Abstract: Implementations include receiving sample data, the sample data being generated as digital data representative of a sample of the product, providing a set of features by processing the sample data through multiple layers of a residual network, a first layer of the residual network identifying one or more features of the sample data, and a second layer of the residual network receiving the one or more features of the first layer, and identifying one or more additional features, processing the set of features using a CNN to identify a set of regions, and at least one object in a region of the set of regions, and determine a type of the at least one object, and selectively issuing an alert at least partially based on the type of the at least one object, the alert indicating contamination within the sample of the product.

Abstract: Implementations include receiving sample data, the sample data being generated as digital data representative of a sample of the product, providing a set of features by processing the sample data through multiple layers of a residual network, a first layer of the residual network identifying one or more features of the sample data, and a second layer of the residual network receiving the one or more features of the first layer, and identifying one or more additional features, processing the set of features using a CNN to identify a set of regions, and at least one object in a region of the set of regions, and determine a type of the at least one object, and selectively issuing an alert at least partially based on the type of the at least one object, the alert indicating contamination within the sample of the product.

Abstract: Implementations include receiving sample data, the sample data being generated as digital data representative of a sample of the product, providing a set of features by processing the sample data through multiple layers of a residual network, a first layer of the residual network identifying one or more features of the sample data, and a second layer of the residual network receiving the one or more features of the first layer, and identifying one or more additional features, processing the set of features using a CNN to identify a set of regions, and at least one object in a region of the set of regions, and determine a type of the at least one object, and selectively issuing an alert at least partially based on the type of the at least one object, the alert indicating contamination within the sample of the product.

Abstract: The steering system according to the invention provides a means of coordinating the movements of the two steering handles on a machine such as light tricycle type vehicles propelled by a pedal (5) and propulsion arms (6). The steering system comprises two handles (8) installed free to pivot, each handle being installed at the end of one of the propulsion arms (6) and a first cable (10) being fixed to the bottom of each handle. The second end is fixed to the steering shaft (9) of the tricycle. A second cable connects the two handles to each other to coordinate the movement of the handles (8). Application to leisure tricycles, light vehicles in general, indoor sports and medical re-education apparatus and static game facilities for leisure parks.

Abstract: The steering system according to the invention provides a means of coordinating the movements of the two steering handles on a machine such as light tricycle type vehicles propelled by a pedal (5) and propulsion arms (6).