A ground system for early forest fire detection based on infrared signal processing

This article presents a ground remote automatic system for forest surveillance based on infrared signal processing applied to early fire detection. Advanced techniques, which are based on infrared signal processing, are used in order to process the captured images. With the aim of determining the presence or absence of fire, the system performs the fusion of different detectors that exploit different expected characteristics of a real fire, such as persistence and increase. Theoretical simulations and practical results are presented to corroborate the control of the probability of false alarm. Results in a real environment are also presented to authenticate the accuracy of the operation of the proposed system. In particular, some experiments have been done to evaluate the delay of the system (tens of seconds on average) in detecting a controlled ground fire in a range of 1-10 km. Moreover, temporary evolution of false alarms and true detections are presented to evaluate the long-term performance of the system in a real environment. We have reached a detection probability of 100% at a false alarm rate of around 1 x 10(-9).This work has been supported by Generalitat Valenciana, under grant GVEMP06/001, and by MEC under the FPU programme.Bosch Roig, I.; Gómez, S.; Vergara Domínguez, L. (2011). A ground system for early forest fire detection based on infrared signal processing. International Journal of Remote Sensing. 32(17):4857-4870. https://doi.org/10.1080/01431161.2010.490245S485748703217Arrue, B. C., Ollero, A., & Matinez de Dios, J. R. (2000). An intelligent system for false alarm reduction in infrared forest-fire detection. IEEE Intelligent Systems, 15(3), 64-73. doi:10.1109/5254.846287Bernabeu, P., Vergara, L., Bosh, I., & Igual, J. (2004). A prediction/detection scheme for automatic forest fire surveillance. Digital Signal Processing, 14(5), 481-507. doi:10.1016/j.dsp.2004.06.003Briz, S. (2003). Reduction of false alarm rate in automatic forest fire infrared surveillance systems. Remote Sensing of Environment, 86(1), 19-29. doi:10.1016/s0034-4257(03)00064-6Pastor, E. (2003). Mathematical models and calculation systems for the study of wildland fire behaviour. Progress in Energy and Combustion Science, 29(2), 139-153. doi:10.1016/s0360-1285(03)00017-0Vergara, L., & Bernabeu, P. (2000). Automatic signal detection applied to fire control by infrared digital signal processing. Signal Processing, 80(4), 659-669. doi:10.1016/s0165-1684(99)00159-0Vergara, L., & Bernabeu, P. (2001). Simple approach to nonlinear prediction. Electronics Letters, 37(14), 926. doi:10.1049/el:20010616Vicente, J., & Guillemant, P. (2002). An image processing technique for automatically detecting forest fire. International Journal of Thermal Sciences, 41(12), 1113-1120. doi:10.1016/s1290-0729(02)01397-

Spatiotemporal relations of primary sensorimotor and secondary motor activation patterns mapped by NIR imaging

Functional near infrared (fNIR) imaging was used to identify spatiotemporal relations between spatially distinct cortical regions activated during various hand and arm motion protocols. Imaging was performed over a field of view (FOV, 12 x 8.4 cm) including the secondary motor, primary sensorimotor, and the posterior parietal cortices over a single brain hemisphere. This is a more extended FOV than typically used in current fNIR studies. Three subjects performed four motor tasks that induced activation over this extended FOV. The tasks included card flipping (pronation and supination) that, to our knowledge, has not been performed in previous functional magnetic resonance imaging (fMRI) or fNIR studies. An earlier rise and a longer duration of the hemodynamic activation response were found in tasks requiring increased physical or mental effort. Additionally, analysis of activation images by cluster component analysis (CCA) demonstrated that cortical regions can be grouped into clusters, which can be adjacent or distant from each other, that have similar temporal activation patterns depending on whether the performed motor task is guided by visual or tactile feedback. These analyses highlight the future potential of fNIR imaging to tackle clinically relevant questions regarding the spatiotemporal relations between different sensorimotor cortex regions, e.g. ones involved in the rehabilitation response to motor impairments

Electromyographic Signal Processing With Application To Spinal Cord Injury

An Electromyogram or Electromyographic (EMG) signal is the recording of the electrical activity produced by muscles. It measures the electric currents generated in muscles during their contraction. The EMG signal provides insight into the neural activation and dynamics of the muscles, and is therefore important for many different applications, such as in clinical investigations that attempt to diagnose neuromuscular deficiencies. In particular, the work in this thesis is motivated by rehabilitation for patients with spinal cord injury. The EMG signal is very important for researchers and practitioners to monitor and evaluate the effect of the rehabilitation training and the condition of muscles, as the EMG signal provides information that helps infer the neural activity in the spinal cord. Before the work in this thesis, EMG analysis required significant amounts of manual labeling of interesting signal features. The motivation of this thesis is to fully automate the EMG analysis tasks and yield accurate, consistent results. The EMG signal contains multiple muscle responses. The difficulty in processing the EMG signal arises from the fact that the transient muscle response is a transient signal with unknown arrival time, unknown duration, and unknown shape. In addition, the EMG signal recorded from patients with spinal cord injury during rehabilitation is very different from the EMG signal of normal healthy people undergoing the same motions. For example, some of the muscle responses are very weak and thus hard to detect. Because of this, general EMG processing tools and methods are either not applicable or insufficient. The primary contribution of this thesis is the development of a wavelet-based, double-threshold algorithm for the detection of transient peaks in the EMG signal. The application of wavelet transform in the detection of transient signals has been studied extensively and employed successfully. However, most of the theories assume certain knowledge about the shapes of the transient signals, which makes it hard to be generalized to the transient signals with arbitrary shapes. The proposed detection scheme focuses on the more fundamental feature of most transient signals (in particular the EMG signal): peaks, instead of the shapes. The continuous wavelet transform with Mexican Hat wavelet is employed. This thesis theoretically derived a framework for selecting a set of scales based on the frequency domain information. Ridges are identified in the time-scale space to combine the wavelet coefficients from different scales. By imposing two thresholds, one on the wavelet coefficient and one on the ridge length, the proposed detection scheme can achieve both high recall and high precision. A systematic approach for selecting the optimal parameters via simulation is proposed and demonstrated. Comparing with other state-of-the-art detection methods, the proposed method in this thesis yields a better detection performance, especially in the low Signal-to-Noise-Ratio (SNR) environment. Based on the transient peak detection result, the EMG signal is further segmented and classified into various groups of monosynaptic Motor Evoked Potentials (MEPs) and polysynaptic MEPs using techniques stemming from Principal Component Analysis (PCA), hierarchical clustering, and Gaussian mixture model (GMM). A theoretical framework is proposed to segment the EMG signal based on the detected peaks. The scale information of the detected peak is used to derive a measure for its effective support. Several different techniques have been adapted together to solve the clustering problem. An initial hierarchical clustering is first performed to obtain most of the monosynaptic MEPs. PCA is used to reduce the number of features and the effect of the noise. The reduced feature set is then fed to a GMM to further divide the MEPs into different groups of similar shapes. The method of breaking down a segment of multiple consecutive MEPs into individual MEPs is derived. A software with graphic user interface has been implemented in Matlab. The software implements the proposed peak detection algorithm, and enables the physiologists to visualize the detection results and modify them if necessary. The solutions proposed in this thesis are not only helpful to the rehabilitation after spinal cord injury, but applicable to other general processing tasks on transient signals, especially on biological signals.</p

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Attack Resilience and Recovery using Physical Challenge Response Authentication for Active Sensors Under Integrity Attacks

Embedded sensing systems are pervasively used in life- and security-critical systems such as those found in airplanes, automobiles, and healthcare. Traditional security mechanisms for these sensors focus on data encryption and other post-processing techniques, but the sensors themselves often remain vulnerable to attacks in the physical/analog domain. If an adversary manipulates a physical/analog signal prior to digitization, no amount of digital security mechanisms after the fact can help. Fortunately, nature imposes fundamental constraints on how these analog signals can behave. This work presents PyCRA, a physical challenge-response authentication scheme designed to protect active sensing systems against physical attacks occurring in the analog domain. PyCRA provides security for active sensors by continually challenging the surrounding environment via random but deliberate physical probes. By analyzing the responses to these probes, and by using the fact that the adversary cannot change the underlying laws of physics, we provide an authentication mechanism that not only detects malicious attacks but provides resilience against them. We demonstrate the effectiveness of PyCRA through several case studies using two sensing systems: (1) magnetic sensors like those found wheel speed sensors in robotics and automotive, and (2) commercial RFID tags used in many security-critical applications. Finally, we outline methods and theoretical proofs for further enhancing the resilience of PyCRA to active attacks by means of a confusion phase---a period of low signal to noise ratio that makes it more difficult for an attacker to correctly identify and respond to PyCRA's physical challenges. In doing so, we evaluate both the robustness and the limitations of PyCRA, concluding by outlining practical considerations as well as further applications for the proposed authentication mechanism.Comment: Shorter version appeared in ACM ACM Conference on Computer and Communications (CCS) 201

Harnessing high-dimensional hyperentanglement through a biphoton frequency comb

Quantum entanglement is a fundamental resource for secure information processing and communications, where hyperentanglement or high-dimensional entanglement has been separately proposed towards high data capacity and error resilience. The continuous-variable nature of the energy-time entanglement makes it an ideal candidate for efficient high-dimensional coding with minimal limitations. Here we demonstrate the first simultaneous high-dimensional hyperentanglement using a biphoton frequency comb to harness the full potential in both energy and time domain. The long-postulated Hong-Ou-Mandel quantum revival is exhibited, with up to 19 time-bins, 96.5% visibilities. We further witness the high-dimensional energy-time entanglement through Franson revivals, which is observed periodically at integer time-bins, with 97.8% visibility. This qudit state is observed to simultaneously violate the generalized Bell inequality by up to 10.95 deviations while observing recurrent Clauser-Horne-Shimony-Holt S-parameters up to 2.76. Our biphoton frequency comb provides a platform in photon-efficient quantum communications towards the ultimate channel capacity through energy-time-polarization high-dimensional encoding