Tactile information is crucial for recognizing physical interactions, manipulation of an object, and motion planning for a robotic gripper; however, concurrent tactile technologies have certain limitations over directional force sensing. In particular, they are expensive, difficult to fabricate, and mostly unsuitable for underwater use. Here, we present a facile and cost-effective synthesis technique of a flexible multi-directional force sensing system, which is also favorable to be utilized in underwater environments. We made use of four flex sensors within a silicone-made hemispherical shell structure. Each sensor was placed 90 Having the presence of a mechanoreceptor that enables sensing both the direction and the magnitude of the applied force on the fingertip instantaneously, human hands can manipulate objects and perform a variety of tasks deftly . RoboticAt present, a broad range of solutions is available for tactile sensor technology depending on different types of hardware employed ,20; howeIn this paper, we propose a simple and cost-effective fabrication technique for a flexible multi-directional force sensing system. We employed four flex sensors and silicone molds to build a hemispherical shell-type sensor structure. The flex sensors are placed at 90In order to develop the multi-directional force sensing system, four flex sensors are embedded in molded silicone (Dragon Skin Fx Pro (DSFP), Reynolds Advanced Materials, Boston, MA, USA) as demonstrated in In order to fabricate the sensor, two molds having different patterns see  have beeCommercially available indentation equipment  with a 10 N force gauge and a 10 mm diameter indenter was used to calibrate the fabricated sensor. Since the equipment is capable of indenting in the vertical direction only, the fabricated sensor was aligned and fixed in a position to apply forces on desired locations precisely. As discussed in the conceptual working principle of the sensor, all the flex sensors would bend in a similar fashion as the normal force exerted on the top of the shell. We also investigated the effect of exerting other directional forces along the x- and y-axis in both positive and negative directions and along the lines with ±45Finally, we estimated force magnitude from the sensor data by employing a calibration matrix equation. The calibration matrix equation can be generated as follows:n = no. of data points.Sensor data can be split into four columns for four flex sensors and force matrix into three i.e., FNext, with the stored compliance matrix data The deformation of the hemispherical shell structure was evaluated using the finite element method (FEM) in the commercial software, ANSYS Mechanical by Ansys Inc., Canonsburg, PA, USA. To simulate the real system, the hemispherical geometry was constructed, including the flex sensors and steel plates with soldering. The hemisphere and flex sensor dimensions were maintained exactly the same as the fabricated sensor; however, to keep the geometry relatively simple, an equivalent area on top of the flex sensor was considered for steel plate and soldering material combined. The geometry is shown in N = number of elements;Finally, the boundary conditions were set according to the experimental setup. The sensor base was kept fixed while forces were applied on a circular area of 10 mm diameter to replicate the force from the indenter in the experimental setup. Multi-directional forces were applied to the sensor in the experiments. The forces from two latitudes that were in the same directions described in the force calibration were considered for the simulations: (i) normal to the equator on the top pole of the hemisphere  for around 30 min and tested its signal. The sensor can provide signals against exerted force while remaining underwater. In recapitulation, we have designed and fabricated a flex sensor-based multi-directional force sensing system employing a simple and low-cost methodology. The force sensing system can effectively detect and estimate forces from multiple directions. A single point of contact on the sensor has been identified on a two-dimensional The future direction of the study would be to map the sensor against the force by employing a machine learning algorithm so that it can generate a force vector  in real-time in response to a general force applied on the sensor. We also plan to integrate the sensor with our previously built soft robotic gripper  to check


Introduction: Autologous native arteriovenous fistula (AVF) created in the non-dominant arm is the gold standard vascular access for dialysis in end-stage renal disease, but the post-surgical vascular access dysfunction causes a reduction in the patient’s quality of life. Creating a functional upper extremity permanent arteriovenous access is limited by the upper limb’s vascular resources, so good management of a complicated arteriovenous fistula may improve patient outcomes. This article highlights the importance of new surgical options in treating complicated AVFs. Case report: We present the case of a patient with a 17-year-old complex radio-cephalic arterio-venous fistula and a series of surgical interventions performed for life salvage in the first place and functional vascular access in the second place. Furthermore, we describe a successfully created uncommon type of fistula in the lower extremity between the great saphenous vein and the anterior tibial artery as the last possible access for hemodialysis in this patient. Results: The patient underwent the first successful dialysis using the newly created lower limb fistula 1 month after the surgery. Conclusion: Applying new surgical techniques to manage AVFs gives a unique chance to improve the quality of life and reduce morbidity and mortality in these patients. Using data from Romanian Renal Registry, the number of dialysis patients exceeded 13,400, of whom 236 patients were using peritoneal dialysis. In 2021, the number of newly admitted patients up until the present day was 2754, with a slightly decreasing tendency [Comparing the data with the European Registry, the incidence of dialysis initialization was higher in 2019, putting Romania in tenth place among all European countries [Dysfunction of vascular access has remained one of the leading causes of aggravation of the disease and increase in mortality in hemodialysis patients; in this context, a functional arteriovenous fistula remains the dialysis patient’s lifeline .The upper limb arterio-venous fistula (AVF) is well described in the literature. However, ankle AVF is not a common site. Still, there are some papers describing vascular access in the lower limb using the posterior tibial artery and the great saphenous vein ,4.The repair of a long-term functional and complicated AVF is always tricky and is a big challenge for the surgeon, but it is worth a try. This paper aims to present the importance of surgical treatment of 17 years’ functional but ruptured radio-cephalic fistula and afterward a successful creation of a new arteriovenous fistula between the great saphenous vein and the anterior tibial artery in a patient left without any other vascular access possibilities except a life-long catheter.We focused on a 54-year-old male patient known to have stage 5 KDOQI chronic kidney disease on hemodialysis, unilateral renal agenesis, right renal artery stenosis, and secondary hypertension, present with a 17-year-old functional but aneurysmatic dilated, calcified left radio-cephalic fistula, with an approximately 1 x 1 cm skin necrosis at the level of the vein. As the first surgical approach, ligation of the fistula using a circular section of a Dacron prosthesis as a cerclage and a new brachiocephalic fistula in the left arm were performed .This new fistula was immediately used for dialysis. This approach was chosen for technical reasons as the cephalic vein was heavily calcified, rendering a cut through it and then suture impossible. Therefore, closing the fistula was merely prophylactic to avoid cardiac insufficiency onset and the disruption of the fistula until the definitive solution of completely resecting the calcified vein, followed by the performing of a new fistula.Before the planned surgical intervention, the patient was presented to the Emergency Room with active bleeding in the left forearm and edema of the left half of the body. A Computed Tomography angiography (CTA) described a left central brachiocephalic vein thrombosis. The surgical exploration showed the recanalization of the ligated radiocephalic fistula and rupture at the level of the skin ulceration. As an emergency intervention, the patient underwent a second ligation and suture of the ruptured fistula for life salvage. Despite the life-saving operation, the fundamental problem was not solved; further interventions were necessary .Complete resection of the ruptured fistula, including a small part of the radial artery at the level of the anastomosis, was performed. A short radial interposition using the great saphenous vein harvested from the left lower limb ended the intervention. In addition, the ligation of the newly created left brachiocephalic fistula became mandatory because of venous hypertension caused by the left central brachiocephalic vein thrombosis. Two reinterventions were needed due to hemorrhagic complications.The histopathological examination of the excised vein confirmed heavy calcification within the atherosclerotic modifications.Completing the patient’s journey, a forearm skin reconstruction with a split-thickness skin graft (STSG) was performed. The patient’s hemodialysis was managed using a temporary tunneled catheter in the left femoral vein during all this time.One month after discharge, a new radiocephalic fistula was performed in the right forearm, which would also be closed later because of symptomatic ischemic steal syndrome. Atherosclerosis-affecting antebrachial arteries led the surgeons not to ligate the radial artery distal to the anastomosis.After multiple surgical procedures involving both upper extremities to reestablish functional vascular access for dialysis, it was decided to try, as the last possibility, an arteriovenous fistula in the lower limb. After ultrasound mapping of the right lower limb vessels, the vascular surgeons performed a fistula between the great saphenous vein and the anterior tibial artery with the aid of a sciatic lumbar plexus block. The ligation of all of the collateral branches of the vein was performed. Due to massive atherosclerosis, the shunt between the saphenous vein and the posterior tibial artery (preferred in this situation) was not possible. The procedure had no hemorrhagic complications .After 4 weeks, the right lower limb became critically ischemic. In the absence of other hemodynamical explanations, peripheral angiography was decided. Several serial critical stenoses of the posterior tibial artery were identified and managed by balloon dilatations and stent implantation at the distal level of the posterior tibial artery. The angiographical result, as well as the clinical evolution, was good.On the seventh postoperative day, the patient was discharged with a functional fistula in the right lower limb. One month later, the patient underwent the first successful hemodialysis on the lower limb fistula .Follow ups after 3 months and 6 months acknowledged the satisfactory performance of the lower leg fistula. Additionally, reconstruction of the left upper limb circulation saved the patient’s life.The radiocephalic arteriovenous fistula performed at wrist level is the recommended first choice of vascular access for hemodialysis. In comparison with grafts and catheters, the native fistula increases the life expectancy of these patients .After an arteriovenous fistula is created, local hemodynamic changes occur, as the fistula is the result of the connection between a high-pressure artery and a low-pressure vein. The rate of complications increases as the patency rate decreases, as was observed on elderly patients benefiting for long periods from hemodialysis. Additionally, the age of the fistula is directly proportional to the occurrence of fistula failure .Vascular access dysfunction is a global problem and the most important cause of mortality in these patients. Current literature describes the following as the most frequent fistula complications: aneurysm, rupture, thrombosis, stenosis , infectiThe Kidney Disease Outcomes Quality Initiative (KDOQI) considers it essential to educate patients in emergencies, such as aneurysm rupture or hemorrhage, and to recognize the symptoms of fistula complications, such as thrombosis, stenosis, or fistula dysfunction . The genUnfortunately, either venous resources in the upper limb often become depleted or the vessels at this level do not have the necessary patency to create reliable fistulas. Whenever preservation of the functionality of the upper limb fistulas is impossible, we have to try to develop fistulas in the lower limbs . DespiteAn arteriovenous fistula in the lower limb is not an experimented option, yet the medical literature describes some cases with relatively high patency rates. The most frequently described fistula in the lower limb is between the posterior tibial artery and the great saphenous vein . An arteThe patient’s follow-up was monthly, consisting of ultrasonographic examination, flow measurement, and peak systolic velocity recording . PostopeThe particularity of this case consists of a complex therapeutical approach and a unique type of arteriovenous fistula in the lower limb. Preoperative angiographic exploration is recommended when this type of fistula is planned. Careful assessment must be made to foresee the possibility of a steal syndrome. As appealing and effective as the ankle approach may look, it should only be performed after ruling out every other access site in the upper limb.As a final idea, we consider that the most critical aspect of the correct management of a complicated arteriovenous fistula is, first, to improve the quality of life in these patients and to decrease the morbidity and mortality and, second, to develop new dialysis techniques that are cost-effective, accessible, and offer good outcomes .The surgical management of the complicated arterio-venous fistulas is essential for decreasing the morbidity and mortality of hemodialysis patients. We consider the particularity of the presented case is the authenticity of the surgical techniques, which are not commonly described in the literature. We also want to emphasize the importance of an arterio-venous fistula in the lower limb, which is not a frequently reported option but is an important one in the long-run benefit of improving the patient’s quality of life. Additionally, an angiography is highly recommended to avoid critical limb ischemia before this procedure.


Cymbal transducers are often used in arrays for underwater communication and detection systems. The working environment of a cymbal array is underwater; therefore, waterproofing, salt-corrosion prevention, and impact resistance are necessary for stable operation of the array. Hence, we simulated potting a cymbal array with 15 different rubber and epoxy materials available in the market, using the finite element method, and analyzed their effect on the transmitting voltage response spectrum of the array. From the analysis results, we selected the material that would achieve the widest frequency bandwidth, while preserving the structural stability of the array. A potting method corresponding to the selected material was suggested as well. This study provides guidelines for the selection of a potting material for use in underwater transducer arrays. Since their inception, cymbal transducers have been recognized for their simple structure, low cost, and excellent acoustic performance in underwater communication and detection systems. The array is an important part of a sonar system, but it encounters a harsh marine environment in practical use. Its performance and efficiency can only be guaranteed if its safety is maintained.To ensure stable operation and avoid the influence of environmental factors, such as moisture, corrosion, and external impacts, the cymbal array can be protected by potting ,2. PottiThe cymbal transducer has been studied and improved upon by many scholars since its invention. Zhang et al. ,5 designDogan et al. ,9 used tAlthough several studies have been conducted on cymbal-array structure designs, as reviewed above, work on insulating the array has not been as active. Tressler et al.  attempteFor underwater acoustic transducers, in addition to offering protection, the potting material must match the acoustic impedance between the array and water. Good acoustic impedance matching results in a wide frequency bandwidth of the array. However, most polymeric materials applicable to potting exhibit certain damping effect on the array. A good potting material will only cause a small decrease in the TVR level of the array. Hence, much care should be taken when selecting the potting material for acoustic transducers.Furthermore, the cymbal transducer is sensitive to the effect of potting, owing to its inherent nature of operation; that is, the flexural vibration of the thin metallic end-caps. However, very few of the polymeric potting materials available for underwater insulation have been tested in previous studies on cymbal transducers. For both good protection and acoustic performance, a more rigorous approach should be adopted to select the material and method for potting cymbal arrays.In this study, we aimed to identify a high-performance potting material and an efficient method for potting cymbal arrays. We selected various epoxy and rubber materials known for good insulation and reliability among those available on the market, and used the FEM to simulate potting a cymbal array with them to determine the best material to widen the array bandwidth. A reduction in the TVR level of the array, owing to potting, was inevitable, but we aimed to minimize this reduction. In means that we wanted to increase the gain-bandwidth product of the array by widening the bandwidth. After determining the potting material, we investigated an appropriate method for potting the cymbal array with the material. An experimental cymbal-array specimen was fabricated and potted with the selected material to verify the efficacy of the potting material.®, a commercial finite element analysis (FEA) software package, to analyze the effect of potting. A cymbal transducer is composed of a piezoceramic disk, a plastic ring, and two caps. The materials of the caps and ring were brass and polyetheretherketone (PEEK), respectively. The PZT-5H piezoceramic was used [This study used PZFlexwas used . In the was used . Figure The array is a double-layered cymbal array, as shown in Because both the cymbal transducer and array have axisymmetric structures, a 3D quarter model was used to save analysis time. The array was surrounded by sufficient water to preserve the far-field distance from the array to a measurement point around the edge. All the edges of the water domain were enforced with sound-absorbing boundary conditions to prevent any reflection of waves at the boundary.The potting material is positioned between the water and array, so it plays not only a protective role but also an acoustic matching role. To ensure proper array insulation, the potting material should not deteriorate the acoustic performance of the array. The TVR level and −3 dB fractional bandwidth (FBW) are two representative parameters that measure the performance of underwater acoustic transducer arrays. A good potting material should preserve a higher TVR level and wider FBW of the array after potting.For the potting material shown in f0, and the frequency scale of the spectrum was normalized to f0, as shown in Using the model shown in f0, which resulted in a sharp notch at 1.7 f0. The high stiffness is reflected in the high acoustic impedance of the materials, as presented in The TVR spectra were significantly different from each other, implying a significant difference in the effect of each material. Five relatively stiffer materials  caused clear harmonics of the fundamental mode at According to In a manner similar to that for the epoxy materials, the effect of the rubber potting materials on the TVR spectrum of the cymbal array was analyzed using FEM with the model shown in f0, whereas other materials showed no considerable downward trend. Aqualene, Devcon, and urethane exhibited relatively large fluctuations. According to the physical properties listed in The results of the analysis are presented in The quantitative acoustic characteristics were extracted from the TVR spectra and are summarized in The purpose of this study was to determine the best potting material to obtain the widest array bandwidth. Hence, of the 15 materials tested, RTV3460 was selected as the final potting material. RTV3460 has been used as an insulating material for many applications . RTV3460As the next step, we attempted to find an appropriate method to pot the cymbal array with the polymeric material determined in To better protect the array, we changed the thickness and pot geometry. An increase in the potting-material thickness was likely to have a negative impact on the TVR level of the array, because polymeric materials have a damping effect on sound waves . HoweverIn The array potted by method 3 in The effects of the three potting methods were analyzed using the FEA model shown in To verify the rationality of selecting the potting material and method, we fabricated a cymbal array specimen and measured its TVR spectrum. In the previous sections, we chose RTV3460 as the potting material and Potting method 2 as the final potting method. Our fabricated array was a 5 × 5 cymbal array, instead of a 3 × 3 array. We increased the number of cymbal transducers in the array to improve the signal-to-noise ratio in the measurement; however, the structure and material of the fabricated 5 × 5 array were exactly the same as those of the 3 × 3 FEA model shown in To facilitate the potting process, we made a mold exclusively for the potting. The underwater TVR spectrum of the fabricated array was measured using the experimental setup illustrated in ®, we simulated potting of the cymbal array with 15 representative epoxy and rubber materials available on the market for insulating underwater acoustic transducers, and analyzed their effect on the TVR spectrum of the array. The goal of this work was to find a high-performance potting material to obtain the widest array bandwidth.Because a cymbal array is typically used as an underwater projector, it must be protected from the harsh underwater environment while preserving its acoustical properties. Therefore, using PZFlexAmong the tested materials, RTV3460 was selected as the potting material, and method 2 in


Connected and autonomous vehicles (CAVs) have witnessed significant attention from industries, and academia for research and developments towards the on-road realisation of the technology. State-of-the-art CAVs utilise existing navigation systems for mobility and travel path planning. However, reliable connectivity to navigation systems is not guaranteed, particularly in urban road traffic environments with high-rise buildings, nearby roads and multi-level flyovers. In this connection, this paper presents TAKEN-Traffic Knowledge-based Navigation for enabling CAVs in urban road traffic environments. A traffic analysis model is proposed for mining the sensor-oriented traffic data to generate a precise navigation path for the vehicle. A knowledge-sharing method is developed for collecting and generating new traffic knowledge from on-road vehicles. CAVs navigation is executed using the information enabled by traffic knowledge and analysis. The experimental performance evaluation results attest to the benefits of TAKEN in the precise navigation of CAVs in urban traffic environments. Connected and autonomous vehicles (CAVs) are emerging as next-generation transportation in terms of growing connectivity and automation devices in vehicles ,2. Most The early advancements in mobility planning for CAVs were majorly based on sensor-oriented predictions . For exaThe recent advancement in advanced machine learning has enabled deep learning techniques to be applied in diverse domains ,16 incluTowards this end, this paper presents a deep learning-enabled framework TAKEN- Traffic Knowledge sharing based Navigation for connected and autonomous vehicles. Different autonomous aspects have been considered such as state estimation, visual perception and path or motion planning for effectively reducing the dependency of autonomous vehicles on the existing navigation systems. The major contributions of the paper can be summarised as follows:The rest of the paper is organised as follows. In The advancements in mobility planning for CAVs can be divided into two major sub-themes including sensor-enabled predictions and advanced machine learning enabled predictions ,21. TowaTowards advanced machine learning-enabled predictions, deep neural network-based calibration automation has been suggested for improving the accuracy of sensor centric distance calculation . SpecifiOverview of the proposed TAKEN framework is presented in Localization and environment perception are one of the most critical tasks while piloting a vehicle ,27. The The lateral and the longitudinal dynamics of the vehicle defines the steering angle and the throttle position respectively. Constrained with different forces/attributes such as the slip, radial forces, friction, centrifugal force, the aerodynamics and the banking of the road, the main goal of the lateral controller is to follow a predefined path such that the resultant (cross-track) error is minimised and that of the longitudinal controller is to maintain the reference velocity. The Stanley controller , a type k. On the other hand, the PID  controller [Equation  24] giv giv24] gntroller  is used Equation  represen∞, and Nonlinear Approaches”, by Dan Simon [These systems work only if there is a contiguous supply of information from sensors such as the GNSS and the wheel odometers without which it would be difficult for a self-driving agent to estimate its state in the environment. The performance of these systems depends heavily upon the hardware devices used to measure them. Typically, measuring devices are subjected to noise and the data captured by such a device are not accurate, making them unreliable. Methods such as least squares, recursive least squares and Kalman filters can be used in deriving such values as expressed in the book named, “Optimal State Estimation: Kalman, Han Simon . Howeverk is linearly spaced values between 0 and p, while sampling coordinates perpendicular to the direction of forwarding velocity of the car and linearly spaced values between 0 and q while sampling along the direction of forwarding velocity of the car. Here, p and q are the vehicle offsets such that, m is the number of training instances.Computing the velocity is trivial because it can be obtained easily by the speedometer, however, calculating pseudo velocity can act as a bypass during the cases of latency and glitches while computing the throttle positions. To consider safety, we force the model to overfit. The advantage of this is that one could use the same model architecture to trace different paths and, thus, the amount of storage required to store any path information remains the same. As a result, even without using GNSS systems, the autonomous vehicle would be able to identify itself in the environment by just keeping the track of its heading and velocity. The system is implemented using a feedback loop such that at every timestep the current position and the heading of the vehicle are estimated from the previous timestep’s output. In other words, the forward velocity of the vehicle inferred from the velocity predictor model or the speedometer is used to estimate the next position and the heading (using the heading predictor model) of the autonomous vehicle which in turn guides its manoeuvring. This process is represented in f), depth of field and frame rate. Cameras are used in tasks such as depth estimation, segmentation, object detection and localisation, lane estimation, etc.For any agents or objects on the road, objects and event identifications are required. Cameras are very important for an SDC because the data generated by such an instrument are very close to what humans can perceive. Therefore, different types of computations can be performed on such data to obtain valuable knowledge. Cameras are generally characterised by metrics such as field of view, resolution, dynamic range, focal length  and taking care of events such as lane changing, obeying traffic rules, identification and tracking of static and dynamic obstacles, collision detection, etc. (low-level planning). The work focuses on low-level planning by designing a prioritised behavioural model which would enable safe overtaking of vehicles, object collision avoidance, roadblocks and abidance by the traffic rules. Behaviour planning involves making high-level decisions to ensure safe driving. The behavioural models are triggered purely based on the visual perception stack’s inferences. The TAKEN system defines them as a state machine to handle pedestrians in the scene A, vehiclAs a result of the training process, the function approximator is associated with parameters tuned to recognise different events. Every SDC in the network bears a database which would store the tuned parameters, the low dimensional representation of scenarios along with the decision that it took and the corresponding outcome. Although the cloud stores a combined copy of such databases across the network, autonomous vehicles in the network can access the processed information from other vehicles. This avoids requesting and packet jamming at the cloud node. To account for reliability, the architecture enables the client (SDC)—cloud interaction for the decision-making process, only if the network latency is beyond a certain threshold. Therefore, the task of decision making reduces to interpreting the scenario as a low dimensional vector using parameters stored in its database and deducing decisions by communicating in the network. The cloud periodically updates the status of the database across the network and fine-tunes the model so as to keep it informed. The advantage of this architecture is that the computational power and the time required for processing data and the amount of data to be stored in an SDC are reduced. This is depicted in The controllers receive control commands from both the knowledge sharing module and the analysis module of the autonomous vehicle. However, if the response is received by the knowledge sharing module before the analysis module finishes its execution and the response of the knowledge sharing module is associated with high confidence, an event destruction trigger is passed to the analysis module to abort its current execution and prepare resources for the next set of events. Optionally, the controllers can also override/reject the output of the knowledge sharing module and continue to wait for the analysis module to complete its execution. This set of control commands is responsible for the movement of the vehicle. As a result of the actions taken by the autonomous vehicle in the environment, the responses and events are sensed/recorded by the various onboard sensors and fed back to the analysis module.The TAKEN system uses the Carla Simulator to simulate the autonomous vehicle and its environment. It is an open-source tool, transparent and closely related to the real world. It supports the simulation of different sensors such as LIDAR, RADAR, GNSS system, odometer, depth cameras, segmentation cameras and RGB cameras. People can build environments using the Unreal engine and associate rules and regulations. Carla simulator provides free access to its various digital assets such as vehicles, pedestrians, buildings and other entities present in the scene. The simulator is characterised by scalable client-server architecture. It also enables the programmers to attach more than one player to the same server. In addition, it facilitates traffic management, recording and tracing, ROS bridging and Autoware implementation, scenario runners and public contributions.The Carla simulator provides us with a variety of towns/maps in which we could test our autonomous models. The working environment registers a set of actions taken by the player and associates with it the corresponding responses. The player on the other hand perceives its environment through various inbuilt simulated sensors and uses it as the source/input for the next action that ought to be taken, this is represented in the The TAKEN system implements a simulated car which is capable of driving on its own in the Carla simulator’s environment. The system uses several functionalities and add-ons provided by Carla. The proposed model falls under level three autonomy. Different situations and scenarios such as the sudden appearance of a pedestrian or a vehicle in front of the car, roadblocks, overtaking, and roundabouts which we generally come across while driving, are addressed in this work using various concepts namely, visual perception, state estimation, planning and controllers. The simulated player can manoeuvre correctly by adhering to traffic rules and regulations in medium traffic conditions. In addition to these, the paper elaborates on strategies through which we could account for sensor failures, navigation systems specific, and the task of knowledge sharing using the centralised/decentralised communication architecture. The knowledge sharing module in the TAKEN system uses images as raw information for detecting potholes on the road F and cauhttps://drive.google.com/file/d/1IXyGhBM2OLqZS4HTRtfFpyoeYW-f11aI/view?usp=sharing (accessed on 30 October 2022).The TAKEN system has implemented various components of an SDC namely, visual perception, state estimator, motion planning and behavioural modelling, using the Carla simulator. The TAKEN system has successfully devised an alternative solution to handle issues faced by self-driving agents due to sensor failures. In addition to this, the work has also introduced how one could infer knowledge from other players in the environment so as to make wiser and quicker decisions. The main motivation behind introducing this concept was from papers such as “Crazyswarm: A large nano-quadcopter swarm”  in which


Current approaches aim to identify the presence of a risky event, such as falls, when it has already occurred. However, there is a need to develop methods capable of identifying the presence of a risk condition in order to prevent the occurrence of the damage itself. The measurement of vital and non-vital physiological parameters enables the worker’s complex state estimation to identify risk conditions preventing falls, slips and fainting, as a result of physical overexertion and heat stress exposure. This paper aims at investigating classification approaches to identify risk conditions with respect to normal physical activity by exploiting physiological measurements in different conditions of physical exertion and heat stress. Moreover, the role played in the risk identification by specific sensors and features was investigated. The obtained results evidenced that k-Nearest Neighbors is the best performing algorithm in all the experimental conditions exploiting only information coming from cardiorespiratory monitoring (mean accuracy  About 84% of all non-fatal injuries and illnesses leading to days away from work in 2020 involved slips, trips, falls, overexertion or exposure to harmful substances or environmental conditions, according to International Labour Organisation (ILO) statistics ,2. With Accelerometers, gyroscopes, pressure sensors, video/depth cameras, microphones and radio frequency sensors are sensor modalities that are generally used to perform fall detection ,5. The aPhysiological monitoring may represent an effective way to collect information about workers’ health and safety statuses . CardiorThis work aims at proposing the best method of achieving risk identification starting from physiological sensing. For this purpose, several classifiers already used in the literature are compared in order to measure their performance in identifying a risky condition between two activities that are difficult to distinguish using conventional accelerometer-based approaches. Moreover, since the physiological parameters are influenced by the activity of the user and the environment, an experiment is designed to collect data in different physical workloads and heat stress conditions. The presence of physical risk of falling was then simulated by means of a stabilometric platform while a treadmill was used to induce physical exertion in the participants.The rest of the paper is structured as follows: This section presents the proposed approach to identify risky conditions shown in The PMS proposed in this work includes measurements of cardiorespiratory activity, GSR and ST, as these were found to be the parameters that most closely reflect the activities of the autonomic nervous system in response to external stimuli, such as risk factors .The electrocardiogram provides multiple pieces of information about the health and cognitive state of the user. Heart Rate (HR) and Heart Rate Variability (HRV) are the two main features that can be extracted from the electrical activity of the heart. Indeed, HR exhibits modifications according to the activity performed and the health status of the user. For instance, HR decelerates after the stimuli administration, and a high varying HR with respect to the baseline value is associated with high aroused conditions . On the Many disorders and/or stimulus administrations generate alterations in respiratory activity. Respiration Rate (RR) is a vital sign sensitive to different pathological conditions and/or stressors including emotional stress, cognitive load, heat, cold, physical effort and exercise-induced fatigue . MoreoveGSR is a physiological parameter commonly used to assess users’ cognitive state . The higST is another important parameter for determining the psychophysiological state and can be used to predict heat stroke conditions . In factData about the acceleration of a user can be retrieved by means of M-IMUs sensors . Such seHR, expressed in beats per minute (bpm), as [Several parameters can be extracted from the recorded raw signals. The ECG allows the computation of HR and HRV. The HR is defined as the number of R-peaks per minute in the ECG trace. Given the Inter-Beat Intervals (bpm), as :(1)HR(i)RMSSD) is a time domain HRV metric, computed as:N represents the number of The Root Mean Square of Successive Differences , can be computed asFrom the breathing waveform, respiratory events are detected by identifying the local maxima which represent the maximum expansion of the rib cage. As for the The GSR requires signal processing to retrieve the tonic and phasic components. They can be extracted by applying a 5 Hz Butterworth low pass filter, to remove noise and motion artifacts, and then applying a stimulus .x is the physiological signals acquired at a given time stamp, All the collected data require a normalization procedure to allow comparisons. In fact, physiological features exhibit high intra- and inter-subject variability as a result of age, gender, time of day and many other factors. Normalization reduces the effect of such variability by evaluating the response of each physiological parameter with respect to the baseline value, i.e., collected in a rest condition of the user. In particular, the participants are asked to sit comfortably, blindfolded and acoustically isolated for 5 min. The normalization is performed as:AVM) is computed as:The M-IMUs placed on the anatomical landmarks of the users return accelerations around the three axes. As a synthetic measure, the Acceleration signal Vector Magnitude (std), minimum (min), maximum (max), mean of the first derivative (fd) and mean of the second derivative (sd) are computed in the time window. Given the SCR is a zero-mean signal, the absolute value of the signal is considered during feature extraction. The computation of the statistical feature from the N_pks), average peak amplitude (mean(A_pks)), the standard deviation of peak amplitude (std(A_pks)) and the energy of the GSR signal response (E_gsr) [The feature extraction should be computed over a temporal window that allows capturing appreciable variation in the signals of interest. In particular, the physiological signals slowly change. Therefore, the time window should be of the order of seconds ,36. A tiion data . The mea (E_gsr) . In partk-nearest sample belonging to the same class (near hits) and the k-nearest sample belonging to another class (near misses). If the distance to the near hits is greater than the distance to the near misses, the weight associated with a feature increases; otherwise, it decreases. The number of samples k is empirically set. Taking into account reduced feature sets may increase the classifiers’ performances.From all the features that have been extracted, the weights of the features that make a positive contribution to the classification have been quantified in this paper. In order to highlight the most informative features that can be used to highlight risk perception, an automatic feature selection method is applied to the PHY dataset. The ReliefF algorithm, along with its derivatives, is an evaluation filter algorithm capable of detecting feature dependencies by using the concept of nearest neighbors to derive feature statistics that indirectly account for interactions . The Relk-Nearest Neighbors (kNN): This computes the prediction according to the majority of the K-nearest patterns in data space. The hyper-parameters characterizing the algorithm behavior are the number of neighbors, i.e., the number of samples of the known class to be considered closest to establish the class of the unknown sample and the metrics used to compute the distance among the samples.Linear Discriminant Analysis (LDA): This model aims at identifying a hyperplane separating the elements of different classes. It fits a Gaussian density to each class, assuming that all classes share the same covariance matrix.Gaussian Support Vector Machine (SVM): This method finds a non-linear separation between samples in a transformed high dimensional feature space. The algorithm performance is mainly influenced by the mathematical functions that can be used to transform the observations before assigning the prediction. Such space transformation is used to highlight a Gaussian-like separation between the samples belonging to different classes.Decision Tree (Tree): The objective of Tree is to create a model that predicts the target value through simple decision rules derived from the features.Four machine learning algorithms are selected to cope with the current classification task. They are k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), Gaussian Support Vector Machine (SVM) and Decision Tree (Tree). kNN is one of the most used machine learning algorithms used to process physiological data . LDA is The aforementioned models were implemented in MATLAB R2020b software using the hyperparameters presented in The accuracy, defined as the proportion of the total number of correct predictions with respect to the total number of tested samples, is used to assess the classification performance. Accuracy is a metric that generally describes how the model performs across all classes. It is useful when all classes are of equal importance and the dataset has a balanced number of observations among classes . MoreoveThe experimental setup used in this study is composed of the commercial sensing devices shown in The MMS is composed of two MTw Xsens Inertial Measurement Units (IMU) that are used to acquire movement information. One is placed on the chest by means of a corsage worn by the participant and another is fixed on the head with an elastic band.TM chest belt is used to measure cardio-respiratory activity. It is a wireless wearable device to be worn in direct contact with the skin, which allows real-time recording of physiological parameters related to the cardio-respiratory system, including HR, HRV and RR [The PMS consists of three sensory systems to monitor vital and non-vital physiological parameters. The Zephyr BioHarnessV and RR . The ShiV and RR .Yet Another Robotic Platform (YARP) [The acquisition of all the sensors was temporally synchronized and collected at 40 Hz by using the m (YARP) .An experiment, involving healthy participants, was carried out in order to validate the possibility to identify risk under different physical workloads and heat stress conditions. The Biodex stabilometric platform, presented in In order to generate two different levels of physical exertion in the participant, the treadmill tasks did not have a fixed duration but conditions were defined that had to be fulfilled in order for the task to be completed. In particular, the participants were asked to walk and/or run on the treadmill until their HR reached a percentage of their critical HR (HRc) , definedworkload .ERC) [T is the environmental temperature in Celsius degrees and RH with a Moreover, the full protocol is repeated in two different environmental conditions, characterized by different heat stress exposure levels. To objectify the effect of the environment on participants, the Environmental Risk Coefficient (ERC)  is compuE1 and E2, respectively. This means that the two environmental conditions are properly exposing the participants to different heat stress levels.In the tested environmental conditions, the risk coefficients are Eight healthy participants, 4 males and 4 females with mean age of For each of the tested environmental conditions, the participants underwent an initial test on the stabilometric platform. The participants were then asked to walk until they reached the first N individuals, this method consists of cyclically using The Leave-One-Subject-Out (LOSO) validation method was used to validate the proposed classification algorithms. Having At first, the four machine learning classifiers were trained and tested by using all the data collected from the accelerometers and the physiological sensors, i.e., the ACC and PHY datasets, respectively. This first validation step serves to assess the actual need to use physiological information to identify risk perception. This first validation step serves to assess the actual need to use physiological information to identify risk perception. Fall detection approaches are in fact already able to identify when a fall has occurred, but the aim of this work is to assess the performance of classifiers in recognizing when users are subjected to the presence of risk before it leads to injury. The rest of the analysis conducted in this study will focus on the PHY dataset.Secondly, an analysis is carried out to assess the effect of the environmental conditions on the algorithms’ performance. Model accuracy is compared by separating the performance obtained in environment E1 from those in the thermally stressful E2. Indeed, physiological parameters can be altered by environmental conditions, especially heat stress. The classifiers’ accuracy in identifying risk conditions whatever the working environment is assessed.The specific contribution to the performance of the implemented classifiers is then quantified by calculating the accuracy of the models by modifying sensory information by removing some of it from the PHY dataset. Starting from the full PHY dataset made of GSR, cardiorespiratory (CR) and ST data, seven configurations can be defined: (I) PHY = GSR + CR + ST, (II) GSR + CR, (III) GSR + ST, (IV) CR + ST, (V) CR, (VI) GSR and (VII) ST. Through this analysis, therefore, it will be possible to identify which sensory information has a greater effect on the performance of the classifiers in recognizing risk perception than in the condition in which all physiological information is present.N highest weighted features returning a higher average accuracy over the eight enrolled participants will be considered.Lastly, the ReliefF feature selection algorithm will be applied to identify the weight of each feature in risk identification with p-values becomes The validation of the risk identification approach goes through a comparative analysis presented in  applied . SpecifiAt the end of the experimental sessions, 320,000 raw samples of physiological parameters and accelerations were collected. Specifically, 20,000 samples were acquired from each subject under each thermal stress condition. After the feature extraction step, performed on 2.5 s time windows (100 samples), the two datasets ACC and PHY consist of 3200 samples per each feature. The first analysis conducted concerns the comparison of the classification accuracy of the four selected machine learning models using the two datasets collected in the experimental acquisitions: ACC and PHY. Accuracy is shown in p-values As evident, all machine learning models are more accurate in recognizing risk situations when using PHY information with respect to ACC. In particular, kNN, LDA and Tree performance are significantly higher with The performance of the classifiers was then detailed by separating the observations collected in the two different environmental conditions, reported in p-values obtained from the statistical tests are 0.8, 0.5, 0.7 and 0.8 for kNN, LDA, SVM and Tree, respectively. This means that machine learning approaches are able to identify perceived risk conditions irrespective of the environmental stresses on the user. Among all the implemented approaches, kNN turned out to be the most robust one, exhibiting the slightest decrease in performance. Precision, recall and F1 scores are presented in Heat stress, administered in E2, induces an alteration of the physiological state of the enrolled participants. This effect is reflected in the performance of the implemented machine learning models, which showed a decrease in accuracy. However, this decrease is not significant. In fact, the p-values of The performance of each model trained with the complete PHY dataset was compared with the accuracy obtained by modifying sensory information by removing some of its one-by-one sensory information. kNN turned out to be one of the most sensible models. The performance is significantly degraded in the condition GSR + CR, GSR + ST and GSR with p-value = LDA is the model that manages to achieve comparable performance in all tested configurations except for the condition where only surface temperature information is used , std(RR) and std(HR) with mean(A_pks) and std(A_pks) obtained positive median weights.The features that obtained the highest median weights from ReliefF are Given the weights of each feature, the four machine learning algorithms were trained by including one feature at a time, from the heaviest to the least important. For each model, the number of features with the best median accuracy is selected, defining the optimal set of physiological features. In particular, kNN, LDA, SVM and Tree obtain the best results for 3, 25, 5 and 18 features, respectively. p-value = All the implemented models exhibit improved performance when trained only with the optimal set of physiological features. LDA, SVM and Tree improve the medians of performance, although not significantly. In contrast, kNN remains the best model and with only three features achieves an accuracy of Among all the models implemented, kNN appears to be the most appropriate for identifying risk conditions. Indeed, it appears to be able to capture knowledge from the observations of the subjects used in the training phase. The physiological responses exhibit highly non-linear behavior that is captured by kNN regardless of the thermal environmental condition to which the participants are subjected. By removing sensory information that does not contribute positively to classification, kNN is able to achieve the best performance among all the implemented models. In particular, cardiorespiratory information alone turns out to be the most suitable for identifying risky conditions. In other words, cardiorespiratory monitoring systems turned out to be the most relevant for integration in intelligent wearable systems for workers. In fact, in the operating environment, it does not appear to be practicable to equip the worker with all the sensory systems used during the laboratory tests carried out in this work.The performance of the approaches presented in this paper strongly depends on the experimental conditions under which the datasets were acquired and the choice of features extracted from the raw data collected. Using different experimental conditions and/or choosing different feature sets could in fact alter the classification performance. Moreover, the optimal feature set that emerged in this work could change if different features were taken into account in the dataset. Each new predictor could positively as well as negatively affect the classification problem faced in this paper.This study presented an analysis to demonstrate the ability of machine learning models to identify risk conditions from physiological information. In contrast to fall-detection algorithms in the literature, where accelerometers are used to identify a fall once it has occurred, in this work awareness of the risk of falling was generated using a stabilometric platform. This condition was studied with respect to different conditions of physical fatigue and thermal stress. In particular, a study was designed and conducted by enrolling eight healthy participants.The dataset composed of physiological information performed better in recognizing the risk condition than a dataset composed only of M-IMU sensor monitoring head and trunk accelerations. This made it possible to demonstrate how the autonomic responses of the autonomic nervous system generate physiological responses in relation to risk perception. The classifiers proved robust to the presence of environmental heat stress, showing non-significant performance degradation. Furthermore, an analysis was conducted to investigate the effect of removing physiological information by excluding one sensor at a time and applying a feature selection method. The cardiorespiratory information turns out to be the most informative with respect to predicting risk conditions. The kNN algorithm achieved an accuracy of Future efforts will be devoted to collecting data from more participants and enrolling a more heterogeneous population. At the same time, exploring different feature sets could be useful for defining further metrics to identify risks. Furthermore, the same algorithmic approaches should be validated by collecting data with sensors integrated within intelligent wearable systems, to be validated in an operational environment.


Due to urbanization, solid waste pollution is an increasing concern for rivers, possibly threatening human health, ecological integrity, and ecosystem services. Riverine management in urban landscapes requires best management practices since the river is a vital component in urban ecological civilization, and it is very imperative to synchronize the connection between urban development and river protection. Thus, the implementation of proper and innovative measures is vital to control garbage pollution in the rivers. A robot that cleans the waste autonomously can be a good solution to manage river pollution efficiently. Identifying and obtaining precise positions of garbage are the most crucial parts of the visual system for a cleaning robot. Computer vision has paved a way for computers to understand and interpret the surrounding objects. The development of an accurate computer vision system is a vital step toward a robotic platform since this is the front-end observation system before consequent manipulation and grasping systems. The scope of this work is to acquire visual information about floating garbage on the river, which is vital in building a robotic platform for river cleaning robots. In this paper, an automated detection system based on the improved You Only Look Once (YOLO) model is developed to detect floating garbage under various conditions, such as fluctuating illumination, complex background, and occlusion. The proposed object detection model has been shown to promote rapid convergence which improves the training time duration. In addition, the proposed object detection model has been shown to improve detection accuracy by strengthening the non-linear feature extraction process. The results showed that the proposed model achieved a mean average precision (mAP) value of 89%. Hence, the proposed model is considered feasible for identifying five classes of garbage, such as plastic bottles, aluminum cans, plastic bags, styrofoam, and plastic containers. The production of solid waste is one of the main concerns in urban clusters. Urbanization and population growth have been identified as major indicators of the growing rate of solid waste, particularly in water bodies, such as rivers, lakes, seas, and oceans . River eManaging solid waste appropriately in the riverine ecosystem is important for building sustainable cities, yet it remains a challenge for certain developing countries and cities. In the absence of automation and modernization of waste management operations, the collection of solid waste remains to be labor-intensive activity. Hence, this work proposed an automatic solid waste collecting system. Several initiatives have been adopted to manage pollution, for example, manual and machine-based cleaning require human supervision constantly. In addition, the requirement of manual labor for cleaning waste can be a threat to the person –10. HencGenerally, computer vision is an area of artificial intelligence (AI) that permits computers and systems to interpret information from various visual inputs. Computer vision aims to replicate the capability of human vision for computers or devices to understand images. Object detection is a computer vision method that aims to detect the location of target objects in images or videos. The detection process includes two parts: (1) the category information and probability of the target and (2) the location of the target by applying bounding boxes with labels –23. ReceA novel object detection algorithm that automatically detects garbage by using a computer vision approach.The proposed object detection model promotes rapid convergence that improves the training time duration.The proposed object detection model improves the detection accuracy by strengthening the non-linear feature extraction process.This paper is arranged as follows. Section Background study discusses the background study of object detection methods. Next, Section Proposed method explains the methodology of the proposed method. Experimental results are tabulated and discussed in Section Results and discussion. Lastly, Section Conclusion concludes the paper.The mainstream object detection algorithms are based on convolution neural networks (CNN), which are one-stage and two-stage detections, using different feature extraction methods. Object detection algorithms that adopt a two-stage detection method include regions with CNN (R-CNN), fast regions with CNN (Fast R)-CNN, and Faster R-CNN, which divide the detection task into (1) region proposal and (2) classification. Meanwhile, the one-stage detection method integrates region proposal and classification into one step, which reduces the detection time. The mainstream methods of one-stage detection are Single Shot Detector (SSD) and YOLO .In recent years, deep learning algorithm in computer vision is a rapidly developing research topic in classifying floating debris. The work in Fulton et al.  classifiOn the other hand, a one-stage detection approach based on the YOLOv3 model is applied in the garbage detection system in Watanabe et al.  and Li eBased on previous works reported in Junos et al. ; Morera Generally, the model structure of YOLOv4 consists of backbone, neck, and prediction. By adopting the learning ability of Cross-Stage Partial Network (CSPNet), YOLOv4 built the CSPDarkNet53. Darknet-53 is a convolutional neural network that consists of 53 layers deep and functions as a backbone of the YOLOv4 model. Meanwhile, the Neck includes the Spatial Pyramid Pooling Network (SPPNet) and Path Aggregation Network (PANet). SPPNet focuses on removing the fixed-size constraint of the network. In SPPNet, the feature layer is convolved three times, followed by the input feature layer, which is maximally pooled by applying the maximum pooling cores using different sizes. Then, PANet convolves the feature layers after the operation of Backbone and SPPNet. PANet enhances the segmentation process by conserving spatial information. This is computed by accurately localizing the signals in lower layers and adopting path augmentation, which lessens the information path between lower layers and the topmost feature. Moreover, YOLOv4 adopted a new mosaic data augmentation technique with the aim to increase the dataset and presented DIOU as the positioning loss function . Hence, In this work, the training images are obtained under various conditions in terms of brightness and positions to prevent overfitting . Five clThis work proposed a novel detection model for the automatic garbage detection system that consists of three stages: backbone feature extraction network, neck network, and object detection stage. The novelty of the proposed work is the development of an improved YOLO model by optimizing the network structure of a YOLOv4 model to detect solid wastes from the image dataset. Several improvements are performed to optimize the network structure of the conventional YOLOv4 model, such as follows: (1) CSPDarkNet53 is improved by adopting CSP1_X with the aim to reduce network modules which will lead to a reduction of parameters in feature extraction, (2) Hard-Swish (H-Swish) activation function is adopted to strengthen the non-linear feature extraction ability of the network, and (3) PANet in the Neck module is improved by adopting CSP2_X to enhance feature extraction and enhance the accuracy of the model. Finally, the performance of the proposed algorithm is compared with previous works to justify the contribution of the proposed model.A residual module is proposed for the conventional YOLO-v4 to improve the network's learning ability and reduce the parameters of the network. The computation of the residual module (Res-unit) can be summarized as follows. First, a 1 × 1 convolution process is performed followed by a 3 × 3 convolution; then, the two outputs of the module are weighted. The weighting computation is performed to increase the information of the feature layer without altering the dimension data. In CSPDarkNet53, the input is the set of feature layers of the image, and then down-sampling convolution is executed continually to obtain better semantic information. Consequently, the last three layers of Backbone have the highest semantic information, and then the last three layers of features are selected as the input of the Spatial Pyramid Pooling Network (SPPNet) and Path Aggregation Network (PANet). Though YOLOv4 applies a residual network to decrease the computing power requirement, its memory requirement still needs to be improved. Therefore, in this work, the network structure of CSPDarkNet53 of YOLO-v4 is modified to the CSP1_X, as shown in Hard-Swish (H-Swish) activation function plays an important role in weight reduction. As compared to CSPDarkNet53, the modified network adopts the H-swish activation function , as presIn this case, the Swish function includes the Sigmoid function, which leads to a higher computation cost compared to the ReLU function. However, the Swish function has been shown to be more effective than the ReLU function , 35. TheIn the fine-tuned module structure as shown in via a cross-stage process. The CSP2_X model architecture is presented in The neck network consists of SPPNet and the modified PANet. In this work, the SPPNet component expands the acceptance range of backbone features efficiently, and this contributed to the separation of the vital contextual features. The high computational cost of model reasoning is mostly produced by recurrent occurrences of gradient data in the network optimization process. Therefore, this paper proposes the CSP2_X module into PANet to split the basic feature layer from Backbone into two sections and then reduce the repeated gradient information The adoption of an improved CSPNet network module will have the ability to improve the network feature fusion stage. The collective process can perform the top to down transmission using deeper semantic features in PANet. Simultaneously, the bottom to up deep features is fused from the SPPNet network, hence appreciating feature combination between different backbone layers and different detection layers in the Neck network to provide discriminant features for the prediction network.SxS grids and computing the weighted feature sets to obtain a probability on each area of cells. If the middle of a possible object belongs to one of the cells, a preliminary bounding box will be created based on the prediction probability as determined by the trained model in (2)  .K numerous boxes and extracts a 3D tensor based on (3), where C denotes the defined number of classes.Then, the detection model performs the prediction process using In During the formation of bounding boxes, the intersection over union (IoU) computes the matching of the prediction with the ground truth image. The confidence score decreases if the originally predicted object does not represent the ground truth, hence, resulting in an unsatisfied prediction. For each cell, C is computed based on Pr (Classi|Object). As shown in (8), the object that fulfills the threshold will obtain an initial bounding box despite having multiple predictions over the specific object.This work adopted Bag of Specials (BoS), cross-stage partial connections (CSP), and multi-input weighted residual connections (MiWRC). BoS is simply defined as a set of modules that have a significant improvement on the accuracy of the object detection with a small increment of inference cost. Meanwhile, the BoS features for the proposed YOLOv4 detector in this work also included distance-IoU-non-maximum suppression (DIoU-NMS). DIoU is applied by computing the IoU and the distance among the central points of two bounding boxes during the suppression of redundant boxes. The rest of the boundary boxes that predict a similar object will be filtered by NMS while maintaining the one with the highest confidence value. This increases its robustness when it comes to cases with occlusions.The experiments in this work are executed using an epoch of 10,000 and the input image size of 416 × 416 × 3. First, this study investigates the contribution of transfer learning to the performance of the object detector by applying the MS-COCO dataset, in which the pre-trained convolutional weight is applied in the training stage. To determine the optimized network structure of the proposed model, the subdivision parameter is varied between 8 and 64 while keeping other parameters constant. Subdivision simply means the batch is split by the value of subdivision into mini batches. For example, for batch=64 and subdivision=8, the training will have 64/8 = 8 images per mini batches. These mini batches will then be sent to the GPU for the computation process. The computation process will be repeated 8 times until a batch is completed. The new iteration begins to be 8 in this work because it produces the lowest average loss during the training process. Averaging over more images helps to speed up the time for training and to generalize the training even more. However, this can be a problem when the subdivision is reduced because a memory issue might occur if the GPU does not have enough memory to process more images at one time. From Moreover, various pre-trained convolutional weights as feature extraction mechanisms are compared. This analysis was performed to determine the optimized convolutional weights for the network. In addition, the loss function of the proposed model is optimized by computing the IoU thresholds, which indicate the degree of overlap area between the target bounding box generated by the proposed model and the original labeled bounding box. In this work, the IoU loss function is optimized by applying the DIoU-NMS. In the training process, the adoption of DIoU loss in the network loss function has been shown to decrease the number of iterations and improve the degree of the overlap area. Hence, this contributes to higher detection accuracy. Several experiments have been performed to verify the effectiveness of the optimization approach. The evaluation parameters used in this work are precision, recall, mAP, and F1 score. Based on It can be seen that the proposed detection model was able to detect multiple objects in an image even under an occluded condition which justifies its feasibility in detecting floating debris for the river cleaning robot. The plastic bag class has the lowest precision value which indicates that detecting plastic bags is the most challenging task as compared to detecting other types of debris. This is because the plastic bag has complex physical characteristics thus making it challenging to be detected. For analysis purposes, the detection of every image is tested using four threshold values. As shown in A Receiver Operating Characteristic (ROC) curve is computed to measure the performance of the proposed classifier. In addition, In conclusion, an automated detection system based on the optimized YOLO model is developed to detect floating solid wastes that include plastic bottles, aluminum cans, plastic bags, styrofoam, and plastic container. In this work, the proposed model optimized the network structure of the conventional YOLOv4 model that includes (i) modification of CSPDark-Net53 into the backbone to overcome limitations due to training time, (ii) adoption of Hard-Swish activation function, and (iii) improved PANet in the Neck module to aid the feature extraction process. The performance of the proposed YOLO model is compared with previous works and has shown promising results with an mAP value of 89%. This research demonstrates that computer vision system plays an important role in environmental monitoring and provides novel insights for improved decision-making and sustainable management. In a nutshell, this study is important for riverine management in urban landscapes since the river is an important part of urban ecological civilization and human health.The raw data supporting the conclusions of this article will be made available by the authors upon request.NZ contributed to the development of the idea and participated in all phases. UK conducted the data collection/analysis and manuscript preparation. KH and AM performed the analysis with constructive discussions. MA prepared the figures and manuscript. All authors have read and approved the final manuscript.This research was supported by Industry-Driven Innovation Grant (IDIG) by Universiti Malaya with project number PPSI-2020-CLUSTER-SD01.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


A tube-integrated flow sensor is proposed in this study by integrating a micro-electro mechanical systems (MEMS) flow-sensing element and electrical wiring structure on the same copper on polyimide (COP) substrate. The substrate was rolled into a circular tube with the flow-sensing element installed at the center of the tube. The signal lines were simultaneously formed and connected to the Cu layer of the substrate during the fabrication of the sensing structure, thus simplifying the electrical connection process. Finally, by rolling the fabricated sensor substrate, the flow sensor device itself was transformed into a circular tube structure, which defined the airflow region. By implementing several slits on the substrate, the sensing element was successfully placed at the center of the tube where the flow velocity is maximum. Compared to the conventional sensor structure in which the sensor was placed on the inner wall surface of the tube, the sensitivity of the sensor was doubled. Micro-electro mechanical systems (MEMS), which are realized by the semiconductor micro- and nano-fabrication technology that enables nano-level processing of minute mechanical structures, are able to provide ultra-compact and multifunctional devices. Today, many MEMS sensors, such as pressure sensors, accelerometers, and gyro-sensors, are indispensable in our daily lives and are used in various fields, such as automobiles, smartphones, and wearable devices. Traditionally, MEMS sensors have been fabricated based on Si materials . This caHow widespread MEMS is can also be attributed to mature back-end processing technologies, such as wire bonding and solder reflow. Such technologies have been readily applicable to Si-based MEMS sensors. In addition to those traditional techniques, some techniques aiming for integration of MEMS devices and other semiconductor devices have been developed. For instance, the development of a through silicon via which allows vertical electrical interconnection has led to the manufacturing of multi-layered devices ,4. WaferWith the development of materials sciences and mechanical engineering, a wide variety of materials have been implemented in MEMS. In particular, MEMS devices using polymer and composite materials have a potential to rival the traditional MEMS devices made of Si materials due to their excellent properties, such as low cost, flexibility, and biocompatibility ,14. For MEMS thermal flow sensors have been traditionally fabricated using Si ,21. Due Our research group has fabricated a micromachined tube-type flow sensor by forming a gold-sensing element on a thin polyimide substrate and mounting it inside a small tube ,29. HoweA MEMS fabrication method using a copper on polyimide (COP) substrate has been proposed to simplify the sensor mounting process . The COPIn our previous work, we fabricated a MEMS flow sensor based on COP substrate with an integrated tube structure as shown in OpenFOAM, an open-source fluid analysis software, was utilized to simulate the fluid flow in a circular tube. The analysis conditions are shown below and in Next, the 3D model was represented by grid meshes using OpenFOAM’s standard utilities, blockMesh and SnappyHexMesh. The total number of meshes was set to 150,000. The CFD analysis was performed with the boundary conditions of 1.0 m/s inlet flow velocity into the tube and static pressure at the tube outlet. The analysis was performed using OpenFOAM’s simpleFOAM solver. The analysis results were visualized using paraView. The simulation result of the flow inside the circular tube is visualized as shown in In the conventional MEMS flow sensor structure based on COP, the sensing element was fabricated on the COP substrate, which was then rolled into a cylindrical shape. Through such a process, the flow-sensing element was installed on the tube wall surface where the flow velocity was the slowest in the tube. Therefore, the sensor sensitivity was limited. According to the above simulation results, a new structure with the flow-sensing element placed at the center of the tube is proposed as depicted in Based on the results of CFD simulation, a new COP flow sensor is proposed with the sensing element placed at the center of the tube. The proposed flow sensor measures the airflow through the tube by using the metal heater (resistive element) as a hot wire anemometer as illustrated in The proposed sensor is also equipped with a flow direction detection function by using temperature sensors installed on both the upstream and downstream sides of the heater. The airflow detection direction mechanism is illustrated in The fabrication process of the sensor is illustrated in Au and Cr thin films were then deposited on the patterned photoresist by sputtering, as shown in The assembly process of the fabricated COP MEMS flow sensor is described as shown in Finally, the performance of the fabricated sensor was evaluated. The sensor output as a response to the amount of airflow through the cylinder was evaluated using a flow measurement equipment as schematically shown in 2 for the conventional sensor and 0.46 V2 for the proposed structure, which shows almost twice the increase in sensitivity. In the future, we plan to confirm the flow direction detection function, and develop a sensor that also integrates a driving circuit on the COP substrate.In this study, a tube-integrated MEMS flow sensor with the sensing element and electrical wiring on the same substrate was proposed and demonstrated. The sensor substrate itself was rolled to form a tube package. Furthermore, the sensing element was successfully placed at the center of the tube during the rolling process, where the flow velocity is maximum. For the proposed tube-integrated MEMS flow sensor, a COP substrate with a thin Cu layer on a polyimide film was used. The flow-sensing element was formed on a photosensitive polyimide layer patterned on the COP substrate. Then, the thin Cu layer was selectively etched through the holes in the photosensitive polyimide layer to form a cavity for thermal insulation and electrical wiring for the flow sensor. The structures fabricated on the COP substrate were incised and rolled to form a tube structure for the flow sensor. The sensing element was placed at the center of the tube during the rolling process, which is the place where the flow velocity is maximum. Finally, the flow characteristics of the fabricated sensor were evaluated, and the sensor sensitivity was approximately doubled in comparison to the conventional structure in which the sensing element is located on the tube wall.


The grain structure of the selective laser melting additive manufactured parts has been shown to be heterogeneous and spatially non-uniform compared to the traditional manufacturing process. However, the complex formation mechanism of these unique grain structures is hard to reveal using the experimental method alone. In this study, we presented a high-fidelity 3D numerical model to address the grain growth mechanisms during the selective laser melting of 316 stainless steel, including two heating modes, i.e., conduction mode and keyhole mode melting. In the numerical model, the powder-scale thermo-fluid dynamics are simulated using the finite volume method with the volume of fluid method. At the same time, the grain structure evolution is sequentially predicted by the cellular automaton method with the predicted temperature field and the as-melted powder bed configuration as input. The simulation results agree well with the experimental data available in the literature. The influence of the process parameters and the keyhole and keyhole-induced void on grain structure formation are addressed in detail. The findings of this study are helpful to the optimization of process parameters for tailoring the microstructure of fabricated parts with expected mechanical properties. Metal additive manufacturing (AM) presents significant advantages in fabricating complex parts, freeing the designers from traditional manufacturing constraints and offering a way to tailor the microstructure and thereby build physical and mechanical properties. It has been attracting tremendous attention in many fields ,2,3, sucThis study focuses on the solidification microstructure of 316L stainless steel printed using the SLM. Much effort has been devoted to the experimental study of the grain structure of the printed steel alloys under different process parameters. Liverani et al.  conducteFor the time being, there are many numerical studies of grain growth during metal additive manufacturing. In these numerical studies, the widely used numerical methods are phase field (PF) method ,17,18,19g et al.  proposedg et al. . HoweverThis study aims to numerically address the process-parameters-dependent grain structure of 316L stainless steel fabricated by SLM using a 3D high-fidelity numerical model. We applied the discrete element method (DEM) to build the powder layer model with randomly distributed metal particles. With the DEM result as input, we used the finite volume method and volume of fluid (VOF) method to solve the thermo-fluid flow problems from single-track to multi-track-by-multi-layer deposition. Finally, we applied CA to predict the grain structure with the powder-scale simulation results of the melt pool and thermal field as input. Experimental results from the literature demonstrate the accuracy and effectiveness of the proposed model. After the validation, the effects of the laser power, scan speed, and scan strategy on the grain structure are presented under the conduction mode melting. For the keyhole mode melting, the keyhole with voids is presented, and their effect on the grain structure formation is addressed in depth.The selective laser melting process includes complex physical phenomena such as heat transfer, mass transfer, and phase transformation associated with the powder bed. In order to accurately simulate the melt pool dynamics and solidification process, we used an integrated modeling framework proposed in our previous work . This frX-axis positive in the laser scanning direction, Z-axis positive in the build direction, and Y-axis positive in the direction perpendicular to the The three-dimensional temperature field and powder bed geometry evolution during SLM of 316L stainless steel are solved by the 3D heat transfer and fluid flow model using the FVM with the VOF method in the Eulerian description. As shown in t is the time, Continuity equation:p represents the pressure. The third term on the right-hand side (RHS) of Equation (Momentum conservation equation:∂∂t(ρu)+u∂∂t(ρu)+u formula .H is the enthalpy of material, k is heat conductivity, and Energy conservation equation:m is the molecular mass, To close the above-mentioned conservation equations, one requires the boundary conditions and initial conditions. For the boundary conditions at the metal/gas interface, recoil pressure and surface tension are applied as follows.At the interface of metal and gas, the boundary conditions of the energy conservation equation are given as follows:rgy loss , hv, is q is then calculated by:ith ray before its jth incident. P is the laser power, r is the radius of laser spot, X-axis, and Due to the high energy input of selective laser melting, the liquid metal over the melt pool surface evaporates, and the keyhole will appear under the action of recoil pressure. At this time, the laser will reflect multiple times on the surface of the keyhole. Conventional surface heat source models often underestimate laser energy input, so a ray tracing model is needed to simulate the laser heat source. The laser beam is first decomposed into For other sides of the material domain, the boundary conditions are set as continuative to achieve a smooth continuation of the flow and heat flux through the boundary. The initial conditions are set based on the real process conditions.F is transported by the following equation.The free surface of the melt pool is captured using the VOF method. The volume fraction The FVM is employed to solve the momentum and continuity equations using an operator-splitting scheme . The advThe three-dimensional 316L stainless steel microstructure evolution associated with the SLM process thermal field is solved by the CA model. In the CA model, the material region is discretized into a set of cubic cells, as shown in In CA, the location of nucleation sites, activation criteria, and crystal orientation of newly nucleated grains are handled by a nucleation model. In the selective laser melting process of 316L stainless steel, both epitaxial grain growth from the partially melted grains at the melt pool boundary and nucleation within the melt pool are frequently experimentally observed. In order to capture these nucleation phenomena, the enriched nucleation model proposed in our previous paper  is used.V is the total volume. In the given discretization domain, i, a new grain is generated if the undercooling at the cell center exceeds the critical value i is changed from liquid state to solid state, and a unique grain ID is assigned to the cell. The crystallographic orientation associated with the new grain using a set of randomly generated Euler angles i. Meanwhile, an envelope representing the shape and size of the grain is generated at the center of the cell. More details of the envelope are explained in the following subsection.For bulk nucleation, the total number of potential nucleation sites is determined by an input parameter, nucleation number density For the epitaxial grain growth from the melt pool boundary, the partially melted grains from the substrate or the previous deposition layer act as seeds and continue to grow. During the scan process, if the moving melt pool engulfs the cell with a solid state, its grain information is erased, and its state label is reset to the liquid state. For the unmelted cells at the melt pool boundary under cooling, if they have at least one adjacent neighboring cell in the liquid state and their temperature is higher than the solidus temperature, they are reactivated and keep their inherent grain information as they grow.In the CA grain growth model, the grain shape and size are resolved by a combination of envelopes, where the dendritic structure details are ignored. Considering that 316L stainless steel has a face-centered cubic crystal structure, the corresponding envelope is set to have an octahedral shape, as shown in v is related to the local undercooling based on the Lipton–Glicksman–Kurz dendrite tip supercooling model [v and the undercooling During the solidification process, the grain growth is captured by expending the six half-diagonal envelopes based on the dendrite tip’s growth kinetics. The dendrite tip velocity ng model ,40. The i, a regular octahedral envelope is placed in the center of the cell. Ignoring the incubation time, the half diagonal length of the octahedron envelope at time t is calculated by the following formula:For either a new grain or a reactivated grain at cell As time proceeds, the octahedron envelope grows up to engulf the center of its neighboring cell. For each captured cell, a new regular octahedral envelope inheriting the grain orientation from the parent envelope is assigned to the cell. However, the center and size of the new envelope are determined via a decentered octahedron method . In thist is interpolated from the coarser FVM cell that covers its center, as follows.I is the node of the FVM cell covering the center of the CA cell I follows the Einstein summation convention. Using the liner interpolation scheme, we calculate n represents the nth time step in the FVM simulation, A one-way coupling method is adopted to obtain the thermal field for the CA model from the thermo-fluid flow model. Compared with the thermo-fluid flow model, the CA model requires finer mesh resolution and smaller time steps to resolve the microstructure evolution. Therefore, two sets of mesh with different cell sizes are used for the FVM and CA, respectively. To obtain the temperature field and the material region, we superpose the CA mesh onto the FVM mesh as shown in Two melting modes of 316L stainless steel fabricated by SLM were conducted to show the process parameter’s effect on the complex grain structure formation. The first set is for the conduction mode melting with different combinations of process parameters. In the conduction mode melting, we first presented the single-track simulations for validation of the integrated modeling framework and the effects of laser power and scan speed. Then, we conducted the multi-track, multi-layer simulations to reveal the development of the complex grain structure in successive tracks and layers with different scan strategies. The second set is for keyhole mode melting with a specific combination of process parameters, where the keyhole with keyhole-induced voids is present. The effect of keyhole mode melting on the microstructure is addressed.In all the simulations, epitaxial grain growth and bulk nucleation are considered. The initial grain structure for the simulations is assumed to be equiaxed grains with an average equivalent sphere diameter of 3.6 The experiment conducted by Pham et al.  was takeIn the following subsections, we first present the validation of reference case for both the melt pool and grain structure predictions. Next, we reveal the laser power and scan speed effects on grain structure via single track cases listed in Following the process parameter settings described in the reference , a case The 3D view of the final simulated grain structure for Case P180V63 is provided in To better probe the spatial distribution of the grains, we plot three 2D views of the simulation results in To reveal the effect of laser power on the grain structure formation, we designed four simulation cases with laser power varying from 140 W to 200 W and other fixed process parameters, as listed in G-R map [G, solidification rate R, morphology factor M, and cooling rate C, of which the calculations are detailed in Four thermal features over the mushy zone based on the well-known G-R map  are usedThe comparison demonstrates that the temperature gradient magnitude profiles share the same trend, i.e., its value decreases from the bottom of the melt pool to the top surface, as listed in The influence of laser power on grain morphology and size distribution is considered. As shown in To illustrate the relationship between laser scan speed and grain structure development, we designed four simulation cases with laser power varying from 140 W to 200 W and other constant parameters shown in The temperature gradient, solidification rate, morphology factor, and cooling rate in the four cases are compared in In this section, three simulations with different layer-wise scan strategies are conducted to further study the layer-wise scan strategy effect. As shown in eference .We first present the simulation results of the case with scan Strategy I. It consists of three layers, and each layer includes three tracks. To compare the three scan strategies, a basic representative unit of a three-track-by-two-layer is used for twofold concerns. One is that the third layer results in a repeated pattern, similar to how the second layer interacts with the first layer when the remelting depth is less than the depth of the previous fusion zone, as demonstrated in the three-layer simulation result of Strategy I. The other is that the two-layer case is a basic unit used to represent the multi-layer depositions with layer-wise rotation of 180° and 90°.For the three different layer-wise raster patterns, a different alignment of melt tracks and thermal fields along the build direction yields different microstructures, as shown in X, Y, and Z directions. In summary, the scanning strategy of rotating the scanning direction between layers can change the arrangement of melting tracks and disrupt the heat distribution along the build direction, which can promote more grains with different orientations to grow.The layer-wise scan strategy effect on the grain structure formation is further quantitatively measured in terms of grain size distribution and texture intensity. In this section, we set the laser power (350 W) and the scan speed (0.63 m/s) in a way such that the keyhole mode melting and keyhole-induced pore defect often observed in experiments can be reproduced. For the grain structure prediction, the region size is set to 1500 Under the high-power, low-scan speed laser melting condition, a topological deep-narrow vapor depression is found in the melt pool under the laser spot, as shown in Y direction attributed to the unique melt pool morphology and the thermal field features.The region size of interest for grain structure prediction is set to 400 Y direction. Therefore, the grains epitaxially grown from both sides of the melt pool have a growth direction parallel to the Y axis. Moreover, the color of these grains is close to red because the grain with orientation best aligned with the temperature gradient can overgrow other grains, as demonstrated by the pole figure shown in In order to detail the influence of keyhole-mode melt pool morphology on microstructure evolution, three 2D views of simulation results are shown in a and b. The black dashed lines indicate the location of the transverse cross sections, two grains right above Pore a are isolated, and a transverse cross-section without pores is plotted for comparison.A close-up of the grain structure around the two pores, as shown in a located very close to the fusion line leads to the absence of epitaxial grain growth from the beneath fusion line; Pore b, located far from the melt pool bottom, blocks the growth of grains marked with the arrows in yellow. Consequently, the presence of pores has a significant influence on the local grain structure. The other effect is that the existence of pores can cause changes in the local thermal field, particularly during the cooling stage, and then alter the local grain morphology. Taking Pore a as an example, a few snapshots of the thermal field around it with cooling rate marks are plotted in a serves as thermal insulation. With such special thermal field features, coarser grains can be expected above Pore a, which is demonstrated in a blocks the epitaxial growth of grains below it, contributing to the coarser grain growth above Pore a.There are two main effects of pores on microstructure evolution. The first is that the pores may block the grain growth from its sides and bottom. From the comparison of This paper conducts a high-fidelity numerical study of the microstructure evolution in selective laser melting 316L stainless steel. The powder scale thermo-fluid flow model using finite volume method and volume of fluid method is adopted to obtain the temperature field, and a 3D cellular automaton model is used to predict the final grain structure, which can link the process details on powder scale with the final microstructure on the micron scale. The calculation accuracy and effectiveness of the numerical model are validated against experimental results. Two sets of numerical examples with different process parameters are simulated to provide insights on grain structure evolution during the conduction mode and keyhole mode melting processes. The main conclusions are as follows.For the conduction mode melting process, it is identified that the grain size increases with the increase in laser power and the decrease in scan speed. For the given ranges, the laser power and scan speed have little effect on the grain structure morphology of the single track. For the given laser power and scan speed, the grain size increases, and the texture gradually becomes strong with the increase in the layers with the unidirectional scan pattern, which can be attributed to the epitaxial grain growth. However, rotating the scan pattern between layers can change the microstructure morphology and weaken the texture.Y direction (perpendicular to the scan and build direction).For the keyhole-mode melting process, the obtained grain structure of the single track is significantly different from its counterpart in the conduction mode melting process. Due to the deep–narrow melt pool and the thermal fields within the mushy zone, the fusion zone is dominated by the columnar grains with a growth direction perpendicular to the build direction. These columnar grains epitaxially grow from the sides of the melt pool and block the columnar grains from the bottom of the melt pool. Consequently, the texture of the single track with the keyhole mode melting condition is stronger, and the preferred grain orientations are best aligned to the It is identified that the keyhole-induced pores have two effects on the microstructure evolution. One is to block the grain growth beneath it. The other is to play a role of heat insulation, which reduces the cooling rate above it and thus increases the possibility of forming coarse grains.