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In Cell On Cell OGS three screen technology science

In-Cell, On-Cell, OGS three-screen technology science

Yesterday article about laminating technology, we also talked about mobile phone screens in the production process requires for the protection of glass, touch screen, display-three part two fitting, if box displays will be undermined, and if fully fit yield is another question. Because of the protective glass, touch screen, display between each pass a joint production process, yield will be greatly reduced, if we can reduce the number of joint, will no doubt improve the fit of the yield rate, there are several directions: led by the original touch-screen manufacturers OGS programme, as well as On Cell, designed by panel makers and In Cell technology solutions.

The display panel makers now more powerful impetus On-Cell or In-Cell programmes, mainly due to the display of its production capacity, which tend to touch is made on the screen and touch module manufacturers or upstream material makers tend to OGS, touch is made on the protection of glass, mainly due to the strong production of process capability and technology.

Common ground between both can reduce the number of fit, so also can save the cost of fitting yield. Because without a touch layer, and thus can achieve savings of material costs, and achieve the purpose of the light, and one Apple iPhone5 is using In-Cell technology.

 

In-Cell

In-Cell is the method of touch panel functions embedded in LCD pixels, that is embedded inside the display touch sensor function, so that the screen becomes thinner. In-Cell screen while also supporting embedded touch IC, otherwise easily lead to errors of the touch sensor signals or excessive noise. Therefore, any display panel makers, cut to the In-Cell/On-Cell the threshold of the touch-screen technology was actually quite high, still needs to tide over this low yield rate. Now using In-Cell technology in addition to Apple’s iPhone 5, Nokia Lumia920. IPhone5 screen thickness estimation of contribution of 2.54mm,In-Cell thin 0.44mm, roughly the thickness down 1.7mm 25%.

In-Cell, On-Cell, OGS three-screen technology science

IPhone5 touch screen, a layer is less than iPhone4S

Although the Giants said that Apple promoted In-Cell technology, but still limited to high-end smartphones in the coming years, the main problem is the yield, because once a In-Cell loss is not only touch-screen display also will work together with scrap, so manufacturers of In-Cell yields higher.

Michael Kors Case for iPad

On-Cell Michael Kors Case for iPad

On Cell is the touch screen embedded in the display color filter substrate plate between the polarizer and method, namely LCD Panel with touch sensors, than In Cell technology difficult to reduce. Samsung, Hitachi, LG touch screen manufacturers in the On-Cell structures such as rapid progress now On applied Cell Samsung Amoled panel products, technology has not been able to overcome on thin, uneven color, touch problem.

In-Cell, On-Cell, OGS three-screen technology science

Comparison between In-Cell and On-Cell

OGS

OGS technologies is the protective glass integrated with touch screen and in the protection of ITO conductive layer on the glass-plated on the inside, directly in the protective coating on the glass and lithography, due to saving a piece of glass and a fitting touch screen can do much thinner and less costly. Domestic mobile phone brand manufacturers, such as Sky Hornet 1, Jin Li Fenghua, millet 2 OGS technologies have been used. Michael Kors Case for iPad

However, OGS are still faced with problems of strength and processing costs. Due to the OGS protection glass and touchscreen are integrated together, usually need to be strengthened, and coating, etching, final cut. In tempered glass cutting is very troublesome, high cost, low yield, and formed the edge of glass capillary crack, these cracks reduce the strength, strength currently become an important factor restricting the development of OGS.

In-Cell, On-Cell, OGS three-screen technology science

 In-Cell are three of the most thin

Learn more about the new cool device, please pay attention to @ love machine

 

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Artificial intelligence in the field of deep learning of past and present

Lei feng’s network (search for “Lei feng’s network” public attention): author of Rancho, this article details a 1) seven important stages in the development of artificial intelligence, 2) depth of learning in the development of artificial intelligence, 3) end author think about deep learning challenges and future development.

Dave Bowman: Hello, HAL do you read me, HAL? HAL, do you see me?

HAL: Affirmative, Dave, I read you. David, I see you FOSSIL iPhone 5

Dave Bowman: Open the pod bay doors, HAL. HAL, open the door

HAL: I’m sorry Dave, I’m afraid I can’t do that. I’m sorry, David, I can’t do

~《2001: A Space Odyssey》~

This bustling two years artificial intelligence not only technology giant with AI technology and product breakthrough, and favor of many startups access to risk capital, almost weekly reports of investment related startups in the area can be seen, and last spring, no doubt Google its Deepmind developed artificial intelligence AlphaGo and South hanlishishi chess battle, AiphaGo win big points on AI with admiration but also led to thinking of how AI will change our lives. In fact, from the 40 ‘s of last century the birth of artificial intelligence, prosperity and valleys of experience again and again, first we look at the past half century history of artificial intelligence.

| Seven chapter in artificial intelligence development

The origins of artificial intelligence: artificial intelligence really was born in 40-50 of the 20th century, this time in mathematics, engineering, and computer scientists in the field of exploring the possibility of an artificial brain, trying to define what is the intelligence of machines. In this context, 1950 Alan Turing published a report entitled “can machines think” paper, became a landmark, the famous Turing test to define what is a machine intelligence, he said as long as there are 30% of human subjects unable to distinguish the tested object in 5 minutes, you can view the machine passes the Turing test.

Artificial intelligence in the field of deep learning of past and present

                                      

Figure 1: the Turing test; Alan Turing himself

The first golden age of artificial intelligence: the origins of artificial intelligence is now recognized as the Dartmouth Conference of 1956, and computer scientist John McCarthy at the meeting to persuade the participants to accept “the AI (Artificial Intelligence)”. More than 10 years after the Dartmouth Conference was the first golden age of artificial intelligence, a large number of researchers have pounced on a new area, is applied to the computer algebra Word problems, geometric theorem proving, top universities to establish artificial intelligence project to ARPA, among others, a lot of money, even researchers believe the machines could soon replace humans do all the work.

Artificial intelligence first downs: in the 70 ‘s, because of your computer’s performance bottlenecks, grow in complexity and the lack of data, many commitments unfulfilled now common computer database to support Visual Basic can’t find enough algorithm to train smart has no way of talking. Later scholars are two types of artificial intelligence: difficult to achieve strong AI and can attempt weak AI. Strong artificial intelligence is the one to think, do the “common tasks” weak artificial intelligence are to address single issues, we are still in the era of weak AI so far, and many of the trends of stagnation has affected the funding of the project, AI was involved in several years of downturn.

FOSSIL iPhone 5

The appearance of expert system: after the 1970, academics come to accept a new idea: not only to study artificial intelligence solutions, and knowledge. So, the expert system was born, which uses digitized knowledge to reason, imitating a field of experts to tackle the problem, “knowledge management” has become the focus of mainstream AI research. In 1977, made by the World Conference on artificial intelligence “knowledge project” inspired by Japan’s fifth generation computer programme, United Kingdom Albi plans, Eureka in Europe and United States star camera introduced, brought rapid development of expert system, the emergence of a Carnegie Mellon’s XCON system and Symbolics, such as IntelliCorp new company.

AI funding crisis for the second time: before in the 1990 of the 20th century, most of the artificial intelligence project is supported by funding from government agencies in research labs, funding to directly influence the development of artificial intelligence. The mid-80, Apple and IBM desktop performance than using expert system of universal computer, fades in the scenery of the expert system, AI encounters the funding crisis.

IBM’s deep blue and Watson: after the expert systems, machine learning has become the focus of AI, the purpose of which is to make machines with automatic learning capabilities, through the algorithm makes the machine from a large amount of historical data in the study and making of new judgment to identify or forecast. At this stage, no doubt IBM is a leader in AI in 1996, deep blue (based on exhaustive search tree) defeated World Chess Champion Garry Kasparov, 2011 Watson (rule-based) against human players in the television quiz show, especially when the latter comes to this day is still the problem of natural language understanding, becoming a milestone in our understanding of human language a step further.

Deep learning of the strong rise: deep learning the second wave of machine learning, in April 2013, the MIT Technology Review magazine first in deep learning in 2013 as the top ten disruptive technologies. In fact, deep learning is not new, it is a traditional neural networks (Neural Network) development, where there is the same between the two, using a similar hierarchical structure, and different is that deep learning using a variety of training mechanisms, with strong communication skills. Traditional neural networks used to be popular directions in the field of machine learning, and later due to parameters difficult to adjust and training issues such as slow fade people’s vision.

But one called Geoffrey Hinton, University of Toronto old professor is very persistent in neural network research, and Yoshua Bengio, and Yann LeCun (invented is now most widely used deep learning neural network model-convolutions CNN) put together a viable deep learning programmes. Iconic thing 2012 Hinton students greatly reduces the error rate on the image classification competition ImageNet (ImageNet Classification with Deep Convolutional Neural Networks), beating industry giant Google, makes academia and industry uproar, not only academic significance Was to attract more industry large-scale investment in deep learning: 2012 16,000 CPU-core computing platform for Google Brain training 1 billion neurons in the deep Web, automatic recognition without outside interference “Cat”; Hinton DNN start-up acquired by Google, Hinton joined Google; and another Daniel LeCun joined Facebook, as the AI Lab Director ; Baidu establishing deep learning Institute, led by a former Google Brain Wu Enda in charge. Technology giants not only increase investment in AI, a large number of startups is riding the wave of deep learning, artificial intelligence hot topic.

| Main engines of artificial intelligence: deep learning

Machine learning is divided into two stages, originated as a shallow 20 ‘s of last century (Shallow Learning) and fire up the depth of learning only in recent years (Deep Learning). Shallow learning algorithm, we first invented the back-propagation algorithm neural network (back propagation), why call it shallow, mainly because of the teaching model is only one hidden layer (middle tier) model of shallow, shallow model has a big weakness is the limited parameters, and calculated cells, characteristic expression ability.

Shang century 90 generation, academia proposed series of shallow layer machine learning model, including rage of support vector machine Support Vector Machine,Boosting,, these model compared neural network in efficiency and accurate rate Shang are has must of upgrade, until 2010 Qian many university research in are is with fashion of SVM, algorithm, including author I (at as one machine learning professional of small Shuo, research of is Twitter text of automatically classification, Is the SVM), mainly because of the shallow model algorithm analysis simple, training methods are relatively easy to master, neural networks but relatively quiet during this period, it is difficult to see in the top academic conferences research based on neural network algorithm.

But actually later people found, even training again more of data and adjustment parameter, recognition of precision seems to to has bottleneck is Shang not to, and many when also need artificial of identifies training data, spent large human, machine learning in the of 5 big steps has features perception, image pretreatment, features extraction, features filter, forecast and recognition, which Qian 4 items is had to personally design of (author after machine learning of hell like of torture finally decided turned). In the meantime, we are dedicated Hinton old professor has studied a number of hidden layer neural network algorithms, hidden layer is actually the shallow depth of the neural network version, try to use more neurons to expression, but why such suffering to achieve it, for three reasons:

1. error back propagation in BP algorithm with hidden layers increased attenuation; optimization problems, in many cases only local optimal solutions;

2. the model increases when the amount of training data there is a high demand, particularly huge identity data is not available, will only lead to overly complex;

3. the hidden layer structure parameters, the size of the training data, you need to take a lot of computing resources.

 

Artificial intelligence in the field of deep learning of past and present

Figure 2: traditional neural networks and hidden-layer neural network

In 2006, R.R Hinton and his students. Salakhutdinov, in the journal Science published a paper (Reducing the dimensionality of data with neural networks), successfully training a neural network, changed the whole pattern of machine learning, although only 3 pages, but now Word’s daughter. This article articles has two a main views: 1) more hidden layer neural network has more badly of learning capacity, can expression more features to description object; 2) training depth neural network Shi, can through drop dimension (pre-training) to achieved, old Professor design out of Autoencoder network can fast found good of global most advantages, used no supervision of method first separate on each layer network for training, then again to fine-tuning.

Artificial intelligence in the field of deep learning of past and present

Figure 3: image and training, encoding decoding → → trimmer

We can see from Figure 3, deep pre-training network is a layer-by-layer by layer, each layer’s output; introduction of encoders and decoders, through primitive input and encoding-decoding error after training, these are unsupervised training process; at last identified samples through supervised training for fine-tuning. Training benefits of the model in a layer by layer close to the optimal location to get better results.

That’s Hinton presented in 2006 the famous deep learning framework, and when the application deep learning network, will inevitably encounter Convolutional neural networks (Convolutional Neural Networks, CNN). CNN’s principle is mimicking human neurons excited structure: number of individual nerve cells in the brain can be made only when a particular edge in the direction of the reaction today is CNN feature extraction method. Playing a analogy, dang we put face very near a Zhang people face pictures observation of when (assumed can very very of near), then only part of neurons is was activated of, we also only see people face Shang of pixel level points, dang we put distance little opened, other of part of neurons will will was activated, we also on can observation to people face of line → pattern → local → people face, whole is a step step get senior features of process.

Artificial intelligence in the field of deep learning of past and present

Figure 4: the basic integrity of deep learning process

Depth study of “deep” (there are many hidden layers), the advantage is obvious – features strong communication skills, have the ability to express a large amount of data; Pretraining unsupervised training and save a lot of human identity than traditional neural networks through layer-by-layer-by-layer method of training reduces the difficulty of training, such as degradation of signal problems. Deep learning in many academic fields than the characteristically tend to have shallow learning algorithms to improve, driven researchers discovered the new world in General flock to deep learning in this field, so now not too shy said with deep learning papers.

| Deep learning of important areas of development

Deep learning, first in image, sound and semantic recognition has made considerable progress, particularly in image and sound field compared to traditional algorithm can greatly enhance the recognition rate, it is easy to understand, deep learning is a humanoid brain algorithms to perceive the outside world, and direct external natural signals than the images, sounds, and text (non-semantic).

Image processing: image is an area of deep learning early attempt, Yann LeCun, Daniel began as early as 1989 Convolutional neural networks research, made in smaller scale (handwriting) image recognition results, but has yet to break through on the pixel-rich picture until 2012, Hinton and his students in ImageNet breakthrough, recognition accuracy improves a great step forward. 2014, Hong Kong Chinese University Professor Tang Xiaoou led of computer Visual research group development has name for DeepID of depth learning model, in LFW (Labeled Faces in the Wild, people face recognition using very widely of test benchmark) database Shang get has 99.15% of recognition rate, people with eye in LFW Shang of recognition rate for 97.52%, depth learning in academic research level Shang has over has people with eye of recognition. FOSSIL iPhone 5

Certainly, when dealing with real scenes of face recognition is still far from satisfactory, for example, face is not clear, light conditions, partial occlusion and other factors will influence the rate of recognition, so in practice combining machine learning and artificial confirmed, more appropriate. Domestic life face was recognition of companies, branch Olson, Sensetime, in which Face++, Linkface, flying is search technology that comes in front of, in the real world use or deep in the vertical segments of data accumulation. In the field of emotion recognition based on facial recognition technology, reading and Facethink of science and technology (Facethink for the angels early investments in the Gulf) was one of the few to enter the field of start-ups.

Speech recognition: speech recognition has long been using Gaussian mixture model to model, is a monopoly for a long time on modeling, but despite its error rate reduced, but commercial grade application remains difficult, which in practice by the noise of the environment issued a usable level. Until the advent of deep learning, the recognition error rate best in the past based on the relative decline of more than 30%, reach the level of commercially available. Microsoft’s Yu Dong Dr and Dr Deng Li, is the earliest practitioner of this breakthrough, their first deep learning is introduced together with the Hinton voice recognition and success. Because the speech-recognition algorithms mature, HKUST, cloud flying know, Si-chi on the general recognition rates differ in HKUST iflytek is a forerunner in the promotion, from military to civilian use, including mobile Internet, telematics, smart home has extensive coverage.

Natural language processing (NLP): even now depth learning in NLP field and no made like image recognition or voice recognition field of results, based on statistics of model still is NLP of mainstream, first through semantic analysis extraction keywords, and keywords match, and algorithm judge sentences function (calculation distance this sentences recently of identifies good of sentences), last again from ahead of prepared of database in provides user output results. Obviously, this is obviously not smart, only implementation of a search function, and the lack of real language abilities. Apple’s Siri, Microsoft’s small ice, Turing robot, Baidu secret and other Giants in the field of power intelligent chat robot, and the scenario in the country is mainly customer service (even if customers hate machine customers, hoping for the first time directly linked to human), I think haven’t appeared on the market of high maturity. Small ice competitors also are quite interesting, her vision is “you just talk to me”, while other competitors focused on segment faces segments still need general chat, personally think that small ice after several years of data accumulated and algorithm improvements have a certain advantage to stand out.

Why the slow progress in the depth of learning in the field of NLP: the voice and image, its constituent elements (contours, lines and voice frames) without pretreatment can clearly reflect the entity or phonemes can be simply applied to neural network in the recognition. And semantic recognition big not same: first a text Word is after brain pretreatment of, is not natural signal; second, words Zhijian of similar does not representative its mean similar, and simple of phrases combination up zhihou mean also will has ambiguity (especially Chinese, like “absolutely didn’t thought”, refers to of is a called absolutely of people didn’t thought does, also is said unexpectedly of didn’t thought does, also is one movie of name does); dialogue need context of context of understanding, need machine has reasoning capacity Human language flexibility, and a lot of communication is needed based on knowledge. Very interesting, deep learning of imitation recognition mechanism of the human brain, the text processed by the human brain signals, but effect is far from satisfactory. Basically, algorithm or weak artificial intelligence now, can go to help mankind to quickly automate (recognition), but I still can’t understand the matter itself.

| Discussion on challenges of deep learning and development

Benefited from the increase of computing power and the emergence of large data, depth of study in the field of computer vision and speech recognition has achieved remarkable results, but we also see some limitations of deep learning, problems to be solved: 

1. the depth of learning in the academic field and achieved good results, but on the business activities of the enterprise to help also was limited, because of deep learning is a process of mapping, a mapping from input to output b, and in the business activity if I already have such a pairing of a → b, why do we need machine learning to predict? Let the machine itself in the data for this pairing relationships or predict, is still a challenge.

2. the lack of theoretical basis, which is plaguing researchers questions. For example, AlphaGo this game won, how do you understand it is very difficult to win, its strategy is like. Study in depth this layer of meaning is a black box, it is also in the process of actually training network-black box: how many hidden layers to train neural networks need, how many valid arguments and so on, have no good theory to explain. I believe that many researchers in multi-layer neural network time, or spend a lot of time on boring parameter adjustment.

3. deep learning requires a lot of training samples. Due to the deep study of multilayer network structure, ability to express its strong features, the parameters of the model will increase, if the sample is too small is difficult to achieve, needs huge amounts of labeled data to avoid overfitting (overfitting) fail to represent the entire data.

4. in the chapter on deep learning NLP application also mentioned that current models or a lack of understanding and reasoning abilities.

Therefore, deep learning the next trend will also involve the solving of these problems, Hinton, and LeCun and Bengio three AI leaders authored a paper (Deep Learning) of the last-mentioned:

(https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf)

1. in unsupervised learning. While the study in depth the study well, overwhelming effects of unsupervised learning in training, but learning is unsupervised learning in humans and animals, we perceive the world through our own observations, to be closer to the model of the human brain, unsupervised learning, needs to be better developed.

2. reinforcement learning. Reinforcement learning refers to the mapping from external environment to conduct learning through trial and error return function to find the optimal behavior. In practice because of the amount of data is increasing, can learn a valid data in the new data and make the correction is very important, depth + feedback mechanism of reinforcement learning can provide incentives to leave the machine independent study (the typical case is AlphaGo).

3. understanding natural language. Old professor said: to let the machine read language!

4. transfer learning. Applied models of migration to the trained data effectively on the mandate of a small amount of data, that is, the learned to effectively solve the distinct but related areas of this look very sexy, but good models existed during migration of trained self bias, so the need for efficient algorithms to eliminate these errors. Fundamentally, is to make the machine as humans do have the ability to quickly learn new knowledge.

Since the publication of deep learning in Science by Hinton, in a short span of less than 10 years, brings vision, voice of revolutionary progress, set off the artificial intelligence boom. Although there are still many poor places, there is a big distance from strong artificial intelligence, it is the closest to the algorithm of the human brain works, I believe that in the future, with the improvement of algorithm and data accumulation, and even hardware-like appearance of neurons in the human brain material, deep learning of intelligent machines will go even further.

Finally, we conclude with Hinton’s words this article: “It has been obvious since the 1980s that backpropagation through deep autoencoders would be very effective for nonlinear dimensionality reduction, provided that computers were fast enough, data sets were big enough, and the initial weights were close enough to a good solution. All three conditions are now satisfied.” (Since the 80 ‘s of last century we know, if you have computers fast enough, data is large enough, the initial weight is perfect enough, based on back-propagation algorithm for automatic coding is very effective. Now, all three are available. )

Lei Feng network Note: article by author starting for reprints please contact the original author. Rancho from Angel Bay ventures, focusing on investment in artificial intelligence and robotics, he worked in Japan for ten years, deep study AI bots, like black technology, welcome all types of AI and robotics entrepreneur tried, micro-hongguangko-Sir.

DuSee is a natural extension of AI Baidu launched the AR platform DuSee

The Verge, it was reported yesterday, Conference on intelligent marketing solutions in 2016, Baidu, Baidu issued an AR platform developed for smartphones DuSee. It is learnt that the hardware platform takes advantage of existing smart phones to “understand” real-world 3D environment, which causes the phone to generate images with real-world interactions. The products from the company deep learning Institute (IDL) and Baidu search product and marketing teams to develop.

iPad mini Hello Kitty case

AR applications currently on the market, such as Pokemon GO mostly to computer-generated images simply superimposed on the screen in the real world. Baidu said DuSee is to use computer vision and depth of what is learning to understand real-time image, which according to the image content for augmented reality interaction in real time. Baidu deep learning Institute Director Lin Yuanqing also said they will put the next AR combined with speech recognition, and natural language processing technologies.

In the General Assembly, referred to Baidu’s Chief Scientist, Wu Enda:

DuSee Baidu is the natural extension of AI research proceeds. It uses complex computer vision and depth of learning, understanding, and enhance certain scenes. AR better needs to be realized through better AI. iPad mini Hello Kitty case

Given the mobile search Baidu has hundreds of millions of monthly active users, DuSee will integrate with application of Baidu. Meanwhile, DuSee platform can be used in interactive advertising, marketing, education, manufacturing and interior design scene, has been for Mercedes-Benz and l ‘ Oréal to develop marketing solutions based on DuSee and shows a Demo: Hello Kitty iPad Mini Case

Through open DuSee-enabled smartphones, 2D map of Shanghai can be turned into a cartoon-3D images.

A shampoo advertisement appeared on the virtual leaf design, is decorated with filter in the face.

Overall, the DuSee provides users with a new way to interact with traditional media, can achieve simply by Smartphones and software, there’s no need to wear extra AR head. But only from a functional perspective, Baidu DuSee and current applications of augmented reality is not very different, in contrast Google Tango platform uses a more sophisticated camera technology.

Baidu currently has not been announced when the DuSee will be released, but officials said the platform will be integrated to Baidu, the most important application.

New Apple patent Touch ID sensors will be integrated in the screen

New Apple patent: Touch ID sensors will be integrated in the screen

According to science and technology Web site AppleInsider reported that today open a new Apple patent application suggests that future iPhone and iPad Touch ID sensor may be integrated in part or all of the touch screen.

iPhone hello kitty


New Apple patent: Touch ID sensors will be integrated in the screen

Earlier media reports said Apple plans to using iris scan technology in the iPhone after 2 years, wait until the technology is mature Iris ID will gradually replace Touch ID status amid speculation that IRIS scanning technology and Touch ID appears in the device there is a high probability. Sources said Apple plans to abolish the Home button using borderless design. But there is a problem, if you remove the Home button means Touch ID device needs to be embedded in other places. From today, Apple new patent applications, iPhone and iPad Touch ID is embedded in the screen seems to have certain things. iPhone hello kitty

The following figure describes the patent as TouchID the entire screen method of sensor: Hello Kitty phone cases

New Apple patent: Touch ID sensors will be integrated in the screen

Meanwhile, there are rumors that the iPhone launch in 2017, not Touch ID sensors embedded in the screen, will also include front facing camera and loud speakers.

Apple charges too ugly See Stacked modular protective case

Apple charges too ugly? See Stacked modular protective case

Apple has recently launched the official Smart Battery charging protection shell Case, built-in 1877mAh battery, used to add power to the iPhone and iPhone 6 6s, and cost up to 848. But its bad form known as the Apple one of the ugliest ever peripheral products. Galaxy S5 Screen Galaxy screen

Galaxy S5 Screen

Apple charges too ugly? See Stacked modular protective case

This Stacked accessories manufacturers introduced the whim of modular charge protection shell, the magnetic connection of the protective case with built-in battery pack, so as to achieve the purpose of charging, charging module with up to 2750mAh.

Apple charges too ugly? See Stacked modular protective case

Shell Shell is made of matte texture, feel good, and shell parts with common shell is not very different, would not be affected because of a lower back shape or weight of large use feel.

Apple charges too ugly? See Stacked modular protective case

Both sides of the battery pack has a magnetic contact, you can contact points on the back of the charger connection charging and suction well, even upright won’t fall off, and you can stack multiple battery pack while charging, and do not fear mobile phones out of power.

Apple charges too ugly? See Stacked modular protective case

This plug-in provides the black, white and gold colors for the user to choose. If this paragraph is full of strange ideas of the casing have any regrets, is the jaw … … A little bit longer, but compared to the Apple Smart Battery Case, this charge is not beautiful?

Apple charges too ugly? See Stacked modular protective case

Of course, appearance and price is proportional to, the Mobile Shell’s selling price is quite expensive, package price up to $ 129, is more expensive than the Apple Smart Battery Case, separately purchased external batteries to $ 58.

IPhone 7 exposed again the battery capacity will be increased by 15

Last night a short video circulating on the Internet on iPhone 7, video shows iPhone 7 back in camera size and headphone jack is canceled two design details, characteristics which was consistent with the rumored information before.

IPhone 7 exposed again, the battery capacity will be increased by 15%

According to the information in the picture display, iPhone 7 headphone jack at the bottom has already been canceled, and only Lightning interface. Therefore, the EarPods Lightning excuses of course. According to the Lei feng’s network (search for “Lei feng’s network” public concern) introduced before, insiders say, Apple used Lightning the headphone in the hope that in virtual reality and put to use in the field of augmented reality, adding accelerometers and other sensors, from the aspects of direction and speed to track the user’s head movements. However, that cannot be ignored is that Lightning interface headphones is likely to increase the power consumption. IPhone 7 battery capacity has become the concern of everyone, after foreign media have claimed, before iPhone 7 battery capacity will raise 6s 1715mah to 1960mah. According to the external exposure of the battery picture, we can see that cell surface display 7.04whr (Watts per hour), power conversion, it is 1960mah.

Ted Baker phone cases

IPhone 7 exposed again, the battery capacity will be increased by 15%

In addition, iPhone 7 will be powered by the A10 has entered mass production stage. In terms of performance, A10 the A9 about 20% before, according to the Web site ran GeekBench score data show, A10 processor performance is strong, at around the 3000-run, close to the iPad Pro A9X.

According to foreign media information display, iPhone 7 will be equipped with 4.7-inch touch screen and a 12 million pixel camera, and simultaneous iPhone 7 Plus is equipped with a 5.5-inch touch screen, equipped with 12 million pixels dual lens. As to whether the adoption of wireless charging technology, and the removal of 16GB, and no definite information yet. Ted Baker phone cases Ted Baker iPhone 5 Case

The ten scientific and technological development how to speed up Internet of

Lei feng’s network (search for “Lei feng’s network” public attention): this article by GfK senior analyst Fang Junlong, Wei Jia, Tan Ying joint finishing writing.

Development of science and technology enables us to reconstruct and defines a new concept of life. GfK elected will have a significant impact on the lives of people in the near future the ten scientific and technological development, we believe that these technologies will greatly affect the brand manufacturers in the future, business models and “Connected Consumers” to promote further opening in the Internet age.

Ubiquitous data analysis

The ten scientific and technological development, how to speed up Internet of things era start?

Every technological trends in this paper are related to data, some technology is directly derived from the data analysis. More and more companies use data collected from their customers better understand customer needs, and optimize the product so that it can better serve their customers. This is the ubiquitous data analysis, it is more focused on data quality, rather than quantity. To maximize data into valuable innovations, using data insight into the market and make informed business judgment on this basis. marvel iPhone 6 case

If you are concerned about data quality, filters all information gathered becomes critical. For example, artificial intelligence will be mentioned in the article, need to quickly complete a series of actions: data collection, analysis, and instantly make a judgment to take action. Strictly speaking, concerns about data quality need to be embedded in the data collection process.

In the data analysis, we have to be concerned with the privacy of consumer information. GfK conducted in 2015, according to results of consumer research, 20 regions in the world, and consumers around the world are worried about how their data is being collected, the sale and use of. Global 16% user said “the illegal use of personal information” was one of the people most concerned about three things (2013 this is 13%). Due to the different markets, fears of varying degrees, but we also believe that consumers are not clearly aware that a large number of data relating to them is being used to market analysis, and they don’t realize the great value, so the industry requires careful use and analyze this data.

Virtual reality (VR)

The ten scientific and technological development, how to speed up Internet of things era start?

In 2016, from industry to consumers, from hardware to software, virtual reality, everything is at the stage of education. With the big three successive launching consumer products, and “sold out” the many virtual reality as the most-watched new intelligence quickly swept the world. Global VR display market is expected in the year 2020 will reach $ 2.8 billion, in which VR equipment for players to use a majority share.

2016 in China retail sales will reach about 3 million units for the year, GfK online market monitoring VR head from January 2016 to April, VR Head retail sales almost double the 20 times, virtual reality hardware products are experiencing an early explosive growth. But is still dominated by VR box. Overall, the VR market in China is still at the stage of barbaric, both in terms of hardware and content are at a very early stage, business model and I dug in further, VR era really still needs to get through the hardware, content as well as the standard VR grave, trying to solve these problems, not overnight thing. But prospects for VR application for all to see, I believe that in future VR applications will gradually live, travel training, medical, decoration, real estate, education and other areas of penetration.

Artificial intelligence (AI)

The ten scientific and technological development, how to speed up Internet of things era start?

Artificial intelligence and ultimately reproduce again the human thinking process. As a man-made machine, the ultimate form of AI will have the same level of intelligence: learning, reasoning, and use of language, the concept of the original idea. However just have learning AI has rapidly penetrated into our lives. Amazon’s use of AI technology to recommend products to consumers, Netflix recommends movies. Facebook and Twitter use to select push content. Siri, and GoogleNow, Cortana has most recently Amazon Echo in the use of AI technology to provide voice-controlled services.

Landing earlier in the speech recognition is artificial intelligence, one of the core areas of investment and research and development. Based on artificial intelligence, individual firms can play in space is very large, different applications and direction is really interesting place. In 2016, the development of AI Assistant might go beyond the smart phone development. Artificial intelligence is an exploration and development markets, this market has a wide range of possibilities.

Wearable products

The ten scientific and technological development, how to speed up Internet of things era start?

Smart health monitoring bracelets, watches, cameras, GPS positioning and heart rate monitoring equipment into the mainstream market for how long? Despite the anticipated Google Glass and Apple Watch issue has captured the imagination of consumers, but only a few consumers have accepted them. Wearable-2016 sales reached 31.6 million units in China, 2015 rose 32% per cent, but with lower-priced product market the majority of the overall sales continued to rise at the same time slowing market after the year 2015 after the jump start to a more rational stage.

If wearable products wants to attract more consumers, we think that the following note:

1, integration with the Internet of things

marvel case

The wearable device and personal technology ecology fusing existing market based initiatives will be expanded. As the consumer with the intelligent home, link has been established between the smart car, this integration will become more important, consumers will be using smart wear devices to control the home and car, as with smart phones can do the same.

2, design and materials

Contour design has become wearable products keep pace with the trend of the times, a major stumbling block. Saw this opportunity, some fashion brands, but also aware of their lack of precipitation technology in the research and development of the wearable device, these brands have chosen to cooperate with technology vendors. For example, high-end Fossil and TAG Heuer watch manufacturer and Intel, Google, these companies cooperate to develop smart devices. Wear devices in appearance the next direction is tailored clothing and medical equipment. New generation of wearable products will require integration of electronic technology and the appearance of traditional wear fashion.

3, accurate and effective information collection

Improve the accuracy of the data and interpretation abilities is to wear manufacturers are working on another big problem. With improved sensors, wearable device output data will be more reliable and accurate. This information will be stored in the cloud and more active through the cloud to provide consumers with a strong correlation and actionable information.

4, notably the new case

We believe that the distinctive characteristics of the product will gradually establish a unique link with consumers, for example, the rhythm can replace handwritten code to open the door or adjust the temperature of the room. In this way, a large number of devices on the market combined with the characteristics of each consumer is realized.

Video consumption

The ten scientific and technological development, how to speed up Internet of things era start?

Video consumption of development speed than zhiqian anyone expected of also to fast, and line Shang has into has people watch video of main channel, from social media Shang of video to video website of movie service and package service even to recently fire of video live, consumers seems to can in any time in any platform see wants to see of video content, actually, was predicted to 2019, 80% of Internet flow consumption will from Yu video watch.

In view of such a large amount of video content is consumed, insight into audience behavior has become particularly important: what people are spending, where consumption, what equipment and how to make them a better experience.

Brand marketing people not only need to cater to new opportunities to optimize their ads in combination with the market, but also use them to provide consumers with the relevant information if they do not keep the video industry as a important part of communicating with consumers, their opponent would be. And as the players more and more into this area, from content producers and publishers to the major brands and manufacturers, mutual cooperation will become a requirement, it is only through such a multilateral exchange of information between be able to unleash more power.

UAV

The ten scientific and technological development, how to speed up Internet of things era start?

According to the GfK estimate, China, in 2014, China consumer market for unmanned aerial for nearly 600 million Yuan by 2018 will surge to 6 billion yuan. While civilian UAV market is expected over the next 10 years a qianyiji scale, a broad space for development in the future.

Drone is not a new product. No machine in airlines took, terrain mapping, commercial transport and rescue deployment, even in automatically mechanized production Shang, are can up to role, no machine technology in many field by can play of uses is was further mining out and will in reduced commercial cost, improve commercial efficiency aspects up to is good of catalytic, but in achieved such of better vision zhiqian still has is long of road to go.

Currently drones are still face many obstacles, such as lack of “sense-avoidance” techniques, cargo weight limits, no night vision and limited battery life. Use drones for cargo transport cost reduction is also in the field of commercial aspects of concern to most people, but its technology and policy obstacles facing is particularly evident. As new players enter the market and Government UAV more improvement and standardization of management policies, these obstacles will be gradually be broken, development of drone technology and industry will become more and more mature.

Mobile payment

The ten scientific and technological development, how to speed up Internet of things era start?

Global mobile payments market is more complex. The traditional means of payment has a strong foundation in many mature markets, cannot be easily shaken. In contrast, some African markets and developing markets in Asia are paid directly into the mobile era. In such a fragmented environment, brands, manufacturers, and retailers to understand the global patterns and the evolution of mobile payments present trends is crucial.

In China, Alibaba group, Tencent and other third-party players had the lead in encouraging Internet users through mobile phones in-store or online payment. Millet, OPPO, Meizu has owned pay phone use also means in this market, mobile payments not only exist, but palpable. And those thought to be the first time for emerging technology markets showed different situation in these areas. For example, in the United Kingdom and the United States, only a few of the high-end cell phone support “tap and pay”, and this is the key investment Visa, Google, Apple and Samsung. Nearly half of consumers (49%) mobile payment is just a “gimmick”, brands need to get rid of this perception of the consumers.

Although the mobile payments market already we talked for at least ten years, but this is really just a market rise in stages. At present, the market still needs to raise consumer awareness of mobile payments, and for other markets, and we want to do is reduce the use of mobile payments barriers.

Smart car

The ten scientific and technological development, how to speed up Internet of things era start?

As the Internet of things technologies become more and more mature, smart cars will come into being. Many luxury cars have been equipped with a big screen, and vehicle-mounted large screen will continue to be the trend at the beginning of the end of 2016 or 2017, designed for front passengers the extra monitor will also appear in the premium. In order to give passengers a better experience “augmented reality (AR)” technology, some OEMs even wants to extend to the entire display screen windscreen or side window. Advances in display technology for those who aim to go to the traditional auto supplier ahead of consumer electronics manufacturers and startups who opened the door.

The past, limited by IOT technologies, OEMs are hard to find the right business model, but for now, opportunities came. By understanding the market segment the needs and preferences of consumers, car app tailored development and support services to obtain the appropriate reward possible. Such as Volvo’s Butler-in-vehicle mobile information systems providing mobile App remotely controls the temperature inside after preheating system and motor function, its user penetration rate in the Nordic countries jumped from the original 5%~10%. Present is the OEM manufacturers effectively use Smartphone App to adjust their products, the best time for the expansion of market presence, so understanding of customers is essential, and find an innovative partner, design, development, customer satisfaction, meeting the needs of their App or feature is particularly important.

3D printing marvel case

The ten scientific and technological development, how to speed up Internet of things era start?

3D printer sales are still relatively small. However, as more manufacturers join the field and consumer awareness has increased, this situation should change over the next year. Take Germany as an example, according to our latest data monitoring report, 3D printer sales last year grew by 71%, but needs to further expand. Our data show that consumers consider 3D printing attractive, 3D printing technology that is most likely to affect their lives in the third. Than the smart car, cloud computing, wearable device also ranked near the top of the Internet of things. Suggesting that knowledge of this new technology is very high in the world.

Price is the main obstacle to popularity of emerging technology. However, as costs fall, prices will no longer be obstacles, advantages of 3D printing technologies will become more obvious and prominent lower Assembly costs, reduce waste, and extremely low cost of transportation and distribution and new products to market faster. Finally, a truly efficient supply chains will bring global market development.

3D printing has potential as an industry changer, manufacturers and designers in order to avoid the latest product to be plagiarism or unauthorized sales in the market, should promptly registered trademarks, patents and copyrights to protect their intellectual property rights. This may mean that the value of the product itself has begun to design the ideas behind and the concept of transfer.

Smart Home

The ten scientific and technological development, how to speed up Internet of things era start?

A wave of smart home “gold rush” is being expanded in various fields, traditional manufacturers, Internet companies, international technology providers and retailers are seeking to maximize the involvement of various organizations, such as the home field of the future. According to GfK global 7 made by the consumer research shows that the vast majority of consumers (90%) know the smart home, 50% of consumers think that smart home can change their life, 78% the consumer agrees that this is an attractive idea.

However, for firms, the challenge now is the consumer smart home and its capacities are still know very little, only 10% of the consumers answered “learn a lot”. On the development potential of the smart home does not mean the application is very fragmented. Consumers considering the purchase of intelligent home devices to meet a specific demand, but when consumers think about smart home for them when, they tend to be high expectations. Consumers want seamless connections between intelligent home products and services. Many people hope that a vendor can provide all of the intelligent home products and services, and are willing to pay for this purchase of an App.

The smart home to obtain the key to success is to get consumers to understand how smart home technologies to enhance their quality of life, and provide a high degree of participation and effective user experience. Now requires industry collaboration to drive demand and consumer education, and to promoting the development of smart homes from manufacturers led to leading innovation in the development of consumer demand. Industry participants need to cooperate and form different relations of cooperation. This would ensure that different devices and services can be connected to each other to meet the demand for convenience in the background. Met this, smart home’s real value can be reflected.

According to GfK China on appliances intelligent of research, China appliances products intelligent of application development speed is located in world forefront, but currently also in manufacturers, and retailers, supply party led of stage, also in intelligent connection, and phone remote control, primary intelligent to more senior intelligent function exploration and attempts to stage; due to no unified standard, the home are in attempts to established himself of “open” intelligent system, for intelligent home aspects of layout, also in active attempts to different level of cooperation including commercial mode innovation attempts to And China’s huge user base of Internet business evolution and for smart home development and innovation and providing a good soil, smart home is expected in the future will bring about a BAT-like levels of new business.

About GfK (GfK global website)

GfK is a reliable source of market trends and consumer information to help customers make more informed business decisions. We are in the world, with more than 13,000 full-time researchers, they are full of enthusiasm, our long-term experience in data analysis, based on more than 100 local markets in several countries proposed matches with important global insight. GfK with innovative technology and data analysis model, turning data into intelligence data, helping customers to enhance competitive advantage and rich consumer experiences and choices.