Abstract: Images of an insect larva are subjected to at least a first convolutional neural network to develop feature maps based on anatomical pixels at corresponding image locations in the respective feature maps. The anatomical pixels correspond to a body part of the insect larva. A computer uses a plurality of feature maps form an integrated feature map and bounding boxes on anatomical pixels corresponding to a head, thorax, abdomen, or terminal abdomen area of the insect larva. A classification network identifies the genus, the species, the sex, or the life stage of the insect larva.

Abstract: An insect trap includes a combination of one or more components used to classify the insect according to a genus and species. The trap includes an imaging device, a digital microphone, and passive infrared sensors at the entrance of the trap to sense wing-beat frequencies and size of the insect (to identify entry of a mosquito). A lamb-skin membrane, filled with an insect attractant such as carbon dioxide mixed with gas air inside, mimics human skin so that the insect can rest on the membrane and even pierce the membrane as if a blood meal is available. An imaging device such as a passive infrared sensor or a camera gathers image data of the insect. The insect may be a mosquito.

Abstract: An insect trap includes a combination of one or more components used to classify the insect according to a genus and species. In one embodiment, an apparatus can include an entry aperture configured to lead one or more insects to an imaging chamber within the apparatus. A device may include a door on a side surface of the apparatus, wherein a rear surface of the door is positioned within the imaging chamber in a closed position, and wherein the rear surface of the door comprises a removable/replaceable adhesive material or surface configured to trap one or more insects thereon. A device may include at least one imaging sensor positioned opposite the rear surface of the door configured to capture images of the one or more insects adhering to the adhesive material or surface.

Abstract: Embodiments of the present disclosure automate the identification of gonotrophic stages in mosquitoes. An example computer-implemented method can include: receiving an image data set; processing at least a portion of the image data set using an image segmentation operation to determine a current gonotrophic phase for each of at least a portion of the plurality of mosquitoes; determining, using a trained machine learning model and based at least in part on the determined current gonotrophic phases, a predictive output indicative of an expected population of mosquitoes at the location during a future time period; and outputting an indication of the expected population of mosquitoes.

Abstract: Images of an insect are subjected to at least a first convolutional neural network to develop feature maps based on anatomical pixels at corresponding image locations in the respective feature maps. The anatomical pixels correspond to a body part of the insect. A computer calculates an outer product of the first feature map and the second feature map to form an integrated feature map. Extracting fully connected layers from respective sets of integrated feature maps and applying the fully connected layers to a classification network for identifying the genus and the species of the insect.

Abstract: A system for authenticating an individual's location activity includes a mobile communications device connected to a network and in electronic communication with at least one other computer. The mobile communications device is configured to authenticate the individual's presence at a location using biometric data entered by the individual. The mobile communications device has applications stored thereon to access location information for the mobile communications device using a GPS application stored on the mobile communications device and to access time information for the mobile communications device from a clock application stored on the mobile communications device. The mobile communications devices creates a digital signature that authenticates an individual's location activity by storing an encrypted digital certificate comprising a hash calculation using the biometric data, a validation key generated by authenticating the biometric data, the location information, and the time information.

Abstract: Images of an insect larva are subjected to at least a first convolutional neural network to develop feature maps based on anatomical pixels at corresponding image locations in the respective feature maps. The anatomical pixels correspond to a body part of the insect larva. A computer uses a plurality of feature maps form an integrated feature map and bounding boxes on anatomical pixels corresponding to a head, thorax, abdomen, or terminal abdomen area of the insect larva. A classification network identifies the genus, the species, the sex, or the life stage of the insect larva.

Abstract: Identifying insect species integrates image processing, feature selection, unsupervised clustering, and a support vector machine (SVM) learning algorithm for classification. Results with a total of 101 mosquito specimens spread across nine different vector carrying species demonstrate high accuracy in species identification. When implemented as a smart-phone application, the latency and energy consumption were minimal. The currently manual process of species identification and recording can be sped up, while also minimizing the ensuing cognitive workload of personnel. Citizens at large can use the system in their own homes for self-awareness and share insect identification data with public health agencies.

Abstract: A public key generated by each user of a plurality of users is used to encrypt the contacts for that user. The results are sent to a server by each user. The key generated by each user is then distributed to every other user in the system, and each recipient encrypts their contacts with the keys. The result of these encryptions for all contacts for all recipients is then received by the server, and the server computes an encrypted computation of equality of two contacts and sends all computations back to the original user. The user can use the homomorphic property of the crypto protocol (e.g., a private key) to determine a set of users that are matched as contacts with the other users. The binary results are returned to the server, and the server computes a graph using the results.

Abstract: Images of an insect are subjected to at least a first convolutional neural network to develop feature maps based on anatomical pixels at corresponding image locations in the respective feature maps. The anatomical pixels correspond to a body part of the insect. A computer calculates an outer product of the first feature map and the second feature map to form an integrated feature map. Extracting fully connected layers from respective sets of integrated feature maps and applying the fully connected layers to a classification network for identifying the genus and the species of the insect.

Abstract: A system for authenticating an individual's location activity includes a mobile communications device connected to a network and in electronic communication with at least one other computer. The mobile communications device is configured to authenticate the individual's presence at a location using biometric data entered by the individual. The mobile communications device has applications stored thereon to access location information for the mobile communications device using a GPS application stored on the mobile communications device and to access time information for the mobile communications device from a clock application stored on the mobile communications device. The mobile communications devices creates a digital signature that authenticates an individual's location activity by storing an encrypted digital certificate comprising a hash calculation using the biometric data, a validation key generated by authenticating the biometric data, the location information, and the time information.

Abstract: Identifying insect species integrates image processing, feature selection, unsupervised clustering, and a support vector machine (SVM) learning algorithm for classification. Results with a total of 101 mosquito specimens spread across nine different vector carrying species demonstrate high accuracy in species identification. When implemented as a smart-phone application, the latency and energy consumption were minimal. The currently manual process of species identification and recording can be sped up, while also minimizing the ensuing cognitive workload of personnel. Citizens at large can use the system in their own homes for self-awareness and share insect identification data with public health agencies.

Abstract: Identifying insect species integrates image processing, feature selection, unsupervised clustering, and a support vector machine (SVM) learning algorithm for classification. Results with a total of 101 mosquito specimens spread across nine different vector carrying species demonstrate high accuracy in species identification. When implemented as a smart-phone application, the latency and energy consumption were minimal. The currently manual process of species identification and recording can be sped up, while also minimizing the ensuing cognitive workload of personnel. Citizens at large can use the system in their own homes for self-awareness and share insect identification data with public health agencies.

Abstract: A system for authenticating an individual's location activity includes a mobile communications device connected to a network and in electronic communication with at least one other computer. The mobile communications device is configured to authenticate the individual's presence at a location using biometric data entered by the individual. The mobile communications device has applications stored thereon to access location information for the mobile communications device using a GPS application stored on the mobile communications device and to access time information for the mobile communications device from a clock application stored on the mobile communications device. The mobile communications devices creates a digital signature that authenticates an individual's location activity by storing an encrypted digital certificate comprising a hash calculation using the biometric data, a validation key generated by authenticating the biometric data, the location information, and the time information.

Abstract: An insect trap includes a combination of one or more components used to classify the insect according to a genus and species. The trap includes an imaging device, a digital microphone, and passive infrared sensors at the entrance of the trap to sense wing-beat frequencies and size of the insect (to identify entry of a mosquito). A lamb-skin membrane, filled with an insect attractant such as carbon dioxide mixed with gas air inside, mimics human skin so that the insect can rest on the membrane and even pierce the membrane as if a blood meal is available. An imaging device such as a passive infrared sensor or a camera gathers image data of the insect. The insect may be a mosquito.

Abstract: In an embodiment, a public key generated by each user of a plurality of users is used to encrypt the contacts for that user. The result of the encryption are sent to a central server by each user. The key generated by each user is then distributed to every other user in the system, and each recipient encrypt their contacts with the public keys that are sent by the server. The result of these encryptions for all contacts for all recipients is then received by the server, and the server computes an encrypted computation of equality of two contacts and sends all computations back to the original user. The user can use the homomorphic property of the crypto protocol (e.g., a private key) to determine a set of users that are matched as contacts with the other users. The binary results are returned to the server, and the server computes a graph using the results.

Abstract: A method of identifying a living creature includes training a convolutional neural network model using pretrained convolutional neural networks to generate proposals about the regions where there might be an anatomical object within a digital image. Introducing a residual connection to get the input from the previous layer to the next layer helps in solving gradient vanishing problem. The next step is to design an object detector network that does three tasks: classifying the boxes with respective anatomies, tightening the boxes, and generating a mask (i.e., pixel-wise segmentation) of each anatomical component. In constructing the architecture of the object detector network, the network uses per-pixel sigmoid, and binary cross-entropy loss function (to identify the k anatomical components) and rigorously train them.

Abstract: Identifying insect species integrates image processing, feature selection, unsupervised clustering, and a support vector machine (SVM) learning algorithm for classification. Results with a total of 101 mosquito specimens spread across nine different vector carrying species demonstrate high accuracy in species identification. When implemented as a smart-phone application, the latency and energy consumption were minimal. The currently manual process of species identification and recording can be sped up, while also minimizing the ensuing cognitive workload of personnel. Citizens at large can use the system in their own homes for self-awareness and share insect identification data with public health agencies.

Abstract: Identifying insect species integrates image processing, feature selection, unsupervised clustering, and a support vector machine (SVM) learning algorithm for classification. Results with a total of 101 mosquito specimens spread across nine different vector carrying species demonstrate high accuracy in species identification. When implemented as a smart-phone application, the latency and energy consumption were minimal. The currently manual process of species identification and recording can be sped up, while also minimizing the ensuing cognitive workload of personnel. Citizens at large can use the system in their own homes for self-awareness and share insect identification data with public health agencies.

Abstract: A system for authenticating an individual's location activity includes a mobile communications device connected to a network and in electronic communication with at least one other computer. The mobile communications device is configured to authenticate the individual's presence at a location using biometric data entered by the individual. The mobile communications device has applications stored thereon to access location information for the mobile communications device using a GPS application stored on the mobile communications device and to access time information for the mobile communications device from a clock application stored on the mobile communications device. The mobile communications devices creates a digital signature that authenticates an individual's location activity by storing an encrypted digital certificate comprising a hash calculation using the biometric data, a validation key generated by authenticating the biometric data, the location information, and the time information.