859 results about "Acoustic sensor" patented technology

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The invention discloses a data center machine room AI energy-saving control method and system, a digital twin model of a machine room operation state is constructed through a holographic perception and heterogeneous data fusion technology, centimeter-level monitoring of an equipment state and environmental parameters is realized, and the system integrates a laser radar array, an acoustic sensor and a gas sensor network. The time-space alignment of multi-modal data is completed by combining edge computing nodes, holographic mapping including thermodynamic characteristics, vibration characteristics and gas leakage risks is formed, historical temperature control strategy characteristics are extracted by adopting a variational auto-encoder based on a dynamic strategy generation mechanism of generative artificial intelligence, and a load trend is predicted by combining a long-short-term memory network. Constructing a self-adaptive strategy pool; the multi-agent reinforcement learning framework enables temperature control, equipment scheduling and power grid response to form game optimization, the strategy robustness in a complex scene is improved, and the system innovatively fuses power grid real-time electricity price and carbon transaction data so as to establish a multi-target decision system.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention relates to the technical field of power transformer state monitoring, and discloses a main transformer winding temperature rise on-line monitoring method and system, and the method comprises the steps: obtaining the calibration flight time of ultrasonic waves through an acoustic sensor array disposed on the outer wall of an oil tank in a quasi-isothermal state of a transformer; calculating a structure additional time delay which is caused by an internal fixed structure and does not change along with the temperature, and establishing an acoustic fingerprint for the transformer; during on-line monitoring, oil way net propagation time reflecting temperature change of the insulating oil is obtained from on-line flight time measured in real time; and then, based on the net propagation time of the oil way, an acoustic tomography algorithm is adopted to reconstruct internal two-dimensional or three-dimensional temperature field distribution, and the winding hot-spot temperature is finally and accurately calculated in combination with the real-time load current. According to the invention, through the acoustic fingerprint correction technology, the measurement interference of an internal fixed structure is eliminated, and non-intrusive, high-precision and visual online monitoring of the winding hot-spot temperature is realized.

The invention provides an offshore wind turbine generator fault diagnosis method and system based on multi-source sensor data fusion. The method comprises the steps that a vibration signal from at least one component of a wind turbine generator is acquired through a vibration sensor; performing time-frequency conversion on the vibration signal by applying synchronous compression wavelet transform to obtain time-frequency representation of the vibration signal; when the reconstruction error exceeds a preset threshold value, it is judged that an abnormal event exists in the vibration signal; obtaining the position of a part corresponding to the abnormal event; starting an image sensor and an acoustic sensor according to the position of the component, and acquiring an image signal and a sound signal of the component according to the image sensor and the acoustic sensor; according to the DS evidence theory, the vibration signal, the image signal and the sound signal, obtaining the confidence of the fault type; the fault type of the component is judged according to the maximum confidence allocation principle, high-resolution time-frequency analysis can be achieved through synchronous compression wavelet transform (SST), and the fault feature identification degree is improved in combination with the self-encoding neural network and the D-S evidence theory.

The invention relates to the technical field of distributed optical fiber acoustic sensors, in particular to a distributed optical fiber sensor vibration identification method and system, and the method comprises the steps: obtaining original data, and carrying out the preprocessing; inputting the preprocessed vibration time sequence into a Mamba model, and extracting a time domain feature vector reflecting a time evolution rule; after obtaining a time-frequency spectrogram of the preprocessed vibration time sequence, extracting to obtain a frequency domain feature vector representing different frequency components and a global association relationship thereof; inputting the time domain and frequency domain feature vectors into a time-frequency feature fusion model, and outputting a vibration event recognition result of the distributed optical fiber acoustic sensor; training the time-frequency feature fusion model, and storing the optimal model weight for prediction; and performing prediction by using the optimal model, storing a prediction result, performing verification, calculating an evaluation index, and finally performing result comparison. The invention aims to solve the problems of insufficient performance and the like of a distributed optical fiber acoustic sensing technology in the prior art.

The invention belongs to the technical field of drainage pipeline detection, and particularly relates to a voiceprint recognition detection method for internal defects of a drainage pipeline, and the method comprises the following steps: S1, collecting sound signals in the pipeline through an acoustic sensor array; s2, preprocessing the collected sound signals, wherein the preprocessing comprises noise filtering, signal enhancement and segmentation processing; s3, extracting time domain, frequency domain and time-frequency domain features of the sound signals, and constructing voiceprint fingerprints; s4, establishing a defect classification model based on the deep neural network, and identifying different types of pipeline defects; s5, carrying out confidence evaluation on the detection result, and determining the position of the defect in the pipeline; s6, outputting a detection result and carrying out graded alarm; according to the method, various defect forms of cracks, blockage, damage and the like of different degrees of the drainage pipeline can be recognized, the recognition accuracy of the internal defects of the drainage pipeline can reach 95% or above through multi-dimensional voiceprint feature extraction and a deep learning algorithm, and continuous monitoring and real-time alarming can be achieved.

The invention relates to a hearing aid intelligent noise reduction and human voice enhancement system based on electroencephalogram signals, and belongs to the field of biomedical engineering and acoustic signal processing. The system comprises an electroencephalogram signal acquisition module, a multi-channel acoustic sensor array, an embedded neural signal processor, an adaptive beam forming module, a dynamic speech enhancement engine and a dual-mode output device, and constructs electroencephalogram-acoustics joint features by extracting an alpha / theta wave power ratio, a P300 component and auditory cortical Gamma phase synchronism. A deep network is driven to separate target voice, a wave beam direction and a frequency response curve are dynamically adjusted based on neural feedback, a closed-loop calibration unit is innovatively adopted, gain is reversely adjusted according to N1-P2 wave amplitude, heart rate variability and eye movement data are fused to optimize decisions, and when the signal-to-noise ratio is-5dB, the voice recognition rate reaches 89%, the auditory fatigue is reduced by 37%, and the decision conflict rate is smaller than 6%. The defects of attention blind area, noise separation failure and physiological adaptation of a traditional hearing aid are overcome. The system is suitable for the fields of hearing impairment rehabilitation, special communication and intelligent cabins.

The invention relates to an FOC control method based on an automobile air conditioner module motor without a Hall sensor, and belongs to the technical field of automobile electronics. The method comprises the following steps: injecting double-frequency voltage pulses into a stator in stages to position a rotor; collecting current through a sampling resistor and filtering to construct a rotor flux linkage distribution model, and presetting rectangular axis current in combination with noise reduction of a miniature acoustic sensor and air speed gear of an air conditioner; an extended Kalman filter observer is adopted to reversely deduce the load torque, and the torque change rate is monitored to trigger instantaneous load impact protection; the voltage space vector amplitude is corrected through voltage feed-forward compensation, a resistor-capacitor series filter circuit is additionally arranged, and phase dislocation pulse width modulation is adopted for resisting interference; and establishing a motor efficiency-rotating speed-load three-dimensional optimization method, starting dynamic flux-weakening control, and estimating the winding temperature through the stator resistance to adjust the flux-weakening depth. Accurate starting and dynamic load adaptation of the Hall-sensor-free motor are achieved, starting out-of-step and noise exceeding are avoided, and stable air volume of an automobile air conditioner and long-term reliable operation of the motor are guaranteed.

An additive manufacturing real-time process parameter optimization method based on reinforcement learning driving comprises the steps that firstly, a multi-mode online monitoring hardware platform is constructed, and a visible light camera, a thermal imaging camera and an acoustic sensor are integrated to sense the working condition of the additive manufacturing process in real time in an omnibearing mode; secondly, extracting key features, including visible light images, thermal imaging and acoustic signal branches, of modal data on line through a lightweight CNN-Transform hybrid network, and generating a unified low-dimensional state vector through convolution feature extraction and fusion of a Transform encoder; then, a reinforcement learning model is established, a multi-target reward function is designed in combination with the extracted feature data, a process jitter penalty term, an overheating penalty term and the like are covered, and dynamic mapping of parameters and performance is achieved. And finally, an online learning and real-time decision-making system is deployed, the trained strategy network is embedded into manufacturing equipment, self-adaptive adjustment and closed-loop control of technological parameters are achieved, and the stability and product performance of the additive manufacturing process are effectively improved.

The invention discloses an intelligent regulation and control method and system for a regulating valve, and the method comprises the following steps: obtaining the current operation condition data of the regulating valve, including fluid parameters and valve execution states; establishing a multi-physics field coupling model according to the operation condition data; dynamically correcting the multi-physics field coupling model by using an acoustic signal acquired by an acoustic sensor; a control instruction of the opening degree of the valve element is generated according to the corrected multi-physics field coupling model, and an actuator is driven to adjust the position of the valve element; in the valve element adjusting process, operation condition data and acoustic signals are collected in real time, and closed-loop feedback based on the condition-model-acoustic signals is formed; and when the acoustic characteristics in the acoustic signals exceed a preset threshold value, a slow opening and slow closing or segmented opening control strategy is triggered. According to the method, prediction errors can be remarkably reduced, the accuracy and reliability of a valve element opening degree control instruction are improved, and therefore the safety and stability of system operation are improved.

The invention relates to the technical field of environmental noise monitoring, and discloses a road traffic noise intelligent monitoring and three-dimensional sound field reconstruction system, which comprises an acoustic sensor module, a data collection and storage module, a data analysis and evaluation module, a three-dimensional sound field construction and display module and a traffic flow feature library. According to the system, a differential geometry principle is adopted, a sound field is regarded as a Riemannian manifold with a local microstructure, and accurate description of an irregular sound field is realized through a curvature self-adaptive sound field manifold construction technology; introducing a covariant derivative in Riemannian geometry, and constructing a sound propagation model adapted to a complex road environment; and realizing hierarchical decomposition and reconstruction of the sound field by using a multi-scale analysis theory. According to the method, the sound source positioning precision and the calculation efficiency are improved, seamless analysis from microcosmic to macroscopic is realized, an innovative solution is provided for traffic and noise collaborative management, and intelligent traffic and environmental noise management are effectively supported.

The invention relates to the technical field of intelligent operation and maintenance of a power system, in particular to an intelligent early warning method and system for preventing external damage of a power transmission line. The method comprises the following steps: acquiring acoustic data of a power transmission line through an acoustic sensor; performing voiceprint feature sparse reconstruction according to the transmission line acoustic data to obtain voiceprint feature data; performing sparse causal graph matching on the voiceprint feature data to obtain sound source behavior causal graph data; performing double-domain attention cross recognition according to the sound source behavior causal atlas data to obtain sound source separation recognition data; and performing scene map generation on the sound source separation identification data to obtain sound source scene map data. According to the invention, by constructing the nested causal chain graph and fusing the propagation weight mechanism, the development evolution and multi-stage trigger relationship of the external damage event of the power transmission line can be accurately captured. Compared with a traditional single-point detection method, the method has higher chain reasoning ability and early risk identification ability, and the response foresight and causal interpretability of an early warning system are effectively improved.

The invention relates to the technical field of noise measurement systems, in particular to a noise measurement system for a new energy tire, which comprises a tire positioning and driving unit, a sensor unit, a data acquisition and synchronization unit, a data processing unit, a data analysis unit and a man-machine interaction and display unit, the tire positioning and driving unit simulates different loads, different speeds and different road conditions of a tire by using a rack type tire positioning device, and drives the tire to rotate by using a brushless mute motor, so that a high-speed rotating effect without introducing extra mechanical noise is realized; the sensor unit is arranged on the two sides of the tire and at the position close to the ground through an acoustic sensor free field type condenser microphone array, is fixed to the suspension support through an acceleration sensor and is used for collecting vibration signals; the running states of the tire under different loads, speeds and road conditions are accurately simulated, noise data of the tire under various actual working conditions are obtained, and the measurement result is closer to the real situation.

The invention relates to the technical field of bag type dust collector filter bag abrasion detection, in particular to a filter bag damage non-contact detection method and system based on acoustic array positioning. The method comprises the following steps: arranging an acoustic sensor array in an air purification chamber of the dust remover, and collecting acoustic signals in real time; after band-pass filtering is carried out on the signals, feature vectors of the signals are extracted and matched with a pre-established acoustic fingerprint database, so that a filter bag damage event is recognized; after identification, based on the time difference of arrival of the signal to at least three sensors in the array, combining the coordinates of the sensors and the sound velocity, and utilizing a TDOA positioning algorithm to calculate the space coordinates of the damaged sound source; and finally, matching the coordinates with a pre-stored filter bag coordinate mapping table, determining the number of a specific damaged filter bag, and performing visual highlight marking on a pattern board plane layout of an application layer terminal. The online, real-time, automatic and accurate positioning of the damaged filter bag is realized, the problem that the prior art can only give an alarm but cannot position is solved, and the operation and maintenance efficiency and safety are improved.

The present disclosure relates to a system (100) for acoustically detecting a temperature change, the system (100) comprising: at least one conversion element (10) for converting a temperature change into a characteristic sound; an acoustic sensor (40) configured to be mechanically connected to the structure (50) at a distance from the at least one conversion element (10); and a processor (42, 62) coupled to the acoustic sensor (40), in which the at least one conversion element (10) comprises an actuation portion (20) having at least one material having thermal expansion properties such that a change in temperature causes a movement of the actuation portion (20) that causes the conversion element (10) to generate a characteristic sound and conduct the characteristic sound into the structure (50), the acoustic sensor (40) is configured to record a characteristic sound generated by the conversion element (10) as a structural sound propagating through the structure (50), and the processor (42, 62) is configured to detect a temperature change based on the characteristic sound recorded by the acoustic sensor (40).

The invention relates to a transformer fault identification method based on voiceprint signals, and belongs to the technical field of cepstrum for extracting parameters in audio decoding or coding. The method comprises the following steps: setting a fault type and establishing a fault identification model for training; arranging an acoustic sensor to collect voiceprint signals of the transformer; utilizing a dream optimization algorithm to optimize the penalty factor and a successive variational mode decomposition method to decompose a plurality of mode components, and dividing the mode components into pure components and noisy components; noise reduction is carried out on the noisy component by adopting a designed threshold function in combination with wavelet threshold noise reduction, and the noisy component and the pure component after noise reduction are input into the recognition model to obtain a probability vector; and finally, fusing into a first fusion probability vector and a second fusion probability vector through fuzzy measurement, and taking the fault type corresponding to the maximum second fusion probability as the fault type of the transformer. The method can accurately capture the mapping relation between the acoustic features and the transformer fault state, and accurately identifies the transformer fault type.

The invention relates to an unmanned aerial vehicle detection and control method and system for regional defense, and the method comprises the steps: dividing a to-be-defended region, and setting a differentiated flight forbidding threshold and a detection response priority; secondly, constructing a multi-dimensional cooperative detection network of radio spectrum monitoring, millimeter wave radar, photoelectric tracking and an acoustic sensor based on sub-region division, and collecting multi-source data of the unmanned aerial vehicle in real time; judging a sub-region to which the unmanned aerial vehicle belongs through coordinate matching, screening out over-limit target data, and extracting a target feature set of flight speed, height, communication protocol type, radar reflection cross section and voiceprint information; and inputting a target dynamic classification model, and dynamically outputting three threat levels of a harmless civil type threat level, a suspicious reconnaissance type threat level and a high-risk aggressive type threat level. The hierarchical management and control are executed in combination with the sub-region priority and the threat level, the management and control effect is dynamically evaluated based on the preset evaluation index, and the detection parameter and the management and control strategy are adaptively adjusted, so that the intrusion risk of the core region is effectively reduced, and the intellectualization and adaptive ability of regional defense are improved.

The invention provides an underwater manipulator motion balance real-time monitoring method based on acoustic sensing, which comprises the following steps: if a frequency shift range exceeds a preset range threshold, filtering a signal data set, and separating superposed components of a friction sound signal and a water flow interference sound to obtain a preliminarily separated friction sound signal; performing short-time Fourier transform processing on the preliminarily separated friction sound signals, extracting time-frequency features of the friction sound signals, and obtaining a friction sound feature set; calculating the signal energy ratio of the friction sound signal to the water flow interference sound by analyzing the frequency spectrum difference between the friction sound feature set and the water flow interference sound, screening the interference sound according to the signal energy ratio, and rejecting the screened interference sound to obtain a separation signal; and when it is recognized that the manipulator executes the complex track task, track complexity is recognized, the position of the acoustic sensor array is dynamically adjusted according to the track complexity, the dynamic layout of the acoustic sensor array is optimized, and array configuration is obtained.

The invention relates to the technical field of intelligent operation and maintenance of urban infrastructures, and particularly discloses an underground pipe gallery water leakage detection method and system based on acoustic features and multi-source fusion, and the method comprises the steps: carrying out the feature extraction of acoustic sensor data, and obtaining optimized acoustic features; performing structure optimization and data enhancement on the optimized acoustic features to obtain a water leakage judgment result; performing positioning correction by using a time difference of arrival algorithm and combining the three-dimensional model of the pipe gallery to obtain a water leakage position coordinate; and the intelligent agent autonomously selects a tool combination, and generates an underground pipe gallery water leakage intelligent detection report in combination with information retrieved in the structured knowledge base. According to the main technical scheme and effects, the intelligent agent with a large language model as a core is constructed, and a plurality of special tool modules and a structured knowledge base are cooperatively called, so that efficient, accurate and intelligent management of underground pipe gallery water leakage events is realized.

Systems and methods for monitoring structural integrity are provided. Acoustic sensors are disposed in relation to an area of interest of a structural member to detect an acoustic emission and output an acoustic emission signal. The signal is collected, and a computing device evaluates acoustic emission signal characteristics and predicts whether a crack is present. The acoustic emission signal characteristics are used to detect and predict aspects associated with formation and growth of cracks at various locations of the structural member.

The invention provides a gas turbine power plant rotating equipment state monitoring method and system based on voiceprint recognition, and relates to the technical field of power plant equipment monitoring, and the method comprises the steps: collecting an original voiceprint signal through deploying an array type acoustic sensor network; performing signal preprocessing, generating a voiceprint signal map, extracting Mel amplitude spectrum and phase spectrum features, constructing a multi-scale feature set, and generating a multi-dimensional voiceprint feature vector after screening; constructing an adaptive feature fusion network based on an attention mechanism, and fusing the multi-dimensional voiceprint feature vectors to generate voiceprint fusion features; inputting the voiceprint fusion feature into a preset voiceprint classification network, and outputting a real-time state recognition result, a confidence value and a fault prediction trend; according to the method, the early warning level is determined through the preset multi-level early warning mechanism, and the early warning information and the state monitoring report are output, so that high-precision state identification of the gas turbine power plant rotating equipment in a complex noise environment can be realized, and the accuracy and the anti-interference capability of fault diagnosis are remarkably improved.

The invention discloses a respiratory intensive care method and system, and the method comprises the steps: deploying an acoustic sensor on a respirator loop, and synchronously obtaining the ventilation parameters and optional temperature and humidity data of a respirator; preprocessing the acquired multi-modal data, and converting an acoustic signal into a time-frequency spectrogram; the preprocessed data are input into a multi-modal fusion fault recognition model, the model comprises a convolutional neural network branch used for extracting acoustic features and a recurrent neural network branch used for capturing time sequence dynamics, and the probability of a specific fault precursor is output by combining the features of all the branches through a fusion layer; and finally, performing early warning decision according to the probability. The invention also comprises a system for implementing the method. According to the invention, through deep fusion of acoustic, pneumatic and thermodynamic data, accurate prediction of fault precursor is realized, and an earlier time window is provided for clinical intervention, so that the monitoring safety is improved.

An assembly is provided which includes a package and a printed circuit board. The package includes a housing bounding a region and an acoustic sensor within the region. The housing includes a base with a first hole. The sensor is configured to generate signals indicative of sound received by the sensor through the first hole. The printed circuit board is in mechanical communication with the base and includes a second hole aligned with the first hole such that sound received by the second hole propagates through the first hole to the sensor. The printed circuit board further includes an electrically conductive layer, at least a portion of which extends across the second hole and is configured to allow the sound to propagate through the second hole and to at least partially shield the region containing the sensor from electromagnetic interference.

The invention discloses an unmanned aerial vehicle detection and directional interference system based on an artificial intelligence technology, which belongs to the technical field of unmanned aerial vehicle detection interference and comprises a data acquisition module, an unmanned aerial vehicle identification module, a geographic information correction module, a trajectory prediction module and a directional interference module. The data acquisition module is used for acquiring geographic information data of a target area based on a geographic information system, comprises a radar, an acoustic sensor and a radio frequency spectrum detection sensor, and is used for acquiring feature data of a moving object in the target area; and the unmanned aerial vehicle identification module obtains feature data of a moving object in the target area based on the data acquisition module. According to the unmanned aerial vehicle detection and directional interference system based on the artificial intelligence technology, the obstacle in the target area is identified, the personalized behavior of the unmanned aerial vehicle is analyzed, the trajectory prediction and interference strategy of the unmanned aerial vehicle is optimized in combination with topographic features, and the trajectory prediction precision and the directional interference effect are improved.

A chemical mechanical polishing apparatus includes a platen to support a polishing pad, a carrier head to a surface of a substrate against the polishing pad, a motor to generate relative motion between the platen and the carrier head so as to polish an overlying layer on the substrate, an in-situ acoustic monitoring system, and a controller. The controller is configured to detect exposure of an underlying layer due to the polishing of the substrate based on measurements from the in-situ acoustic monitoring system. The in-situ acoustic monitoring system may detect exposure of an underlying layer based on comparison of the signal to prior measurements of acoustic signals generated by stress energy of test substrates.

An aerial vehicle configured for operating within indoor or outdoor spaces is equipped with acoustic sensors for detecting reflections of sound, or echoes, from objects. Distances and bearings to such objects may be calculated based on such echoes. The echoes may be reflections of sound actively emitted by the aerial vehicle, such as by a speaker, or sound radiating from operating components aboard the aerial vehicle, such as rotating motors or propellers. The echoes may be captured by multiple sensors such as microphones provided around the aerial vehicle and used to calculate distances or bearings to the objects, such as by trilateration, triangulation, or in any other manner. Such distances or bearings may also be utilized along with distances or bearings determined from cameras, range sensors, or other systems, and used to generate a navigation map of the space, or compared to a navigation map generated for that space.

Acoustic sensor systems with different heights, as well as methods for configuring and operating such sensor systems are disclosed. In some embodiments, a sensor system described herein may include an acoustic transmitter element; and an acoustic receiver element; wherein the first portion of the piezoelectric material of the acoustic transmitter element comprises a first thickness, and the second portion of the piezoelectric material of the acoustic receiver element comprises a second thickness different from the first thickness; and wherein the isolation layer is structured to create a gap that physically separates the first portion of the second electrode layer from the second portion of the second electrode layer.

Fabrication and processing of flexible acoustic sensor systems and various configurations of sensor stacks associated therewith are disclosed. In some embodiments, a method for fabrication of a flexible acoustic sensing apparatus may include: obtaining a stack of materials on a glass wafer, the stack of materials including: a thin-film transistor (TFT) component having a flexible substrate; a piezoelectric component on the TFT component, the piezoelectric component configured to, responsive to receipt of an acoustic signal, provide an electrical signal to one or more receiver elements having associated circuitry on the TFT component; and an electrode component on the piezoelectric component, the electrode component including one or more acoustic transmitter elements configured to transmit one or more acoustic signals; performing a laser liftoff (LLO) process to separate the stack of materials from the glass wafer; and implementing at least a portion of the stack of materials with a display component.

Disclosed herein are a microelectromechanical system (MEMS) devices comprising a first electrode layer, an aluminum nitride based piezoelectric layer, and a second electrode layer. The aluminum nitride based piezoelectric layer comprises a piezoelectric material of formula AlxScyYbzN, wherein x+y+z=1. Also disclosed are acoustic components comprising the piezoelectric material.