6382 results about "Recognition system" patented technology

The present invention provides a facial recognition system using deep learning methodologies while integrating privacy-preserving capabilities. This system employs convolutional neural networks (CNNs) to extract and classify facial features, ensuring high accuracy in recognition tasks. Moreover, the system addresses privacy concerns by incorporating techniques such as facial feature encryption and anonymization, thereby enhancing user privacy and data security. This invention is applicable across various domains, including security, surveillance, access control, and personalized services, where facial recognition is utilized while preserving individual privacy.

The invention provides an intelligent Internet of Things public security management and control system and method based on multi-source data fusion, and belongs to the technical field of Internet of Things security. According to the system, unified collection and standardized processing of multi-source data are achieved by recognizing a system operation scene and loading corresponding model parameters and response strategies, and a unified semantic representation structure is constructed. And the system executes anomaly detection at an edge node, completes risk scoring and grading alarm judgment in combination with a semantic structure and anomaly feature information, and generates a control response instruction based on strategy matching. Meanwhile, dynamic optimization of the scoring model and identity authentication, behavior auditing and data compliance export in the operation process are supported. The system has intelligent identification, adaptive analysis and response closed loop capabilities, and improves the precision and credibility of public security management in the Internet of Things environment.

The invention discloses a deep learning-based small target defect recognition model training method, relates to the technical field of defect recognition model training, and aims at meeting small defect detection requirements, starting with high-resolution diversified data construction and accurate labeling, highlighting weak targets through multi-scale feature fusion and spatial attention, and realizing high-resolution target defect recognition. A hard case scene is processed in cooperation with layer-by-layer screening and secondary intensified training, real-time iterative optimization is achieved through multi-model fusion and online dynamic adjustment and optimization, finally, multi-mode and time sequence dimensions are expanded to capture deeper and dynamic defect information, the missing detection and false detection rate is greatly reduced, and the detection efficiency is improved. The detection efficiency and adaptability of micron-sized defects under a complex process background are improved; furthermore, by means of multi-source data such as infrared, X-ray or 3D morphology and a time sequence modeling means, multiple dimensions are fused, and hidden or early cracks are brought into a detection and prediction range, so that a high-reliability and evolvable intelligent recognition system for the tiny target defects is constructed.

The invention discloses a robot real-time potential safety hazard recognition system based on multi-modal sensor fusion, and particularly relates to the technical field of intelligent inspection and safety monitoring, the system comprises five parts of data acquisition, information fusion, behavior response, trajectory analysis and risk output, and the potential safety hazard recognition system is used for recognizing potential safety hazards through image acquisition, thermal imaging, gas concentration and temperature and humidity information. Carrying out numerical value normalization and feature extraction, identifying potential abnormity and generating early warning; triggering data enhanced acquisition and track recording in the target area, and constructing a space-time path model to analyze an abnormal evolution trend; and finally, outputting a potential safety hazard assessment result according to a risk level classification rule by combining the enhanced information and the trajectory features. According to the method, high-precision early warning is realized through multi-source data acquisition and normalization fusion, the local recognition capability is improved based on dynamic enhanced acquisition of a behavior response mechanism, an abnormal development trend is tracked by combining track evolution modeling, risk level assessment is output according to the abnormal development trend, and accurate recognition and dynamic management and control of hidden dangers are realized.

The invention discloses a large-model real-time voice text intention recognition method and system based on text prediction, and the method comprises the steps: obtaining the real-time voice data of a user, carrying out the real-time voice recognition processing through a streaming voice recognition interface, and obtaining a part of transcriptional text; inputting the partial transcription text into a mask language model for text prediction, and generating a plurality of high-credibility complete sentence candidates; based on the complete sentence candidates, the complete sentence candidates are input into a large language model in parallel for intention recognition, a corresponding intention result is obtained, and a mapping relation between the candidate sentences and the intention recognition result is established; and obtaining a sentence completely expressed by the user, calculating the similarity between the complete actual sentence and a plurality of high-credibility complete sentence candidates through a multi-level text similarity algorithm, selecting the candidate sentence with the highest similarity score, and directly obtaining a corresponding final intention recognition result based on the mapping relationship. The objective of the invention is to solve the technical problem of high response delay of an existing voice intention recognition system.

The invention discloses an automatic geological disaster recognition system and method based on multi-source remote sensing data, and particularly relates to the field of geological disaster recognition, and the system comprises a multi-modal remote sensing data acquisition module, a cross-domain physical fusion module, a spatio-temporal evolution decision module, a multi-cascade early warning decision module, an optimization control module and a visualization module. According to the geological disaster automatic identification system and method based on the multi-source remote sensing data, virtual features are generated through a cross-domain physical fusion module by using a domain adversarial network, the model generalization ability during cross-domain application is improved, physical association among the multi-source remote sensing data is deeply mined, and dependence on manual design rules is eliminated; through a three-layer processing chain technology composed of a spatial-temporal feature extraction layer, a dynamic graph evolution layer and a critical recognition layer, the capability of capturing disaster features in a complex geological environment is effectively improved, especially the recognition precision of precursor tiny deformation is improved, and the risk of missing report is reduced.

The invention relates to the technical field of image recognition, in particular to a license plate recognition system and method based on an image technology and a medium. The method comprises the following steps: acquiring area sensing data and a camera image set, and performing deformation effect compensation to obtain an environment compensation image set; performing image diffusion reverse enhancement on the environment compensation image set to obtain a license plate area enhanced image set; performing character region high-dimensional topological mapping based on the license plate region enhanced image set to obtain a character segmentation matrix; extracting character morphological characteristics according to the character segmentation matrix, and performing character recognition on the character morphological characteristics to obtain a character recognition result; and carrying out cross-character semantic compensation on the character recognition result to obtain a semantic compensation license plate character vector, and carrying out multi-target cross verification on the semantic compensation license plate character vector to obtain a license plate recognition result. According to the invention, the accuracy and robustness of license plate recognition can be improved.

The invention discloses an article recognition system based on computer vision. The article recognition system comprises a multi-modal data acquisition module, a multi-modal data processing module and a computer vision processing module, wherein the multi-modal data acquisition module is used for acquiring multi-modal data through a multi-modal sensor array; the data preprocessing module is used for standardizing a multi-modal data format and generating a time-space aligned multi-modal tensor; the feature extraction module is used for respectively extracting modal specific features from texture, spectrum and geometric dimensions by adopting ResNet50, 3D-CNN and PointNet + +; the multi-modal fusion module is used for constructing cross-modal joint representation; the adaptive sensing module is used for modeling illumination invariance and scene dynamics based on self-supervised comparative learning and a 3D-STMN space-time memory network, predicting a shielded target trajectory by using Kalman filtering in combination with the shielding sensing propagation module, and generating an environment sensing parameter set; and the recognition engine module is used for integrating YOLOv8 detection, Mask R-CNN segmentation and multi-modal decision tree classification, outputting a target bounding box, a category and confidence in combination with the depth data, and generating three-dimensional space coordinates combined with the depth data.

The invention discloses an electrical fire intelligent identification system based on multi-dimensional sensor fusion. The system comprises the following steps: constructing a reference environment model through multi-sensor scanning, data dimension reduction and intelligent node deployment; multi-sensor time sequence alignment is carried out through edge calculation denoising and dynamic time warping, and high-priority data is processed in real time through a layering mechanism; establishing a fire feature modeling system through LSTM time sequence analysis, mutual information correlation mining and self-supervised learning; through multi-level data fusion, GAN abnormal data generation and fuzzy logic reasoning; through grading alarm, an intelligent fire extinguishing strategy and remote control, full-process coverage from fire detection to emergency response is realized. A fire scene is visualized by means of a three-dimensional thermodynamic diagram, flame dynamic analysis and an augmented reality technology. The system is suitable for fire detection, alarm and response in a complex industrial scene, and can be widely applied to intelligent management of electrical fire.

The invention provides an emotion recognition method based on electroencephalogram eye movement multi-mode cross-attention feature fusion, and the method comprises the steps: firstly carrying out the preprocessing of an emotion recognition public data set, and building a corresponding training set and a test set; secondly, constructing an electroencephalogram eye movement multi-mode cross-attention feature fusion emotion recognition model; then carrying out model training and performance testing; finally, an electroencephalogram eye movement multi-mode online emotion recognition system is built, and the effectiveness of the method is verified. The method has the advantages that an electroencephalogram eye movement multi-mode cross-attention feature fusion emotion recognition model is designed, a dynamic graph convolutional network and an attention mechanism are flexibly applied, meanwhile, multi-dimensional data are used, the problem that single-mode emotion recognition information is insufficient is solved, and the recognition accuracy is improved; and an electroencephalogram eye movement multi-mode online emotion recognition system is built, so that the interactivity is enhanced. The average recognition accuracy of the method reaches 97.94% and is superior to that of an existing optimal method, and meanwhile the accuracy of online emotion recognition reaches 87.4%.

The invention discloses an unmanned car washer stain panorama identification system. The system operation process specifically comprises the following steps: acquiring panorama image data of a target car; preprocessing the panoramic image data to obtain a standardized panoramic image set; performing stain area identification on the standardized panoramic image set based on a deep learning model to generate an initial stain distribution diagram; performing stain type classification on the initial stain distribution diagram according to a stain feature database to generate a stain classification result set; generating a dynamic cleaning path instruction set based on the stain classification result set and a cleaning strategy library; real-time images in the cleaning process are collected in real time, real-time stain residue analysis is conducted, and finally a cleaning effect feedback report is generated. The method has the following advantages and effects that the system of multi-dimensional stain feature recognition, classification and dynamic decision can be fused, so that the core contradiction that the cleaning strategy is not matched with the stain features in the prior art is solved.

The invention discloses a stacking machine motion speed parabolic trajectory deviation correction comprehensive method, and relates to the technical field of stacking machine trajectory deviation correction, and the method comprises the following steps: carrying out the data collection of the real-time motion trajectory of a stacking machine through an acceleration sensor, an encoder and a visual recognition system, building a trajectory deviation detection model, and carrying out the calculation of the trajectory deviation detection model; the motion speed, the acceleration and the position information are extracted, and the deviation value of the current trajectory deviating from the ideal parabolic trajectory is calculated. According to the invention, through multi-sensor fusion and Kalman filtering, the accuracy and stability of track correction of the stacker are improved; an LSTM neural network is adopted to predict inertial errors, and correction failures are reduced in combination with an adaptive compensation strategy; a double-closed-loop control and anomaly detection mechanism is introduced, intelligent safety protection is achieved, the fault recovery capacity is improved, it is ensured that the stacking machine stably operates in a complex environment, and the reliability and working efficiency of an automatic warehousing system are enhanced.

The embodiment of the invention provides a project research and development data key information processing method and device. A research and development content recognition system and a self-adaptive research and development knowledge graph are constructed. Unified processing and time sequence alignment of text, voice and image contents are realized through multi-modal information decomposition and fusion. Semantic completion and error correction are carried out based on research and development of a semantic analysis model, a knowledge graph structure is dynamically constructed and optimized, and a structured document with a traceability relation is generated. The system adopts a deep neural network model to extract research and development key information, constructs a multi-level document framework, and realizes intelligent conversion from research and development data to a project application document. According to the method, the defects of the traditional technology in the aspects of multi-modal information processing and knowledge structure optimization are effectively overcome, and the research and development data management and project declaration efficiency is remarkably improved.

Systems and methods are disclosed for condition identification. One or more processors may receive a member data object with indicators and dimensions, access a member-specific graph network with nodes representing attributes and weighted edges indicating associations, modify the nodes and edges based on the member data object, generate a multi-modal graph database by combining the modified member-specific graph network and a disease graph network, apply the multi-modal graph database to an attention-based graph neural network (GNN) that identifies associations between nodes by dynamically allocating attention weights to edges, generate an embedding data object with node identifiers and vectors representing features and relationships, select a target node associated with condition data, apply the embedding data object to a classification layer that outputs predicted conditions for the target node, and generate the probability of predicted conditions appearing in the target node.

The invention provides a dynamic calibration method and system for a vehicle-mounted emotion recognition system, and the method comprises the steps: S1, obtaining multi-source data which comprises a facial image, a voice signal and a physiological signal; the obtained multi-source data are preprocessed, and preprocessed multi-source data are obtained; s2, performing feature extraction based on the preprocessed facial image, the voice signal and the physiological signal to obtain a facial expression feature vector, an audio feature vector and a physiological state feature vector; s3, evaluating the current environment credibility based on an environment credibility evaluation function; s4, dynamically distributing the weight of the multi-source data according to the credibility of the current environment and the real-time scene; and S5, constructing a multi-modal fusion vector based on the dynamically distributed weight of the multi-source data, the facial expression feature vector, the audio feature vector and the physiological state feature vector, and performing emotion recognition by using the constructed emotion recognition model based on the multi-modal fusion vector.

The invention relates to the technical field of soil pollution identification, and discloses a soil heavy metal pollution identification system. A multispectral remote sensing sensing module of the system obtains surface reflectance data and soil in-situ spectral data through a satellite load and a vehicle-mounted mobile platform respectively, and the surface reflectance data and the soil in-situ spectral data are processed by a heterogeneous data fusion gateway to generate multiband spectral response signals. In the pollution risk assessment module, a spatial distribution analysis unit outputs a heavy metal spatial distribution map, a migration risk prediction unit generates a pollution migration probability cloud map in combination with meteorological and hydrological data, and a pollution threshold defining unit outputs a soil remediation safety threshold. In the treatment decision execution module, an in-situ remediation execution unit adjusts passivator injection parameters, a pollution source management and control unit regulates pollution source blocking equipment and collects monitoring signals, and a three-dimensional dynamic early warning platform generates a comprehensive pollution risk index. According to the system, the cooperative operation of soil heavy metal pollution identification, evaluation and treatment is realized.

The invention relates to the technical field of nondestructive testing, and discloses a steel structure weld defect ultrasonic detection and identification system. A data acquisition module of the system acquires an original ultrasonic signal of a steel structure welding seam through ultrasonic detection equipment and acquires geometric attribute data of the welding seam; the model construction module constructs a welding seam three-dimensional digital model based on the data; a feature extraction module performs feature mining on the three-dimensional digital model and extracts a weld defect feature index set; the difference analysis module carries out deviation calculation on the characteristic index set and a reference index set in a standard welding seam characteristic database, and an abnormal area is identified; the risk assessment module calculates a defect sensitivity index according to the abnormal region in combination with real-time environmental parameters, and assesses a defect risk level; and the report generation module formulates a detection scheme according to the defect risk level, generates a detection instruction, executes ultrasonic scanning, collects performance data and generates a defect detection report. The system has the advantages of high detection precision, high reliability, automatic and standardized process and the like.

The invention discloses a Beidou strong deception jamming detection processing method and system, and relates to the technical field of satellite navigation anti-jamming. The invention provides a solution based on dynamic gain monitoring and multi-dimensional feature collaborative analysis to solve the problems that a Beidou / GNSS system is prone to being affected by strong deception interference signals in a complex electromagnetic environment to suppress radio frequency link gain, and positioning failure is caused by the fact that deception signals and real signals are difficult to distinguish in a traditional technology. According to the method, radio frequency gain change is tracked in real time through a digital AGC module to generate an interference-to-signal ratio, an equivalent carrier-to-noise ratio is calculated in combination with a signal carrier-to-noise ratio for primary judgment, and accurate recognition is achieved by fusing verification of multi-dimensional characteristics such as code phase consistency, carrier frequency stability and signal strength fluctuation. The system comprises a radio frequency front end, a digital AGC module, a correlator channel and an interference processing module, and precise suppression of strong deception interference is realized through dynamic reference gain learning, interference signal mark elimination and robust positioning resolving mechanisms.

The invention provides an image recognition system and method based on deep learning, and the system comprises a self-adaptive optical collection module, a heterogeneous preprocessing pipeline, a hierarchical reconfigurable convolutional network, a multi-dimensional training optimization engine and a cross-modal verification output interface, aperture parameters are dynamically adjusted through deep reinforcement learning; the heterogeneous preprocessing pipeline comprises a quantum noise modeling non-local mean noise reduction unit, a double-discriminator generative adversarial network enhancement unit and a dynamic normalization unit; the hierarchical reconfigurable convolutional network adopts a staged feature distillation structure and comprises a separable convolution module, a mixed pooling layer and a three-dimensional attention fusion module. According to the invention, through a multi-modal data fusion and dynamic optimization mechanism, the image acquisition quality in a complex illumination and noise scene is improved, the adaptability of the model to different environments is enhanced, and all modules work cooperatively to realize an end-to-end efficient identification process.

The invention relates to the technical field of digital economic risk control, and discloses a digital economic risk identification system and method based on artificial intelligence. A risk data acquisition engine of the system obtains transaction behavior data streams from a plurality of digital economic transaction platforms in real time, and converts the transaction behavior data streams into a structured transaction feature matrix; an abnormal mode detection engine extracts time sequence abnormal features through a deep residual network to generate an abnormal feature vector set; the risk association analysis engine constructs a risk propagation path map through a graph neural network, and outputs a risk association degree scoring matrix; the dynamic threshold adjustment engine performs adaptive threshold calibration according to the historical risk event database to generate a dynamic risk threshold vector; and the risk decision engine compares the scoring matrix with a dynamic threshold value, marks risk transaction nodes and generates a risk early warning instruction set. The system can adapt to digital economic transaction characteristics, and the comprehensiveness and accuracy of risk identification are improved.

The invention relates to the technical field of smart power grids and artificial intelligence, in particular to a substation autonomous intelligent inspection method and system based on multi-modal data, and the method comprises the steps: obtaining inspection data of multiple modals, and generating fusion features; identifying system alarm information; performing intention recognition and task classification to generate an executable task sequence; generating a multi-device cooperative scheduling scheme; executing the multi-device cooperative scheduling scheme; iterative optimization is carried out; according to the intelligent inspection method provided by the invention, more accurate and more robust multi-mode perception and diagnosis are realized, and deep understanding of complex instructions and safe and efficient cooperation of multiple devices are also realized; and meanwhile, through dynamic re-planning and a verification type feedback learning mechanism, high real-time performance and robustness are ensured, and meanwhile, the system is endowed with the capability of iterative optimization.

The invention discloses a power transmission line constructor identity verification method based on face recognition, and relates to the technical field of electric power engineering safety management, and the method comprises the steps: collecting multispectral face data, and generating a spectral stereo feature matrix; micro blood flow pulsation characteristics, skin texture characteristics and thermal imaging temperature distribution characteristics are extracted from the spectrum stereo characteristic matrix, a three-layer cascade anti-counterfeiting verification mechanism is constructed, and comprehensive living body judgment is carried out; establishing a distributed feature matching network, and matching the spectrum stereo feature matrix with a pre-stored feature library; collecting a geographic position track and an operation behavior mode of a constructor, and carrying out multi-dimensional cross validation on the geographic position track and the operation behavior mode and an identity matching result to generate a multi-level safety evaluation index; and writing the identity verification process and the multi-level security evaluation index into a distributed account book, and generating a verification voucher. According to the invention, cross analysis is carried out on the identity matching result of the constructor and the behavior characteristics, so that the problem that a traditional face recognition system is easily falsely used by the identity is effectively solved.

The invention relates to the technical field of audio and video identity recognition, in particular to an audio and video identity recognition system based on multimode clue driving. The audio and video identity recognition system comprises an audio feature extraction module, a video feature extraction module, a multimode clue fusion module, a living body detection module and an identity recognition and verification module. The audio feature extraction module is used for capturing a voice signal of a user and extracting key voiceprint features, the video feature extraction module is used for acquiring facial features or limb features of the user and extracting related features, and the multi-mode clue fusion module is used for performing intelligent weighted fusion on the features of audio and video modes. The method comprises the following steps: firstly, using a living body detection module to comprehensively analyze the dynamic characteristics of audio and video, judging whether a user is a real individual, and finally, completing the final verification of the identity of the user through an identity recognition and verification module. The problem of feature extraction and modal fusion in the prior art is effectively solved.

The invention discloses a multi-target pedestrian re-identification system based on multiple modes and a vector database, relates to the technical field of network communication and positioning, and solves the problem of cross-target and cross-mode trajectory association in a complex multi-camera scene. The multi-target pedestrian re-recognition system comprises a monocular tracking module, a multimode extraction module, a trajectory generation module, a multi-objective matching module and a global retrieval module, through organic combination of multi-modal features and a multi-modal multi-path recall strategy, the accuracy and applicability of cross-modal pedestrian re-identification are significantly improved. Through track-level feature generation and storage design, the modeling capability of dynamic features of a target in a complex scene is enhanced; through collaborative design of a space-time constraint mechanism and multi-modal features, logic consistency and global optimality of target person trajectory association are ensured.

The invention relates to the technical field of artificial intelligence, in particular to a real-time anti-fraud monitoring system and method based on behavior reasoning and sentiment analysis, and the system comprises a multi-modal data collection unit, an edge preprocessing unit, a feature fusion and behavior reasoning unit, a large language model context reasoning unit, a risk assessment and decision unit, and an intervention execution unit. A log recording and federal incremental learning unit; the method has the beneficial effects that the traditional isolated single-mode detection is evolved into an emotion and behavior dual-channel collaborative multi-mode recognition system through millisecond-level coaxial alignment of voice, video and user operation logs; the robustness of dialect, noise and expression shielding is greatly improved through the multi-modal fusion model, so that the cross-scene recognition accuracy is improved by nearly three percent compared with that of a traditional single-voice scheme, and high-sensitivity capture of hidden and emotion control type fraud is truly achieved.

The Marker Fingerprinting system provides a method for identifying and correlating serial or sequential data patterns across diverse domains such as geological, biological, and financial datasets. This innovation transforms single- or multi-attribute data series into feature matrices, generating unique hash tokens—or fingerprints—that encapsulate specific data patterns. Using advanced signal analysis and spectral transformations, it enables efficient processing and pattern recognition within complex datasets. Fingerprints from reference patterns are matched against target datasets, with quantitative confidence metrics derived from weighted algorithms assessing match accuracy. Iterative data conditioning enhances robustness by addressing noise and inconsistencies, ensuring reliability at scale. The invention improves decision-making by delivering rapid and accurate pattern identification with quantified reliability, making it particularly suited for applications like geological top picking, seismic data analysis, and other fields requiring precise data correlation

The invention relates to the technical field of fire safety monitoring, in particular to a firework identification system and method based on multi-modal fusion perception, and the core of the scheme is a visible light, multispectral and temperature three-modal framework: feature extraction optimization of each modal, improved YOLOv8s for visible light branches, dynamic background modeling and flame color screening, and multi-modal fusion perception. False positive is rejected by a multispectral branch depending on a waveband ratio and an index, an error compensation algorithm is introduced into a thermopile branch, and finally, a final smoke and fire area and the confidence coefficient thereof are determined by associating a three-mode area through collaborative decision. According to the scheme, the complex environment adaptability and the recognition reliability can be improved, the false alarm risk is reduced, the extremely-early smoke and fire detection capability is enhanced, good real-time performance and deployment flexibility are achieved, and the method is suitable for various types of fire safety monitoring scenes.

The invention discloses a semantic recognition system and method based on a heterogeneous graph attention network and dynamic normalization, and belongs to the technical field of natural language processing and artificial intelligence. The system adopts a dual-channel architecture and comprises a general semantic channel and a domain semantic channel, semantic feature extraction is performed through a DIFF attention mechanism and a ToST statistical attention mechanism, and training stability is improved by adopting a DyT dynamic normalization module. Adaptive fusion of cross-channel semantic features is realized through a GeGLU gating mechanism, and high-precision semantic recognition is realized by combining an improved SimCSE + + comparison learning loss and a local minimization editing strategy of semantic perception. According to the method, the problems of inaccurate semantic expression, poor context adaptability and the like in the prior art are solved, the accuracy and applicability of cross-domain semantic recognition are remarkably improved, and the method can be widely applied to scenes of legal document processing, financial document analysis and the like.

The invention discloses an intelligent steel pipe surface defect recognition system based on deep learning, and particularly relates to the technical field of pipe surface defect analysis. An annular polarization light source array and a high-frame-rate CMOS sensor are adopted to synchronously collect visible light and near-infrared multi-polarization images; a surface normal is calculated based on Stokes parameters, mirror surface suppression and diffuse reflection enhancement are realized, a defect candidate area is generated by fusing multi-scale Laplacian pyramid residual error and Renyi entropy segmentation threshold positioning, multi-physical quantity registration is completed through white light interference and infrared thermal imaging, a six-channel feature cube is constructed, and a three-dimensional image is obtained. According to the method, space, spectrum and thermal characteristics are jointly extracted in the multi-head attention convolutional neural network, the confidence coefficient is evaluated in combination with Jensen-Shannon divergence, and the polarization angle and the focal length are dynamically adjusted according to the confidence coefficient, so that closed-loop parameter self-optimization is realized, and the micro-scale pitting corrosion and millimeter-scale crack detection precision is remarkably improved.

A system for material detection and identification includes an interface configured to access a material database associating each of a plurality of materials with one or more corresponding resonance frequencies; an RF transmitter configured to, for each material of at least a subset of the plurality of materials in the material database, transmit into an environment an RF signal at a first resonance frequency for the material; an RF receiver configured to receive a response signal from the environment for each RF signal; and a processor configured to analyze each response signal for resonance characteristics that indicate a presence of the material and identifying the material to a user if the presence of the material is indicated by the resonance characteristics.