Thursday, 13 August 2015

Bottom-up Assembly of Metallic Germanium

Chip makers were aggressively researching on finding new materials to boost the chip performance in respect to bringing further miniaturization. Chips are known to get smaller and better in performance over each passing decade. This is achieved by finding new materials which can boost the performance at the same time working efficiently with the present platform. Currently chip manufacturers are utilizing the metallic germanium in order to maximize the potential of the chip in terms of performance and functionalities which integrates well with the silicon platform.

The features of metallic germanium

Germanium fits well all the requirements and it is widely used as a high mobility channel materials, a plasmonic conductor and a light emitting medium in the silicon integrated lasers. The reason for its being successful is it possesses a high electron density in three dimensions (3D).

A simple approach can help in demonstrating the 3D assembly of the atoms found in the germanium by repeated stacking of two dimensional (2D) high density phosphorus layers. This results in producing a high density with relatively low resistivity metallic germanium of precisely defined thickness.

How germanium happens to work so efficiently?

In order to demonstrate the ability of the free electrons of metallic germanium converting from 2D dopant layers into a homogeneous 3D conductor certain specific measurements are used such as quantum interference measurements, density functional theory and atom probe topography. Metallic germanium is doped with homogenous concentrations of free electron which helps in creating low resistivity source or drain extensions in high mobility transistors.

It is not feasible to use the mainstream top-down implantation for this purpose as enhanced dopant diffusion and formation of neutral complexes will create an electrical deviation. In order to control doping process from the bottom up self limiting surface reactions can be used.

This approach will lead to the creation of monolayer-doped semi conductors with much needed high density, two dimensional electron gases which are strongly confined. Utilizing the two approaches- the bottom and top down- it helps in extending the monolayer doping from just 2D to 3D. In this specific case the dopants are easily deposited in a single 2D layer while their effective distribution in 3D is obtained by thermal diffusion. The bottom up assembly of the metallic Germanic is only achieved by the repeated deposition of N nearly identical phosphorus doped layers. This approach helps in preserving the vertical atom precision which is associated with the monolayer doping and helps in creating homogenous 3D system.

Metallic germanium stamps its great performance over others

Mostly bottom up approach is used by the researchers and manufacturers which helps in achieving doping in Ge capable of producing high electron densities and low resistivity metallic conductors. The application of this technology and substance is of great relevance in the electronic industry along with photonics, plasmonics and others. Metallic germanium is actively used in the development of the high mobility transistors, mid-IR plamonics bio-sensors and industrially viable Si-integrated lasers.

Google’s Alphabet

Google took the world by surprise when it announced its creation of a new parent named “Alphabet Inc”. Google had made it clear at the announcement that this particular entity is intended to build any products and brands. However many companies having similar name were quite taken aback with this development and viewed it as a potential trademark infringement instance if Google goes further by bringing in products and services under this brand name.

Google founders Page and Brin had cunningly given the name Alphabet to its newly created parent entity, which will specifically house the Google search business along with other smaller holding. Among the smaller holding, the most important ones are Nest, which is a maker of smart thermostats, Calico, which is focused on longevity and a division, which focuses on developing smart contact lenses and driverless cars.

A lot of firms use Alphabet in their names

Alphabet is rather a common name, which finds itself in many derivates and moderations among the companies listed in US and worldwide. Within US there are 103 trademark registered which includes the word Alphabet or in some other variations as per the database with U.S. Patent and Trademark Office. These registrations had been done by companies coming from various fields such as computer games, furniture, clothing label, books, toys, children accessories makers and others.

On Wall Street, there is a company which goes by name Alphabet Funds. Another company Alphabet Energy in Hayward, an Alphabet Plumbing in Prescott, an Alphabet Record Company in Austin along with numerous inns, restaurants and preschools using Alphabet in its name with different variations.

Conflicts might arise with Google’s Alphabet

BMW a premier carmaker already has a robust subsidiary car division named Alphabet, which manages the fleet management at BMW. On a good note, it already possesses the premium domain name There are chances of Google finding itself broiled into trademark infringement by using the same name and providing similar kind of services to the end users. Since Google is not going to offer similar goods and services there is no likelihood of creating confusion among the consumers at the moment.

Larry Page envisions a separate road for Alphabet

During the announcement, Google CEO Larry Page had made it clear that the new parent company namely Alphabet is not intended for creating a big consumer brand with related products rather it will provide the independence in creating and developing their own brands. Regarding the choosing of name Page emphasized that alphabet actively represents a language and it even signifies “core of how we index” in a Google Internet search.

A history getting into troubles

Google had even experienced legal over intellectual property in the past. Google is frequently targeted by the companies, which claim it off aggressively violating their patents. Currently Google is handling a severe copyright battle with the Oracle Corp exclusively over the royalties, which are used in the Java programming language and being used by Google in its Android operating system.

Wednesday, 12 August 2015

Deep Neural Nets Can Now Recognize Your Face in Thermal Images


Neural Network – Connecting Mid-or-Far Infrared Image

Cross modal matching of the face between thermal and visible range is a desired capability especially during night time scrutiny as well as security applications. Owing to huge modality gap, thermal to visible recognition of the face seems to be one of the challenging face matching issue.

Recently Saquib Sarfraz and Rainer Stiefelhagen at Karlsruhe Institute of Technology in Germany has worked out for the first time, a way in connecting a mid-or far-infrared image of a face with a visible light counterpart, a trick they have achieved in teaching a neural network to do all the task. Corresponding to an infrared image of a face to its visible light counterpart is not an easy work, but which deep neural networks are beginning to surface.

The issue with infrared observed videos or infrared CCTV images is that it could be difficult in recognising individuals where the faces tend to look different in the infrared images. Matching of these images to their usual look could be an important uncertain experiment. The issue could be that the connection between the way one may tend to look in infrared and visible light could be very nonlinear. This could be very complicating for footage which could be taken in midand far-infrared that could use passive sensors detecting emitted light instead of the reflected range.

Visible Light Images- High Resolution/Infrared Images – Low Resolution

The way in which a face emits infrared light is completely different from the way it reflects it where the emissions differ as per the temperature of the air as well as that of the skin. This in turn is based on the activity level of the individual, like having a fever or not. Another issue which could make comparison difficult is that visible light images could have a high resolution while far infrared images could have a much lower resolution due to the nature of the camera from which the images have been taken.

Collectively, these factors could tend to make it difficult in matching an infrared face with its visible light corresponding image. With the recent developments in deep neural networks in overcoming all types of difficult issues, it gave rise to the idea to Sarfraz and Stiefelhagen. They speculated on training a network to recognize visible light faces by looking at infrared types. Two major factors have been pooled in, recently in making neural networks very powerful.

Better Understanding/Availability of Interpreted Datasets

Better understanding, being the first, on how to build and tweak the networks in the performance of their task which is a procedure leading to the development of the supposed deep neural nets which was something that Sarfraz and Stiefelhagen learnt from other work.The second is the availability of largely interpreted datasets which could be utilised in training these networks.

For instance accurate computerized face recognition has been possible due to the creation of massive banks of images wherein people’s faces have been remote as well as identified by observers because of crowdsourcing services like Amazon’s Mechanical Turk. These data sets seem to be much difficult to come by for infrared or visible light evaluations.

Nevertheless, Sarfrax and Stiefelhagenhandled this issue. It was created at the University of Notre Dame comprising of 4,585 images of 82 individuals which were taken either in visible light at a resolution of 1600 x 1200 pixels or in the far infrared at 312 x 239 pixels.The data is said to comprise of images of individuals, laughing, smiling together with neutral expressions taken in various sessions in order to capture the way their appearance seem to change from day to day and in two various lighting conditions.

Fast/Capable of Running in Real Time

Each image was then divided into sets of overlapping patches of 20 x 20 pixels in size in order to vividly increase the size of the database. Eventually Sarfraz and Stiefelhagen utilised the images of the first 41 individuals in training their neural net together with the images of the other 41 people for the purpose of testing. The outcome of it seemed to be interesting.

Sarfraz and Stiefelhagen have commented saying that `the presented approach improves the state-of-the art by more than 10 percent. It is said that the net can now match a thermal image to its noticeable counterpart in a mere 35 milliseconds. They further added that `this is very fast as well as capable of running in real time at ∼ 28 fps’. Though it is by no means flawless, at best its precision is over 80 percent when it has anextensivearray of visible images when compared against the thermal image.

The one-to-one contrast accuracy is only 55 percent. Improved accuracy could be possible with larger datasets together with much more powerful network, out of which, the creation of a data set that is higher by order of magnitude would be the more difficult of the two jobs.

However, it is not an issue to imagine this type of database to be created rather quickly provided the interested individuals could be the military, law enforcement agencies and government who tend to have deeper pockets with regards to security related technology.

One Camera is all This Self-Driving Car Needs


Self-Driving Vehicles with One Camera

Vision systems are now considered to be adequate in enabling a car to drive automatically with only a camera. Several self-driving vehicles, inclusive of Google’s prototypes are impressed with the sensors like the cameras; high accuracy GPS, ultrasound as well as the expensive laser ranging instruments called `lidar’.

These devices tend to support the cars in building a composite image of the world around for the purpose of safe driving, though some of the components like lidar tend to be quite costly. A demo portrayed how quickly some of the technology has been progressing.

A company – Magna which provides components to several huge carmakers has recently shown that it could make a car drive itself with the use of a single camera which is embedded in the windshield. Cost of the technology has not been disclosed by the company but the vehicle camera system would probably cost hundreds of dollars instead of thousands.

 This achievement has been possible due to the speedy progress in the software that comes from MobileEye, an Israeli company, which is good at interpreting a scene.

Software Recognized Traffic Signs

Lead control algorithm engineer at Magna, Nathaniel Johnson, organised a ride in a Cadillac with the installed technology in it and after pulling onto the I-94 north of Ypsilanti, Michigan, he pressed a button on the steering wheel in order to activate the system and then sat back enabling the car to take control.

Johnson explained that `it could drive itself in several situations and as the car followed the curve of the road, it used various image processing techniques’. Entertainment display on the dashboard of the car portrayed the video feed which was being processed by MobileEye’s software.

Besides this, the lane marked were emphasized in green while green boxes were drawn around each vehicle ahead with their numbers indicating their distance in feet. Moreover the software also recognized instantly, the traffic signs. He also clarified that the automated driving method could be organized to stick to whichever speed the sign indicated. He took the opportunity of taking the wheel for a few seconds then abandoned it, enabling the self-driving system to retake control.

Technology Linked with Other Sensors

The company had been testing the technology, in trails for the past several years in U.S., Germany, and U.K and recently in China. It is said that the technology would not be utilised this way by carmaker but would probably be linked with other sensor systems, though it portrays that automated driving abilities can be added quite cheaply to vehicles.

Johnson has informed that `for higher levels of autonomy, they would need more sensors, but this seems a good introductory level of autonomy. It is something people could afford and get into their cars’.Currently, automated driving systems like adaptive cruise control as well as hands-free parallel parking are only provided on high-end vehicles.

 Mercedes S-Class sedan that can automatically follow the car ahead in stop-and-go-traffic and can take the wheel to support swing over obstacles, comes at a cost of $94,400 in the U.S. and could cost as much as $222,000.If the technology should have an great impact on the consumers, the price of sensors as well as the related systems will need to come down considerably.

Tuesday, 11 August 2015

Chinese Carmaker is Testing Car-to-Car Communications


Chinese Testing Technology

One of the leading carmakers in China has been testing technology which could prevent accidents and reduce overcrowding by enabling vehicles and traffic signals with wireless communication. Though there is no standard for the technology that has surfaced in China so far, representative at the company state that it could introduce some sort of car-to-car communication by 2018 ahead of several U.S. automakers.

 A state owned car manufacturers, Changan; based in Chongquing, in central China has been testing vehicle-to-vehicle – V2V as well as vehicle-to-infrastructure – V2I technology at its U.S. R&D centre in Plymouth, Michigan. The company does not sell vehicles in the U.S. and has stated that it has no plans in entering the U.S. market.

However, testing car-to-car technology at its U.S. centre indicates that it envisages a future for it in its home country. The car-to-car technology has been promoted in the U.S. and Europe as cost effective way in helping vehicles to avoid crashes as well as to control traffic flow in an efficient manner.

Technology to Be Introduced in High-End Cadillac- 2017

Vehicles which are equipped with useful broadcast information inclusive of location, direction of travel, speed and computers on-board on each car could use that information in identifying an approaching crash and send a warning. Some of the companies are also making headway in custom communication systems to enable commercial vehicles to travel in highly efficient high-speed convoys.

The U.S. Department of Technology, after a successful test of the technology involving thousands of cars around Ann Arbour, Michigan, is expected to issue specifications for the technology somewhere later this year. The technology is said to be introduced in a high-end Cadillac towards 2017 and would eventually be delegated for new cars in the U.S.

The scenario is less clear in China wherein the government is studying vehicle-to-vehicle technology though has not yet provided any clues on when it could be implemented.

Will Take Time to Get Universal

A ride was organised around Ann Arbor in one of the Changan’s car which was a small SUV known as the CS35 and was fitted with vehicle-to-infrastructure technology. The SUV was fitted with a wireless transmitter as well as a receiver that was connected to an Android tablet attached to the dashboard. When another car which was equipped with the technology approached along a blind crossing, a warning flashed out. Another warning was also received as the car travelled around a sharp bend too quickly.

The challenge with car-to-car technology is that it would take some time to get universal. Though the Chinese car market tends to be the largest auto market in the world, per capita car ownership is still lower in China than in the U.S., Japan of Europe. China also tends to lag behind U.S., Europe and Japan with regards to the development of technology. A PhD student at Carnegie Mellon University, John Helveston, studying the adoptions of electric vehicles in China has stated that the foreign car developers which control the market in China favour selling older technology there. If domestic car makers tend to be interested in car-to-car systems, it would not be interesting if only five out of every 100 cars could communicate with each other’.