One of the biggest challenges for energy companies is that power prices have recently remained at very low levels. At the same time, competition has continued to increase in the Texas retail electricity market with more than 50 providers competing for market share—often by using low prices to attract and retain customers. Dell EMC Enterprise Hybrid Cloud built on two Vblock Systems 720 and XtremIO storage arrays enable EFH to run IT for a lot less, so the organization can bring those savings back to the business and invest in new innovation. EFH believes this innovation for the business is going to enable leaner operation for its power plants. It’s also going to enable and improve the customer experience on the retail side.Read case study here; Watch videos here and here; Read blog here Customers are at the heart of everything we do at the Converged Platforms and Solution Division of Dell EMC and a big part of my role as a global sales leader is talking to customers to understand what they want from their IT, not just today but also in the future. I’m always keen to find out how we can help them to modernize and transform their data center and expedite their journey to the cloud. It is this laser focus on our customers that has enabled us to deliver success after success in 2016.Since it’s that time of the year when we all make lists, here’s mine, consisting of some of the best stories that show how our customers have benefitted by leveraging converged infrastructure.The scope of Ph.D candidates’ research has grown in recent years with new valuable insights available through analysis of Twitter, Facebook, and YouTube. Annenberg’s IT infrastructure could not keep up with the big data generated by the social media networks. Its small IT staff also struggled to keep systems up and running, leaving little time for IT innovation and planning. A Vblock System enabled the school to capture one percent of all Twitter feeds to aid research; reduced firmware updates from taking five days and 12 hours of downtime to one day with zero downtime; and freed IT to pursue innovative projects.Read case study here; Watch video here; Read blog hereFollowing a period of foreign ownership, a private equity firm purchased Brownes in 2010. The sudden shift in ownership meant that Brownes not only had to deal with the divestment from its previous owner, they also had to migrate quickly from an IT infrastructure that was being run from New Zealand. Since Brownes was faced with a full migration, it had to completely transform its IT model, and quickly. On top of that, the new ownership had also acquired award-winning West N Fresh Gourmet Yoghurt and Casa Dairy, and was beginning to merge those companies with Brownes. This required an agile infrastructure able to scale across multiple business units. A Vblock System allowed Brownes to rapidly scale their IT operations to meet changing business needs. Simplicity was the key to the Brownes IT overhaul and the Vblock ensured that they maintained the highest levels of availability without compromising security, speed, or agility.Read case study here Insight is a Fortune 500-ranked global provider of hardware, software, cloud and service solutions.The company chose Dell EMC’s all-flash CI solution to eliminate operational technical debt, simplify its environment, and increase agility, availability, and performance to better serve the business. Insight’s converged platform supports a number of workloads including Dell EMC Enterprise Hybrid Cloud as well as Insight’s revenue-generating applications. Insight will be moving its SAP landscapes to its Vblock in the near future.You can watch video hereIn part-2 I’ll put the spotlight on more great customer stories that help turn 2016 into the year of converged infrastructure.
Co-Author: Benita Mordi, Artificial Intelligence and IoT StrategistOverviewIncreasing hours of video footage, combined with the limits of human biology, make video analytics software essential to processing large amounts of video streams. Information is most valuable when it is most needed, and is also as valuable as the incidents that can be actively captured and acted upon in real-time. Video management systems assist security and surveillance personnel by monitoring video streams 24/7 and alerting them to activity which requires attention.Advances in storage capabilities, video and image resolution drove video analytics adoption over the last decade. The global video analytics market was valued at $2.77 billion in 2017 and is estimated to increase to a staggering $8.55 billion by 2023. GPUs make it easier to process videos on low-cost accelerators, making the case for advanced video analytics. Signal stabilizing technologies also improve the effectiveness of video analytics which relies on quality video streams.Video analytics has use cases across many industries. Some examples include:Retail – Counting customers in a store, tracking movement, optimizing store design and merchandise restockingTransportation – Left luggage identification in airportsHealthcare – Thermal imaging for elevated temperature monitoringManufacturing/Industrial/Construction – Quality control, and health and safety complianceFood Processing – Quality controlEntertainment and Sports – People counting to manage crowd trafficCity Operations – License plate recognition and vehicle counting for urban planningLaw Enforcement – Searching video content to help investigationsMultiple video streams are often paired to enable use cases, and combined with IoT sensor data for comprehensive insight and situational awareness. Video management systems can also integrate with third party security apps to help organizations take a holistic approach to their video analytics strategy.How Video Analytics WorksIncoming streams of video are dense combinations of pictorial and audio data packaged as a collection of consecutive frames. For analytics, an individual frame would not provide any more insightful information than a typical photo. The sequential continuity of consecutive frames provides the dynamic needed to extract insight from video data.Video data is processed in two stages: first motion detection and Aanalysis, followed by pattern recognition. During motion detection and analysis, changes in pixel content are monitored to identify movement, then pattern recognition classifies objects in motion, their trajectory, and considers other moving objects.Analytics-enabled cameras use a mathematical function to detect objects in motion by calculating the difference between frames. If the difference is anything but zero, movement is said to have occurred. This is a simple task computationally, while analyzing movement requires more complex computational gymnastics, mathematical functions included. This is where AI comes in. Movement is viewed as a trajectory drawn from an initial frame and tracked to an object’s position in subsequent frames.As an example, consider tracking one automobile’s movement in traffic for five seconds without confusing its trajectory with other automobiles in the extracted video stream. An image segmentation algorithm segments the vehicle in the initial frame and connects the identified image from frame to frame, thus drawing out the trajectory. Basic Computer Vision solutions with reasonable computational power support this easily with CPU cores and a lavish amount of RAM. In the case of 30 automobiles, 30 segmented images in 30 different spots need to be tracked without confusion when images overlap. For video analytics solutions to be valuable here, they need to process many objects per frame as well as their movement across frames, when the average rate is 60FPS. Imagine looking at 60 photos in one second and fully understanding what was in each photo. More advanced computational capacity is needed to optimize for scale in scenarios like this.Tools and DevelopmentDeep learning techniques used in intelligent video analytics vary. One common approach is converting video frames to image files and applying Convolutional Neural Networks (CNN) to detect objects in each frame. Hybrid models of CNNs and Recurrent Neural Networks (RNN) are recommend for motion analysis. It’s outside the scope of this blog to cover all candidate tools and frameworks available to implement video analytics applications; however, here are some resources where a variety of options are reviewed for tooling and development:Open Source Tools – 33 Open Source products on GitHub available for Video Analytics. The major difference amongst these is the use case they enable.VidSaga, a global video marketers community suggests 2020’s top 10 commercial Video Analytics tools for Business Intelligence.The core power of such tools and other popular frameworks are the APIs they offer to enrich correspondence in algorithm implementation. Google Object Detection is popular for rapid creation of object detection models. It provides APIs leveraged over 330,000 classes in the COCO Data Set for object classification. It also allows use of libraries like OpenCV for segmentation and enforces appropriate object-labeling as prerequisites for object tracking. This blog walks through a step by step approach to developing a video analytics application in Python, using the TensorFlow framework and OpenCV library, also applied via Google Object Detection APIs. For a complete implementation example here’s how a soccer game is analyzed. Players (objects) are detected by segmenting them in video frames using OpenCV to assign attributes like jersey color, then labeled and tracked using Google Object Detection APIs.Related SolutionsAt Dell, we have an extensive video analytics partner ecosystem powered by our solutions. See our partner validation page for details.NVIDIA and Dell have a joint Intelligent Video Analytics solution that’s been implemented to support loss prevention in Retail, and another with Intellisite using thermal vision technology for a wide range of use cases. We also partner with companies like NTT in the Smart Cities space, and Converge, who bring great consultative and custom approaches to video analytics.To find out more, you can read about our Edge/IoT and Analytics capabilities and customer success stories.
WARSAW, Poland (AP) — The daughter of former Warsaw Zoo directors Jan and Antonina Zabinski, who saved hundreds of Jews from the Holocaust by hiding them at the zoo and whose story was told in a Hollywood movie, has died. The zoo said on Facebook Sunday that Teresa Zabinska-Zawadzki had died the previous night, at age 77. It did not give the cause of her death. She was born at the zoo in 1944, during World War II. Israel has recognized her parents as Righteous Among the Nations for having risked the family’s lives in order to save up to 300 Jews by hiding them on the zoo grounds. In 2017 she attended Warsaw’s gala screening of “The Zookeeper’s Wife,” a movie starring Jessica Chastain, about her parents’ heroism.
The coronavirus pandemic has worsened child malnutrition in Guatemala, already the country with the worst rate of it in the Western Hemisphere. So Bonifaz Díaz has been riding his bike all across the high-altitude city of Quetzaltenango to deliver books in exchange for donations of a popular and healthy cereal mix. It’s part of a books-for-food barter program started by a local NGO that feeds hundreds of families scraping by on less than a dollar a day. Díaz has pedaled some 1,200 miles (2,000 kilometers) to ensure that donations keep flowing despite lockdowns and fear of contagion.
TORONTO (AP) — Prime Minister Justin Trudeau says Canada will eventually be able to manufacture COVID-19 vaccines. Canada does not have domestic production but Trudeau expects to use vaccines made in Europe to vaccinate all Canadians who want to be vaccinated by September. He says the European Union is reassuring him that it will respect Canada’s contracts with Pfizer and Moderna. But Trudeau says Canada needs as much domestic capacity for vaccine production as soon as possible. Trudeau says two companies – Precision NanoSystems and Novavax – will manufacture vaccines in Canada eventually.