#dnn programmers

[currenthunt]

Digital University Kerala introduces the state's first silicon-certified artificial intelligence chip – Kairali Chip

Digital University Kerala has introduced State’s maiden silicon-proven Artificial Intelligence (AI) chip—Kairali AI Chip, that offers Speed, Power Efficiency and Scalability for various applications. Kairali AI Chip - This chip leverages edge intelligence (or edge AI) to deliver high performance and low power consumption for a wide range of applications. - Edge artificial intelligence (AI), or AI at the edge, is the implementation of AI in an edge computing environment, which allows computations to be done close to where data is actually collected, rather than at a centralized cloud computing facility or an offsite data center. - It entails deploying Machine Learning algorithms on the edge device where the data is generated, rather than relying on cloud computing. - Edge intelligence can provide faster and more efficient data processing while also protecting the privacy and security of both data and users. Potential Applications - Agriculture: The chip can enable precision farming techniques by providing real-time monitoring of crop health, soil conditions and environmental factors. This can help in optimizing the use of resources and enhancing the crop yields. - Mobile Phone: The chip can improve the efficiency and performance of smartphones by enabling advanced features such as real-time language translation, enhanced image processing and AI-powered personal assistants. - Aerospace: The chip can augment the capabilities of Unmanned Aerial Vehicles (UAVs) and satellites by providing advanced processing power for navigation, data collection and real-time decision-making, all with minimal power consumption. The chip can also enhance the navigation and autonomous decision-making capabilities of drones, which are useful for applications such as delivery services and environmental monitoring. - Automobile: The chip can be a game-changer for autonomous vehicles by providing the necessary computing power for real-time processing of sensory information, which is essential for safe and efficient autonomous driving. - Security and surveillance: The chip can enable faster and efficient facial recognition algorithms, threat detection and real-time analytics by using its edge computing capability. AI chips - AI chips are built with specific architecture and have integrated AI acceleration to support deep learning-based applications. - Deep learning, more commonly known as Active Neural Network (ANN) or Deep Neural Network (DNN), is a subset of Machine Learning and comes under the broader umbrella of AI. Functions - It combines a series of computer commands or algorithms that stimulate activity and brain structure. - DNNs go through a training phase, learning new capabilities from existing data. - DNNs can then inference, by applying these capabilities learned during deep learning training to make predictions against previously unseen data. - Deep learning can make the process of collecting, analysing, and interpreting enormous amounts of data faster and easier. - Chips like these, with their hardware architectures, complementary packaging, memory, storage, and interconnect solutions, make it possible for AI to be integrated into applications across a wide spectrum to turn data into information and then into knowledge. Types of AI Chips Designed for Diverse AI Applications - Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), Central Processing Units (CPUs) and GPUs. Applications - AI applications include Natural Language Processing (NLP), computer vision, robotics, and network security across a wide variety of sectors, including automotive, IT, healthcare, and retail. Benefits of AI Chips Faster Computation - Artificial intelligence applications typically require parallel computational capabilities in order to run sophisticated training models and algorithms. - AI hardware provides more parallel processing capability that is estimated to have up to 10 times more competing power in ANN applications compared to traditional semiconductor devices at similar price points. High Bandwidth Memory - Specialized AI hardware is estimated to allocate 4-5 times more bandwidth than traditional chips. - This is necessary because due to the need for parallel processing, AI applications require significantly more bandwidth between processors for efficient performance. Read the full article

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DNN Module Development per Your Requirements. Custom DotNetNuke modules are the power of the DotNetNuke framework and why? It gives us the ability to extend the codebase and create custom DNN modules to fit our client's Case Study DNN module.

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[2topnews]

What is deep learning?

Deep Learning is a subset of Machine Learning and it has the effect of helping computers train themselves to be able to perform all tasks similarly to humans. This is to help computers imitate the way humans learn and think.

Deep Learning systems are likely to improve their performance with more access to data.

Usually, the machine version will have more experience; Machines that have enough experience will be brought to serve tasks such as driving and detecting weeds.

Deep Learning has support for language translation, image classification, and speech recognition. Therefore, it can be applied to solve all the needs of pattern recognition without human intervention.

In addition to the concept of what Deep Learning is, you should learn more about the concept of neural networks as follows: Deep Learning works based on an artificial neural network and it includes many layers containing data that simulate how to operate. the behavior of the human brain.

This artificial neural network is similar to the human brain, including Nodes (it is the neural unit in an artificial neural network) are neurons. Most nodes themselves are usually only able to answer the most simple and basic questions; With difficult tasks, they will link together to answer.

You can teach or you can train them with specific algorithms. Nodes that answer complex questions are called deep neural networks (DNNs), it is defined as follows: Deep neural networks are capable of performing complex operations such as representation, abstraction, and abstraction. images have meanings of sound, text, and images. They are considered to be the most developed area in Machine Learning.

How Deep Learning Works

Deep Learning is considered a method of methodical machine learning: an AI programmer will be trained to predict the output based on a set of inputs. Specific example: Predict the behavior of a cat when it meets a mouse. Train it with supervised learning.

When you want to predict its action on the input will be done as follows:

Choose the right prey

At this time: the body parts of the cat such as the eyes, claws, and ears of the cat will become very sensitive.

Where will the mouse appear?

Basically, Deep Learning will not be different from regular machine learning. However, with the above example, it takes a lot of time to design the features that represent the cat. What it needs to do is provide the system with a number of cat images, and cat-and-mouse videos, then the system can learn the features that represent a cat.

For tasks like computer vision, speech recognition, robotics, or machine translation, the performance of Deep Learning can far exceed that of other machine systems. However, building a Deep Learning system is not so easy compared to a conventional mechanical system.

What are the outstanding advantages of Deep Learning?

Deep Learning is highly accurate, can learn extensively, and achieves extremely high recognition accuracy. This will ensure that consumer electronics can meet all needs and expectations of users. Deep Learning is very important for the safety of driverless car models.

The Deep Learning data will be labeled by: The development of driverless cars requires millions of images as well as thousands of hours of video viewing.

GPU has high performance and parallel knowledge so it is very effective for Deep Learning. If combined with cloud computing or clusters, it allows the development team to reduce the training time for the learning network to a maximum of weeks or hours.

How is Deep Learning applied in life?

In the high-tech industry

The outstanding application of Deep Learning that cannot be ignored is the construction of Robots. Currently, human-like versions of robots with the ability to sense and react to the environment are gradually being born.

Robots can now cooperate with human activities, and they can perform specific tasks to their strengths. Robots are replacing humans in performing more difficult jobs. This is a great invention thanks to the application of Deep Learning.

In agriculture

Now, thanks to Deep Learning, farmers can deploy devices that can distinguish weeds from crops. From there, the herbicide spraying machines can selectively spray on the weeds to ensure that the crops are not affected.

In addition to the role of eliminating weeds with herbicides, thanks to Deep Learning, agricultural output is increasingly improved. In addition, Deep Learning is being expanded further into activities such as harvesting, irrigation, fertilizing, planting, etc.

[vegxcodes]

You better know what Artificial Intelligence is since it is here to stay

A super-short introduction to what AI is all about for a layman

Artificial Intelligence is making waves in the tech world. It’s the next exciting frontier, and it’s already transforming industries from healthcare to finance. But what is artificial intelligence? How does it work? To answer those questions, let’s dive into the basic concepts of AI: machine learning and deep learning.

Artificial Intelligence

Artificial intelligence (AI) is a field of computer science that involves giving computers the ability to reason, learn, and solve complex problems. AI has been around for over 60 years but has recently seen a surge in interest because of recent advances in computing power and big data processing. The goal of AI is to develop systems that allow computers or machines to perform tasks normally requiring human intelligence for example visual perception, speech recognition, or decision making under uncertain conditions. The concept behind machine learning is that software can be trained on large amounts of data (i.e., millions of pieces of information), without being explicitly programmed with all the knowledge required to make decisions when given new inputs. Deep learning is a subset of machine learning that uses deep neural networks which are loosely inspired by biological processes like neurons in the brain or DNA transcription/translation reactions within cells.

Machine Learning

Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed.

Machine learning is a subset of artificial intelligence (AI), which itself is a subfield of computer science. So what’s AI? It’s the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. The study of how machines can do these things is known as machine learning.

Data science is an interdisciplinary field that uses scientific techniques to analyze data and extract knowledge from it — it’s where statistics meets programming meets computer science! Machine learning falls under this umbrella: it’s a subset of data science because it deals specifically with computational methods for making predictions based on data.

Deep Learning

In the field of artificial intelligence, deep learning is considered to be a subfield of machine learning. In computer science, it’s considered a subfield of artificial intelligence (AI).

In statistics, it’s considered one of many subfields under the overarching umbrella of statistical modeling. And in mathematics? Well, that’s where things get interesting.

Many mathematicians think about deep learning as being part of their domain — but others disagree because they think that deep learning is too broad and isn’t really “mathematical.” Of course, there are some who would argue that all fields are ultimately mathematical in nature.

Machine Learning vs. Deep Learning

Many people find it difficult to distinguish between Machine Learning and Deep Learning. While they are both subsets of AI, there are some important differences worth noting.

Machine learning is a field that focuses on the development of algorithms that can learn from data. More specifically, machine learning algorithms make inferences from data without being explicitly programmed to do so. This is different from traditional software programming where the programmer would have to enter rules or dependencies for every single possibility (for example: if x then y). Instead, machine learning uses statistics and probability theory to give computers this ability by finding patterns in large amounts of data — for example in images or text documents — and making predictions about new data based on those associations.

Deep Learning is a particular branch of machine learning that involves deep neural networks (DNNs) which consist of multiple layers stacked together in order to recognize patterns in complex datasets such as images or sound files; these systems can derive context from one layer to another resulting in an outcome more accurate than traditional methods using smaller datasets consisting only.

Conclusion

AI is an exciting field and we have barely scratched the surface of its potential to improve our lives. This article should give you an overview of AI, ML and DL, which are all tools that can be used to achieve this goal. There are many more topics in this realm but hopefully, this introduction has given you an overview of what makes up these fields.

I really appreciate every kind of support! Every interaction you are doing with the content will help me grow and deliver better content with time. 🚀

Thank you, VEGXCODES

Resources ⚡️

Link 1: https://www.guru99.com/artificial-intelligence-tutorial.html Link 2: https://www.mygreatlearning.com/blog/what-is-artificial-intelligence/ Link 3: https://levity.ai/blog/difference-machine-learning-deep-learning

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DNN Module Development per Your Requirements. Custom DotNetNuke modules are the power of the DotNetNuke framework and why? It gives us the ability to extend the codebase and create custom DNN modules to fit our client's Case Study DNN module.

[manektechge-blog]

Mieten Sie DotNetNuke Programmer. ManekTech bietet eine vollständige Suite flexibler Einstellungsservices für das DotNetNuke (DNN) -Projekt für Ihr Unternehmen.

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13 Below provides a full range of flexible hiring services for DNN project. Our skilled and professional Dot Net Nuke programmers are capable of building Dot Net Nuke applications at affordable prices.

[craigbrownphd-blog-blog]

What’s going on on PyPI

Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way. • azure-eventhub-checkpointstoreblob Microsoft Azure Event Hubs checkpointer implementation with Blob Storage Client Library for Python • disc An accurate and scalable semi-supervised deep learning method for imputing dropouts for single-cell transcriptome • topological-clustering Implementation of the ToMATo clustering algorithm, with clique complex and KNN nearest neighbors graph. • wai.tfrecords Converting ADAMS annotations to tfrecords. • aghasher An implementation of the Anchor Graph Hashing algorithm (AGH-1), presented in Hashing with Graphs (Liu et al. 2011). • cf-text-embeddings Text Embeddings for ClowdFlows • clustering-jhk practice for K-Means algorithm • condensa Condensa Programmable Model Compression Framework. Condensa is a framework for _programmable model compression_ in Python. It comes with a set of built-in compression operators which may be used to compose complex compression schemes targeting specific combinations of DNN architecture, hardware platform, and optimization objective. To recover any accuracy lost during compression, Condensa uses a constrained optimization formulation of model compression and employs an Augmented Lagrangian-based algorithm as the optimizer. • CoreNLG CoreNLG is an easy to use and productivity oriented Python library for Natural Language Generation. It aims to provide the essential tools for developers to structure and write NLG projects. Auto-agreement tools based on extra-resources are not provided in this library. • errant-qordoba ERRor ANnotation Toolkit: Automatically extract and classify grammatical errors in parallel original and corrected sentences. http://bit.ly/2ThSvaG

[analyticsindiam]

Intel Readies For An AI Revolution With A Comprehensive AI Solutions Stack

Global technology player Intel has been a catalyst for some of the most significant technology transformations in the last 50 years, preparing its partners, customers and enterprise users for a digital era. In the area of artificial intelligence (AI) and deep learning (DL), Intel is at the forefront of providing end-to-end solutions that are creating immense business value. But there’s one more area where the technology giant is playing a central role. Intel is going to the heart of the developer community by providing a wealth of software and developer tools that can simplify building and deployment of DL-driven solutions and take care of all computing requirements so that data scientists, machine learning engineers and practitioners can focus on delivering solutions that grant real business value. The company’s software offerings provide a range of options to meet the varying needs of data scientists, developers and researchers at various levels of AI expertise. So, why are AI software development tools more important now than ever? As architectural diversity increases and the compute environment becomes more sophisticated, the developer community needs access to a comprehensive suite of tools that can enable them to build applications better, faster and more easily and reliably without worrying about the underlying architecture. What Intel is primarily doing is empowering coders, data scientists and researchers to become more productive by taking away the code complexity. Intel Makes AI More Accessible For The Developer Community In more ways than one, software has become the last mile between the developers and the underlying hardware infrastructure, enabling them to utilise the optimization capabilities of processors. Analytics India Magazine spoke to Akanksha Bilani, Country Lead – India, Singapore, ANZ at Intel Software to understand why, in today’s world, transformation of software is key to driving effective business, usage models and market opportunity. “Gone are the days where adding more racks to existing platforms helped drive productivity. Moore’s law and AI advocates that the way to take advantage of hardware is by driving innovation on software that runs on top of it. Studies show that modernization, parallelisation and optimization of software on the hardware helps in doubling the performance of our hardware,” she emphasizes. Going forward, the convergence of architecture innovation and optimized software for platforms will be the only way to harness the potential of future paradigms of AI, High Performance Computing (HPC) and the Internet of Everything (IoE). Intel’s Naveen Rao, Corporate Vice President and General Manager, Artificial Intelligence Products Group at Intel Corporation, summed up the above statement at the recently concluded AI Hardware1 summit.  It’s not just a ‘fast chip’ - but a portfolio of products with a software roadmap that can enable the developer community to leverage the capabilities of the new AI hardware. “AI models are growing by 2x every 3 months. So it will take a village of technologies to meet the demands: 2x by software, 2x by architecture, 2x by silicon process and 4x by interconnect,” he stated.  Simplifying AI Workflows With Intel® Software Development Tools As the global technology major leads the way forward in data-driven transformation, we are seeing Intel® Software2 solutions open up a new set of possibilities across multiple sectors. In retail,  the Intel® Distribution of OpenVINO™ Toolkit is helping business leaders3 take advantage of near real-time insights to help make better decisions faster. Wipro4 has built groundbreaking edge AI solutions on server class Intel® Xeon® Scalable Processors and the Intel® Distribution of OpenVINO™ Toolkit. Today, data scientists who are building cutting-edge AI algorithms rely very heavily on Intel® Distribution for Python to get higher performance gains. While stock Python products bring a great deal performance to the table, the Intel performance libraries that come already plugged in with Intel® Distribution for Python help programmes gain more significant speed-ups as compared to the open source scikit-learn. Now, those working in distributed environments leverage BigDL, a DL library for Apache Spark. This distributed DL library helps data scientists accelerate DL inference on CPUs in their Spark environment. “BigDL is an add-on to the machine learning pipeline and delivers an incredible amount of performance gains,” Bilani elaborates.  Then there’s also Intel® Data Analytics Acceleration Library (Intel® DAAL), widely used by data scientists for its range of algorithms, ranging from the most basic descriptive statistics for datasets to more advanced data mining and machine learning algorithms. For every stage in the development pipeline, there are tools providing APIs and it can be used with other popular data platforms such as Hadoop, Matlab, Spark and R. There is also another audience that Intel caters to — the tuning experts who really understand their programs and want to get the maximum performance out of their architecture. For these users, the company offers its Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) — an open source, performance-enhancing library which has been abstracted to a great extent to allow developers to utilise DL frameworks featuring optimized performance on Intel hardware. This platform can accelerate DL frameworks on Intel architecture and developers can also learn more about this tool through tutorials.  The developer community is also excited about yet another ambitious undertaking from Intel, which will soon be out in beta and that truly takes away the complexity brought on by heterogeneous architectures. OneAPI, one of the most ground-breaking multi-year software projects from Intel, offers a single programming methodology across heterogeneous architectures. The end benefit to application developers is that they need no longer maintain separate code bases, multiple programming languages, and different tools and workflows which means that they can now get maximum performance out of their hardware. As Prakash Mallya, Vice President and Managing Director, Sales and Marketing Group, Intel India, explains, “The magic of OneAPI is that it takes away the complexity of the programme and developers can take advantage of the heterogeneity of architectures which implies they can use the architecture that best fits their usage model or use case. It is an ambitious multi-year project and we are committed to working through it every single day to ensure we simplify and not compromise our performance.” According to Bilani, the bottomline of leveraging OneAPI is that it provides an abstracted, unified programming language that actually delivers a one view/OneAPI across all the various architectures. OneAPI will be out in beta in October. How Intel Is Reimagining Computing As architectures get more diverse, Intel is doubling down on a broader roadmap for domain-specific architectures coupled with simplified software tools (libraries and frameworks) that enable abstraction and faster prototyping across its comprehensive AI solutions stack. The company is also scaling adoption of its hardware assets — CPUs, FPGAs, VPUs and the soon to be released Intel Nervana™ Neural Network Processor product line. As Mallya puts it, “Hardware is foundational to our company. We have been building architectures for the last 50 years and we are committed to doing that in the future but if there is one thing I would like to reinforce, it is that in an AI-driven world, as data-centric workloads become more diverse, there’s no single architecture that can fit in.”  That’s why Intel focuses on multiple architectures — whether it is scalar (CPU), vector (GPU), matrix (AI) or spatial (FPGA). The Intel team is working towards offering more synchrony between all the hardware layers and software. For example, Intel Xeon Scalable processors have undergone generational improvements and are now seeing a drift towards instructions which are very specific to AI.  Vector Neural Network Instruction (VNNI), built into the 2nd Generation Intel Xeon Scalable processors, delivers enhanced AI performance. Advanced Vector Extensions (AVX), on the other hand, are instructions that have already been a part of Intel Xeon technology for the last five years. While AVX allows engineers to get the performance they need on a Xeon processor, VNNI enables data scientists and machine learning engineers to maximize AI performance. Here’s where Intel is upping the game in terms of heterogeneity — from generic CPUs (2nd Gen Intel Xeon Scalable processors) running specific instructions for AI to actually having a complete product built for both training and inference. Earlier in August at the Hot Chips 2019, Intel announced the Intel Nervana Neural Network processors4, designed from the ground up to run full AI workloads that cannot run on GPUs which are more general purpose. The Bottomline: a) Deploy AI anywhere with unprecedented hardware choice  b) Software capabilities that sit on top of hardware  c) Enriching community support to get up to speed with the latest tools  Winning the AI Race For Intel, the winning factor has been staying closely aligned with its strategy of ‘no one size fits all’ approach and ensuring its evolving portfolio of solutions and products stays AI-relevant. The technology behemoth has been at the forefront of the AI revolution, helping enterprises and startups operationalize AI by reimagining computing and offering full-stack AI solutions, spanning software and hardware that add additional value to end customers. Intel has also heavily built up a complete ecosystem of partnerships and has made significant inroads into specific industry verticals and applications like telecom, healthcare and retail, helping the company drive long-term growth. As Mallya sums up, the way forward is through meaningful collaborations and making the vision of AI for India a reality using powerful best-in-class tools.  Sources 1AI Hardware Summit: https://twitter.com/karlfreund 2Intel Software Solutions: https://software.intel.com/en-us 3Accelerate Vision Anywhere With OpenVINO™ Toolkit: https://www.intel.in/content/www/in/en/internet-of-things/openvino-toolkit.html 4At Hot Chips, Intel Pushes ‘AI Everywhere’: https://newsroom.intel.com/news/hot-chips-2019/#gs.8w7pme Read the full article

[ncodetechnologiesinc-blog]

How DotNetNuke is feature-rich in providing extensibility through addons?

Most of the businesses look for a CMS platform that can provide add-ons to simplify the complexity of managing contents, provides accordant user-interface experience throughout all administrative controls. Its architecture allows users to create multiple websites beyond the basic web application framework pertaining to ASP.NET technology. Interestingly, ASP.NET WebForums handles each and every website’s list of pages which might include modules controlled by Web API layer providing specific functionalities to every end-user. For every business, DNN helps them to store data on cloud storage which is trusted, secure and safe.

Every feature offered by DNN is never constant but keeps improving to the next level. The skinning engine of DNN is so smooth and easy to handle. DNN specializes in providing role assignments, access to the dynamic content in the most flexible in quick steps. Again DNN is highly advantageous for administrative tools to completely integrate into the website when compared to other CMS platforms.

When we compare DNN with Wordpress, the prior is highly preferable or mid-size companies to enterprise-level companies.

What makes DotNetNuke is so special?

DotNetNuke development company india, being an open-source Web Application Framework is a perfect match for creating portals, intranets, extranets, and customized websites. Even if you have very little knowledge about the programming language, still you can manage DNN administrative tools that are easily accessible and also create pages like FAQs, discussion forums, feedback forms and man other similar pages. DNN provides important tools and features that are required to handle and manage a website. The best part is its capability to give complete control over the content, user management, security controls and layouts of the website.

How extensibility feature in DNN is productive for your website?

One of the most important aspects of DotNetNuke where it stands tall and steady among other CMS-based platforms is its extensibility. Whenever you are working on a project there is every possibility that you may need to integrate third-party modules with customized websites. Top DNN CMS Development Companies USA  is advantageous over providing built-in cloud computing support that makes it highly scalable.

DNN has the capability to implement pluggable modules adding new functionalities to the website. The skins within DNN can be very resourceful in changing the look and feel of a website with improved design, functionalities, and user-experience. Each and every interface provided for skins, modules, and providers are well-structured and documented that are supported by programmer’s manuals giving a handful of opportunities for third-party developers helping them to create customized components. To use third party modules within a website are provided by developers and open source communities in order to reduce the pain of using programming and you can easily install them after uploading through administrative pages.

DNN’s extensibility feature is a conceptualized implementation for websites keeping in mind future growth. Today, if you look more than 10,000+ low budget extensions are enough to help to create an engaging and appealing website from a client perspective.

[dotnetnuke19-blog]

Hire DotNetNuke Programmer. we provide a Hourly basis of hiring services for DotNetNuke (DNN) project for your business. Dotnetnuke Application Development is the leading web application development company in the USA.

[allianceinternational-blog]

Hire Asp Net Developer -Important Information that help for Develop the Website

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Hire Net Developers

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[our13belowconsulting-blog]

dotnetnuke development company | dnn services

Hire the best DotNetNuke programmers. We are a full-service project-based consulting company.  Our main areas are focused on Microsoft .net and DotNetNuke development. We can handle your project needs in the house or supply the IT consultants to match your needs onsite. We continue to serve, maintain, and establish new client relationships as we head into future business relationships.

[mdp-blog2019]

Computer Assisted Detection System (Existing Computer Vision and image recognition methods)

OpenCV (Computer Vision Library)

OpenCV (Open source computer vision) is a library of programming functions mainly aimed at real-time computer vision.The library is cross-platform and free for use under the open-source BSD license.This library is used by the next few detection system

Using OpenCV 4.0.0 in conjunction with Visual Studio 2017 ,we are able to put video feed of the FPV camera into a C++ programme that will run it through pre-trained image classifier (haar cascade files) that detects human body.This is supposed to assist in a search and rescue.

OpenCV's application areas include:

2D and 3D feature toolkits

Egomotion estimation

Facial recognition system

Gesture recognition

Human–computer interaction (HCI)

Mobile robotics

Motion understanding

Object identification

Segmentation and recognition

Stereopsis stereo vision: depth perception from 2 cameras

Structure from motion (SFM)

Motion tracking

Augmented reality

To support some of the above areas, OpenCV includes a statistical machine learning library that contains:

Boosting

Decision tree learning

Gradient boosting trees

Expectation-maximization algorithm

k-nearest neighbor algorithm

Naive Bayes classifier

Artificial neural networks

Random forest

Support vector machine (SVM)

Deep neural networks (DNN) 

TensorFlow (Machine Learning Library )

TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

TensorFlow is used with OpenCV to train the computer to learn and recognise a specific thing which the user desire

YOLO,You Only Look Once (Image recognition method)

Currently the fastest Open source image recognition system ,YOLO which is able to use graphic processing unit to process its image unlike the other current conventional methods where only CPU can be used.

Other current open sources detection systems repurpose classifiers or localizers to perform detection.They apply the model to an image at multiple locations and scales. High scoring regions of the image are considered detections.

YOLO use a totally different approach. it apply a single neural network to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities.

YOLO model has several advantages over classifier-based systems. It looks at the whole image at test time so its predictions are informed by global context in the image. It also makes predictions with a single network evaluation unlike systems like R-CNN which require thousands for a single image. This makes it extremely fast, more than 1000x faster than R-CNN and 100x faster than Fast R-CNN. See our paper for more details on the full system. Compiled with OpenCV and CUDA , it can make use of Nvdia GPU to achieve high speed image recognition and detection

Haar-Cascade (Image recognition method)

Haar-like features are digital image features used in object recognition. They owe their name to their intuitive similarity with Haar wavelets and were used in the first real-time face detector.

Viola and Jones adapted the idea of using Haar wavelets and developed the so-called Haar-like features.

A Haar-like feature considers adjacent rectangular regions at a specific location in a detection window, sums up the pixel intensities in each region and calculates the difference between these sums. This difference is then used to categorize subsections of an image. For example, let us say we have an image database with human faces. It is a common observation that among all faces the region of the eyes is darker than the region of the cheeks. Therefore a common Haar feature for face detection is a set of two adjacent rectangles that lie above the eye and the cheek region. The position of these rectangles is defined relative to a detection window that acts like a bounding box to the target object (the face in this case).

In the detection phase of the Viola–Jones object detection framework, a window of the target size is moved over the input image, and for each subsection of the image the Haar-like feature is calculated.

The key advantage of a Haar-like feature over most other features is its calculation speed. Due to the use of integral images, a Haar-like feature of any size can be calculated in constant time (approximately 60 microprocessor instructions for a 2-rectangle feature

Results

We decided to only use OpenCV and its built-in Haar Cascade method.

Compiling Opencv on c++ and using the video from the video receiver we are able do real time object detection with relatively low latency.

Thou it is the most early methods and least accurate ,it takes up the least amount of processing time and it is relatively easy to setup.Reason we didn’t use tensorflow to train a specific model is because that requires 1000+ picture of the dummy and we don’t have the picture of the dummy.The next step from here will probably be making use of the YOLO method with GPU to offer a off-board solution.

The Receiver used to connect the computer to fpv cam is a High Quality Eachine ROTG01 UVC OTG 5.8G 150CH.