A method of designing a microwave filter using a computerized filter optimizer, comprises generating a filter circuit design in process (DIP) comprising a plurality of circuit elements having a plurality of resonant elements and one or more non-resonant elements, optimizing the DIP by inputting the DIP into the computerized filter optimizer, determining that one of the plurality of circuit elements in the DIP is insignificant, removing the one insignificant circuit element from the DIP, deriving a final filter circuit design from the DIP, and manufacturing the microwave filter based on the final filter circuit design.

An autonomous tilting three-wheeled vehicle comprises a pair of front wheels coupled to a tiltable chassis by a mechanical linkage, such that the pair of wheels and the chassis are configured to tilt in unison with respect to a roll axis of the chassis. An electronic controller of the autonomous vehicle controls a tilt actuator to selectively tilt the chassis. Optionally, a steering actuator is coupled to the front wheels and controlled by the electronic controller to selectively steer the wheels. A sensor configured to measure orientation-dependent information may be coupled to the chassis by a gimbal configured to compensate for vehicle tilt. In some examples, the autonomous vehicle comprises an autonomous delivery robot.

Disclosed systems and methods relate to generating and optimizing an architectural design. According to embodiments, a method can include receiving metadata of digital communications and collaboration between members of an organization. The method can also include generating a target network model indicating a work style of the members of the organization; a level of interaction between the members of the organization; a spatial clustering of the members; or a number, a size, and an allocation of rooms with a specialized purpose. The method can further include selecting at least one criterion for the architectural design, where the criterion can include: cost, wellness, community, work style, choice, privacy, or vibrancy. The method can further include determining criteria scores for one or more architectural design prototypes. Moreover, the method can include selecting the architectural design based on the criterion scores.

A method and system provide the ability to propagate object connections. A three-dimensional (3D) model (that has 3D object components) is acquired. Two or more of the 3D object components are selected as input elements. A connection between the input elements is defined and selected. Data is autonomously collected from the selected connection. The data includes a number of the input elements, a section type of the input elements, and a relative geometrical position between the input elements. A rule autonomously created based on the data. The 3D model is autonomously searched based on the rule to identify other instances of other 3D object components that are consistent with the data. The connection is then autonomously propagated to the identified other 3D object components.

A system and method for implementing a neural network based vehicle dynamics model are disclosed. A particular embodiment includes: training a machine learning system with a training dataset corresponding to a desired autonomous vehicle simulation environment; receiving vehicle control command data and vehicle status data, the vehicle control command data not including vehicle component types or characteristics of a specific vehicle; by use of the trained machine learning system, the vehicle control command data, and vehicle status data, generating simulated vehicle dynamics data including predicted vehicle acceleration data; providing the simulated vehicle dynamics data to an autonomous vehicle simulation system implementing the autonomous vehicle simulation environment; and using data produced by the autonomous vehicle simulation system to modify the vehicle status data for a subsequent iteration.

A system and method are described for rendering a characteristic for a set of particles passing through a measurement volume of a particle optical measurement system. The method includes acquiring raw particle data for the particles passing through the measurement volume. The raw particle data comprises a set of raw particle records. Each particle record comprises at least: a trajectory of at least one particle, and a second primary mark of the at least one particle whose value influences an effective sampling area corresponding to the measurement volume. The method includes generating and storing an effective sampling area based upon: the trajectory of the at least one particle, and the second primary mark. Thereafter, an ensemble characteristic is rendered for the set of particles by performing an operation on the sampling area-corrected set of particle records.

Accordingly, there are disclosed herein geologic modeling methods and systems employing reference-based inversion of seismic image volumes. An illustrative method embodiment includes: (a) obtaining a measured seismic image volume; (b) determining a reference seismic image volume based on a reference model; (c) deriving a synthesized seismic image volume from a geologic model; (d) detecting at least one geologic model region where the synthesized seismic image volume and the measured seismic image volume are mismatched; (e) finding a reference model region where the reference seismic image volume best matches the measured seismic image volume; (f) replacing content of the at least one geologic model region with content of the reference model region to obtain an improved geologic model; and (g) outputting the improved geologic model.

A random sequence generation of defined values may be provided. A method comprises pre-loading a RAM block with an initial list comprising the defined values of a sequence of values to be updated, and shuffling the defined values of the sequence using a counter and a random offset for indices in the list.

Digital design of an area is provided by obtaining input parameters for designing the area, comparing the input parameters to a design profile for the user, in which the design profile indicates user preferences regarding design elements, identifying design element(s) to be included in digital design(s) for the area based on correlating the input parameters with design element(s) from the design profile, generating the digital design(s), each of which incorporates at least one of the identified design elements, and displaying a digital design for potential selection to guide designing the area.

Identifying pathways that are significantly impacted in a given condition is a crucial step in the understanding of the underlying biological phenomena. All approaches currently available for this purpose calculate a p-value that aims to quantify the significance of the involvement of each pathway in the given phenotype. These p-values were previously thought to be independent. Here, we show that this is not the case, and that pathways can affect each other's p-values through a “crosstalk” phenomenon that affects all major categories of existing methods. We describe a novel technique able to detect, quantify, and correct crosstalk effects, as well as identify novel independent functional modules. We assessed this technique on data from four real experiments coming from three phenotypes involving two species.