Virtual Reality Aided High-Quality 3D Reconstruction by Remote Drones

Artificial intelligence including deep learning and 3D reconstruction methods is changing the daily life of people. Now, an unmanned aerial vehicle that can move freely in the air and avoid harsh ground conditions has been commonly adopted as a suitable tool for 3D reconstruction. The traditional 3D reconstruction mission based on drones usually consists of two steps: image collection and offline post-processing. But there are two problems: one is the uncertainty of whether all parts of the target object are covered, and another is the tedious post-processing time. Inspired by modern deep learning methods, we build a telexistence drone system with an onboard deep learning computation module and a wireless data transmission module that perform incremental real-time dense reconstruction of urban cities by itself. Two technical contributions are proposed to solve the preceding issues. First, based on the popular depth fusion surface reconstruction framework, we combine it with a visual-inertial odometry estimator that integrates the inertial measurement unit and allows for robust camera tracking as well as high-accuracy online 3D scan. Second, the capability of real-time 3D reconstruction enables a new rendering technique that can visualize the reconstructed geometry of the target as navigation guidance in the HMD. Therefore, it turns the traditional path-planning-based modeling process into an interactive one, leading to a higher level of scan completeness. The experiments in the simulation system and our real prototype demonstrate an improved quality of the 3D model using our artificial intelligence leveraged drone system.

Wearable Device for Observation of Physical Activity with the Purpose of Patient Monitoring Due to COVID-19

In late 2019, a new genre of coronavirus (COVID-19) was first identified in humans in Wuhan, China. In addition to this, COVID-19 spreads through droplets, so quarantine is necessary to halt the spread and to recover physically. This modern urgency creates a critical challenge for the latest technologies to detect and monitor potential patients of this new disease. In this vein, the Internet of Things (IoT) contributes to solving such problems. This paper proposed a wearable device that utilizes real-time monitoring to detect body temperature and ambient conditions. Moreover, the system automatically alerts the concerned person using this device. The alert is transmitted when the body exceeds the allowed temperature threshold. To achieve this, we developed an algorithm that detects physical exercise named “Continuous Displacement Algorithm” based on an accelerometer to see whether a potential temperature rise can be attributed to physical activity. The people responsible for the person in quarantine can then connect via nRF Connect or a similar central application to acquire an accurate picture of the person’s condition. This experiment included an Arduino Nano BLE 33 Sense which contains several other sensors like a 9-axis IMU, several types of temperature, and ambient and other sensors equipped. This device successfully managed to measure wrist temperature at all states, ranging from 32 °C initially to 39 °C, providing better battery autonomy than other similar devices, lasting over 12 h, with fast charging capabilities (500 mA), and utilizing the BLE 5.0 protocol for data wireless data transmission and low power consumption. Furthermore, a 1D Convolutional Neural Network (CNN) was employed to classify whether the user is feverish while considering the physical activity status. The results obtained from the 1D CNN illustrated the manner in which it can be leveraged to acquire insight regarding the health of the users in the setting of the COVID-19 pandemic.

Diagnosis of Scoliosis in Juvenile Patients

In this paper, a wearable device used to monitor the posture variations. This device is useful in early detection and monitoring of patient having spine related disease such as scoliosis, kyphosis. Scoliosis is a 3-dimensional deformation of spine. The most common characteristics are bending of backbone in coronal plane and rotation of vertebrae, which results in various deformations of human postures. It mostly occurs in juvenile stage (3-10 years). The existing system consists of wearable sensor network for posture data acquisition, wireless data transmission and conventional smartphone for data processing. The biofeedback device helps to improve the self-awareness in natural environment, but it is not suitable in case of severe deformity. The flex sensor used because of its High level of reliability, consistency, repeatability and harsh temperature resistance. Keywords: scoliosis, microcontroller, flex sensor

In-Body Electromagnetic Sensor Combined with AI-Enhanced Electrocardiography for Pacemaker Working Status Telemonitoring

This paper describes the design and implementation of an in-body electromagnetic sensor for patients with implanted pacemakers. The sensor can either be mounted on myocardial tissue and monitor the electrocardiography (ECG) with contact electrodes or implanted under the skin and monitor the ECG with coaxial leads. A 16-bit high-resolution analog front-end (AFE) and an energy-efficient 32-bit CPU are used for instantaneous ECG recording. Wireless data transmission between the sensor and clinician’s computer is achieved by an embedded low-power Bluetooth transmitter. In order to automatically recognize the working status of the pacemaker and alarm the episodes of arrhythmias caused by pacemaker malfunctions, pacing mode classification and fault diagnosis on the recorded ECG were achieved based on an AI algorithm, i.e., a resource allocation network (RAN). A prototype of the sensor was implemented on a human torso, and the in vitro test results prove that the sensor can work properly for the 1-4-meter transmission range.

Real-Time Continuous Acoustic Monitoring of Marine Mammals in the Mediterranean Sea

The passive acoustic monitoring of cetaceans is a research method that can provide unique information on the animal’s behaviour since the animals can be studied at great depths and at a long-range without interference. Nevertheless, the real-time data collection, transfer, and analysis using these techniques are difficult to implement and maintain. In this paper, a review of several experiments that have used this approach will be provided. The first class of detectors consists of hydrophone systems housed under buoys on the sea surface with wireless data transmission, while the second type comprises several acoustic detector networks integrated within submarine neutrino telescopes cabled to the shore.

Pilot-Assisted OFDM for Underwater Acoustic Communication

Multicarrier techniques have made it possible to wirelessly transmit data at higher rates for underwater acoustic (UWA) communication. Several multicarrier techniques have been explored in the past for wireless data transmission. OFDM is known to fight off inter-symbol interference due to the orthogonality of its subcarriers. However, due to time variations, OFDM suffers from intercarrier interference. As the UWA channel is both a time and frequency variant, channel estimation becomes complex. We propose a pilot-based channel estimation technique and explore two equalizers for improving the error performance of an OFDM-based UWA system. Both the equalizers employ pilot subcarriers to estimate the UWA channel. One equalizer is a least squares (LS) equalizer and the other is a zero forcing (ZF) equalizer. Using computer simulations, it is observed that, for an acceptable error performance, the number of pilots should be one-fourth the number of subcarriers. Moreover, if the energy of the pilots is increased without changing the overall symbol energy, the error performance degrades. It is also noted that both the LS and ZF equalizers give an acceptable error performance with the ZF performing marginally better than the LS. Furthermore, the error performance of the proposed system is evaluated as a function of the transmitter-receiver distance and an acceptable error performance is observed even at 1250 m.

Wearable Sensory Apparatus for Real-Time Feedback in Wearable Robotics

We describe the Wearable Sensory Apparatus (WSA) System, which has been implemented and verified in accordance with the relevant standards. It comprises the Inertial Measurement Units (IMUs), real-time wireless data transmission over Ultrawideband (UWB), a Master Unit and several IMU dongles forming the Wireless Body Area Network (WBAN). The WSA is designed for, but is not restricted to, wearable robots. The paper focuses on the topology of the communication network, the WSA hardware, and the organization of the WSA firmware. The experimental evaluation of the WSA incorporates the confirmation of the timing using the supply current WSA profile, measurements related to determining the less error prone position of the master device on the backpack, measurements of the quality of the data transfer in a real environment scenario, measurements in the presence of other microwave signals, and an example of raw IMU signals during human walking. Placement of the master device on the top of the backpack was found to be less error prone, with less than 0.02% packet loss for all the IMU devices placed on different body segments. The packet loss did not change significantly in public buildings or on the street. There was no impact of Wi-Fi bands on the WSA data transfer. The WSA hardware and firmware passed conformance testing in a certified lab. Most importantly, the WSA performed reliably in the laboratory and in clinical tests with exoskeletons and prostheses.

Design, Development and Implementation of Real Time Canal and Weather Monitoring Devices

In the present study, canal depth, velocity and weather monitoring sensors are designed and implemented in the field irrigation laboratory, Aditya Engineering College, Surampalem, Andhra Pradesh, India. The depth sensor which is used in this project is HC-SR04 sensor and the velocity sensor is YF-S403. A method of data acquisition and transmission based on ThingSpeak IOT is proposed. To record weather data (i.e., temperature, humidity, rainfall depth and wind speed) DHT11 sensor, ultrasonic sensor and IR sensors are used. The purpose of this project is to evaluate the performance of real time canal and weather monitoring devices. A structure of real time weather monitoring devices based on sensors and ThingSpeak IOT, a design was developed to realize the independent operation of sensors and wireless data transmission can help in minimizing the error in data collection. Arduino UNO is connected with canal depth and velocity sensor to generate the output, similarly NodeMCU is connected with weather monitoring device. The results revealed that observed sensor data showed good results when compared/calibrated with the existing conventional measurement system. In order to decrease the time and to get accurate value, it is recommended to consider the sensors for the proper use and to access weather data easily. The developed device worked satisfactorily with minimum or no errors.

A Method for Underwater Wireless Data Transmission in a Hydroacoustic Channel under NLOS Conditions

Wireless data transmission in the hydroacoustic channel under non-line-of-sight (NLOS) propagation conditions, for example, during a wreck penetration, is difficult to implement reliably. This is mostly due to the multipath propagation, which causes a reduction in the quality of data reception. Therefore, in this work an attempt has been made to develop a reliable method of wireless underwater communication test it under the NLOS conditions. In our method, we used multiple frequency-shift keying (MFSK) modulation, sending a single bit on two carriers, and diversity combining. The method was tested in laboratory conditions which simulated underwater signal propagation during the penetration of the wreck. The propagation conditions were investigated by determining the impulse responses at selected measurement points using the correlation method. Additionally, for comparison, the data transmission quality was determined by the bit error rate (BER) under the same conditions using direct sequence spread spectrum (DSSS) and binary phase shift keying (BPSK) modulation. The obtained results confirmed the usefulness of the application of the developed method for wireless data transmission in a hydroacoustic channel under NLOS conditions.

Federated learning enables intelligent reflecting surface in fog-cloud enabled cellular network

The intelligent reflecting surface (IRS) is a ground-breaking technology that can boost the efficiency of wireless data transmission systems. Specifically, the wireless signal transmitting environment is reconfigured by adjusting a large number of small reflecting units simultaneously. Therefore, intelligent reflecting surface (IRS) has been suggested as a possible solution for improving several aspects of future wireless communication. However, individual nodes are empowered in IRS, but decisions and learning of data are still made by the centralized node in the IRS mechanism. Whereas, in previous works, the problem of energy-efficient and delayed awareness learning IRS-assisted communications has been largely overlooked. The federated learning aware Intelligent Reconfigurable Surface Task Scheduling schemes (FL-IRSTS) algorithm is proposed in this paper to achieve high-speed communication with energy and delay efficient offloading and scheduling. The training of models is divided into different nodes. Therefore, the trained model will decide the IRSTS configuration that best meets the goals in terms of communication rate. Multiple local models trained with the local healthcare fog-cloud network for each workload using federated learning (FL) to generate a global model. Then, each trained model shared its initial configuration with the global model for the next training round. Each application’s healthcare data is handled and processed locally during the training process. Simulation results show that the proposed algorithm’s achievable rate output can effectively approach centralized machine learning (ML) while meeting the study’s energy and delay objectives.