Air Vehicle Integrated Design (United States)

Most power sources, like motors or engines, provide power to a device by rotating a shaft. But, from there, this rotation often needs to be transformed into some other kind of motion. For example, there may be a need to change the speed of the rotation of a LEGO motor. Or perhaps the need is to change the direction of the rotation or make the motor push harder than it currently does. Gears allow all these transformations. This chapter starts with a tour of the gears that come in the Robot Inventor set. After that, a series of exercises teaches how to increase or decrease the speed of rotation with techniques called gearing up and gearing down, how a force called torque affects the ability to speed up a gear system, and how to build a system with multiple gear pairings. This chapter's project is to build a device called a transmission that allows a switch back and forth from a low-speed to a high-speed gear configuration.

This research provides a comprehensive review of prevailing Automatic Speech Recognition (ASR) methods and their profound impact on media production. Central to this exploration is the pivotal role of the transformer model, showcasing its unique self-attention mechanism ideal for tasks demanding comprehension of temporal relationships. We conduct an in- depth comparison, evaluating leading ASR models like Multilingual Machine Speech (MMS) from Meta, Whisper from OpenAI, and Google's Universal Speech Model (USM). Their performance is gauged against commercial services from tech giants such as Microsoft Azure, Amazon Web Services, and Google Cloud Platform. The paper covers aspects of ASR systems like voice activity detection, language identification, multilanguage support and the metrics used to characterize the accuracy of the systems. The research also identifies important challenges, like the dearth of data for some languages and the intricacies associated with linguistic nuances. Further, we discuss the role of ASR in media production, ranging from generating time-based captions to revolutionizing editing methodologies. Through a detailed breakdown of the ASR process, from audio preprocessing to postprocessing, this study endeavors to merge academic insights with practical applications, empowering media producers to leverage the immense capabilities of contemporary ASR technologies.

One of the most challenging problems in computer vision and image processing is the detection of the semantic regions of interest (SRoI). In this paper, we propose a method using OpenAI's CLIP model to find SRoI by performing semantic search for objects in the image which are detected by an object detection model called Generic RoI Extractor (GRoIE). Finding the semantic regions of interest can be used in different image processing tasks such as image and video compression, enhancement, and reformatting. By knowing the semantic region of interest within images, we can improve the visual quality of images by compressing the more important parts with higher quality and the less important parts, such as the background, with a lower quality. This operation can be achieved without changing the overall compression ratio and the Peak Signal-to-noise Ratio (PSNR) quality metric. Finding the SRoI can make the processes of image enhancement and color correction more accurate by focusing only on the important parts. Moreover, for the image reformatting process, the important parts of the image may be lost. But by using the SRoI, we can reformat the image in a better way by keeping the most important regions in the frame.

Arduino shields can offer sophisticated functionality, such as the connection to a smartphone that will be explored in this chapter. The shield to accomplish this connection to a smartphone is the 1Sheeld+, shown in Figure 12-1. This shield features a Bluetooth connection between the smartphone and the Arduino, as well as an app for the smartphone to facilitate connection. Much of the functions and sensors built into a smartphone can be accessed through the Arduino and hence also to LEGO MINDSTORMS. Two projects are built in this chapter. The first, called the Tilt Mimic, will use a LEGO motor to tilt a Technic liftarm to the same angle of orientation of the phone. The second invention, called the Intrusion Monitor, uses a MINDSTORMS Ultrasonic Sensor as a trigger for a smartphone to take a picture and send a notification email message.

The airfoil blades built in the previous chapter are the basis for the horizontal-axis turbine A horizontal-axis turbine produces much more electrical power than a vertical-axis design does because the blades catch a much larger area of wind. This area of capture is called the swept area and is defined by the diameter encircled by the airfoil blades as they spin. For comparison, the swept area of the vertical-axis turbine is the diameter of one cup of the spinning rotor, or three cups in the three-layer stack shown in Figure 4-1. The larger swept area of a horizontal-axis turbine results in a higher power-generation ability than a vertical-axis turbine. Figure 5-1 shows a completed horizontal-axis turbine consisting of the blades from Figure 4-3, a generator mount, a rotor, and a pillar. The pillar is the same tower design used for the vertical-axis turbine.

(...) It is shown that the recent characterization of strong feasibility of interval linear equations by Karademir and Prokopyev was in fact published by this author thirteen years earlier. The original result was di‐cult to discover, having been embedded, together with its proof, into the proof of another theorem.