April 2019

Computer Vision Solutions for the New Age Automotive

The application of computer vision solutions in automotive, which primarily include computer vision algorithms, outward facing cameras and inward facing-cameras (dash cams), continue to grow. These systems help improve driving safety, enable autonomous driving, and record information that can be used an evidence in case of accidents or injuries. Read More

A Quick Guide to Building a Holistic IoT Testing Strategy

The Internet of Things (IoT) is getting smarter day by day through the convergence of digital and physical worlds. IoT is becoming more complex with the integration of multiple systems like sensors, platforms, mobile apps, web interfaces, and more. Hence, there is a need for holistic IoT testing strategy catering to these multiple endpoints to ensure the best customer experience. Read More

Edge Intelligence is Paving the Way for Smarter Manufacturing, Warehousing, and Transportation

Edge intelligence has the potential to transform the manufacturing industry. In fact, the process has already begun. Ranging from pipeline safety and smart metering to fleet management and warehouse management, intelligent edge solutions have already helped improve safety and efficiency, while delivering cost savings and improved user experiences.Read More

How IoT is Making Heavy Equipment Safer and More Efficient

Heavy equipment represents a large list of heavy vehicles, engineering equipment, and bulky industrial machinery. Things or characteristics that one would expect from heavy equipment are oversized dimensions, long life expectancy, and improved equipment performance, as these machines are a fundamental part of the workflow process in many industries. Safety and efficiency are the key concerns for companies that extensively use such equipment. Read More

Addressing Challenges Associated with Imbalanced Datasets in Machine Learning

A common problem that is encountered while training machine learning models is imbalanced data. An imbalanced dataset can lead to inaccurate results even when brilliant models are used to process that data. If the data is biased, the results will also be biased, which is the last thing that any of us will want from a machine-learning algorithm. Let’s find out what problems an imbalanced dataset can cause and how to handle them. Read More

How Avionics Systems and Services Providers can Ensure ITAR Compliance

ITAR Compliance impacts a number of businesses that are involved with the manufacturing of the avionics systems, or which sell, distribute or provide services to this sector. The key areas that are impacted include project information, technical datasheet, development code, product/solution, and end-user related data. Stringent physical and digital security is required to comply with ITAR. Read More

Low Power Design is a Game Changer in ASIC Physical Design

Power reduction has been on the top priority list of chip designers. The question at hand is: are we positioned to address this problem? Are we getting the best combination of power, performance, and area in our chips? Are the EDA tools that we are using equipped to do so? What steps can we take to resolve this challenge? Read More

A Heuristic Approach to Fix Design Rule Check (DRC) Violations in ASIC Designs @7nm FinFET Technology (DnR)

The intent of this paper is to explain the varied kinds of DRCs (Design Rule Checks) that are encountered in the Physical Design flow. This paper will discuss the Metal DRC violations (7nm Technology) generally seen at the block level and outline the practical approach to fix them. Read More

Reducing DFT Footprints: A Case in Consumer SoC (DnR)

Nowadays, placing multiple IPs on a single chip plays the most vital role in satisfying System on Chip ASIC specification requirements. Most of the time, these different IPs will have different clock domains. In this scenario, the use of multi-clock domains is becoming vital in elaborate designs for balancing scan chains as a limitation to Scan I/O ports. Read More

Reinvent Security with AI-based Video Surveillance

AI-based video surveillance and analytics are seeing growth in adoption, owing to their ability to reduce the workload of security staff and management. Using artificial intelligence in video analytics can bring significant benefits to enterprises in terms of detecting unusual incidents and generating alerts. Read More