"... the research area that studies the operation and design of systems that recognize patterns in data." It includes statistical methods like discriminant analysis, feature extraction, error estimation, cluster analysis.
– Pattern Recognition Laboratory at Delft University of Technology
RAILWAY AGE, SEPTEMBER 2020 ISSUE: Whether it's the track structure or the equipment that operates on it, there are many things that the naked eye cannot readily see. Increasingly, machine vision technology is becoming the best way to identify potential flaws before they lead to failures. "The various machine vision technologies deployed detect thousands of conditions each year that could potentially lead to accidents," says Robert Coakley, Director of Business Development, ENSCO Rail. Compared to manual visual inspections, he says, autonomous machine vision offers advantages of speed, reduced track occupancy, inspection frequency and consistency. The equipment is installed on revenue service trains, can perform inspections at track speed and does not require the additional occupancy of a hi-rail vehicle.
Portland's 2016 entry for a $50 million federal contest called the Smart City Challenge described a Pacific Northwest tech-topia. It promised autonomous shuttles, trucks, and cars on city streets, through partnerships with Daimler and Lyft. Sensors from Alphabet's Sidewalk Labs would monitor people walking and biking around the city to analyze traffic patterns. The Rose City didn't win, and four years later there are no self-driving Lyfts on its streets. One thing that has changed: Portland's conception of what makes a city smart. This month, Portland adopted the nation's most restrictive laws on face recognition, banning private as well as government use of the technology.
Non-Metris Space Library or shortly NMSLIB is an efficient similarity search package. We have mentioned similarity search solutions of tech giants: Spotify Annoy and Facebook Faiss. However, this package was developed by just a few PhD students. Amazon adopted nmslib in Elasticsearch recently. Product and service recommendations, image, document and video search are some use cases for similarity search.
Artificial Intelligence is one of the most innovative technologies in recent times and is taking mobile technology to the next level. AI technology enhances user experience with various features like face recognition, voice commands, image labeling. Alan Turing, an American Computer Scientist has sowed the seeds of the concept of artificial intelligence in 1956. He also developed a'Turing test' to determine whether a computer (machine) can intelligently think like a human. Today, every industry has realized the fact that AI is the next big technology that will transform human machine interactions. AI automates specific tasks and helps in problem solving.
In the above code segment, we print out the training & validation accuracies along with the training & validation losses for character recognition. We have successfully developed Handwritten character recognition (Text Recognition) with Python, Tensorflow, and Machine Learning libraries. Handwritten characters have been recognized with more than 97% test accuracy. This can be also further extended to identifying the handwritten characters of other languages too. Did you like our efforts?
Have you ever considered how much data exists in our world? Data growth has been immense since the creation of the Internet and has only accelerated in the last two decades. Today the Internet hosts an estimated 2 billion websites for 4.2 billion active users. In one day, you can expect 5.5 billion Google searches, 223 million emails, and 5.9 billion video views. The rate at which we create data far outpaces the rate which humans can absorb and interpret that data. That is where artificial intelligence comes in.
Speaker recognition is a well known and studied task in the speech processing domain. It has many applications, either for security or speaker adaptation of personal devices. In this paper, we present a new paradigm for automatic speaker recognition that we call Interactive Speaker Recognition (ISR). In this paradigm, the recognition system aims to incrementally build a representation of the speakers by requesting personalized utterances to be spoken in contrast to the standard text-dependent or text-independent schemes. To do so, we cast the speaker recognition task into a sequential decision-making problem that we solve with Reinforcement Learning. Using a standard dataset, we show that our method achieves excellent performance while using little speech signal amounts. This method could also be applied as an utterance selection mechanism for building speech synthesis systems.
The FBI's Criminal Justice Information Services, nearly seven years after piloting the concept, will add iris recognition technology to its portfolio of identification services for law enforcement agencies. Kimberly Del Greco, the FBI's deputy assistant director for criminal justice information services, said the CJIS Advisory Policy Board and FBI Director Chris Wray recently approved the iris-recognition technology. Capturing iris images, Del Greco added, can be "easily integrated" into the existing biometric process using near-infrared cameras. All iris images added into the FBI's searchable iris image repository must be associated with fingerprints submitted as part of an arrest. The bureau launched its iris recognition pilot in 2013, according to a recent Government Accountability Office report, with the intention of helping criminal justice agencies quickly and accurately identify or confirm someone's identity. "An iris offers highly accurate, contactless and rapid biometric identification option for agencies.
The why and how the human brain develops religious beliefs may stem from our ability to learn, a new study reveals. Researches found individuals who can unconsciously predict complex patterns in the environment believe there is a god who creates order and intervenes in an otherwise chaotic universe. The study used a cognitive test to measure implicit pattern learning, which showed a sequence of dots appeared and disappeared on a computer screen. Participants were told to push a button when a dot appeared, but some learned the distinct patter and were able to predict when it would appear - and sometimes before. The data showed those who noted they had faith in a higher power performed better overall during the experiment.