What Is Bullet Camera?

A bullet camera is a small CCTV camera used for security applications. These cameras have a unique design that makes them easy to conceal to keep intruders from knowing they are being monitored. Bullet cameras are also convenient for non-security applications such as nanny cams and home monitoring systems. Bullet cameras can be placed in many different positions. They can be mounted to the ceiling or placed on the floor. The function of the camera will impact the view and field of view. The angle of the lens will also have an impact on the view. Most bullet cameras have a wide-angle lens, but you can purchase models with a fisheye lens. The wide-angle lens will have a more comprehensive view, but the image will be less focused. The field of view will depend on the distance from the subject. The closer the camera is to the issue, the narrower the view. Bullet cameras are often used in banks or casinos, where security is paramount. These cameras are also used in places like hospitals and nursing homes, where the protection of patients is essential. And, of course, these cameras are used in schools, where the safety and security of children are imperative. Due to their small size, these cameras are often installed in hard-to-reach areas. As such, they are most often powered by PoE. However, many bullet cameras also come with a 12V DC adapter. It makes them easy to install as part of a surveillance system. These cameras are easy to install and maintain. They are cost-effective solutions for a variety of monitoring situations. IP-based systems allow you to view live or recorded video from your computer or smartphone wherever you have an Internet connection. The bullet camera works to your advantage in the security world. It's small but powerful.

By TechDogs Editorial Team

Related Terms by Surveillance

Scanning Electron Microscope (SEM)

The scanning electron microscope combines two of the most valuable types of microscopes: They function in the same way as a standard microscope but are superior. Imagine you are looking at the very tip of your nose right now and attempting to see what's there. To get a close look at those minuscule hairs, you would need a powerful microscope, and if you squinted your eyes that intently at your face, you would probably have a headache. Imagine instead employing a scanning electron microscope, in which case the electrons would perform all the work for you. Since electrons make it possible for visual display results to have better integrity and resolution, objects can be seen more clearly and be used for cutting-edge research and engineering. You may not believe anything like this might be beneficial in regular life, but it absolutely is. We wouldn't be able to see how the tiny parts of bugs work together to form a whole, nor would we be able to see how much space there is between each atom in our bodies if we didn't have scanning electron microscopes. We would know nothing about our world if it weren't for the scanning electron microscopes that are currently in use. An electron beam is used to analyze whatever is being viewed in a scanning electron microscope, which is a type of microscope. It is also known as an SEM, and it is really interesting. The SEM traces the paths that electrons go through in an experiment. An electron gun is responsible for releasing electrons, which can be thought of as a light bulb that releases electrons rather than photons (light particles). Then, after passing through a few different components, such as scanning coils and a detector for backscattered electrons. You now possess some images obtained from the SEM! The backscattered electrons are transformed into signals and then delivered to a display screen. So as you're doing it, you're looking at photographs of your product on your computer or television screen - that's awesome!

Secure Hash Algorithm (SHA)

Secure Hash Algorithm is a set of algorithms developed by the National Institutes of Standards and Technology and other government and private parties. Cryptographic hashes (or checksums) have been used for electronic signatures and file integrity for decades. However, these functions have evolved to address some of the cybersecurity challenges of the 21st century. The NIST has developed a set of secure hashing algorithms that act as a global framework for encryption and data management systems. The initial instance of the Secure hash Algorithm (SHA) was in 1993. It was a 16-bit hashing algorithm and is known as SHA-0. The successor to SHA-0, SHA-1, was released in 1995 and featured 32-bit hashing. Eventually, the next version of SHA was developed in 2002, and it is known as SHA-2. SHA-2 differs from its predecessors because it can generate hashes of different sizes. The whole family of secure hash algorithms goes by the name SHA. SHA-3, or Keccak or KECCAK, is a family of cryptographic hash functions designed by Guido Bertoni, Joan Daemen, Michaël Peeters, and Gilles Van Assche. SHA-3 competition to develop a new secure hash algorithm was held by the United States National Security Agency (NSA) in 2007. To be a super safe and fast hashing algorithm, SHA3 was developed from this contest. The evolution of cybersecurity has led to the development of several "secure hash algorithms." Security is a crucial concern for businesses and individuals in today's digital world. As a result, many types of encryption have been developed to protect data in various scenarios. One of these is hash algorithms. All secure hash algorithms are part of new encryption standards to keep sensitive data safe and prevent different types of attacks. These algorithms use advanced mathematical formulas so that anyone who tries to decode them will get an error message that they aren't expected in regular operation.

Segregated Witness (SegWit)

It is time to get this party started! SegWit is an agreement implemented in the Bitcoin cyber currency community. It is also a soft fork in the Bitcoin chain and has been widely accepted by miners and users. So what does it all mean? In short, if you are running a node (a piece of software that helps keep the Bitcoin network stable), you need to upgrade your software by April 27th, or else your node will stop working. SegWit was activated as part of a hard fork on August 24th, 2017. The most important thing to note about SegWit is that it fixes transaction malleability, which has plagued miners and users for years. However, you do not need to worry if you do not want to upgrade your software. You will still be able to use Bitcoin just fine! It is confusing, but it is not that confusing. Segregated Witness (SegWit) is a proposal to improve Bitcoin implemented in August 2017. It allows for more transactions per block, which means lower fees and faster transactions.SegWit2x is a proposal that would include a hard fork months after the initial adoption of SegWit, creating two bitcoins. One of these versions would have SegWit, and one wouldn't, but both would be called "Bitcoin" and act as separate currencies. BIP 148 is another proposal that includes a user-activated hard fork and proposes implementing SegWit.SegWit is a soft fork, not a hard fork. SegWit is a technical improvement that allows more transactions to be processed simultaneously, making the network faster and more efficient. A hard fork is when developers propose changes to the protocol. If most users accept those changes, there will be two versions of that particular cryptocurrency, one for each side. The Bitcoin Cash (BCH) chain split from Bitcoin in August 2017 as an example of a crypto hard fork. Bitcoin Cash is the result of a hard fork.

Trending Definitions

Botnet Herder

Are you ready to jump into the world of Botnets and Botnet Herders? This is both a technical and exciting subject! A Botnet is a computer network hacked and controlled by one person or group, usually a cybercriminal. These Botnets can be used for bad things like spamming, Distributed Denial of Service (DDoS) attacks, and stealing information. A Botnet Herder or Botmaster is the person in charge of running a Botnet. Think of them as the people who control the bots and tell them what to do. They use advanced tools and methods to get into weak computers and add them to their network. Once a computer is part of a Botnet, the Botmaster has complete control over it and can use it to launch cyberattacks. Now comes the hard part! Usually, a Command and Control (C&C) infrastructure is used to build a botnet. The Botmaster sends commands and gets information from the bots through the C&C server. Protocols like HTTP, IRC, and BitTorrent are used for this communication. The bots are programmed to take orders from the C&C server, do the work, and then send the results back. The great thing about botnets is that the bots do all the dirty work while the botmaster stays anonymous. The bots can be found anywhere in the world, which makes it very hard for the police to find the Botmaster. Due to this, Botnets are often used for illegal things like DDoS attacks, which can shut down websites and cost much money. So, how do Botnet Herders make money? There are many ways to sell access to the Botnet for DDoS attacks, send spam emails to make money through affiliate marketing, or steal sensitive information like passwords and credit card numbers. The good news is that people who work on security are always coming up with new ways to find and stop Botnets. Honeypots, sinkholes, and Botnet takedowns are some of the tools and methods that can be used. Botnet takedowns involve law enforcement, Internet service providers, and security companies working together to shut down the C&C server and stop botmasters and bots from talking to each other. Botnets and the people who run them are a big problem for cybersecurity. They do illegal things with computers that have been hacked, and the Botmaster can stay anonymous. But people in the security community are working hard to find and stop Botnets, which makes it harder for Botmasters to run their networks. People and businesses should keep their computers from being hacked and added to a Botnet. This can be done by updating software often, using strong passwords, and being careful when you're on the web. So, don't forget to stay safe and keep your computer safe!"

Multilayer Perceptron (MLP)

What's that, you say? Do you want to know more about MLPs? If you're looking for a neural network like no other, you've come to the right place. A Multilayer Perceptron (MLP) is a feedforward synthetic neural Network that generates a fixed of outputs from a set of inputs. MLP is outstanding with the aid of using many layers of enter nodes linked as a directed graph among the enter and output layers. MLP makes use of backpropagation for education the Network. The process of preparing an MLP involves two phases: forward propagation and backpropagation. During forward propagation, the inputs are propagated through the network to produce intermediate activations at each node. These activations are then passed on to subsequent layers. The activation function used by each node is defined by its transfer function or weight value. Backpropagation involves calculating the expected error in the output layer based on these intermediate activations, adjusting these weights accordingly, then propagating this information back through each layer until it reaches the input layer again. An MLP consists of three layers: input layer, hidden layer(s) and output layer. Each node in each layer is an artificial neuron. The input layer receives external inputs from a real-world problem and transforms them into an internal representation (input vector). This internal representation passes through the hidden layer(s) and gets converted into another internal representation (hidden vector). Finally, this isolated vector passes through another layer of neurons to produce an output vector representing our model's prediction for the answer to our natural world problem. The input vector is then multiplied by weights and added or subtracted by bias terms before passing through an activation function depending on whether it's going through hidden layers (multilayer perceptron). So, you're looking for a neural network. You want a deep learning technique but need to know what it is, and you don't want to read all that stuff about backpropagation. Let me tell you: a multilayer perceptron is just the thing! It's like a neural network, except there are multiple layers of neurons. And it's supervised learning, so you can use it on data sets where you already know the answers—you have to figure out how to get your MLP to learn them too. It's that easy!