Lightweight Sensor Research Articles

Autonomous Control and Transportation of Underslung Load With Single and Dual Lift Helicopter Systems

Abstract This paper discusses the development of a single-lift and dual-lift helicopter underslung load transportation system for practical applications. A control law is developed to damp the load swing and stabilize the oscillation while performing the transportation task. For the dual-lift system, the load transportation is achieved by using a load distribution controller, developed for this purpose, to maintain equal load distribution among the vehicles. The load damping and load distribution controllers require accurate measurement of load states, which is achieved through the design and development of innovative, simple, and lightweight sensors units, namely, load tension measurement unit (LTMU) and load swing measurement unit (LSMU). LTMU sensor consists of a unique design that utilizes a flexi-force sensor, capable of measuring compressive load, for measurement of cable tension. The cable inclination in the longitudinal and lateral directions is measured by the LSMU sensor. These units are integrated with the helicopter autopilot for autonomous flight. The performance of the developed system is experimentally validated in the outdoor environment with single and dual-lift systems.

YOLOv5-Tassel: Detecting Tassels in RGB UAV Imagery With Improved YOLOv5 Based on Transfer Learning

Unmanned aerial vehicles (UAVs) equipped with lightweight sensors, such as RGB cameras and LiDAR, have significant potential in precision agriculture, including object detection. Tassel detection in maize is an essential trait given its relevance as the beginning of the reproductive stage of growth and development of the plants. However, compared with general object detection, tassel detection based on RGB imagery acquired by UAVs is more challenging due to the small size, time-dependent variable shape, and complexity of the objects of interest. A novel algorithm referred to as YOLOv5-tassel is proposed to detect tassels in UAV-based RGB imagery. A bidirectional feature pyramid network is adopted for the path-aggregation neck to effectively fuse cross-scale features. The robust attention module of SimAM is introduced to extract the features of interest before each detection head. An additional detection head is also introduced to improve small-size tassel detection based on the original YOLOv5. Annotation is performed with guidance from center points derived from CenterNet to improve the selection of the bounding boxes for tassels. Finally, to address the issue of limited reference data, transfer learning based on the VisDrone dataset is adopted. Testing results for our proposed YOLOv5-tassel method achieved the mAP value of 44.7%, which is better than well-known object detection approaches, such as FCOS, RetinaNet, and YOLOv5.

A lidar-only SLAM algorithm for marine vessels and autonomous surface vehicles

Research into autonomous surface vehicles is noticeably limited in regards to the functionality of the vehicles themselves. Specifically, testing and evaluation typically occurs at speeds considerably lower than what is allowed in an operational setting. For a vessel to be able to take advantage of higher speeds, there must be a robust and tested method for determining localisation and navigation. With an emphasis of development for small vessels with higher impulse capabilities, working in confined and restricted environments, the decision was made to develop a method of navigation that relied solely upon lightweight sensors. For this, a single light ranging sensor was utilised to develop both simultaneous localisation and mapping for the vessel, using the normal distribution transform and iterative closest point methods. Evaluation of the algorithm accuracy as the vessel moved above speeds greater than two metres per second was conducted, and it was feasibly evaluated that there was no observable drift of mapping in horizontal planes, however, there was a accumulated drift in the vertical plane and a transient response in localisation deviation as the vessel changed impulse through the two metre per second window.

A high-performance porous flexible composite film sensor for tension monitoring.

Flexible, lightweight sensors with a wide strain-sensing range are showing increasing significance in structural health monitoring compared with conventional hard sensors, which typically have a small strain range, are heavyweight, and have a large volume. In this work, salt particle precipitation and mechanical coating methods are used to fabricate porous graphene nanoplatelet (GNP)/polydimethylsiloxane (PDMS) flexible sensors for tension monitoring in structural health applications. The signal transformation through the Back Propagation (BP) algorithm is integrated to provide monitoring data that are comparable with other sensors. The results reveal that the flexible sensors with a low content of GNPs (0.1–0.25 wt%) possess better flexibility, allowing tensile strains over 200% to be measured. In addition, due to the enhanced deformation capacity of the pore structures, they can achieve high sensitivity (1–1000) under 65% strain, and a fast response time (70 ms) under 10% strain at 60 mm min−1. They also show high performance in the fatigue test (20 000 cycles) under 5% strain, and can effectively respond to bending and torsion. In addition, the sensors show an obvious response to temperature. Overall, the prepared flexible composite sensors in this work have the advantages of a wide strain-sensing range, a full-coverage conductive network, and being lightweight, and show potential for structural health monitoring in the near future.

SA1D-CNN: A Separable and Attention Based Lightweight Sensor Fault Diagnosis Method for Solar Insecticidal Lamp Internet of Things

Sensor faults can produce abnormal and spurious observations in the solar insecticidal lamp Internet of Things (SIL-IoTs) system. Early detection and identification of the sensor node's abnormality are critical to ensure the SIL-IoTs system's reliability. In this study, we propose a lightweight separable 1D convolution neural network that can be implemented in SIL-IoTs nodes to identify sensor faults, reduce detecting delay, and decrease data transmission. However, the reliability of data acquired by sensors is decreased because a SIL-IoTs node releases high voltage pulse discharge (a kind of electromagnetic interference) when pests collide with its metal mesh. This kind of data fluctuation impacts fault diagnosis accuracy. Consequently, fault-related feature maps and temporal signals are characterized via a novel time and channel attention module (TCAM) method, which contributes to separating electromagnetic interference noise from sensor faults of SIL-IoTs nodes. A real-world testbed is applied to validate the effectiveness of the proposed method on sensor fault diagnosis in the SIL-IoTs system. Experimental results demonstrate that the proposed method can detect four typical sensor faults with the best trade-off between accuracy (99.9% average accuracy and 97.6% average F1-score) and efficiency (351KB inference model size and 4.33W average energy consumption on Raspberry Pi).

Delamination ASSESSMENT during MACHINING OF laminated POLYMER nanocomposite

Nanomaterials are gaining extensive application in the manufacturing sector due to favorable properties. Its rapid growth in highly sensitive, robust, and lightweight sensors and biomedical components has attracted considerable attention worldwide. Nanomaterial uses with fiber-reinforced polymeric material have increased significantly. In order to manufacture structural components in a near-net shape, laminated nanocomposite machining is required. Due to the need for product assembly in mechanical structures, Milling is the primarily machining process in the manufacturing industry to create slots, channels, etc. The present work optimized the process variables affecting the Milling process by adopting the minimize criterion to control the delamination factor using the Taguchi method. The process parameters include cutting speed, feed, depth of cut, and filler material Graphene Oxide. The optimized conditions were found as cutting speed (Vc) 37.12 m/min, spindle feed (F) 80 mm/min, depth of cut (D) 0.5 mm and filler material Graphene Oxide (G) 1 wt.%. The percentage contribution of the process parameter on the delamination factor (Fd) was determined using the Analysis of Variance (ANOVA) method, and it has been found the feed rate (62.60%) is the most influencing factor. The delamination factor obtained in the confirmatory experiments carried out under optimized conditions was found lower than the Taguchi design test runs. The findings indicate that process parameter optimization under the given set of experimental conditions is effective for a manufacturing environment.

Size and charge distribution characteristics of fine and ultrafine particles in simulated lunar dust: Relevance to lunar missions and exploration

Astronaut anecdotal data, biologic studies, and multi-perspective reports indicate that lunar dust will be a significant hindrance to exploring the Moon, both robotically and by humans. Hence, important characteristics of the lunar dust must be established. Critically needed are detailed particulate matter size, dust cloud concentration, and charge distribution analyses to determine the viability of methods for real-time measurement on the lunar surface. In this study, we applied various state-of-the-art aerosol measurement techniques to characterize fine (0.1–2.5 ​μm) and ultrafine (<0.1 ​μm) JSC-1A lunar simulant particles. The different techniques were assessed at 1 ​atm and evaluated for viability on the lunar surface (i.e., hard vacuum). To represent suspended dust, the simulant was dispersed as an aerosol and size distributions were measured in real-time using multiple aerosol instruments. Results show a unimodal distribution centered at approximately 0.2 ​μm, in fair agreement with previously reported measurements. To demonstrate portability and networkability, two low-cost particulate matter sensors were evaluated for measurement of suspended dust concentration. Strong correlations with an industry standard instrument support the potential candidacy of these sensors for future in-situ measurements on the Moon. Evaluating the capability of compact, low-cost instruments and larger, more robust instruments to precisely and accurately measure particle size distributions is a critical step to determine the prospect of measuring particulate matter with lightweight sensors in multiple lunar environments. In addition, assessing the compact instrument mass, power, portability, complexity, and measurement speed will provide key guidance into mitigating dust-related challenges to future lunar missions.

Bearing Sensor for 3D Localization and Swarm Centroid Detection

A bearing sensor measures its orientation relative to a landmark. Such sensors enable tracking and localization. Since small Unmanned Aerial Vehicles (UAVs) suffer from payload limitations, they must be equipped with lightweight sensors with low power consumption. This paper presents two implementations for bearing sensors. First, a novel vision-based sensor based on a camera and a spherical mirror that enables wide view. This sensor is lightweight and low cost ( 20 [g] and under 8 [USD]). Second, a novel, laser-based beacon together with a photo-diode based sensor which enables to better distinguish between multiple landmarks while keeping inclination mismatch robustness. We present the scheme for detecting the relative angular position of an aerial agent to a light-based landmark in 3D. We also present algorithms for the localization of a UAV, and swarm centroid detection, both using such bearing observations. Finally, we demonstrate our sensors and algorithms in real-world experiments and provide their accuracy.

A Secure Mobile Wireless Sensor Networks based Protocol for Smart Data Gathering with Cloud

The lightweight sensors gain rapid growth for the development of emerging technologies due to multi aspects. Recently, many applications are integrating with the cloud services for storage and processing a large amount of observing data, however, the transitory and time-dependent environment needs valuable performance. Also, reliable and error-free data transmission is an important factor for green systems. Moreover, most of the proposed green networks may be exposed to unspecified events due to the broadcast nature of the unreserved medium. This research paper presents a secured mobile sensor network based protocol for smart data gathering with cloud services (S-SDC), which aims to attain information distribution in dynamic networks using mobile sensors with minimum loss and power consumption. Also, it ensures network availability and consistency for the collected data with the cloud corporations and increases authoritative routing. The simulation results and their analysis are proven the significant performance of the proposed protocol.

Secure Lightweight Key Exchange Using ECC for User-Gateway Paradigm

Internet of Things (IoT)- based services connect the user with sensing devices through intermediary devices like a gateway. The authentication with secure key exchange assures security trio of confidentiality, integrity, and availability for the complete IoT-based system. It also ensures trusted privacy of communicated information. Recently, numerous authors proposed a Remote User Authentication scheme (RUA) on User-Gateway based secure key exchange for the tiny and lightweight sensor based paradigms such as an IoT and a wireless sensor network (WSN) using lightweight mathematical approaches. In this article, we propose a reliable and efficient lightweight key exchange scheme with the secure RUA for the user-gateway model. For designing a lightweight RUA scheme, we use elliptic curve cryptography (ECC). We use BAN-Logic for the validation of mutual authentication. We use widely accepted random oracle-based ROR model for formal security analysis and Dolev-Yao channel for informal security analysis. For the implementation and result collection of the proposed scheme, we used publisher-subscriber-based lightweight Message Queuing Telemetry Transport (MQTT) protocol. Overall, in this article, we propose a highly secure and efficient RUA scheme for the sensor-based environment with the formal security analysis and real-time implementation.

Mapping Surgeons Hand/Finger Movements to Surgical Tool Motion During Conventional Microsurgery Using Machine Learning

Purpose: Recent developments in robotics and artificial intelligence (AI) have led to significant advances in healthcare technologies enhancing robot-assisted minimally invasive surgery (RAMIS) in some surgical specialties. However, current human–robot interfaces lack intuitive teleoperation and cannot mimic surgeon’s hand/finger sensing required for fine motion micro-surgeries. These limitations make teleoperated robotic surgery not less suitable for, e.g. cardiac surgery and it can be difficult to learn for established surgeons. We report a pilot study showing an intuitive way of recording and mapping surgeon’s gross hand motion and the fine synergic motion during cardiac micro-surgery as a way to enhance future intuitive teleoperation. Methods: We set to develop a prototype system able to train a Deep Neural Network (DNN) by mapping wrist, hand and surgical tool real-time data acquisition (RTDA) inputs during mock-up heart micro-surgery procedures. The trained network was used to estimate the tools poses from refined hand joint angles. Outputs of the network were surgical tool orientation and jaw angle acquired by an optical motion capture system. Results: Based on surgeon’s feedback during mock micro-surgery, the developed wearable system with light-weight sensors for motion tracking did not interfere with the surgery and instrument handling. The wearable motion tracking system used 12 finger/thumb/wrist joint angle sensors to generate meaningful datasets representing inputs of the DNN network with new hand joint angles added as necessary based on comparing the estimated tool poses against measured tool pose. The DNN architecture was optimized for the highest estimation accuracy and the ability to determine the tool pose with the least mean squared error. This novel approach showed that the surgical instrument’s pose, an essential requirement for teleoperation, can be accurately estimated from recorded surgeon’s hand/finger movements with a mean squared error (MSE) less than 0.3%. Conclusion: We have developed a system to capture fine movements of the surgeon’s hand during micro-surgery that could enhance future remote teleoperation of similar surgical tools during micro-surgery. More work is needed to refine this approach and confirm its potential role in teleoperation.

Combining Residual and LSTM Recurrent Networks for Transportation Mode Detection Using Multimodal Sensors Integrated in Smartphones

In recent years, with the rapid development of public transportation, the ways people travel has become more diversified and complicated. Transportation mode detection, as a significant branch of human activity recognition (HAR), is of great importance in analyzing human travel patterns, traffic prediction and planning. Though many works have been devoted to transportation mode detection, there remains challenge for accurate and robust transportation pattern identification. In this paper, we propose a residual and LSTM recurrent networks-based transportation mode detection algorithm using multiple light-weight sensors integrated in commodity smartphones. Feature representation learning is adopted separately on multiple preprocessed sensor data using deep residual and LSTM network, which can enhance the identification accuracy and support one or more sensors. Residual units are introduced to accelerate the learning speed and enhance the accuracy of transportation mode detection. Furthermore, we also leverage the attention model to learn the significance of different features and different timesteps to enhance the recognition accuracy. Extensive experimental results on three datasets indicate that using our proposed model can achieve the best recognition accuracy for eight transportation modes including being stationary, walking, running, cycling, taking a car, taking a bus, taking a subway and taking a train, which outperforms other benchmark algorithms.

A digital sensor to measure real-time leaf movements and detect abiotic stress in plants.

Plant and plant organ movements are the result of a complex integration of endogenous growth and developmental responses, partially controlled by the circadian clock, and external environmental cues. Monitoring of plant motion is typically done by image-based phenotyping techniques with the aid of computer vision algorithms. Here we present a method to measure leaf movements using a digital inertial measurement unit (IMU) sensor. The lightweight sensor is easily attachable to a leaf or plant organ and records angular traits in real-time for two dimensions (pitch and roll) with high resolution (measured sensor oscillations of 0.36 ± 0.53° for pitch and 0.50 ± 0.65° for roll). We were able to record simple movements such as petiole bending, as well as complex lamina motions, in several crops, ranging from tomato to banana. We also assessed growth responses in terms of lettuce rosette expansion and maize seedling stem movements. The IMU sensors are capable of detecting small changes of nutations (i.e. bending movements) in leaves of different ages and in different plant species. In addition, the sensor system can also monitor stress-induced leaf movements. We observed that unfavorable environmental conditions evoke certain leaf movements, such as drastic epinastic responses, as well as subtle fading of the amplitude of nutations. In summary, the presented digital sensor system enables continuous detection of a variety of leaf motions with high precision, and is a low-cost tool in the field of plant phenotyping, with potential applications in early stress detection.

Dynamic Target Tracking and Ingressing of a Small UAV Using Monocular Sensor Based on the Geometric Constraints

In many applications of airborne visual techniques for unmanned aerial vehicles (UAVs), lightweight sensors and efficient visual positioning and tracking algorithms are essential in a GNSS-denied environment. Meanwhile, many tasks require the ability of recognition, localization, avoiding, or flying pass through these dynamic obstacles. In this paper, for a small UAV equipped with a lightweight monocular sensor, a single-frame parallel-features positioning method (SPPM) is proposed and verified for a real-time dynamic target tracking and ingressing problem. The solution is featured with systematic modeling of the geometric characteristics of moving targets, and the introduction of numeric iteration algorithms to estimate the geometric center of moving targets. The geometric constraint relationships of the target feature points are modeled as non-linear equations for scale estimation. Experiments show that the root mean square error percentage of static target tracking is less than 1.03% and the root mean square error of dynamic target tracking is less than 7.92 cm. Comprehensive indoor flight experiments are conducted to show the real-time convergence of the algorithm, the effectiveness of the solution in locating and tracking a moving target, and the excellent robustness to measurement noises.

Development of a Smart Leg Splint by Using New Sensor Technologies and New Therapy Possibilities.

Nowadays, after suffering a fracture in an upper or lower limb, a plaster cast is placed on the affected limb. It is a very old and efficient technique for recovery from an injury that has not had significant changes since its origin. This project aims to develop a new low-cost smart 3D printed splint concept by using new sensing techniques. Two rapidly evolving Advanced Manufacturing (AM) technologies will be used: 3D scanning and 3D printing, thus combining engineering, medicine and materials evolution. The splint will include new small and lightweight sensors to detect any problem during the treatment process. Previous studies have already incorporated this kind of sensor for medical purposes. However, in this study it is implemented with a new concept: the possibility of applying treatments during the immobilization process and obtaining information from the sensors to modify the treatment. Due to this, rehabilitation treatments like infrared, ultrasounds or electroshock may be applied during the treatment, and the sensors (as it is showed in the study) will be able to detect changes during the rehabilitation process. Data of the pressure, temperature, humidity and colour of the skin will be collected in real time and sent to a mobile device so that they can be consulted remotely by a specialist. Moreover, it would be possible to include these data into the Internet of Things movement. This way, all the collected data might be compared and studied in order to find the best treatment for each kind of injury. It will be necessary to use a biocompatible material, submersible and suitable for contact with skin. These materials make it necessary to control the conditions in which the splint is produced, to assure that the properties are maintained. This development, makes it possible to design a new methodology that will help to provide faster and easier treatment.

Ranging with Frequency Dependent Ultrasound Air Attenuation.

Measuring the distance between two points has multiple uses. Position can be geometrically calculated from multiple measurements of the distance between reference points and moving sensors. Distance measurement can be done by measuring the time of flight of an ultrasonic signal traveling from an emitter to receiving sensors. However, this requires close synchronization between the emitter and the sensors. This synchronization is usually done using a radio or optical channel, which requires additional hardware and power to operate. On the other hand, for many applications of great interest, low-cost, small, and lightweight sensors with very small batteries are required. Here, an innovative technique to measure the distance between emitter and receiver by using ultrasonic signals in air is proposed. In fact, the amount of the signal attenuation in air depends on the frequency content of the signal itself. The attenuation level that the signal undergoes at different frequencies provides information on the distance between emitter and receiver without the need for any synchronization between them. A mathematical relationship here proposed allows for estimating the distance between emitter and receiver starting from the measurement of the frequency dependent attenuation along the traveled path. The level of attenuation in the air is measured online along the operation of the proposed technique. The simulations showed that the range accuracy increases with the decrease of the ultrasonic transducer diameter. In particular, with a diameter of 0.5 mm, an error of less than ± 2.7 cm (average value 1.1 cm) is reached along two plane sections of the typical room of the office considered (4 × 4 × 3 m3).

MONITORING TERRAIN DEFORMATIONS CAUSED BY UNDERGROUND MINING USING UAV DATA

Abstract. Underground mining causes terrain surface deformations that lead to various threats to the environment and people, thus a systematic deformation monitoring needs to be performed. This monitoring mainly focuses only on the vertical part of the deformation and remote sensing techniques are currently very often used for this purpose. The development of Unmanned Aerial Systems (UASs) open new possibilities in this context. Most commonly, the mapping UASs are equipped with RGB cameras but also other lightweight sensors are utilized. In this work, the usefulness of UAS photogrammetry and LiDAR data is investigated in the context of detection and measurement of terrain deformations caused by underground mining. The accuracy of the methods was compared in reference to TLS data. The UAS and TLS measurements were performed in 2018 and 2019 but the subsidence was also evaluated in regards to ALS data acquired in 2011. The standard methodology based on Digital Terrain Models of Difference (DoDs) was applied to detect the subsidence. The DoD analysis was restricted to the hard surfaces. The profiles along the roads were also analysed to validate the accuracy of the data. The analysis showed that the UAS photogrammetry enables to obtain less noisy data and more accurate results of the terrain subsidence measurement than the UAS LiDAR sensors. The comparison of the DoDs showed about 33 cm subsidence between 2011 and 2018, which gives a subsidence rate of about 5 cm/year. The observed subsidence between years 2018 and 2019 was equal to about 5 to 15 cm depending on the measurement technique and investigated area.

A low-cost stand-alone platform for measuring motor behavior across developmental applications

SummaryMotion tracking provides unique insights into motor, cognitive, and social development by capturing subtle variations into how movements are planned and controlled. Here, we present a low-cost, wearable movement measurement platform, KiD, specifically designed for tracking the movements of infants and children in a variety of natural settings. KiD consists of a small, lightweight sensor containing a nine-axis inertial measurement unit plus an integrated processor for computing rotations. Measurements of three-dimensional acceleration using KiD compare well with those of current state-of-the-art optical motion capture systems. As a proof of concept, we demonstrate successful classification of different types of sinusoidal right arm movements using KiD.

Piezo-based flexural vibration suppression for an annular rotor via rotating-frame H2 control optimization

Elastic vibration can arise in annular and thin-walled rotor structures, impacting on operating performance and the risk of failure. Feedback control to reduce flexural vibration can be realized using lightweight actuators and sensors embedded in the rotor structure. To design optimal controllers, rotating-frame models of both the structural dynamics and sources of excitation are required. This paper describes a solution to this problem for the case of an annular rotor equipped with piezo patch actuators and sensors. To account for space-fixed external excitation sources, a forcing function is considered involving specified spatial and frequency domain distributions. A model-based [Formula: see text] synthesis is used to compute optimal control solutions. These are tested experimentally on a thin-walled cylindrical steel rotor for cases with narrowband and broadband excitation sources, applied from the fixed frame. The results show that frequency-splitting within the rotating-frame dynamics plays a key role in predicting and controlling resonance. The effectiveness of the optimal control methodology in reducing circumferential vibration of the annular rotor is also confirmed.

Halide-modulated self-assembly of metal-free perovskite single crystals for bio-friendly X-ray detection

Halide-modulated self-assembly of metal-free perovskite single crystals for bio-friendly X-ray detection