Recently, a paper entitled "Using Deep Learning and Google Street View to EsTImate the Demographic Makeup of the US" was posted to arxiv.org. As one of the co-authors of this paper, Li Feifei was on her Twitter. The public recommended this paper. This paper mainly discusses how to combine the motor vehicle data collected by Google Street View vehicles with machine learning algorithms to estimate the characteristics and composition of the population in the region, and even the political inclination of residents in this area. Below are some excerpts from this paper. For thousands of years, rulers and policy makers have conducted national censuses to collect demographic data. In the United States, the most detailed census work is the American Community Adjustment (ACS), which is performed by the US Census Bureau and costs $1 billion and more than 6,500 people per year. This is a labor intensive data collection process. In recent years, the rise of computing methods has become an effective way to solve problems in the social sciences. For example, using the data on Twitter to predict the unemployment rate, using a large amount of text analysis culture in the book, and so on. These examples show that computational methods can promote research and development in the socio-economic field, and ultimately, detailed and real-time analysis of demographic trends, and the cost is very cheap. Our research shows that combined with public data and machine learning methods, socioeconomic data and American political tendencies can be obtained. In our process, it takes a small amount of manpower to collect data for several cities, and then used to predict the situation in the United States. Specifically, we analyzed 50 million images collected by Google Street View cars in 200 cities. Our data is mainly about motor vehicles, because 90% of American households own at least one car, and people's choice of car is affected by a variety of demographic factors, including family needs, personal preferences and funds. The deep learning-based CNN computer vision framework not only identifies cars in complex streetscapes, but also identifies a range of vehicle features, including materials, models and vintages. For an untrained person, the difference between cars is hard to detect. For example, in the same model, there are minor changes in the taillights in different years (such as the Honda Accord produced in 2007 and the Honda Accord in 2008). However, our system is able to divide the car into 2,657 classes, with an analysis time of only 0.2 seconds per image. The system can classify 50 million images in 2 weeks, and a professional human classifier, assuming that he takes 10 seconds each, will take 15 years to complete the task. Using Google Street View cars to collect 50 million images, we use the Deformable Part Model to learn to automatically collect car images. After collecting images of each car, we deployed a CNN model to classify objects to determine the material, model, model and year of each car. Then, we classify the database according to the town name and divide it into two databases. The first one is the "training library", which contains all the regions whose names begin with A, B, and C. This database includes 35 cities and is trained to produce models. The second is the "test library", including all names with D, Z is the starting area, this database is used to promote the model. We collected a total of 22 million vehicles (8% of the total number of cars in the US) to accurately estimate the region's revenue, race, education, and voting program (voTIng pattern). The results show an unexpectedly simple and powerful relationship. For example, if a car is more than a truck in a 15-minute drive in a city, then the city tends to vote for the Democratic Party in the next general election (88% chance); otherwise it tends to vote for the Republican Party (82). %). Our results show that automated system monitoring uses good spatial resolution and can monitor population trends in near real-time, effectively assisting labor-intensive survey methods.
PCB
Connectors: Backplane, Wire-to-Board, Board-to-Board Connectors
These types of connector systems are
mounted or processed to a printed circuit board (PCB). There are a variety of PCB connectors and accessories best designed for specific uses. To name some, they include:Din41612 Connector,Board To Board Connectors,battery holders Clips Contacts,Future Bus Connectors,PLCC Connectors.
Din41612 Connector
Board To Board Connectors
Battery Holders Clips Contacts
Future Bus Connectors
PLCC Connectors
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What is a PCB Connector?
Other names for PCB Connectors
PCB Connectors can be known as PCB Interconnect product. Specific terms are also used for the two sides of the connection. Male PCB Connectors are often referred to as Pin Headers, as they are simply rows of pins. Female PCB Connectors can be called Sockets, Receptacles, or even (somewhat confusingly) Header Receptacles.
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Printed Circuit Board connectors are connection systems mounted on PCBs. Typically PCB Connectors are used to transfer signals or power from one PCB to another, or to or from the PCB from another source in the equipment build. They provide an easy method of Design for Manufacture, as the PCBs are not hard-wired to each other and can be assembled later in a production process.
PCB Connector orientations
The term PCB Connector refers to a basic multipin connection system, typically in a rectangular layout. A mating pair of PCB Connectors will either be for board-to-board or cable-to-board (wire-to-board). The board-to-board layouts can give a range of PCB connection orientations, all based on 90 degree increments:
Parallel or mezzanine – both connectors are vertical orientation;
90 Degree, Right Angle, Motherboard to Daughterboard – one connector is vertical, one horizontal;
180 Degree, Coplanar, Edge-to-Edge – both connectors are horizontal orientation.