Khalifa Aliyu Ibrahim | Engineering and Technology | Best Researcher Award

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Khalifa Aliyu Ibrahim | Engineering and Technology | Best Researcher Award

Mr Khalifa Aliyu Ibrahim, Cranfield University, United Kingdom

Mr. Kamilu A. Ibrahim is a dedicated researcher and academic with expertise in AI-driven high-frequency power electronics. Currently pursuing a PhD at Cranfield University, he has a strong background in physics, energy, and power systems. His research focuses on sustainable energy solutions, incorporating artificial intelligence and machine learning. With numerous publications in reputable journals and conferences, Mr. Ibrahim has made significant contributions to renewable energy, hydrogen systems, and power electronics. His academic career includes roles as a lecturer and research assistant, demonstrating his passion for knowledge dissemination. Recognized for his excellence, he has received prestigious scholarships and awards.

Publication Profile

Google Scholar

Education

Mr. Kamilu A. Ibrahim is currently pursuing a PhD in AI-driven design of high-frequency power electronics at Cranfield University, where he explores innovative approaches to sustainable energy and power systems. He previously earned a Master of Science in Energy Systems and Thermal Processes from Cranfield University (2020-2021), gaining expertise in energy efficiency, renewable energy, and thermal management. Further advancing his research skills, he completed a Master’s by Research (M.Res.) in Energy and Power (2022-2023), focusing on advanced power systems and their optimization. His academic journey began with a Bachelor of Science in Physics from Kaduna State University (2013-2016), where he built a strong foundation in physical sciences and energy applications. Throughout his education, Mr. Ibrahim has demonstrated a commitment to innovation in power electronics, artificial intelligence, and sustainable energy solutions. His multidisciplinary background equips him with the technical and analytical skills essential for driving advancements in renewable energy and intelligent power systems. 🎓⚡🔋

Experience

Mr. Kamilu A. Ibrahim has a strong background in research, teaching, and project management, with a focus on power systems, renewable energy, and AI applications. Since 2022, he has been working as a Research Assistant at Cranfield University, contributing to cutting-edge studies in AI-driven high-frequency power electronics. Prior to this, he served as a Lecturer at Kaduna State University from 2021 to 2022, where he taught physics and energy-related courses while mentoring students in research projects. From 2020 to 2021, he was a Lecturer at Nuhu Bamalli Polytechnic, Zaria, where he played a key role in curriculum development and academic instruction in energy systems. Throughout his career, Mr. Ibrahim has combined his expertise in energy and artificial intelligence to drive innovation in sustainable energy solutions. His experience spans teaching, publishing research in top-tier journals, and collaborating on interdisciplinary projects, making significant contributions to the advancement of renewable energy technologies.

Awards and Honors

Mr. Kamilu A. Ibrahim has been recognized for his academic excellence and research contributions through several prestigious awards and honors. He received the Petroleum Technology Development Fund Scholarship (2021), a highly competitive award supporting outstanding researchers in energy and power systems. In 2020, he was also honored with the Merit-based Foreign Scholarship, which enabled him to pursue advanced studies in energy systems and AI-driven power electronics. In addition to these distinguished scholarships, Mr. Ibrahim has earned multiple certificates of completion from various specialized training programs, focusing on sustainable energy, artificial intelligence applications in power systems, and cutting-edge advancements in high-frequency electronics. His commitment to continuous learning and innovation has positioned him as a leader in his field, contributing significantly to research in renewable energy, hydrogen storage, and machine learning applications. These achievements underscore his dedication to academic excellence and groundbreaking contributions to the future of energy technologies.

Publication Top Notes

  • 📊 Revolutionizing Power Electronics Design Through Large Language Models: Applications and Future Directions (2024)
  • 🌊 Floating Solar Wireless Power Transfer System for Electric Ships: Design and Laboratory Tests (2025)
  • 🔋 Harnessing Energy for Wearables: A Review of Radio Frequency Energy Harvesting Technologies (2023)
  • ☀️ The Effect of Solar Irradiation on Solar Cells (2019)
  • 🌡️ Cooling of Concentrated Photovoltaic Cells—A Review and the Perspective of Pulsating Flow Cooling (2023)
  • 🌱 High-Performance Green Hydrogen Generation System (2021)
  • ⚗️ Advancing Hydrogen: A Closer Look at Implementation Factors, Current Status, and Future Potential (2023)
  • 📡 Survey and Assessment of Radiation Levels Associated with Mobile and Wireless Telecommunication Mast in Residential and Office Areas within Kaduna Metropolis (2019)
  • 🚀 Decision Support System for Sustainable Hydrogen Production: Case Study of Saudi Arabia (2025)
  • 🛢️ Measurements of Pour Points, Flash Points, Water Contents, and Viscosity of Some Selected Automobile Oils Used as Lubricants in Nigeria (2022)

Jiantao Shi | Robotics and Automation | Best Researcher Award

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Jiantao Shi | Robotics and Automation | Best Researcher Award

Mrs Jiayun Nie, chongqing Jiaotong University, China

Jiayun Nie is a distinguished professor at Nanjing Tech University, China, specializing in cooperative control of multi-robot systems, fault diagnosis, and fault-tolerant control of distributed systems. 📡 With a Ph.D. in Control Science and Engineering from Tsinghua University, she has made pioneering contributions to multi-agent systems, UAV adaptive control, and reinforcement learning-based fault diagnosis. ✈️🔍 Her research has led to high-impact publications on fault estimation, bipartite consensus, and deep learning models for system diagnostics. 🤖 She has served as a research fellow at the Nanjing Research Institute of Electronic Technology and has received recognition as an Outstanding Reviewer for the Journal of the Franklin Institute (2017). 📚 Her latest work explores AI-driven fault-tolerant frameworks for autonomous systems and aerospace applications. 🚀 With a stellar academic record and transformative research, she is a deserving recipient of the Best Researcher Award. 🏅

Publication Profile

Orcid

Education

Jiayun Nie’s academic journey is marked by excellence in control science and automation engineering. She earned her Ph.D. in Control Science and Engineering from Tsinghua University (2011-2016), focusing on fault-tolerant systems and adaptive control strategies for multi-robot cooperation. 🤖🔍 Her doctoral research introduced novel iterative learning algorithms for fault estimation and compensation, improving system reliability. Before this, she completed her B.E. in Electrical Engineering and Automation at Beijing Institute of Technology (2007-2011), where she laid the groundwork in robotic control, embedded systems, and automation engineering. 🎛️⚡ During her studies, she was actively involved in research projects on UAV dynamics and cooperative control theory, leading to early publications and innovative designs for fault-resilient robotics. 🚀 Her strong educational foundation, combined with rigorous research, has positioned her as a global leader in fault diagnosis and control engineering. 📚

Experience

Jiayun Nie has an extensive academic and research career, currently serving as a Professor at Nanjing Tech University (2021-present), where she leads groundbreaking work on distributed control and autonomous systems. 🤖🔍 Prior to this, she was a Research Fellow (2019-2021) and Associate Research Fellow (2016-2018) at the Nanjing Research Institute of Electronic Technology, contributing to fault detection models for phased array radar transceivers and advanced control strategies for UAVs. ✈️📡 Her expertise in adaptive control and AI-driven fault detection has been instrumental in developing next-generation intelligent robotic networks. 🚀 Throughout her career, she has collaborated with leading research institutions, advancing the state-of-the-art in reinforcement learning-based fault diagnosis, consensus control, and multi-agent fault-tolerant frameworks. 🏅 Her work continues to influence aerospace, robotics, and autonomous vehicular technologies, making her an authority in the field. 📚

Awards and Honors

Jiayun Nie’s outstanding contributions to robotics and fault-tolerant control have earned her several prestigious accolades. 🎖️ She was recognized as an Outstanding Reviewer for the Journal of the Franklin Institute (2017) 📚, reflecting her expertise in control systems and automation engineering. 🚀 Her innovative research on fault diagnosis in distributed robotic systems has been cited extensively, leading to multiple best paper awards at international IEEE and IFAC conferences. 🏅 She has received multiple grants and funding awards for her pioneering work in multi-agent cooperative control and AI-driven adaptive learning control. 🤖 As a highly regarded professor and researcher, her contributions continue to impact autonomous systems, aviation safety, and smart robotics. ✈️ With her extensive publications and transformative research, she is a deserving recipient of the Best Researcher Award, recognized for her excellence in innovation and scientific advancement. 🏆📡

Research Focus

Jiayun Nie’s research revolves around cooperative control, fault diagnosis, and learning-based fault-tolerant strategies in autonomous systems. 🤖 She has made significant breakthroughs in multi-robot cooperation, bipartite consensus, and AI-driven adaptive fault detection. 📊 Her work in fault-tolerant control enhances resilience in UAVs and aerospace systems, ensuring robustness against unknown disturbances and failures. ✈️ She has developed deep learning and reinforcement learning models for self-healing robotic networks, transforming distributed control frameworks. 🏅 Her studies in event-based control, collision avoidance, and system stability have contributed to advancements in autonomous vehicle technology. 🚀 By integrating data-driven methods with real-time fault estimation, her research provides solutions for smart transportation, defense, and aerospace industries. 📡 With numerous high-impact publications, Jiayun Nie’s pioneering work defines the future of adaptive robotics and autonomous systems. 🎓

Publications Top Notes

  1. “A parallel weighted ADTC-Transformer framework with FUnet fusion and KAN for improved lithium-ion battery SOH prediction” 🔋 (2025)
  2. “Bipartite Fault-Tolerant Consensus Control for Multi-Agent Systems with a Leader of Unknown Input Under a Signed Digraph” 🤖 (2025)
  3. “Iterative learning based fault estimation for stochastic systems with variable pass lengths and data dropouts” 📊 (2025)
  4. “A Two-Stage Fault Diagnosis Method With Rough and Fine Classifiers for Phased Array Radar Transceivers” 📡 (2024)
  5. “An intuitively-derived decoupling and calibration model to the multi-axis force sensor using polynomials basis” 📊 (2024)
  6. “Event-Based Adaptive Fault Tolerant Control and Collision Avoidance of Wheel Mobile Robots With Communication Limits” 🤖 (2024)
  7. “Reinforcement Learning-Based Fault Tolerant Control Design for Aero-Engines With Multiple Types of Faults” ✈️ (2024)

Ebrahim Farrokh | Engineering and Technology | Best Researcher Award

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Ebrahim Farrokh | Engineering and Technology | Best Researcher Award

Assoc. Prof. Dr Ebrahim Farrokh, Amirkabir University of Technology, Iran

Assoc. Prof. Dr. Ebrahim Farrokh is a distinguished expert in rock mechanics and mining engineering, serving as the Head of Rock Mechanics and Mining Engineering at Amirkabir University of Technology. With a career spanning academia and industry, he specializes in tunnel boring machines (TBMs), underground excavation, and rock stability analysis. He has played a key role in major tunneling projects, providing expertise on TBM operations, rock fragmentation, and ground control. His research has led to numerous influential publications, advancing TBM performance prediction and tunnel design methodologies. Alongside his academic role, he consults for Tunnel Saz Machin Co. and has held managerial positions at Hyundai Engineering and Construction. Recognized with prestigious awards, including the Hardy Memorial Award and SME’s NAT Conference Scholarship, his contributions continue to shape the field of mining engineering. His work combines theoretical advancements with practical applications, ensuring safer and more efficient underground construction projects. 🚆💡

Publication Profile

Google Scholoar

Education

  • Ph.D. in Mining Engineering, Penn State University (2009-2012) 🏗️
    Dr. Farrokh earned his Ph.D. at Penn State University, focusing on TBM performance evaluation, advance rate prediction, and rock behavior analysis. His research contributed to innovative methodologies for assessing TBM cutter wear and ground stability.

  • M.Sc. in Mining Engineering, Tehran University (2001-2004) ⛏️
    During his master’s studies, he specialized in underground excavation, tunnel stability, and mine planning. His thesis examined rock fragmentation techniques and their applications in mechanized tunneling.

  • B.Sc. in Mining Engineering, Yazd University (1997-2001) 🌍
    He completed his undergraduate degree at Yazd University, gaining foundational knowledge in rock mechanics, mineral extraction, and geotechnical engineering. His early research explored TBM operational parameters and ground convergence in tunneling projects.

Experience

  • Associate Professor & Head, Rock Mechanics & Mining Engineering, Amirkabir University of Technology (2018-present) 🎓
    Leads research and academic initiatives in TBMs, tunnel stability, and underground mining.

  • Consultant, Tunnel Saz Machin Co. (2018-present) 🏗️
    Provides technical expertise in TBM operations, ground support, and excavation efficiency.

  • TBM Specialist & Manager, Hyundai Engineering & Construction (2013-2017) 🚜
    Managed TBM operations in major tunneling projects, optimizing performance and reducing downtime.

  • Research Assistant, Penn State University (2009-2012) 📊
    Conducted cutting-edge research on TBM cutter wear, penetration rate estimation, and tunnel convergence.

Awards and Honors 🏆

  • Outstanding Business Performance Award, Hyundai Engineering & Construction (2015) 🌟
    Recognized for leadership in TBM project execution and efficiency improvements.

  • Outstanding Research Award, Hyundai Engineering & Construction (2014, 2015) 🏅
    Awarded for contributions to TBM performance evaluation and geotechnical risk mitigation.

  • NAT Student Conference Scholarship Award, SME (2012) 🎓
    Acknowledged for excellence in mining engineering research and academic achievements.

  • Hardy Memorial Award, Penn State University (2010) 🏆
    Prestigious recognition for outstanding research contributions in mining and rock mechanics.

Research Focus

Dr. Farrokh’s research focuses on Tunnel Boring Machines (TBMs) 🚜, specializing in performance evaluation, advance rate prediction, and cutterhead design optimization. In Rock Mechanics 🏗️, he investigates rock properties, ground convergence, and stability assessment for underground projects. His work in Mining Engineering ⛏️ explores underground mining methods, rock fragmentation, and geotechnical risk analysis. By integrating theoretical advancements with real-world applications, Dr. Farrokh enhances the efficiency and safety of tunneling and mining operations. His research contributes to optimizing excavation processes, reducing operational risks, and advancing sustainable underground construction. 📊🔬

Publications Top Notes

  1. Tunnel Face Pressure Design and Control 📊 (2020)
  2. Concrete Segmental Lining: Procedure of Design, Production, and Erection of Segmental Lining in Mechanized Tunneling 📚 (2006)
  3. Study of Various Models for Estimation of Penetration Rate of Hard Rock TBMs 📊 (2012)
  4. Effect of Adverse Geological Conditions on TBM Operation in Ghomroud Tunnel Conveyance Project 🌎 (2009)
  5. Correlation of Tunnel Convergence with TBM Operational Parameters and Chip Size in the Ghomroud Tunnel, Iran 📊 (2008)
  6. A Discussion on Hard Rock TBM Cutter Wear and Cutterhead Intervention Interval Length Evaluation 💡 (2018)
  7. Evaluation of Ground Convergence and Squeezing Potential in the TBM-Driven Ghomroud Tunnel Project 🌎 (2006)
  8. Study of Utilization Factor and Advance Rate of Hard Rock TBMs 📊 (2013)
  9. A Study of Various Models Used in the Estimation of Advance Rates for Hard Rock TBMs 📊 (2020)
  10. Analysis of Unit Supporting Time and Support Installation Time for Open TBMs 🕒 (2020)

Eduardo Garcia Magraner | Engineering and Technology | Best Researcher Award

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Eduardo Garcia Magraner | Engineering and Technology | Best Researcher Award

Dr Eduardo Garcia, Magraner Ford Motor Company, Spain

🔧 Dr. Eduardo García Magraner is a distinguished expert in industrial automation, manufacturing, and occupational safety. With a Ph.D. in Computer Engineering & Industrial Production (Cum Laude, CEU Cardenal Herrera, 2016) 🎓, he has led key roles at Ford Spain 🚗, from Electromechanical Maintenance to Manufacturing Manager. A Lean Six Sigma Black Belt ⚙️, he has earned multiple innovation and safety awards 🏆, including the Henry Ford Technical Award. A sought-after speaker 🎤 and researcher 📖, his contributions span Industry 4.0, predictive maintenance, and AI-driven efficiency. Passionate about smart factories 🏭, he bridges academia and industry for sustainable progress. 🌍

Publication Profile

Orcid

Academic and Professional Background 

Dr. Eduardo Garcia Magraner 🎓⚙️ is an expert in industrial engineering, automation, and occupational safety. His academic journey began with vocational training in electrical and telecommunications, followed by a Ph.D. in Computer Engineering and Industrial Production (2016) 🏅. Certified in Lean Six Sigma (Black Belt) and occupational risk prevention, he has mastered integrated management systems 🏭✅. With expertise in robotics 🤖, energy efficiency 🌱, and human resources 🤝, he has undertaken extensive additional training in automation and safety. His research explores machine deterioration and throughput optimization 📊. A leader in engineering and innovation, he continuously enhances industry standards 🚀.

Experience

Dr. Eduardo Garcia Magraner has built an impressive career at Ford S.L, starting as a First-Class Electromechanical Maintenance Operator (1990-2001) ⚙️. He advanced to Equipment Engineer (2001-2006) and later became a Maintenance Supervisor (2006) 🔩. His expertise led him to roles such as Senior Equipment Engineer in Maintenance & Automation (2007-2012) and Production Supervisor (2012) 🏭. He progressed to Manufacturing Manager for Body & Stamping (2014) and Champion of M.O.S. (2016) 🔧. As SPOC for Cyber Security (2019) and now Manufacturing Manager for Assembly & Battery Plants (2023), he continues shaping Ford’s production excellence ⚡🚗.

Awards and Recognitions 

Dr. Eduardo Garcia Magraner has been recognized for his outstanding contributions to safety, productivity, and innovation at Ford Spain 🚗🏆. His accolades include multiple Maximum Awards for enhancing press line efficiency (1997, 1999, 2002) and the prestigious Henry Ford Technical Award (2019) for IIoT-based predictive maintenance 🔧📊. He received honors for workplace safety (1995, 2010, 2012) and academic mentorship at the Polytechnic University of Valencia (2014) 🎓👏. His work in AI and cybersecurity earned global recognition (2019, 2020) 🤖🔐. A leader in industrial innovation, he continues to push the boundaries of engineering excellence 🚀⚙️.

Academic Impact 

Dr. Eduardo Garcia is a distinguished researcher in automation and smart manufacturing 🤖🏭. Beyond his groundbreaking research, he has delivered keynote presentations at global conferences, sharing insights on smart factories and AI-driven manufacturing 🔍🎤. His expertise has influenced industry advancements, making him a sought-after speaker in the field. In recognition of his contributions, he was honored with the prestigious CEU Ángel Herrera Prize in 2023 🏆🎓, further solidifying his reputation as a leading researcher. Through his work, Dr. Garcia continues to shape the future of industrial automation, bridging innovation and practical applications for smarter, more efficient production systems ⚙️🌍.

Presentation

Dr. Eduardo García is a distinguished expert in industrial robotics and Industry 4.0 🤖🏭, actively contributing to global conferences and forums. He has delivered impactful presentations at events like EVERii2020, PENTEO2020, and INNOVATION TALKS 2020, addressing smart manufacturing and supply chains 🚀📦. A sought-after speaker, he has shared insights at Advanced Factories, the Global Robot Expo, and the International Conference on Big Data, AI, and IoT 🌍📊. As a Master Professor at PEAKS BUSINESS SCHOOL, he fosters innovation in Industry 4.0. In 2023, he received the prestigious CEU Ángel Herrera Prize for outstanding research excellence 🏆📖.

Research Focus

Dr. Eduardo García Magraner’s research focuses on Industrial Internet of Things (IIoT) 🏭📡, smart manufacturing 🤖, and predictive maintenance 🔧⚙️ within Industry 4.0. His work includes real-time condition monitoring 🕒, virtual sensors 📊, and AI-driven automation 🤖🔍 to enhance efficiency in industrial environments. Key contributions involve sub-bottleneck detection 🚀, autonomous mobile warehouses 🚗🏭, and manufacturing process optimization 📈 using big data and simulation tools 📡💡. His research advances smart factory management 🏭💻, ensuring reliability, reducing downtime, and boosting productivity. His innovations drive next-gen industrial automation 🚀🏗️ for intelligent and self-optimizing manufacturing ecosystems.

Publication Top Notes

I3oT (Industrializable Industrial Internet of Things) Tool for Continuous Improvement in Production Line Efficiency by Means of Sub-Bottleneck Detection Method

Survival 1.0: A Novel Autonomous Industrial Mobile Warehouse (AIMW) for Industrial Environments

Virtual Sensor of Gravity Centres for Real-Time Condition Monitoring of an Industrial Stamping Press in the Automotive Industry

Influence of Mechanical Failures of the Welding Gun on the Magnetic Field Generated in the Measurement of Misalignment

Mini-Sym 4.0. a Novel Simulation Tool to Calculate Real-time Production Line Productivity

Miniterm, a Novel Virtual Sensor for Predictive Maintenance for the Industry 4.0 Era

Manufacturing Maps, a Novel Tool for Smart Factory Management Based on Petri Nets and Big Data Mini-Terms

Ali Othman Albaji | Computer Science and Artificial Intelligence | Best Researcher Award

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Ali Othman Albaji | Computer Science and Artificial Intelligence | Best Researcher Award

Dr Ali Othman Albaji, Libyan Authority for Scientific Research, Libya

With a Bachelor’s in Electrical Engineering and a Master’s in Electronics & Telecommunications from Universiti Teknologi Malaysia, this accomplished professional has extensive experience in academia and industry. Currently an Assistant Professor at the Higher College of Science and Technology in Libya, they also lead CR Technology System in the MENA region. Their research interests include optical wireless technologies and machine learning applications in environmental monitoring. Fluent in Arabic and English, with a diploma in Italian, they are also the President of the Postgraduate Student Society at UTM. 📡🎓🌍✍️

Publication profile

google scholar

Academic Background 

With a diverse academic journey, the individual holds a Master’s in Public Management and Leadership from the London School of Economics (LSE), UK, completed in 2010. They further pursued a Master in Electronics and Telecommunications at the University Technology Malaysia from 2019 to 2023. Their foundational education includes a Bachelor’s degree in Electrical Engineering, specializing in General Communications, from the Civil Aviation Higher College in Tripoli, Libya, earned in 2007. Additionally, they completed a Diploma in the Italian language during the 2011/2012 academic year. 🎓📡

Experience

Dr. Ali Othman Albaji boasts a diverse work history across reputable organizations. He started as a Sales Advisor at Akida Company (LG) and ZTE/Telecom China, honing his expertise in the telecommunications field. His academic journey includes significant roles as an Assistant Professor and lecturer in Electronics and Telecommunications. In addition to his teaching, he has demonstrated leadership as the Chairman of CR Technology System (CRTS Group) and the President of the Postgraduate Student Society at Universiti Teknologi Malaysia. Dr. Albaji’s commitment to both academia and industry underscores his dedication to advancing technology and education. 📡🎓💼🌟

Main Hard Skills 

Dr. Albaji possesses a robust set of technical skills, including proficiency in CAD Design, MATLAB Simulation Analysis, Python, and data visualization tools like Tableau. His capabilities extend to qualitative and quantitative analysis, SCADA systems, and programming languages like Verilog and HTML. These skills enable him to tackle complex research problems and contribute innovatively to his field. 

Languages 

Fluent in Arabic and English, with an IELTS Band Score of 8.5, Dr. Albaji also has a very good command of Italian. This linguistic proficiency allows him to collaborate with international researchers and disseminate his work to a broader audience. 

Research focus

Ali Othman Albaji’s research focus centers on machine learning applications in environmental noise classification, emphasizing smart cities and mobile communications. His work includes developing algorithms for monitoring and classifying noise pollution using MATLAB, contributing to urban planning and public health. He has also explored traffic noise impacts on residential areas and mobile telecommunications in Libya. His diverse research interests extend to the design and implementation of communication systems, highlighting the integration of technology in environmental studies. Through these contributions, Albaji aims to enhance noise management and promote sustainable urban environments. 🌍📊🔊📡

Publication top notes

Investigation on Machine Learning Approaches for Environmental Noise Classifications

A Machine Learning for Environmental Noise Monitoring and Classification Using Matlab

Machine Learning for Environmental Noise Classification in Smart Cities

Designing the Global System for Mobile Communications GSM-900 Cellular Network up to the Nominal Cell Plan in Tripoli, Libya

Conclusion and Recommendations

A Review of Traffic Highway Noise Towards Residential Area

NOISE POLLUTION DATA REPORTING AND WAREHOUSING USING TABLEAU SOFTWARE

Designing and Implementing a Signed Multiplier Radix-2 Using Booth’s Algorithm