Sensors, Volume 24, Issue 9 (May-1 2024) – 262 articles

This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared lights simultaneously for photoplethysmography (PPG) acquisition. Leveraging the different penetration depths and tissue reflection characteristics of these light wavelengths, the device can provide richer and more comprehensive physiological information. Furthermore, a Multi-Channel Convolutional Neural Network–Long Short-Term Memory–Attention (MCNN-LSTM-Attention) evaluation model was developed. This model, constructed based on multiple convolutional channels, facilitates the feature extraction and fusion of collected multi-modality data. Additionally, it incorporated an attention mechanism module capable of dynamically adjusting the importance weights of input information, thereby enhancing the accuracy of rehabilitation assessment. To validate the effectiveness of the proposed system, sixteen volunteers were recruited for clinical data collection and validation, comprising eight stroke patients and eight healthy subjects. Experimental results demonstrated the system’s promising performance metrics (accuracy: 0.9125, precision: 0.8980, recall: 0.8970, F1 score: 0.8949, and loss function: 0.1261). This rehabilitation evaluation system holds the potential for stroke diagnosis and identification, laying a solid foundation for wearable-based stroke risk assessment and stroke rehabilitation assistance. Full article

This article presents a study on the neurobiological control of voluntary movements for anthropomorphic robotic systems. A corticospinal neural network model has been developed to control joint trajectories in multi-fingered robotic hands. The proposed neural network simulates cortical and spinal areas, as well as the connectivity between them, during the execution of voluntary movements similar to those performed by humans or monkeys. Furthermore, this neural connection allows for the interpretation of functional roles in the motor areas of the brain. The proposed neural control system is tested on the fingers of a robotic hand, which is driven by agonist–antagonist tendons and actuators designed to accurately emulate complex muscular functionality. The experimental results show that the corticospinal controller produces key properties of biological movement control, such as bell-shaped asymmetric velocity profiles and the ability to compensate for disturbances. Movements are dynamically compensated for through sensory feedback. Based on the experimental results, it is concluded that the proposed biologically inspired adaptive neural control system is robust, reliable, and adaptable to robotic platforms with diverse biomechanics and degrees of freedom. The corticospinal network successfully integrates biological concepts with engineering control theory for the generation of functional movement. This research significantly contributes to improving our understanding of neuromotor control in both animals and humans, thus paving the way towards a new frontier in the field of neurobiological control of anthropomorphic robotic systems. Full article

Structural engineers are often required to draw two-dimensional engineering sketches for quick structural analysis, either by hand calculation or using analysis software. However, calculation by hand is slow and error-prone, and the manual conversion of a hand-drawn sketch into a virtual model is tedious and time-consuming. This paper presents a complete and autonomous framework for converting a hand-drawn engineering sketch into an analyzed structural model using a camera and computer vision. In this framework, a computer vision object detection stage initially extracts information about the raw features in the image of the beam diagram. Next, a computer vision number-reading model transcribes any handwritten numerals appearing in the image. Then, feature association models are applied to characterize the relationships among the detected features in order to build a comprehensive structural model. Finally, the structural model generated is analyzed using OpenSees. In the system presented, the object detection model achieves a mean average precision of 99.1%, the number-reading model achieves an accuracy of 99.0%, and the models in the feature association stage achieve accuracies ranging from 95.1% to 99.5%. Overall, the tool analyzes 45.0% of images entirely correctly and the remaining 55.0% of images partially correctly. The proposed framework holds promise for other types of structural sketches, such as trusses and frames. Moreover, it can be a valuable tool for structural engineers that is capable of improving the efficiency, safety, and sustainability of future construction projects. Full article

Heart failure (HF) is a complex clinical syndrome associated with significant morbidity, mortality, and healthcare costs. It is characterized by various structural and/or functional abnormalities of the heart, resulting in elevated intracardiac pressure and/or inadequate cardiac output at rest and/or during exercise. These dysfunctions can originate from a variety of conditions, including coronary artery disease, hypertension, cardiomyopathies, heart valve disorders, arrhythmias, and other lifestyle or systemic factors. Identifying the underlying cause is crucial for detecting reversible or treatable forms of HF. Recent epidemiological studies indicate that there has not been an increase in the incidence of the disease. Instead, patients seem to experience a chronic trajectory marked by frequent hospitalizations and stagnant mortality rates. Managing these patients requires a multidisciplinary approach that focuses on preventing disease progression, controlling symptoms, and preventing acute decompensations. In the outpatient setting, patient self-care plays a vital role in achieving these goals. This involves implementing necessary lifestyle changes and promptly recognizing symptoms/signs such as dyspnea, lower limb edema, or unexpected weight gain over a few days, to alert the healthcare team for evaluation of medication adjustments. Traditional methods of HF monitoring, such as symptom assessment and periodic clinic visits, may not capture subtle changes in hemodynamics. Sensor-based technologies offer a promising solution for remote monitoring of HF patients, enabling early detection of fluid overload and optimization of medical therapy. In this review, we provide an overview of the CardioMEMS device, a novel sensor-based system for pulmonary artery pressure monitoring in HF patients. We discuss the technical aspects, clinical evidence, and future directions of CardioMEMS in HF management. Full article

Interoperability is a central problem in digitization and sos engineering, which concerns the capacity of systems to exchange information and cooperate. The task to dynamically establish interoperability between heterogeneous cps at run-time is a challenging problem. Different aspects of the interoperability problem have been studied in fields such as sos, neural translation, and agent-based systems, but there are no unifying solutions beyond domain-specific standardization efforts. The problem is complicated by the uncertain and variable relations between physical processes and human-centric symbols, which result from, e.g., latent physical degrees of freedom, maintenance, re-configurations, and software updates. Therefore, we surveyed the literature for concepts and methods needed to automatically establish sos with purposeful cps communication, focusing on machine learning and connecting approaches that are not integrated in the present literature. Here, we summarize recent developments relevant to the dynamic interoperability problem, such as representation learning for ontology alignment and inference on heterogeneous linked data; neural networks for transcoding of text and code; concept learning-based reasoning; and emergent communication. We find that there has been a recent interest in deep learning approaches to establishing communication under different assumptions about the environment, language, and nature of the communicating entities. Furthermore, we present examples of architectures and discuss open problems associated with ai-enabled solutions in relation to sos interoperability requirements. Although these developments open new avenues for research, there are still no examples that bridge the concepts necessary to establish dynamic interoperability in complex sos, and realistic testbeds are needed. Full article

In this paper, a six-degree-of-freedom analog tactile probe with a new, simple, and robust mechanical design is presented. Its design is based on the use of one elastomeric ring that supports the stylus carrier and allows its movement inside a cubic measuring range of ±3 mm. The position of the probe tip is determined by three low-cost, noncontact, 2D PSD (position-sensitive detector) sensors, facilitating a wider application of this probe to different measuring systems compared to commercial ones. However, several software corrections, regarding the size and orientation of the three LED light beams, must be carried out when using these 2D sensors for this application due to the lack of additional focusing or collimating lenses and the very wide measuring range. The development process, simulation results, correction models, experimental tests, and calibration of this probe are presented. The results demonstrate high repeatability along the X-, Y-, and Z-axes (2.0 µm, 2.0 µm, and 2.1 µm, respectively) and overall accuracies of 6.7 µm, 7.0 µm, and 8.0 µm, respectively, which could be minimized by more complex correction models. Full article

Concussions, a prevalent public health concern in the United States, often result from mild traumatic brain injuries (mTBI), notably in sports such as American football. There is limited exploration of smart-textile-based sensors for measuring the head impacts associated with concussions in sports and recreational activities. In this paper, we describe the development and construction of a smart textile impact sensor (STIS) and validate STIS functionality under high magnitude impacts. This STIS can be inserted into helmet cushioning to determine head impact force. The designed 2 × 2 STIS matrix is composed of a number of material layered structures, with a sensing surface made of semiconducting polymer composite (SPC). The SPC dimension was modified in the design iteration to increase sensor range, responsiveness, and linearity. This was to be applicable in high impact situations. A microcontroller board with a biasing circuit was used to interface the STIS and read the sensor’s response. A pendulum test setup was constructed to evaluate various STISs with impact forces. A camera and Tracker software were used to monitor the pendulum swing. The impact forces were calculated by measuring the pendulum bob’s velocity and acceleration. The performance of the various STISs was measured in terms of voltage due to impact force, with forces varying from 180 to 722 N. Through data analysis, the threshold impact forces in the linear range were determined. Through an analysis of linear regression, the sensors’ sensitivity was assessed. Also, a simplified model was developed to measure the force distribution in the 2 × 2 STIS areas from the measured voltages. The results showed that improving the SPC thickness could obtain improved sensor behavior. However, for impacts that exceeded the threshold, the suggested sensor did not respond by reflecting the actual impact forces, but it gave helpful information about the impact distribution on the sensor regardless of the accurate expected linear response. Results showed that the proposed STIS performs satisfactorily within a range and has the potential to be used in the development of an e-helmet with a large STIS matrix that could cover the whole head within the e-helmet. This work also encourages future research, especially on the structure of the sensor that could withstand impacts which in turn could improve the overall range and performance and would accurately measure the impact in concussion-causing impact ranges. Full article

The Timed-Up and Go (TUG) test is widely utilized by healthcare professionals for assessing fall risk and mobility due to its practicality. Currently, test results are based solely on execution time, but integrating technological devices into the test can provide additional information to enhance result accuracy. This study aimed to assess the reliability of smartphone-based instrumented TUG (iTUG) parameters. We conducted evaluations of intra- and inter-device reliabilities, hypothesizing that iTUG parameters would be replicable across all experiments. A total of 30 individuals participated in Experiment A to assess intra-device reliability, while Experiment B involved 15 individuals to evaluate inter-device reliability. The smartphone was securely attached to participants’ bodies at the lumbar spine level between the L3 and L5 vertebrae. In Experiment A, subjects performed the TUG test three times using the same device, with a 5 min interval between each trial. Experiment B required participants to perform three trials using different devices, with the same time interval between trials. Comparing stopwatch and smartphone measurements in Experiment A, no significant differences in test duration were found between the two devices. A perfect correlation and Bland–Altman analysis indicated good agreement between devices. Intra-device reliability analysis in Experiment A revealed significant reliability in nine out of eleven variables, with four variables showing excellent reliability and five showing moderate to high reliability. In Experiment B, inter-device reliability was observed among different smartphone devices, with nine out of eleven variables demonstrating significant reliability. Notable differences were found in angular velocity peak at the first and second turns between specific devices, emphasizing the importance of considering device variations in inertial measurements. Hence, smartphone inertial sensors present a valid, applicable, and feasible alternative for TUG assessment. Full article

Wireless power transfer (WPT) technology is a contactless wireless energy transfer method with wide-ranging applications in fields such as smart homes, the Internet of Things (IoT), and electric vehicles. Achieving optimal efficiency in wireless power transfer systems has been a key research focus. In this paper, we propose a tracking method based on full current mode impedance matching for optimizing wireless power transfer efficiency. This method enables efficiency tracking in WPT systems and seamless switching between continuous conduction mode and discontinuous mode, expanding the detection capabilities of the wireless power transfer system. MATLAB was used to simulate the proposed method and validate its feasibility and effectiveness. Based on the simulation results, the proposed method ensures optimal efficiency tracking in wireless power transfer systems while extending detection capabilities, offering practical value and potential for widespread applications. Full article

Shrimp fry counting is an important task for biomass estimation in aquaculture. Accurate counting of the number of shrimp fry in tanks can not only assess the production of mature shrimp but also assess the density of shrimp fry in the tanks, which is very helpful for the subsequent growth status, transportation management, and yield assessment. However, traditional manual counting methods are often inefficient and prone to counting errors; a more efficient and accurate method for shrimp fry counting is urgently needed. In this paper, we first collected and labeled the images of shrimp fry in breeding tanks according to the constructed experimental environment and generated corresponding density maps using the Gaussian kernel function. Then, we proposed a multi-scale attention fusion-based shrimp fry counting network called the SFCNet. Experiments showed that our proposed SFCNet model reached the optimal performance in terms of shrimp fry counting compared to CNN-based baseline counting models, with MAEs and RMSEs of 3.96 and 4.682, respectively. This approach was able to effectively calculate the number of shrimp fry and provided a better solution for accurately calculating the number of shrimp fry. Full article

Robot-Assisted Minimally Invasive Surgery (RAMIS) marks a paradigm shift in surgical procedures, enhancing precision and ergonomics. Concurrently it introduces complex stress dynamics and ergonomic challenges regarding the human–robot interface and interaction. This study explores the stress-related aspects of RAMIS, using the da Vinci XI Surgical System and the Sea Spikes model as a standard skill training phantom to establish a link between technological advancement and human factors in RAMIS environments. By employing different physiological and kinematic sensors for heart rate variability, hand movement tracking, and posture analysis, this research aims to develop a framework for quantifying the stress and ergonomic loads applied to surgeons. Preliminary findings reveal significant correlations between stress levels and several of the skill-related metrics measured by external sensors or the SURG-TLX questionnaire. Furthermore, early analysis of this preliminary dataset suggests the potential benefits of applying machine learning for surgeon skill classification and stress analysis. This paper presents the initial findings, identified correlations, and the lessons learned from the clinical setup, aiming to lay down the cornerstones for wider studies in the fields of clinical situation awareness and attention computing. Full article

To address the various challenges in aluminum surface defect detection, such as multiscale intricacies, sensitivity to lighting variations, occlusion, and noise, this study proposes the AluDef-ClassNet model. Firstly, a Gaussian difference pyramid is utilized to capture multiscale image features. Secondly, a self-attention mechanism is introduced to enhance feature representation. Additionally, an improved residual network structure incorporating dilated convolutions is adopted to increase the receptive field, thereby enhancing the network’s ability to learn from extensive information. A small-scale dataset of high-quality aluminum surface defect images is acquired using a CCD camera. To better tackle the challenges in surface defect detection, advanced deep learning techniques and data augmentation strategies are employed. To address the difficulty of data labeling, a transfer learning approach based on fine-tuning is utilized, leveraging prior knowledge to enhance the efficiency and accuracy of model training. In dataset testing, our model achieved a classification accuracy of 98.01%, demonstrating significant advantages over other classification models. Full article

The burgeoning interest in intelligent transportation systems (ITS) and the widespread adoption of in-vehicle amenities like infotainment have spurred a heightened fascination with vehicular ad-hoc networks (VANETs). Multi-hop routing protocols are pivotal in actualizing these in-vehicle services, such as infotainment, wirelessly. This study presents a novel protocol called multiple junction-based traffic-aware routing (MJTAR) for VANET vehicles operating in urban environments. MJTAR represents an advancement over the improved greedy traffic-aware routing (GyTAR) protocol. MJTAR introduces a distributed mechanism capable of recognizing vehicle traffic and computing curve metric distances based on two-hop junctions. Additionally, it employs a technique to dynamically select the most optimal multiple junctions between source and destination using the ant colony optimization (ACO) algorithm. We implemented the proposed protocol using the network simulator 3 (NS-3) and simulation of urban mobility (SUMO) simulators and conducted performance evaluations by comparing it with GSR and GyTAR. Our evaluation demonstrates that the proposed protocol surpasses GSR and GyTAR by over 20% in terms of packet delivery ratio, with the end-to-end delay reduced to less than 1.3 s on average. Full article

Traditionally, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and moving axes. In this study, we attempted to estimate the shoulder joint internal/external rotation angle using the combination of pose estimation artificial intelligence (AI) and a machine learning model. Videos of the right shoulder of 10 healthy volunteers (10 males, mean age 37.7 years, mean height 168.3 cm, mean weight 72.7 kg, mean BMI 25.6) were recorded and processed into 10,608 images. Parameters were created using the coordinates measured from the posture estimation AI, and these were used to train the machine learning model. The measured values from the smartphone’s angle device were used as the true values to create a machine learning model. When measuring the parameters at each angle, we compared the performance of the machine learning model using both linear regression and Light GBM. When the pose estimation AI was trained using linear regression, a correlation coefficient of 0.971 was achieved, with a mean absolute error (MAE) of 5.778. When trained with Light GBM, the correlation coefficient was 0.999 and the MAE was 0.945. This method enables the estimation of internal and external rotation angles from a direct-facing position. This approach is considered to be valuable for analyzing motor movements during sports and rehabilitation. Full article

Within the context of a smart home, detecting the operating status of appliances in the environment plays a pivotal role, estimating power consumption, issuing overuse reminders, and identifying faults. The traditional contact-based approaches require equipment updates such as incorporating smart sockets or high-precision electric meters. Non-constant approaches involve the use of technologies like laser and Ultra-Wideband (UWB) radar. The former can only monitor one appliance at a time, and the latter is unable to detect appliances with extremely tiny vibrations and tends to be susceptible to interference from human activities. To address these challenges, we introduce HomeOSD, an advanced appliance status-detection system that uses mmWave radar. This innovative solution simultaneously tracks multiple appliances without human activity interference by measuring their extremely tiny vibrations. To reduce interference from other moving objects, like people, we introduce a Vibration-Intensity Metric based on periodic signal characteristics. We present the Adaptive Weighted Minimum Distance Classifier (AWMDC) to counteract appliance vibration fluctuations. Finally, we develop a system using a common mmWave radar and carry out real-world experiments to evaluate HomeOSD’s performance. The detection accuracy is 95.58%, and the promising results demonstrate the feasibility and reliability of our proposed system. Full article

Modern society increasingly recognizes brain fatigue as a critical factor affecting human health and productivity. This study introduces a novel, portable, cost-effective, and user-friendly system for real-time collection, monitoring, and analysis of physiological signals aimed at enhancing the precision and efficiency of brain fatigue recognition and broadening its application scope. Utilizing raw physiological data, this study constructed a compact dataset that incorporated EEG and ECG data from 20 subjects to index fatigue characteristics. By employing a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) for fatigue state recognition, this study achieved recognition rates of 95.71% and 96.13% on the DROZY public dataset and constructed dataset, respectively. These results highlight the effectiveness of the multi-modal feature fusion model in brain fatigue recognition, providing a viable solution for cost-effective and efficient fatigue monitoring. Furthermore, this approach offers theoretical support for designing rest systems for researchers. Full article

The advancement of novel water treatment technologies requires the implementation of both accurate data measurement and recording processes. These procedures are essential for acquiring results and conducting thorough analyses to enhance operational efficiency. In addition, accurate sensor data facilitate precise control over chemical treatment dosages, ensuring optimal water quality and corrosion inhibition while minimizing chemical usage and associated costs. Under this framework, this paper describes the sensoring and monitoring solution for a hybrid system based on a cooling tower (CT) connected to mechanical vapor compression (MVC) equipment for desalination and brine concentration purposes. Sensors connected to the data commercial logger solution, Almemo 2890-9, are also discussed in detail such as temperature, relative humidity, pressure, flow rate, etc. The monitoring system allows remote control of the MVC based on a server, GateManager, and TightVNC. In this way, the proposed solution provides remote access to the hybrid system, being able to visualize gathered data in real time. A case study located in Cartagena (Spain) is used to assess the proposed solution. Collected data from temperature transmitters, pneumatic valves, level sensors, and power demand are included and discussed in the paper. These variables allow a subsequent forecasting process to estimate brine concentration values. Different sample times are included in this paper to minimize the collected data from the hybrid system within suitable operation conditions. This solution is suitable to be applied to other desalination processes and locations. Full article

This paper reports on the design, modeling, and characterization of a multi-resonant, directional, MEMS acoustic sensor. The design builds on previous resonant MEMS sensor designs to broaden the sensor’s usable bandwidth while maintaining a high signal-to-noise ratio (SNR). These improvements make the sensor more attractive for detecting and tracking sound sources with acoustic signatures that are broader than discrete tones. In-air sensor characterization was conducted in an anechoic chamber. The sensor was characterized underwater in a semi-anechoic pool and in a standing wave tube. The sensor demonstrated a cosine-like directionality, a maximum acoustic sensitivity of 47.6 V/Pa, and a maximum SNR of 88.6 dB, for 1 Pa sound pressure, over the bandwidth of the sensor circuitry (100 Hz–3 kHz). The presented design represents a significant improvement in sensor performance compared to similar resonant MEMS sensor designs. Increasing the sensitivity of a single-resonator design is typically associated with a decrease in bandwidth. This multi-resonant design overcomes that limitation. Full article

During the operation of space gravitational wave detectors, the constellation configuration formed by three satellites gradually deviates from the ideal 60° angle due to the periodic variations in orbits. To ensure the stability of inter-satellite laser links, active compensation of the breathing angle variation within the constellation plane is achieved by rotating the optical subassembly through the telescope pointing mechanism. This paper proposes a high-performance robust composite control method designed to enhance the robust stability, disturbance rejection, and tracking performance of the telescope pointing system. Specifically, based on the dynamic model of the telescope pointing mechanism and the disturbance noise model, an H_∞ controller has been designed to ensure system stability and disturbance rejection capabilities. Meanwhile, employing the method of an H_∞ norm optimized disturbance observer (HODOB) enhances the nonlinear friction rejection ability of the telescope pointing system. The simulation results indicate that, compared to the traditional disturbance observer (DOB) design, utilizing the HODOB method can enhance the tracking accuracy and pointing stability of the telescope pointing system by an order of magnitude. Furthermore, the proposed composite control method improves the overall system performance, ensuring that the stability of the telescope pointing system meets the 10 nrad/Hz^1/2 @0.1 mHz~1 Hz requirement specified for the TianQin mission. Full article

The purpose of this article is to establish a prediction model of joint movements and realize the prediction of joint movemenst, and the research results are of reference value for the development of the rehabilitation equipment. This will be carried out by analyzing the impact of surface electromyography (sEMG) on ankle movements and using the Hill model as a framework for calculating ankle joint torque. The table and scheme used in the experiments were based on physiological parameters obtained through the model. Data analysis was performed on ankle joint angle signal, movement signal, and sEMG data from nine subjects during dorsiflexion/flexion, varus, and internal/external rotation. The Hill model was employed to determine 16 physiological parameters which were optimized using a genetic algorithm. Three experiments were carried out to identify the optimal model to calculate torque and root mean square error. The optimized model precisely calculated torque and had a root mean square error of under 1.4 in comparison to the measured torque. Ankle movement models predict torque patterns with accuracy, thereby providing a solid theoretical basis for ankle rehabilitation control. The optimized model provides a theoretical foundation for precise ankle torque forecasts, thereby improving the efficacy of rehabilitation robots for the ankle. Full article

Underwater visual detection technology is crucial for marine exploration and monitoring. Given the growing demand for accurate underwater target recognition, this study introduces an innovative architecture, YOLOv8-MU, which significantly enhances the detection accuracy. This model incorporates the large kernel block (LarK block) from UniRepLKNet to optimize the backbone network, achieving a broader receptive field without increasing the model’s depth. Additionally, the integration of C2fSTR, which combines the Swin transformer with the C2f module, and the SPPFCSPC_EMA module, which blends Cross-Stage Partial Fast Spatial Pyramid Pooling (SPPFCSPC) with attention mechanisms, notably improves the detection accuracy and robustness for various biological targets. A fusion block from DAMO-YOLO further enhances the multi-scale feature extraction capabilities in the model’s neck. Moreover, the adoption of the MPDIoU loss function, designed around the vertex distance, effectively addresses the challenges of localization accuracy and boundary clarity in underwater organism detection. The experimental results on the URPC2019 dataset indicate that YOLOv8-MU achieves an [email protected] of 78.4%, showing an improvement of 4.0% over the original YOLOv8 model. Additionally, on the URPC2020 dataset, it achieves 80.9%, and, on the Aquarium dataset, it reaches 75.5%, surpassing other models, including YOLOv5 and YOLOv8n, thus confirming the wide applicability and generalization capabilities of our proposed improved model architecture. Furthermore, an evaluation on the improved URPC2019 dataset demonstrates leading performance (SOTA), with an [email protected] of 88.1%, further verifying its superiority on this dataset. These results highlight the model’s broad applicability and generalization capabilities across various underwater datasets. Full article

Demand is strong for sensitive, reliable, and cost-effective diagnostic tools for cancer detection. Accordingly, bead-based biosensors have emerged in recent years as promising diagnostic platforms based on wide-ranging cancer biomarkers owing to the versatility, high sensitivity, and flexibility to perform the multiplexing of beads. This comprehensive review highlights recent trends and innovations in the development of bead-based biosensors for cancer-biomarker detection. We introduce various types of bead-based biosensors such as optical, electrochemical, and magnetic biosensors, along with their respective advantages and limitations. Moreover, the review summarizes the latest advancements, including fabrication techniques, signal-amplification strategies, and integration with microfluidics and nanotechnology. Additionally, the challenges and future perspectives in the field of bead-based biosensors for cancer-biomarker detection are discussed. Understanding these innovations in bead-based biosensors can greatly contribute to improvements in cancer diagnostics, thereby facilitating early detection and personalized treatments. Full article

Recent advancements in polymer-assisted layer-by-layer (LbL) fabrication have revolutionized the development of wearable sensors for health monitoring. LbL self-assembly has emerged as a powerful and versatile technique for creating conformal, flexible, and multi-functional films on various substrates, making it particularly suitable for fabricating wearable sensors. The incorporation of polymers, both natural and synthetic, has played a crucial role in enhancing the performance, stability, and biocompatibility of these sensors. This review provides a comprehensive overview of the principles of LbL self-assembly, the role of polymers in sensor fabrication, and the various types of LbL-fabricated wearable sensors for physical, chemical, and biological sensing. The applications of these sensors in continuous health monitoring, disease diagnosis, and management are discussed in detail, highlighting their potential to revolutionize personalized healthcare. Despite significant progress, challenges related to long-term stability, biocompatibility, data acquisition, and large-scale manufacturing are still to be addressed, providing insights into future research directions. With continued advancements in polymer-assisted LbL fabrication and related fields, wearable sensors are poised to improve the quality of life for individuals worldwide. Full article

Electrochemical measurements are vital to a wide range of applications such as air quality monitoring, biological testing, food industry, and more. Integrated circuits have been used to implement miniaturized and low-power electrochemical potentiostats that are suitable for wearable devices. However, employing modern integrated circuit technologies with low supply voltage precludes the utilization of electrochemical reactions that require a higher potential window. In this paper, we present a novel circuit architecture that utilizes dynamic voltage at the working electrode of an electrochemical cell to effectively enhance the supported voltage range compared to traditional designs, increasing the cell voltage range by 46% and 88% for positive and negative cell voltages, respectively. In return, this facilitates a wider range of bias voltages in an electrochemical cell, and, therefore, opens integrated microsystems to a broader class of electrochemical reactions. The circuit was implemented in 180 nm technology and consumes 2.047 mW of power. It supports a bias potential range of 1.1 V to −2.12 V and cell potential range of 2.41 V to −3.11 V that is nearly double the range in conventional designs. Full article

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR’s capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material’s complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced. Full article

Soft robots claim the architecture of actuators, sensors, and computation demands with their soft bodies by obtaining fast responses and adapting to the environment. Sensory-motor coordination is one of the main design principles utilized for soft robots because it allows the capability to sense and actuate mutually in the environment, thereby achieving rapid response performance. This work intends to study the response for a system that presents coupled actuation and sensing functions simultaneously and is integrated in an arbitrary elastic structure with ionic conduction elements, called as soft sensory-motor system based on ionic solution (SSMS-IS). This study provides a comparative analysis of the performance of SSMS-IS prototypes with three diverse designs: toroidal, semi-toroidal, and rectangular geometries, based on a series of performance experiments, such as sensitivity, drift, and durability. The design with the best performance was the rectangular SSMS-IS using silicon rubber RPRO20 for both internal and external pressures applied in the system. Moreover, this work explores the performance of a bioinspired soft robot using rectangular SSMS-IS elements integrated in its body. Further, it investigated the feasibility of the robot to adapt its morphology online for environment variability, responding to external stimuli from the environment with different levels of stiffness and damping. Full article

Bats play a pivotal role in maintaining ecological balance, and studying their behaviors offers vital insights into environmental health and aids in conservation efforts. Determining the presence of various bat species in an environment is essential for many bat studies. Specialized audio sensors can be used to record bat echolocation calls that can then be used to identify bat species. However, the complexity of bat calls presents a significant challenge, necessitating expert analysis and extensive time for accurate interpretation. Recent advances in neural networks can help identify bat species automatically from their echolocation calls. Such neural networks can be integrated into a complete end-to-end system that leverages recent internet of things (IoT) technologies with long-range, low-powered communication protocols to implement automated acoustical monitoring. This paper presents the design and implementation of such a system that uses a tiny neural network for interpreting sensor data derived from bat echolocation signals. A highly compact convolutional neural network (CNN) model was developed that demonstrated excellent performance in bat species identification, achieving an F1-score of 0.9578 and an accuracy rate of 97.5%. The neural network was deployed, and its performance was evaluated on various alternative edge devices, including the NVIDIA Jetson Nano and Google Coral. Full article

In the context of the rapid development of the Internet of Vehicles, virtual reality, automatic driving and the industrial Internet, the terminal devices in the network show explosive growth. As a result, more and more information is generated from the edge of the network, which makes the data throughput increase dramatically in the mobile communication network. As the key technology of the fifth-generation mobile communication network, mobile edge caching technology which caches popular data to the edge server deployed at the edge of the network avoids the data transmission delay of the backhaul link and the occurrence of network congestion. With the growing scale of the network, distributing hot data from cloud servers to edge servers will generate huge energy consumption. To realize the green and sustainable development of the communication industry and reduce the energy consumption of distribution of data that needs to be cached in edge servers, we make the first attempt to propose and solve the problem of edge caching data distribution with minimum energy consumption (ECDDMEC) in this paper. First, we model and formulate the problem as a constrained optimization problem and then prove its NP-hardness. Subsequently, we design a greedy algorithm with computational complexity of $O\left(n^2\right)$ to solve the problem approximately. Experimental results show that compared with the distribution strategy of each edge server directly requesting data from the cloud server, the strategy obtained by the algorithm can significantly reduce the energy consumption of data distribution. Full article

In recent years, satellite communication systems (SCSs) have rapidly developed in terms of their role and capabilities, promoted by advancements in space launch technologies. However, this rapid development has also led to the emergence of significant security vulnerabilities, demonstrated through real-world targeted attacks such as AcidRain and AcidPour that demand immediate attention from the security community. In response, various countermeasures, encompassing both technological and policy-based approaches, have been proposed to mitigate these threats. However, the multitude and diversity of these proposals make their comparison complex, requiring a systemized view of the landscape. In this paper, we systematically categorize and analyze both attacks and defenses within the framework of confidentiality, integrity, and availability, focusing on specific threats that pose substantial risks to SCSs. Furthermore, we evaluate existing countermeasures against potential threats in SCS environments and offer insights into the security policies of different nations, recognizing the strategic importance of satellite communications as a national asset. Finally, we present prospective security challenges and solutions for future SCSs, including full quantum communication, AI-integrated SCSs, and standardized protocols for the next generation of terrestrial–space communication. Full article

In order to efficiently identify early tea diseases, an improved YOLOv8 lesion detection method is proposed to address the challenges posed by the complex background of tea diseases, difficulty in detecting small lesions, and low recognition rate of similar phenotypic symptoms. This method focuses on detecting tea leaf blight, tea white spot, tea sooty leaf disease, and tea ring spot as the research objects. This paper presents an enhancement to the YOLOv8 network framework by introducing the Receptive Field Concentration-Based Attention Module (RFCBAM) into the backbone network to replace C2f, thereby improving feature extraction capabilities. Additionally, a mixed pooling module (Mixed Pooling SPPF, MixSPPF) is proposed to enhance information blending between features at different levels. In the neck network, the RepGFPN module replaces the C2f module to further enhance feature extraction. The Dynamic Head module is embedded in the detection head part, applying multiple attention mechanisms to improve multi-scale spatial location and multi-task perception capabilities. The inner-IoU loss function is used to replace the original CIoU, improving learning ability for small lesion samples. Furthermore, the AKConv block replaces the traditional convolution Conv block to allow for the arbitrary sampling of targets of various sizes, reducing model parameters and enhancing disease detection. the experimental results using a self-built dataset demonstrate that the enhanced YOLOv8-RMDA exhibits superior detection capabilities in detecting small target disease areas, achieving an average accuracy of 93.04% in identifying early tea lesions. When compared to Faster R-CNN, MobileNetV2, and SSD, the average precision rates of YOLOv5, YOLOv7, and YOLOv8 have shown improvements of 20.41%, 17.92%, 12.18%, 12.18%, 10.85%, 7.32%, and 5.97%, respectively. Additionally, the recall rate (R) has increased by 15.25% compared to the lowest-performing Faster R-CNN model and by 8.15% compared to the top-performing YOLOv8 model. With an FPS of 132, YOLOv8-RMDA meets the requirements for real-time detection, enabling the swift and accurate identification of early tea diseases. This advancement presents a valuable approach for enhancing the ecological tea industry in Yunnan, ensuring its healthy development. Full article