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What is the Structure of C program:

C Program Basic Structure

 

Here you will learn the basic concepts of C program. 

The C program consists of six main sections. Below you will find a brief explanation for each of them.

 

C program Basic Structure
Documentation Section
Link Section
Definition Section
Global Declaration Section
Main() Function Section {                                        . Declaration Section Executable Section }                                        .
Subprogram Section

 

 

Basic C Program Structure

 

Documentation Section:

This section consists of a comment line that includes the name of the programmer, the author and other details such as the time and date of writing of the program. The Documentation section helps anyone get an overview of the program.

 

Link Section:

The link section consists of the header files of the functions used in the program. It gives the compiler instructions on how to link functions from the system library.

 

Definition Section:

All of the symbolic constants are written in the definition section. Macros is known as symbolic constants.

 

Global Declaration Section:

Global variables that can be used anywhere in the program are stated in the Global Declaration section. The user defined functions are also declared in this section.

 

main() Function Section:

It is necessary to have one main) (function section in each C program. This section includes two parts, a declaration and an executable part. The declaration part declares all the variables used in the executable part. These two parts must be written between the opening and the closing braces. Each declaration and executable part of the declaration must end with a semicolon (;). The execution of the program starts with the opening of the braces and ends with the closing braces.

 

Subprogram Section:

The subprogram section contains all user defined functions used to perform a specific task. In the main ( ) function, these user defined functions are called.

Output of preview program is:

Top Programming Libraries for c++

8 great C++ programming libraries

 

C++ programmers look to these libraries to help build desktop applications, mobile applications, machine learning and scientific applications, and more.

C++ is a general-purpose programming language system that is now more than 40 years old and was designed in 1979. Far from losing steam, C++ still ranks close to the top of multiple programming language popularity indexes.

Smoothing the path to C++ usage is a broad support for language among IDE makers, editors, compilers, test frameworks, code quality, and other tools. Software developers also have at their disposal many excellent libraries to assist in the development of C++ applications.

Here are eight that C++ developers are relying on.

 

Library of active templates

From Microsoft, Active Template Library (ATL) is a set of C++ classes for building COM (Common Object Model) objects, supported by COM features such as dual interfaces, standard COM enumerator interfaces, connection points, and ActiveX controls. ATL can be used with Visual Studio IDE to build single-threaded objects, apartment-model objects, free-threaded model objects, or both free-threaded and apartment-model objects.

 

Library of Asio C++

The Asio C++ library is used for network and low-level I / O programming with a consistent asynchronous model. Asio has been used for applications ranging from smartphone apps and games to highly interactive websites and real-time transaction systems, providing basic building blocks for competition, C++ networking, and other types of I / O. Projects that use Asio include the WebSocketPP library and the DDT3 remote debugger for the Lua language. Asio is available for free download open source under the Boost Software License and is supported by Linux , Windows, FreeBSD and MacOS.

 

Poco C++ Libraries

The Poco (Portable Components) C++ Libraries are cross-platform C++ libraries designed to build Internet and network applications that run on systems ranging from desktops and servers to mobile and IoT devices. The libraries can also be used to build microservices with REST APIs for machine learning or data analytics. The Poco libraries are similar in concept to the Java Class Library, the Microsoft. NET Framework, or the Apple Cocoa.

Developers can use Poco libraries to build C++ application servers that talk to SQL databases, Redis, or MongoDB, or build software for IoT devices that talk to cloud backends. The library features include a cache framework, HTML form handling, an FTP file transfer client, and an HTTP server and client. Poco libraries are available free of charge under the Boost Software License and can be downloaded from GitHub.

 

FloatX

FloatX, or Float eXtended, is a low-precision, floating point type library for header-only emulation. Although natively compatible with C++ compilers, FloatX can be used in other languages such as Python or Fortran. Floating point types are extended beyond single and double precision native types. Template types are provided that allow the user to select the number of bits used by the exponent as well as the significant parts of the floating point number. FloatX is based on the FlexFloat library 's idea of emulating reduced-precision floating  types, but implements the FlexFloat functionality superset in C and provides C++ wrappers. FloatX has emerged from the Open Transprecision Computing initiative. It is available free of charge under the Apache License 2.0.

 

Eigen

Eigen is a C++ template library for linear algebra, including matrices, vectors, numeric solvers, and related algorithms. All sizes of the matrix are supported, from small, fixed matrices to arbitrarily large , dense matrices. Algorithms are chosen for reliability. All of the standard numeric types are supported. For speed, set your own expression templates to intelligently remove timeframes and enable lazy evaluation. Freely available under Mozilla Public License 2 and downloadable from the own project page, Eigen has been equipped with an API described by the proponents as expressive and clean and natural to C++ programmers. The test suite for Eigen has been run against a number of compilers to ensure reliability.

 

OpenCV

OpenCV, or Open Source Computer Vision Library, is a computer vision and machine learning library written in a native C++ format and available under a BSD license. OpenCV was designed to provide a common infrastructure for computer vision applications and accelerate the use of machine perception in commercial products. Over 2,500 optimized algorithms for face recognition, object detection, object classification, 3D model extraction, image search, and more, OpenCV has amassed a user community of more than 47,000 people. Available from the OpenCV project website, the library provides interfaces to C++, Java , Python, and Matlab, and supports Windows , Linux, Android, and MacOS. The CUDA and OpenCL interfaces are under development.

 

Windows Template Library

Originally from Microsoft, the Windows Template Library (WTL) has been an open source library for building lightweight Windows applications and UI components since 2004. Positioned as an alternative to the Microsoft Foundation Classes toolkit, WTL expands ATL and provides a set of classes for controls, dialogs, frame windows, and GDI objects.

 

Wt

Wt is a modern C++ web GUI library that allows developers to build interactive web interfaces with widgets without having to write JavaScript. A server-side solution, Wt requests handling and page rendering, providing built-in security, PDF rendering, a 2D and 3D painting system, an object-related mapping library, a charting library, and an authentication framework. The core library is open source, providing a single-page hybrid framework that can be deployed on Linux , Unix or Windows.

Developed by Emweb, Wt is compatible with both HTML5 and HTML4 browsers and plain HTML user agents. And Wt can integrate JavaScript libraries with third parties. With Wt, the application is defined as a widget hierarchy — ranging from generic widgets such as push buttons to specialized widgets such as the entire blog widget. The widget tree is rendered in the browser as HTML / JavaScript. Wt can be downloaded from the web page of the project. Both the terms of open source and commercial use are available.

Google's Project Zero team will not apply for Apple's SRD program

Google's Project Zero team will not apply for 

Apple's SRD program

 

Other security researchers have expressed similar intentions to skip the Apple SRD after the program rules give Apple full control of the vulnerability disclosure process.

Some of the top names in the iPhone Vulnerability Research field have announced plans to skip Apple's new Security Research Device (SRD) program today due to Apple 's restrictive disclosure rules that effectively muzzle security researchers.

The list includes Project Zero (Google's elite bug-hunting team), Will Strafach (CEO of Guardian Mobile Security), ZecOps (mobile security firm that has recently discovered a series of iOS attacks) and Axi0mX (iOS vulnerability researcher and author of Checkm8 iOS exploit).

 

What's the Apple SRD program?

The Security Research Device (SRD) program is unique among smartphone manufacturers. Through the SRD program, Apple has promised to provide security researchers with pre-sale iPhones.

These iPhones are modified to have fewer restrictions and allow deeper access to the iOS operating system and hardware of the device, so security researchers can search for bugs that they would not normally be able to detect on standard iPhones where the phone's default security features prevent security tools from looking deeper into the phone.

Apple officially announced the SRD program in December 2019, when it also extended its bug bounty program to include more of its operating systems and platforms.

However, while the company tampered with the program last year, it wasn't until today that Apple launched it by publishing an official SRD website and emailing selected security researchers and bug hunters to invite them to apply for the review process needed to receive an untapped iPhone.

 

New Restrictive Rule

This new website also included the official rules of the SRD program, which security researchers have not had the opportunity to review in detail.

But while Apple's SRD announcement was welcomed by the security community with joy last year, considering it a first step in the right direction, they weren't very happy with Apple today.

According to complaints shared by social media, one specific clause was wrong for most security researchers:

"If you report a vulnerability that affects Apple products, Apple will provide you with a release date (usually the date on which Apple releases the update to resolve the issue). Apple will work in good faith to resolve any vulnerabilities as soon as possible. You can not discuss the vulnerability with others until the release date."

The clause effectively makes it possible for Apple to muzzle security researchers. The clause gives Apple full control of the process of disclosure of vulnerabilities. It allows the iPhone maker to set the release date when security researchers are allowed to talk or publish anything about vulnerabilities found in iOS and iPhone while part of the SRD program.

Many security researchers are now afraid that Apple will abuse this clause to delay major patches and drag its feet on delivering much-needed security updates by postponing the release date after which they are allowed to talk about iOS bugs. Others fear that Apple will use this clause to silence their work and prevent them from even publishing their work.

 

Project Zero and others will decide not to apply

The first to notice and understand the implications of this clause was Ben Hawkers, leader of the Google Project Zero team.

"It looks like we're not going to be able to use the Apple 'Security Research Device' because of the vulnerability restrictions that seem specifically designed to exclude Project Zero and other researchers using a 90-day policy," Hawkes said on Twitter today.

Hawkes' tweet received a lot of attention from the infosec community, and other security researchers soon followed the team's decision. Speaking to ZDNet's sister site, CNET, Will Strafach also said that he was not going to join the program because of the same clause.

On Twitter, the cybersecurity firm ZecOps also announced that it would skip the SRD program and continue to hack iPhones in the old fashion way.

In a conversation with ZDNet, security researcher Axi0mX said they were thinking of not participating as well.

"Disclosure time limits are standard practice in the industry. They are necessary," said the researcher.

"Apple requires researchers to wait for an unlimited amount of time, at Apple's discretion, before any bugs found in the Security Research Device Program can be revealed. There is no time limit. This is a poison pill," he added.

Alex Stamos, Facebook's former Chief Information Security Officer, also criticized Apple 's move, which was part of a larger set of decisions that the company has taken in recent months against the cybersecurity and vulnerability research community — which also included a lawsuit against a mobile device virtualization company that helped security researchers track iOS bugs.

It's one thing to see no-name security researchers talking about a security program, but it's another thing to see the industry's biggest names attacking one.

 

Apple Security Programs are not well viewed

The fear that Apple might abuse the rules of the SRD program to bury important iOS bugs and research is justified for those who followed Apple's security programs. Apple has previously been accused of the same practice.

In a series of tweets published in April, macOS and iOS developer Jeff Johnson attacked the company for not being serious enough about its security work.

"I 'm thinking about withdrawing from the Apple Security Bounty program," said Johnson. "I don't see any evidence that Apple is serious about the program. I've heard of just 1 bounty payment, and the bug wasn't Mac-specific. Also, Apple Product Security has ignored my last email to them for weeks.

"Apple announced the program in August, did not open it until a few days before Christmas and has not yet paid a single Mac security researcher to my knowledge. This is a joke. I think the goal is to keep researchers quiet about bugs for as long as possible, "Johnson said.

Self Driving Car Project For Computer Science

Self Driving Cars

 

INTRODUCTION

The usage and production of these cars have become a leading industry in almost every area of the world. Over the years and centuries, this industry has gone through enormous development, as the first vehicles were only powered by the steam engine, then petrol and diesel came to the public mind, and currently, it seems that the electric propulsion will be the future. Of course, with this development, faster and more useful vehicles can be produced, but in our accelerated world with more and more cars, unfortunately, the number of accidents has increased.

In most cases, these accidents are the fault of the driver, therefore it could be theoretically replaceable with the help of self-propelled cars. The human presence is the most important part of transport at present, although there are many areas where you can use a tool or feature that helps people achieve greater efficiency. Some examples of these features are the autopilot on aircraft, cruise control in cars, and many other tools that help decision-making.

 

EVOLUTION OF SELF-DRIVING CARS

Autonomous cars are those vehicles that are driven by digital technologies without any human intervention. They are capable of driving and navigating themselves on the roads by sensing the environmental impacts. Their appearance is designed to occupy less space on the road to avoid traffic jams and reduce the likelihood of accidents.

The dream of self-propelled cars goes back to the Middle Ages, centuries before the invention of the car. A piece of evidence for this statement comes from sketches of Leonardo De Vinci, in which he made a rough plan of them. Later, in literature and several science fiction novels, the robots and the vehicles controlled by them appeared. The first driverless cars were prototyped in the 1920s, but they looked different than they are today. Although the "driver" was nominally lacking, these vehicles relied heavily on specific external inputs. One of these solutions is when the car is controlled by another car behind it. Its prototype was introduced in New York and Milwaukee is known as, the American Wonder" or "Phantom Auto".

Most of the big names – Mercedes Benz, Audi, BMW, Tesla, Hyundai, etc. – have begun developing or forming partnerships around autonomous technology. They invested sizable resources into this, and by making this step they wanted to be leaders at the market of self- driving cars.

Up to this point, numerous aids, software, and sensors have been put into these cars, but we are still far from full autonomy.

They use lasers that are testing the environment with the help of LIDAR (Light Detection and Ranging). This optical technology senses the shape and movement of objects around the car; combined with the digital GPS map of the area, they detect white and yellow lines on the road, as well as all standing and moving objects on their perimeter. Autonomous vehicles can only drive themselves if the human driver can take over control if needed.

 

These are those features that driverless cars already use:

•Collision avoidance

•Drifting warning

•Blind-spot detectors

•Enhanced cruise control

•Self-parking

 

Below we briefly present some companies that play the most important role in the innovation of this segment, to show how this industry has developed.

 

Tesla 

Elon Musk, the Chief Executive Officer of Tesla, claims that every Tesla car will be completely autonomous within two years. Tesla's "S" model is a semi-self-propelled car, where different cars can learn from each other while working together. The signals processed by the sensors are sent to other cars thus they can develop each other. This information teaches cars about changing lanes and detecting obstacles, and are continually improving from day today. From October 2016, all Tesla vehicles have been being built by Autopilot Hardware 2, with a sensor and computing package that the company claims to allow complete self-driving without human interference.

Google 

The Google team has been working on driverless cars for years, and last year a working prototype was presented (by them). Furthermore, Google also supports other car manufacturers with self-driving car technologies such as Toyota Prius, Audi TT, and Lexus RX450h. Their autonomous vehicle uses Bosch sensors and other equipment manufactured by LG and Continental companies. In 2014, Google planned a driverless car that would be available without pedals and wheels to make it available to the general public by 2020, but according to the current trends, its fulfillment is still unlikely.

 

nuTonomy 

A small group of graduates of the Massachusetts Institute of Technology (MIT) created the nuTonomy software and algorithm, especially to self-propelled cars. In Singapore, nuTonomy has already put sensors to the Mitsubishi i-MiEV electric car prototype, thus nuTonomy algorithms can control the car on these complex urban roads by using GPS and LiDAR sensors. Besides that, in November 2016, they announced that self-propelled cars will be tested in Boston as well.

The National Highway Traffic Safety Administration (NHTSA) adopted the levels of the Society of Automotive Engineers for automated driving systems, which provides a broad spectrum of total human participation to total autonomy. NHTSA expects automobile manufacturers to classify each vehicle in the coming years using SAE 0 to 5 levels.

These are the levels of SAE: 

Level 0: No Automation

In this case, there is 100% of human presence. Acceleration, braking, and steering are constantly controlled by a human driver, even if they support warning sounds or safety intervention systems. This level also includes automated emergency braking.

 

Level 1: Driver Assistance 

The computer never controls steering and accelerating or braking simultaneously. In certain driving modes, the car can take control of the steering wheel or pedals. The best examples for the first level are adaptive cruise control and parking assistance.

 

Level 2: Partial Automation 

The driver can take his hands off the steering wheel. At this level, there are set-up options in which the car can control both pedals and the steering wheel at the same time, but only under certain circumstances. During this time the driver has to pay attention and if it is necessary, intervene. This is what Tesla Autopilot has known since 2014.

 

Level 3: Conditional Automation 

It approaches full autonomy, but this is dangerous in terms of liability, so therefore, paying attention to them is a very important element. Here the car has a certain model that can take full responsibility for driving in certain circumstances, but the driver must take the control back when the system asks. At this level, the car can decide when to change lanes and how to respond to dynamic events on the road and it uses the human driver as a backup system.

 

Level 4: High Automation 

It is similar to the previous level, but it is much safer. The vehicle can drive itself under suitable circumstances, and it does not need human intervention. If the car meets something that it cannot handle, it will ask for human help, but it will not endanger passengers if there is no human response. These cars are close to the fully self-driving car.

 

Level 5: Full Automation 

At this level, as the car drives itself, human presence is not a necessity, only an opportunity. The front seats can turn backward so passengers can talk more easily with each other because the car does not need help in driving. All driving tasks are performed by the computer on any road under any circumstances, whether there's a human on board or not.

These levels are very useful as with these we can keep track of what happens when we move from human-driven cars to fully automated ones. This transition will have enormous consequences for our lives, our work, and our future travels. As autonomous driving options are widespread, the most advanced detection, vision, and control technologies allow cars to detect and monitor all objects around the car, relying on real-time object measurements.

Besides, the information technology built into the vehicle is fully capable of delivering both external (field) and internal (machine) information to the car

 

DECISIONS

Self-driving cars may be the future of transportation but we do not know whether it is safer than non- autonomous driving or not. There are unexpected events during driving that force us to decide, often these are only tiny things such as passing through the yellow light or not but sometimes situations arise where we have to decide on the lives of others or our own.

Trusting in new technologies is expected to be a significant challenge for the public. Few people feel comfortable about using a new and unproven transportation technology, which can be seen after studying aviation history.

 

These problems may arise:

• How should the car be programmed to act in the event of an unavoidable accident?
• Should it minimize the loss of life even if it means sacrificing the occupants, or should it protect the occupants at all costs?
• Should it choose between these extremes at random?

 

Answers to these ethical questions are important because they can have a great impact on the ability to accept autonomous cars in society. Who would buy a car that is programmed to sacrifice the owner?

 

Careful about:

1. An Unregulated Industry

Because information about the technology is limited, and although 200 car companies are jumping into the self-driving car space, there are not enough solid facts to create a baseline for safety standards. As yet, the industry is unregulated which is excellent for manufacturers but bad for consumers.

2. More Accidents Blending Self-Driving and Manual Cars

Sometimes self-driving cars give the passengers a sense of false security when really, they should be extra cautious and ready to take the wheel at any given moment should the need arise.

3. Vulnerability to Hacking & Remote Control

Any computer device connected to the internet is vulnerable to hacking. These cars also rely heavily on the software that runs their components, and if a hacker gets into the system, they can control every aspect of the car.

Other dangers to be aware of are the theft of private data and even gaining remote access to a cell phone connected to the car via Bluetooth. Self-driving vehicles may also be more susceptible to computer viruses.

4. Computer Malfunctions

Most self-driving cars are made up of not one but 30 to 100 computers. That is a lot of technology where things could go wrong. The software that runs self-driving cars is admittedly sophisticated. However, one of the more difficult challenges that engineers struggle to solve is how to operate smoothly in all weather conditions. Correctly controlling sensors on the rear camera is also an issue. A particularly dangerous glitch is how to know when to execute a quick stop when someone is in the crosswalk in front of the car. Other concerns that should be solved before these cars hit the road are freeze-ups during autopilot mode, and how to account for the unpredictable behavior of other motorists. 

5. Exposure to Radiation

With all the goodies on board like GPS, remote controls, power accessories, Bluetooth, Wi-Fi, music, and radio components drivers will be increasingly exposed to higher levels of electromagnetic field radiation. Exposure to electronic radiation can cause a myriad of serious health problems. Some of the more serious issues are high blood pressure, difficulty breathing, migraine headaches, eye issues, exhaustion, and sleeplessness.

 

 

CONCLUSIONS

This is a quite new topic, and this is still closer to a piece of science fiction literature, but several companies try to solve this task, even if there are many problems with it. We showed that the main problem is the fear of losing control. If a computer decides instead of us, we do not control the processes. Every computer and program may have a back door, and the question arising is what can be done if someone enters into the computer that can save our lives.