Global Closed Circuit Television (CCTV) Camera Sales Market Report 2018

In this report, the global Closed Circuit Television (CCTV) Camera market is valued at USD XX million in 2017 and is expected to reach USD XX million by the end of 2025, growing at a CAGR of XX% between 2017 and 2025.

Geographically, this report split global into several key Regions, with sales (K Units), revenue (Million USD), market share and growth rate of Closed Circuit Television (CCTV) Camera for these regions, from 2013 to 2025 (forecast), covering
United States
China
Europe
Japan
Southeast Asia
India

Global Closed Circuit Television (CCTV) Camera market competition by top manufacturers/players, with Closed Circuit Television (CCTV) Camera sales volume, Price (USD/Unit), revenue (Million USD) and market share for each manufacturer/player; the top players including
Bosch Security Systems, Inc
Axis Communications AB
Geovision Inc
Hanwha Techwin Co. Ltd
Honeywell International Inc
Panosonic System Network Co. Limited
Pelco Inc
Toshiba Corporation
Hangzhou Hikvision Digital Technology Co Ltd
Zhejiang Dahau Technology Co. Ltd
On the basis of product, this report displays the production, revenue, price, market share and growth rate of each type, primarily split into
By Model Type
PTZ Camera
Box Camera
Dome Camera
Bullet Camera
Others
By Technology
Analog CCTV Systems
Wireless CCTV Systems
IP-based CCTV Systems
Hybrid CCTV Systems
On the basis on the end users/applications, this report focuses on the status and outlook for major applications/end users, sales volume, market share and growth rate for each application, including
Retail
Hospitality
BFSI
Commercial Infrastructure
Home Security
Government
Others

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Global BFSI security market - CMR

In the past couple of decades, there is an incessant inroads made by digitization in the banking, financial services, and insurance (BFSI) sector. Supported by unhindered penetration of internet, this has led to an upsurge in demand for electronic platforms for a vast spectrum of banking services and products. With this, there is a staggering rise in incidents of cybercrimes such as identity thefts and hacks and financial frauds affecting in various parts of the world. The increasing drive for robust BFSI security technologies stems from the need to secure various communication and payment channels and critical information systems from internal as well as external digital and physical threats. Physical security and information security systems help in mitigating these concerns and addressing these challenges.

The global market for mobile payment technology is poised to reach a valuation of $2136.67 Billion by 2025

Over the past few years, banking and particularly payments have undergone considerable changes. How consumers practice payments today is almost nothing less than a virtual exchange, wherein numbers are shifting from one account to another. In this era of digitization, mobile payment technologies are on the rise and they are projected to stay here for long. According to a report by Crystal Market Reports, the global market for mobile payment technology is poised to reach a valuation of $2136.67 Billion by 2025 as compared to $338.72 Billion in 2015, progressing at a phenomenal CAGR of 20.5% from 2016 to 2025.

Biological Circuits Behavior Prediction Via Machine Learning

Biomedical engineers have dived deep into neural networks, facilitating a better understanding of biological circuits. These researchers from Duke University used a machine learning approach to model interactions in an engineered bacterium. Now, these variables’ interaction pattern helps provide a peek into inner working that would otherwise be too complex to grasp. Besides, scientists believe that these algorithms will help understand other biological systems as well. Researchers associated with the study worked with a neural network in a bacterium-culture. Like in any machine learning set-up, this too needs training of algorithm to function well. So, the researchers took up the task and found the system was x30000 more efficient. Thus, it looks like this is the future of behavioral prediction of biological circuits. Moreover, the team worked on bettering precision by devising means of retraining the model. So, they retrained the algorithm several times to compare results. This showed that the model can work in different biological systems, at different levels of complexity. According to a professor of biomedical engineering at the University, Lingchong You, Google inspired the study. When Google showed that neural networks once well-trained can beat humans at board games, people took notice and the conception of study took place. Primarily, the fact that despite several rules in a game, the computer could determine the best move changed the playfield. Now, researchers were interested in whether the biological circuits could use this approach.