#DNN Module Development

[edgythoughts]

How Does AI Generate Human-Like Voices? 2025

How Does AI Generate Human-Like Voices? 2025

Artificial Intelligence (AI) has made incredible advancements in speech synthesis. AI-generated voices now sound almost indistinguishable from real human speech. But how does this technology work? What makes AI-generated voices so natural, expressive, and lifelike? In this deep dive, we’ll explore: ✔ The core technologies behind AI voice generation. ✔ How AI learns to mimic human speech patterns. ✔ Applications and real-world use cases. ✔ The future of AI-generated voices in 2025 and beyond.

Understanding AI Voice Generation

At its core, AI-generated speech relies on deep learning models that analyze human speech and generate realistic voices. These models use vast amounts of data, phonetics, and linguistic patterns to synthesize speech that mimics the tone, emotion, and natural flow of a real human voice. 1. Text-to-Speech (TTS) Systems Traditional text-to-speech (TTS) systems used rule-based models. However, these sounded robotic and unnatural because they couldn't capture the rhythm, tone, and emotion of real human speech. Modern AI-powered TTS uses deep learning and neural networks to generate much more human-like voices. These advanced models process: ✔ Phonetics (how words sound). ✔ Prosody (intonation, rhythm, stress). ✔ Contextual awareness (understanding sentence structure). 💡 Example: AI can now pause, emphasize words, and mimic real human speech patterns instead of sounding monotone.

2. Deep Learning & Neural Networks AI speech synthesis is driven by deep neural networks (DNNs), which work like a human brain. These networks analyze thousands of real human voice recordings and learn: ✔ How humans naturally pronounce words. ✔ The pitch, tone, and emphasis of speech. ✔ How emotions impact voice (anger, happiness, sadness, etc.). Some of the most powerful deep learning models include: WaveNet (Google DeepMind) Developed by Google DeepMind, WaveNet uses a deep neural network that analyzes raw audio waveforms. It produces natural-sounding speech with realistic tones, inflections, and even breathing patterns. Tacotron & Tacotron 2 Tacotron models, developed by Google AI, focus on improving: ✔ Natural pronunciation of words. ✔ Pauses and speech flow to match human speech patterns. ✔ Voice modulation for realistic expression. 3. Voice Cloning & Deepfake Voices One of the biggest breakthroughs in AI voice synthesis is voice cloning. This technology allows AI to: ✔ Copy a person’s voice with just a few minutes of recorded audio. ✔ Generate speech in that person’s exact tone and style. ✔ Mimic emotions, pitch, and speech variations. 💡 Example: If an AI listens to 5 minutes of Elon Musk’s voice, it can generate full speeches in his exact tone and speech style. This is called deepfake voice technology. 🔴 Ethical Concern: This technology can be used for fraud and misinformation, like creating fake political speeches or scam calls that sound real.

How AI Learns to Speak Like Humans

AI voice synthesis follows three major steps: Step 1: Data Collection & Training AI systems collect millions of human speech recordings to learn: ✔ Pronunciation of words in different accents. ✔ Pitch, tone, and emotional expression. ✔ How people emphasize words naturally. 💡 Example: AI listens to how people say "I love this product!" and learns how different emotions change the way it sounds. Step 2: Neural Network Processing AI breaks down voice data into small sound units (phonemes) and reconstructs them into natural-sounding speech. It then: ✔ Creates realistic sentence structures. ✔ Adds human-like pauses, stresses, and tonal changes. ✔ Removes robotic or unnatural elements. Step 3: Speech Synthesis Output After processing, AI generates speech that sounds fluid, emotional, and human-like. Modern AI can now: ✔ Imitate accents and speech styles. ✔ Adjust pitch and tone in real time. ✔ Change emotional expressions (happy, sad, excited).

Real-World Applications of AI-Generated Voices

AI-generated voices are transforming multiple industries: 1. Voice Assistants (Alexa, Siri, Google Assistant) AI voice assistants now sound more natural, conversational, and human-like than ever before. They can: ✔ Understand context and respond naturally. ✔ Adjust tone based on conversation flow. ✔ Speak in different accents and languages. 2. Audiobooks & Voiceovers Instead of hiring voice actors, AI-generated voices can now: ✔ Narrate entire audiobooks in human-like voices. ✔ Adjust voice tone based on story emotion. ✔ Sound different for each character in a book. 💡 Example: AI-generated voices are now used for animated movies, YouTube videos, and podcasts. 3. Customer Service & Call Centers Companies use AI voices for automated customer support, reducing costs and improving efficiency. AI voice systems: ✔ Respond naturally to customer questions. ✔ Understand emotional tone in conversations. ✔ Adjust voice tone based on urgency. 💡 Example: Banks use AI voice bots for automated fraud detection calls. 4. AI-Generated Speech for Disabled Individuals AI voice synthesis is helping people who have lost their voice due to medical conditions. AI-generated speech allows them to: ✔ Type text and have AI speak for them. ✔ Use their own cloned voice for communication. ✔ Improve accessibility for those with speech impairments. 💡 Example: AI helped Stephen Hawking communicate using a computer-generated voice.

The Future of AI-Generated Voices in 2025 & Beyond

AI-generated speech is evolving fast. Here’s what’s next: 1. Fully Realistic Conversational AI By 2025, AI voices will sound completely human, making robots and AI assistants indistinguishable from real humans. 2. Real-Time AI Voice Translation AI will soon allow real-time speech translation in different languages while keeping the original speaker’s voice and tone. 💡 Example: A Japanese speaker’s voice can be translated into English, but still sound like their real voice. 3. AI Voice in the Metaverse & Virtual Worlds AI-generated voices will power realistic avatars in virtual worlds, enabling: ✔ AI-powered characters with human-like speech. ✔ AI-generated narrators in VR experiences. ✔ Fully voiced AI NPCs in video games.

Final Thoughts

AI-generated voices have reached an incredible level of realism. From voice assistants to deepfake voice cloning, AI is revolutionizing how we interact with technology. However, ethical concerns remain. With the ability to clone voices and create deepfake speech, AI-generated voices must be used responsibly. In the future, AI will likely replace human voice actors, power next-gen customer service, and enable lifelike AI assistants. But one thing is clear—AI-generated voices are becoming indistinguishable from real humans. Read Our Past Blog: What If We Could Live Inside a Black Hole? 2025For more information, check this resource.

How Does AI Generate Human-Like Voices? 2025 - Everything You Need to Know

Understanding ai in DepthRelated Posts- How Does AI Generate Human-Like Voices? 2025 - How Does AI Generate Human-Like Voices? 2025 - How Does AI Generate Human-Like Voices? 2025 - How Does AI Generate Human-Like Voices? 2025 Read the full article

[inestwebnoida]

 .NET Based CMS Platforms For Your Business

In today’s digital landscape, Content Management Systems (CMS) play a crucial role in helping businesses manage their online presence efficiently. For companies utilizing .NET, selecting the appropriate CMS is vital for seamless content creation, publishing, and management. Let’s explore the top 5 .NET-based CMS platforms and their key features:

Kentico:

Robust CMS platform with features tailored for businesses of all sizes.

User-friendly interface and extensive customization options.

Key features include content editing, multilingual support, e-commerce capabilities, and built-in marketing tools.

Sitecore:

Renowned for scalability and personalization capabilities.

Enables businesses to deliver personalized digital experiences across various touchpoints.

Advanced analytics and marketing automation tools drive customer engagement and conversion.

Umbraco:

Open-source CMS known for flexibility and simplicity.

Ideal for businesses seeking lightweight yet powerful content management.

User-friendly interface, extensive customization options, and seamless integration with Microsoft technologies.

Orchard Core:

Modular and extensible CMS built on ASP.NET Core framework.

Allows developers to create custom modules and extensions, adapting to diverse business needs.

Offers flexibility and scalability for building simple blogs to complex enterprise applications.

DNN (formerly DotNetNuke):

Feature-rich CMS trusted by thousands of businesses worldwide.

Drag-and-drop page builder, customizable themes, and robust security features.

Offers modules and extensions for creating powerful websites, intranets, and online communities

In conclusion, selecting the right .NET-based CMS platform is crucial for establishing a strong online presence and engaging effectively with the audience. Each platform offers unique features and benefits to suit various business needs. By evaluating factors like flexibility, scalability, personalization, and community support, businesses can choose the ideal CMS platform to drive digital success.

[dynnamite11]

Our experience as a DNN developer has given the DyNNamite team unique insight and capabilities in DNN custom module development.

[laitkor-info-solutions-blog]

[our13belowconsulting-blog]

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.

[mindfiresolutions-blog]

ONLINE WEIGHT MANAGEMENT SYSTEM

Executive Summary

Online weight management system assists its customer to lose obesity and help them stay fit. The system can well track daily activities of subscribed users like: their diet plans, exercise frequency, online health training history, appointments etc. This system is defined with an automated SMS gateway, so that the one month trial user receives an e-mail in regular interval of time to encourage them to convert as paid subscription member. PayPal system helps trial users to convert into a subscribed user. The SMS system also sends out automated notice and alert to all level of users in the system if they miss any training session, appointment slot or diet plan entry.

Mindfire was approached to develop different modules that would serve users and meet client’s objective of developing such a system. The modules developed were: Appointment module, Doctor and health history module, Recipe module, SMS and Email for different role based member for this CMS. Also, a number of 3rd party DNN modules have been integrated which includes Dataspring’s Dynamic Form module, Ultra Video Module, SunBlog, WhosOn Live etc to bring greater functionality and feasibility to this DotNetNuke health care system.

About our Client

Client Description: Online Weight Management System

Client Location: Australia

Industry: Healthcare

Business Situation

Our client was aiming to create an online health management system, which can integrate Cognitive Behavior Therapy, Nutrition and Activity all delivered online. Webcam counseling sessions and webcam online Activity Sessions was also needed. To merge all the requirement and needs in a one place was a bit tedious job. The client had invested a lot by purchasing 3rd party module and wanted us to find out a way to use it in his system for better result. It was a very tough situation for the client because he could not enter into the market having spent so much of money.

With these many number of objectives and critical business logic implementation in mind, the client approached Mindfire Solutions DotnetNuke team to find and propose a feasible solution. Mindfire’s DNN experts took no time to start discussing about the specifications sent by the client and finally proposed a solution.

Technologies

ASP.NET 4.0 + ADO.Net, SQL Server 2008, HTML,CSS , XSLT, Jquery

Download Full Case Study

[techoriz22222]

Best PHP Training in Calicut

Techoriz Digital Academy provides best PHP Training in Calicut, India. We have a team of expert experts who would guide you for best IT Training. The courses are meant for beginners and are delivered by optimized DNN Certified trainers in Calicut. The curriculum includes different modules like PHP basics, Database Handling and more.https://www.techorizdigitalacademy.com/php-full-stack-developer-training

[astromains]

Big pipe timeslice

#Big pipe timeslice manual

It alerts and mitigates even before the defended service fails.The "engine" is self-adjusting and adaptive to changes. There is no need for user intervention to configure DoS thresholds or to maintain them.Characterizing the offending traffic and automatically mitigating on the offending traffic.Automatic detecting of (D)DoS attacks using behavioral data.Some of the advantages of behavioral mitigation are: We improved the presentation of tightening suggestions to enable violations and sub-violations.ĭDoS mitigation based on behavior analysisį5 developed a new innovative technology, that mitigates DDoS attacks, not just by leveraging the rules and signatures on ASM, but also by capturing the attacks from behavior analysis using machine learning and big data analytics.Previously, the "Accept Suggestion" operation worked only in automatic mode, and it accepted all suggestions.

#Big pipe timeslice manual

You can perform the “Accept Suggestion" operation also in manual mode and accept only violation-triggered suggestions.You no longer configure different values for different sources. The Tighten Policy criteria is configured for both trusted and untrusted sources.We changed the default of the minimum number of requests needed to tighten (stabilize) the security policy.The Tighten Policy criteria is now based on the number of requests, time, and learning suggestions with a specific learning score instead of the number of requests, time, sessions, and IP addresses.We modified the enforcement learning logic to speed up the learning and reduce memory consumption by making the following changes in the Policy Building Process area of the Learning and Blocking Settings screen:.The previous method led to a delay in signature enforcement. In Automatic Learning Mode, the Policy Builder enforces attack signatures individually, rather than all signatures at once.We performed the following improvements to the Policy Builder: The following list applies for all memory levels: Most of the support guidelines relate to memory. This section provides general guidelines for module support. These platforms support various licensable combinations of product modules. Second, we introduce a cost-balanced recomputing algorithm to reduce memory usage in the pipeline mode. First, we propose a model partition algorithm that accelerates pipeline-hybrid parallelism training between heterogeneously network-connected GPUs. To contend with this, we introduce two execution optimization methods based on pipeline-hybrid parallelism (using both data and model parallelism) in a GPU cluster with heterogeneous networking. On the other hand, the high memory requirements to train a DNN model make running the training processes on GPUs onerous. Workload schedulers then end up having to consider hardware topology and requirements for workload communication, in hopes of allocating GPU resources to optimize execution time and improve usage in a heterogeneous environment. This approach has its detriments, though on one hand, a GPU's expanded capacity to compute also produces bigger bottlenecks in inter-GPU's communications during model training, and multi-GPU systems lead to complex connectivity. Often researchers deploy an approach that uses distributed parallel training to acquire larger models faster on GPUs. Exorbitant resources (computing and memory) are required to train a deep neural network (DNN).

[researchtrendingmarket-blog]

Deep Learning Neural Networks Market Demand To Massive Growth with Leading Players | NEURALWARE, NVIDIA CORPORATION, SKYMIND INC.

Deep Learning Neural Networks Market rapidly increasing digitization is boosting global deep learning neural networks market. The digital transformation helps to adapt the advanced technology which provides the ease to collect the data, while the data is important and essential part of the artificial intelligence. The data helps Europe deep learning neural networks (DNNs) market to recognize the pattern and do the prediction.

Europe deep learning neural networks (DNNs) market is projected to register a healthy CAGR in the forecast period of 2019 to 2026.

 Get Sample Report at :

https://www.databridgemarketresearch.com/request-a-sample/?dbmr=europe-deep-learning-neural-networks-dnns-market

Competitive Analysis: Europe Deep Learning Neural Networks Market

Few of the major competitors currently working in Europe Deep Learning Neural Networks Market are ALYUDA RESEARCH, LLC, ALPHABET INC.(google), IBM, Micron Technologies, Inc., Neural Technologies Limited, NEURODIMENSION, INC., NEURALWARE, NVIDIA CORPORATION, SKYMIND INC, SAMSUNG, Qualcomm Technologies, Inc., Intel Corporation, Amazon Web Services, Inc., Microsoft, GMDH LLC., Sensory Inc., Ward Systems Group, Inc., Xilinx Inc., Starmind and  among others.

 Key Pointers Covered in the Europe Deep Learning Neural Networks Market Trends and Forecast to 2026

Europe   Deep Learning Neural Networks Market New Sales Volumes

Europe   Deep Learning Neural Networks  Market Replacement Sales Volumes

Europe   Deep Learning Neural Networks Market Installed Base

Europe   Deep Learning Neural Networks Market By Brands

Europe   Deep Learning Neural Networks Market Size

Europe   Deep Learning Neural Networks  Market Procedure Volumes

Europe   Deep Learning Neural Networks Market Product Price Analysis

Europe   Deep Learning Neural Networks Market Healthcare Outcomes

Europe   Deep Learning Neural Networks Market Cost of Care Analysis

Europe   Deep Learning Neural Networks Market Regulatory Framework and Changes

Europe   Deep Learning Neural Networks Market Prices and Reimbursement Analysis

Europe   Deep Learning Neural Networks Market Shares in Different Regions

Recent Developments for Europe   Deep Learning Neural Networks Market Competitors

Europe   Deep Learning Neural Networks Market Upcoming Applications

Europe   Deep Learning Neural Networks Market Innovators Study

Get Detailed TOC:

https://www.databridgemarketresearch.com/toc/?dbmr=europe-deep-learning-neural-networks-dnns-market

Key Developments in the Market:

In May 2019, Google has announced that its own chip which will accelerate Deep Learning training and inference, known as the TensorFlow Processing Unit (TPU), this will doubles down on NVIDIA GPUs for inference. To predict properties of new input data AI inference processing is used which is a trained neural network. This helps Google customers to express a preference for a cost-effective GPU for training and inference.

In February 2019, IBM launched drive next-generation AI hardware to develop nanotechnology, nucleus of a new ecosystem of research and commercial partners will be the IBM Research AI Hardware Centre. The collaboration with partners will help in accelerate the development of AI-optimized hardware innovations.

 Segmentation: Europe Deep Learning Neural Networks (DNNs) Market

Europe deep learning neural networks (DNNs) market is segmented into three notable segments which are component, application and end-user.

On the basis of component, Europe deep learning neural networks (DNNs) market is segmented into hardware, software and services

On the basis of application, Europe deep learning neural networks (DDNs) market is segmented into image recognition, speech recognition, natural language processing, data mining

On the basis of end-user, Europe deep learning neural networks (DNNs) market is segmented into banking, financial services & insurance (BFSI), it & telecommunication, healthcare, retail, automotive, manufacturing, aerospace & defence, security and others 

 Inquire Before Buying:

https://www.databridgemarketresearch.com/inquire-before-buying/?dbmr=europe-deep-learning-neural-networks-dnns-market

Research Methodology: Europe Industrial Services Market

Data collection and base year analysis is done using data collection modules with large sample sizes. The market data is analyzed and forecasted using market statistical and coherent models. Also market share analysis and key trend analysis are the major success factors in the market report. To know more please request an analyst call or can drop down your enquiry.

The key research methodology used by DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation.

Apart from this, other data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Top to Bottom Analysis and Vendor Share Analysis. To know more about the research methodology, drop in an inquiry to speak to our industry experts.

Key insights in the report:

Complete and distinct analysis of the market drivers and restraints

Key Market players involved in this industry

Detailed analysis of the Market Segmentation

Competitive analysis of the key players involved

About Us:

Data Bridge Market Research set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market.

Contact:

Data Bridge Market Research

Tel: +1-888-387-2818

Email: Sopan.gedam@databridgemarketresearch.com

Browse Related Report Here:

LED Matrix Boards Outdoor LED Display Market

Cloud ERP Market

[emeraldcharos]

How does ECommerce Web Design Benefits Socio-Economically?

Competition, Competition, Competition! It is intimidating auto dealers worldwide. The automotive market has really gone intensely competitive. If you are also an automobile dealer, additionally you should be seeking new techniques to enhance the connection between your organization investment and efforts. The right kind of auto dealer website can provide you with strength becoming a step in advance of the competition. It's really needless to cover the value of online presence in the current hi-tech world where all the details and facts are obtained from the internet. However, when lots of people have understood the value of online presence and are having their websites for business promotion, you'll be able to differ from the bunch by developing a matchless website.

Earlier, big businesses had the impeachment of sole dictatorship making use of their explosive arsenal of capital and hours contrary to the inadequate and limited capabilities with the small-scale enterprises. In addition to this, the tiny businesses weren't availed with any mainstream market and, when the fortune conferred, they are able to only avail the success in local areas which, obviously, didn't comprised the opportunity of earning large profits as well as the prospect of augmentation. Today, online shopping trend has levelled this pitch of difference between the small and large-scale businesses. With the power of internet along with the evolution from the social networking, the caged bird are now able to soar in the open sky of global market. Fixed up which has a cautiously fabricated e-commerce web design, its marketplace eradicates all of the hindrances and, the chance to flourish and earn huge profits becomes able to crossing each of the boundaries similar to the key players who once ruled within the consumers.

Building my first site would be a headache. I had to download multiple programs, discover ways to make use of them first, then learn to make a fully optimized page. At  I learned a lot about building websites, the way to optimize them, and how you can lay things out so that my visitors will make purchases, but developing a site is no easy task.

Firstly, Turn Key websites comprise a sizable diverse market part offering numerous ?themed? sites from corporate and business to private ones. All of them are ready-to-go solutions that is a good base both for companies and non-business individuals to launch on the web. Good design characteristics as well as a group of DNN modules will help to get this to ?freshman? of your site presentable and trustworthy. So if you?re trying to find a ?themed? predesigned site, you?re in for a treat. They?re around for you.

The years that the company has spent in developing websites might have seasoned the company rendering it able to handle any sort of requirement. The essential skills and knowledge needed to convert ideas in a website could only come through experience. Therefore when search for web page design Company bhopal https://seocybernetics.com/website-design-company/bhopal/, it's best to approach a firm that has been doing the work for many years.

[our13belowconsulting-blog]

13 Below Consulting is an IT consulting and IT staffing company focused on .NET development and DotNetNuke development services.

[researchtrendingnews]

Deep Learning Neural Networks Market Shows Strong Growth with Leading Players | NEURALWARE, NVIDIA CORPORATION, SKYMIND INC, SAMSUNG.

Deep Learning Neural Networks Market rapidly increasing digitization is boosting global deep learning neural networks market. The digital transformation helps to adapt the advanced technology which provides the ease to collect the data, while the data is important and essential part of the artificial intelligence. The data helps Europe deep learning neural networks (DNNs) market to recognize the pattern and do the prediction.

Europe deep learning neural networks (DNNs) market is projected to register a healthy CAGR in the forecast period of 2019 to 2026.

 Get Sample Report at :

https://www.databridgemarketresearch.com/request-a-sample/?dbmr=europe-deep-learning-neural-networks-dnns-market

Competitive Analysis: Europe Deep Learning Neural Networks Market

Few of the major competitors currently working in Europe Deep Learning Neural Networks Market are ALYUDA RESEARCH, LLC, ALPHABET INC.(google), IBM, Micron Technologies, Inc., Neural Technologies Limited, NEURODIMENSION, INC., NEURALWARE, NVIDIA CORPORATION, SKYMIND INC, SAMSUNG, Qualcomm Technologies, Inc., Intel Corporation, Amazon Web Services, Inc., Microsoft, GMDH LLC., Sensory Inc., Ward Systems Group, Inc., Xilinx Inc., Starmind and  among others.

 Key Pointers Covered in the Europe Deep Learning Neural Networks Market Trends and Forecast to 2026

Europe   Deep Learning Neural Networks Market New Sales Volumes

Europe   Deep Learning Neural Networks  Market Replacement Sales Volumes

Europe   Deep Learning Neural Networks Market Installed Base

Europe   Deep Learning Neural Networks Market By Brands

Europe   Deep Learning Neural Networks Market Size

Europe   Deep Learning Neural Networks  Market Procedure Volumes

Europe   Deep Learning Neural Networks Market Product Price Analysis

Europe   Deep Learning Neural Networks Market Healthcare Outcomes

Europe   Deep Learning Neural Networks Market Cost of Care Analysis

Europe   Deep Learning Neural Networks Market Regulatory Framework and Changes

Europe   Deep Learning Neural Networks Market Prices and Reimbursement Analysis

Europe   Deep Learning Neural Networks Market Shares in Different Regions

Recent Developments for Europe   Deep Learning Neural Networks Market Competitors

Europe   Deep Learning Neural Networks Market Upcoming Applications

Europe   Deep Learning Neural Networks Market Innovators Study

Get Detailed TOC:

https://www.databridgemarketresearch.com/toc/?dbmr=europe-deep-learning-neural-networks-dnns-market

Key Developments in the Market:

In May 2019, Google has announced that its own chip which will accelerate Deep Learning training and inference, known as the TensorFlow Processing Unit (TPU), this will doubles down on NVIDIA GPUs for inference. To predict properties of new input data AI inference processing is used which is a trained neural network. This helps Google customers to express a preference for a cost-effective GPU for training and inference.

In February 2019, IBM launched drive next-generation AI hardware to develop nanotechnology, nucleus of a new ecosystem of research and commercial partners will be the IBM Research AI Hardware Centre. The collaboration with partners will help in accelerate the development of AI-optimized hardware innovations.

 Segmentation: Europe Deep Learning Neural Networks (DNNs) Market

Europe deep learning neural networks (DNNs) market is segmented into three notable segments which are component, application and end-user.

On the basis of component, Europe deep learning neural networks (DNNs) market is segmented into hardware, software and services

On the basis of application, Europe deep learning neural networks (DDNs) market is segmented into image recognition, speech recognition, natural language processing, data mining

On the basis of end-user, Europe deep learning neural networks (DNNs) market is segmented into banking, financial services & insurance (BFSI), it & telecommunication, healthcare, retail, automotive, manufacturing, aerospace & defence, security and others 

 Inquire Before Buying:

https://www.databridgemarketresearch.com/inquire-before-buying/?dbmr=europe-deep-learning-neural-networks-dnns-market

Research Methodology: Europe Industrial Services Market

Data collection and base year analysis is done using data collection modules with large sample sizes. The market data is analyzed and forecasted using market statistical and coherent models. Also market share analysis and key trend analysis are the major success factors in the market report. To know more please request an analyst call or can drop down your enquiry.

The key research methodology used by DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation.

Apart from this, other data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Top to Bottom Analysis and Vendor Share Analysis. To know more about the research methodology, drop in an inquiry to speak to our industry experts.

Key insights in the report:

Complete and distinct analysis of the market drivers and restraints

Key Market players involved in this industry

Detailed analysis of the Market Segmentation

Competitive analysis of the key players involved

About Us:

Data Bridge Market Research set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market.

Contact:

Data Bridge Market Research

Tel: +1-888-387-2818

Email: Sopan.gedam@databridgemarketresearch.com

Browse Related Report Here:

LED Matrix Boards Outdoor LED Display Market

Cloud ERP Market

[ndoos-blag]

Building libfreenect2 on Fedora 32

For an upcoming facial recognition project, I wanted to use OpenCV's (relatively) new DNN module together with an Xbox One Kinect Sensor, requiring libfreenect2 and pylibfreenect2 as prerequisites.

As I'm prototyping this on a laptop with an Nvidia GPU, and plan to eventually run this on an Nvidia Jetson Nano, I will be building libfreenect2 with CUDA support.

However, most Nvidia/CUDA-related development appears to take place on Ubuntu systems, if the READMEs of these projects are any indication. As somebody who uses Fedora on his laptops, that presented me with several hurdles to getting my Kinect sensor working. Hopefully documenting these here helps someone.

General Build Preparation

Right off the bat, you will want to have Fedora's build binaries and headers installed; sudo dnf install @development-tools will take care of that.

I already had this installed on my laptop, but I'm just leaving this here for posterity.

libfreenect2 Dependencies

To build libfreenect2, I had to satisfy some build dependencies to get the following features working:

CUDA

OpenGL

TurboJPEG

CUDA

This one is pretty tricky to get right without some help; CUDA 10 does not support GCC newer than GCC 8, and Fedora does not provide an easy way to have multiple GCC versions installed through dnf and update-alternatives, which means dealing with RPM files and dependency hell.

Fortunately, negativo17 provides pre-built binaries for CUDA development (and various other Nvidia-related binaries), that takes care of all that.

First, I added the repository to DNF:

sudo dnf config-manager --add-repo=https://negativo17.org/repos/fedora-nvidia.repo

Then, I installed the prerequisite CUDA development packages:

sudo dnf install cuda-devel cuda-gcc cuda-gcc-c++

Unfortunately, Fedora does not package OpenNI2 headers, but as I probably won't need it for my use case, I built without OpenNI2 support.

Other Prerequisites, OpenGL & TurboJPEG

These are pretty straightforward to satisfy with Fedora packages:

sudo dnf install libusb-devel glfw-devel turbojpeg-devel

Building and Installing libfreenect2

Now, I am ready to download libfreenect2's source and start building.

git clone https://github.com/OpenKinect/libfreenect2.git && cd libfreenect2 mkdir build && cd build CUDA_INC_PATH=/usr/include/cuda cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local -DCMAKE_C_COMPILER=/usr/bin/cuda-gcc

Do note the following variables that I used, which you may wish to tune depending on your use case:

CUDA_INC_PATH environment variable points to where negativo17's cuda-devel package installs its headers

CMAKE_INSTALL_PREFIX CMake cache variable determines where make install installes files to, I chose this because pylibfreenect2's install script looks for libfreenect2 here

CMAKE_C_COMPILER CMake cache variable points to where negativo17's cuda-gcc/cuda-gcc-c++ package installs its binaries

If everything went well, you should see CMake end with:

-- Build files have been written to: …/libfreenect2/build

Now, we're ready to build and install, like so:

make make install

If you used a system folder as your CMAKE_INSTALL_PREFIX like I did, you have to run make install as root or with sudo.

Environment Variables

To get the demo working, I had to add the installation path's lib folder to my LD_LIBRARY_PATH:

echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib" >> ~/.bashrc . ~/.bashrc

USB Device Permissions

As Fedora doesn't seem to use a group to control USB device access, I had to use Udev to set appropriate permissions on the Kinect device:

echo 'SUBSYSTEM=="usb", ATTRS{idVendor}=="045e", ATTRS{idProduct}=="02c4", MODE="0666"' | sudo tee /etc/udev/rules.d/99-kinect.rules sudo udevadm control --reload-rules && sudo udevadm trigger

Trying It Out

Now, all that's left to do is to make sure everything works! Assuming you are in the build folder:

./bin/Protonect

You should see a 4-up video feed of the RGB, IR, depth and merged streams.

Hopefully this worked for you like it did for me!