Interactive Digital Signage
An Innovative Media Platform and Service to Deliver Targeted and Personalized Contents with Real-Time Interaction to Bring Audience Engagement
With the growing needs for in-store business, retailers are seeking for digital solutions that brings more immersive interactions with potential customers, providing real-time and targeted advertisements, collecting customer's behaviors and processing data analysis to drive business goals. GIGABYTE and NOEMA 's interactive digital signage is a well-integrated visual display solutions for retail markets to improve customer's experience.
Use Case Scenarios
Audience easily watch and switch movie previews before deciding which one to see in the theater, or promote scheduled events best suited for users in outdoor activities.
Promote the cruisine or combo meals for customers with digital interactions, attracting customers to walk in to the restaurant.
Halls of Large transportation hubs like airports are great to place and show targeted interactive digital signage Ads for passengers to find boarding gate directions and get the discount coupon of duty free shop from the interaction with interactive digital signage.
With user roles, corporation can generate customized groups of user permission to access restricted areas, search for classified database or provide floor plan for visitors to find directions.
NOEMA's Software Brings User Experience to New Level
NOEMA's Interactive Digital Signage software solution is the first software product including audience measurement, sight direction tracking and gesture recognition in one package:
・Audience measurement detects age and gender and counts unique customers.
・Sight direction tracking enables touchless video management and heatmap analysis of the displayed content.
・Gesture recognition adds interactivity and user interaction with displayed content.
・All analytics can be done at the edge.
The Benefits of Interactive Digital Signage
SDM supports hot swap and is easy to carry & insert in digital signage. Multi-media contents can be pre-installed in the SDM or remotely uploaded.
Interacting with customers by detecting their age and gender to provide target contents to drive further interaction immediately.
Collects statistics and performs analysis on all features: Audience traffic by gender or age, video watch time, etc.
Highly Integrated Hardware for Interactive Digital Signage
Smart Display Module (SDM) : Compact Size but Powerful Performance
As the display panels in the market keep getting thinner, a slim computing module is a necessity for an all-in-one display solution. Smart Display Module (SDM), a form factor defined by Intel®, is the next generation of display hardware solution that delivers the same level of intelligence and interoperability as the Open Pluggable Specification (OPS), yet the housing is only a 1/3 of the size. GIGAIPC SDM series, supported by Intel® Celeron® N4000 to Core™ i3/i5 processors, is designed for low power consumption, which significantly improves longevity and high computing performance and stability.
With the dimensions of 175mm x 100mm, SDM is designed and built-in a thin client or PC box, but also acts as a standalone module with Windows or Linux operating systems preinstalled for users to "plug-and-play". It is also a convenience for system upgrades and ease of maintenance. It offers multiple I/O interfaces to be connected with various devices based on users' needs, and supports dual independent display outputs of 1 x HDMI and 1 x Display Port. It delivers 4K Ultra HD resolution for eye-catching video content used on multi-screen digital signage. In addition, it incorporates high-speed PCIe connectivity with expansion slots that eliminate the need for external I/O devices.
Smart Display Module with Intel® i5-1135G7 Processor
Smart Display Module with Intel® i3-1115G4 Processor
Smart Display Module with Intel® i5-7300U Processor
Smart Display Module with Intel® i3-7100U Processor
Big Data describes the large volume of data – both structured, semi-structured and unstructured – that is collected by a business on a daily basis. This data can be generated by both humans (such as a customer's financial transactions), as well as by machines and processes (such as sensor readings and event logs). By its nature, the amount of Big Data is often massive – ranging from terabytes, petabytes and even exabytes of data captured over time.
What is edge computing? Edge computing is a type of computing network architecture, where computation is moved as close to the source of data as possible, in order to reduce latency and bandwidth use. The aim is to reduce the amount of computing required to be performed in a centralized, remote location (i.e. the "cloud") far away from the source of the data or the user who requires the result of the computation, thus minimizing the amount of long-distance communication that has to happen between a client and server. Rapid advances in technology allowing for miniaturization and increased density of computing hardware as well as software virtualization have made edge computing more feasible in recent years.Learn More:《What is Edge Computing? Definition and Cases Explained.》
The Internet of Things (IoT) refers to a network of devices connected to the internet that can record or receive data without requiring any human to machine interaction. These devices can be any kind of physical object in daily life, business or industry – for example, the thermostat in your home, a trash can on the roadside, or a piece of equipment on a factory production line.
Artificial Intelligence (AI) is a broad branch of computer science. The goal of AI is to create machines that can function intelligently and independently, and that can work and react the same way as humans. To build these abilities, machines and the software & applications that enable them need to derive their intelligence in the same way that humans do – by retaining information and becoming smarter over time. AI is not a new concept – the idea has been in discussion since the 1950s – but it has only become technically feasible to develop and deploy into the real world relatively recently due to advances in technology – such as our ability to now collect and store huge amounts of data that are required for machine learning, and also the rapid increases in processing speeds and computing capabilities which make it possible to process the data collected to train a machine / application and make it "smarter".
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