Smart railway is a technologically advanced approach to efficiently manage railway operations through sharing of rail data across rail infrastructure components, such as passengers, control centers, ticketing department, and freight. Smart railways incorporate a range of technologies such as Internet of Things (IoT), big data, cloud analytics, artificial intelligence (AI), global positioning system (GPS), and machine learning (ML). Smart railway solutions results in interconnected trains, infrastructure, and passengers to attain higher safety, optimized mobility, and lower costs. For the purpose of analysis, the report segments the global smart railways market into system, offering type, and region. Under the scope of the research, market comprises solutions, devices & components, and services that supports the rail companies to collect data to monitor and analyses all railway-related operations with high precision and accuracy.

The global AI-enabled imaging modalities market consists of numerous large-scale as well as smallscale manufacturers and vendors. Presently, with the increasing adoption of AI in healthcare, the manufacturers in the market have an ample number of opportunities to expand their offerings and to establish a strong foothold in the market. In the past years (February 2016 - July 2020), the market witnessed approximately 40 new offerings, 32 partnerships, alliances & business expansions, 27 regulatory & legal approvals, 13 funding activities, six mergers & acquisitions, and one procurement & sale. Most of the manufacturers in the market are incorporating collaborations and partnerships with not only other companies but also the university and research institutions as the key strategy to develop novel AI-embedded medical imaging systems and attain a strong financial position in the market.

A GaN power device is an electronic device equipped with artificial intelligent algorithms and IoT for smart connection for human assistance, monitoring, and other intelligent functions. The GaN power devices are used in various applications such as consumer electronics, healthcare, automotive, military & defense, media & entertainment, and others. The consumer electronics segment accounted for the highest revenue in 2018. Whereas, military & defense segment is expected to create future opportunities for the market. The GaN power device market is analyzed and estimated in accordance with the impacts of the drivers, restraints, and opportunities. The period studied in this report is 2019–2027. The GaN power device market is segmented on the basis of type, application, and region. Based on type, the market is divided into smart watch, smart glasses, smart earwear, smart gloves, and others. Depending on application, it is categorized into consumer electronics, healthcare, automotive, military & defense, media & entertainment, and others. Region wise, as the market is analyzed across North America, Europe, Asia-Pacific, and LAMEA.

An intelligent virtual assistant (IVA) is a software that is developed on artificial intelligence system. It stimulates or provides responses as similar to human interactions and perform particular tasks such as customer service. Virtual assistants are widely being adopted across industry verticals due to machine learning, deep neural networks, and other advancements in artificial intelligence technologies. The intelligence technology integrated into these systems indicates the capacity for understanding, reasoning, and learning. These three are primary key elements that emulate a customer service agent’s ability to response to queries. IVA systems use various interaction methods, which include text-to-text, speech-to-text, text-to-speech, and speech-to-speech, among others to assist users in executing their respective tasks. The global intelligent virtual assistant market has exhibited notable growth in the recent past. However, it is poised to demonstrate an even more robust growth trend during the forecast period. The report focuses on the growth prospects, restraints, and market analysis. The study provides Porter’s five forces analysis of the intelligent virtual assistant industry to understand the impact of various factors such as bargaining power of suppliers, competitive intensity of competitors, threat of new entrants, threat of substitutes, and bargaining power of buyers on the market.

The need to manage limited space and high rack densities, in order to reduce IT system downtime, has been driving the data center infrastructure management (DCIM) market’s growth, since the past few years. According to AFCOM estimates, the current rack density is valued at 16.9 kW in data centers. According to a study conducted by the Data Centers Association (AFCOM), close to 67% repor ted an increase in rack density in the past three years. Moreover, the study highlights that with an average power density of about 7 kilowatts per rack, most data centers face challenges in managing their IT workloads with traditional air-cooling methods. Factors driving the rack density include the growth of cloud computing, Big Data, and AI. It is highly essential to mange IT, since more equipment is fit into the data center space. Such facts indicate the need for solutions such as DCIM. Initiatives, such as the Data Center Optimization Initiative (DCOI), are aimed at reducing the costs of physical data centers and maintaining minimum PUE rates. This is encouraging enterprises to consolidate and maximize the use of their existing infrastructure. Such initiatives prompt the need for data center services. Moreover, the high penetration of cloud computing augmented the competitiveness of the industry, as various companies, such as Google and AWS, are actively investing more in this technology. This is expected to aid in the scaling of data center resources. Furthermore, it is expected to gain more visibility in data activities.

Automation is the major trend that is driving the software development in the freight and logistics space, and transportation management systems are rapidly adopting this approach. Automation, coupled with artificial intelligence, has made it easy to build a routing guide that ensures the freight moves with the best, and not just the cheapest, carrier for the job. TMS adoption rate is comparatively on the higher side with the large enterprises, owing to the sheer volume of product variety and services offered. However, the increasing adoption of cloud (SaaS) across various industries is reducing the high initial cost, in turn, providing an opportunity for adoption of TMS by small enterprises as well.

After the collection of waste, the traditional way of sorting the waste is by manual ways. But this is not considered to be efficient. Thus, the development of automated robot-based sorting systems was conceptualized in 2016. Furthermore, these systems have been improved using Artificial Intelligence(AI). But the development and adoption of these robotic waste sorting systems have been slow, as the municipalities highly depend on either dumping or landfills to get rid of their waste. This is evident from how late these robotic systems were introduced. The United States started testing the Robotic sorting systems in 2016. The adoption of robotic sorting systems is expected to explode in the coming years, due to the anti- dumping regulation imposed by China and the new landfill regulation imposed by each country.

A self-driving bus is a robotic vehicle designed to travel between destinations without a human operator. They combine sensors and software to control, navigate, and drive the vehicle. This vehicle uses LiDAR and RADAR and several other sensors for its operations. These self-driving systems create and maintain an internal map of their surroundings, based on a wide array of sensors. Artificial Intelligence (AI) software controls all the functionalities related to sensor working and RADAR sensor is used to detect obstacles. Such advance technologies help control these vehicles. Self-driving bus provides advantages such as reduction in accidents caused by driver negligence or error and reduction of hazardous gas (CO2) from the vehicle. The global self-driving bus market is segmented on the basis of level of automation, component, and region. By level of automation, it is divided into level 3, level 4, and level 5. By component, it is divided into hardware, software, and services. Region wise, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA. The key players analyzed in the self-driving bus market include AV Volvo, Continental AG, Volkswagen AG, Tesla, Scania AB, Daimler AG, Proterra, Hyundai Motor Company, Hino Motors, Ltd., Navya, and others.

The explosion in data generation has fueled the demand for improved memory and storage types at the workplace. The traditional memory systems have been struggling to keep up with the increased volume of data, the bandwidth requirements in the systems, and the speed requisite in the current generation systems. The emerging Big Data and artificial intelligence (AI) applications, including machine learning, drive innovations across many industries. The issues in making advancements in memory technologies to meet the evolving computing requirements present several challenges for the AI industry. Next-generation memory has fit in as the missing part of the puzzle. For instance, the existing tech DRAM works fast but consumes a lot of power, while both NAND and hard drives are cheap but work slow. The new memory types combine the speed of SRAM and the non- volatility of flash drives with almost unlimited endurance. Although the research on these technologies has been going on for a long time, there was a delay in reaching the commercial market. A significant proportion of the total power is spent on memory systems, as most of the area of current systems-on-chips (SOC) is occupied by memories. Moreover, the processing elements need to be fed with instructions and data from memories where memory plays a vital role in the system's performance.

Digital Scent technology has witnessed exponential development from its initial deployment by the US Army in the Vietnam war to massive e-nose networks setup, around the world, presently. The digital scent technology has gained momentum recently with the development of emerging technologies like artificial intelligence and IoT. E-nose technology has seen widespread adoption in the waste management industry. Countries, such as the Netherland, UAE, Kuwait, and Italy have the most comprehensive e-nose networks. Countries like South Africa, Oman, and Estonia, have also invested in developing such e-nose networks. Odor pollution has become a sensitive issue in many parts of the world, with many severe instances reported by different countries in the world. The latest was from Malaysia in July 2019, when the authorities in Malaysia had to dispatch several teams to comb Batang Kali, which was considered to be the source of foul smell which spread through the region.