse by utilizing millimeter waves, however, the relationship between blood pressure and millimeter waves needs further investigation.

In this paper, we propose a system for visualization of radio signal using drone in the field. It is possible to measure the directivity of antennas if we use anechoic chamber. However, it is difficult to obtain the directivity of antennas in the field. In order to get the directivity of antennas in the field, we propose the system which uses detector circuits which convert radio signals to light, receive antennas, and drone. To obtain the directivity in the field, we carried out an experiment. In this experiment, we used Yagi-antennas. The results show that precision of the system is a moderate match to the directivity obtained from the chamber. The results also confirm the proposed system is practical in the field.

This paper introduces a novel Vehicular Ad Hoc Networks (VANET) clustering scheme, titled “Normalized MultiDimensional Parameter based Affinity Propagation Clustering (NMDP-APC).” The similarity function of NMPD-APC consists of normalized multi-dimensional parameters in order to represent VANET's motion dynamics. This similarity function is the key in the Affinity Propagation Clustering (APC) scheme to adequately representing the homogeneity of traffic dynamics of VANET. In contrast to the previous research on APC scheme for VANET, our proposal does not associate any un-stable future prediction term. We also applied recently introduced Cellular V2X radio capability in this study, which significantly contributed to the reduction of communication latency. In conducting simulations, Gazis-Herman-Rothery (GHR) car following model was applied on the real sampled traffic data. The simulation resulted remarkable effects on the normalized multidimensional parameters along with successful VANET clustering. The simulation also demonstrated required minimum number of iterations for the clustering and controllability of clustering granularity in the proposed NMDP-APC scheme on the real traffic data.

In this paper, a cooperative traffic light controlling algorithm for urban road network aiming at reducing traffic congestion is proposed. Dedicated Short Range Communications (DSRC) is applied to detect the real time traffic flow. Based on the traffic flow at the current traffic light cycle and the historical data, we use machine learning technique to predict the variation of the traffic flow at the next traffic light cycle. With the purpose of reducing the road network's average waiting time and balancing the traffic pressure between different intersections, the traffic light control system adjusts the timing plan cooperatively. The genetic algorithm is used to calculate the optimum traffic light timing plan. In addition, a novel state transition model of the road network for dynamic numerical simulation is utilized to verify the effectiveness of the proposed algorithm. According to a 4-nodes road network simulation result, the vehicles in the traffic flow with congestion problems will have a shorter waiting time while the vehicle in the other traffic flows will have an increased waiting time. More importantly, the average waiting time of the road network declines.

In this paper, we propose a power control algorithm dedicated for machine type communications (MTC) in future non-orthogonal multiple access (NOMA) networks employing game theory. In MTC networks, communication reliability should be considered prior to power consumption or energy efficiency. Once the reliability is satisfied, discussions about power consumption makes sense. We build a cost function for each device based on a non-cooperative game model. The cost function reflects the power consumption as well as received signal-to-interference plus noise ratio (SINR) of each device. Since we assume the devices are battery-driven, the objective is to minimize the power consumption as much as possible provided that the received SINR of each device is kept beyond an acceptable level so that the reliability can be guaranteed. We derive the power control algorithm function and prove the convergence of this iteration algorithm and the unique existence of Nash equilibrium as well. The simulation results show that under the same constraints of maximum power consumption and minimum acceptable SINR, the proposed algorithm outperforms the conventional algorithms in terms of power consumption and power efficiency.

This paper explores a cooperative partnership and data forwarding model in wireless sensor networks using unmanned aerial vehicle (UAV) with the goal of enhancing the data collection efforts. A UAV-based data acquisition architecture is presented to suppress the limitations of the traditional wireless sensor network. For this, we introduce a flexible and fast approach to collect data by taking into consideration the mobility of the mobile sink (UAV) and sensor nodes in the network. In other words, leveraging the mobility of the UAV and the location of sensor nodes, we adopt a novel frame selection technique that classifies sensor nodes into different frames. Then we present a cooperative partnership model that allows sensor nodes in the network to individually pair with their peers and thus transmitting data simultaneously. We also aim to alleviate the packet loss originated from certain sensor nodes located in the rear edge-side of the UAV's coverage area. This situation happens when the UAV is moving in the forward direction while collecting data. Thus, to alleviate these packet losses while guaranteeing a higher success rate of packet reception ratio, we propose a novel data forwarding scheme to closely integrate with the aforementioned partnership model. We conduct simulations to verify our proposed framework, and results show huge performance gain is obtained over the traditional data collection technique.

This paper presents a novel ticket gate system employing optical wireless communication used at a train station in Japan. The proposed system needs two optical wireless card readers attached to two inner sides of the gate. According to our study, we found that the proposed system has higher received power compared to the conventional train ticket gate system. It also has higher probability of success as long as the communication distance is less than 0.28 meters. As a result, the proposed system has a better performance compared to the conventional one and its availability is guaranteed.

In this paper, we propose a system for detection of the missing people employing UAV which is able to move quickly and has a wide view from the air in the disaster area. However, an airplane type UAV does not always have a stable flight due to strong wind, rolling, and pitching. In order to detect and to respond the missing people promptly using the UAV, we propose the system which uses a phased array antenna and an angle detector. To implement the system, we assemble the phased array antenna and carry out an experiment to measure the characteristics of directivity, the directivity control characteristics, the return loss of the antenna, and the received power level to compare using and not using the phased array antenna in terms of efficiency and functionality. The experiment results show that the proposed system increase the received power level by adjusting the directivity of the antennas with short delay time. The results also confirm that the proposed system is practical in decreasing victims of disasters.

This paper proposes a self-optimizing based energy-efficient scheme with a dynamic coverage expansion of femtocells in the macro-femto two-tier networks. High SINR and low power consumption can be benefited by coordinating downlink cross-tier interference and intra-interference with a neuron control based adaptive power adjustment when sleep mode is involved in the macro base station (MBS). Moreover, by allowing open access in the hybrid femtocells, more near-indoor macro user equipments (MUEs), especially located around the edge of the macrocell can be served by indoor femto access point (FAP), which results in MBS having more of a probability to maintain sleep mode when the traffic is low. Many related performances are evaluated with a comparison of utility-based power control (UBPC) scheme, the energy impact of both MBS and FAP can be largely improved with optimal transmit power, which means the sleep mode technology can be enhanced, as shown in the simulation.

This paper presents an optical wireless identification (OWID) scheme utilizing high-energy harvesting thinfilm embedded onto the corner cube retroreflector (CCR). We apply optical wireless communication (OWC) for transmitting information. The thinfilm is used as a rechargeable battery that absorbs the power when the incident light beam goes through the CCR and reflects On-Off-Keying (OOK) modulated signals back to the reader. The energy harvested during the transmission is used to drive the CCR inside the tag to modulate the OWC signals back to the reader. The proposed system is proved to be more secure and having much higher available stored energy. The performance of the proposed scheme is evaluated based on the simulations and the results are discussed. According to the simulations, by using a 550nm wavelength with 0.4W of the transmitted power, the scheme achieves a data rate of 100bps and about 0.19W of the harvested power while the communication distance is up to 0.25m.

This paper presents a novel position based access scheme for indoor optical wireless communication systems. In this scheme, we apply the Intensity Modulation-Direct Detection (IM/DD) with Quadrature Phase Shift Keying (QPSK) modulation. The I (in-phase) and Q (Quadrature) signal are transmitted separately in Additive White Gaussian Noise (AWGN) channel. The probability of error is analyzed with consideration of different mode numbers of the radiation lobe of Lambertian source, and position of the transmitters. The analytical result of this paper demonstrates that high level security for optical wireless communication systems can be achieved by specifying the receivers' position where the signal can be demodulated successfully.

This is a study on the effect of reflected light on optical code-division multiple-access (CDMA) system over indoor optical wireless communication (OWC). The indoor propagation channel model which takes multiple reflecting surfaces into account was addressed. Theoretical analysis of the pulse response of direct light and reflected light is given. Bit error rate (BER) of this system is analyzed considering reflected light, background light, avalanche photo-diode (APD) noise, thermal noise, and multi-user interference. The results prove that the BER of the system is influenced by the reflected light and the effect of reflected light is related to the room size and receiver position. With the increase of user number, the effect of reflected light becomes stronger and it is a significant source of inter-symbol interference. It becomes clear that when OCDMA is used to an indoor optical wireless system, the effect of reflected light is a key issue to be considered.

This paper presents a wireless relay scheme for indoor healthcare communication system, in which an optical relay link, instead of a traditional radio frequency (RF) relay link, is established between the relay station (RS) and the mobile terminal (MT). To simplify the structures of RS and the additional optical parts that shall be installed in the MT, the subcarrier-modulation based intensity-modulated direct- detection (IM/DD) is also adopted for both the up-link and the down-link. The proposed relay scheme not only enhances the transmission power, but also creates safe signal propagation environment, which is much meaningful for wireless and pervasive healthcare communication. Simulation results show that the proposed interference-reduced relay scheme provides a stable and high quality communication link when the average optical receiving power is larger than -25 dBm.

This paper presents an optical intensity-modulated direct-detection (IM/DD) based relay scheme for indoor optical wireless communication system. Multiple distributed optical relay links are set up between relay stations (RS) and mobile terminals (MT) to create multiple safe signal propagation links for wireless communication so that at least one link is available for data streaming. Different from conventional relay scheme, the phase shift of RF signals is pre-adjusted based on maximum ratio transmission (MRT) technique, and then relayed from multiple distributed optical transmitters which are set up in different places. The proposed relay scheme not only enhances signal power and achieves diversity, but also avoids possible obstruction of line-of-sight (LOS) signal. Furthermore since propagation phase variation of incoherent optical signal can be neglected, the propagation space becomes a non-directional scalar space, resulting in a much simpler system structure, where multiple optical antenna elements can be freely arranged, phase shift due to different transmitting paths is solely decided by the distance between transmitter and receiver, etc. The paper presents these features and also provides computer simulation results to demonstrate the performance. Simulation results show that the proposed relay system is much feasible and attractive, and it would be one of the promising candidates for future wireless and pervasive healthcare communication systems.

There has been many developments in long distance optical wireless communication between buildings using rooftop- mounted units over Point-to-Point wireless links. We propose an Angle Division/Time Division Multiple Access scheme to perform Point-to-Multi-Point communication employing reflectors. A single reflector controlled by a stepping motor and an array of fixed reflectors were used. The width of the beam of the infrared rays is measured and the relationship between the width and the stepping angle deviation of the motor are demonstrated. Moreover, the received power, the bit error rate, and throughput of the proposal scheme are measured. The angle of the reflector and the reflection characteristics when using one or two reflectors were compared. In addition, the throughput characteristics of the proposal scheme were made clear by experiments.