At the end of 2021, the artificial intelligence market was estimated to be a value of $58.3 billion. This figure is bound to increase and is estimated to grow tenfold over the next 5 years and reach $309.6 billion by 2026. Given such popularity of AI technology, companies extensively want to build and deploy solutions with AI applications for their businesses. In today’s technology-driven world AI has become an integral part of our life. As per a report by McKinsey, AI adoption is continuing its steady rise: 56% of all respondents report AI adoption in at least one business function, up from 50% in 2020. This increase in adoption is due to evolving strategies for building and deploying AI applications. Various strategies are evolving to build and deploy AI models. Container applications are one such strategy.
MLOps are becoming increasingly stable. If you are unfamiliar with Machine Learning operations, then it is a collection of principles, practices, and technologies that help to increase the efficiency of machine learning workflows. It is based on DevOps, and just as DevOps has streamlined the SDLC from development to deployment, MLOps accomplish the same for machine learning applications. Containerization is one of the most intriguing and emerging technologies for developing and delivering AI applications. A container is a standard unit of software packaging that encapsulates code and all its dependencies in a single package, allowing programs to move from one computing environment to another rapidly and reliably. Docker is at the forefront of application containerization.
What Are Containers?
Containers are logical boxes that contain everything an application requires to execute. The operating system, application code, runtime, system tools, system libraries, binaries, and other components are all included in this software bundle. Optionally, some dependencies might be included or excluded based on the availability of specific hardware. These containers run directly within the host machine kernels. The container will share the host machine’s resources (like CPU, disks, memory, etc.) and eliminate the extra load of a hypervisor. This is the reason why containers are “lightweight “.
Why Are Containers So Popular?
First, they are lightweight since the container shares the machine operating system kernels. It doesn’t need an entire operating system in place to run the application. VirtualBox, popularly known as VM’s, require installation of complete OS making them quite bulky.
Containers are portable and can easily be transported from one machine to another machine with all the required dependencies within it. They enable developers and operators to improve CPU and memory utilization of physical machines.
Among container technology, Docker is the most popular and widely used platform. Not only the Linux-powered Red Hat and Canonical have embraced Docker, but also companies like Microsoft, Amazon, and Oracle are relying on it. Today, almost all IT and cloud companies have adopted docker, and are widely used to provide their solution with all the dependencies.
Virtual Machines vs Containers
Is There Any Difference between Docker and Containers?
Docker has widely become a synonym for containers because it is open-source, has a huge community base, and is a quite stable platform. But container technology isn’t new, it has been incorporated into Linux in the form of LXC for more than 10 years, and similar operating-system-level virtualization has also been offered by FreeBSD jails, AIX Workload Partitions, and Solaris Containers.
Dockers can make the process easier by merging OS and package needs into a single package, which is one of the differences between containers and dockers.
We’re often perplexed as to why docker is employed in the field of data science and artificial intelligence, yet it’s mostly used in DevOps. ML and AI, like DevOps, have inter-OS dependencies. As a result, a single code can run on Ubuntu, Windows, AWS, Azure, Google Cloud, ROS, a variety of edge devices, or anywhere else.
Container Application for AI / ML:
Like any software development, AI applications also face SDLC challenges when assembled and run by various developers in a team or in collaboration with multiple teams. Due to the constant iterative and experimental nature of AI applications, there comes a point where the dependencies might wind up crisscrossing, causing inconveniences for other dependent libraries in the same project.
To Explain:
The need for Container Application for AI / ML
The issues are true, and as a result, there is a requirement for acceptable documentation of each step to follow if you’re presenting a project that requires a specific method of execution. Imagine you have multiple python virtual environments for different models of the same projects, and without updated documentation, you may wonder what are these dependencies for? Why do I get conflicts while installing newer libraries or updated models etc.?
Developers constantly face this dilemma “It works on my machine” and constantly try resolving it.
Why it’s working on my machine
Using Docker, all of this can be made easier and faster. Containerization can help you save a lot of time updating documents and make the development and deployment of your program go more smoothly in the long term. Even by pulling multiple images which will be platform-agnostic, we can serve multiple AI models using docker containers.
The application written fully on the Linux platform can be run on the Windows platform using docker, which can be installed on a Windows workstation, making code deployment across platforms much easier.
Deployment of code using docker container
Benefits of Converting entire AI application development to deployment pipeline into a container:
Separate containers for each AI model for different versions of frameworks, OS, and edge devices/ platforms.
Having a container for each AI model for customization of deployments. Ex: One container is developer-friendly while another is user-friendly and requires no coding to use.
Individual containers for each AI model for different releases or environments in the AI project (development team, QA team, UAT (User Acceptance Testing), etc.)
Container applications truly accelerate the AI application development-deployment pipeline more efficiently and help maintain and manage multiple models for multiple purposes.
Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.
Contact us at business@softnautics.com for any queries related to your solution or for consultancy.
[elementor-template id=”12005″]
With new-age technologies, customers now have higher expectations from their vehicles than ever before. Many are more concerned with in-car interfaces than with aesthetics or engine power. The majority of drivers desire a vehicle that makes their lives easier and supports their favourite smartphone apps. HMI (Human-Machine Interface) solutions for automobiles are features and components of car hardware and software that enable drivers and passengers to interact with the vehicle and the outside environment. Automotive HMI solutions improve driving experiences by allowing interaction with multi-touch dashboards, voice-enabled vehicle infotainment, control panels, built-in screens, and other features. They turn a vehicle into an ecosystem of interconnected parts that work together to make driving more personalized, adaptive, convenient, safe, and enjoyable. FuSa (ISO 26262) complied HMIs, which are powered by embedded sensors and smart systems, enabling the vehicle to respond to the driver’s intent and preferences. The global automotive HMI market size is projected to reach $33.59 billion by 2025 with a 9.90% growth rate as per the reports stated by the allied market research group.
Let us see a few applications of HMI in the automotive industry and how it enhances the driver/passenger experience.
Application & Benefits of HMI Solutions
Digital Instrumental Clusters
An instrument cluster is seen in every vehicle. An instrument cluster is a board that houses a variety of gauges and indicators. The instrument cluster is located right behind the steering wheel in the dashboard. To keep track of the vehicle’s status, the driver relies on the gauges and indicators. The modern car cockpit’s full electronics features are accessible through the digital instrument cluster. With the help of digital clusters vehicle driving information, such as speed, gasoline or charge level, trip distance calculator, route planning graphics, and so on, are combined with comfort information, such as outside temperature, clock, and air vent control. In addition, these digital clusters connect with the vehicle’s entertainment system to control multimedia, browse a phone book, make a call, and choose a navigation destination location. For instance, with use of a tachometer, indicates how fast the engine is turning.
Heads Up Display (HUD)
The Heads Up Display (HUD) is a transparent display fitted on the dashboard of a car that displays important information and data without diverting the driver’s attention away from their normal viewing position. Whether it’s speed or navigation, you have it all in one place. It gives critical information to drivers so that they are not distracted. Driver tiredness is reduced greatly since they are not forced to search for information within the vehicle, allowing them to concentrate more on the road.
Automotive HMI Solutions
Rear-Seats Entertainment (RSE)
Rear-Seats Entertainment (RSE) is a fast-growing car entertainment system that heavily relies on graphics, video, and audio processing. TV, DVD, Internet, digital radio, and other multimedia content sources are all integrated into RSE systems. One can keep the whole family engaged while traveling with the Rear-Seat Entertainment System. As the system is installed with wireless internet connectivity, they can surf the web, manage their playlist, interact with their social media platforms, and can get access to many more services.
Voice-Operated Systems
Modern voice-activated systems enable very natural communication with a vehicle. They can even understand accents and request additional information if necessary. This is made possible by the incorporation of Artificial Intelligence and Machine Learning, as well as general advances in Natural Language Processing and cognitive computing. Apple CarPlay apps, for example, will allow users to navigate, send and receive messages, make phone calls, play music, and listen to podcasts or audiobooks. All of this is controlled by voice command, ensuring a safer atmosphere and allowing the driver to concentrate on the road.
Haptic Technology
It’s also known as 3D touch, and it’s a technology that gives the user a tactile sensation by applying forces, vibrations, or motions. Haptics can be used, when consumers need to touch a screen or operate some functionalities. In its most basic form, a Haptic system will consist of a sensor – such as a touchpad key – that sends the input stimulus signal to a microprocessor. The microprocessor generates a suitable output, which is amplified and transmitted to the actuator. The actuator then produces the vibration that the system requires. Automobiles are also becoming increasingly adept at recognizing their surroundings and reacting properly by issuing safety warnings and alarms. Information can easily be communicated to the driver by vibration alerting, rather than unpleasant lights or noises. For instance, when a lane change is detected without warning, the steering wheel can produce vibrations to alert the driver. The seats can also vibrate to alert the driver if they move lanes too slowly. As in the case of General Motors in 2015 under the Chevrolet brand, they introduced the Safety Alert Seat. The car can share collision risk and lane departure with the driver via haptic input in the seat. It was one of the first automobiles that employ the touch sensation to communicate with the driver.
In-Car Connected Payments
The concept of connected commerce is gaining popularity and creating opportunities for brands and OEMs. In this case, users will receive an e-wallet with biometric identification verification that will allow them to pay for nearly anything on the go, including tolls, coffee, and other billers and creditors. While in-car payments may not appear to be a huge advantage at present, the future of such HMI services may include more than just parking and takeaway.
This cannot be restricted to only advertisements, age and gender detection can also help businesses in taking quick decisions by managing appropriate support staff in retail stores, what age and gender people prefer visiting your store, businesses, etc. All this is more powerful and effective if you are very quick to determine and act. So, even more, a reason to have this solution on Edge TPU.
Driver Monitoring System
A driver-monitoring system is a sophisticated safety system that uses a camera positioned on the dashboard to detect driver tiredness or distraction and deliver a warning or alert to refocus the driver’s attention on the road. If the system detects that the driver is distracted or drowsy, it may issue auditory alarms, and illuminate a visual signal on the dashboard to grab the driver’s attention. If the driver’s internal sensors indicate that he or she is distracted, and the vehicle’s external sensors indicate that a collision is imminent, the system can automatically apply the brakes, integrating inputs from both the interior and outside sensors.
The interface between the vehicle and the human has transformed as we move towards smart, interconnected, and autonomous mobility. Today’s HMI solutions not only improve in-vehicle comfort and convenience but also provide personalized experiences. These smart HMI solutions convey critical information which is important and needs attention from the driver. This reduces driver distraction and improves vehicle safety. HMI makes information processing and monitoring simple, intuitive, and dependable.
At Softnautics, we help automotive businesses to design HMI & Infotainment-based solutions such as gesture recognition, voice recognition, touch recognition, infotainment sub-menu navigation & selection, etc. involving FPGAs, CPUs, and Microcontrollers. Our team of experts has experience working with autonomous driving platforms, functions, middleware, and compliances like adaptive AUTOSAR, FuSa (ISO 26262), and MISRA C. We support our clients in the entire journey of intelligent automotive solution design.
Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.
Contact us at business@softnautics.com for any queries related to your solution or for consultancy.
Machine Vision has exploded in popularity in recent years, particularly in the manufacturing industry. Companies can profit from the technology’s enhanced flexibility, decreased product faults, and improved overall production quality. The ability of a machine to acquire images, evaluate them, interpret (the situation), and then respond appropriately is known as Machine Vision. Smart cameras, image processing, and software are all part of the system. Vision technology can assist the manufacturing industry on many levels, thanks to significant advancements in imaging techniques, smart sensors, embedded vision, machine and supervised learning, robot interfaces, information transmission protocols, and image processing capabilities. By decreasing human error and ensuring quality checks on all goods traveling through the line, vision systems improve product quality. The Industrial Machine Vision market is valued at $53.38 billion by the end of 2028 and is expected to grow at a rate of 9.90% as per the reports stated by the Data Bridge Research group. Furthermore, an increase in the demand for inspection in the manufacturing units/factories with higher product quality measures, are likely to drive up demand for industrial Machine Vision under AI technologies and propel the market forward.
Applications of Machine Vision in Manufacturing
Predictive Maintenance Manufacturing enterprises need to use a variety of large machinery to produce vast quantities of goods. To avoid equipment downtime, certain pieces of equipment must be monitored regularly. Examining each piece of equipment in a manufacturing facility by hand is not only time-consuming but also costly and gaffe. The idea was to only fix the equipment when it failed or became problematic. However, utilizing this technique to restore the equipment can have significant consequences for worker productivity, manufacturing quality, and cost. What if, on the other hand, manufacturing organizations could predict the state of their machinery’s operation and take proactive steps to prevent a breakdown from occurring? Let’s examine the situation where some production processes take place at high temperatures and in harsh environments, material deterioration and corrosion are prevalent. As a result, the equipment deforms. If not addressed promptly, this can lead to significant losses and the halting of the manufacturing process. Machine vision systems can monitor the equipment in real-time and predict maintenance based on multiple wireless sensors that provide data of a variety of parameters. If any variation from metrics indicates corrosion/over-heating, the vision systems can notify the appropriate supervisors, who can then take pre-emptive maintenance measures.
Goods Inspection
Manufacturing firms can use machine vision systems to detect faults, fissures, and other blemishes in physical products. Moreover, when the product is being built, these systems may easily check for accurate and reliable component or part dimensions. Images of goods will be captured by machine vision systems. The trained Machine Vision model will compare these photographs with acceptable data limit & will then pass or reject the goods. Any errors or flaws will be communicated via appropriate notification/alert. This is how manufacturers may use machine vision systems to do automatic product inspections and accurate quality control, resulting in increased customer satisfaction.
Scanning Barcodes
Manufacturers can automate the complete scanning process by equipping machine vision systems with enhanced capabilities such as Optical Character Recognition (OCR), Optical Barcode Recognition (OBR), Intelligent Character Recognition (ICR), etc. As in the case of OCR text contained in photographed labels, packaging, or documents can be retrieved and validated against databases. This way, products with inaccurate information can be automatically identified before they leave the factory, limiting the margin for error. This procedure can be used to apply information on drug packaging, beverage bottle labels, and food packaging information such as allergies or expiration dates.
Role of Machine Vision in Manufacturing
3D Vision System
A machine vision inspection system is used in a production line to perform tasks that humans find difficult. Here, the system creates a full 3D model of components and connector pins using high-resolution images.
As components pass through the manufacturing plant, the vision system captures images from various angles to generate a 3D model. When these images are combined and fed into AI algorithms, they detect any faulty threading or minor deviations from the design. This technology has a high level of credibility in manufacturing industries for automobiles, oil & gas, electronic circuits, and so on.
Vision-Based Die Cutting
The most widely used technologies for die-cutting in the manufacturing process are rotary and laser die-cutting. Hard tooling and steel blades are used in rotary, while high-speed laser light is used in laser. Although laser die cutting is more accurate, cutting tough materials is difficult, while rotary cutting can cut any material.
To cut any type of design, the manufacturing industry can use machine vision systems to do rotary die cutting that is as precise as laser cutting. After feeding the design pattern to the vision system, the system will direct the die cutting machine, whether laser or rotary, to execute accurate cutting.
As a result, Machine Vision with the help of AI and deep learning algorithms can transform the manufacturing industry’s efficiency and precision. Such models, when combined with controllers and robotics, can monitor everything that happens in the industrial supply chain, from assembly to logistics, with the least amount of human interaction. It eliminates the errors that come with manual procedures and allows manufacturers to focus on higher cognitive activities. As a result, Machine Vision has the potential to transform the way a manufacturing organization/unit does business.
At Softnautics, we help the manufacturing industry to design Vision-based ML solutions such as image classification & tagging, gauge meter reading, object tracking, identification, anomaly detection, predictive maintenance and analysis, and more. Our team of experts has experience in developing vision solutions based on Optical Character Recognition, NLP, Text Analytics, Cognitive Computing, etc.
Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.
Contact us at business@softnautics.com for any queries related to your solution or for consultancy.
With new-age technologies like the Internet of Things, Machine Learning, Artificial Intelligence, companies are reimagining and creating intelligent multimedia applications by merging physical reality and digital information in innovative ways. A multimedia solution involves audio/video codec, image/audio/video processing, edge/cloud applications, and in a few cases AR/VR as well. This blog will talk about the software infrastructure involved for an embedded video processor core in any multimedia solution.
The video processor is an RTL-based hardened IP block available for use in leading FPGA boards these days. With this embedded core, users can natively support video conferencing, video streaming, and ML-based image recognition and facial identification applications with low latencies and high resource efficiency. However, there are software level issues pertaining to OS support, H.264/265 processing, driver development, and so forth that could come up before deploying the video processor.
Let us begin with an overview of the video processors and see how such issues can be resolved for semiconductor companies enabling the end-users to reap its product benefits.
The Embedded Video Processor Core
The video processor is a multi-component solution, consisting of the video processing engine itself, a DDR4 block, and a Synchronization block. Together, these components are dedicated to supporting H.264/.265 encoding and decoding at resolutions up to 4k UHD (3840x2160p60) and, for the top speed grades of this FPGA device family, up to 4096x2160p60. Levels and profiles supported include up to L5.1 High Tier for HEVC and L5.2 for AVC. All three are RTL-based embedded IP products that are deployed in the programmable logic fabric of the targeted FPGA device family and are optimized/’hardened’ for maximum resource efficiency and performance.
The video processor engine is capable of simultaneous encoding and decoding of up to 32 video streams. This is achieved by splitting up the 2160p60 bandwidth across all the intended channels, supporting video streams of 480p30 resolution. H.264 decoding is supported for bitstreams up to 960Mb/s at L5.2 2160p60 High 4:2:2 profile (CAVLC) and H.265 decoding of bitstreams up to 533Mb/s L5.1 2160p60 Main 4:2:2 10b Intra profile (CABAC.)
There is also significant versatility built into the video processor engine. Rate control options include CBR, VBR, and Constant QP. Higher resolutions than 2160p60 are supported at lower frame rates. The engine can handle 8b and 10b color depths along with YCbCr Chroma formats of 4:0:0, 4:2:0, and 4:2:2.
The microarchitecture includes separate encoder and decoder sections, each administered by an embedded 32b synthesizable MCU slaved to the Host APU through a single 32b AXI-4 Lite I/F. Each MCU has its L1 instruction and data cache supported by a dedicated 32b AXI-4 master. Data transfers with system memory are across a 4 channel 128b AXI-4 master I/F that is split between the encoder and decoder. There is also an embedded AXI performance monitor which measures bus transactions and latencies directly, eliminating the need for further software overhead other than the locked firmware for each MCU.
The DDR4 block is a combined memory controller and PHY. The controller portion optimizes R/W transactions with SDRAM, while the PHY performs SerDes and clock management tasks. There are additional supporting blocks that provide initialization and calibration with system memory. Five AXI ports and a 64b SODIMM port offer performance up to 2677 MT/s.
The third block synchronizes data transactions between the video processor engine encoder and DMA. It can buffer up to 256 AXI transactions and ensures low latency performance.
The company’s Integrated Development Environment (IDE) is used to determine the number of video processor cores needed for a given application and the configuration of buffers for either encoding or decoding, based on the number of bitstreams, the selected codec, and the desired profile. Through the toolchain, users can select either AVC or HEVC codecs, I/B/P frame encoding, resolution and level, frames per second color format & depth, memory usage, and compression/decompression operations. The IDE also provides estimates for bandwidth requirements and power consumption.
Embedded Software Support
The embedded software development support for any hardware into video processing can be divided into the following general categories:
Video codec validation and functional testing
Linux support, including kernel development, driver development, and application support
Tools & Frameworks development
Reference design development and deployment
Use of and contributions to open-source organizations as needed
Validation of the AVC and HEVC codecs on the video processor is extensive. It must be executed to 3840x2160p60 performance levels for both encoding and decoding in bare metal and Linux-supported environments. Low latency performance is also validated from prototyping to full production.
Linux work focused on multimedia frameworks and levels to customize kernels and drivers. This includes the v4l2 subsystem, the DRM framework, and drivers for the synchronization block to ensure low latency performance.
The codec and Linux projects lent themselves effectively to the development of a wide variety of reference designs on behalf of the client. Edge designs for both encoding and decoding, developments ranging from low latency video conferencing to 32 channel video streaming, Region of Interest-based encoding, and ML face detection, all of this can be accomplished via the use of a carefully considered selection of open-source tools, frameworks, and capabilities. Find below a summary of these offerings:
GStreamer – an open-source multi-OS library of multimedia components that can be assembled pipeline-fashion, following an object-oriented design approach with a plug-in architecture, for multimedia playback, editing, recording, and streaming. It supports the rapid building of multimedia apps and is available under the GNU LGPL license.
The GStreamer offering also includes a variety of incredibly useful tools, including gst-launch (for building and running GStreamer pipelines) and gsttrace (a basic tracer tool.)
StreamEye – an open-source tool that provides data and graphical displays for in-depth analysis of video streams.
Gstshark – available as an open-source project from Ridgerun, this tool provides benchmarking and tracing capabilities for analysis and debugging of GStreamer multimedia application builds.
FFmpeg and FFprobe – both part of the FFmpeg open-source project, these are hardware-agnostic, multi-OS tools for multimedia software developers. FFmpeg allows users to convert multimedia files between many formats, change sampling rates, and scale video. FFprobe is a basic tool for multimedia stream analysis.
OpenMAX – available thru the Khronos Group, this is a library of API and signal processing functions that allow developers to make a multimedia stack portable across hardware platforms.
Yocto – a Linux Foundation open-source collaboration that creates tools (including SDKs and BSPs) and supporting capabilities to develop Linux custom implementations for embedded and IoT apps. The community and its Linux versioning are hardware agnostic.
Libdrm – an open-source set of low-level libraries used to support DRM. The Direct Rendering Manager is a Linux kernel that manages GPU-based video hardware on behalf of user programs. It administers program requests in an arbitration mode through a command queue and manages hardware subsystem resources, in particular memory. The libdrm libraries include functions for supporting GPUs from Intel, AMD, and Nvidia as well.
Libdrm includes tools such as modetest, for testing the DRM display driver.
Media-ctl – a widely available open-source tool for configuring the media controller pipeline in the Linux v4l2 layer.
PYUV player – another widely available open-source tool that allows users to play uncompressed video streams.
Audacity – a free multi-OS audio editor.
The above tools/frameworks help design efficient and quality multimedia solutions under video processing, streaming and conferencing.
The Softnautics engineering team has a long history of developing and integrating embedded multimedia and ML software stacks for many global clients. The skillsets of the team members extend to validating designs in hardware with a wide range of system interfaces, including HDMI, SDI, MIPI, PCIe, multi-Gb Ethernet, and more. With hands-on experience in Video Processing for Multi-Core SoC-based transcoder, Streaming Solutions, Optimized DSP processing for Vision Analytics, Smart Camera Applications, Multimedia Verification & validation, Device Drivers for Video/Audio interfaces, etc. Softnautics enable multimedia companies to design and develop connected multimedia solutions.
Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.
Contact us at business@softnautics.com for any queries related to your solution or for consultancy.
The concept of autonomous vehicles is now becoming a reality with the advancement of Computer Vision technologies. Computer Vision helps in the areas of perception building, localization and mapping, path planning, and making effective use of controllers to actuate the vehicle. The primary aspect is to understand the environment and perceive it by using the camera to identify other vehicles, pedestrians, roads, pathways and with the use of sensors such as Radar, LIDAR, complement those data obtained by the camera.
Histogram of oriented Gradients (HOG) and classifiers for object detection got a lot of attention with the use of machine learning. Classifiers train a model for the identification of shape by examining its different gradients and HOG retains the shapes and directions of each pixel. A typical vision system consists of near and far radars, front, side, and rear cameras with ultrasonic sensors. This system assists in safety-enabled autopilot driving and retains data that can be useful for future purposes.
The Computer Vision market size stands at $9.45 billion and is expected to reach $41.1 billion by 2030 as per the report by Allied market research. Global demand for autonomous vehicles is growing. It is expected that by 2030, nearly 12% to 17% of total vehicle sales will belong to the autonomous vehicle segment. OEMs across the globe are seizing this opportunity and making huge investments in ADAS, Computer Vision, and connected car systems.
Computer Vision with Sensor
How does Computer Vision enable Autonomous Vehicles?
Object Detection and Classification
It helps in identifying both stationary as well as moving objects on the road like vehicles, traffic lights, pedestrians, and more. For the avoidance of collisions, while driving, the vehicles continuously need to identify various objects. Computer Vision uses sensors and cameras to collect entire views and make 3D maps. This makes it easy for object identification, avoiding collision, and makes it safe for passengers.
Information Gathering for Training Algorithms
Computer Vision technology makes use of cameras and sensors to gather large sets of data inducing type of location, traffic and road conditions, number of people, and more. This helps in quick decision-making and assists autonomous vehicles to make use of situational awareness. This data can be further used in training the deep learning model to enhance performance.
Low-Light Mode with Computer Vision
The complexity of driving in a low light mode is much different than driving in daylight mode as images captured in a low light mode are often blurry and unclear which makes driving unsafe. With Computer Vision vehicles can detect low light conditions and make use of LIDAR sensors, HDR sensors, and thermal cameras to create high-quality images and videos. This improves safety for night driving.
Vehicle Tracking and Lane Detection
Cutting lanes can become a daunting task in the case of autonomous vehicles. Computer Vision with assistance from deep learning can use segmentation techniques to identify lanes on-road and continue in the stipulated lane. For tracking and understanding behavioral patterns of a vehicle, Computer Vision uses bounding box algorithms to assess its position.
Assisted Parking
The development in deep learning with convolutional neural networks (CNN) has drastically improved the accuracy level of object detection. With the help of outward-facing cameras, 3D reconstruction, parking slot marking recognition makes it is quite easy for autonomous vehicles to park in congested spaces, thereby eliminating wastage of time and effort. Also, IoT-enabled smart parking systems determine the occupancy of the parking lot and send a notification to the connected vehicles nearby.
Insights to Driver Behaviour
With the use of inward-facing cameras, Computer Vision can monitor driver’s gestures, eye movement, drowsiness, speedometer, phone usage, etc. which have a direct impact on road accidents and passengers’ safety. Monitoring all the parameters and giving timely alerts to drivers, avoids fatal road incidents and augments safety. Especially in the case of logistics & fleet companies, the vision system can identify and provide real-time data for the improvement of driver performance for maximizing their business.
The application of vision solutions into automotive is gaining immense popularity. With the inception of deep learning algorithms such as route planning, object detection, and decision making driven by powerful GPUs along with technologies ranging from SAR/thermal camera hardware, LIDR & HDR sensors, radars, it is becoming simpler to execute the concept of autonomous driving.
At Softnautics, we help automotive businesses to design Computer Vision-based solutions such as automatic parallel parking, traffic sign recognition, object/lane detection, driver attention system, etc. involving FPGAs, CPUs, and Microcontrollers. Our team of experts has experience working with autonomous driving platforms, functions, middleware, and compliances like adaptive AUTOSAR, FuSa (ISO 26262), and MISRA C. We support our clients in the entire journey of intelligent automotive solution design.
Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.
Contact us at business@softnautics.com for any queries related to your solution or for consultancy.
At its core, Edge AI is the combination of Edge computing and Edge intelligence to run machine learning tasks directly on end devices which generally consists of an in-built microprocessor and sensors, while the data processing task is also completed locally and stored at the edge node end. The implementation of machine learning models in edge AI will decrease the latency rate and will improve the network bandwidth. Edge AI helps applications that rely on real-time data processing by assisting with data, learning models, and inference. The edge AI hardware market valued at USD 6.88 billion is expected to reach USD 39 billion by 2030 at a CAGR of 18.8% as per the report by Valuates Reports.
The advancement of IoT & adoption of smart technologies by consumer electronics and automotive among others are fuelling the AI hardware market forward. Edge AI processors with on-device analytics are going to enhance the opportunities for the AI hardware market. NVIDIA, Google, AMD, Lattice, Xilinx, and Intel are some of the edge computing platforms providers for such cognitive AI applications design. Furthermore, the advancement of emerging technologies such as deep learning, AI hardware accelerators, neural networks, computer vision, optical character recognition, natural language processing, etc. opens all-new horizons of opportunities. While businesses are rapidly moving towards a decentralized computer architecture, they are also discovering new ways to use this technology to increase productivity.
What is Edge Computing?
Edge computing brings the computing and storage of data closer to the devices that collect it, rather than relying on a primary site that might be far away. This ensures that data does not suffer from latency and redundancy issues that limit an application’s efficiency. The amalgamation of Machine Learning into edge computing gives rise to new, resilient, and scalable AI systems in a wide range of industries.
Myth: Will Edge Computing suppress Cloud Computing?
No, edge computing is not going to replace nor suppress cloud computing, instead, the edge will complement with cloud environment for better performance and leverage machine learning tasks to a greater extent.
Need for Edge AI Hardware Accelerators
Running complex machine learning tasks on edge devices requires specialized AI hardware accelerators, which boost speed & performance, offer greater scalability, maximum security, reliability & efficient data management.
VPU (Vision Processing Unit)
A vision processing unit is a sort of microprocessor aimed at accelerating machine learning and AI algorithms. It balances Edge AI workload with high efficiency and supports tasks like image processing, which is like a video processing unit used with neural networks. It works on low power and high-performance precision.
GPU (Graphical Processing Unit)
An electronic circuit capable of producing graphics for display on an electronic device is referred to as a GPU. It can process multiple data simultaneously, making them ideal for machine learning, video editing, and gaming applications. With their ability to perform complex machine learning tasks, they are being extensively used in mobiles, tablets, workstations, and gaming consoles nowadays.
TPU (Tensor Processing Unit)
Google introduced the Tensor Processing Unit (TPU), an ASIC for executing Machine Learning (ML) algorithms based on neural networks. It uses less energy and operates more efficiently. Google Cloud Platform with TPUs is a good choice for ML applications that don’t require a lot of cloud infrastructure.
Applications of Edge AI across industries
Smart Factories
Edge AI can be applied for predictive maintenance belonging to the equipment industry, by which edge devices can perform analysis on stored data to identify scenarios wherein a failure might occur before the actual failure happens.
Autonomous Vehicles
Self-driving vehicles are one of the best examples of incorporating edge AI technology into the automobile industry, where the integration helps detection and identification of objects thereby eliminating chances for accidents. It aids in avoiding collision with pedestrians/other vehicles, detecting roadblocks, which requires immediate real-time data processing, as plenty of lives are at stake.
Edge AI
Industrial IoT
With the enablement of Computer Vision to Industrial IoT, visual inspections can be done effortlessly without much human intervention thereby increasing operational efficiency and improving the productivity in assembly lines.
Smart Healthcare
Edge Artificial intelligence can help in the healthcare industry via wearables enhancing surveillance of a patient’s health and forecasting early disorders. These details can also be used to provide patients with effective treatments in real-time. Patient data can be secured with HIPAA compliance in place.
Benefits of using Machine Learning on Edge
Higher Scalability
As the demand for the number of interconnected IoT devices is on the rise across industries, Edge AI is becoming an absolute choice due to its efficient and timely data processing without relying heavily on a cloud-based centralized network.
Data Protection & Security
Since Edge devices are not completely dependent on cloud resources, attackers cannot bring down the whole cloud data center/server system to a standstill point.
Low Operational Risks
Since Edge AI is based on a distributed model, in case of failure it will not affect the entire system chain, as in the case of cloud, which is based on a centralized model. The failure of individual edge devices will not pose a huge threat to the entire system.
Reduced Latency Rate
With the implementation of Edge AI, the computation can be performed in milliseconds. This is possible as there is no need to send data to the cloud for initial processing, thereby saving time and reduction of latency in the data processing.
Cost-effectiveness
Edge AI saves a lot of bandwidth, as the transfer of data is minimized. This also reduces the capacity requirements for cloud services which makes Edge AI a cost-effective solution, when compared to cloud-based ML solutions.
In several instances, machine learning models are complex and quite big. In such situations, it becomes extremely difficult to shift these models to compact edge devices. Without proper precautions if efforts are put to reduce the complexity of the algorithms, the processing perfection will take a toll, and also, the computation power will become limited. Hence at the initial development stage, it’s crucial to evaluate all the failure points. Most priority should be given to testing the trained model perfectly on different types of devices and operating systems.
At Softnautics, we provide machine learning services and solutions with expertise on edge platforms (TPU, RPi), NN compiler for the edge, and tools like TensorFlow, TensorFlow Lite, Docker, GIT, AWS deepLens, Jetpack SDK, and many more targeted for domains like automotive, Multimedia, Industrial IoT, Healthcare, Consumer, and Security-Surveillance. Softnautics can help businesses to build high-performance edge ML solutions like object/lane detection, face/gesture recognition, human counting, key-phrase/voice command detection, and more across various platforms. Our team of experts has years of experience working on various edge platforms, cloud ML platforms, and ML tools/ technologies.
Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.
Contact us at business@softnautics.com for any queries related to your solution or for consultancy.
Private, public or hybrid, cloud solutions for any business domain are designed to provide the freedom to grow and security for the organization and customer data. For cloud-based multimedia solutions, there is cloud-based custom transcoder IP that supports automated Video-On-Demand (VOD) pipelines. Cloud services offer solutions that ingest source videos, processes video for playback on a wide range of devices using cloud media converter, and store transcoded media files for on-demand delivery to end-users.
Custom IP integration along with other cloud services showcases better feasibility of using Open-Source codec, to use one’s transcoder instead of cloud media-converter for multimedia solutions. In this blog, we will see how an Open-Source codec like AV1 is selected as a custom IP for encoding to integrate over the cloud as a service.
Thus, the video files uploaded on the cloud can be encoded with AV1 codec, without using cloud media-converter service. The solution is automated in such a way that the content provider just needs to upload video on cloud input file storage service and the further encoding happens automatically. It stores the content on cloud storage services after completion and the end-user gets notified about content availability.
Modules’ usage
Local PC can be used to upload input video on target AWS S3 bucket and EC2 instance is used to transcode input video into AV1 codec output. Encoding can be done through FFmpeg as well GStreamer, here FFmpeg is used considering the strong support community and extra features available. EC2 cloud instance can be used on any Linux-based system server. Further, the S3 cloud output file link is integrated into AWS Sumerian, to view it using VR set in 3D scene mode.
To overcome the limitations of cloud media converter, one can have own custom IP i.e. Transcoder solution, which can be used along with other cloud services. It will make faster the encoding or provide the same speed as of cloud media converter with reduced cost per encoding job, as compared to a cloud media converter. It is also easy to integrate any codec and provides a choice of multiple encoders per codec.
Benefits of using AOMedia Video 1 (AV1) codec
It is an Open-Source, royalty-free video coding format for video transmissions over the Internet
AV1 Quality and Efficiency: Based on measurements with PSNR and VMAF at 720p, AV1 was about 25% more efficient than VP9 (libvpx). Similar conclusions with respect to quality were drawn from a test conducted by Moscow State University researchers, where it was found that VP9 requires 31% and HEVC 22% more bitrate, than AV1 for the same level of quality
Comparing AV1 against H.264 (x264) and VP9 (libvpx), Facebook showed about 45-50% bitrate savings using AV1 over H.264 and about 40% over VP9, when using a constant quality encoding mode
At Softnautics, we have incorporated multimedia solutions having market trending features like image overlay, timecode burn-in, bitrate control mode, advertise, rotation, motion image overlay, sub-title, cropping and more. Such features are required to build solutions like end-to-end pipeline orchestration, live and recorded streaming (VOD), transcoding, cloud services, Content Delivery Network (CDN) integration and interactive VR scene creation.
Flow Diagram:
Cloud-based-solutions
In the flow diagram of a Virtual Reality solution, the user uploads the video to the Watch Folder of the bucket in AWS S3. The multipart upload complete event will trigger the lambda function, which starts the EC2 instance. Encoding will then be performed through FFmpeg to encode output with the AV1 codec. If encoding is successful, then only the encoded file will be uploaded to the “output” directory in the AWS S3 bucket. If encoding is failed, then the input media file will be deleted from the “input” directory of AWS S3. The content provider will receive an email notification for failure or success of encoding job, using AWS SNS service. AWS SNS will trigger further AWS Lambda function and Lambda will stop the AWS EC2 instance. Lambda will also check whether the trigger is for output file upload or not, if yes, then it will send an email notification to the end user, using AWS SES service, to notify new content’s availability. Further AWS S3 output file link can be integrated into AWS Sumerian, to view it using VR set, in 3D scene mode. Python3 can be used for entire automation scripts.
Using cloud media custom IP-based solution services, one can stream videos to end-users at scale, deliver low-latency content, secure videos from unexpected downloads, remove the complexity of building development steps manually, and construct solutions in own environment for demo purposes.
Softnautics can help media companies design multimedia solutions across various platforms, merging physical reality and digital information in innovative ways, using advanced technologies. Softnautics multimedia experts have experience working on Augmented Reality, Virtual Reality, AV codecs development, image/video analytics, computer vision, image processing, and more.
Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.
Contact us at business@softnautics.com for any queries related to your solution or for consultancy.
SOM is an entire CPU architecture built in a small package, of size like a credit card. It is a board-level circuit that integrates a system function and provides core components of an embedded processing system – processor cores, communication interfaces, and memory blocks on a single module. Designing any product based on the SOM is a much faster process than designing the entire system from the ground up.
There are multiple System on Module manufacturers available in the market worldwide with an equal amount of open-source automated testing frameworks. If you plan to use System-on-Module (SOM) in your product, the first thing required is to identify the test automation framework from the ones available out in the market and check for a suitable module for your requirement.
Image/Video intensive industries face difficulty in designing and developing customized hardware solutions for explicit application, with reduced time and cost. It is linked with quick evolving processors with increasing complexity, requiring product companies to constantly introduce upgraded variants in a short span. System on Module (SOM) ensures reduced development and design risk for any application. SOM is a re-usable module embracing maximum hardware/processor complexity, leaving behind reduced work on the carrier/mainboard, thus accelerating Time-to-Market.
System-on-Module is a small PCB board having CPU, RAM, Flash, Power Supply, and various IOs (GPIOS, UART, USB, I2C, SPI, etc.). In new-age electronics, SOM is becoming a quite common part of the design, specifically in industrial, medical electronics. It reduces the design complexity and the time-to-market which is critical for a product’s success. These System-on-Modules runs an OS and are mainly used in applications where Ethernet, file systems, high-resolution display, USB, Internet, etc. are required and the application needs high computing with less development effort. If you are building a product with less than 20-25K volume, it is viable to use a ready SOM for the product development.
Test Automation frameworks for SOM
A testing automation framework is a set of guidelines used for developing test cases. A framework is an amalgamation of tools and practices designed to support quality assurance experts test more efficiently. The guidelines involve coding standards, methodologies to handle test data, object repositories, processes to store test results, or information on accessing external resources. Testing frameworks are an essential part of any successful product release that goes under testing automation. Using a framework for automated testing will enhance a team’s testing efficiency, accuracy, and will reduce time and risks.
There are different types of Automated Testing Frameworks, each having its architecture and merits/demerits. Selecting the right framework is very crucial for your SOM application testing.
Below mentioned are few frameworks used commonly:
Linear Automation Framework
Modular Based Testing Framework
Library Architecture Testing Framework
Data-Driven Framework
Keyword-Driven Framework
Hybrid Testing Framework
From above, the Modular and Hybrid testing frameworks are best suitable for SOM and their development kit verification. The ultimate goal of testing is to ensure that software works as per the specifications and in line with user expectations. The entire process involves quite a few testing types which are preferred or prioritized over others depending on the nature of the application and organization. Let us see some of the basic testing involved in the end-to-end testing process.
Unit testing: Full software stack is made of many small components, so instead of directly testing the full software stack one should cover individual module level testing first. Here unit testing makes sure to have module/method level input/output testing coverage. Unit testing offers a base for complex integrated software and provides fine quality application code, speeding up continuous integration and development process. Often unit tests are executed through test automation by developers.
Smoke testing: Smoke testing is to verify whether the deployed software build is stable or not. To go ahead with further testing depends on smoke test results. It is also referred to as build verification testing which checks whether functionality meets its objective. There is still some development work required if SOM does not clear the smoke.
Sanity testing: The changes or proposed functionality that are working as expected is defined by sanity testing. Suppose we fix some issue in the boot flow of the embedded product, then it should go to the validation team for sanity testing. Once this test is passed it should not impact other basic functionality. Sanity testing is unscripted and specifically targets the area that has undergone a code change.
Regression testing: Every time the program is revised/modified, it should be retested to assure that the modifications didn’t unintentionally “break” some unrelated behavior. This is called regression testing; these tests are usually automated through a test script. Each time the program/design is tested, it should give a smooth result.
Functional testing: Functional testing specifies what the system does. It is also known as black-box testing because the test cases for functional tests are developed without reference to the actual code, i.e., without looking “inside the box.”
Any embedded system has inputs, outputs, and implements some drivers between them. Black-box testing is about which inputs should be acceptable and how they should relate to the outputs. The tester is unaware of the internal structure of the module or source code. Black-box tests include stress testing, boundary value testing, and performance testing.
Over the past years, Softnautics has developed complex software around various processor families from Lattice, Xilinx, Intel, Qualcomm, TI, etc., and has successfully tested the boards for applications like vision processing, AI/ML, multimedia, industrial IoT, and more. Softnautics has market proven process for developing a verification and validation automation suite with zero compromises on feature and/or performance coverage as well as executing test automation with in-house STAF and open-source frameworks. We also provide testing support for product/solution future releases, release management, and product sustenance/maintenance.
Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.
Contact us at business@softnautics.com for any queries related to your solution or for consultancy.
DevOps has evolved over the last decade as a combination of practices that combine software development and IT operations. Because of its utility, flexibility and sophistication, DevOps has become an essential ingredient of success in supporting basic software engineering principles such as CI/CD (continuous integration/continuous deployment) and the exploratory iterations of Agile development.
Organizations that follow DevOps practices create a reusable development pipeline and overarching methodology for software development. These frameworks include highly automated workflows that facilitate rapid and repeatable coding efforts, experimentation, test automation and production-level deployment. New software products can be conceptualized, created and then stored systematically with archived and auditable data, code versions, documentation, toolchain configurations/dependencies and scripts. These archives serve purposes such as re-creating original SW development environments, tracking changes, ensuring version reproducibility, and facilitating further enhancement & evolution of software products.
However, the interplay between embedded software stacks and underlying hardware provides an unexpected and highly salutary effect stemming from DevOps with respect to the development of system hardware solutions. This blog explores just such an example – the definition, development, and deployment of the Board Resource Depot for large semiconductor companies. The Board Resource Depot is a cluster arrangement of servers intended to be a shared, remotely accessible application, for the development groups scattered globally.
Board-resource
To explain, semiconductor industry vendors offer a vast selection of product families, along with supporting IDEs, embedded IP, and many reference designs. A major component of the support portfolio consists of development boards, and there is a plethora of such boards for those semiconductor vendor’s devices.
Despite the tremendous variety of such boards, every design share general software commonality. Among them are the device bootup code, JTAG boundary scan testing, application stack installation, OS implementation and IDE/toolchain access. The same scenario follows for other large semiconductor manufacturers as well. Here, Continuous Integration Continuous Deployment would save time and effort for their engineering teams, permitting those teams to focus on the application stack development for which the board was intended.
The first choice for building the DevOps pipeline is always, an open-source product – specifically, the Jenkins automation server. A multi-OS, java-based server that automates the creation and deployment of DevOps flows for CI/CD software development. The server supports a wide variety of versioning and software build automation tools thru a library of plugins, making Jenkins highly flexible and extensible.
Jenkins also lends itself easily and effectively to server hardware cluster deployments in order to support larger development organizations and parallelization of projects. The IBM Spectrum Load Sharing Facility (LSF) software can be used to configure a server cluster to support the Board Resource Depot. LSF is a multi-OS compatible, scalable, and fault-tolerant job scheduler that distributes and load-balances HPC workloads across hardware platforms. An administrator can manage the hosting hardware hierarchically, set policies and leave the LSF to control hardware resources, queue jobs and execute them in accordance with those policies while the administrator monitors activity.
Based on java servlet containers, Jenkins uses a web interface for setup and configuration and creates a CI/CD pipeline from user-chosen plug-ins. The pipeline provides multiple build, test and deployment stages that are codified in a Jenkins-specific coding language & syntax and automatically stored in a text file as part of the project archive. The pipeline architecture offers a high degree of flexibility to developers, including the ability to fork an existing project, workflow loops for A/B testing or Agile development, and task parallelization to accelerate code module development.
The Board Resource Depot enables globally scattered development teams/users to create a new application from a range of boards available across the board resource depot.
There is a cascade of benefits accrued to semiconductor companies’ board developers. Some of the major benefits included:
Increased board re-use
The ability to reserve boards in advance
Real-time notification of board release to production, along with a time stamp
Automated email notifications for the completion of tasks or milestones in new board development via Jenkins server
A regular schedule of ‘health checks’ for boards to alert users of boards that were experiencing functional problems
Automation of new board additions to the Farm resource reserve through a set of dedicated scripts
The board support software can be tested by creating a library of Python-coded test modules. This test module library is developed using Pytest, a unit testing framework available with its own library of over 300 plug-ins that help developers quickly develop testing regimens for their python code. Tests can be simple (unit) or complex, and the test setup is flexible, modular, and scalable. Tests can be re-used, repeated (with results logged) and multiple test fixtures can be set up at the same time.
The final piece of the puzzle is finding a way to implement a robust, repeatable, and automated board testing schema. The Robot framework, an open-source framework available under Apache license is widely used for such scenarios of test automation and robotic process automation. Implemented in Python, it nonetheless supports multiple object-oriented and procedural languages. The framework is keyword-driven and can be extended using available libraries.
Using Robot, a full JTAG Boundary Scan-based test suite can be created in Python for scanning the Power Management (PM) Bus, FPGA I/O and board level peripherals (UART, I2C, clocks and so forth.) This board test suite was integrated into the Jenkins pipeline to facilitate re-use, traceability, and versioning. MySQL scripts are used to produce statistics involving reports and graphs on archive activity and the operations of the server cluster. The Board Resource Depot also helps to plot the real time usage statistics per user, per user-group, per board.
Huge semiconductor companies like Xilinx, Lattice, Microsemi, etc. has over 1,000 development boards to offer its customers, all of which can be a part of such Board Resource Depot and its Jenkins server pipeline. The key to the success of Board Resource Depot is its reformulation as a DevOps pipeline. By streamlining the building, testing and deployment, the Board Resource Depot DevOps pipeline has provided engineering teams the means of continuous and efficient application/product lifecycle management which, in the end, accelerates embedded software development cycle.
Softnautics enable semiconductors to bring in next-gen products with its semiconductor design, embedded software development and end-to-end test automation services with custom and inhouse framework STAF. Our team of experts also have in-depth experience working on DevOps, Agile, device security and compliance testing. We enabled semiconductor companies with geographically separate teams to access and work on various development boards through Board Resource Depot management.
Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.
Contact us at business@softnautics.com for any queries related to your solution or for consultancy.
We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept All”, you consent to the use of ALL the cookies. However, you may visit "Cookie Settings" to provide a controlled consent.
This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
Cookie
Duration
Description
cookielawinfo-checkbox-analytics
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional
11 months
The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy
11 months
The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
