Intelligent Recognition Using Ultralight Multifunctional Nano‑Layered Carbon Aerogel Sensors with Human‑Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.

Functional Optical Fiber Sensors Detecting Imperceptible Physical/Chemical Changes for Smart Batteries

The battery technology progress has been a contradictory process in which performance improvement and hidden risks coexist.Now the battery is still a“black box”,thus requiring a deep understanding of its internal state.The battery should“sense its internal physical/chemical conditions”,which puts strict requirements on embedded sensing parts.This paper summarizes the application of advanced optical fiber sensors in lithium-ion batteries and energy storage technologies that may be mass deployed,focuses on the insights of advanced optical fiber sensors into the processes of one-dimensional nano-micro-level battery material structural phase transition,electrolyte degradation,electrode-electrolyte interface dynamics to three-dimensional macro-safety evolution.The paper contributes to understanding how to use optical fiber sensors to achieve“real”and“embedded”monitoring.Through the inherent advantages of the advanced optical fiber sensor,it helps clarify the battery internal state and reaction mechanism,aiding in the establishment of more detailed models.These advancements can promote the development of smart batteries,with significant importance lying in essentially promoting the improvement of system consistency.Furthermore,with the help of smart batteries in the future,the importance of consistency can be weakened or even eliminated.The application of advanced optical fiber sensors helps comprehensively improve the battery quality,reliability,and life.

Characteristic Study of Self-Powered Sensors Based on Native Protein Composite Film

Flexible electronic sensors composed of flexible film and conductive materials play an increasingly important role in wearable and internet information transmission.It has received more and more attention and made some progress over the decades.However,it is still a great challenge to prepare biocompatible and highly transparent conductive films.Egg white is a pure natural protein-rich material.Hydroxypropylmethyl cellulose has a good compatibility and high transparency,which is an ideal material for flexible sensors.Here,we overcome the problem of poor mechanical flexibility and electrical conductivity of protein,and develop a high transparency and good flexibility hydroxypropylmethyl cellulose/egg white protein composite membrane-based triboelectric nanogenerator('X'-TENG).The experimental results show that the flexible pressure sensor based on'X'-TENG has a high sensitivity,fast response speed,and low detection limit.It can even be used as a touch/pressure sensing artificial electronic skin.It can also be made into an intelligent waffle keyboard for recording and tracking users of the keyboard.Our strategy may provide a new way to easily build flexible electronic sensors and move toward practical applications.

Resource Allocation for IRS Assistedmm Wave Wireless Powered Sensor Networks with User Cooperation

In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET)phase first and then cooperatively transmit information to a hybrid access point(AP)in the wireless information transmission(WIT)phase,following which the IRS is deployed to enhance the system performance of theWET andWIT.We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots,power allocations,and the phase shifts of the IRS.Due to the non-convexity of the original problem,a semidefinite programming relaxation-based approach is proposed to convert the formulated problem to a convex optimization framework,which can obtain the optimal global solution.Simulation results demonstrate that the weighted sum throughput of the proposed UC scheme outperforms the non-UC scheme whether equipped with IRS or not.

Mechanoluminescent-Triboelectric Bimodal Sensors for Self-Powered Sensing and Intelligent Control

Self-powered flexible devices with skin-like multiple sensing ability have attracted great attentions due to their broad applications in the Internet of Things(IoT).Various methods have been proposed to enhance mechano-optic or electric performance of the flexible devices;however,it remains challenging to realize the display and accurate recognition of motion trajectories for intelligent control.Here,we present a fully self-powered mechanoluminescent-triboelectric bimodal sensor based on micronanostructured mechanoluminescent elastomer,which can patterned-display the force trajectories.The deformable liquid metals used as stretchable electrode make the stress transfer stable through overall device to achieve outstanding mechanoluminescence(with a gray value of 107 under a stimulus force as low as 0.3 N and more than 2000 cycles reproducibility).Moreover,a microstructured surface is constructed which endows the resulted composite with significantly improved triboelectric performances(voltage increases from 8 to 24 V).Based on the excellent bimodal sensing performances and durability of the obtained composite,a highly reliable intelligent control system by machine learning has been developed for controlling trolley,providing an approach for advanced visual interaction devices and smart wearable electronics in the future IoT era.

Visualization of real-time displacement time history superimposed with dynamic experiments using wireless smart sensors and augmented reality

Wireless smart sensors(WSS)process field data and inform inspectors about the infrastructure health and safety.In bridge engineering,inspectors need reliable data about changes in displacements under loads to make correct decisions about repairs and replacements.Access to displacement information in the field and in real-time remains a challenge as inspectors do not see the data in real time.Displacement data from WSS in the field undergoes additional processing and is seen at a different location.If inspectors were able to see structural displacements in real-time at the locations of interest,they could conduct additional observations,creating a new,information-based,decision-making reality in the field.This paper develops a new,human-centered interface that provides inspectors with real-time access to actionable structural data during inspection and monitoring enhanced by augmented reality(AR).It summarizes and evaluates the development and validation of the new human-infrastructure interface in laboratory experiments.The experiments demonstrate that the interface that processes all calculations in the AR device accurately estimates dynamic displacements in comparison with the laser.Using this new AR interface tool,inspectors can observe and compare displacement data,share it across space and time,visualize displacements in time history,and understand structural deflection more accurately through a displacement time history visualization.

Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual agriculture.Accelerating the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart agriculture.Automatic irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation techniques.Automatic learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an agronomist.With this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent agriculture.The MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination purposes.Besides,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not needed.The MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention.Fortunately,digital watermarking provides an effective method to solve this problem.In order to improve the robustness of the medical image watermarking scheme,in this paper,we propose a novel zero-watermarking algorithm with the integer wavelet transform(IWT),Schur decomposition and image block energy.Specifically,we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,then we decompose the sub-blocks by Schur decomposition.After that,the feature matrix is constructed according to the relationship between the image block energy and the whole image energy.At the same time,we encrypt watermarking with the logistic chaotic position scrambling.Finally,the zero-watermarking is obtained by XOR operation with the encrypted watermarking.Three indexes of peak signal-to-noise ratio,normalization coefficient(NC)and the bit error rate(BER)are used to evaluate the robustness of the algorithm.According to the experimental results,most of the NC values are around 0.9 under various attacks,while the BER values are very close to 0.These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods,which indicates it is more suitable for medical image privacy and security protection.

The application of intelligent sensors in medical research:a review

The rapid advancement of biomedicine in the twenty-first century has been facilitated by the constant innovation in biomedical technology.The most crucial issue in the field of medicine is to use sensor technology to gather information from primitive organisms,particularly the human body.Design,development,and application of biomedical sensors in the study of clinical diseases’diagnosis and therapy have all been significantly aided by the advancement of medicine.The interest in creating sensors significantly increased in the 1960s.Chemical and biological sensors have been swiftly created in response to an urgent practical necessity,enabling the creation of selective sensors for the direct detection of diverse ions and compounds.The traditional large-size sensors are quickly turning into miniature sensors and are rapidly applied in biological and medical fields.Currently,wearable electronic blood pressure monitors,home blood glucose meters,and quick body surface digital thermometers are commonly used.The advent of a wide variety of medical-grade wearable sensors that will enable real-time biometric data tracking of a large range of physiological characteristics will likely be one of the most revolutionary,exciting,and difficult changes to come to medicine over the next several years.For possible uses in the entertainment,health monitoring,and medical care industries,high-performance flexible strain sensors connected to clothing or human skin are necessary.The use of sensors in the development of biomedical diagnostic tools and medical equipment will enhance human quality of life in the twenty-first century.This article will introduce the current medical sensor field related to sensors for physical quantities,sensors for chemical quantities,sensors for biological quantities such as electronic nose,electronic tongue,and their applications.

Driving Activity Classification Using Deep Residual Networks Based on Smart Glasses Sensors

Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road accidents.As a result,reckless driving behaviour can cause congestion and delays.Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem.Previous research has also collected and analyzed a wide range of data,including electroencephalography(EEG),electrooculography(EOG),and photographs of the driver’s face.On the other hand,driving a car is a complicated action that requires a wide range of body move-ments.In this work,we proposed a ResNet-SE model,an efficient deep learning classifier for driving activity clas-sification based on signal data obtained in real-world traffic conditions using smart glasses.End-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning model.Sensor data from 3-point EOG electrodes,tri-axial accelerometer,and tri-axial gyroscope from the Smart Glasses dataset was utilized in this study.We performed various experiments and compared the proposed model to base-line deep learning algorithms(CNNs and LSTMs)to demonstrate its performance.According to the research results,the proposed model outperforms the previous deep learning models in this domain with an accuracy of 99.17%and an F1-score of 98.96%.

Intelligent emergency service system based on wireless sensor and actuator networks

An intelligent emergency service（ IES） system is designed for indoor environments based on a wireless sensor and actuator network（ WSAN） composed of a gateway, sensor nodes, and a multi-robot system（ MRS）. If the MRS receives accident alarm information, the group of robots will navigate to the accident sites and provide corresponding emergency services.According to the characteristics of the MRS, a distributed consensus formation protocol is designed, which can assure that the multiple robots arrive at the accident site in a specified formation. The prototype emergency service system was designed and implemented, and some relevant simulations and experiments were carried out. The results showthat the MRS can successfully provide emergency lighting and failure node replacement services when accidents happen. The effectiveness of the algorithm and the feasibility of the system are verified.

Multi-Sensor Intelligent System for On-Line and Real-Time Moneitoring Tool Cutting State in FMS

The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sensor (AE) with motor current sensor was presented. The parallel communication between control system of machine tools, the monitoring intelligent system,and several decision-making systems for identifying tool cutting state was established It can auto - matically select the sensor way ,monitoring mode and identifying method in machining process- ing so as to build a successful and effective intelligent system for on -line and real-time moni- toring cutting tool states in FMS.

APPLICATIONS OF PIEZOELECTRIC MATERIAL SENSORS IN SMART STRUCTURES

The use of piezoelectric material sensors in smart composite structures is investigated. An experimental structure bonded with lead zirconate titanate piezoelectric ceramic(PZT) sensors is developed. These bonded sensors are employed to monitor load variations and transient impacts in the structure. Incorporated with pattern recognition approach, PZT sensors have succeeded in detecting the onset and location of damages.

Smart Sensor与MEMS

讨论了SmartSensor与MEMS的全新概念。结合微机械加工技术的发展，概述了现代传感器技术智能化、功能集成化的新趋势，及其对现代微电子、微型电子机械乃至整个信息技术领域的深远影响。

DEVELOPMENT OF EXTRINSIC FABRY PEROT FIBER SENSOR AND ITS APPLICATION TO SMART MATERIALS

In the work of developing extrinsic fabry perot interferometric (EFPI), the key technology of polishing fiber optic endfaces and coating the multilayer of dielectric films on them is raised and resolved to settle the disturbance and stability problem of EFPI, which simplifies the sensing system, improves the sensor performance and reduces the cost. In this paper, the relations between the output interferential light intensity and the F P cavity length are calculated based on the theory of mode field coupling. The EFPI fiber optic sensor is adhered to a distributed smart laminate beam to detect vibration frequency and axial strain value, the results coincident with the results tested by PZT.

Smart gas sensor arrays powered by artificial intelligence

Mobile robots behaving as humans should possess multifunctional flexible sensing systems including vision,hearing,touch,smell,and taste.A gas sensor array(GSA),also known as electronic nose,is a possible solution for a robotic olfactory system that can detect and discriminate a wide variety of gas molecules.Artificial intelligence(AI)applied to an electronic nose involves a diverse set of machine learning algorithms which can generate a smell print by analyzing the signal pattern from the GSA.A combination of GSA and AI algorithms can empower intelligent robots with great capabilities in many areas such as environmental monitoring,gas leakage detection,food and beverage production and storage,and especially disease diagnosis through detection of different types and concentrations of target gases with the advantages of portability,low-powerconsumption and ease-of-operation.It is exciting to envisage robots equipped with a"nose"acting as family doctor who will guard every family member's health and keep their home safe.In this review,we give a summary of the state-of the-art research progress in the fabrication techniques for GSAs and typical algorithms employed in artificial olfactory systems,exploring their potential applications in disease diagnosis,environmental monitoring,and explosive detection.We also discuss the key limitations of gas sensor units and their possible solutions.Finally,we present the outlook of GSAs over the horizon of smart homes and cities.

A Review of Smart Materials for the Boost of Soft Actuators,Soft Sensors,and Robotics Applications

With the advance of smart material science,robotics is evolving from rigid robots to soft robots.Compared to rigid robots,soft robots can safely interact with the environment,easily navigate in unstructured fields,and be minimized to operate in narrow spaces,owning to the new actuation and sensing technologies developed by the smart materials.In the review,different actuation and sensing technologies based on different smart materials are analyzed and summarized.According to the driving or feedback signals,actuators are categorized into electrically responsive actuators,thermally responsive actuators,magnetically responsive actuators,and photoresponsive actuators;sensors are categorized into resistive sensors,capacitive sensors,magnetic sensors,and optical waveguide sensors.After introducing the principle and several robotic prototypes of some typical materials in each category of the actuators and sensors.The advantages and disadvantages of the actuators and sensors are compared based on the categories,and their potential applications in robotics are also presented.

Improved Implementation and Evaluation of Wireless Sensor Networks in Intelligent Building

A complete study for the implementation of wireless sensor networks in the intelligent building is presented. We carry out some experiments to find out the factors affecting the network performance. Several vital parameters which are related to the link quality are measured before deploying the actual system. And then, we propose an optimized routing protocol based on the analysis of the test data. We evaluate the deployment strategies to ensure the excellent performance of the wireless sensor networks under the real working conditions. And the evaluation results show that the presented system could satisfy the requirements of the applications in the intelligent building.

Distributed Information Flow Verification for Secure Service Composition in Smart Sensor Network

Accelerate processor, efficient software and pervasive connections provide sensor nodes with more powerful computation and storage ability, which can offer various services to user. Based on these atomic services, different sensor nodes can cooperate and compose with each other to complete more complicated tasks for user. However, because of the regional characteristic of sensor nodes, merging data with different sensitivities become a primary requirement to the composite services, and information flow security should be intensively considered during service composition. In order to mitigate the great cost caused by the complexity of modeling and the heavy load of single-node verification to the energy-limited sensor node, in this paper, we propose a new distributed verification framework to enforce information flow security on composite services of smart sensor network. We analyze the information flows in composite services and specify security constraints for each service participant. Then we propose an algorithm over the distributed verification framework involving each sensor node to participate in the composite service verification based on the security constraints. The experimental results indicate that our approach can reduce the cost of verification and provide a better load balance.

Selection of Metaheuristic Algorithm to Design Wireless Sensor Network

The deployment of sensor nodes is an important aspect in mobile wireless sensor networks for increasing network performance.The longevity of the networks is mostly determined by the proportion of energy consumed and the sensor nodes’access network.The optimal or ideal positioning of sensors improves the portable sensor networks effectiveness.Coverage and energy usage are mostly determined by successful sensor placement strategies.Nature-inspired algorithms are the most effective solution for short sensor lifetime.The primary objective of work is to conduct a comparative analysis of nature-inspired optimization for wireless sensor networks(WSNs’)maximum network coverage.Moreover,it identifies quantity of installed sensor nodes for the given area.Superiority of algorithm has been identified based on value of optimized energy.The first half of the paper’s literature on nature-inspired algorithms is discussed.Later six metaheuristics algorithms(Grey wolf,Ant lion,Dragonfly,Whale,Moth flame,Sine cosine optimizer)are compared for optimal coverage of WSNs.The simulation outcomes confirm that whale opti-mization algorithm(WOA)gives optimized Energy with improved network coverage with the least number of nodes.This comparison will be helpful for researchers who will use WSNs in their applications.