The future energy grid is expected to integrate more distributed and renewable energy resources with significantly enhanced communications infrastructure for timely and reliable data exchanges between the control center and various grid control and monitoring points [1]. Smartgrid is an example of the cyber-physical system (CPS) that integrates different communications, control, and computing technologies [2]. Smartgrid communications infrastructure is an important component of the future smartgrid that enables to support many critical grid control, monitoring, and management operations and emerging smartgrid applications [2]-[4]. The smartgrid communications infrastructure is typically hierarchical, i.e., data communications between customer premises (smart meters (SMs)) and local concentrators and between local concentrators and the utility company are supported by field/neighborhood area networks and long-haul wide area networks, respectively [5]-[8]. The former is usually based on the low bandwidth communications technologies such as Zigbee, WiFi, and power line communications (PLC) while the later is required to have higher capacity, which can be realized by employing LTE, 3G cellular, WiMAX, and fiber optics for example. Manuscript received December 23, 2014; accepted June 11, 2016. The editor coordinating the review of this paper and approving it for publication is Dr. Yun Rui. L. T. Tan is with the School of Electrical Computer and Energy Engineering, Arizona State University (ASU), Tempe, AZ, USA.

On Monday, Apple exec Craig Federighi quickly mentioned how Apple is going to try and catch up to Google's smart Photo app: "on-device intelligence." Similarly, Microsoft offers developers a cloud service that will automatically caption photos. "It's disappointing to see that Apple is not offering developers and customers the option to tap into the power of applying machine learning to data hosted in cloud services. Apple has increased the privacy and encryption capabilities in their operating systems, but at the expense of not taking advantage of powerful machine learning capabilities," he adds.

Once June hits, thoughts of homework, after school activities and the hustle and bustle are quickly replaced with visions of fun camps, long days at the beach and weekend family trips. Summertime activities can be enhanced with the right tech gadgets, whether you're boating, beaching or basking. Waterproof, wireless and solar are the name of the game for these summer tech essentials. UE Roll 2 Every outdoor adventure should be accompanied by summer tunes and the UE Roll 2 is a great option. It's portable, waterproof and most importantly, can boom your tunes of choice at the beach, pool or patio. Thermacell Mosquito Repeller Those irritating mosquitoes have the ability to ruin any outdoor event.

How do you bring a bad drone down? New kinds of drones that can fly autonomously can't be stopped with traditional techniques, the US Air Force has warned. It's put out a call for ideas to yank drones right out of the sky. Millions of drones are sold worldwide each year. Most are flown for fun, but a few have been put to criminal use: carrying cameras to bedroom windows, flying into secure airspace over nuclear power stations, and smuggling contraband into prisons.

In a lab in California, ten robots are gearing up for a project that might solve some of the biggest mysteries in our universe. Scientists in Berkeley plan to use 5,000 galaxy-seeking robots to create the most detailed 3D map of the universe yet. Now the scaled-down prototype system is about to be tested. DESI robots (right) will poke out from 10 wedge-shaped'petals' that will be fitted together in a Focal Plate Assembly. Over its lifetime, the ends of the fibre-optic cables will be turned almost 200,000 times.

The company said the new Edgeline EL1000 (pictured above) and Edgeline EL4000 converged systems target enterprise clients deploying IoT devices in remote environments, also known as the "edge of the network." While the recent proliferation of IoT devices has made it increasingly possible for companies to gather massive amounts of data from remote locations in the field, retrieving that information and performing intensive analytics on it has been challenging. Some companies, such as those in the oil and gas, manufacturing, and telecommunications industries face particular challenges when it comes to harnessing big data in remote environments, HPE said. "Until now, the remote data would have to be transported to a data center or cloud for analysis, which can be a slow, risky and inefficient process," the company said in a statement. The two new systems are aimed at enabling organizations to harness their data onsite by delivering real-time analytics and machine learning at the point of data collection.

It's a strong contender for the geekiest video ever made: a close-up of a smartphone with line upon line of numbers and symbols scrolling down the screen. But when visitors stop by Nicola Marzari's office, which overlooks Lake Geneva, he can hardly wait to show it off. "It's from 2010," he says, "and this is my cellphone calculating the electronic structure of silicon in real time!" Even back then, explains Marzari, a physicist at the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, his now-ancient handset took just 40 seconds to carry out quantum-mechanical calculations that once took many hours on a supercomputer -- a feat that not only shows how far such computational methods have come in the past decade or so, but also demonstrates their potential for transforming the way materials science is done in the future. Instead of continuing to develop new materials the old-fashioned way -- stumbling across them by luck, then painstakingly measuring their properties in the laboratory -- Marzari and like-minded researchers are using computer modelling and machine-learning techniques to generate libraries of candidate materials by the tens of thousands.

Nuclear energy provides electricity for a large segment of the global population, but it has one Achilles' heel that remains to be adequately addressed: the waste. According to a report from Business Standard, however, scientists from the DOE/Lawrence Berkeley National Laboratory have made a breakthrough finding that could lead to a viable solution to this lingering problem. A team of scientists has developed a new material that can clean up nuclear waste gases created as a byproduct of fuel reprocessing plants. The researchers say the material allows for the efficient, safe and cheap disposal of harmful byproducts of generating nuclear energy. The study was carried out by an international team based in Switzerland at the Ecole Polytechnique Federale de Lausanne (EPFL).

There is a lot of buzz around Machine Learning (the most implemented type of Artificial Intelligence) these days and its applications. The RigBasket technology team thought of offering a quick explanation of what it means and how it could help your business. To begin, let's answer whether machine learning really works. In order for us to understand how Machine Learning can help solve inventory management problems, we first need to understand what it is and how it works. To most people, Machine Learning is a black box where you throw something in, some advanced calculations are made and the computer predicts the outcome or the future.

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) trained a neural network to recognize materials (e.g., metal grate, plants, concrete sidewalk) being hit with a drumstick, and synthesize sounds to accompany the actions. It did well enough to fool humans into thinking the sounds were real. Objects make distinctive sounds when they are hit or scratched. These sounds reveal aspects of an object's material properties, as well as the actions that produced them. In this paper, we propose the task of predicting what sound an object makes when struck as a way of studying physical interactions within a visual scene.