Video analytics

Applications and Opportunities for Video Analytics

Multimedia Engineering /

In recent years, the development of video analytics based video solutions have come up as a high-end technology that has changed the way we interpret and analyze video data. Video analytics uses the most advanced algorithms and artificial intelligence (AI) to track the behaviour and understand the data in real-time, allowing to automate necessary actions. This technology has found many applications across different industries, providing valuable insights, intensifying security, improving the safety and optimizing operations. According to Verified Market Research group, video analytics is experiencing rapid market growth, with its global market value to reach USD 35.88 billion by 2030, representing a CAGR of 21.5% from its valuation of USD 5.65 billion in 2021. This growth trend highlights the increasing demand for video analytics solutions as organizations seek to enhance their security and surveillance systems. 

Video analytics is closely related to video processing which is an essential part of any multimedia solution, as it involves the extraction of meaningful insights and information from video data using various computational techniques. Video analytics leverages the capabilities of video processing to analyze and interpret video content, enabling computers to automatically detect, track, and understand objects, events, and patterns within video streams. Video processing techniques are used in a wide range of applications, including surveillance, video streaming, multimedia communication, autonomous vehicles, medical imaging, entertainment, virtual reality, and many more.

In this article, we will see some Industrial applications and use cases of video analytics in different areas.

Industrial Applications of Video Analytics
Automotive
One of the most used and inescapable uses of video analytics is in the automotive industry for Advanced Driver Assistance System (ADAS) in highly-automate vehicles (HAVs). The HAVs use multiple cameras to identify pedestrians, traffic signals, other vehicles, lanes, and other indicators, they are integrated with the ECU and programmed in such a way as to identify the real-time situation and then respond accordingly. Automating this process, requires integration of various system on a chip (SoC). These chipsets help actuators to connect with the sensors through interface and ECUs. It analyses the data with deep learning based machine learning (ML) models that uses neural networks to learn patterns in data. Neural networks are structured with layers of interconnected processing nodes, typically comprising multiple layers. This deep learning algorithms are used to detect and track objects in real-time videos, as well as to recognize specific actions. 

Sports
In the sports industry, video analytics is being utilized by coaches, personal trainers, and professional athletes to optimize performance through data-driven insights. In sports such as rugby and soccer, tracking metrics like ball possession and the number of passes has become a standard practice for understanding game patterns and team performance. Detailed research on a soccer game has shown that analyzing ball possession can even impact the outcome of a match. Video analytics can be used to gain insights into the playing style, strategies, passing patterns, and weaknesses of the opponent team, enabling a better understanding of their gameplay.

Video Analytics Applications

Retail
Intelligent video analytics is a valuable tool for retailers to monitor storefront events and promptly respond to improve the customer experience. Real-time video is captured by cameras, which cover areas such as shelf inventory, curbside pickup, and cashier queues. On-site IoT Edge devices analyze the video data in real-time to detect key metrics, such as the number of people in checkout queues, empty shelf space, or cars in the parking lot.

Anomaly events can be avoided by metrics analysis, alerting store managers or stock supervisors to take corrective actions. Additionally, video clips or events can be stored in the cloud for long-term trend analysis, providing valuable insights for future decision-making.

Health Care
Video analytics has emerged as a transformative technology in the field of healthcare, offering significant benefits in patient care and operational efficiency. By utilizing cutting-edge machine learning algorithms and computer vision, these systems can analyze video data in real-time to automatically detect and interpret various diseases into human body. It can also be leveraged for patient monitoring, detecting emergencies, identifying wandering behaviour in dementia patients, and analyzing crowd behaviour in waiting areas. These capabilities enable healthcare providers to proactively address potential issues, optimize resource allocation, and enhance patient safety, leading to improved patient outcomes and a higher quality of care. With ongoing advancements in technology, video analytics is poised to play a crucial role in shaping the future of healthcare, making it more intelligent, efficient, and patient-centric.

To summarize, video analytics is a rapidly growing field that leverages various technologies such as computer vision, deep learning, image and video processing, motion detection and tracking, and data analysis to extract valuable insights. Video analytics has found applications in diverse domains, including security and surveillance, healthcare, automotive, sports, and others. By automating the analysis of video data, video analytics enables organizations to efficiently process large amounts of visual information, identify patterns and behaviours, and make data-driven decisions in more effective and less expensive.

With continuous advancements in technology, we at Softnautics help businesses across various industries to provide intelligent media solutions involving the simplest to the most complex multimedia technologies. We have hands-on experience in designing high-performance media applications, architect complete video pipelines, audio/video codecs engineering, applications porting, ML model design, optimize, test and deploy.

We hope you enjoyed this article and got a better understanding of how video analytics based intelligent solutions can be implemented for various businesses to automate processes, improve efficiency/accuracy, and take better decisions.

Read our success stories related to intelligent media solutions to know more about our multimedia engineering services.

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An overview of Machine Learning pipeline and its importance

An overview of Machine Learning pipeline and its importance

AI Engineering /

A Machine Learning (ML) pipeline is used to assist in the automation of machine learning processes. They work by allowing a sequence of data to be transformed and correlated in a model that can be tested and evaluated to achieve a positive or negative outcome. Starting from data extraction and pre-processing to model training & tuning, analysis of the model and deployment would run in a single entity in mainstream design. This means that the data will be extracted, cleaned, prepared, modelled, and deployed using the same script. Because machine learning models typically contain far less code than other software applications, keeping all resources in one place makes perfect sense. Because of advancements in deep learning, and neural network algorithms, the global market is expected to gain traction. Furthermore, many companies are tightening their deep learning capabilities to drive innovation, which is expected to drive ML market growth across industries like automotive, consumer electronics, media & entertainment, and others. According to the precedence research group, the global ML as a service market was valued at USD 15.47 billion in 2021 and it is predicted to reach USD 305.62 billion by 2030, with a CAGR of 39.3 percent from 2022 to 2030. 

Overview of machine learning pipeline

A machine learning pipeline is a method for fully automating a machine learning task’s workflow. This can be accomplished by allowing a series of data to be converted and associated in a model that can be examined to determine the output. A general ML pipeline consists of data input, data models, parameters, and predicted outcomes. The process of creating a machine learning model can be codified and automated using a machine learning pipeline. The deployment of various versions of the same model, model expansion, and workflow setup difficulties may arise while executing the ML process and must be handled manually. We can utilize a machine learning pipeline to address all of the aforementioned issues. Each step of the workflow functions independently using the ML pipeline. Therefore, one may select that module and use it as needed for any updates at any stage.

Overview of ML Pipeline

Data input
The Data input step is the first step in every ML pipeline. The data is organized and processed in this stage so that it can be applied to subsequent steps. 

Validation of data
Data validation is the next step, which must be completed before training a new model. The statistics of the new data, such as the scope, number of classifications, distribution of subgroups, etc., are the main focus of data validation. We can compare various datasets to find anomalies using a variety of data validation tools like Python, R, Python Pandas, etc.

Pre-processing of data 
One of the most important phases of each ML lifecycle as well as the pipeline is data pre-processing. As it might produce a sudden and unexpected result, we cannot input the collected data directly to train the model without first processing it. The pre-processing stage entails getting the raw data ready for the ML model. The procedure is divided into several parts, such as attribute scaling, data cleansing, information quality assessment, and data reduction. The final dataset that can be utilised for model training and testing is the result of the data pre-processing procedure. In machine learning, a variety of methods like normalization, aggregation, numerosity reduction, etc. are available for pre-processing data.

Data model training
Each ML pipeline’s central step is model training. In this step, the model is trained to predict the output as accurately as possible given the input (a pre-processed dataset). Larger models or training data sets, however, might present some challenges. Therefore, efficient model training or model tuning distribution is needed for this. Because pipelines are scalable and can process many models at once, they can address the problem of the model training stage. Different sorts of ML algorithms like Supervised, Unsupervised, and Reinforcement learnings can be utilized for building data models. 

Deployment of model
It’s time to deploy the model after training and analysis. Three methods exist for deploying ML models: through the model server, a browser, and an edge device. However, employing a model server is the typical method of deployment for the model. ML pipeline ensures smooth functioning of ML inference at edge level devices where the data generation plays a crucial part and offers features like lower cost, real time processing, and increased privacy. And for cloud services, the ML pipeline ensures proper utilization of resource demand and reduces processing power and consumes fewer data storage space. The ability to host different versions concurrently on model servers makes it possible to do A/B tests on models and can yield insightful feedback for model improvement.

Benefits of a machine learning pipeline include.

  • Providing a comprehensive view of the entire series of phases by mapping a complex process that incorporates input from various specialties.
  • Concentrating on particular steps in the sequence one at a time allows for the automation of individual phases. It is possible to integrate machine learning pipelines, increasing productivity and automating processes.
  • It offers the flexibility to easily debug the entire code and trace out the issues in a particular step.
  • Easily deployable, upscaling modular machine learning pipeline components as necessary.
  • Offers the flexibility of using multiple pipelines which are reliably coordinated over heterogeneous system resources as well as different storage locations.

Each machine learning pipeline will be slightly different depending on the model’s use case and the organization using it. However, since the pipeline frequently adheres to a typical machine learning lifecycle, the same factors must be taken into account when developing any machine learning pipeline. Consider the various phases of machine learning and divide each phase into distinct modules as the first step in the process. A modular approach facilitates the gradual enhancement of each component of the machine learning pipeline and makes it easier to concentrate on the individual parts of the pipeline.

Softnautics with its AI engineering and machine learning services helps businesses build intelligent solutions in the areas of computer vision, cognitive computing, artificial intelligence & FPGA acceleration. We possess the capability to handle a complete Machine Learning (ML) pipeline involving dataset, model development, optimization, testing, and deployment. We collaborate with organizations to develop high-performance cloud-to-edge machine learning solutions like face/gesture recognition, people counting, object/lane detection, weapon detection, food classification, and more across a variety of platforms.

Read our success stories related to Machine Learning services and AI engineering solution design to know more about our expertise for the domain.

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FPGAs and GPUs for AI Based Applications

Selection of FPGAs and GPUs for AI Based Applications

AI Engineering, Platform Engineering /

Artificial Intelligence (AI) refers to non-human, machine intelligence capable of making decisions in the same way that humans do. This includes contemplation, adaptability, intention faculties, and judgment. Machine vision, robotic automation, cognitive computing, machine learning, and computer vision are all applications in the AI market. AI is rapidly gaining traction in a variety of industry sectors like automotive, consumer electronics, media & entertainment, and semiconductors, heralding the next great technological shift. The scope for semiconductor manufactures is expected to grow in the coming years. As the demand for machine learning devices grow around the world, many major market players belonging to EDA (Electronic Design Automation), graphics cards, gaming, multimedia industries are investing to provide innovative and high-speed computing processors. While AI is primarily based on software algorithms that mimic human thoughts and ideas, hardware is also an important component. Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) are the two main hardware solutions for most AI operations. According to the precedence research group, the global AI in hardware market was valued at USD 10.41 billion in 2021 and it is predicted to reach USD 89.22 billion by 2030, with a CAGR of 26.96 percent from 2022 to 2030.

FPGA vs GPU

Overview of FPGA
A hardware circuit with reprogrammable logic gates is known as a field-programmable gate array (FPGA). While a chip is being used in the field, users can design a unique circuit by overwriting the configurations. This contrasts with standard chips, which cannot be reprogrammed. With an FPGA chip, you can build anything from simple logic gates to multi-core chipsets. The usage of FPGAs is very much popular where intrinsic circuitry is essential, and changes are expected. ASIC prototyping, automotive, multimedia, consumer electronics, and many more areas are covered by FPGA applications. Based on the application requirement, either low-end, mid-range, or high-end FPGA configurations are selected. ECP3 and ECP5 series from Lattice semiconductor, Artix-7/Kintex-7 series from Xilinx, and Stratix family from Intel are some of the popular FPGA designs for low power & low design density.

The logic blocks are built using look-up tables (LUTs) with a limited of inputs and are built using basic memory such as SRAM or Flash to store Boolean functions. Each LUT is linked to a multiplexer and a flip-flop register to support sequential circuits. Similarly, many LUTs can be used to create complex functions. Read our FPGA blog to know more about its architecture.

FPGAs are more suitable for embedded applications and use less power than CPUs and GPUs. These circuits are not constrained by design like GPUs and can be used with bespoke data types. Additionally, FPGAs’ programmability makes it simpler to modify them to address security and safety issues.

Advantages of using FPGAs
Energy efficient
Designers can precisely adjust the hardware to meet the requirements of the application, thanks to FPGAs. With its low power consuming capability, overall power consumption for AI and ML applications can be minimized. This could increase the equipment’s lifespan and reduce the training’s overall cost.

Ease of flexibility
FPGA offers the flexibility of programmability for handling AI/ML applications. One can program one individual block or an entire block depending on the requirements.

Reduced latency
FPGAs excel at handling short batch phrases with reduced latency. Reduced latency refers to a computing system’s ability to respond with minimal delay. This is critical in real-time data processing applications such as video surveillance, video pre and post processing, and text recognition, where every microsecond counts. Because they operate in a bare-metal environment without an operating system, FPGAs and ASICs are faster than GPUs.

Parallel processing
The operational and energy efficiency of FPGAs is substantially improved by their ability to host several tasks concurrently and even designate specific sections of the device for particular functions. Small quantities of distributed memory are included in the fabric of the FPGAs’ special architecture, bringing them closer to the processor.

Overview of GPU
The original purpose of graphic processing units (GPUs) was to create computer graphics, and virtual reality environments that depended on complex computations and floating-point capabilities to render geometric objects. A modern artificial intelligence infrastructure would not be complete without them and are very much suitable for the deep learning process.

Artificial intelligence needs a lot of data to study and learn from to be successful. To run AI algorithms and move a lot of data, demands a lot of computational power. GPUs can carry out these tasks because they were created to quickly handle the massive volumes of data required for generating graphics and video. Their widespread use in machine learning and artificial intelligence applications is due in part to their high computing capabilities.

GPUs can handle several computations at once. As a result, training procedures can be distributed, which greatly speeds up machine learning activities. With GPUs, you may add several cores with lower resource requirements without compromising performance or power. Various types of GPUs are available in the market and generally fall into the following categories such as data center GPUs, consumer grade GPUs, and enterprise grade GPUs.

Advantages of using GPUs
Memory bandwidth
GPUs have good memory bandwidth due to which they tend to perform computation quickly in the case of deep learning applications. GPUs consume less memory when training the model on huge datasets. With up to 750GB of memory bandwidth, they can really accelerate quick processing of AI algorithms.

Multicores
Typically, GPUs consists of many processor clusters that can be grouped together. This makes it possible to greatly boost a system’s processing power particularly to AI applications with parallel inputs of data, convolutional neural network (CNN), and training of ML algorithms.

Flexibility
Because of a GPU’s parallelism capabilities, you can group GPUs into clusters and distribute jobs among those clusters. Another option is to use individual GPUs with dedicated clusters for training specific algorithms. GPUs with high data throughput can perform the same operation on many data points in parallel, allowing them to process large amounts of data at unrivalled speed.

Dataset Size
For model training, AI algorithms require a large dataset, which accounts for memory-intensive computations. A GPU is one of the best options for efficiently processing datasets with many datapoints that are larger than 100GB in size. Since the inception of parallel processing, they have provided the raw computational power required for efficiently processing largely identical or unstructured data.

The two major hardware choices for running AI applications are FPGAs and GPUs. Although GPUs can handle the massive volumes of data necessary for AI and deep learning, they have limitations regarding energy efficiency, thermal issues, endurance, and the ability to update applications with new AI algorithms. FPGAs offer significant benefits for neural networks and ML applications. These include ease of AI algorithm updates, usability, durability, and energy efficiency.

Additionally, significant progress has been made in the creation of software for FPGAs that makes compiling and programming them simpler. For your AI application to be successful, you must investigate your hardware possibilities. As it is said, carefully weigh your options before settling on a course of action.

Softnautics AI/ML experts have extensive expertise in creating efficient Machine Learning solutions for a variety of edge platforms, including CPUs, GPUs, TPUs, and neural network compilers. We also offer secure embedded systems development and FPGA design services by combining the best design methodologies and the appropriate technology stacks. We help businesses in building high-performance cloud and edge-based AI/ML solutions like key-phrase/voice command detection, face/gesture recognition, object/lane detection, human counting, and more across various platforms.

Read our success stories related  to Artificial Intelligence and Machine Learning expertise to know more about the services for accelerated AI solutions.

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Emerging Trends and Challenges in Embedded System Design

Emerging Trends and Challenges in Embedded System Design

Platform Engineering /

An embedded system is a microprocessor based hardware system integrated with software, designed to handle a particular function or entire system functionalities. With the rapid growth in terms of technology and development in microcontrollers, embedded systems have also evolved in various forms. Embedded software is typically developed for handling specialized hardware in operating systems such as RTOS, Linux, Windows, and others. Furthermore, with the drastic increase in the adoption of embedded systems in the areas of machine learning, smart wearables, home automation, electronic design automation, and the advancement of multicore processing, the future of the embedded system market looks quite appealing. Between 2022 and 2031, the global market for embedded systems is anticipated to expand at a 6.5 percent CAGR and reach about $163.2 billion, as per Allied market research group reports.

An Overview of Embedded System design

In general, an embedded system consists of hardware, software, and embedded OS. The hardware comprises a user interface, memory, power supply, and communication ports. In the software section machine level code is being created with the use of programming languages like C and C++. RTOS (Real Time Operating System) is the most sorted out OS which is often used for the embedded operating system. Embedded system generally falls into three categories starting with small scale, medium scale, and sophisticated ones.

If you approach embedded system design without a plan, it can be overwhelming. A systematic approach, on the other hand, helps to divide the design cycle into manageable stages, allowing for proper planning, implementation, and collaboration.

The embedded system design consists of the following steps

Embedded system design process

Product identification/Abstraction
It all starts with requirement analysis, which starts with analysing product requirements and turning them into specifications. The number of inputs/outputs and the logic diagram are not the only considerations but investigating usage and operating conditions aid in determining the appropriate specifications for the embedded system.

Layout design
The hardware designer can begin building the blueprint once the requirements have been translated into specifications. At this stage, the design team must select the appropriate microcontrollers based on power consumption, peripherals, memories, and other circuit components keeping in mind the cost factor.

Printed circuit board
A PCB is an assembly that employs copper conductors to link various components electrically and to support them mechanically. A printed circuit board design involves a brainstorming process in which best practices for features and capabilities, and reliability must be followed. When working with high-speed mixed-signal circuits, microprocessors, and microcontrollers it becomes more complicated. The common types of PCBs include single & double sided, multi-layer, flex, ceramic, etc.

Prototype development
When creating a new product for a specific market segment, time is very essential and plays a crucial part. Creating a prototype allows you to identify flaws and design advantages early on. It aids in identifying design flaws earlier, allows ideas to be tested, determines product feasibility, and streamlines the design process.

Firmware development
Writing code for embedded hardware (microprocessor, microcontroller, FPGA), as opposed to a full-fledged computer, is known as firmware development. Software that controls the sensors, peripherals, and other components is known as firmware. To make everything function, firmware designers must use coding to make the hardware come to life. Utilizing pre-existing driver libraries and example codes provided by the manufacturer will speed up the process.

Testing & validation
Stringent testing must be passed before an embedded system design is authorized for production or deployment. The circuit must undergo reliability testing in addition to functionality testing, especially when operating close to its limitations.

Trends in embedded system
Technology trends are accelerating, and devices have developed into distinctive qualities that fit in many categories and sectors, including embedded. Due to its outcomes being application-oriented and advance development areas in focus, embedded systems and devices will gain more popularity in the coming future while considering various business sectors and their applications. Let us see recent trends under embedded systems.

System-on-Chip Solution
System on Chip (SoC) solution is another new trend in embedded system technology. Many businesses provide SoC based embedded devices, and among these solutions is the market delivery of analog and mixed-signal integrated circuits as a popular one. ASIC with great performance, small size, low cost, and IP protection is one such solution. Due to their size, weight, and power performance, it is very popular for application specific system needs.

Wireless technology
The primary goal of building wireless embedded software solutions is information transmission and reception. The wireless embedded system plays an important role where physical connections are impossible in any setting, and the use of IoT peripherals and devices becomes vital. With the technological advances in the areas of wireless solutions like Z-Wave, Bluetooth, Wi-Fi, and ZigBee the applicability of embedded wireless systems has drastically increased.

Automation
Every system in use today is becoming more automated. Every sector of growth has some level of automation, largely due to developments in computers, robots, and advancement in intelligent technologies like artificial intelligence and machine learning. The usage of embedded devices speeds up the connection of multiple storage components and can easily link up with cloud technology to power the device’s quick expansion of cognitive processing. The applications based on facial recognition and vision solution offers benefits like image identification & capturing, image processing, post processing, etc, and alerting for security in real time. For example, a smart factory outfitted with IoT, and artificial intelligence can significantly boost productivity by monitoring operations in real time and allowing AI to make decisions that prevent operational errors.

Low power consumption
The optimization of battery-powered devices for minimal power consumption and high uptime presents a significant challenge for developers. For monitoring and lowering the energy usage of embedded devices, a number of technologies/modules and design techniques are currently being developed and these include Wi-Fi modules, enhanced Bluetooth that use less power at the hardware layer optimizing embedded systems.

Challenges in embedded systems design
Embedded system design is an important component and is rapidly evolving; however, certain challenges must be addressed, such as issues related to security & safety, updating system hardware and software, consumption of power, seamless integration, and verification & testing which plays a crucial part in improving the performance of the system. When developing an embedded system, it is critical to avoid unexpected behaviour that could endanger users. It should be designed so that there are no problems with life-saving functionality in critical environments. Most of the time embedded device is controlled using mobile applications, where it is critical to ensure that there is no risk of data takeover or breach.

Writing code for embedded hardware (microprocessor, microcontroller, FPGA), as opposed to a full-fledged computer, is known as firmware development. Software that controls the sensors, peripherals, and other components is known as firmware. To make everything function, firmware designers must use coding to make the hardware come to life. Utilizing pre-existing driver libraries and example codes provided by the manufacturer will speed up the process

Embedded technologies will continue to grow, manufacturers are now heavily relaying the usage of embedded devices starting from automobiles to security systems, consumer electronics to smart home solutions, and others. Admittedly, the embedded system may now be the most important factor driving device cognition and performance advancements.

Softnautics offers the best design practices and the right selection of technology stacks to provide secured embedded systems, software development, and FPGA design services. We help businesses in building next-gen systems/solutions/products with services like platform enablement, firmware & driver development, OS porting & bootloader optimization, and Middleware Integration, and more across various platforms.

Read our success stories related to embedded system design to know more about our platform engineering services. 

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Pytest root framework

Pytest for Functional Test Automation with Python

Quality Engineering /

Today’s modern businesses require faster software feature releases to produce high-quality products and to get to market quickly without sacrificing software quality. To ensure successful deployments, the accelerated release of new features or bug fixes in existing features requires rigorous end-to-end software testing. While manual testing can be used for small applications or software, large and complex applications require dedicated resources and technologies like python testing frameworks, automation testing tools, and so on to ensure optimal test coverage in less time and faster quality releases. PyTest is a testing framework that allows individuals to write test code in Python. It enables you to create simple and scalable test cases for databases, APIs, and user interfaces. PyTest is primarily used for writing API tests. It aids in the development of tests ranging from simple unit tests to complex functional tests. According to a report published by future market insights group, the global automation testing market is expected to grow at a CAGR of 14.3% registering a market value of US$ 93.6 billion by the end of 2032.

Why choose Pytest?

Selection of the right testing framework can be difficult and relies on parameters like feasibility, complexity, scalability, and features provided by a framework. PyTest is the go-to test framework for a test automation engineer with a good understanding of Python fundamentals. With the PyTest framework, you can create high-coverage unit tests, complex functional tests, and acceptance tests. Apart from being an extremely versatile framework for test automation, PyTest also has a plethora of test execution features such as parameterizing, markers, tags, parallel execution, and dependency.

  • There is no boilerplate while using Pytest as a test framework 
  • Pytest can run tests written in unittest, doctest, and nose
  • Pytest supports plugins for behaviour driven testing
  • There are more than 150 plugins available to support different types of test automation
  •  

The diagram below shows a typical structure of a Pytest framework.

Pytest root framework

As shown above in the structure, the business logic of the framework core components is completely independent of Pytest components. Pytest makes use of the core framework just like instantiating the objects and calling its functions in the test script. Test script file name should either start with `test_` or end with `_test`. The test function name should also be in the same format. Reporting in Pytest can be taken care of by Pytest-html reporting.

Important Pytest features
Pytest fixtures
The most prominently used feature of Pytest is Fixtures. Fixtures, as the name suggests are decorator functions that are used in pytest to generate a specific condition that needs to be arranged for the test to be run successfully. The condition can be any precondition like creating objects of the classes required, bringing an application to a specific state, bringing up the mockers in case of unit tests, initializing the dependencies, etc. Fixtures also take care of the teardown or reverting of the conditions that were generated after the test execution is completed. In general, fixtures take care of the setup and teardown conditions for a test.

Fixture scope
The setup and teardown do not have to be just for the test function. Scope of the setup may differ from a test function to as large as the whole test session. This means the setup-teardown is executed only once per defined scope. To achieve the same, we can define the scope along with the fixture decorator i.e., session, module, class, function..

Fixture usage
Pytest provides the flexibility to use a fixture implicitly or call it explicitly, with autouse parameter. To call the fixture function by default, the autouse parameter value needs to be set to True, else to False.

Conftest.py
All the fixtures that are to be used in the test framework are usually defined in conftest.py. It is the entry point for any Pytest execution. Fixtures need not be autouse=True. All defined fixtures can be accessed by all the test files. conftest.py needs to be placed in the root directory of the Pytest framework.

Pytest hooks
Pytest provides numerous hooks that will be called in to perform a specific setup. Hooks are generator functions that yield exactly once. Users can also write wrappers in conftest for the Pytest hooks.

Markers
Pytest provides markers to group a set of tests based on feature, scope, test category, etc. The test execution can be auto-filtered based on the markers. i.e., acceptance, regression suit, login tests, etc. Markers also act as an enabler for parameterizing a test. The test will be executed for all the parameters that are passed as the argument. Note, Pytest considers a test for one parameter as a completely independent test. Many things can be achieved with markers like marking a test to skip, skipping on certain conditions, depending on a specific test, etc.

Assertion
Pytest does not require the test scripts to have their assertions. It works flawlessly with Python inbuilt assertions.

Pytest.ini
All default configuration data can be put in pytest.ini and the same can be read by the conftest without any specific implementation.
PyTest supports a huge number of plugins with which, almost any level of a complex system can be automated. A major benefit of Pytest is that any kind of implementation of the structure is done using raw Python code without any boilerplate code. It means implementing anything in Pytest is as flexible and clean as implementing anything in Python itself.
Amidst shorter development cycles, test automation provides several benefits that are critical for producing high-quality applications. It reduces the possibility of unavoidable human errors taking place during manual testing methods. Automated testing improves software quality and reduces the likelihood of defects jeopardizing delivery timelines.
At Softnautics, we provide Quality Engineering Services for both embedded and software products to help businesses create high-quality solutions that will enable them to compete in the market. Our complete QE services include embedded software and product testing, DevOps and automated testing, ML platform testing, and compliance with industry standards such as FuSa – ISO 26262, MISRA C, AUTOSAR, etc. Our internal test automation platform, STAF, supports businesses in testing end-to-end solutions with increased testing efficiency and accelerated time to market.

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Artificial Intelligence and Machine Learning based Image Processing

AI Engineering /

Image processing is the process of converting an image to a digital format and then performing various operations on it to gather useful information. Artificial Intelligence (AI) and Machine Learning (ML) has had a huge influence on various fields of technology in recent years. Computer vision, the ability for computers to understand images and videos on their own, is one of the top trends in this industry. The popularity of computer vision is growing like never before and its application is spanning across industries like automobiles, consumer electronics, retail, manufacturing and many more. Image processing can be done in two ways: Physical photographs, printouts, and other hard copies of images being processed using analogue image processing and digital image processing is the use of computer algorithms to manipulate digital images. The input in both cases is an image. The output of analogue image processing is always an image. However, the output of digital image processing may be an image or information associated with that image, such as data on features, attributes, and bounding boxes. According to a report published by Data Bridge Market Research analyses, the Image processing systems market is expected to grow at a CAGR of 21.8% registering a market value of USD 151,632.6 million by 2029. Image processing is used in a variety of use cases today, including visualisation, pattern recognition, segmentation, image information extraction, classification, and many others.

Image processing working mechanism

Artificial intelligence and Machine Learning algorithms usually use a workflow to learn from data. Consider a generic model of a working algorithm for an Image Processing use case. To start, AI algorithms require a large amount of high-quality data to learn and predict highly accurate results. As a result, we must ensure that the images are well-processed, annotated, and generic for AIML image processing. This is where computer vision (CV) comes in; it is a field concerned with machines understanding image data. We can use CV to process, load, transform, and manipulate images to create an ideal dataset for the AI algorithm.
Let’s understand the workflow of a basic image processing system

An Overview of Image Processing System

Acquisition of image
The initial level begins with image pre-processing which uses a sensor to capture the image and transform it into a usable format.

Enhancement of image
Image enhancement is the technique of bringing out and emphasising specific interesting characteristics which are hidden in an image.

Restoration of image
Image restoration is the process of enhancing an image’s look. Picture restoration, as opposed to image augmentation, is carried out utilising specific mathematical or probabilistic models.

Colour image processing
A variety of digital colour modelling approaches such as HSI (Hue-Saturation-Intensity), CMY (Cyan-Magenta-Yellow) and RGB (Red-Green-Blue) etc. are used in colour picture processing.

Compression and decompression of image
This enables adjustments to image resolution and size, whether for image reduction or restoration depending on the situation, without lowering image quality below a desirable level. Lossy and lossless compression techniques are the two main types of image file compression which are being employed in this stage.

Morphological processing
Digital images are processed depending on their shapes using an image processing technique known as morphological operations. The operations depend on the pixel values rather than their numerical values, and well suited for the processing of binary images. It aids in removing imperfections for structure of the image.

Segmentation, representation and description
The segmentation process divides a picture into segments, and each segment is represented and described in such a way that it can be processed further by a computer. The image’s quality and regional characteristics are covered by representation. The description’s job is to extract quantitative data that helps distinguish one class of items from another.

Recognition of image
A label is given to an object through recognition based on its description. Some of the often-employed algorithms in the process of recognising images include the Scale-invariant Feature Transform (SIFT), the Speeded Up Robust Features (SURF), and the PCA (Principal Component Analysis).

Frameworks for AI image processing

Open CV
OpenCV is a well-known computer vision library that provides numerous algorithms and utilities to support the algorithms. The modules for object detection, machine learning, and image processing are only a few of the many that it includes. With the help of this programme, you may do picture processing tasks like data extraction, restoration, and compression.

TensorFlow
TensorFlow, created by Google, is one of the most well-known end-to-end machine learning programming frameworks for tackling the challenges of building and training a neural network to automatically locate and categorise images to a level of human perception. It offers functionalities like work on multiple parallel processors, cross platform, GPU configuration, support for a wide range of neural network algorithms, etc.

PyTorch
Intended to shorten the time it takes to get from a research prototype to commercial development, it includes features like a tool and library ecosystem, support for popular cloud platforms, a simple transition from development to production, distribution training, etc.

Caffe
It is a deep learning framework intended for image classification and segmentation. It has features like simple CPU and GPU switching, optimised model definition and configuration, computation utilising blobs, etc.

Applications

Machine vision
The ability of a computer to comprehend the world is known as machine vision. Digital signal processing and analogue-to-digital conversion are combined with one or more video cameras. The image data is transmitted to a robot controller or computer. This technology aids companies in improving automated processes through automated analysis. For instance, specialised machine vision image processing methods can frequently sort parts more efficiently when tactile methods are insufficient for robotic systems to sort through various shapes and sizes of parts. These methods use very specific algorithms that consider the parameters of the colours or greyscale values in the image to accurately define outlines or sizing for an object.

Pattern recognition
The technique of identifying patterns with the aid of a machine learning system is called pattern recognition. The classification of data generally takes place based on previously acquired knowledge or statistical data extrapolated from patterns and/or their representation. Image processing is used in pattern recognition to identify the items in an image, and machine learning is then used to train the system to recognise changes in patterns. Pattern recognition is utilised in computer assisted diagnosis, handwriting recognition, image identification, character recognition etc.

Digital video processing
A video is nothing more than just a series of images that move quickly. The number of frames or photos per minute and the calibre of each frame employed determine the video’s quality. Noise reduction, detail improvement, motion detection, frame rate conversion, aspect ratio conversion, colour space conversion, etc. are all aspects of video processing. Televisions, VCRs, DVD players, video codecs, and other devices all use video processing techniques.

Transmission and encoding
Today, thanks to technological advancements, we can instantly view live CCTV footage or video feeds from anywhere in the world. This indicates that image transmission and encoding have both advanced significantly. Progressive image transmission is a technique of encoding and decoding digital information representing an image in a way that the image’s main features, like outlines, can be presented at low resolution initially and then refined to greater resolutions. An image is encoded by an electronic analogue to multiple scans of the exact image at different resolutions in progressive transmission. Progressive image decoding results in a preliminary approximate reconstruction of the image, followed by successively better images whose adherence is gradually built up from succeeding scan results at the receiver side. Additionally, image compression reduces the amount of data needed to describe a digital image by eliminating extra data, ensuring that the image processing is finished and that it is suitable for transmission.

Image sharpening and restoration
The technique of identifying patterns with the aid of a machine learning system is called pattern recognition. The classification of data generally takes place based on previously acquired knowledge or statistical data extrapolated from patterns and/or their representation. Image processing is used in pattern recognition to identify the items in an image, and machine learning is then used to train the system to recognise changes in patterns. Pattern recognition is utilised in computer assisted diagnosis, handwriting recognition, image identification, character recognition etc.

Image processing can be employed to enhance an image’s quality, remove unwanted artefacts from an image, or even create new images completely from scratch. Nowadays, image processing is one of the fastest-growing technologies, and it has a huge potential for future wide adoption in areas such as video and 3D graphics, statistical image processing, recognising, and tracking people and things, diagnosing medical conditions, PCB inspection, robotic guidance and control, and automatic driving in all modes of transportation.

At Softnautics, we help industries to design Vision based AI solutions such as image classification & tagging, visual content analysis, object tracking, identification, anomaly detection, face detection and pattern recognition. Our team of experts have experience in developing vision solutions based on Optical Character Recognition, NLP, Text Analytics, Cognitive Computing, etc. involving various FPGA platforms.

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.

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Multimedia and Artificial Intelligence

Multimedia Intelligence: Confluence of Multimedia and Artificial Intelligence

AI Engineering, Multimedia Engineering /

In contrast to traditional mass media, such as printed material or audio recordings, which feature little to no interaction between users, a multimedia is a form of communication that uses a combination of different content forms such as audio, text, animations, images, or video into a single interactive presentation. This definition now seems outdated because coming to 2022, multimedia has just exploded with more complex forms of interactions. Alexa, Google Assistant, Twitter, Snapchat, Instagram Reels, and many more such apps are becoming a daily part of the common man’s life. Such an explosion of multimedia and the rising need for artificial intelligence are bound to collide, and that is where multimedia intelligence comes into picture. Multimedia market is being driven forward by the increasing popularity of virtual creation in the media and entertainment industries, as well as its ability to create high-definition graphics and real-time virtual worlds. The growth is such that between 2022 to 2030, the global market for AI in media & entertainment is anticipated to expand at a 26.9% CAGR and reach about USD 99.48 billion, as per the Grand View Research, Inc. reports.

What is multimedia intelligence?

The rise and consumption of ever-emerging multimedia applications and services are churning out so much data, giving rise to conducting research and analysis on it. We are seeing great forms of multimedia research already like image/video content analysis, video or image search, recommendations, multimedia streaming, etc. Also, on the other hand, Artificial Intelligence is evolving at a faster pace, making it the perfect time for tapping content-rich multimedia for more intelligent applications.
Multimedia intelligence refers to the eco-system created when we apply artificial intelligence to multimedia data. This eco-system is a 2-way give-and-take relationship. In the first relation, we see how multimedia can boost research in artificial intelligence, enabling the evolution of algorithms and pushing AI toward achieving human-level perception and understanding. In the second relation, we see how artificial intelligence can boost multimedia data to become more inferable and reliable by providing its ability to reason. Like in the case of on-demand video streaming applications use AI algorithms to analyse user demographics and behaviour and recommend content that they enjoy streaming or watching. As a result, these AI-powered platforms focus on providing users with content tailored to their specific interests, resulting in a truly customized experience. Thus, multimedia intelligence is a closed cyclic loop between multimedia and AI, where they mutually influence and enhance each other.

Evolution and significance
The evolution of multimedia should be credited to the evolution of smartphones. Video calling through applications like skype, and WhatsApp truly marked that multimedia is here to dominate. This was a significant move because they completely revolutionized long distance communication. This has evolved further to even more complex applications like video streaming apps like discord, twitch, etc. Then AR/VR technology took it a step ahead by integrating motion sensing and geo-sensing into audio, and video.
Multimedia contains multimodal and heterogenous data like images, audio, video, text, etc. together. Multimedia data has become very complex, and this will be incremental. Normal algorithms are not capable enough to co-relate and derive insights from such data and this is still an active area of research, even for AI algorithms it’s a challenge to connect and establish a relationship between different modalities of the data.

Difference between media intelligence and multimedia intelligence
There is a significant difference between media and multimedia intelligence. Text, drawings, visuals, pictures, film, video, wireless, audio, motion graphics, web, and so on are all examples of media. Simply put, multimedia is the combination of two or more types of media to convey information. So, to date, when we talk about media intelligence, we are already seeing applications that exhibit it. Voice Bots like Alexa and Google Assistant are audio intelligent, Chatbots are text intelligent, and drones that recognize and follow hand gestures are video intelligent. There are very few multimedia intelligent applications. To name one: There is EMO – An AI Desktop robot that utilizes multimedia for all its interactions.

Industrial landscape for multimedia intelligence
Multimedia is closely tied to the media and entertainment industry. Artificial Intelligence enhances and influences everything in multimedia.

Landscape for Multimedia Intelligence

Let’s walk through each stage and see how artificial intelligence is impacting them:

Media devices
The media devices that have increasingly become coherent with artificial intelligence applications are cameras and microphones. Smart cameras are not just limited to capturing images and videos these days, but they increasingly do more stuff like detecting objects, tracking items, applying various face filters, etc. All these are driven by AI algorithms and come as part of the camera itself. Microphones are also getting smarter where AI algorithms do active noise cancellations and filter out ambient sounds. Wake words are the new norm, thanks to Alexa and Siri like applications that next-gen microphones are having in-built wake-word or key-phrase recognition AI models.

Image/Audio coding and compression
Autoencoders consists of two components namely encoder, and decoder and are self-supervised machine learning models that use recreating input data to reduce its size. These models are trained as supervised machine learning models and inferred as unsupervised models, hence the name self-supervised models. Autoencoders can be used for image denoising, image compression, and, in some cases, even the generation of image data. This is not limited to images only, autoencoders can be applied to audio data too for the same requirements.
GAN (General Adversarial Networks) are again revolutionary deep neural networks that have made it possible to generate images from texts. OpenAI’s recent project DALLE can generate images from textual descriptions. GFP (Generative Facial Prior)-GAN is another project that can correct and re-create any bad image. AI has shown quite promising results and has proven the feasibility of Deep learning-based image/audio encoding and compression.

Audio / Video distribution
Video streaming platforms like Netflix and Disney Hotstar extensively use AI for improving their content delivery across a global set of users. AI algorithms dominate personalization and recommendation services for both platforms. AI algorithms are also used for the generation of video meta-data for improving search on their platforms. Predicting content delivery and caching appropriate video content geographically is a challenging task that has been simplified to a good extent by AI algorithms. AI has honestly proven its potential to be a game-changer for the streaming industry by offering effective ways to encode, distribute, and organize data. Not just for video streaming platforms, but also for game streaming platforms like Discord, and Twitch and communication platforms like Zoom, and Webex, AI will become an integrated part of AV distribution. 

Categorization of content
On the internet, data is created in a wide range of formats in just a few seconds. Putting stuff into categories and organizing it could be a huge task. Artificial intelligence (AI) steps in to help with the successful classification of information into relevant categories, enabling users to find their preferred topic of interest faster, improving customer engagement, creating more enticing and effective targeted content, and boosting revenue.

Regulating and identifying fake content
Several websites generate and spread fake news in addition to legitimate news stories to enrage the public about events or societal issues. AI is assisting with the discovery and management of such content, as well as with the moderation or deletion of such content before distribution on internet platforms like social media sites. All platforms including Facebook, LinkedIn, Twitter, Instagram, etc. employ powerful AI algorithms in most of their features. Targeted ads services, recommendation services, job recommendations, fraud profile detections, harmful content detections, etc. has AI in it.

We have tried to cover how multimedia and artificial intelligence are interrelated and how they are impacting various industries. Still, this is a broad research topic since media intelligence is still in cogs where AI algorithms are still learning from single media, and we build other algorithms to co-relate them. There is still scope for the evolution of AI algorithms that would understand the full multimedia data in a singularity like how a human does it.

Softnautics has a long history of creating and integrating embedded multimedia and ML software stacks for various global clients. Our multimedia specialists have experience dealing with multimedia devices, smart camera applications, VoD & media streaming, multimedia frameworks, media infotainment systems, and immersive solutions. We work with media firms and the domain chipset manufacturer to create multimedia solutions that integrate digital information with physical reality in innovative and creative ways across a wide range of platforms.

Read our success stories related to Machine Learning services around multimedia to know more about our expertise.

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Right Python Framework Selection for Automation Testing

Right Python Framework Selection for Automation Testing

Quality Engineering /

Test automation is the practice of automating test execution using frameworks and tools to carry out tests more quickly and reduce the need for human testers. In this method of software testing, reusable test scripts are created to test the functioning of the application, cutting down on overall regression time and facilitating quicker software releases. Utilizing test automation shortens the testing life cycle’s regression time and improves quality of releases. According to a report published by future market insights group, the global automation testing market is expected to grow at a CAGR of 14.3% registering a market value of US$ 93.6 billion by the end of 2032. Automated test scripts can be written in several different programming languages, such as Python, C#, Ruby, Java, etc. Among them, Python is by far the most popular language used among automation engineers for automation testing. It provides various useful tools and libraries used for automation testing. Python also extensively supports many different types of test automation frameworks. Apart from the default Python testing framework, unit test (or PyUnit), various python frameworks are available that can be more suitable for the project. The most appropriate test framework for the project can be selected based on the project requirement, size, and the automation framework practiced, for example, TDD (Test Driven Development), BDD (Behaviour Driven Development), ATDD (Acceptance Test Driven Development), KDD (Keyword Driven Development), etc.

Types of Python testing frameworks

Test Automation Framework

PyTest:

PyTest is an open-source framework, and it supports unit testing, API testing, and functional testing. In PyTest, the test cases follow a particular format where tests either start with test_ or end with _test.

Prerequisites:

  • Basic knowledge of Test-Driven Development framework
  • Working knowledge of Python

Pros:

  • Can be used for projects that practice TDD
  • Helps in writing test suits in a compact manner
  • Fixtures and parameterized tests cover numerous test case combinations without rewriting them
  • Markers can be used to group tests or skip them when running the entire test suite
  • Many inbuilt and third-party plugin support that can add new features like report generation etc.
  • Supports parallel execution of test cases using the pytest-xdist plugin
  • Huge community support
  • Implements python decorators and can leverage python programming flexibility completely

Cons:

  • It is not compatible with other python frameworks. All the tests must be rewritten if someone wants to move to another python framework.
  • It is purely based on python programming which requires to have sound knowledge over python programming

Robot

The Robot is an open-source framework. It is widely used for Selenium Test Automation.

Prerequisites:

  • Basic knowledge of Keyword Driven Development framework
  • Working knowledge of python is required to create new keywords

Pros:

  • Can be used for projects that practice ATDD, BDD, or keyword driven development
  • No prior programming knowledge is required if using pre-defined keywords
  • Easy to understand for clients and higher management who are from a non-technical background.
  • Many libraries and inbuilt keywords, especially for selenium testing
  • Good built-in reporting mechanism
  • Good community support

Cons:

  • Hard to customize HTML Reports
  • No built-in feature for parallel test execution. Pabot can be used to execute test cases parallelly
  • Creating new keywords can be time-taking or can be a restriction to testers with coding knowledge and therefore less flexible

Behave

Behave is an open-source framework. It is best suited for web testing. The scripts or feature files syntax is very close to the layman English language.

Prerequisites:

  • Basic knowledge of Behaviour Driven Development framework
  • Working knowledge of Python

Pros:

  • Can be used for projects that practice BDD
  • Availability of environmental functions, configuration settings, fixtures, etc. enables easy setup and clean-up
  • Easy to understand the framework
  • Can be integrated with other web development frameworks like flask, etc.
  • Simple to add new test cases
  • Report generation in JUnit format
  • Excellent support for documentation

Cons:

  • Parallel execution of test cases is not supported
  • Can only be used for black-box testing
  • Not suitable for integration testing

PyUnit

PyUnit (Unit Test) is the default testing framework for unit testing that comes with Python. Similar to PyTest, in PyUnit also the test cases follow a particular format where tests either start with test_ or end with _test.

Prerequisites:

  • Working knowledge of Python

Pros:

  • No additional package installation is required
  • Test report generation is faster
  • Individual tests can be run just by typing the test name on terminal
  • The default output is easy to understand

Cons:

  • Using PyUnit for large projects is significantly hampered by the support for too much abstraction and the abundance of boilerplate code

Nose2

Nose2 is the extension to the unit test. Nose2 adds support to the PyUnit framework by providing plugins.

Prerequisites:

Working knowledge of Python

Pros:

  • Easy to install
  • Have features like fixtures, parameterized tests, etc. like PyTest
  • Tests can be executed in parallel with multiple processes by using the (multiprocess) mp plugin
  • Lots of plugins can be added with features like reporting, selenium test automation, etc.

Cons:

  • Documentation is not extensive

Despite shorter development cycles, automated testing offers several advantages that are essential for producing high-quality applications. It minimizes the possibility of inevitably occurring human mistakes in manual testing procedures. Software quality is improved and the likelihood of defects endangering the delivery timeline is decreased by automated testing.

At Softnautics, we offer Quality Engineering Services for both software and embedded devices to assist companies in developing high-quality products and solutions that will help them succeed in the marketplace. Embedded software and product testing, DevOps and test automation, Machine Learning application/platform testing, and compliances with industrial standards like FuSa – ISO 26262, MISRA C, AUTOSAR, etc. are all part of our comprehensive QE services. STAF, our in-house test automation framework, helps businesses test end-to-end products/solutions with enhanced testing productivity and faster time to market.

Read our success stories related to to Quality Engineering services to know more about our expertise in the domain.

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Voice assistant solutions

Next-Generation Voice Assisted Solutions

AI Engineering /

In recent years, voice technology has steadily increased in popularity, from voice control in vehicles to smart speakers in homes. A voice assistant solution is built using machine learning, NLP (Natural Language Processing), and voice recognition technology. These solutions merge cloud computing to combine AI and can converse with the end users in natural language. As modern buyers continue to want simplicity and user-friendly voice-enabled interactions, businesses now consider designing and implementing a conversational-first approach to forge closer bonds with them. According to Market Research Future, the global voice assistant market is predicted to reach USD 30.74 Billion by the end of 2030, with a CAGR of 31.2 % from 2020 to 2030. Voice assistants have a bright future and will get better at understanding the context of instructions, phrases, meanings and will be able to help us in a unique and more customized manner.

Voice Assisted Solution Model

A voice assistant model uses voice recognition and synthesis to listen to specific voice commands and perform specific functions as requested by the user. A voice assistant system comprises different stages starting with automatic speech recognition which entitles the device to identify and translate voice from speech to text. Afterward, text interpretation is performed using NLP (Natural Language Processing) which analyses speech in text form and understands the user’s intent, once the intent has been identified the desired action is carried through API (Application Programming Interface) connection and returned to the user as feedback using Text-to-Speech (TTS) technology.

Voice Assistant Solution Model

With the emergence of advanced AI technologies, companies may create synthetic speech that sounds like a human voice to resolve customer queries more effectively. Businesses across a variety of industries, including retail, automotive, media & entertainment, and healthcare, are realising the benefits of the technology and are using it to provide better, and more individualized customer experiences.

Applications of Voice Assisted Solutions

Automotive
Automakers are ensuring that their vehicles have the latest speech AI technology to suit customer demands and expectations, as more consumers desire in-car voice assistants. Voice assistant system can be easily integrated into the vehicle’s HMI (Human Machine Interface) through which different vehicle functions like music playback, windows adjustments, temperature control, and smartphone connectivity can be operated quite conveniently. With the use of voice assistant setting up the destination in the navigation system can be performed by voice commands, it also assists in calling other people without the use of any physical interference, as well as operating entertainment services that reduce the chances of driver distraction which lowers the number of accident cases. In addition, the voice assistant can communicate information to the victim’s family and the closest medical facility in the event of an accident. As a result, the level of safety is increased. With the advancement of AI in the fields of text-to-speech, NLP automakers can use different modes of voice as instructions depending on the driver’s situation. Auto manufacturers who want to stay ahead of the competition should seriously consider investing in voice AI technology.

Media & Entertainment
The media & entertainment sector is utilising voice assistants to offer people a tailored experience, rapid access to their favourite media, and quick, relevant search results. Voice assistants facilitate a rich and immersive experience with features like media asset management and interactive media where with voice instructions one can access all the media content. Users of voice assistants can control music, adjust the volume up and down, and skip tracks. In the case of smart TVs equipped with cutting-edge speech AI technology, they can comprehend difficult and compound inquiries as well as remember questions that have already been asked, making them more interesting and conversational. Voice assistants are also being used by entertainment apps to provide hands-free, rapid, and convenient user experiences for either the entire app or a specific feature.

Home Automation
It is now possible to use voice-controlled systems and devices to automate routine tasks because of technological breakthroughs. Having voice-controlled home automation gives you the ability to group all your home’s smart gadgets. Along with providing a remote-control option for entertainment devices like radios, music and songs, audiobooks, podcasts, etc., it assists with managing tasks like turning on and off the lights, fans, AC, door locks, curtains, etc. Home automation systems like Alexa and Google Home boost overall efficiency and can connect with several devices hassle-free. In addition, the integration of voice commands into the installation of such home automation with voice control offers greater security alternatives by streamlining the available security options. It helps reduce human efforts, especially for the elderly and disabled people.

Consumer Industry
As more voice-activated devices enter the market and more electronics makers start to integrate voice capability into current products and services, the popularity of voice assistants in the consumer industry is only anticipated to increase. Consumers will increasingly prefer using this technology to engage with their devices such as refrigerators, smart TVs, air conditioners, and all kinds of gadgets it comes up with in this era. Voice integrations make entertainment and social interaction more accessible to those with physical disabilities. Through information lookups, reminders, and routines to make calls, read/send emails, etc., they can help persons with memory impairments. Some of the areas under consumer electronics that are rapidly becoming popular with the inception of voice technology are smart wearable & fitness tracking, smart security & surveillance system, and digital personal assistants.

Voice assistants are becoming increasingly popular due to their capabilities that cut down on handling time and costs while maintaining accuracy and precision. They are getting better at decoding questions to provide timely, relevant answers. There are numerous opportunities for far richer and more in-depth interactions with clients. Voice assistant is becoming a technology that can’t be missed out, especially with the eventual rollout of 5G and the advancement in machine learning.

At Softnautics, we provide AI engineering and machine learning services and solutions with expertise on edge platforms (TPU, Rpi, FPGA), NN compilers for the edge, cloud platforms accelerators like AWS, Azure, AMD, and many more targeted for domains like Automotive, Multimedia, Industrial IoT, Consumer, and Security-Surveillance. Softnautics helps businesses in building high-performance cloud and edge-based ML solutions like key-phrase/voice command detection, VUI (Voice User Interface) design, hand gesture recognition, object/lane detection, and more across various platforms.

Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.

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