A system for detection of human or vehicle activity comprising at least one sensor adapted to generate a signal and at least one processor operating to denoise the signal; generate an autocorrelation of the signal; partition the signal into a predetermined number of overlapping segments to form a time series of data; generate symbols for the overlapping segments; compare the pattern of generated symbols with known predetermined patterns of symbols representing human or vehicular activity; determine whether a threshold probability is exceeded which attributes the data signal to human or vehicular activity; analyze the patterns presented in the data signal by transforming the patterns of symbols into states; determine the transitions between states; and classify the signal as to being attributable to human or vehicular activity based upon the transitions between states. A method of detection and classification of sensor data signals via detecting patterns using time series analysis.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for detection of at least one of human activity and vehicle activity comprising: at least one sensor adapted to generate a data signal in response to detecting human or vehicular activity, at least one processor operatively connected to the sensor, the at least one processor operating to perform an analysis of the signal without human intervention in order to recognize and classify the type of activity detected by the sensor; the at least one processor being configured to denoise the data signal from the sensor; generate an autocorrelation of the data signal; partition the data signal into a predetermined number of overlapping segments to form a time series of data; generate symbols for the overlapping segments; compare the pattern of generated symbols with known patterns of symbols representing human or vehicular activity; determine whether a threshold probability is exceeded which attributes the data signal to human or vehicular activity; analyze the patterns presented in the data signal by transforming the patterns of symbols into states and determining the transitions between states; and classify the activity based upon the transitions between states. 2. The system of claim 1 wherein the sensor is at least one of a seismic detector, a magnetic detector an acoustic detector, and a passive infra-red detector and wherein the human activity is classifiable as digging or walking 3. The system of claim 1, wherein the at least one sensor comprises a seismic sensor configured to generate a seismic signature and the at least one processor performs a detection analysis using a multi-scale symbolic time series analysis framework for real-time activity recognition from the seismic signature. 4. The system of claim 3 wherein the at least one processor utilizes a short-length symbolic time-series online classifier (SSTOC) via Dirichlet Compound-Multinomial model (DCM) which works on symbol sequences generated from seismic time-series and intermediate event class time-series at different time-scales and wherein the classification of the activity utilizes a symbolic sequence modeled as a probabilistic finite state automaton. 5. The system of claim 4 wherein the at least one processor utilizes different window sizes for different time scales that are cascaded in multiple layers for event detection and activity classification. 6. The system of claim 1 wherein the detected human activity comprises walking and digging and wherein the detection correctly classifies the event without human intervention in at least 92% of the activity occurrences. 7. The system of claim 1 wherein the activity is sensed using a seismic sensor to detect ground movement. and wherein the signal is divided into a predetermined number of sequential segments having a two second duration with approximately 80% overlap, and wherein the determining of the probability of occurrence of a human event comprises of generating a compound probability distribution, where prior uncertainty is modeled by Dirichlet distribution and posterior uncertainty is modeled by multinomial distribution. 8. The system of claim 1 wherein the classifying of the activity comprises generating a compound probability distribution, and wherein the at least one processor is programmed using the input of known human activity that is modeled using Dirichlet distribution and during subsequent system operation the posterior uncertainty is modeled by multinomial distribution. 9. The system of claim 1 wherein whether or not an activity has occurred is determined using a short length symbolic time series online classifier procedure that estimates probability of activity, and wherein the partial classification portion comprises a division of the probabilities into 40 segments to output a threshold and wherein during classification of the activity symbols are again generated for the segments and the short term symbolic time-series online classifier is used on a first predetermined number of probabilities, then the window is shifted a predetermined number of probabilities to capture the next predetermined number of probabilities; in order to capture variations, and wherein the classification comprises a probabilistic finite state automaton construction. 10. A method for detection and classification of human or vehicular activity using a sensor and at least one processor comprising the following steps not necessarily in the following order: sensing activity using a seismic sensor; the sensor being operatively connected to at least one processor and providing a seismic data signal extending for a predetermined time frame; denoising the data signal from the sensor and removal of the mean from the data signal; generating an autocorrelation of the data signal;’ partitioning data signal into a predetermined number of overlapping segments to form a time series of data; generating symbols for the overlapping segments; comparing the pattern of generated symbols with known predetermined patterns of symbols representing human or vehicular activity; determining the probability of whether or not the seismic data signal is attributable to human activity; determining whether or not the probability determination meets or exceeds a predetermined threshold; recognizing the data signal as being possibly attributable to an activity when the threshold is met or exceeded; accumulating additional time frames of data signals repeating the steps of denoising, autocorrelation, partitioning the data signal into a predetermined number of overlapping segments and generating symbols for overlapping segments; transforming the generated symbols representing the initial and additional time frames into states and determining the transitions between states; based upon the transitions, determining whether or not a human or vehicular activity has occurred and classifying the activity based upon the transitions between states. 11. The method of claim 10 wherein the activity is sensed using a seismic sensor to detect ground movement. and wherein the signal is divided into a predetermined number of sequential segments having a two second duration with approximately 80% overlap, and wherein the determining of the probability of occurrence of a human event comprises of generating a compound probability distribution, where prior uncertainty is modeled by Dirichlet distribution and posterior uncertainty is modeled by multinomial distribution. 12. The method of claim 10 wherein the step of determining whether or not a human or vehicular activity has occurred comprises generating a compound probability distribution, where prior uncertainty is modeled by Dirichlet distribution and posterior uncertainty is modeled by multinomial distribution. 13. The method of claim 12 wherein for a predetermined symbol string Si in the training phase, the probability of observing a symbol string {tilde over (S)} in the testing phase is obtained using Pr  ( S ~ | S i ) = ∫ …   ∫ Pr  ( S ~ | Π i = θ i )  f Π i | S i i  ( θ i | S i )   θ = ∏ m = 1  Q    ( N ~ m ) !  ( N m i   Σ  - 1 ) ! ( N ~ m + N m i +  Σ  - 1 ) ! × ∏ n = 1  Q    ( N ~ mn + N mn i ) ! ( N ~ mn ) !  ( N mn i ) ! . 14. The method of claim 10 wherein the predetermined symbols for known human activity are generated in a testing phase wherein human activity is recorded using the predetermined symbols. 15. The method of claim 10 wherein the data signal is a time series of data and wherein the denoising of the data signal further comprises temporal envelop detection utilizing Hilbert transform of the time series to reduce phase distortions and an autocorrelation of the temporal envelop is obtained with different window sizes for recognizing and classifying the activity. 16. The method of claim 15 wherein the autocorrelation is used as an input to a multi-scale symbolic time series algorithm so as to capture the difference in the periodicity pattern in the autocorrelation between walking and digging. 17. The method of claim 10 further comprising using a seismic sensor and wherein the recognition of detection of an activity and classification comprise using a time series analysis comprising a processing routine configured to process the generated data based at least in part an multi-scale symbolic time series analysis algorithm; and a communication routine configured to communicate the processed data. 18. A system for detection of at least one of human activity and vehicle activity comprising: at least one seismic sensor adapted to generate a data signal in response to detecting human or vehicular activity on or in the ground, at least one processor operatively connected to the sensor, the at least one processor operating to perform an analysis of the signal without human intervention in order to recognize and classify the type of activity detected by the sensor; the at least one processor being configured to perform an autocorrelation of the data signal; partition the data signal into a predetermined number of overlapping segments to form a time series of data; generate symbols for the overlapping segments; compare the pattern of generated symbols with known patterns of symbols representing human or vehicular activity; transform the patterns of symbols into a Markov chain; and classify the activity based upon the transitions in the Markov chain. 19. The system of claim 18 wherein the detected human activity comprises walking and digging. 20. The system of claim 18 wherein the activity is sensed using a seismic sensor to detect ground movement. and wherein the signal is divided into a predetermined number of sequential segments having a two second duration with approximately 80% overlap, and wherein the determining of the probability of occurrence of a human event comprises of generating a compound probability distribution, where prior uncertainty is modeled by Dirichlet distribution and posterior uncertainty is modeled by multinomial distribution.