July 2025

Over the years, we’ve worked with hundreds of engineering teams across different industries, and one pattern consistently emerges: the most successful engineers are those who master automation through shell scripting. While technologies come and go, the fundamental ability to automate repetitive tasks and streamline operations remains invaluable. Through our experience training engineers at Fortune 500 companies and fast-growing startups, we’ve identified five shell scripting examples that consistently deliver the highest return on investment. These aren’t theoretical exercises—they’re proven solutions that our clients use daily in production environments. The Reality of Modern Engineering Operations When we visit client sites, we often see talented engineers spending hours on tasks that could be automated in minutes. Database administrators manually checking disk space across dozens of servers. DevOps engineers running the same deployment commands repeatedly. Security teams generating compliance reports by hand every month. This manual approach doesn’t just waste time —it introduces human error and prevents engineers from focusing on strategic initiatives. The organizations that recognize this early and invest in automation capabilities consistently outperform their competitors. We’ve noticed that companies with strong scripting cultures tend to have fewer outages, faster deployment cycles, and more satisfied engineering teams. The correlation isn’t coincidental – automation creates a foundation for reliability and innovation. 1. Intelligent System Monitoring Solutions One of the most common requests we get is help with monitoring and alerting. Many teams start with basic tools that generate too many false positives or miss critical issues entirely. The solution isn’t always buying expensive monitoring software —sometimes a well-crafted shell script provides exactly what’s needed. We recently worked with a financial services company that was drowning in alerts. Their monitoring system generated hundreds of notifications daily, most of which were noise. We helped them build custom monitoring scripts that understood their application patterns and business cycles. The key breakthrough was implementing context-aware thresholds. Instead of static limits, the scripts learned normal behavior patterns for different times of day and days of the week. A CPU spike that’s normal during month-end processing becomes an alert during quiet periods. These scripts also implement intelligent escalation. Minor issues generate internal tickets, while critical problems immediately page the on-call engineer. Self-healing capabilities handle common problems automatically —restarting stuck processes, clearing full log directories, or failing over to backup systems. What impressed the client most was how these scripts reduced their mean time to resolution. Instead of engineers manually diagnosing problems, the scripts provide detailed context about what’s wrong and often suggest specific remediation steps. 2. Bulletproof Backup and Recovery Systems Backup failures are devastating, but they’re also preventable with proper automation. We’ve helped dozens of companies recover from data loss incidents, and in almost every case, better backup scripts could have prevented the crisis. One healthcare client learned this lesson the hard way when a ransomware attack encrypted their primary data. Their existing backup system had been failing silently for weeks, leaving them with no clean recovery point. We rebuilt their backup strategy from scratch, focusing on verification and testing. The new backup scripts don’t just copy data – they validate integrity at every step. Checksums verify that copied files match the originals. Automated restore tests ensure that backups actually work when needed. Encryption protects data both in transit and at rest. For database-heavy environments, we implement sophisticated backup strategies that minimize performance impact. Hot backups run against read replicas when possible. For systems where that’s not feasible, the scripts coordinate with application teams to schedule backups during maintenance windows. Cloud integration has transformed backup strategies for many of our clients. Scripts automatically upload encrypted backups to multiple cloud providers, implementing geographic redundancy that would have been prohibitively expensive just a few years ago. 3. Automated Log Analysis and Intelligence Log analysis is where many engineering teams miss huge opportunities. Logs contain incredible insights about system performance, user behavior, and potential security threats – but only if you know how to extract and interpret the information. We worked with an e-commerce company that was struggling with intermittent performance issues. Their engineers would spend hours manually correlating logs from different systems, trying to identify patterns. We automated this process with scripts that continuously analyze logs and identify anomalies. The scripts parse logs from web servers, application containers, databases, and load balancers. They correlate timestamps across systems, accounting for clock drift and timezone differences. Machine learning algorithms identify patterns that human analysts might miss. Real-time alerting capabilities notify engineers when unusual patterns emerge. A sudden increase in database query times combined with specific error patterns might indicate an impending outage. User behavior anomalies could signal a security breach or bot attack. What really impressed this client was the business intelligence aspect. The scripts generate daily reports showing user engagement patterns, popular features, and performance trends. This information helps product managers make data-driven decisions about feature development and infrastructure investments. 4. Development Environment Standardization Environment setup is often overlooked, but it’s critical for team productivity. We’ve seen too many projects delayed because new team members spent days configuring their development environments, or because subtle differences between developer setups caused mysterious bugs. At a software company we worked with, new engineers needed almost a week to get productive. They had a 50-page setup guide that was always outdated, and everyone’s environment was slightly different. We helped them create comprehensive setup scripts that get new developers running in under an hour. These scripts handle everything: installing required software, configuring development tools, setting up databases, pulling the latest code, and running initial tests. They’re idempotent, meaning you can run them multiple times safely, and they handle different operating systems gracefully. Container integration was a game-changer for this client. Their scripts now build consistent Docker development environments that match production exactly. This eliminated the “works on my machine” problem that had plagued their deployments. The scripts also integrate with their continuous integration pipeline. When new dependencies are added to projects, the setup scripts automatically update, ensuring that all developers stay synchronized with the latest

The Internet of Things (IoT) is transforming communication and interaction between all digital devices in a vastly interconnected world. Over the last few years many billions of smart devices of every kind, ranging from home appliances to industrial sensors, have emerged. All these devices use an operating system specifically designed for the special conditions addressed by IoT and are less commonly known as IoT Operating Systems or IoT OS. Constructors of these systems are working diligently to extend the capabilities of their IoT OS while keeping the unique challenges of the IoT environment in mind, including limited processing power, battery constraints, and the need to communicate in real-time. This article tackles some of the most widely used IoT operating systems by 2025, what they do, and how these operating systems are helping to facilitate our IoT world. Key Takeaways IoT operating systems tend to be designed to be lightweight, and are geared toward utilizing lower power and memory. IoT OS also provides base capabilities for common communication protocols, like MQTT and CoAP, to facilitate the communication of data. Security and OTA (over-the-air) updates are important features of today’s IoT OS. Some of the popular IoT OS include TinyOS, FreeRTOS, Zephyr, RIOT, and Mbed OS. Selecting an IoT OS requires consideration of the hardware used, application, and security requirements. Introduction to Popular IoT Operating Systems Unlike conventional computer or mobile operating systems, IoT OS run on resources with less memory and less CPU power, often relying on battery power as well. This means IoT devices run on a specialized platform, which is optimized to run in a resource-constrained environment, while also being transparent, reliable, and secure. In the world of IoT, operating system software enables devices to receive, process, and transmit data in real-time while managing energy consumption and communicating through different networks. It is because of these abilities, along with support for multiple OS across various hardware and application domains, that has allowed IoT operating systems to become so popular. What Are Popular IoT Operating Systems? Core Definition & Importance Famous IoT OS are software platforms in embedded hardware that is operating as IoT devices. The characteristics of IoT OS include: Operating with limited resources – less processing power, memory, etc. Real-time data processing to provide immediate response. Power / energy efficiency to prolong the life of battery powered devices. Support for secure communication and remote updates. The use of operating systems is meaningful because they provide functioning and secure infrastructure and support for IoT devices, which is important when devices are responsible for sensitive information and/or control important functions. Top Popular IoT Operating Systems and Their Key Functions Let’s explore some of the leading operating systems dominating the IoT landscape in 2025. 1.TinyOS TinyOS is known for its extreme lightweight-ness. TinyOS is used for wireless sensor networks and where extreme battery life is needed. It has been programmed by using nesC. The programming structure of nesC with TinyOS is intentional to run efficiently with as little power consumed as possible, and with the lowest latency for power efficiency on each tiny processing device, resulting in longer times between each charging or maintenance event for a unit. 2.Contiki OSThis open-source system features powerful networking capabilities with support for current protocols including the IPv6, 6LoWPAN, and CoAP. It is a multitasking system that allows devices to carry out multiple operations in the same amount of time. Contiki is well suited for applications involving sensor networks for smart cities and industrial applications. 3.FreeRTOS and Amazon FreeRTOSFreeRTOS, a real-time operating system, has deterministic timing which is required for applications such as health monitoring or industrial automation. FreeRTOS is small enough to run on microcontrollers. Amazon FreeRTOS takes FreeRTOS, add cloud integration, and provides a means for developers to connect devices securely to AWS services. This has potential to ease the development of cloud-connected IoT applications as we move forward. 4.Zephyr OSThe Zephyr operating system is acclaimed for its architectural scalability and modularity, coupled with its strong security focus. It supports numerous hardware platforms and is chiefly deployed in industrial IoT and safety-critical systems, where security and reliability are of utmost priority. 5.RIOT OS RIOT operates with a microkernel architecture which supports multithreading and full protocol stacks, allowing it to span professional and academic IoT development while supporting a diverse range of microcontrollers to make connected, efficient devices. 6.Mbed OS Mbed OS is built for ARM Cortex hardware and offers commercial-grade security and connectivity. Mbed OS allows developers to rapidly build robust embedded IoT applications and offers significant support through an expansive ecosystem. 7.Ubuntu CoreUbuntu Core is a lightweight and secure Linux operating system with excellent cloud capabilities. It is suitable for IoT devices that need a fully featured Linux operating system and a secure and resilient infrastructure. 8.Tizen OSTizen OS is Linux-based operating system used in many different devices, including wearables and smart TVs. It is fast and secure and provides a cleaner interface. It has quite substantial backing from various companies that will ensure that Tizen OS is continually updated and stable. 9.Windows 10 IoT Windows 10 IoT includes both Core and Enterprise versions, which provide a comfortable and familiar environment for industrial and edge devices. Windows is commonly used for IoT as it has broad hardware compatibility, and it works well with Microsoft enterprise software.. 10.OpenWrt OpenWrt is a Linux-based operating system used for routers and network equipment. OpenWrt has excellent customizability while also having strong security which makes it a great option for gateways and security hubs. Key Functions Shared Across Popular IoT Operating Systems There are a few fundamental functions that are consistent across these well-known IoT OS: Efficient Resource Management: They efficiently use limited CPU and memory to ensure devices work effectively. Real-time Task Scheduling: When many IoT applications require real-time responses, the characteristics of a real-time OS enable efficient task sequencing and scheduling. Communication Protocols: Include support for MQTT, CoAP, TCP/IP and many others in the IoT ecosystem so that devices work together and seamlessly exchange

The automotive industry is moving fast, and the ability for vehicle systems to communicate with each other is more important than ever. As an automotive engineer, it is crucial to understand the top five automotive protocols to help ensure vehicles operate safely, reliably, and efficiently. Key Takeaways: Automotive communication protocols allow data to flow between Electronic Control Units (ECUs). For many years, the Controller Area Network (CAN) protocol has been the foundation of most automotive networks. There are protocols even more specialized than CAN. These other protocols like LIN, FlexRay, MOST, and Automotive Ethernet serve functions with different objectives, from simple controls to high-speed multimedia communications. Understanding these protocols can help engineers design vehicles that are increasingly safer and better connected. Keeping up to speed with the changes in communications, relay protocols and standards may potentially improve your career and the automotive industry. 1.What Are Automotive Communication Protocols? Automotive protocols are the rules and standards that allow various vehicle parts – called Electronic Control Units (ECUs) – to communicate effectively. Consider a car to be a tiny networked computer with multiple “brains” controlling the engine, brakes, airbags, infotainment, etc. One task of protocols is to ensure messages are communicated clearly and without errors, essential for performance and safety. Today’s cars contain dozens, or, in some situations, hundreds of ECUs. Without sound protocols in place the communication these critical components provide could not effectively work together and could ultimately cause failures and safety risks. Why CAN Is the Most Popular Protocol Developed by Bosch in the 1980s, the CAN protocol is the primary communication bus within the vast majority of cars today. The protocol allows for the communication of different ECUs to one another in a manner which can be accomplished quickly, reliably and with built-in error correcting features. The CAN protocol offers communication speeds up to 1 MB, and a multi-master communication model which allows for a number of ECUs to initiate communication, preventing the accidental interrupting of other messages. The CAN protocol provides message priorities, meaning critical information (e.g., a brake message) would never be delayed. The use of the CAN protocol typically includes engine controls, ABS, and airbags, as well as versions of the protocol including Standard CAN and CAN-FD (Flexible Data Rate) which increase bandwidth or throughput capabilities for modern applications. 2. Local Interconnect Network (LIN) Protocol Where LIN Shines: Simple and Cost-Effective Although CAN is fast and powerful, not all car systems ought to be fast. There are systems that are slower and do not deal with critical parts of the car, where the LIN protocol can be used instead. LIN, or Local Interconnect Network employs a single wire connection and uses a master-slave relationship with one master and many slaves. LIN is limited to a communication speed of up to 20 kbps making it ideal for applications like door locks, window winders, and interior lighting. Because LIN is cheaper and simpler, it allows manufacturers to cut costs on low-cost, low-demand, body-related subsystems that do not require safety margins. 3. FlexRay Protocol High Speed and Safety for Advanced SystemsFor safety-critical applications that demand very precise timing, such as steer-by-wire and brake-by-wire systems, FlexRay is the primary protocol of choice. FlexRay supports a communications speed of up to 10 Mbps and employs both time-triggered and event-triggered communication methods. FlexRay incorporates redundant channels for greater fault tolerance (if one path fails, then communication still occurs). FlexRay is perfectly suited for advanced driver assistance systems (ADAS) and autonomous driving technology. 4. Media Oriented Systems Transport (MOST) Protocol The Multimedia Network Inside Your CarToday’s vehicles are equipped with entertainment systems, ranging from high-quality music players to video streaming screens. The MOST protocol is designed to handle multimedia data in a more efficient way. MOST employs a ring topology and synchronizes audio and video to allow for seamless playback without buffering or delays. MOST is also used for head units, navigation systems, and rear seat entertainment. Most importantly, MOST is not designed to handle safety critical control, and principally supports seamless multimedia playback. 5. Automotive Ethernet The Future Network for Connected and Autonomous VehiclesAs vehicles get more connected and autonomous, data requirements are increasing dramatically. Automotive Ethernet provides the bandwidth needed to support systems such as ADAS, infotainment, and vehicle-to-everything (V2X) communication. Automotive Ethernet supports speeds from 100 Mbps up to 10 Gbps. With an IP based communication framework, it has a lot of built in IP networking standards and it is scalable to future technologies. It will also enable simple integration to external networks which will provide cloud-connected vehicle diagnostic capabilities and the potential for over-the-air/software updates. Conclusion Knowledge of the major automotive protocols including CAN, LIN, FlexRay, MOST and Automotive Ethernet are critical for any automotive engineer. The speed of advancement in vehicle technology necessitates corporate training in the best and most efficient manner available in order for engineers to learn and hone their skills or at least leverage the most up to date standards for communications. Further, investing in corporate training ensures that teams are prepared to innovate where they can, and make safer and smarter vehicles.

Field Programmable Gate Arrays (FPGAs) have become a cornerstone technology in industries like aerospace, automotive, communications, and embedded systems. Whether you’re a student, engineer, or professional looking to expand your skill set, an industry-focused FPGA course equips you with practical, in-demand abilities. Key Takeaways: Understanding FPGA architecture is the fundamental principle for developing virtually any hardware solution. Proficiency in Hardware Description Languages (HDL), namely VHDL and Verilog, means you can design and simulate reliable architectures. Practical experience with tools such as Vivado and Quartus Prime prepares you for anything related to FPGA or digital hardware. Having experience with debugging, optimization, and power management contributes to developing effective and reliable hardware. Understanding FPGA Architecture and Design Basics Before you really dive into using FPGAs for your design efforts, it’s helpful to understand what an FPGA is and how it operates. An FPGA is a semiconductor device that can be customized. It can be programmed for different digital logic operations after it’s been manufactured. It allows you to “field-program” an hardware circuit in a way that is designed for a specific application, without needing to have a new chip manufactured.Core Components You’ll Learn Configurable Logic Blocks (CLBs): The basic logic fabric which you will configure. Input/Output Blocks (IOBs): This is the interface to the FPGA from external devices Routing Networks: This connects the logic blocks and I/Os to create more complex circuits. Courses begin by teaching you the fundamentals of digital logic as you learn about how to understand gates like AND, OR, NOT; how to use flip-flops for storing data; and using multiplexers to route signals. After that you move on to learn about Hardware Description Languages (HDL), which allows you to accurately describe the behavior of this digital hardware. Mastery of Hardware Description Languages (HDL) Hardware description languages such as VHDL and Verilog are the “programming languages” of FPGAs. Although HDL languages do not create software programs, you write hardware behaviors describing how circuits operate at the same time instead of instructing on which to execute first. Why Learn HDL? HDL provides a way to express digital circuits that can be complicated, while still remaining easy to read. You can simulate the design on a computer to verify the logic of the design before you assemble the hardware. HDLs allow for hardware designs to be reused and modular designing of large systems. 1) VHDL and Verilog HDL languages are widely used in many industries: 2) VHDL is strong in aerospace and defense industries. Verilog is more common in commercial electronics, such as electronics and consumer devices. Developing and Testing FPGA Designs with Testbenches Building the design is only half the journey. Testing to guarantee your FPGA works correctly under all situations is equally as important. What Are Testbenches? Testbenches are simulation scripts or programs that provide inputs to your FPGA design and automatically examine outputs. They are a representation of your design in the real world and test your design to check if it gives the expected response. Techniques and Tools You’ll Learn Writing testbenches in SystemVerilog or UVM (Universal Verification Methodology). Using a Python based framework, such as cocotb, to run automated test cases. Running simulations by Vivasdo or Quartus as tools. This way of working allows you to find bugs early and mitigates expensive mistakes on actual hardware, while also developing your debugging skills and forming good design practices. You will become familiar with the technical terms, including “assertions,” “coverage” and “waveform analysis,” all of which are essential for any professional working with FPGAs. Hands-On Experience with FPGA Development Tools and Software FPGA design is all about hands-on skills. You will experience the complete hardware design flow in a very industry-like manner: You will learn how to use Vivado Design Suite (from Xilinx) and Quartus Prime (from Intel/Altera). You will learn how to synthesize your HDL code to a gate-level netlist. You will learn how to place and route the circuit into the FPGA fabric. You will learn how to generate the configuration bitstream. You will learn how to program the physical FPGA development board. You will learn how to upload your design and test it live. You will also practice hardware debugging! You will learn how to use Integrated Logic Analyzers (ILA) that allow you to see real-time internal signals in the FPGA, which is extremely helpful for resolving timing and logic errors. The reason this “learn by doing” experience is important: It is important for some of the graduate students to go into the job market successfully. It is one thing to be able to write HDL code on your laptop or PC, but being able to load it on real hardware and being able to look at real signals is a whole different and necessary level of skill. Formal Verification and Design Validation Techniques In safety-critical areas, such as aerospace and medicine, formal verification provides an additional degree of confidence. Formal methods have a distinct value proposition relative to simulation – simulation tests a small number of sampled operational scenarios; formal methods are able to mathematically prove that your design meets the specification in every possible case. You will come away with a general understanding of the following techniques Equivalence checking – when you optimize designs, equivalent testing checks that they still operate as expected. Model checking – explores all system states to find bugs. Assertions and coverage to rigorously validate your designs. Formal verification will be the most advanced subject we discuss, but knowing what it and how to effectively apply it in practice will help you stand out among candidates applying for higher reliability level positions. Industry-Relevant Applications and Use Cases In order to relate the theory to tangible relevance, courses offer FPGA-based projects directly from many sectors. For example: Communication Systems: digital modulation, encryption, error correction (just to name a few). Aerospace & Defense: real-time processing, radar systems, autonomous vehicles. Automotive Electronics: advanced driver assistance programming, control of electric vehicles. Medical Devices: imaging, diagnostics, patient monitoring. Industrial Automation: robotics, data acquisition,

Teamed with C++, Qt is an excellent way to build modern cross platform applications. Whether you’re looking to develop desktop applications or embedded devices, you will be able to work with many more opportunities as a developer by knowing Qt. You will understand why it is used and respected by developers across the world from working in automotive, medical, and many more. Key Takeaways: Qt is a cross-platform GUI framework that has been designed to utilize a C++ backend for the purpose of creating fast, native-like applications. Signals and slots simplify the process of handling events inside your application. The extensive range of modules and APIs provided will enhance the productivity of your development. Depending on your current development experience with Qt, learning Qt can increase your productivity, increase your cross-platform capabilities, and increase your application’s performance. Qt is also a possible solution for desktop, embedded, and mobile application development. Introduction: What is Qt with C++ Qt is a widely used open-source framework written in the programming language C++. It enables developers to create apps for numerous operating systems, including Windows, macOS, Linux, and mobile platforms, without rewriting most of the code. This is often referred to as write once, compile anywhere. C++ is a powerful programming language with speed and flexibility, and Qt expands C++ by providing tools to create graphical user interfaces (GUIs), as well as manage system resource. This combination creates a powerful platform for app development, with great performance and beautiful design, so it is no wonder that Qt is popular among developers who are designing apps. Why combine Qt with C++? C++ has the capability to do low-level tasks and perform higher-level processing very well. Qt extends C++ with useful functionality to simplify writing apps that utilize a UI, as well as databases, networking, and a lot more. Thus, C++ and Qt can let you build high-performance apps with modern user interfaces. Core Concepts of Qt with C++ To get started with Qt, it’s helpful to understand some core concepts. Qt as a GUI toolkit and beyondWhile Qt is primarily known for building graphical user interfaces (GUIs), its capabilities expand far beyond that: Includes a variety of widgets like buttons, labels, and sliders Signals and slots for event handling Handles threading and data handling Provides networking and database support Has multimedia and OpenGL graphics support The Meta-Object System and QObjectAt the foundation is the QObject class, which employs a meta-object system to provide runtime type information, signals, and slots. This meta-object system allows for simple communication between different areas of your application — e.g. a button click triggers some behavior. What Are Signals and Slots?Qt uses signals (events) and slots (handlers) instead of classic callbacks: Signal: An event that was emitted whenever something happens, like when a button is clicked. Slot: A function that is connected to a signal which responds to it.This mechanism makes it easier to deal with user actions and cleaner code. Key Qt Modules for C++ DevelopmentQt has several modules. The primary modules or classes include: QtCore: Core features that are not related to GUI, including timers and threads QtWidgets: User interface widgets, also with layout management QtNetwork: APIs for network programming QtSql: Accessing and querying databases QtMultimedia: Using multimedia functions for audio and video Benefits of Learning Qt with C++ Cross-Platform AdvantageWhen you code your application once using Qt, it runs on many platforms: Windows, macOS, Linux, Android, iOS. This saves you time and money! High Performance and Native LookQt applications run almost at native speed – they offer responsive performance. It uses the native platform’s style which provides a familiar, native experience.Rich, Mature APIs Qt has been evolving for over 20 years, and has many built in features like networking, file handling, database support, multimedia etc. So you don’t have to reinvent the wheel. Faster Development and Code Reuse Qt’s object-oriented design and features such as signals and slots make it faster and easier to develop applications. It allows you to write modular, reusable code that is easier to maintain. Extends C++ Capabilities for Modern AppsQt provides more than just standard C++, like GUI and event-driven programming capabilities, so you have a powerful toolbox at your disposal. Common Use Cases and IndustriesQt powered many applications in the following categories: Desktop applications for example productivity, media player, and IDEs Embedded systems like automotive infotainment and industrial controllers Mobile applications on Android and iOS with native performance. Medical and IoT applications with strong UI and application/system management. Some common industries using Qt are; automotive, healthcare, embedded systems, and mobile. Conclusion Qt with C++ is a powerful framework for applications that let developers create cross-platform applications with beautiful GUI, great performance, and deep backend integration. Whether you are writing for desktop, mobile, or embedded systems, learning Qt with C++ will put you in a position to compete amongst the technology elite. For companies looking to speed up innovation and development cycles, corporate training in Qt with C++ will ensure that teams are developing applications that can be supported, scalable, and modern. This course is not just an improvement to your skillset; it is an investment in preparing your future software! FAQ 1. What is Qt with C++ used for? Qt with C++ allows you to develop high performance, cross platform applications with graphical user interfaces (GUI), plus embedded systems and real-time applications. Is Qt with C++ suitable for beginners? Yes, good understanding of C++ is advantageous, but Qt also has an extensive amount of documentation and there are many tools to help a beginner. Why should I learn Qt with C++? Learning Qt with C++ provides you with the capabilities to create cross platform applications and makes you more marketable as someone who is part of industries like embedded systems, automotive, and software development.

Electronic Design Automation (EDA) has undoubtedly changed the design of chips by automating many of the more complex tasks we have to do. However, we are now at the next level with Circuit Foundation Models (CFMs) powered by AI. CFMs are shifting the landscape of circuit design, verification, and optimization, and making chip development faster, smarter, and more efficient. Key Takeaways Circuit Foundation Models use self-supervised learning and fine-tuning to learn the complexity of circuit tasks. CFMs provide enhanced accuracy, automation, and efficiency for EDA tasks such as synthesis, verification, and layout. AI-native EDA will enable faster design cycles and optimization of power, performance, and area (PPA). There are integration issues with CFMs including data quality, explainability, and workflow compatibility. Important advances in the future will be multi-modal models and enhanced partnerships between industry and academia. What Are Circuit Foundation Models? Circuit Foundation Models are a new generation of AI explicitly designed for EDA and chip design. Unlike conventional artificial intelligence models designed for narrow specific tasks, CFMs are large models that are trained in two parts: First is self-supervised pre-training—here, the models learn universal behaviors and characteristics by automatically mining enormous volumes of unlabeled circuit data. This phase is critical as it allows the models to learn deeply about circuit structures and behaviors using unlabeled data. Next is fine-tuning: adapting the model to perform specific tasks such as verifying the correctness of a design or predicting the behavior of a circuit. The two-step process offers CFMs flexibility and usefulness to a myriad of chip design tasks. CFMs typically come in two main types: Encoder models: These are learning a broad representation of circuits and can be used to make predictions about power or timing, for instance. Decoder models: These are often based on large language models (LLMs) and can produce outputs such as RTL code or scripted commands that can automate design processes. By exercising less reliance on large, labeled datasets and more reliance on learning from unwritten circuit data, CFMs can deliver new efficiencies and value add in EDA. Role of Circuit Foundation Models in EDA Electronic Design Automation encompasses numerous activities throughout the chip development process: synthesis verification, layout, routing, and more, and CFMs affect these processes in very important ways. They increase predictive fidelity as they identify design quality problems much earlier in the design process. For example, CFMs can indicate a potential timing violation or power bottleneck prior to embarking on the expensive physical design phase. Additionally, CFMs generalize well across different types of EDA tasks. Instead of maintaining a collection of isolated tools each intended to tackle a specific narrow task, a single CFM can approach synthesis optimization, verification assertions, or layout recommendations in a manner tuned to that specific phase of the chip design and development process. One of the biggest benefits is automation. CFMs have the ability to auto-generate verification test scripts or assertions, thereby saving engineers potentially hours of effort because CFMs automate tedious manual tasks. Plus, scripting an entire workflow with artificial intelligence introduces minimal human error and saves substantial turnaround time. How CFMs Transform Chip Design Processes Chip design primarily focuses on improving three parameters: power, performance, and area—often referred to as PPA. CFMs allow designers to achieve even better PPA metrics through: (i) Faster design cycles because AI-native EDA tools significantly reduce the simulation and verification steps, (ii) Better optimization as CFMs build knowledge of managing their poorly -defined and conflicting goal of minimizing power while not degrading performances, and (iii) more reliable functional correctness since CFMs can leverage smarter error detection during verification. For example, a CFM can work alongside the designers and examine a partially completed design and then suggest optimized RTL or reasonable layout modifications to improve the overall chip’s effort to deliver. If we think in terms of innovation, CFMs can operate on the development versions of code to rapidly code RTL design, or adapt design flows to its designs, which represents a fundamental shift from traditional rule-based EDA. Why CFMs Matter for Chip Designers Chip designers consistently endure strict deadlines, heavy complexity, and perpetual pressure to accelerate all efforts while improving yield rates. CFMs can deliver many smart assistant-like capabilities, which can benefit designers in several different ways: Allowing designers to receive early warnings whenever a design error is made. Accelerating repetitive work effort. Providing design optimizations based on prior designs learned through deep learning. Combined, design cycles improve and allow for more opportunity for innovation, because engineers will be able to use extra time to explore new designs and a lot less time doing repetitive work. Future Directions and Exciting Trends The use of CFMs in EDA is a burgeoning field with an accelerating trajectory. In theory, researchers have made practical advances in EDA with CFMs since approximately 2022, and these have involved: Multimodal Learning: Models that simultaneously process circuit code, circuit layouts (or shapes), circuit specifications, and circuit simulation/pre/post-layout results. Large Language Models (LLMs): The use of powerful generative AI that understands circuit languages and design intent. Industry Partnerships: Partnerships between semiconductor companies, AI researchers, and EDA tool vendors to rapidly develop specialized AI-native design tools. We can anticipate new AI-native design paradigms that will not only automate the current task and workflow, but fundamentally change how chips are designed. Conclusion Circuit Foundation Models (CFMs) are undoubtedly reshaping the world of Electronic Design Automation and chip design. CFMs are able to leverage the power of AI to provide more fine-tuned, faster, and optimized chip development with regards to power, performance, and area. For engineers and students looking to break into this field, taking a VLSI design course in Bangalore is an enticing option because Bangalore is a technology hub, there will be better access to state-of-the-art resources, and students and engineers will be exposed to the cutting-edge workflows of modern EDA tools like CFMs . From a semiconductor design and development standpoint it will be crucial to understand AI-driven tools and use them effectively in order to remain competitive. FAQ

Mastering MATLAB can dramatically improve your engineering skills and open doors to exciting career opportunities. Engineers across many fields rely on MATLAB for everything from solving complex problems to visualizing data and simulating systems. This article will guide you on how MATLAB training can truly boost your engineering capabilities, enhance efficiency, and help you excel professionally. Key Takeaways MATLAB training improves problem-solving and automation abilities for engineers Specialized training tailors skills for different engineering disciplines Training formats include online and classroom to suit your style MATLAB skills increase career opportunities and earning potential Real-world applications span simulations, data analysis, and design projects What Is MATLAB and Why Is It Important for Engineers? Understanding MATLAB MATLAB stands for Matrix Laboratory. It is a high-level programming platform that is specifically geared to engineers and scientists. It simplifies complex numerical operations and matrix manipulations, as well as visualizes data. Unlike many other programming languages, it relies on mathematical notation which engineers can grasp quickly. Why Do Engineers Need MATLAB? Handles complex calculations easily Provides specialized toolboxes for different engineering fields Visualizes data clearly with 2D and 3D plotting tools Supports algorithm development, simulations, and model prototyping According to MathWorks, the company behind MATLAB, engineers experience a 144% increase in MATLAB competence after taking training courses, showing the clear value of focused learning. Role of MATLAB in Different Engineering Fields Whatever field of engineering you are interested in—mechanical, electrical, aerospace, or civil—you can rely on MATLAB for important applications that support your engineering projects. Mechanical Engineering: Used for finite element analysis and control system simulations. Electrical Engineering: Used for signal processing and circuit modeling. Aerospace Engineering: Used for flight dynamics and trajectory simulations. Civil Engineering: Used for stress analysis and material testing. There can be no doubt that MATLAB is highly versatile—it’s a Swiss army knife for engineers! Core Benefits of MATLAB Training for Engineers Why You Should Invest in MATLAB Training Independent learning is great for those who can self-motivate, but structured learning produces faster results and provides clarity on which toolboxes are essential and how to use them. Top benefits of structured MATLAB learning include: Improved Problem-Solving: Learn to break down complex challenges and solve them using MATLAB’s powerful functions. Increased Time Efficiency: Automate repetitive tasks and save hours of manual work. Enhanced Visualization: Create attractive, clear graphs and 3D models for communicating results. Job Readiness: Gain certification, boost your résumé, and access more job offers with higher earning potential. Practical Application: Learn to apply MATLAB directly in your engineering projects and research. Tailored MATLAB Training for Specific Engineering FieldsTraining Designed for Your Specialty MATLAB training is not a “one size fits all” experience. It can be customized for your specific engineering discipline: Mechanical Engineers: Training covers kinematics and mechanical system simulations. Electrical Engineers: Modules include signal and image processing techniques. Aerospace Engineers: Focus on modeling flight and propulsion systems. This specialized training ensures you master the toolboxes essential for your field. For example, electrical engineers benefit from the Signal Processing Toolbox, which enables advanced analysis of noisy data that would be hard to analyze manually. Practical Skills You Learn Through MATLAB TrainingWhat You Will Gain MATLAB training offers more than theory. It’s hands-on, enabling students to: Create efficient MATLAB scripts and functions Use loops, conditionals, and data structures to automate workflows Apply numerical methods (e.g., integration, differentiation, root finding) using built-in tools Develop 2D and 3D data visualizations Build simulation models and simple graphical user interfaces (GUIs) Integrate MATLAB with hardware or other languages like C or Python These capabilities let you handle simulations, design optimization, and advanced analyses confidently. Conclusion Learning MATLAB is an essential employee development training type that builds engineering skills and workplace value. Improving your MATLAB knowledge gives you a way to solve problems more quickly, and it makes a tangible difference to the quality of your projects. Whether you are an individual engineer or a company that wants to provide staff training, focused MATLAB training will broaden your growth opportunity, build productivity, and drive career development success. Do your training today to become an engineering super-charged future-focused engineer FAQ Why should engineers learn MATLAB? MATLAB enables engineers to perform complex calculations more quickly, automate engineering processes, and visualize data in meaningful ways. Learning MATLAB will make you more efficient and valuable in your career What kind of engineering problems can MATLAB solve? MATLAB provides engineers with tools for calculation, simulation, data analysis, signal processing, control system design, and many other processes. It is used in mechanical, electrical, aerospace, and civil engineering Is MATLAB used more in industry or academia? MATLAB is widely used in university level engineering education, as well as for research. In industry, companies use MATLAB to design, test, and optimize engineering systems.

Embedded Linux is changing the way we think about edge computing, especially with regards to the new wave of AI-based devices at the edge. Embedded Linux can be open source, customizable, and approachable, which positions it to power the ‘smart’ devices at the edge of networks today. The field of embedded Linux in AI edge devices will be our topic of exploration as we explore current trends, advantages and disadvantages, and potential innovations for the future leap in technology. Key Takeaways    • Embedded Linux remains the operating platform for many AI-oriented edge                 devices because of its flexibility and open source ecosystem.   • AI frameworks such as TensorFlow Lite make it possible to offload real-time ML        (machine learning) processing on embedded Linux devices that are resource               constrained.   • Architectures such as ARM and RISC-V are gaining traction, and improving Linux       support for edge AI hardware.   • Secure boot, OTA updates, and security features are key to securing AI at the             edge.  • Developers will come up against hurdles such as optimizing Linux kernels and            limited resources.  • Available tools such as the Yocto Project and Buildroot help create custom                 embedded Linux build environments for AI workloads and applications. What Is Embedded Linux in AI-Powered Edge Devices? Embedded Linux is a simplified, customized edition of the Linux operating system, specifically designed for use on small, limited hardware. Whereas desktop versions of Linux (like Mint or Ubuntu) are being used in small, resource-limited devices for things like smart cameras, industrial sensors, wearable health monitors, and autonomous robots. Why is all of this important? Because running AI-powered applications directly on the device—rather than sending data to the cloud—decreases latency, minimizes the cost of networks, and protects user privacy. Processing information on the “edge” of the network requires an operating system that is reliable and lightweight. Embedded Linux brings all of this together. Developers can create specific combinations of the Linux kernel and packages to exactly match the specific hardware specs and AI workloads required by the device. From scaling embedded Linux from very small sensor nodes to more complex but still lightweight gateways, it aligns perfectly for AI-powered edge applications. The Current Landscape of Embedded Linux in Edge AI Devices Embedded Linux is the foundation for many types of AI-based edge devices in many industries: automotive, healthcare, smart cities, and manufacturing. As an open-source platform, it enjoys broad community support and has a huge degree of hardware compatibility. Popular Embedded Linux Solutions for AI at the Edge Yocto Project: A highly customizable framework ideal for building specialized Linux distributions that fit specific AI hardware needs.Buildroot: A lightweight tool to generate small Linux systems, perfect for minimalistic AI devices.OpenWRT: Primarily for networking devices, often used in smart gateways bridging sensors and cloud AI. From smart factory robots running on Linux to AI-enabled traffic cameras processing inference locally, embedded Linux powers everything. On the hardware side, we have ARM-based processors that dominate the embedded space for their power/dollar and balancing power consumption/performance. RISC-V can be seen as a parallel development, an open-source architecture that is viable as an ARM alternative based on current trends in embedded Linux distributions that support AI workloads. Emerging Trends Driving the Future of Embedded Linux in Edge AIIntegration of AI and Machine Learning Frameworks AI frameworks like TensorFlow Lite, PyTorch Mobile, and smaller ML runtimes now run efficiently on embedded Linux devices. These frameworks provide the ability for AI models to perform low-latency inference in real-time which is required for applications that include autonomous vehicles, drones, and industrial automation. At the same time, developers place emphasis on optimizing the Linux kernel in order to prioritize AI-related tasks often using real-time patches like PREEMPT-RT, which further minimize system response delay that is important for edge AI. Enhanced Security for Edge AI Devices As AI devices become more prevalent, security is critical. Embedded Linux offers cutting-edge security features like:    • Secure boot for validated software execution – prevents tampering  • Encrypted over-the-air (OTA) updates – enables secure AI model upgrades and           remote system updates  • Hardened Linux kernels with a security module like SELinux or AppArmor Large deployments in smart cities and healthcare mean the above security features are non-negotiable. Edge AI Hardware Accelerators and Linux Support New AI accelerators are being integrated into embedded Linux systems increasingly. Newly branded accelerators, such as Neural Processing Units (NPUs), GPUs, and Google’s TPU edge chips are appearing everywhere. All of these accelerators are now standard features, baked directly into embedded Linux systems. The Linux kernel continues to evolve with new drivers and middleware to utilize these additional accelerators in ways that make AI inference faster and more power efficient. Benefits of Embedded Linux for AI-Powered Edge Devices Embedded Linux has a number of benefits, which help make it the OS of choice for edge AI devices: • Flexibility & Customizability: Developers can remove the components they do not use when building for hardware constraints.• Open-Source Ecosystem: Developers have a massive code library of tools, frameworks, and community support that helps accelerate innovation by building upon shared ideas.• Scalability: Embedded Linux can scale from tiny sensor nodes to intelligent gateways and autonomous systems to suit a variety of use cases.• Power and Reliability: Embedding Linux creates a long-term stable kernel so the devices can be relied on in the field for years. Challenges and Considerations in Embedded Linux for AI Edge While there is much to appreciate about embedded Linux in AI devices, some challenges still exist: • Kernel Optimization: When optimizing Linux for AI workloads, we need to carefully change kernel parameters, maintaining appropriate response time and minimizing latency.• Memory and Resource Constraints: Although models can be optimized to run minimal footprints, they still require sufficient RAM, storage, and CPU time to execute

Real-time operating systems (RTOS) are important for embedded devices because they allow time-sensitive operations to be performed both reliably and on time. In all sorts of scenarios—whether it is controlling an infusion pump in healthcare, managing safety systems in vehicles, or running smart home devices—an RTOS enables repeatability and fast reaction times. In this article, we identify the top 7 real-time operating systems (RTOSs) you would use in embedded devices in 2025, the properties and applications of them, and how to choose which RTOS would be best for your project. Key Takeaways RTOS, also called Real-time Operating Systems, enables tasks in an embedded system to be executed in a timely and predictable manner. Examples of popular RTOS include FreeRTOS, Zephyr, VxWorks, QNX, SafeRTOS, Integrity, and embOS, to name just a few. Considerations in choosing an RTOS include things like latency, memory footprint, certification for safety systems, and license type. RTOS are used in numerous sectors, particularly and everywhere an embedded system is used, including automotive, industrial, Internet of Things (IoT), medical, aerospace, etc. Some of the new trends look at security, multi-core and cloud support features. What is a Real-Time Operating System? Understanding RTOS A Real-Time Operating System (RTOS) is an operational system that is more specialized than a standard operational system, designed to manage the hardware resources and run multiple tasks on embedded systems where time is critical. An RTOS is different from a general-purpose operating system (such as Windows or Linux) in that a standard OS does not have to guarantee that a high-priority task will complete in a certain amount of time. If it does not, the system may fail—especially if it is a safety-critical system. For example, in an ABS system (anti-lock braking system), an accident may be avoided through early detection and response of wheel slip. This is accomplished by the responsiveness of an RTOS, which is due to prioritized tasks and low-latency. Why RTOS are Essential in Embedded Systems Embedded devices typically work under strict constraints of power, memory, and CPU. However, they must respond instantly when an event occurs. Ideally, RTOS offer: Deterministic task scheduling: Tasks execute as scheduled. Low interrupt latency: Hardware signals are responded to immediately. Priority multitasking: Tasks are preempted for higher priority tasks. When these factors are addressed properly, RTOS minimizes timing delays and improves safety and reliability of embedded systems. How to Choose the Right RTOS for Embedded Devices When selecting an RTOS, there are many competing considerations: Performance and latency: The OS needs to meet real-time deadlines as well as minimize jitter (variation in response time). Memory footprint: Embedded systems often have limited memory (either flash or RAM). The RTOS should use as little of either as comfortably possible. Safety certifications: The RTOS may need to comply with a safety standard if you’re working in a safety-critical application (e.g., IEC 61508, ISO 26262). Hardware compatibility: The RTOS must support the specific microcontroller or processor in the device. Licensing: Open-source RTOS (e.g., FreeRTOS, Zephyr) are free but offer limited commercial support. Proprietary RTOS (e.g., VxWorks, QNX) offer premium features with commercial support. Community and vendor support: Tools, documentation, forums, and official support can speed up development. New RTOS trends focus on security, multi-core support, and cloud integration—especially relevant in IoT applications. Top 7 Real-Time Operating Systems in Embedded Devices 1. FreeRTOS FreeRTOS is the most popular RTOS—open-source or not—around the globe. It provides lightweight preemptive scheduling ideal for small microcontrollers typically found in consumer electronics and IoT devices. FreeRTOS is easily portable and has a very active community.FreeRTOS is now provided by Amazon Web Services (AWS), with added features for secure cloud connectivity. 2. Zephyr OS Zephyr OS is a modular, open-source RTOS designed for resource-constrained embedded and IoT applications. It supports many architectures including ARM Cortex-M and RISC-V, and features a full networking stack.Zephyr is well-suited for industrial and smart home devices due to its focus on security and evolving safety certifications. 3. VxWorks VxWorks is a commercial RTOS by Wind River, built for aerospace, defense, automotive, and similar industries. It offers hard real-time guarantees, deterministic scheduling, and low-latency operations.VxWorks has powered NASA Mars rovers and numerous avionics systems. 4. Integrity Integrity RTOS is designed for safety and security in embedded systems. It’s used in medical, aerospace, and defense applications that demand strict safety and fault tolerance.Integrity offers strong memory isolation, fault containment, and industry-required safety certifications. 5. SafeRTOS SafeRTOS is based on FreeRTOS and is built for applications needing compliance with functional safety standards like IEC 61508 SIL 3 and ISO 26262 ASIL D. It’s used in automotive, medical, and regulated markets.SafeRTOS brings the simplicity of FreeRTOS with added certification support. 6. QNX QNX is a Unix-like microkernel RTOS known for fault tolerance and security. It’s heavily used in automotive infotainment, industrial automation, and healthcare devices.Its modular architecture and multicore support make it suitable for real-time and high-throughput applications. 7. embOS embOS is a commercial RTOS developed for minimal latency and high efficiency on low-resource devices. It’s widely used in safety-critical automotive and industrial systems.embOS is certified to standards like IEC 61508 and ISO 26262, and supports deterministic behavior with middleware flexibility. Conclusion Selecting the appropriate real-time operating system (RTOS) is determined by factors such as performance, memory requirements, safety and security assurance (safety is a huge area of development in the RTOS space) and hardware support. FreeRTOS, Zephyr, VxWorks, Integrity, SafeRTOS, QNX and embOS are all leading examples of RTOS that support various styles of embedded applications. However, with modern trends in areas such as security, cloud-based systems and modern hardware support, maintaining ongoing employee development training is essential to ensure that teams remain up to date. An appropriate RTOS enables developers to build dependable real-time systems for an increasingly connected world FAQ What is the difference between an RTOS and a general-purpose OS? General-purpose operating systems such as Windows and Linux usually prioritize throughput and user experience, but can make no guarantees about the timing of tasks. Real-Time Operating Systems, or RTOSs, make

ARM architecture is behind millions of devices used today in the Internet of Things (IoT). It is stumbling into this burgeoning moment; ARM is the go-to processor for the world’s connected devices because they are energy efficient, scalable, and secure. The energy efficiency, performance, and security of ARM is the best option for IoT developers for applications ranging from tiny sensors to smart home hub devices. Key takeaways The ability to use ARM’s RISC architecture provides an ideal option for battery-based IoT devices because of its low power consumption. With transition from data processing to IoT, ARM provides high-performance per watt with greater processing enabled while drawing minimal power. The capability to scale is unparalleled with ARM, ranging from basic sensor characteristics to OEM edge products. With security threats increasing, the TrustZone has a wide variety of security features to protect data and functions related to IoT. Software is plentiful in the ARM ecosystem allowing for fast-tracked innovation and development. Why ARM Architecture Is Perfect for IoT Devices What is ARM Architecture?ARM means Advanced RISC Machine, a family of processor designs based on Reduced Instruction Set Computing (RISC). Unlike x86 processors which use a complex instruction set, ARM uses a small, simple instruction set that reduces power consumption and aloows strong performance, especially for embedded or battery powered devices. With a simple instruction set there is less energy consumed every time the instruction is run which is really important for IoT devices that run on a small battery or limited power.. ARM & IoT: A Natural FitIoT devices range from simple temperature sensors to sophisticated AI-based smart devices. Most importantly in IoT technology are certain key properties that ARM provides. It’s ARM’s flexibility, energy efficiency, and rich software ecosystem which makes it ideal for the highly diverse environment as the IoT devices market. In fact, ARM has shipped over 310 billion chips worldwide, a significant commercial share of which has been related to powering IoT innovations. Top 5 Features of ARM Architecture Ideal for IoT Devices 1.Energy Efficiency and Low Power Consumption ARM’s architecture considers energy efficiency its primary concern. The RISC architecture has a simple design and executes fewer instructions. With fewer instructions, fewer transistors switch, which results in less battery drain. ARM Cortex-M series microcontroller examples are popular with fitness trackers, industrial sensors, etc. because they consume practically zero current. Low power consumption allows many IoT devices to run for years on even tiny batteries, needing replacement or charging less frequently. 2. High Performance per WattARM has a superior performance per watt which means there is more work done for a given power budget available to IoT devices. This is possible through features like superscalar execution (the ability to process several instructions at once), branch prediction (the ability to guess the next instruction), and the number of registers available internally within processors, e.g. ARM E31M or Cortex-A. This foundation allows IoT devices to process data, do analytics, or execute AI workloads (machine learning) at the edge and remain in real time and connected without having to query the cloud and drain excessive power budgets. As a result, many of the IoT gateways and edge computing devices on the market, are based on ARM core architecture, Cortex-A processers 3.Scalability and Flexibility Across IoT ApplicationsOne of the great strengths of ARM is scalability. It provides a large variety of processor cores for different types of IoT devices:   Cortex-M cores are included in simple sensors and wearables. Cortex-R cores are designed for real-time systems, e.g., automotive controls. Cortex-A cores are oriented towards managing complex applications in IoT gateways and smart appliances. Such diversity allows manufacturers to build a full product portfolio with ARM as a platform, keeping the same or compatible software with little development effort. Also, ARM licenses that provide companies the capability to customize their chips (SoCs) for specific IoT requirements allows for improved products for smart agriculture, industrial monitoring, medical applications, etc. 4. Built-in Robust Security FeaturesGiven that IoT devices manage sensitive data and may control critical systems, security must be the highest priority in the design and architecture of these devices. All ARM processors provide security, with TrustZone technology being its standout feature. TrustZone allows secure and non-secure operating environments to coexist on an SoC, thereby separating sensitive applications from those not trusted. This level of hardware-based sandboxing greatly minimizes the attack surface for an IoT device and protects personal data such as health, financial transactions and operational integrity in smart homes, industrial plants, or connected health devices. In addition to TrustZone, ARM chips also offer secure boot and cryptography (both standard and hardware-based), and secure key storage to provide protection for the startup and communications of a system. 5.Extensive Software Ecosystem and Developer CommunityYou need hardware to play with. ARM supports the architecture with a huge software ecosystem. Developers can use general support for popular RTOS, as well as many variants of Linux and IoT-specific platforms.Alongside that ecosystem, come a myriad of tools, debug environments, libraries, and open-source projects that are tailored to ARM. There are starter kits like the Arduino (Cortex-M), and Raspberry Pi (Cortex-A), which are in the hands of millions of developers around the world. These projects support prototyping and idea sharing.The community and extensiveness of tooling provide a lower barrier for companies and hobbyists, allowing for faster innovation of ARM-based IoT platforms with lower risk. ConclusionThe critical role that ARM plays in the emergence of IoT underlines the value of corporate training for employees to learn about ARM technology. Providing formal training for the teams will help them fully exploit ARM’s energy efficiency, scalability, and security in building resilient IoT devices. Cranes Varsity offers practical, hands-on courses involving ARM microcontrollers, embedded systems including IoT Security, for organizations seeking to upskill professionals.