Plant health estimation is required to be performed so as to detect any health issues in early stages, so as to take counter measures. Existing systems for the plant health estimation perform the health estimation by considering data obtained from satellite images of the plants being monitored. However this alone may not be much effective as the satellite images fail to provide information on many parameters which have direct or indirect impact on health of the plants. Disclosed herein are a method and a system for plant health estimation, wherein the system performs health estimation at a macro level and a micro level. The macro level health estimation is done using satellite images of the plants as inputs, whereas the micro level health estimation is done by collecting and processing sensor data with respect to various parameters that affect health of a plant.

A method includes acquiring a first data column output from a plurality of sensors, generating a model for estimating data from the plurality of sensors on the basis of the first data column, acquiring a second data column output from the plurality of sensors, obtaining an estimated data column corresponding to the second data column based on the model by using regularization for making an error between the second data column and the estimated data column sparse, and identifying a sensor in which a change occurred between the first data column and the second data column on the basis of the error between the second data column and the estimated data column. A corresponding computer program product and apparatus are also disclosed herein.

A method includes acquiring a first data column output from a plurality of sensors, generating a model for estimating data from the plurality of sensors on the basis of the first data column, acquiring a second data column output from the plurality of sensors, obtaining an estimated data column corresponding to the second data column based on the model by using regularization for making an error between the second data column and the estimated data column sparse, and identifying a sensor in which a change occurred between the first data column and the second data column on the basis of the error between the second data column and the estimated data column. A corresponding computer program product and apparatus are also disclosed herein.

A comparison system includes a portable terminal including image capturing unit configured to capture a comparison image of a predetermined comparison region of a comparison object; and a server including comparison unit configured to compare the comparison object with a registered object on the basis of a statistical value for the luminance value of pixels in a registered image of the registered object, the pixels of the registered image existing at pixel positions corresponding to pixel positions estimated to be generating specular reflection in the comparison image captured by the portable terminal.

A method and a device for searching for images in memory containing at least one or more images and, for each image, at least one sensor fingerprint associated with the image and related to the sensor that acquired the image includes a reading phase, wherein a search sensor fingerprint is read, a compression phase, wherein the search sensor fingerprint and at least one of the sensor fingerprints associated with the images are compressed by using a random projection technique, and a searching phase, wherein the images contained in the memory are either selected or discarded by comparing the sensor fingerprints of the images with the search sensor fingerprint.

A method and system that efficiently selects sensors without requiring advanced expertise or extensive experience even in a case of new machines and unknown failures. An abnormality detection system includes a storage unit for storing a latent variable model and a joint probability model, an acquisition unit for acquiring sensor data that is output by a sensor, a measurement unit for measuring the probability of the sensor data acquired by the acquisition unit based on the latent variable model and the joint probability model stored by the storage unit, a determination unit for determining whether the sensor data is normal or abnormal based on the probability of the sensor data measured by the measurement unit, and a learning unit for learning the latent variable model and the joint probability model based on the sensor data output by the sensor.

The present invention is a verification method comprising: designating as a verification area at least a part of a predetermined area of an article that has a textured pattern provided thereto, and previously storing at least one or more image features of the textured pattern of the verification area; acquiring the image feature of the textured pattern that is provided to the verification area of an article to be verified; and verifying the article to be verified by using the acquired image feature and the stored image features.

A method and a system secure a remote video transmission. A marker digitally encoded in bits is generated for an image. The encoded marker contains an identifier of the camera and a time variable. The encoded marker is inserted into the image. The insertion is carried out by adding, for each bit of the encoded marker, a pattern to the image in the spatial domain. The conversion of the pattern into the spectral domain is a predefined sub matrix of which a frequency coefficient encodes the bit of the marker. The encoded marker is extracted from the secure image. The time variable is verified by comparing with the time reference and the identifier of the camera to check the temporal freshness of the secure image and the origin of same. A failure is flagged if one of the items of information included in the encoded marker violates a security criterion.

A method includes acquiring a first data column output from a plurality of sensors, generating a model for estimating data from the plurality of sensors on the basis of the first data column, acquiring a second data column output from the plurality of sensors, obtaining an estimated data column corresponding to the second data column based on the model by using regularization for making an error between the second data column and the estimated data column sparse, and identifying a sensor in which a change occurred between the first data column and the second data column on the basis of the error between the second data column and the estimated data column. A corresponding computer program product and apparatus are also disclosed herein.

Methods and apparatuses for authenticating communication devices and securely transmitting and/or receiving encrypted voice and data information. A biometric scanner, for example a fingerprint scanner, is utilized for authenticating the communication device and for generating the encryption key. The fingerprint scanner can be an area or swipe type of scanner is registered to a particular user and has unique intrinsic characteristics (the scanner pattern) that are permanent over time and can identify the scanner even among scanners of the same manufacturer and model. The unique scanner pattern of the scanner generates a unique encryption key that cannot be reproduced using another fingerprint scanner.