The new development will enable taking photos of race cars, runners, birds in flight, and dunking basketballs into hoops.

Researchers from Tel Aviv University have developed a computational photography process based on an optical element that encodes motion information and a corresponding digital image processing algorithm, enabling clear, sharp photography of moving objects without motion blur, i.e. avoiding the movement being “smeared” over the picture.

This integrated processing method was developed by PhD student Shay Elmalem from the School of Electrical Engineering in the Iby and Aladar Fleischman Faculty of Engineering, under the joint guidance of Prof. Emanuel Marom and Dr. Raja Giryes. The results of the study have been published in the prestigious Optica Journal (by OSA Publishing).

The term ‘long exposure’ always refers to the velocity of the photographed object”, explains Shay Elmalem. “If you photograph a racing car, even an exposure of a tenth of a second could be too long, and if you’re photographing a person walking, long exposure could be a second or longer. According to the conventional camera design approach, the lens is designed to produce the best possible image, i.e., the most similar to what the human eye sees, and thereafter digital image processing algorithms are applied to remove the optical distortions. However, as anyone with a camera in their phone knows, this isn’t always effective; hence, it is still very difficult to photograph moving objects”.

Through integrated design of the optical components and image post-processing algorithms, Elmalem and his colleagues have encoded motion information cues in the raw optical image; these cues are in turn decoded by the image processing algorithm which utilizes them for motion deblurring.. The cues have been encoded using two optical components integrated in a conventional lens: a clear phase plate developed by the researchers, and a commercial electronic focusing lens. The phase plate contains a micro-optical structure designed to introduce a color-focus dependency, whereas the focusing lens is synchronized in order to make a gradual focus change during the image exposure. As a result, moving objects are colored with various colors as they move. Encoding the colors enables the algorithm to decode the direction and velocity of the object’s movement, which enables it to correct the motion blur and restore the image sharpness.

“In every split second of exposure, our lens generates a bit different image”, Elmalem explains; “thus, the blur of a moving object will not be uniform, but rather change gradually with its movement. In order to understand where and how fast the object in the image is going, we use color. Thus, for example, a white ball suddenly thrown into the frame will be colored with different colors over the course of its movement, like passing light through a prism. According to these colors, our algorithm knows where the ball has been thrown from and at what velocity. It will thus know how to correct the blur. With a regular camera we’d see a white wake that would compromise the sharpness of the whole picture, whereas with our camera the final image will be a clear focused white ball.”

According to Elmalem, the computational image technique they developed can enhance any camera – and at minimum cost. “The potential is very broad: from basic uses like smartphone cameras to research, medical and industrial uses such as for production line controllers, microscopes and telescopes. They all suffer from the same smearing problem, and we offer a systemic solution to it.”

Ramot, the Technology Transfer Company of Tel-Aviv University has filed several patent applications covering this breakthrough technology, which is generating great interest among industry players. 

Prof. Marom passed away during the study, and the paper has been published in his memory. The late Prof. Marom was among the founders of the Faculty of Engineering at Tel Aviv University, served as its Dean in 1980-1983 and Vice President of Tel Aviv University in 1992-1997. After his retirement, Prof. Marom continued dealing in active research and advising graduate students, until his very last day.

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Tel Aviv University’s future engineers flew to Africa to connect a school with 1,000 students to clean water

The children living in the villages of Babati district, Tanzania will now have drinkable water during the dry season, thanks to the student delegation from the Iby and Aladar Fleischman Faculty of Engineering. Unfortunately, there are still many places in the world where fresh, clean water, which many in developed countries take for granted, is a rare commodity. This is a common problem in many African countries. Each year, a delegation of engineers from the University of Tanzania sets out to build and improve water and electricity infrastructure. They do so within the framework of the “Engineers Without Borders” association, which works to promote and improve the quality of life of the Israeli population and developing populations worldwide. This year, as in previous years, students responded to the organization’s call, and a delegation of six students flew out during the Sukkot holiday break to contribute their time and knowledge, gained during their studies, and install systems at the regional school that would store 40,000 liters of water.

Months without water

“In Babati district, children sometimes have to walk 10 kilometers to reach clean water reservoirs, or settle for stagnant, contaminated water containing high levels of fluoride, which is detrimental to their health,” says Natalie Lubelchick, a University delegation graduate who is currently completing her master’s degree in Astrophysics. In Tanzania, during the dry season, the local rural population has to cope with three long, hot and dry months. In the absence of a solid infrastructure, the search for drinkable water sources is particularly difficult. If that’s not enough, the same water is also sought by wild animals, who often damage the few existing artificial water reservoirs. The mission of the Israeli delegation, which has sent volunteers for the fifth time, was to build a 40,000-liter water collection system from the rooftops of the Babati district school, where approximately a 1,000 students study, maintain existing systems installed by previous delegations, and also establish a new library, together with the local community. The project is overseen by Prof. Dror Avisar, head of the Water Research Center.   Working together: members of the delegation with locals from the school in Babati district

A glass of water a day for 1,000 children

After a 12-hour flight and before they embarked on two weeks of challenging physical labor, the delegation acquired all the necessary equipment they didn’t bring with them from Israel in Arusha, the city where they landed: dozens of huge 2000-liter containers, each to be installed in a school, as well as pipes and work tools. The main difficulty of this delegation and its predecessors is in being funded. “The ideal situation is that we would have a regular annual budget and know that our operations are guaranteed,” explains Natalie. Sometimes it’s unclear until the day of departure how much money will be at their disposal. On the eve of leaving the city for the village, the delegation members met the Tanzanian community representative, Julius, a school teacher who accompanies the project, and met his family. “He is the delegation’s angel and takes care of all of us,” says Natalie.   Smiles all around: Julius and his family with members of the delegation At the biggest, most central school in Babati district, where the largest system was about to be installed, the delegation was greeted with an enthusiastic welcome. Knowing that soon every student would be able to enjoy clean water was exciting for the children.   Hope and excitement: the delegation is recieved by the young students   ​ At the principal’s office The first order of business for the delegation was teaching a group of boys and girls from local Scouts how to help with the construction and then later on how to maintain the systems. “The idea is not just to build a system, but to work collaboratively with the community, which includes education and instruction, which will lead to long-term results,” Natalie explains. Left: local Scouts learning the new system. Right: children from the school using it to get clean water.   The construction process included installing gutters, cleaning the water tanks and preparing the infrastructure. In one of the schools, where systems had already been installed in the past, the delegation had to replace containers, destroyed by elephants that came in search of water, and build anti-elephant concrete walls around them.   While the systems were being installed, the delegation members taught the students about proper use of the system and “water discipline”, and in return the students taught them local songs and dances. Singing while you work: two members of the delgation with young students When it was clear that the work was progressing quickly and efficiently, the delegation decided to visit local families and get to know the community. “We started asking them questions about their daily lives and their needs,” Natalie says. “We realized that in addition to building the systems in schools, we also want to think of a home solution. Most people here live in extended families, sometimes numbering up to 50 people. So, a solution for one family can spare them a walk to the nearest water source, which can take hours, and also give them clean water, as opposed to reservoirs that are very polluted. Our challenge was to think of a simple, creative and inexpensive solution, using local materials, so we could easily distribute and duplicate it, and they could easily maintain the systems.”     Sometimes the villagers are forced to drink polluted water

As the largest system was assembled at the Minjingu Elementary School, the principal, together with the teachers, some of the parents and a thousand students, conducted a moving farewell ceremony. “They thanked us with a song, and promised to maintain the system, and we, for our part, got to see and understand how important the system is to them,” Natalie concludes.

TAU Computer Science master’s student Vered Zilberstein applies machine learning to identify schizophrenics

Tel Aviv University student, Vered Zilberstein, pursuing an MSc in Computer Science at the Blavatnik School of Computer Science, has co-led a study that will help detect schizophrenia patients using artificial intelligence.

She and her research partners applied a machine learning algorithm that identified which study participants were afflicted by schizophrenia and which were not.

 “We used participant scores in a language experiment to train a machine learning classifier to differentiate between schizophrenia patients and a control group of the same sex and age. It managed to do it at an 81.5% accuracy rate,” says Zilberstein, “This procedure is done through a sub-area called natural language processing.”

Collaborating with Beer Yaacov Mental Health Center, Zilberstein set out to explore how AI and computing can assist in the world of mental health – specifically in dealing with schizophrenia.

The disorder is very tricky to diagnose and is characterized by abnormal behavior, speech impairments and a diminished ability to understand reality.

Examples of thought and language disorders characterizing people with schizophrenia include jumping between unrelated issues, called “derailment,” while engaging in conversation. “Tangentiality” occurs when a sufferer replies to a question in an oblique and irrelevant manner. Grammatical mistakes and incoherent, illogical speech are also among the symptoms.

“However,” says Zilberstein, “you need to be very skilled to succeed in identifying speech difficulties affecting schizophrenia sufferers as well as those affecting other groups, such as people on the autistic spectrum.”

Zilberstein’s study included two experiments which examined two types of thought disorders. One focused on derailment, which is dissociative weakness. “It means that one is jumping from one subject to another during a conversation,” explains Zilberstein, “for example, someone can say: ‘I’ve always liked geography. My last teacher in that subject was Prof. August A. He was a man with black eyes. I also like black eyes. There are blue and grey eyes and other sorts too…’ and so on. You can clearly see that they jump very quickly between subjects and by the end of the sentence they have completely derailed from the initial topic, which was geography.”

The other experiment focused on incoherence caused by peculiar vocabulary and incorrect grammar. It is hard to understand what is meant. For example: “Oh, it was superb, you know, the trains broke, and the pond fell in the front doorway.”

Both experiments utilized interviews, questionnaires and photo descriptions. They involved 24 male patients affected by schizophrenia aged 30-40 and 27 mentally healthy males, serving as a control group.

The test results showed that, predictably, the control group tended to maintain focus on the conversation topics whereas the patients were more inclined to changing the subject. More important, it was the machine learning algorithm that could analyze and identify who was whom.

As a computer science master’s student, who comes from the world of exact sciences, what draws you to the world of mental health?

“I wanted to be involved in a combination of disciplines, and not only computer science. I wanted to write a thesis based on real data.”

How widespread, if at all, is the intertwining of artificial intelligence and mental health?

“While artificial intelligence gathers pace in the academic, industrial, educational and social media worlds, combining computer science and mental health is still very much in its infancy. However, artificial intelligence is inevitably going to affect almost all aspects of our lives.

“My study examined the way patients and healthy people talk but further studies may explore and compare between the way patients and healthy people write, for example on social media, which is what I intend on looking into in my research going forward.”