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Controlled manipulation of microscale robotic devices in complex fluidic networks is critical for various applications in biomedical endovascular sensing, lab-on-chip biochemical assays, and environmental monitoring. However, achieving controlled transport and active retention of microscale robots with flow sensing capability has proven to be challenging. Here, we report the dynamic tweezing of an anisotropic magnetic microrobot in a rotating magnetic trap for active retention and localized flow sensing under confined fluidic conditions. We reveal a series of unconventional motion modes and the dynamics of the microrobot transporting in a confined fluidic flow, which manifest themselves as transitions from on-trap centre rolling to large-area revolution and off-trap centre rolling with varying rotating frequencies. By retaining the robot within the magnetic trap and its motion modulated by the field frequency, the offcentre rolling of the microrobot endows it with crucial localized flow sensing capabilities, including flow rate and flow direction determination. The magnetic microrobot serves as a mobile platform for measuring the flow profile along a curved channel, mimicking a blood vessel. Our findings unlock a new strategy to determine the local magnetic tweezing force profile and flow conditions in arbitrary flow channels, revealing strong potential for microfluidics, chemical reactors, and in vivo endovascular flow measurement. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) enables mobile robots to autonomously perform localization and mapping tasks in unknown environments. Despite significant progress achieved by visual SLAM systems in ideal conditions, relying solely on a single robot and point features for mapping in large-scale indoor environments with weak-texture structures can affect mapping efficiency and accuracy. Therefore, this paper proposes a multi-robot collaborative mapping method based on point-line fusion to address this issue. This method is designed for indoor environments with weak-texture structures for localization and mapping. The feature-extraction algorithm, which combines point and line features, supplements the existing environment point feature-extraction method by introducing a line feature-extraction step. This integration ensures the accuracy of visual odometry estimation in scenes with pronounced weak-texture structure features. For relatively large indoor scenes, a scene-recognition-based map-fusion method is proposed in this paper to enhance mapping efficiency. This method relies on visual bag of words to determine overlapping areas in the scene, while also proposing a keyframe-extraction method based on photogrammetry to improve the algorithm's robustness. By combining the Perspective-3-Point (P3P) algorithm and Bundle Adjustment (BA) algorithm, the relative pose-transformation relationships of multi-robots in overlapping scenes are resolved, and map fusion is performed based on these relative pose relationships. We evaluated our algorithm on public datasets and a mobile robot platform. The experimental results demonstrate that the proposed algorithm exhibits higher robustness and mapping accuracy. It shows significant effectiveness in handling mapping in scenarios with weak texture and structure, as well as in small-scale map fusion. [ABSTRACT FROM AUTHOR]

The present study addresses advanced monitoring techniques for particles and airborne molecular contaminants (AMCs) in cleanroom environments, which are crucial for ensuring the integrity of semiconductor manufacturing processes. We focus on quantifying particle levels and a representative AMC, hydrogen chloride (HCl), having known detrimental effects on equipment longevity, product yield, and human health. We have developed a compact laser sensor based on open-path cavity ring-down spectroscopy (CRDS) using a 1742 nm near-infrared diode laser source. The sensor enables the high-sensitivity detection of HCl through absorption by the 2-0 vibrational band with an Allan deviation of 0.15 parts per billion (ppb) over 15 min. For quantifying particle number concentrations, we examine various detection methods based on statistical analyses of Mie scattering-induced ring-down time fluctuations. We find that the ring-down distributions' 3rd and 4th standard moments allow particle detection at densities as low as ~105 m−3 (diameter > 1 μm). These findings provide a basis for the future development of compact cleanroom monitoring instrumentation for wafer-level monitoring for both AMC and particles, including mobile platforms. [ABSTRACT FROM AUTHOR]

In autonomous driving of the mobile robot, the robot's current location should be identified first to plan and move a path to the target location. Accordingly, research on the robot's localization using GPS, 3D LiDAR, and Vision has been actively conducted. However, there is a limitation in that it is difficult to locate robots in indoor spaces where signals are disturbed by walls or ceilings, or in areas where sufficient environmental information cannot be obtained. This paper introduces the robot's position estimation method to overcome these environmental problems by using sensor fusion in an indoor tennis court. We propose a localization method that has low latency performance and high location accuracy through the use of Kalman filters to fuse data from wheel odometry and visual-inertial odometry. To evaluate its performance, this method was compared against wheel odometry, visual-inertial odometry, and LIO-SAM after the robot completed three rectangular paths. The resultant mean absolute errors in the x and y directions were 1.908 m and 0.707 m for wheel odometry, 1.169 m and 1.430 m for visual-inertial odometry, and 0.400 m and 0.383 m for LIO-SAM, respectively. In contrast, the wheel-visual-inertial odometry introduced in this study reported errors of 0.209 m and 0.103 m in the x and y directions, respectively, indicating superior accuracy compared to the other algorithms. This underscores the effectiveness of the proposed method in indoor environments where signals can be obstructed by walls or ceilings, or in areas lacking abundant environmental information. [ABSTRACT FROM AUTHOR]

This paper explores design of finite impulse response (FIR) filters for controlling underdamped systems while dealing with uncertainties in model parameters. By setting magnitude constraints in the frequency domain within a convex programing framework, it ensures that dominant resonant modes are attenuated at the end of the maneuver, high-frequency unmodeled modes are not excited, and there is no inordinate accentuation of frequencies in the passband of the filter. A mobile platform with an attached flexible beam serves as a testbed to validate the designs for rest to rest maneuvers, demonstrating how different cost functions of error between the desired and optimized magnitude response affect the filter performance. The study also examines robustness in the notch area by shifting the natural frequencies of the system by shifting a tip mass at the free end of the beam. The total energy at the final maneuver time of the first three system modes is calculated as a vibration suppression metric and is used to compare established input shapers with the proposed finite impulse response filters. [ABSTRACT FROM AUTHOR]

This study investigates the pricing strategies of a mobile payment platform connecting merchants and customers who may bypass the platform and trade in cash. Our analytical model incorporates cross-side network externalities at both sides of the platform. We find that the optimal pricing strategy involves cross-subsidization, including subsidizing customers to register for the service while charging the merchants per transaction. Moreover, the technology option adopted by the platform influences this optimal pricing format. In particular, the platform should subsidize customers more if the payment technology imposes larger transaction costs on customers and charge merchants more if banks demand a higher payment processing fee. Our major findings remain qualitatively valid when per-transaction subsidy is available and the marginal benefit of cross-side network externalities diminishes. Finally, the presence of a platform improves social welfare if the per-transaction subsidy sufficiently stimulates consumption on the platform. [ABSTRACT FROM AUTHOR]

The educational recommendation system with an artificial intelligence (AI) chatbot is a system designed to operate on smartphones and is intended for use at the College of Industrial Technology in Thailand. Compatible with both iOS and Android operating systems, the educational recommendation system in this research is capable of providing educational recommendations and valuable information about the engineering degree program. It includes information about the Electronics Engineering Technology Department, relevant bodies of knowledge that can enhance awareness and public relations, and a proactive introduction to information. The main objectives of this study are to develop the capacity to analyze educational issues and provide useful and accurate information for educational recommendations, as well as to explore the possibilities for enhancing this system. The results of the study address the three research questions and demonstrate that the process and structure of the educational recommendation system with an AI chatbot can be utilized to create educational recommendation tools in higher education. These tools are accessible anywhere and at any time through small mobile devices. The system is also compatible with popular social media applications and is capable of analyzing user questions precisely and accurately to find the optimal answers that can meet the user's needs. However, there are still gaps in the research that need to be investigated in the future. These studies should include exploring a broader range of applications among the general population and educational settings to assess the effectiveness of the educational recommendation system with an AI chatbot. Additionally, they should examine the skills that impact the utilization of educational technology in various contexts. [ABSTRACT FROM AUTHOR]

AbstractSocial cues play a critical role in the exchange of information during patient–physician communication. Although previous research has recognized the impact of social cues on patients’ perceptions and behaviors in mobile medical consultations, little is known about how different combinations of social cues affect the interactions. Employing a within-subject factorial design experiment, this study examined the effects of three social cues—empathic cues, interactivity cues, and social validation—on patients’ perceptions of interactivity, social presence, and trust. A total of 103 participants was recruited. The results revealed the various significant impacts of empathic cues, interactivity cues, and social validation on perceived interactivity, and social validation affected trust, as well as the relationships among these perceptions. The findings suggested strategies for integrating various social cues to enhance patients’ positive perceptions, offering potential benefits to patient–physician communication in mobile medical platforms. [ABSTRACT FROM AUTHOR]

Social platforms facilitate the daily interactions of billions of people globally. Prior research generally concludes that social platforms negatively affect people's welfare. This research reopens this debate by using a robust methodology to examine the time series effects of social platform use on users' subjective well‐being, psychological well‐being, physical health, and financial security. We report a 6‐month longitudinal study of 1029 adults. Participants' daily time using social platforms on their mobile device was unobtrusively tracked and their well‐being was measured every 2 weeks. The findings suggest a small, positive effect of time spent using social platforms on both subjective well‐being and psychological well‐being (but no significant effects on physical health or financial security). Further, it is time spent using social platforms that facilitate interactions with intimate/close ties, that is correlated with positive subjective and psychological well‐being. [ABSTRACT FROM AUTHOR]

Point clouds captured using laser scanners mounted on mobile platforms contain errors at the centimetre to decimetre level due to motion distortion. In applications such as lidar odometry or SLAM, this motion distortion is often ignored. However, in applications such as HD mapping or precise vehicle localisation, it is necessary to correct the effect of motion distortion or ‘deskew’ the point clouds before using them. Existing methods for deskewing point clouds mostly rely on high frequency IMU, which may not always be available. In this paper, we propose a straightforward approach that uses the registration of consecutive point clouds to estimate the motion of the scanner and deskew the point clouds. We introduce a novel surface‐based evaluation method to evaluate the performance of the proposed deskewing method. Furthermore, we develop a lidar simulator using the reverse of the proposed deskewing method which can produce synthetic point clouds with realistic motion distortion. [ABSTRACT FROM AUTHOR]

With the dynamic nature of today's market demands, industries increasingly require flexible and adaptable systems to navigate through variable operational needs. In-plane manipulation systems, characterized by their ability to handle objects over an active surface, have emerged as promising solutions to address these challenges. This systematic literature review (SLR) investigates the landscape of in-plane manipulation systems, focusing on their classification, technological aspects, and emerging trends. Drawing upon a comprehensive search strategy, this review identifies and synthesizes relevant literature on the topic. Through structured data extraction and analysis, the review categorizes these devices based on their fields of application, employed technologies, common characteristics, advantages, disadvantages, and existing gaps in research and development. The main technologies identified for material handling include MEMS, vibrations, cilia, pneumatic surfaces, variable-morphology surfaces, the use of rotors, and mobile platforms. These technologies are described based on their primary features and fields of application and are compared to highlight their unique advantages and limitations. Accordingly, this SLR aims to provide insights into the current state of the art, identify areas for further investigation, and inform future developments in in-plane material handling. Additionally, the findings of this review contribute to a deeper understanding of the capabilities, limitations, and potential applications of this kind of manipulation across various industries. Furthermore, the identification of gaps in existing literature, particularly regarding the complexity and efficiency of rotor systems, serves as a foundation for future research aimed at advancing the field and addressing the evolving needs of industries in an increasingly dynamic market landscape. [ABSTRACT FROM AUTHOR]

This paper presents a procedure for estimating the motion capabilities of an omnidirectional mobile platform with three omni wheels arbitrarily distributed and oriented. This procedure is based on the analysis of the bidirectionality compliance between the inverse and forward kinematics of a mobile platform for a wide set of discrete motion commands. This procedure has been applied to analyze eleven alternative mobile platform configurations with three omni wheels. The estimation of the omnidirectional motion capabilities of these platforms agrees with state-of-the-art methods while providing new differentiated information on the translational capabilities of each platform. The procedure can be applied in the design stage of new omnidirectional mobile platforms in order to verify the motion capabilities of new designs with omni wheels. [ABSTRACT FROM AUTHOR]

Pressure ulcers are serious healthcare concerns, especially for the elderly with reduced mobility. Severe pressure ulcers are accompanied by pain, degrading patients' quality of life. Thus, speedy and accurate detection and classification of pressure ulcers are vital for timely treatment. The conventional visual examination method requires professional expertise for diagnosing pressure ulcer severity but it is difficult for the lay carer in domiciliary settings. In this study, we present a mobile healthcare platform incorporated with a light-weight deep learning model to exactly detect pressure ulcer regions and classify pressure ulcers into six severities such as stage 1–4, deep tissue pressure injury, and unstageable. YOLOv8 models were trained and tested using 2800 annotated pressure ulcer images. Among the five tested YOLOv8 models, the YOLOv8m model exhibited promising detection performance with overall classification accuracy of 84.6% and a mAP@50 value of 90.8%. The mobile application (app) was also developed applying the trained YOLOv8m model. The mobile app returned the diagnostic result within a short time (≒3 s). Accordingly, the proposed on-device AI app can contribute to early diagnosis and systematic management of pressure ulcers. [ABSTRACT FROM AUTHOR]

Telecommunications offers an alternative or supplement to community-based interventions as a means of extending healthcare services and improving health outcomes in remote settings but can fail to reach target communities and achieve the desired impact if barriers to access are not overcome. We conducted seven focus group discussions and 26 interviews with community health workers, community leaders, and female members of the public who declared that they had or had not previously accessed free audio health messages provided via a mobile platform in two rural communities of Mali, Koulikoro and Bougouni. A content analysis showed that participants accessed and trusted health information from a range of sources, including radio, telephone and television, as well as town criers, local relays and community health centres. Barriers to access faced by women included economic factors, lack of network or electricity, and social factors such as illiteracy, cultural restrictions and being unaware of mobile communication. Through analysis and interpretation of the participants' responses, we have made recommendations for future campaigns for the dissemination of health-related information for women in remote settings. [ABSTRACT FROM AUTHOR]

In this paper, we present the first machine learning package developed specifically for fish calls identification within a specific range (0-500Hz) that encompasses four Caribbean grouper species: red hind (E. guttatus), Nassau (E. striatus), yellowfin (M. venenosa), and black (M. bonaci). Because of their ubiquity in the soundscape of the grouper's habitat, squirrelfish (Holocentrus spp.) sounds along with vessel noise are also detected. In addition themodel is also able to separate grouper species call types. This package called FADAR, the Fish Acoustic Detection Algorithm Research is a standalone user-friendly application developed in Matlab™. The concept of FADAR is the product of the evaluation of various deep learning architectures that have been presented in a series of published articles. FADAR is composed of a main algorithmthat can detect all species calls including their call types. The architecture of this model is based on an ensemble approach where a bank of five CNNs with randomly assigned hyperparameters are used to form an ensemble of classifiers. The outputs of all five CNNs are combined by a fusion process for decisionmaking. At the species level, the output of the multimodel is thus used to classify the calls in terms of their types. This is done by species specific deep learningmodels that have been thoroughly evaluated in the literature on the species concerned here, including transfer learning for red hind and yellowfin groupers and custom designed CNN for Nassau grouper, which has a greater number of known call types than the other species. FADARwasmanually trained on a diversity of data that span various regions of the Caribbean Sea and also two recorder brands, hydrophone sensitivities, calibrations and sampling rates, including a mobile platform. This strategy has conferred FADAR substantive robustness to a diversity of noise level and sources that can be found in the grouper calls frequency band such as vessels and marine mammals. Performance metrics based on sensitivity (recall) and specificity showed the same performance level for both balanced and unbalanced datasets and at locations not used in the training set. [ABSTRACT FROM AUTHOR]

In order to solve the problems of loss of moving targets due to the flight status of the unmanned aerial vehicle (UAV) itself, the interference of the environment the randomness of the target and other reasons in the process of collecting moving target image information by the UAV platform, a deep deterministic policy gradient (DDPG) algorithm UAV following method based on moving target recognition was proposed. Facing the vehicle target on the highway, the relationship among the height, posture and high-speed vehicle motion of the UAV was analyzed. the velocity adaptive model of the target detection frame rate of the mobile platform was estab lished, and the flight attitude and speed of the UAV were corrected in real time according to the motion state of the target, so that the UAV could maintain the relative position and angle with the target. Then, based on the value network of DDPG algorithm, the value of UAV taking spe- cific actions in different states was estimated; the strategy network generates the strategy of UAV taking actions in a given state, and gives UAV flight altitude and speed control parameters for target tracking, so that the UAV could automatically adjust the flight state according to the movement change of the target, and realize the adaptive tracking of the moving target. Simulation experiments show that the DDPG algorithm can provide stable flight attitude data and a reliable control basis for the UAV following task, and the UAV can track the ground moving target in a circular area with a speed range of 0~33 m/s and a radius of 120 m in real time, and can achieve continuous and stable tracking within the endurance range. [ABSTRACT FROM AUTHOR]

To solve the complicated steering control of small vehicles in the agriculture and difficult steering on complex roads, this study designed a wheel-track composite vehicle. The vehicle incorporated a novel power differential steering mechanism with dual driving, enabling steering through the differential rotation of the rear two wheels. The vehicle is simple to control, small in size, and is able to work under the conditions of complex roads, such as hills, mountains, and muddy land. The study initially focused on presenting the design, theoretical analysis, and dynamic simulation analysis of the power differential steering mechanism with dual driving. Subsequently, the vehicle underwent modeling and simulation using UG software to validate the reasonability of the values. Finally, utilizing test data, four mathematical models for the actual steering radius of the vehicle on four road surfaces were derived through neural network fitting. The maximum relative error between the model results and the actual steering radius value was reported to be 3.53%. The advantages of the vehicle included continuous radius steering, deceleration and torsion increase, differential lock, etc. This made it well-suited for applications in all-terrain military and civilian vehicles, as well as various special equipment mobile platforms equipped with walking devices. [ABSTRACT FROM AUTHOR]

The object detection based on video has the problem of poor recognition effect in bad weather, so it is necessary to make up for video defects and improve the robustness of detection framework. In view of this problem, this study designs an object detection framework based on radar and video fusion. YOLOv5 (You Only Look Once version 5) network is used to obtain image feature map and image detection frame, density-based clustering is used to obtain radar detection frame, and radar data is encoded to get object detection results based on radar information. Finally, the detection boxes of the two are superimposed to obtain a new ROI (Region of Interest), and the classification vector after fusion radar information is obtained, which improves the detection accuracy in extreme weather. The experimental results show that the mAP(mean Average Precision) of the framework reaches 60.07%, and the parameter number is only 7.64 x 10°, which indicates that the framework has the characteristics of lightweight, fast computing speed and high robustness, and can be widely used in embedded and mobile platforms. [ABSTRACT FROM AUTHOR]

This study presents a robot-assisted solution for the automated identification of bridge frequencies and high-spatial-resolution mode shapes using a minimal number of sensors. The proposed approach employs programmable wheeled robots, whose movement can be remotely controlled, as the mobile platform carrying accelerometers. The output-only frequency domain decomposition (FDD) algorithm is adopted for use with the proposed stop-and-go mobile sensing scheme, resulting in the identification of frequencies and high-resolution mode shapes. The solution was verified via two numerical case studies and was validated on a full-scale test of a footbridge. The results reveal that the frequencies and high-resolution shapes of the excited structural modes are identified successfully using only two accelerometers, confirming the satisfactory practicality and efficiency of the proposed solution. [ABSTRACT FROM AUTHOR]

Keeping up with the fast evolution of mobile operating systems is challenging for developers, who have to frequently adapt their apps to the upgrades and behavioral changes of the underlying API framework. Those changes often break backward compatibility. The consequence is that apps, if not updated, may misbehave and suffer unexpected crashes if executed within an evolved environment. Being able to quickly identify the portion of the app that should be modified to provide compatibility with new API versions can be challenging. To facilitate the debugging activities of problems caused by backward incompatible upgrades of the operating system, this paper presents FILO, a technique that is able to recommend the method that should be modified to implement the fix by analyzing a single failing execution. FILO can also provide additional information and key symptomatic anomalous events that can help developers understand the reason for the failure, therefore facilitating the implementation of the fix. We evaluated FILO against 18 real compatibility problems related to Android upgrades and compared it with Spectrum-Based Localization approaches. Results show that FILO is able to efficiently and effectively identify the fix-locus in the apps. [ABSTRACT FROM AUTHOR]

Mobile manipulators require whole-body inverse kinematics (IK), encompassing both the mobile base and arm, to execute manipulation tasks. One of the challenges associated with whole-body IK is the redundancy of degrees of freedom when combining those of the mobile base and arm, which generates an infinite number of solutions. Therefore, in this study, we focus on the trend in mobile manipulators where the positional precision of the mobile base is lower than that of the manipulator. We propose a definition for the robustness value to base position control error and discuss a methodology to quickly obtain robust whole-body IK solutions. In an assessment utilizing a dataset that simulates domestic environments and a human support robot, which is a research platform for mobile manipulators designed to operate with humans, we validated that the quick acquisition of IK solutions robust to base position control error is achievable. In addition, we integrated the proposed IK approach into motion planning and evaluated its performance. Our results revealed that motion planning integrated with the proposed IK approach was superior to conventional methods in terms of both computation time and robustness for base position control error. [ABSTRACT FROM AUTHOR]

In this article, an updated dynamic model of mobile manipulators is presented, which incorporates the effects of wheel slipping and skidding in the mobile base motion equations. This modified model is then used to design an improved control algorithm. The interaction between the robot's mobile platform and manipulator often leads to inaccuracies when using traditional control methods. To address this, a modified control strategy is proposed. Unlike previous research, which faced difficulties directly measuring wheel slippage and traction forces, this work implements a disturbance observer to estimate these unknown parameters. The control algorithm is then designed using the feedback from the observer estimates. First, kinematic control is used to guide the robot along the desired trajectory. Next, dynamic control augmented with the disturbance observer enables robust tracking. Specifically, a disturbance observer-based sliding mode controller is developed for dynamic control of the system. This is further optimized using the state-dependent Riccati equation method. Lyapunov analysis proves system stability and guarantees disturbance estimation errors converge to zero. Simulations on a model of a mobile manipulator confirm the effectiveness of the proposed method. When combining the disturbance observer and sliding mode control, the consumed torque of the wheels and arms is reduced on average by 0.42 Nm and 0.04 Nm, respectively. Defining a cost function and optimizing the torques with the optimal sliding mode control approach further decreases the required torque to 5.17 Nm compared to the basic SMC, while also reducing tracking error by 4.9 mm. Despite platform slippage, the controller performance keeps end effector errors small and within allowable bounds. Experimental validation on a Scout robot demonstrates the feasibility of implementing this method on physical systems. The robot is able to track desired trajectories with acceptable errors. The tracking error in experiments is approximately 54 mm, compared to 13 mm in simulation. [ABSTRACT FROM AUTHOR]

Although short-video sharing apps (e.g., TikTok and Instagram Reels) on mobile platforms have grown exponentially due to their playful, interactive, and participatory nature, the effectiveness of branded content on these apps for desirable brand outcomes is empirically under-explored. Integrating theories of uses and gratifications, brand experience, and brand relationship quality, this study investigated how app users' motivations for branded content interactions influence their attitude toward branded content and their responses to brands promoted on these apps. An online survey via Qualtrics was conducted to recruit short-video sharing app users who had engaged with branded content. Our findings showed that, among app users' five motivations, entertainment, social connection, and information-seeking produced a favorable effect on their attitude toward branded content, while social connection and information-seeking generated their positive brand experience. Subsequently, both attitude toward branded content and brand experience affected relationship quality with the brands encountered on these apps. Based on these findings, theoretical and practical contributions are deliberated for academic researchers and marketing professionals. [ABSTRACT FROM AUTHOR]

IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user's health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability. [ABSTRACT FROM AUTHOR]

To improve the current oil painting teaching mode in Chinese universities, this study combines deep learning technology and artificial intelligence technology to explore oil painting teaching. Firstly, the research status of individualized education and related research on image classification based on brush features are analyzed. Secondly, based on a convolutional neural network, mathematical morphology, and support vector machine, the oil painting classification model is constructed, in which the extracted features include color and brush features. Moreover, based on artificial intelligence technology and individualized education theory, a personalized intelligent oil painting teaching framework is built. Finally, the performance of the intelligent oil painting classification model is evaluated, and the content of the personalized intelligent oil painting teaching framework is explained. The results show that the average classification accuracy of oil painting is 90.25% when only brush features are extracted. When only color features are extracted, the average classification accuracy is over 89%. When the two features are extracted, the average accuracy of the oil painting classification model reaches 94.03%. Iterative Dichotomiser3, decision tree C4.5, and support vector machines have an average classification accuracy of 82.24%, 83.57%, and 94.03%. The training speed of epochs data with size 50 is faster than that of epochs original data with size 100, but the accuracy is slightly decreased. The personalized oil painting teaching system helps students adjust their learning plans according to their conditions, avoid learning repetitive content, and ultimately improve students' learning efficiency. Compared with other studies, this study obtains a good oil painting classification model and a personalized oil painting education system that plays a positive role in oil painting teaching. This study has laid the foundation for the development of higher art education. [ABSTRACT FROM AUTHOR]

For real-world applications, autonomous mobile robotic platforms must be capable of navigating safely in a multitude of different and dynamic environments with accurate and robust localization being a key prerequisite. To support further research in this domain, we present the INSANE datasets (Increased Number of Sensors for developing Advanced and Novel Estimators)—a collection of versatile Micro Aerial Vehicle (MAV) datasets for cross-environment localization. The datasets provide various scenarios with multiple stages of difficulty for localization methods. These scenarios range from trajectories in the controlled environment of an indoor motion capture facility, to experiments where the vehicle performs an outdoor maneuver and transitions into a building, requiring changes of sensor modalities, up to purely outdoor flight maneuvers in a challenging Mars analog environment to simulate scenarios which current and future Mars helicopters would need to perform. The presented work aims to provide data that reflects real-world scenarios and sensor effects. The extensive sensor suite includes various sensor categories, including multiple Inertial Measurement Units (IMUs) and cameras. Sensor data is made available as unprocessed measurements and each dataset provides highly accurate ground truth, including the outdoor experiments where a dual Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) setup provides sub-degree and centimeter accuracy (1-sigma). The sensor suite also includes a dedicated high-rate IMU to capture all the vibration dynamics of the vehicle during flight to support research on novel machine learning-based sensor signal enhancement methods for improved localization. The datasets and post-processing tools are available at: https://sst.aau.at/cns/datasets/insane-dataset/ [ABSTRACT FROM AUTHOR]

As higher spatiotemporal resolution tactile sensing systems are being developed for prosthetics, wearables, and other biomedical applications, they demand faster sampling rates and generate larger data streams. Sparsifying transformations can alleviate these requirements by enabling compressive sampling and efficient data storage through compression. However, research on the best sparsifying transforms for tactile interactions is lagging. In this work we construct a library of orthogonal and biorthogonal wavelet transforms as sparsifying transforms for tactile interactions and compare their tradeoffs in compression and sparsity. We tested the sparsifying transforms on a publicly available high-density tactile object grasping dataset (548 sensor tactile glove, grasping 26 objects). In addition, we investigated which dimension wavelet transform—1D, 2D, or 3D—would best compress these tactile interactions. Our results show that wavelet transforms are highly efficient at compressing tactile data and can lead to very sparse and compact tactile representations. Additionally, our results show that 1D transforms achieve the sparsest representations, followed by 3D, and lastly 2D. Overall, the best wavelet for coarse approximation is Symlets 4 evaluated temporally which can sparsify to 0.5% sparsity and compress 10-bit tactile data to an average of 0.04 bits per pixel. Future studies can leverage the results of this paper to assist in the compressive sampling of large tactile arrays and free up computational resources for real-time processing on computationally constrained mobile platforms like neuroprosthetics. [ABSTRACT FROM AUTHOR]

Visual Place Recognition (VPR) aims to determine whether a robot or visual navigation system locates in a previously visited place using visual information. It is an essential technology and challenging problem in computer vision and robotic communities. Recently, numerous works have demonstrated that the performance of Convolutional Neural Network (CNN)-based VPR is superior to that of traditional methods. However, with a huge number of parameters, large memory storage is necessary for these CNN models. It is a great challenge for mobile robot platforms equipped with limited resources. Fortunately, Binary Neural Networks (BNNs) can reduce memory consumption by converting weights and activation values from 32-bit into 1-bit. But current BNNs always suffer from gradients vanishing and a marked drop in accuracy. Therefore, this work proposed a BinVPR model to handle this issue. The solution is twofold. Firstly, a feature restoration strategy was explored to add features into the latter convolutional layers to further solve the gradient-vanishing problem during the training process. Moreover, we identified two principles to address gradient vanishing: restoring basic features and restoring basic features from higher to lower layers. Secondly, considering the marked drop in accuracy results from gradient mismatch during backpropagation, this work optimized the combination of binarized activation and binarized weight functions in the Larq framework, and the best combination was obtained. The performance of BinVPR was validated on public datasets. The experimental results show that it outperforms state-of-the-art BNN-based approaches and full-precision networks of AlexNet and ResNet in terms of both recognition accuracy and model size. It is worth mentioning that BinVPR achieves the same accuracy with only 1% and 4.6% model sizes of AlexNet and ResNet. [ABSTRACT FROM AUTHOR]

Currently, telemedicine and telehealth have grown, prompting healthcare institutions to seek innovative ways to incorporate them into their services. Challenges such as resource allocation, system integration, and data compatibility persist in healthcare. Utilizing an open data approach in a versatile mobile platform holds great promise for addressing these challenges. This research focuses on adopting such an approach for a mobile platform catering to personalized care services. It aims to bridge identified gaps in healthcare, including fragmented communication channels and limited real-time data access, through an open data approach. This study builds upon previous research in omnichannel healthcare using prototyping to design a mobile companion for personalized care. By combining an omnichannel mobile companion with open data principles, this research successfully tackles key healthcare gaps, enhancing patient-centered care and improving data accessibility and integration. The strategy proves effective despite encountering challenges, although additional issues in personalized care services warrant further exploration and consideration. [ABSTRACT FROM AUTHOR]

The hub motor-driven mobile platform has gradually replaced the traditional centralized electric drive system. There is ever-increasing demand for flowers, fruits, vegetables, and tea horticulture for the new energy-powered operating equipment, particularly with high efficiency, energy saving, compact structure, independent and controllable advantages. Compared with direct-drive hub motors, the geared hub motors have been the mainstream of the drive unit for facility horticulture, due to the higher overload and torque output. The operational requirements of large load and torque can be fully met in the operation scenarios of facility horticulture. However, the geared hub motors are limited to the size of the space inside the hub. The high heat generation has seriously restricted the operational performance of electric drive mobile platforms. In this study, a decelerated inner-rotor axial flux hub motor was designed to meet the requirements of the low-speed and high-torque traction for electric-driven mobile platforms in facility horticulture operation scenarios. The electromagnetic finite element (FE) model was established to analyze the electromagnetic and loss features of the hub motor. Moreover, an improved magnetic-thermal coupling FE model of the hub motor was also constructed using the thermal network. The effect of temperature on the winding resistivity was considered to reduce the prediction accuracy of the simulation model, compared with the conventional magnetic-thermal bidirectional model. The prediction accuracy of the torque was improved by 5.33% using the improved model, compared with the bench tests. The correctness of the model was verified using the magnetic-thermal coupling FE model. A systematic investigation was made on the influence of the temperature and phase current on the torque of the motor. The experiments were also conducted on the external efficiency of the hub motor under peak operating conditions. The simulation results reveal that the average output torque of the motor decreased, as the steady-state temperature increased. The prediction value of torque was deviated by less than 1.56% from the measured at the motor speed of 800 r/min. The output torque of the motor also decreased with increasing motor temperature. The output torque of 15.23 N·m was reduced by 7.66% at the current of 32 A, compared with the conventional. The experimental results demonstrate that the power of the hub motor increased with the increase of the rotational speed in the middle and low-speed ranges. The peak power was up to 4.59 kW, while the torque almost remained constant. The output torque also decreased with the increase of motor temperature. The average output torque of the motor was linearly related to the phase current in the range of 0 to 20 A. The efficiency of the motor was up to 93.2% in the middle speed range with the torques in the range of 300 to 600 N·m. This finding can provide a direct guideline for the structural design of hub motors and the magnetic-thermal multi-physical field coupling performance analysis. [ABSTRACT FROM AUTHOR]

The research in the field of mobile assisted language learning has a long history. It basically follows the route from theory to application practice, but there are few process studies on learners' individual language skills learning behavior based on mobile platform data. This research takes vocabulary learning as the starting point, and constructs a mobile user English learning pattern recognition model with improved Stacking integration algorithm. The purpose of this study is to identify different learning modes by analyzing the learning behavior and learning data of mobile users, and to provide personalized learning suggestions for users. The evaluation goal of this study is the accuracy and robustness of the mobile users' English learning pattern recognition model, and the accuracy is the classification accuracy of the model for different learning patterns. Robustness is the stability and consistency of the model in different situations. In order to evaluate the integrated English learning mode of mobile users, the study first divides the collected learning data set into training set and test set. In this step, the method used in the study is cross-validation, which aims to reduce the difference of evaluation results caused by different data sets. For the relevant features in mobile users' learning data, the features extracted by the research include learning behavior and learning progress, which can accurately reflect the learning mode. Then, the integrated learning method is used to train the model, and the best parameter combination is selected through the training set. Finally, the study uses the test set to evaluate the trained model, and calculates the accuracy and recall index of the model on the test set. Through this evaluation method, the evaluation results of the integrated learning pattern recognition model in terms of accuracy and robustness are obtained, and reference is provided for the improvement of the model. The model proposed in this study is suitable for a large number of user data. Because the learning behavior of users is influenced by personal habits, there are limitations in obtaining enough high-quality data, so the labeling of data is subjective. It is still a challenge to select the most representative features of user learning behavior extracted from the model. The feature selection method can lead to different results, and the process requires a lot of human intervention. The experiment conducted mining and analysis on user learning behavior data of a domestic English vocabulary learning APP. Compared with the confusion matrix of the traditional Stacking model, the improved Stacking model has a stronger ability to distinguish user learning patterns. According to the formula, the accuracy of the improved Stacking model is 91.29%; The accuracy of traditional Stacking model is 90.71%. The ROC curve of the improved Stacking model is smoother than the three single models. Its AUC value is 0.85, which is the same as that of XGBoost. The function is also improved compared with the traditional Stacking model, Logistic Regression (LR) and Random Forest (RF) model. Therefore, the Stacking integrated model owns the best forecast performance and can accurately predict the long-term learning mode of users. In this study, the English learning patterns of mobile users are identified by the method of integrated learning, so that the prediction results of multiple basic learners can be integrated, and the complementarity between different learners can be effectively dealt with, thus improving the generalization ability of the model. This model aims at identifying the English learning patterns of mobile users, and can accurately identify the learning patterns of users by analyzing their learning behaviors on mobile devices. This is of great significance for personalized English learning recommendation. The English learning pattern recognition model for mobile users proposed in this study can identify users' learning patterns by analyzing their learning behaviors, thus providing personalized learning support and suggestions. In the process of digital manufacturing, they can learn from the idea of learning pattern recognition model and identify the patterns and laws in the production process by analyzing production data and process parameters, so as to optimize the production process and improve production efficiency. [ABSTRACT FROM AUTHOR]

This article addresses the growing need in resource-constrained edge computing scenarios for energy-efficient convolutional neural network (CNN) accelerators on mobile Field-Programmable Gate Array (FPGA) systems. In particular, we concentrate on register transfer level (RTL) design flow optimization to improve programming speed and power efficiency. We present a re-configurable accelerator design optimized for CNN-based object-detection applications, especially suitable for mobile FPGA platforms like the Xilinx PYNQ-Z2. By not only optimizing the MAC module using Enhanced clock gating (ECG), the accelerator can also use low-power techniques such as Local explicit clock gating (LECG) and Local explicit clock enable (LECE) in memory modules to efficiently minimize data access and memory utilization. The evaluation using ResNet-20 trained on the CIFAR-10 dataset demonstrated significant improvements in power efficiency consumption (up to 22%) and performance. The findings highlight the importance of using different optimization techniques across multiple hardware modules to achieve better results in real-world applications. [ABSTRACT FROM AUTHOR]

Skiing technique and performance improvements are crucial for athletes and enthusiasts alike. This study presents SnowMotion, a digital human motion training assistance platform that addresses the key challenges of reliability, real-time analysis, usability, and cost in current motion monitoring techniques for skiing. SnowMotion utilizes wearable sensors fixed at five key positions on the skier's body to achieve high-precision kinematic data monitoring. The monitored data are processed and analyzed in real time through the SnowMotion app, generating a panoramic digital human image and reproducing the skiing motion. Validation tests demonstrated high motion capture accuracy (cc > 0.95) and reliability compared to the Vicon system, with a mean error of 5.033 and a root-mean-square error of less than 12.50 for typical skiing movements. SnowMotion provides new ideas for technical advancement and training innovation in alpine skiing, enabling coaches and athletes to analyze movement details, identify deficiencies, and develop targeted training plans. The system is expected to contribute to popularization, training, and competition in alpine skiing, injecting new vitality into this challenging sport. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) plays an important role in the automation of various tasks related to activity tracking in such areas as healthcare and eldercare (telerehabilitation, telemonitoring), security, ergonomics, entertainment (fitness, sports promotion, human–computer interaction, video games), and intelligent environments. This paper tackles the problem of real-time recognition and repetition counting of 12 types of exercises performed during athletic workouts. Our approach is based on the deep neural network model fed by the signal from a 9-axis motion sensor (IMU) placed on the chest. The model can be run on mobile platforms (iOS, Android). We discuss design requirements for the system and their impact on data collection protocols. We present architecture based on an encoder pretrained with contrastive learning. Compared to end-to-end training, the presented approach significantly improves the developed model's quality in terms of accuracy (F1 score, MAPE) and robustness (false-positive rate) during background activity. We make the AIDLAB-HAR dataset publicly available to encourage further research. [ABSTRACT FROM AUTHOR]

A floating greenhouse for growing oyster mushrooms can be operated remotely via a mobile phone. This innovative system can enhance mushroom production and quality while saving time. By using the Android OS operating system on a mobile phone (Internet Mobile Device with Android OS, MGT Model: T10), users can adjust the humidity and temperature within the greenhouse. This approach is particularly beneficial for older adults. Create a smart floating greenhouse that can be controlled remotely to cultivate oyster mushrooms. It would help to enhance the quality of the mushrooms, reduce the time required for cultivation, and increase the yield per planting area. We carefully examined the specifications and proceeded to create a greenhouse that could float. In addition, we have developed a unit that could control temperature and humidity, a solar cell unit, and a rack for growing mushrooms. Our greenhouses were operated remotely. To determine the best conditions for growing plants in a floating greenhouse, we conducted a test to measure temperature and humidity. We then compared our findings to those of a traditional greenhouse test and determined the optimal parameters for floating greenhouse growth. These parameters include growth time, temperature, humidity, and weight. A mushroom nursery that can be controlled remotely and floats on water consists of four main components: a structure to regulate temperature and humidity, solar cells, and mushroom racks. Research shows that mushrooms grown under this automated control system grow better than those grown through traditional methods. The harvest period is shorter, and the yield is higher than the typical yield of 1.81–1.22. When considering the construction and use of remote-controlled floating mushroom nurseries, the daily weight of mushrooms accounted for 20.22%. The company's investment return rates were found to be 3.47 years, or 580.21 h per year, which is higher than the yield of traditional methods. This mobile phone remote control system, created by Arduino, is tailor-made for cutting-edge floating greenhouses that grow grey oyster mushrooms. It can be operated with ease via mobile devices and is especially user-friendly for elderly individuals. This system enables farmers to produce a high volume of quality breeds. Furthermore, those with fish ponds can utilize the system to increase their profits. [ABSTRACT FROM AUTHOR]

Underwater optical wireless communication (UOWC) has gained interest in recent years with the introduction of autonomous and remotely operated mobile systems in blue economic ventures such as offshore food production and energy generation. Here, we devised a model for estimating the received power distribution of diffused line-of-sight mobile optical links, accommodating irregular intensity distributions beyond the beam-spread angle of the emitter. We then used this model to conduct a spatial analysis investigating the parametric influence of the placement, orientation, and angular spread of photodiodes in array-based receivers on the mobile UOWC links in different Jerlov seawater types. It revealed that flat arrays were best for links where strict alignment could be maintained, whereas curved arrays performed better spatially but were not always optimal. Furthermore, utilizing two or more spectrally distinct wavelengths and more bandwidth-efficient modulation may be preferred for received-signal intensity-based localization and improving link range in clearer oceans, respectively. Considering the geometric implications of the array of receiver photodiodes for mobile UOWCs, we recommend the use of dynamically shape-shifting array geometries. [ABSTRACT FROM AUTHOR]

Android is the most targeted mobile operating system for malware attacks. Most modern anti-malware solutions largely incorporate deep learning or machine learning techniques to detect malwares. In this paper, we conduct a comprehensive analysis on 10 deep learning and 5 machine learning classifiers in their abilities to identify Android malware applications. We used 1-gram dataset, 2-gram dataset and image dataset generated from the system call co-occurrence matrix for our experiments. Among the machine learning classifiers, XGBoost with 2-gram dataset showed the highest F1-score of 0.98. Also, the deep learning classifiers such as extreme learning machine with the system call images demonstrated the best F1-score of 0.952. We experimented using Gabor filters to investigate classifier performance on textures extracted from system call images. We observed an F1-score of 0.953 using the extreme learning machine with the Gabor images. We generated the Gabor image dataset by combining the images generated by passing system call images through 25 different Gabor configurations. In addition, to enhance the performance of the baseline classifiers, we considered the combination of autoencoders with machine learning classifiers. We observed that the amalgam of autoencoder with Random Forest displayed the best F1-score of 0.98. To evaluate the effectiveness of the aforesaid classifiers with diverse features on adversarial examples, we simulated a black-box based attack using a Generative Adversarial Network. The True Positive Rate of XGBoost on the 1-gram dataset, Random Forest on the 2-gram dataset and the Extreme Learning Machine on the system call image dataset significantly dropped to 0 from 0.98, 0.001 from 0.99 and 0 from 0.984 after the attack. Our experiments exposed a crucial vulnerability in classifiers used in modern anti-malware systems. A similar event in a real-world system could potentially render grave catastrophes. To defend against such probable attacks, we should continue further research and develop adequate security mechanisms. [ABSTRACT FROM AUTHOR]

This study investigates the potential of enhancing collaborative learning in mobile environments through the integration of interactive virtual reality (VR) simulations. With the ubiquity of mobile devices and advancements in VR technology, there is a growing interest in using immersive experiences to promote collaborative learning among students. The study explores the design and implementation of interactive VR simulations customized for mobile platforms. The goal is to create engaging and immersive learning experiences that foster collaboration and knowledge sharing. By immersing students in virtual environments where they can interact with digital objects and manipulate scenarios, the study aims to facilitate active participation and teamwork, thereby enhancing learning outcomes. Furthermore, the study examines the impact of interactive VR simulations on student engagement, motivation, and knowledge retention in collaborative learning settings. Through empirical studies and user evaluations, the effectiveness of interactive VR simulations as a tool for collaborative learning in mobile environments is assessed. The findings provide valuable insights into the design and pedagogical integration of VR technologies in mobile learning contexts. They offer guidance for educators and instructional designers who aim to leverage the potential of immersive experiences to improve collaborative learning outcomes. [ABSTRACT FROM AUTHOR]

The situation awareness, especially for collaborative robots, plays a crucial role when humans and machines work together in a human-centered, dynamic environment. Only when the humans understands how well the robot is aware of its environment can they build trust and delegate tasks that the robot can complete successfully. However, the state of situation awareness has not yet been described for collaborative robots. Furthermore, the improvement of situation awareness is now only described for humans but not for robots. In this paper, the authors propose a metric to measure the state of situation awareness. Furthermore, the models are adapted to the collaborative robot domain to systematically improve the situation awareness. The proposed metric and the improvement process of the situation awareness are evaluated using the mobile robot platform Robotino. The authors conduct extensive experiments and present the results in this paper to evaluate the effectiveness of the proposed approach. The results are compared with the existing research on the situation awareness, highlighting the advantages of our approach. Therefore, the approach is expected to significantly improve the performance of cobots in human–robot collaboration and enhance the communication and understanding between humans and machines. [ABSTRACT FROM AUTHOR]

The maximum linear (or translational) velocity achievable by an omnidirectional platform is not uniform as it depends on the angular orientation of the motion. This velocity is limited by the maximum angular velocity of the motors driving the wheels and also depends on the mechanical configuration and orientation of the wheels. This paper proposes a procedure to compute an upper bound for the translational velocity, named the consistent velocity of the omnidirectional platform, which is defined as the minimum of the maximum translational velocities achievable by the platform in any angular orientation with no wheel slippage. The consistent velocity is then a uniform translational velocity always achievable by the omnidirectional platform regardless of the angular orientation of the motion. This paper reports the consistent velocity for a set of omnidirectional platforms with three omni wheels that have the same radius and angular distribution but different angular orientations. Results have shown that these platforms can achieve different maximum velocities in different angular orientations although the consistent velocity is the same for all of them. Results have also shown that the consistent velocity has a linear relation with the angular velocity of the motion. The consistent velocity of a mobile platform can be used by its path-planning algorithm as an upper bound that guarantees the execution of any omnidirectional motion at a uniform and maximum translational velocity. [ABSTRACT FROM AUTHOR]

Android stands out as one of the most prevalent mobile operating systems globally, due to its widespread adoption and open-source nature. However, its susceptibility to malware attacks, facilitated by the ability to install third-party applications without centralized control, poses significant security challenges. Despite efforts to integrate security measures, the proliferation of malicious activities and vulnerabilities emphasizes the need for advanced detection techniques. This study implemented and optimized Long Short-Term Memory (LSTM) and Neural Network (NN) models for malware detection on the Android platform. Leveraging meticulous hyperparameter tuning and robust data preprocessing techniques, this study aimed to increase the efficacy of LSTM and NN models in identifying and mitigating various forms of malware. The results demonstrate remarkable performance, with the LSTM model achieving an accuracy of 99.24%, precision of 99.07%, recall of 98.79%, and F1-score of 98.93%, and the NN model attaining an accuracy of 99.18%, precision of 99.02%, recall of 98.84%, and F1-score of 98.93%. By addressing these challenges and achieving such high levels of accuracy and effectiveness, this study contributes significantly to the ongoing endeavor to fortify defenses against cyber threats, thus fostering a safer digital environment for users worldwide. [ABSTRACT FROM AUTHOR]

In order to develop the target object mobile platform for vehicle Automatic Emergency Braking (AEB) system test, this article studied the configuration design, strength analysis, fatigue life estimation and path tracking of test scene. Through the analysis of the test scenarios stipulated in C-NCAP Management Regulation (2024 Edition), the moving path of the target was extracted, and the expected trajectory was obtained according to the boundary conditions, then the trajectory tracking results were obtained through dynamic simulation. The comparison shows that the simulation results are basically consistent with the specified requirements, indicating that the designed mobile platform can meet the needs of AEB testing. [ABSTRACT FROM AUTHOR]

In order to improve the automation and safety of underground steel belt support in coal mines, a method for the intelligent identification and positioning of steel belt anchor holes in roadway support using inductive sensors is proposed. Using STM32F407ZGT6 as the main control chip, tasks such as data acquisition and processing, motor motion control, etc., are assigned based on the real-time operating system FreeRTOS. Using the XY mobile platform equipped with inductive sensors to detect steel belts, The collected data includes coordinate values and voltage values. Adaptive threshold generation and correction strategies are used for threshold segmentation and extraction of anchor hole boundary points. The principle of Hough circle transformation is used to fit the extracted boundary points into circles. The results show that this method can perform anchor hole positioning with a positioning error of within 5 mm, meeting the design requirements. [ABSTRACT FROM AUTHOR]

As important auxiliary equipment, rehabilitation robots are widely used in rehabilitation treatment and daily life assistance. The rehabilitation robot proposed in this paper is mainly composed of an omnidirectional mobile platform module, a lower limb exoskeleton module, and a support module. According to the characteristics of the robot's omnidirectional mobility and good stiffness, the overall kinematic model of the robot is established using the analytical method. Passive and active training control strategies for an omnidirectional mobile lower limb exoskeleton robot are proposed. The passive training mode facilitates the realization of the goal of walking guidance and assistance to the human lower limb. The active training mode can realize the cooperative movement between the robot and the human through the admittance controller and the tension sensor and enhance the active participation of the patient. In the simulation experiment, a set of optimal admittance parameters was obtained, and the parameters were substituted into the controller for the prototype experiment. The experimental results show that the admittance-controlled rehabilitation robot can perceive the patient's motion intention and realize the two walking training modes. In summary, the passive and active training control strategies based on admittance control proposed in this paper achieve the expected purpose and effectively improve the patient's active rehabilitation willingness and rehabilitation effect. [ABSTRACT FROM AUTHOR]

Autonomous machines must self‐maintain proper functionality to ensure the safety of humans and themselves. This pertains particularly to its cameras as predominant sensors to perceive the environment and support actions. A fundamental camera problem addressed in this study is noise. Solutions often focus on denoising images a posteriori, that is, fighting symptoms rather than root causes. However, tackling root causes requires identifying the noise sources, considering the limitations of mobile platforms. In this work, a real‐time, memory‐efficient, and reliable noise source estimator that combines data‐based and physically based models is investigated. To this end, a deep neural network that examines an image with camera metadata for major camera noise sources is built and trained. In addition, it quantifies unexpected factors that impact image noise or metadata. This study investigates seven different estimators on six datasets that include synthetic noise, real‐world noise from two camera systems, and real‐field campaigns. For these, only the model with most metadata is capable to accurately and robustly quantify all individual noise contributions. This method outperforms total image noise estimators and can be plug‐and‐play deployed. It also serves as a basis to include more advanced noise sources, or as part of an automatic countermeasure feedback loop to approach fully reliable machines. [ABSTRACT FROM AUTHOR]

Today, malware is arguably one of the biggest challenges organisations face from a cybersecurity standpoint, regardless of the types of devices used in the organisation. One of the most malware-attacked mobile operating systems today is Android. In response to this threat, this paper presents research on the functionalities and performance of different malicious Android application package analysis tools, including one that uses machine learning techniques. In addition, it investigates how these tools streamline the detection, classification, and analysis of malicious Android Application Packages (APKs) for Android operating system devices. As a result of the research included in this article, it can be highlighted that the AndroPytool, a tool that uses machine learning (ML) techniques, obtained the best results with an accuracy of 0.986, so it can be affirmed that the tools that use artificial intelligence techniques used in this study are more efficient in terms of detection capacity. On the other hand, of the online tools analysed, Virustotal and Pithus obtained the best results. Based on the above, new approaches can be suggested in the specification, design, and development of new tools that help to analyse, from a cybersecurity point of view, the code of applications developed for this environment. [ABSTRACT FROM AUTHOR]

Heart infarction has become one of the major causes of global death in recent decades. As the aging society intensifies, many elderly people living alone are facing life-threatening situations brought on by sudden heart attacks or infarctions. Researchers have been investigating vision-based, economical, and effective ways to monitor health status in real time in recent years. Motivated by this, this work presents a dual-stream network framework that takes into account both facial features and general whole image features and employs the feature interaction strategies in the framework structure. Furthermore, to cope with the lack of relevant datasets, we propose a novel image dataset for the detection of chest pain and falls which are the significant vital signs of myocardial infarction. With an accuracy of 88.98 % , our approach validates the viability of identifying cases of myocardial infarction in practical situations. Numerous experiments demonstrate our proposed framework's effectiveness and its extensibility to be deployed on mobile smart platforms. [ABSTRACT FROM AUTHOR]

Existing direct and inverse kinematic models of planar parallel robots assume that the robot's active joints are all at the bases. However, this approach becomes excessively complex when modeling a planar parallel robot in which the active joints are within one single kinematic chain. To address this problem, our article unveils an alternative for a 3RRR symmetric planar robot modeling technique for the derivation of the robot workspace and the analysis of its direct and inverse kinematics. The workspace was defined using a system of inequalities, and the direct and inverse kinematics models were generated using vectorial analysis and an optimized geometrical approach, respectively. The resulting models are systematically presented and validated. Two final model renditions are delivered supplying a thorough equation analysis and an applicability discussion based on the importance of the robot's mobile platform orientation. The advantages of this model are discussed in comparison to the traditional modeling approach: whereas conventional techniques require the solution of complex eighth-degree polynomials for the analysis of the active joint configuration of these robots, these models provide an efficient back-of-the-envelope analysis approach that requires the solution of a simple second-degree polynomial. [ABSTRACT FROM AUTHOR]

Air pollution monitoring using mobile ground-based measurement platforms can provide high-quality spatiotemporal air pollution information. As mobile air quality monitoring campaigns extend to entire fleets of vehicles and integrate smaller-scale air quality sensors, it is important to address the need to assess these measurements in a scalable manner. We explore the collocation-based evaluation of air quality measurements in a mobile platform using fixed regulatory sites as a reference. We compare two approaches: a standard collocation assessment technique, in which the mobile platform is parked near the fixed regulatory site for a period of time, and an expanded approach, which uses measurements while the mobile platform is in motion in the general vicinity of the fixed regulatory site. Based on the availability of fixed-reference-site data, we focus on three pollutants (ozone, nitrogen dioxide, and nitric oxide) with distinct atmospheric lifetimes and behaviors. We compare measurements from a mobile laboratory with regulatory site measurements in Denver, CO, USA, and in the San Francisco Bay Area, CA, USA. Our 1-month Denver dataset includes both parked collocation periods near the fixed regulatory sites and general driving patterns around the sites, allowing a direct comparison of the parked and mobile collocation techniques on the same dataset. We show that the mobile collocation approach produces similar performance statistics, including coefficients of determination and mean bias errors, to the standard parked collocation technique. This is particularly true when the comparisons are restricted to specific road types, with residential streets showing the closest agreement and highways showing the largest differences. We extend our analysis to a larger (yearlong) dataset in California, where we explore the relationships between the mobile measurements and the fixed reference sites on a larger scale. We show that using a 40 h running median converges to within ± 4 ppbv of the fixed reference site for nitrogen dioxide and ozone and up to about 8 ppbv for nitric oxide. We propose that this agreement can be leveraged to assess instrument performance over time during large-scale mobile monitoring campaigns. We demonstrate an example of how such relationships can be employed during large-scale monitoring campaigns using small sensors to identify potential measurement biases. [ABSTRACT FROM AUTHOR]

Portable instruments that can measure the number concentration and size of airborne nanoparticles are very useful for assessing their impacts on human health and climate, mainly because they can enable personal monitoring when carried by individuals, and/or 2- or 3-dimensional mappings when employed onboard mobile platforms. Partector 2 (P2), which is a lightweight and portable instrument manufactured by Naneos Particle Solutions GmbH (Windisch, Switzerland), can determine the concentration (up to 106 #/cm3) and average diameter of aerosol particles having sizes from 10 to 300 nm, making it an excellent candidate for such measurements. Although its performance has been investigated at standard conditions (i.e., ground level pressure and room temperatures), it has not been assessed under reduced pressure and temperature conditions that are typically encountered at higher altitudes; e.g., when employed outdoors in mountainous environments and/or onboard Unmanned Aerial Systems; UASs. Here we assess the counting and sizing capabilities of P2 at temperatures from ca. 22 down to 4 °C, and pressures from 1013 down to 710 hPa that correspond to altitudes from sea level to ca. 3 km. Our results show that the performance of the instrument is not substantially affected when operated at these conditions, remaining within the accuracy thresholds of ±30% reported by the manufacturer. P2, therefore, qualifies for outdoor use at higher altitudes, and can be employed in such environments to determine the number concentration and mean size of sub-300 nm aerosol particles, complementing existing portable optical particle counters that are already employed onboard aerial systems. [ABSTRACT FROM AUTHOR]