Abstract: A vehicle air compressor apparatus is provided for a heavy vehicle air braking system. The apparatus comprises a crankcase, and a cylinder head disposed on the crankcase. The cylinder head includes (i) an air inlet port through which air can be received for compression within the crankcase and the cylinder head, (ii) a discharge port through which compressed air can be delivered from the cylinder head, (iii) a first port through which compressed air can pass, and (iv) a second port through which cooled compressed air can pass. The apparatus further comprises a discharge air cooling jumper connected externally of the cylinder head between the first and second ports. The jumper is arranged to cool compressed air passing from the first port through a discharge air path of the jumper to the second port to provide cooler compressed air to be delivered from the cylinder head through the discharge port.

Abstract: Systems and methods for recognizing and identifying items located on the lower shelf of a shopping cart in a checkout lane of a retail store environment for reducing or preventing loss or fraud and increasing the efficiency of a checkout process efficiency. The system includes one or more visual sensors that can take images of items and a computer system that receives the images and automatically identifies the items. The system can be trained to recognize the items using images taken of the items. The system relies on matching visual features from training images to match against features extracted from images taken at the checkout lane. Using the scale-invariant feature transformation (SIFT) method, for example, the system can compare extracted visual features of the images to the features stored in a database to find one or more matches, where the found one or more matches are used to identify the items.

Abstract: Systems and methods for recognizing and identifying items located on the lower shelf of a shopping cart in a checkout lane of a retail store environment for the purpose of reducing or preventing loss or fraud and increasing the efficiency of a checkout process. The system includes one or more visual sensors that can take images of items and a computer system that receives the images from the one or more visual sensors and automatically identifies the items. The system can be trained to recognize the items using images taken of the items. The system relies on matching visual features from training images to match against features extracted from images taken at the checkout lane. Using the scale-invariant feature transformation (SIFT) method, for example, the system can compare the visual features of the images to the features stored in a database to find one or more matches, where the found one or more matches are used to identify the items.

Abstract: Systems and methods for recovery based social networking are presented. An analysis module analyzes past and current activity of users on the social networking platform. The analysis module predicts, based on user activity, when particular users will need support from user identified supporters and healthcare professionals. The analysis module sends alert messages to the pre-determined supporters and healthcare professionals soliciting support responsive to the predictions.

Abstract: Systems and methods for recovery based social networking are presented. Users of the social network platform can select different groups of individuals that have access to different portions of the content that the user generates through the social networking platform. Private health information can be maintained separately from information generated by the user through the social networking platform. Healthcare professionals can access both private and user generated information in order to support the user's recovery.

Abstract: Systems and methods for recovery based social networking are presented. Users of the social network platform can select different goals that are tracked via the social networking platform. In addition, healthcare professionals and supporters can make goals for users that are similarly tracked. Individualized discharge plans can be created for users to track and support recovery on an individualized basis. Location based services can be used to verify completion of goals and the discharge plan.

Abstract: Systems and methods for recovery based social networking are presented. Users of the social network platform are presented with selectable status indicators relating to their recovery. The selected indicators are shared with user selected supporters and health care professionals so that the supporters and healthcare professionals can support recovery. Automated analysis of the users' interactions with the social networking is also performed to predict when users will need additional support so that the supporters and healthcare professionals can be notified.

Abstract: Systems and methods for recognizing and identifying items located on the lower shelf of a shopping cart in a checkout lane of a retail store environment for the purpose of reducing or preventing loss or fraud and increasing the efficiency of a checkout process. The system includes one or more visual sensors that can take images of items and a computer system that receives the images from the one or more visual sensors and automatically identifies the items. The system can be trained to recognize the items using images taken of the items. The system relies on matching visual features from training images to match against features extracted from images taken at the checkout lane. Using the scale-invariant feature transformation (SIFT) method, for example, the system can compare the visual features of the images to the features stored in a database to find one or more matches, where the found one or more matches are used to identify the items.

Abstract: Methods and computer readable media for recognizing and identifying items located on the belt of a counter and/or in a shopping cart of a store environment for the purpose of reducing/preventing bottom-of-the-basket loss, checking out the items automatically, reducing the checkout time, preventing consumer fraud, increasing revenue and replacing a conventional UPC scanning system to enhance the checking out speed. The images of the items taken by visual sensors may be analyzed to extract features using the scale-invariant feature-transformation (SIFT) method. Then, the extracted features are compared to those of trained images stored in a database to find a set of matches. Based on the set of matches, the items are recognized and associated with one or more instructions, commands or actions without the need for personnel to visually see the items, such as by having to come out from behind a check out counter or peering over a check out counter.

Abstract: Systems and methods for recognizing and identifying items located on the lower shelf of a shopping cart in a checkout lane of a retail store environment for the purpose of reducing or preventing loss or fraud and increasing the efficiency of a checkout process. The system includes one or more visual sensors that can take images of items and a computer system that receives the images from the one or more visual sensors and automatically identifies the items. The system can be trained to recognize the items using images taken of the items. The system relies on matching visual features from training images to match against features extracted from images taken at the checkout lane. Using the scale-invariant feature transformation (SIFT) method, for example, the system can compare the visual features of the images to the features stored in a database to find one or more matches, where the found one or more matches are used to identify the items.

Abstract: Systems and methods for recognizing and identifying items located on the lower shelf of a shopping cart in a checkout lane of a retail store environment for the purpose of reducing or preventing loss or fraud and increasing the efficiency of a checkout process. The system includes one or more visual sensors that can take images of items and a computer system that receives the images from the one or more visual sensors and automatically identifies the items. The system can be trained to recognize the items using images taken of the items. The system relies on matching visual features from training images to match against features extracted from images taken at the checkout lane. Using the scale-invariant feature transformation (SIFT) method, for example, the system can compare the visual features of the images to the features stored in a database to find one or more matches, where the found one or more matches are used to identify the items.

Abstract: Methods and computer readable media for recognizing and identifying items located on the belt of a counter and/or in a shopping cart of a store environment for the purpose of reducing/preventing bottom-of-the-basket loss, checking out the items automatically, reducing the checkout time, preventing consumer fraud, increasing revenue and replacing a conventional UPC scanning system to enhance the checking out speed. The images of the items taken by visual sensors may be analyzed to extract features using the scale-invariant feature-transformation (SIFT) method. Then, the extracted features are compared to those of trained images stored in a database to find a set of matches. Based on the set of matches, the items are recognized and associated with one or more instructions, commands or actions without the need for personnel to visually see the items, such as by having to come out from behind a check out counter or peering over a check out counter.