Adaptive 3D ray tracing approach for indoor radio signal prediction at 3.5 GHz

This paper explained an adaptive ray tracing technique in modelling indoor radio wave propagation. As compared with conventional ray tracing approach, the presented ray tracing approach offers an optimized method to trace the travelling radio signal by introducing flexibility and adaptive features in ray launching algorithm in modelling the radio wave for indoor scenarios. The simulation result was compared with measurements data for verification. By analyzing the results, the proposed adaptive technique showed a better improvement in simulation time, power level and coverage in modelling the radio wave propagation for indoor scenario and may benefit in the development of signal propagation simulators for future technologies.

Constructing Floor Plan through Smoke Using Ultra Wideband Radar

Floor plan construction has been one of the key techniques in many important applications such as indoor navigation, location-based services, and emergency rescue. Existing floor plan construction methods require expensive dedicated hardware (e.g., Lidar or depth camera), and may not work in low-visibility environments (e.g., smoke, fog or dust). In this paper, we develop a low-cost Ultra Wideband (UWB)-based system (named UWBMap) that is mounted on a mobile robot platform to construct floor plan through smoke. UWBMap leverages on low-cost and off-the-shelf UWB radar, and it is able to construct an indoor map with an accuracy comparable to Lidar (i.e., the state-of-the-art). The underpinning technique is to take advantage of the mobility of radar to form virtual antennas and gather spatial information of a target. UWBMap also eliminates both robot motion noise and environmental noise to enhance weak reflection from small objects for the robust construction process. In addition, we overcome the limited view of single radar by combining multi-view from multiple radars. Extensive experiments in different indoor environments show that UWBMap achieves a map construction with a median error of 11 cm and a 90-percentile error of 26 cm, and it operates effectively in indoor scenarios with glass wall and dense smoke.

Unobtrusive Pedestrian Identification by Leveraging Footstep Sounds with Replay Resistance

The ability to identify pedestrians unobtrusively is essential for smart buildings to provide customized environments, energy saving, health monitoring and security-enhanced services. In this paper, we present an unobtrusive pedestrian identification system by passively listening to people's walking sounds. The proposed acoustic system can be easily integrated with the widely deployed voice assistant devices while providing the context awareness ability. This work focuses on two major tasks. Firstly, we address the challenge of recognizing footstep sounds in complex indoor scenarios by exploiting deep learning and the advanced stereo recording technology that is available on most voice assistant devices. We develop a Convolutional Neural Network-based algorithm and the footstep sound-oriented signal processing schemes to identify users by their footstep sounds accurately. Secondly, we design a "live" footstep detection approach to defend against replay attacks. By deriving the novel inter-footstep and intra-footstep characteristics, we distinguish live footstep sounds from the machine speaker's replay sounds based on their spatial variances. The system is evaluated under normal scenarios, traditional replay attacks and the advanced replays, which are designed to forge footstep sounds both acoustically and spatially. Extensive experiments show that our system identifies people with up to 94.9% accuracy in one footstep and shields 100% traditional replay attacks and up to 99% advanced replay attacks.

Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition

There are various techniques to approach learning in autonomous driving; however, all of them suffer from some problems. In the case of imitation learning based on artificial neural networks, the system must learn to correctly identify the elements of the environment. In some cases, it takes a lot of effort to tag the images with the proper semantics. This is also relevant given the need to have very varied scenarios to train and to thus obtain an acceptable generalization capacity. In the present work, we propose a technique for automated semantic labeling. It is based on various learning phases using image superposition combining both scenarios with chromas and real indoor scenarios. This allows the generation of augmented datasets that facilitate the learning process. Further improvements by applying noise techniques are also studied. To carry out the validation, a small-scale car model is used that learns to automatically drive on a reduced circuit. A comparison with models that do not rely on semantic segmentation is also performed. The main contribution of our proposal is the possibility of generating datasets for real indoor scenarios with automatic semantic segmentation, without the need for endless human labeling tasks.

COVID surveillance robot: Monitoring social distancing constraints in indoor scenarios

Observing social/physical distancing norms between humans has become an indispensable precaution to slow down the transmission of COVID-19. We present a novel method to automatically detect pairs of humans in a crowded scenario who are not maintaining social distancing, i.e. about 2 meters of space between them using an autonomous mobile robot and existing CCTV (Closed-Circuit TeleVision) cameras. The robot is equipped with commodity sensors, namely an RGB-D (Red Green Blue—Depth) camera and a 2-D lidar to detect social distancing breaches within their sensing range and navigate towards the location of the breach. Moreover, it discreetly alerts the relevant people to move apart by using a mounted display. In addition, we also equip the robot with a thermal camera that transmits thermal images to security/healthcare personnel who monitors COVID symptoms such as a fever. In indoor scenarios, we integrate the mobile robot setup with a static wall-mounted CCTV camera to further improve the number of social distancing breaches detected, accurately pursuing walking groups of people etc. We highlight the performance benefits of our robot + CCTV approach in different static and dynamic indoor scenarios.

Throughput performance for full-duplex DF relaying protocol in hybrid wireless power transfer systems

Most of the existing studies on energy harvesting (EH) cooperative relaying networks are conducted for the outdoor environments which are mainly characterized by Rayleigh fading channels. However, there are not as many studies that consider the indoor environments whereas the state-of-the-art internet of things (IoT) and smart city applications are built upon. Thus, in this paper, we analyze a namely hybrid time-power splitting relaying (HTPSR) protocol in a full-duplex (FD) decode-and-forward (DF) battery-energized relaying network in indoor scenarios modelled by the unpopular log-normal fading channels. Firstly, we formulate the analytical expression of the outage probability (OP) then the system throughput. Accordingly, we simulate the derived expressions with the Monte Carlo method. It is worth mentioning that in our work, the simulation and the theory agree well with each other. From the simulation results, we know how to compromise either the power splitting (PS) or the time splitting (TS) factors for optimizing the system performance.

A performance predictor of beamforming versus time-reversal based far-field wireless power transfer from linear array

For far-field wireless power transfer (WPT) in a complex propagation environment, a time-reversal (TR) based WPT that can overcome the drawbacks of conventional beamforming (BF) by taking advantage of multipath has been recently proposed. However, due to the WPT performance of BF and TR depending on the complexity of the propagation environment, the performance prediction between BF versus TR would be required. We present a detailed and generalized analysis of the recently proposed performance metric referred to as the peak received power ratio (PRPR) for linear array-based WPT. Here, the effectiveness of PRPR is verified via measurement for free space and indoor scenarios. The results demonstrate that PRPR is directly related to the complexity of the propagation environment and the corresponding power transmission capability of BF and TR. That is, the higher the complexity, the greater the value of PRPR and TR outperforms BF with higher peak power given the same average transmit power and vice versa. The mode decision between BF and TR based on PRPR potentially promises efficient far-field WPT even in a dynamic propagation environment.

Outage performance analysis of non-orthogonal multiple access systems with RF energy harvesting

Non-orthogonal multiple access (NOMA) has drawn enormous attention from the research community as a promising technology for future wireless communications with increasing demands of capacity and throughput. Especially, in the light of fifth-generation (5G) communication where multiple internet-of-things (IoT) devices are connected, the application of NOMA to indoor wireless networks has become more interesting to study. In view of this, we investigate the NOMA technique in energy harvesting (EH) half-duplex (HD) decode-and-forward (DF) power-splitting relaying (PSR) networks over indoor scenarios which are characterized by log-normal fading channels. The system performance of such networks is evaluated in terms of outage probability (OP) and total throughput for delay-limited transmission mode whose expressions are derived herein. In general, we can see in details how different system parameters affect such networks thanks to the results from Monte Carlo simulations. For illustrating the accuracy of our analytical results, we plot them along with the theoretical ones for comparison.

Mobile-Based 3D Modeling: An In-Depth Evaluation for the Application in Indoor Scenarios

Indoor environment modeling has become a relevant topic in several application fields, including augmented, virtual, and extended reality. With the digital transformation, many industries have investigated two possibilities: generating detailed models of indoor environments, allowing viewers to navigate through them; and mapping surfaces so as to insert virtual elements into real scenes. The scope of the paper is twofold. We first review the existing state-of-the-art (SoA) of learning-based methods for 3D scene reconstruction based on structure from motion (SFM) that predict depth maps and camera poses from video streams. We then present an extensive evaluation using a recent SoA network, with particular attention on the capability of generalizing on new unseen data of indoor environments. The evaluation was conducted by using the absolute relative (AbsRel) measure of the depth map prediction as the baseline metric.