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New computer vision method helps speed up screening of electronic materials Boosting the performance of solar cells, transistors, LEDs, and batteries will require better electronic materials, made from novel compositions that have yet to be discovered. To speed up the search for advanced functional materials, scientists are using AI tools to identify promising materials from hundreds of millions of chemical formulations. In tandem, engineers are building machines that can print hundreds of material samples at a time based on chemical compositions tagged by AI search algorithms. But to date, there’s been no similarly speedy way to confirm that these printed materials actually perform as expected. This last step of material characterization has been a major bottleneck in the pipeline of advanced materials screening. Now, a new computer vision technique developed by MIT engineers significantly speeds up the characterization of newly synthesized electronic materials. The technique automatically analyzes images of printed semiconducting samples and quickly estimates two key electronic properties for each sample: band gap (a measure of electron activation energy) and stability (a measure of longevity). The new technique accurately characterizes electronic materials 85 times faster compared to the standard benchmark approach. The researchers intend to use the technique to speed up the search for promising solar cell materials. They also plan to incorporate the technique into a fully automated materials screening system. “Ultimately, we envision fitting this technique into an autonomous lab of the future,” says MIT graduate student Eunice Aissi. “The whole system would allow us to give a computer a materials problem, have it predict potential compounds, and then run 24-7 making and characterizing those predicted materials until it arrives at the desired solution.” “The application space for these techniques ranges from improving solar energy to transparent electronics and transistors,” adds MIT graduate student Alexander (Aleks) Siemenn. “It really spans the full gamut of where semiconductor materials can benefit society.” Aissi and Siemenn detail the new technique in a study appearing today in Nature Communications. Their MIT co-authors include graduate student Fang Sheng, postdoc Basita Das, and professor of mechanical engineering Tonio Buonassisi, along with former visiting professor Hamide Kavak of Cukurova University and visiting postdoc Armi Tiihonen of Aalto University. Power in optics Once a new electronic material is synthesized, the characterization of its properties is typically handled by a “domain expert” who examines one sample at a time using a benchtop tool called a UV-Vis, which scans through different colors of light to determine where the semiconductor begins to absorb more strongly. This manual process is precise but also time-consuming: A domain expert typically characterizes about 20 material samples per hour ...

What degree is suitable to land an AI job? Unlocking the secret for non-IT professionals. Actually, there is no need for any specific degree to land a job in AI. You can be a - mechanical engineer, or - electrical engineer, - come from the medical domain, or - even from arts and science, such as accounts or commerce. Anyone can pursue AI careers. In fact, if you're an electrical engineer and have self-taught or completed any AI courses online or offline, improving your AI skills, you'll receive higher priority from electrical industries for their digital transformation projects. Since most successful projects not only depend on AI skills but also require domain experience, it works best when combined! Have any of you experienced this truth? #artificialintelligence #datascience #thepartheee #nonit

Role: Image Processing Engineer / Signal Processing Engineer Company - Panacea Medical Technologies Pvt Ltd. No of positions: 10 Qualification: M.S / M. Tech in Signal Processing / Image Processing / Medical Electronics / Video Processing / Biomedical Experience:    1 to 6 years Job Location: Malur, Kolar District E-Mail ID [email protected] Job Description: Algorithm development related to image enhancement and 3D reconstruction. UI development. Architecture development. Developing interface between hardware and application for image grabbing. Developer testing. Candidate Profile: Hands on experience in C , QT, Python, PyQt, VTK, ITK and APIs DICOM, GPU architecture, CUDA programming GUI design, SQL, efficient thread handling, OOPS, Debugging skills, image processing, computer vision. #ImageProcessingEngineer #ImageProcessing #signalprocessing #medicalimage #videoprocessing #c #cplusplus #QT #Python #PyQt #VTK #ITK #APIs #DICOM #GPU #architecture #CUDAprogramming #CUDA #GUI #design #SQL #threadhandling #OOPS #Debugging #computervision #Biomedical #Engineering #electricalengineering #digitalart #cfd #powerelectronics #phdresearch #pythonprogramming #simulation #programmer #fiverr #ros #generativeart #pcb #biomedicalengineering #neuralnetwork #clippingpath #backgroundremoval #digitalimaging #technology #processing #assignmenthelp #finalyearproject #gazebo #autonomousvehicles #arm #fea #photoshop #engineering #creativecoding #atmega #electronicsengineering #imageprocessing #photoshop #image #research #programming #python #programmer #consultant #robotics #artificialintelligence #scientist #machinelearning #esa #datascience #fira #pythonprogramming #deeplearning #softwareengineering #fiverr #gis #digitalimaging #imageprocessing #image #research #programming #python #programmer #consultant #robotics #artificialintelligence #scientist #machinelearning #esa #imageprocessing #datascience #fira #pythonprogramming #deeplearning #softwareengineering #fiverr #gis #digitalimaging #sentinel #keras #datascientist #computervision #datamining #neuralnetworks #imageediting #upwork #arcgis #earthexplorer #tensorflow #remotesensing

Often, people ask me what electrical engineers have to do with algorithms and AI. It's not all wires and circuits. There is a whole branch called Digital Signal Processing, which is a core component of electrical engineering. It involves techniques for analyzing, modifying, and interpreting data and signals. This knowledge is directly applicable to data science, particularly in areas such as image and signal processing. Engineers also work with control algorithms and optimization techniques, which align with the principles of AI/data science in terms of modeling, prediction, and system optimization. Several prominent figures in AI, who may be less known for their electrical engineering background, include: Dr Fei-Fei Li: AI Researcher & Professor at Stanford University Dr Anima Anandkumar: Sr. Director of AI Research at NVIDIA  Dr Yann LeCun:  Founding father of convolutional nets Dr Chelsea Finn: AI & Robotics researcher at Stanford University Mira Murati : CTO at OpenAI (Mechanical Engineer) My point is, if you hold an engineering degree and are interested in exploring non-traditional paths, remember that your skills are incredibly versatile. Engineering isn't just about building bridges; it's a way of thinking, a problem-solving mindset that can be applied in numerous fields. From data science to finance, from entrepreneurship to environmental conservation, the analytical and logical skills you develop in engineering can open doors to many opportunities. #engineering #ai #luceaai ----------------------------------------------- 🚀 Hi, this is Amy from Lucea AI We simplify AI for Leaders and Non-Tech Professionals, getting you up and running with AI in less than a month. Join our AI for Leaders Community here: https://lnkd.in/gxcRk7wj ✅ Message me to find out more!

#Technology #Thread #Semiconductor #Manufacturing #Process The Semiconductor Manufacturing Process: 1/ - The Semiconductor Manufacturing Process (From FAB To Assembly To Test) Is Made Up Several Crucial Steps. - The Attached Image From Tokyo Electron Limited Provides A Good View Of How A Wafer Turns Into An End-Product. - Credit: Tokyo Electron Limited ---- 2/ - For Students: -- Each Of This Process Steps Is A Career Option In Itself -- One Need Not Be Master Of All The Process Steps -- You Can Focus On One Aspect Of The Process And Make A Career Out Of It -- Not Only Electrical Or Semiconductor Fabrication/Process Majors But Also Several Other Disciplines ---- 3/ - As An Example: - At Lithography: -- A Mechanical Engineer Can Be Vital In Equipment Development Along With Different Simulations And Skills Related To Litho Research -- A Physics And/Or Photonics Engineer Can Drive The Development Of Next-Gen Lithography -- A Data Scientist Can Work With A Litho Expert To Deep Dive Into Data Based Lithography (Read Inverse lithography) -- A Chemical Expert Drives The Development Of The Chemistry Of Photolithography -- A Mathematics And/Or Computer Science Major Can Find Use Cases Of ML/AI To Drive Next-Gen Lithography - The List Goes On. ---- 4/ - Many Students And Early Professionals Focus On The Larger Picture Of Being Part Of The Full Semiconductor Manufacturing Process. - However, In Many Such Cases, It Will Not Give You Domain Expertise Of Specific Process Steps. - If You Are Looking To Get Into Semiconductor Fabrication, Focusing On A Specific Process Step Is Also A Good Way To Build A Career In Semiconductor Fabrication. - In Between, As Usual: Always Build Data Skills. Much Needed For The Fabrication Engineers. ---- 5/ - What Do You Think Are The Major Bottlenecks In Understanding Career Opportunities In Semiconductor Fabrication Versus Skills Needed? - Please Do Comment Below. ---- #chetanpatil - Chetan Arvind Patil - www.ChetanPatil.in

🚀Exiting News for Tech World ! 🚀 🔍 Amidst the rapid advancements in Artificial Intelligence (AI), there's a question on everyone's mind: Will AI replace hardware design engineer jobs in the future? 🤔 🛠️ As a hardware design engineer myself, I'm here to share some insights. While AI is revolutionizing many industries, it won't replace the critical role of hardware design engineers anytime soon. Here's why: 1️⃣ Complexity of Hardware Design: Hardware design is intricate and multifaceted, requiring a deep understanding of electrical engineering, physics, and material science. While AI can assist in certain aspects, it can't replicate the creativity and problem-solving skills of human engineers. 2️⃣ Innovation and Creativity: Hardware design often involves pushing the boundaries of technology, creating new solutions, and optimizing designs for performance and efficiency. These tasks require human creativity and intuition, which AI struggles to emulate. 3️⃣ Varied Skill Set: Hardware design engineers possess a diverse skill set that goes beyond technical expertise. They need strong communication skills, teamwork, and the ability to adapt to evolving technologies and requirements. 4️⃣ Human Oversight and Assurance: In critical industries like aerospace, automotive, and healthcare, human oversight is essential to ensure safety and reliability. Hardware design engineers play a vital role in verifying and validating designs, which cannot be solely entrusted to AI systems. 💡 Instead of replacing jobs, AI is augmenting and enhancing the capabilities of hardware design engineers. AI tools can streamline certain tasks, accelerate simulations, and improve design optimization, allowing engineers to focus on higher-level challenges and innovation. 👩💻👨💻 So, to all my fellow hardware design engineers out there, rest assured that your expertise and skills will continue to be in high demand as we navigate the exciting intersection of AI and hardware innovation! share your views #HardwareDesign #AI #Engineering #Innovation #FutureOfWork

We partner with companies to integrate computer vision and AI into their embedded systems by helping machines perceive and understand visual data to solve real-world challenges. Through close collaboration, we work to fully understand our customers' needs and constraints. Only then can we recommend solutions that balance performance, cost-effectiveness and ease of integration. Some of our key capabilities: 🧠 Feasibility Studies - Assessing the viability of computer vision initiatives 📈 Algorithms Design - Creating tailored or state-of-the-art algorithms 👥 Dataset Curation - Defining rigorous data acquisition protocols 🤖 Model Development - Crafting AI models with precision using ML/DL techniques 📸 3D Reconstruction - Reconstructing 3D models from images/video ⚡ Model Optimization - Compressing models for resource-constrained devices 🛠 Prototyping - Validating ideas in real-world scenarios 💻 Software Engineering - Building robust CV/AI-powered solutions 📟 Embedded Systems - Accelerating algorithms in FPGA/ASIC for edge devices Our case studies highlight how we've partnered to enable visual intelligence across industries, providing the most effective computer vision solutions while maximizing performance and minimizing cost. 🌐 Read more about our Computer Vision & AI services here: https://lnkd.in/dD6i2UPi #technology #artificialintelligence #machinelearning #computervision #innovation

In a future driven by artificial intelligence, new skills and industries are constantly emerging, and the types of talents required for jobs are also changing. Which jobs deserve highlight? 1, AI and machine learning specialists 2, Robotics engineers 3, Cybersecurity experts 4, Data and systems analysts 5, Augmented Reality (AR) and Virtual Reality (VR) developers 6, Healthcare technologists 7, Renewable energy technicians /environmental engineers 8, Human-machine interaction designers like UX/UI designers 9, Education and training professionals 10, Creative industries 11, Finance and legal tech experts 12, Advanced manufacturing technicians like 3D printing specialists and industrial IoT engineers. 13, HR These roles emphasize the need for a blend of technical skills, creativity, ethical considerations, and adaptability. As AI evolves, continuous learning and flexibility will be crucial for professionals in these emerging fields. #career #jobs #education

The concept of an “AI Multimeter” can be approached in two main ways. The first interpretation involves enhancing a traditional multimeter with AI-powered features. This upgraded tool would retain fundamental functionalities such as measuring voltage, current, and resistance while incorporating AI capabilities. For instance, the AI could automatically identify components based on measured values, provide troubleshooting guidance by analyzing circuit schematics, and perform advanced analyses like predicting component failures or calculating power consumption. The second interpretation takes a more revolutionary approach, envisioning an entirely new AI-based diagnostic tool. This tool would surpass the capabilities of a traditional multimeter, using AI to diagnose problems in electronic circuits in a more comprehensive manner. This could include analyzing waveforms and signals to identify issues like noise or distortion, learning from a database of known circuit problems to improve accuracy over time, and not only diagnosing problems but also suggesting repairs or improvements to the circuit. The traditional multimeter with AI features would be a practical choice for those who want to augment existing tools with intelligent functionalities. It blends the familiarity of a conventional multimeter with the efficiency and insight provided by AI, making it a valuable asset for both beginners and experienced technicians. On the other hand, the entirely new AI-based diagnostic tool takes a bold step forward by introducing advanced diagnostic capabilities beyond traditional measurements. It aims to provide a holistic approach to circuit diagnostics, leveraging AI to offer in-depth analyses, learn from experience, and propose actionable solutions for circuit optimization. Ultimately, the success of these concepts hinges on the effective integration of AI algorithms with the practical needs of technicians. User-friendly interfaces and clear presentation of information are crucial aspects to ensure the seamless adoption of these AI-powered tools in the field of electronic circuit testing and maintenance. . #technology #ai #artificialintelligence #coder #electronics #girls #coding #engineer #machinelearning #tech #techlover #electricalengineering #electricalengineer #EEE #PCB #pubs #jobs #freshersjobs

Closing the design-to-manufacturing gap for optical devices https://ift.tt/gkX4A5F Photolithography involves manipulating light to precisely etch features onto a surface, and is commonly used to fabricate computer chips and optical devices like lenses. But tiny deviations during the manufacturing process often cause these devices to fall short of their designers’ intentions. To help close this design-to-manufacturing gap, researchers from MIT and the Chinese University of Hong Kong used machine learning to build a digital simulator that mimics a specific photolithography manufacturing process. Their technique utilizes real data gathered from the photolithography system, so it can more accurately model how the system would fabricate a design. The researchers integrate this simulator into a design framework, along with another digital simulator that emulates the performance of the fabricated device in downstream tasks, such as producing images with computational cameras. These connected simulators enable a user to produce an optical device that better matches its design and reaches the best task performance. This technique could help scientists and engineers create more accurate and efficient optical devices for applications like mobile cameras, augmented reality, medical imaging, entertainment, and telecommunications. And because the pipeline of learning the digital simulator utilizes real-world data, it can be applied to a wide range of photolithography systems. “This idea sounds simple, but the reasons people haven’t tried this before are that real data can be expensive and there are no precedents for how to effectively coordinate the software and hardware to build a high-fidelity dataset,” says Cheng Zheng, a mechanical engineering graduate student who is co-lead author of an open-access paper describing the work. “We have taken risks and done extensive exploration, for example, developing and trying characterization tools and data-exploration strategies, to determine a working scheme. The result is surprisingly good, showing that real data work much more efficiently and precisely than data generated by simulators composed of analytical equations. Even though it can be expensive and one can feel clueless at the beginning, it is worth doing.” Zheng wrote the paper with co-lead author Guangyuan Zhao, a graduate student at the Chinese University of Hong Kong; and her advisor, Peter T. So, a professor of mechanical engineering and biological engineering at MIT. The research will be presented at the SIGGRAPH Asia Conference. Printing with light Photolithography involves projecting a pattern of light onto a surface, which causes a chemical reaction that etches features into the substrate. However, the fabricated device ends up with a slightly different pattern because of miniscule deviations in the light’s diffraction and tiny variations in the chemical reaction. Because photolithography is complex and hard to model, many existing desi...