Blockchain could revolutionize the oil and gas industry, but there are several open research challenges hindering its successful implementation.��

Legacy systems, approaches, and technologies leveraged for managing oil and gas supply-chain operations fall short of providing operational transparency, traceability, audit, security, and trusted–data-provenance features. They also tend to be centralized, manual, and not integrated, which make them vulnerable to manipulation and the single-point-of-failure problem.

A Khalifa University team researched the issue and found reason to be excited for blockchain technology’s potential in the industry. But there are challenges ahead despite the fact that the industry has already begun adopting blockchain solutions.

Dr. Raja Ahmad, Postdoctoral Research Fellow, Prof. Khaled Salah, Department of Electrical Engineering and Computer Science, Dr. Raja Jayaraman, Associate Professor, Department of Industrial and Systems Engineering, Dr. Ibrar Yaqoob, Research Scientist, and Dr. Mohammed Omar, Chair of the Department of Industrial and Systems Engineering, have investigated the use of blockchain in the oil and gas industry, analyzing the applications, challenges and future trends of this technology in one of the world’s most important industries. Their research was published in.

Industry players believe digital technologies could boost their productivity by 10 to 15 percent. Trading of oil and gas products, such as gasoline and diesel, is a highly standardized and quality-sensitive process that requires high security, privacy and fast data processing, but the majority of systems that currently exist to monitor and manage this trade are centralized, unreliable and non-transparent.

Blockchain, however, is a secure, distributed ledger of transactions that uses cryptographic hash algorithms, which the researchers believe can make oil and gas operations more efficient, transparent, and trustworthy.

A Shell, BP, and Statoil research study estimates that adopting blockchain could reduce the oil and gas industry’s transaction-execution time by 30 percent, Dr. Yaqoob said.

Blockchain offers an immutable and tamper-proof ledger, where each record created forms a block, and each block is confirmed by the community among which the platform is shared before it can be paired up with the previous entry in the chain. The blockchain is a shared database, validated by a wider community rather than a central authority, making it a public ledger that cannot be easily tampered with, as no one person can go back and change things.

Many blockchain solutions use programmable smart contracts – simple programs that can be used to automatically exchange information under predetermined conditions.

Dr. Yaqoob said. “More specifically, blockchain assists in securing and simplifying oil and gas trading, shipment tracking, inventory control, documentation, and billing and payments. It simplifies the unwieldy and complex supply chain processes by introducing transparency to the involved business processes.”

The researchers say blockchain technology uses resource-efficient consensus algorithms and irreversible hashing-based data encryption methods to secure the data and transactions relating to this industry. However, the successful adoption of blockchain technology into the oil and gas industry is affected by many factors, including immature and globally differing blockchain standards, which need to be standardized across the world for such an international industry. Additionally, blockchain technology has a high implementation cost, and legal and regulatory frameworks for blockchain need to be built.

Additionally, the computing processes behind blockchain require a large amount of energy and computing resources to unlock the mathematical challenges of building each block. The energy demands result in increased carbon-dioxide emissions, so finding a more energy-efficient mining process is a crucial research challenge.

Dr. Yaqoob said. “While there are many open challenges still hindering its implementation, we present these as future research directions, and believe there are several systems using blockchain-based smart contracts that can greatly improve critical services in the oil and gas industry.”

Jade Sterling
Science Writer
22 June 2022

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Dr. Deepak Puthal, Assistant Professor in the Department of Electrical Engineering and Computer Science, was honored by the IEEE Smart Computing Special Technical Community (SCSTC) with the Early Career Award, a recognition for young researchers and scientists who have made outstanding strides in their respective industries early on in their careers.��

Nominees for the Early Career Award should have significant and relevant research contributions and a strong academic record, achieving these within 10 years after obtaining their PhD degrees.��

Dr. Puthal is making a name for himself in the field of smart computing with his groundbreaking research in cybersecurity, blockchain, and Internet of Things (IoT)/edge computing. A few of his notable works are Proof of Authentication (PoAh), the first-ever blockchain consensus for IoT; PUFChain, a hardware-assisted blockchain; and CryptoClinqIn, a graph-theoretic cryptography using clique injection. He is also a co-developer of two open-source simulators, the IoTSim-Edge and the IoTSim-SDWAN.��

He is also active in the scientific community, serving in different capacities namely as a technical board member of the IEEE Hyper-Intelligence Technical Committee and as an associate editor for several publications such as IEEE Transactions on Computational Social Systems, IEEE Transactions on Big Data, and IEEE Consumer Electronics Magazine.��

“I am both humbled and excited to receive this award. It is an honor that the potential of my research is recognized by my colleagues in the industry,” Dr. Puthal said. Prior to his IEEE SCSTC Early Career Award, Dr. Puthal has also received the 2019 Best IEEE ComSoc Young Researcher Award for Europe, Middle East, and Africa and the 2018 IEEE TCSC Award for Excellence in Scalable Computing Early Career Researcher.��

“I believe that we should put ourselves forward and join competitive international awards. Recognitions like these bring honor not only to me as an individual, but to Khalifa University as well and I would like to thank the university for its continuous support.”

Ara Maj Cruz
Creative Writer
8 April 2022

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Computer Engineering sophomore student Muhamed Nebuhan Shajahan was part of the team who won first place in the recently concluded RTA Transport Hackathon 2022. Muhamed’s team was composed of other students from universities around the UAE.

The Hackathon, one of the biggest competitive events in the UAE, aims to promote the culture of digital transformation in the country. The event focuses on enhancing RTA’s services through technology and research, with the wellbeing of the community at the center. It also helps identify the future of mobility and infrastructure by addressing the needs of the next generation of users.��

Muhamed’s team developed the winning app, Scooty, to provide a safe riding experience for e-scooter users. As the number of e-scooter users steadily grows, it is important to ensure that not only the riders but everyone in the community is safe.��

The Scooty app includes regulatory features wherein the RTA can incorporate functionalities such as adding scooter licenses that users can apply for within the app. With safety as the main goal, the app monitors adherence to�� riding rules. Scooty uses computer vision and machine learning techniques to detect if the rider is wearing a helmet, and a rider’s speed and navigation in following e-scooter lanes are tracked through GPS technology. The app also rewards riders with points when they follow correct riding practices.��

Joining the Hackathon was a great learning experience for Muhamed. “Khalifa University is committed to nurturing students’ potential and skills, particularly teamwork and collaboration. The opportunity to be able to participate in competitions such as the RTA Hackathon has helped me collaborate with individuals from different schools to brainstorm and conceptualize innovative solutions to some of the pressing challenges in our communities,” he said.��

Ara Maj Cruz
Creative Writer
8 April 2022

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Dr. Vinod Khadkikar, Professor in the Department of Electrical Engineering and Computer Science, has been named IEEE Fellow. The distinction of IEEE Fellow is given to only select IEEE members who have made extraordinary accomplishments in the fields of interests of IEEE.��

This achievement is significant for Dr. Khadkikar as he becomes the youngest researcher in the UAE to receive the recognition. “Becoming a fellow of the IEEE is the ultimate career accomplishment for electrical engineers and researchers. It is a global recognition of the extraordinary career accomplishments that have made an impact on society,” he said.��

Dr. Khadkikar is recognized for his work in unified power quality conditioners (UPQCs), one of the most versatile power quality enhancement devices that can simultaneously provide shunt (current related) and series (voltage related) compensations. He has pioneered the theory of a power-angle control UPQC that demonstrates a new functionality of the load reactive power support through an underutilized series inverter. This new concept of sharing and supporting the load reactive power demand through both shunt and series inverters offers several notable merits such as better utilization of series inverters, and a considerable reduction (20%–40%) in shunt inverter rating, thus reducing the overall cost, weight, and volume of the UPQC. Dr. Khadkikar meticulously correlated the load active and reactive power with the amount of reactive power that the series inverter should share. This is a noteworthy addition to the existing control, operation, and usability of the UPQC. His findings on UPQC have been widely referenced in research articles and documented in several books.��

Another notable contribution of Dr. Khadkikar to the industry is successfully implementing an artificial neural network (ANN)-based phase-locking scheme for active power filters (APFs). In APFs, the knowledge of supply voltage fundamental frequency and phase is very crucial in achieving synchronization and unity power factor (UPF) operation. He developed a novel technique using adaptive��linear neurons (ADALINE) and systematically executed it by splitting the control tasks in outer (fundamental frequency and phase estimation) and inner (effective APF operation) loops. These solutions allowed Dr. Khadkikar to apply his cutting-edge approach easily in general-purpose signal processors (DSPs). His approach is versatile and can be applied in several applications including grid-connected solar and wind energy systems.��

“This milestone is possible because of the substantial research funding from Khalifa University and the Abu Dhabi Government at large. I am honored to be elevated as IEEE Fellow and I strongly believe that my graduate students, research engineers, post-docs, and collaborators have played a crucial role in achieving this feat,” Dr. Khadkikar commented.��

And to inspire fellow researchers, he says, “Keep pursuing your dreams with dedicated and consistent efforts and things will definitely follow through.”

Ara Maj Cruz
Creative Writer
26 January 2022

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A team of four Electrical Engineering and Computer Science senior students have won 2nd place at the 15th IEEE UAE Student Day 2021 competition in the category “Senior Design Project – Power and Renewable Energy,” for their innovative electric vehicle wireless charging system. The Competition took place virtually on 6 November 2021.

The 7kW multi-coil wireless charging system the team designed was based on a thorough research/literature review of wireless charging systems. The team studied the various compensation strategies, selected the best compensation strategy, modeled and designed the wireless charging system, verified it through computer simulation, and finally built an experimental prototype.����

The team included senior students Faris Alazzani, Esmaeil Alhajeri, Ali Alzaabi, and Saeed Al Qubaisi. They were supervised by Dr. Balanthi Beig, Associate Professor, Dr. Khaled Al Jaafari, Assistant Professor, and Dr. Khalid AlHammadi, Assistant Professor, all from KU’s EECS Department. Dr. Nazar Ali, Associate Professor of EECS is the course instructor.��

“Participating in such competitions motivates students to work hard, and winning 2nd place encourages us to develop our project further,” Faris, the Team Leader, shared.��

“Since the sky’s the limit at Khalifa University, we will do our best to participate in more competitions and conferences with our project and aim for first place. Many thanks to Khalifa University and the EECS department for giving us this opportunity to participate in this competition and for providing all the equipment we needed to develop and test our system. Moreover, many thanks to our supervisor Dr. Balanthi Beig. He supported us from the beginning and he encouraged us to participate in this competition. Lastly, we are thankful to our supervisors Dr. Khaled Al Jaafri and Dr. Khalid Al Hammadi, and to researchers Dr. Ahmed Shehada, Dr. Motiur Mohammed and Mr. Nguyen The Hoach for their help in the lab. We would also like to thank Dr. Shihab Jimaa, Associate Professor of EECS and KU coordinator of the IEEE Student competition for his encouragement.”

The KU multi-coil wireless EV charging system consists of a rectifier with capacitor filter – a device which converts an alternating current (from the Abu Dhabi Distribution Company’s�� distribution supply socket at 230 volts and 50 Hertz),�� into a direct�� current.��

Then this DC power is converted to 85 kilohertz high frequency AC using a silicon carbide-metal-oxide-semiconductor field-effect transistor (SiC-MOSFET) based DC to AC converter.

This high frequency AC power is then transmitted wirelessly through a magnetic field using transmitter and receiver coils.��

The electric vehicles are fitted with receiver coils and the high frequency AC signal is converted to DC using another high frequency rectifier. This DC is then used to charge the car battery.

The KU students selected the series compensation strategy after conducting a thorough literature review. In the first stage, they modelled and designed a wireless charging system based on a single coil arrangement. Through simulation studies, they found that the energy transfer is reduced due to misalignment and the distance between transmitter and receiver coil. To increase energy-transfer efficiency, three coils were used at the transmitter end. The design was then verified through computer simulation using MATLAB/SIMULINK software.

The team then fabricated a scaled down experimental prototype and successfully tested it at the power electronics and sustainable energy research lab in KU’s Advanced Power and Energy Center.��

Erica Solomon
Senior Publication Specialist
20 December 2021

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Teaching robots to follow a moving object is more difficult than you think, requiring complex algorithms and a different way of thinking.

Take a look around. What do you see? Most of us have two eyes and we use those eyes to collect light that reflects off the objects around us. The eyes convert that light into electrical signals that are processed by our brain. This builds a representation of the world and we use that to navigate during our everyday lives. Even robots that are the most like humans in appearance, however, don’t see the world the way we do.

Instead, algorithms recognize features in images collected by a robot’s sensors and cameras. The software may create a very basic map of the environment and learn to recognize patterns to help the robot understand its surroundings. This means that robots are being programmed by humans to see things the human thinks the robot will need to see. While this has many very successful examples, no robot is capable of navigating the world using just vision for static recognition.

If you spot a bird outside, you can watch that bird fly through the sky until it lands or disappears from view. This is visual object tracking, and it’s a simple task for humans: spot the object and follow it. For robots, it’s much more difficult.

To improve visual object tracking in robotic applications, Dr. Sajid Javed, Assistant Professor, Dr. Jorge Dias, Professor, Dr. Lakmal Seneviratne, Professor, and Dr. Naoufel Werghi, Professor, all from the Department of Electrical Engineering and Computer Science at Khalifa University, collaborated with Dr. Arif Mahmood from Information Technology University, Pakistan, to develop an AI algorithm that is both highly accurate and quick when detecting and tracking a generic object. Their proposed solution was published in.

“Visual object tracking is a fundamental and challenging task in many high-level vision and robotics applications,” Dr. Javed explains. “Typically, the difficulties lie in developing detection algorithms that can handle blurred images from fast motion, ignore background clutter and deal with significant scale and light variations.”

Object tracking is an application of deep learning where a program takes an initial set of object detections and follows them as they move around frames in a video. The algorithms allow the robot to automatically identify an object in a video and interpret it as a set of trajectories to predict where it will end up.

The first step in tracking an object is to detect it. The research team’s solution narrows a search area down and instructs the robot to find all object instances of one or more pre-determined object classes. The algorithm is trained on a series of examples of these object classes to learn what it is looking for, regardless of the object’s scale, location or pose and despite any partial occlusions or poor lighting conditions.

Once the object has been identified, it needs to be followed. Robots can do this by continuously re-identifying the object in subsequent images, but for visual object tracking to be useful in robotic applications, interpreting the object as a set of trajectories with high accuracy is required.

Algorithms for tracking objects need to accurately perform detections and localize objects of interest in the least amount of time possible. This is especially imperative for real-time object tracking models.

“Discriminative correlation filters (DCF) are well suited to object tracking because of their impressive performance in terms of speed and accuracy,” Dr. Javed says. “In most DCF methods, an online correlation filter is trained from the region of interest in the current frame and then employed to track the target object in subsequent frames.”

High detection accuracy and fast processing speed are difficult to combine: More accurate tracking tasks often require longer processing times, while quicker responses are more prone to errors. In the research team’s solution, accuracy and speed are achieved by constructing a spatiotemporal graph that models and predicts where an object is likely to appear based on its previous identified location. Out of a series of possible trajectories, the most probable is selected by the DCF, which filters the background noise and any other distractions.

To evaluate their algorithm, the team tested it on six challenging benchmark datasets and compared it with 33 existing state-of-the-art trackers. Their results were excellent, achieving higher accuracy than existing trackers on many tests and ranking among the top three for the remaining tests.

As mobile robots and autonomous machines are increasingly deployed, object detection systems are becoming more important. Although great progress is being made, we are still far from achieving human-level performance, but solutions like this are a vital step towards that level of performance.��

Jade Sterling
Science Writer
28 November 2021

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Muhamed Nebuhan Shajahan, BSc in Computer Engineering student, was invited to be a part of the Mohamed Bin Zayed Majlis for Future Generations (MBZMFG) Virtual Future Lab program.

Titled ‘Beyond Ideas, To Next Steps,’ the two-day intense program took place from 25 – 26 September 2021. Muhamed joined one hundred other students from around the UAE to discuss the challenges and opportunities in developing the UAE’s sustainable future.

“It was a beneficial experience that helped me and my��co-participants understand the importance of critically thinking about��designing��our��future as well the��communities’ future,” Muhamed shared.

The program was led by former Harvard professor Dr. Maurizio Travaglini, who encouraged the students to be future planners who, as Muhamed explained “open the immediate doors before the farthest ones and close the doors that stop us.”

“The program inspired me to think about the world, as a place of opportunities and the doors we can open to make a difference,” Muhamed said.

The event provided a safe space for students to collaborate and exchange ideas, while being guided to think about their own future through the lens of different design concepts.��

The students discussed the complexity of the concept of “entanglement” and “knotty objects,” which refers to how various practices, technologies, practices, and processes become “entangled” in an object, and how this affects societies and is in turn affected by society.��

In another exercise, the students discussed the L5 space colony. L5 is an area of the solar system where the gravitational force is neutral, making it suitable for operating satellites and a launch pad for future space explorations.��

“We imagined ourselves as a NASA team, that was asked to find innovative ideas to recruit people to live in this society. The groups shared their ideas, which were very different from conventional methods of recruiting people for space exploration. Also, we described a day-in-the-life of a person in the L5 colony and the importance of togetherness among the people in this society,” Muhamed shared.

“The teamwork and communication skills that I acquired from KU have truly helped me throughout this session. The program itself was a different experience than the usual virtual meeting to think about our future.”��

Erica Solomon
Senior Publication Specialist
2 November 2021

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After taking first place in the EmiratesSkills competition in the CNC Turning category, BSc in Computer Science student Hanan Ahmed Alshamsi went on to represent the UAE in the same category at EuroSkills 2021 in Graz, Austria.��

After taking first place in the EmiratesSkills competition in the CNC Turning category, BSc in Computer Science student Hanan Ahmed Alshamsi went on to represent the UAE in the same category at EuroSkills 2021 in Graz, Austria. She was the first UAE representative to compete in the CNC category, which stands for Computer Numerical Control Turning, used to produce the precise and interactive parts of complex products from smartphones to airplanes.

EuroSkills is a vocational skills competition staged as a European championship every two years. The competition focuses on the outstanding achievements of young, talented, and skilled professionals, with around 400 participants competing in vocational categories from the industrial, craft, and service sectors.

“Having the opportunity to participate in EuroSkills was one of the best and most challenging experiences in my life,” Hanan said. “I trained for 7 to 10 hours every day, including weekends, for three months before the competition, while still keeping up with my university workload to stay on my graduation plan.

The UAE was one of only three guest countries, which meant that while we were part of the competition, we didn’t get medals, but the opportunity to go and gain experience and make connections was our reason for going.

It was definitely challenging. I was up against people who had been training full-time for over three years on a completely different machine than the one I trained on. Despite that, I performed really well and took third place.”

For her first time competing in an international competition and despite her limited time training and other commitments to her studies, Hanan showcased her talent and expertise in CNC turning, taking third place in her category. Her visit to EuroSkills 2021 was a stop on the journey to the 2022 WorldSkills Competition in Shanghai, where she will test her abilities against the best of the best in the world.

Jade Sterling
Science Writer
2 November 2021

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The ‘memimpedance’ device can control current flow in a circuit, and could make electronics like wearable sensors, flexible medical devices and biodegradable electronics more efficient

A team from Khalifa University has developed a novel electronic ‘memimpedance’ device that can act as a switch and induce tunable resistor and capacitor behavior simultaneously in an electronic circuit.

A resistor is an electrical component that regulates the flow of electrons in a circuit, while a capacitor is an electrical component that collects and stores electrical charge.

The need to control electron flow is what gave rise to transistors, which are at the heart of all electronics today. Transistors are three terminal electronic switches that either permit or prevent electrons from flowing from one terminal to another based on the control provided by the third terminal, which serves as a gate. Other elements, including resistors and capacitors, also play a role in regulating current flow in electronics.

Enter memristors. Memristors are resistors with memory. They were physically realized for the first time in 2008, though they were conceptualized theoretically for decades before that, and have gained popularity for their potential use in computers.�� They are simpler than transistors, smaller, use less energy, can alter their resistance and “remember” the most recent resistance they had. This means they have the potential to replace silicon-based transistors and could be used to create faster, more efficient computer chips that integrate memory with logic.

When memristor and memcapacitor behaviors happen simultaneously in the same device, it is called memimpedance. A memimpedance device, therefore, is designed to control, or tune, the memristor and memcapacitor behavior in an integrated circuit.

Dr. Heba Abunahla, Research Scientist in the Electrical Engineering and Computer Science Department at Khalifa University, and a team from the KU System-on-Chip Lab (SoCL), developed a memimpedance device made out of silver-reduced graphene oxide-silver that can tune the resistance and capacitance behaviors in a circuit.

Dr. Abunahla published her research in the journal, with co-authors Dr. Baker Mohammad, Professor, Dr. Yawar Abbas, Research Scientist, and Dr. Anas Alazzam, Associate Professor.

“Memimpedance has many advantages compared to resistance or capacitance only devices, especially its ability to tune the overall circuit impedance,” Dr. Abunahla said.

Circuit impedance measures how much a circuit impedes the flow of charge. As electrons move through a circuit, they collide with the internal structure of the conductor, which creates friction and slows them down.

The amount of resistance depends on the conductor’s material, shape and size, but conductors generally have low resistance to current. In addition to resistance, circuit impedance also considers capacitance, which is the ability of a component to collect and store electrical charge.

A device that can tune a circuit’s overall impedance would be particularly useful in applications like wearable sensors, flexible medical devices and biodegradable electronics.

Dr. Abunahla and the SoCL team successfully demonstrated that their memimpedance device would, when a suitable voltage was applied, tune the circuit resistance and capacitance concurrently.

They developed the memimpedance device with a unique structure using silver-reduced graphene oxide-silver.

“Using graphene-related materials as a switching material is a great asset due to their low cost and adaptability, and they are environmentally friendly,” Dr. Abunahla said.

The team’s memimpedance device has a planar structure, meaning all the atoms of the molecule sit on a single two-dimensional plane.

“Fabricating the device with a planar design boosts its potential to be deployed in sensing applications, such as wearable electronics. The planar structure allows for a bigger surface area and better interaction with the environment, which increases the efficiency of the sensing unit,” Dr. Abunahla added.

The researchers fabricated the device on a flexible��polymer substrate using a lithography process. They deposited the graphene oxide directly onto the polymer substrate, then immersed it in an acid to create a thin layer of reduced graphene oxide measuring around 60 nanometers thick. They then used a standard lift-off process to pattern a film of silver electrodes onto the substrate.

They intentionally selected a polymer substrate instead of a silicon-based substrate, which is traditionally used to make memristors, because silicon-based devices pose challenges when they are stacked together to create 3D circuits.

The KU memimpedance device, however, is well suited for stacking and to produce 3D integrated circuits, which can achieve better performance than traditional 2D circuits.

Using silver-reduced graphene oxide-silver in a planar structure, fabricated on a flexible substrate using a standard production process, makes the resulting device cost-effective and deployable in flexible electronics and many other potential applications

“This work will be a great asset for tunable emerging applications, especially for communication and AI systems,” Dr. Abunahla said.

Jade Sterling
Science Writer
6 October 2021

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���ճԹ� Team Also Retains 6^th Spot in Design Segment to Remain Ahead of Renowned International Universities

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���ճԹ� has announced that a 21-member student team led by Sultan Al Hassanieh has won the top spot in verbal presentation for the first time during the Knowledge Event at the 42^nd Society of Automotive Engineers (SAE) Collegiate Design Series Supermileage Competition for 2021. The team also retained 6^th spot overall in the Design Event, the same position it won last year in Michigan, US.

The verbal design report demonstrates the Khalifa University team’s understanding and application of the engineering principles that support their design of the one-person, single-cylinder engine vehicle, capable of achieving fantastic fuel economy during a 6-lap attempt around an approximately 1.6-mile-long, banked, oval track.

Since the traditional validation event at the 42nd SAE Collegiate Design Series Supermileage Competition was cancelled due to the pandemic this year, the organizers opted to score teams based on two major criteria – the submitted design report that details the design and fabrication of the vehicle, and a verbal presentation on Zoom in which team members present their design process and final outcome to a panel of judges. The student team was then critically questioned, and the Khalifa University students proved their mettle to reach the top spot. They were also assessed on team participation, program knowledge and technical knowledge.

Dr. Arif Sultan Al Hammadi, Executive Vice-President, Khalifa University, said: “Our faculty members and the students have once again demonstrated that Khalifa University remains the top-ranked, globally-acknowledged academic institution with unparalleled expertise. Winning an international competition of this stature not only brings honor to the university but also to the UAE. Congratulations to the student team and the faculty members who guided them through training and support.”

The Khalifa University team achieved this remarkable feat even though it entirely consisted of only first-time participants, demonstrating the world-class faculty’s role in imparting knowledge, as well as the talented students’ capability in designing and presenting the project to the international judges. Also for the first time, this year’s team comprised students from all majors.

Dr. Bashar Khasawneh, Associate Professor, Mechanical Engineering, and Dr. Reyad El-Khazali, Associate Professor, Electrical and Computer Engineering, advised the students on the project.

In line with the healthcare protocols being implemented on campus at Khalifa University, team members were allowed to visit the campus and work on fabricating parts of the vehicle that they are responsible for, while complying with all necessary safety precautions. Team members used online video communications applications to ensure safety while discussing the general concept of the design, which was then developed into working theoretical models of the vehicle’s subsystems.

Dr. Khasawneh said: “Though the student team leaders were initially worried that the majority of the work would be held online, they achieved the top position because of team skills. What made the Khalifa University Supermileage team one of the best in the world was how every member ensured to constantly support and motivate each other, creating an inherently supportive atmosphere.”

The SAE Collegiate Design Series Supermileage Competition challenges students to design and construct a single-person, fuel efficient vehicle that will run a specified course to obtain the highest combined km/L (mpg) rating. Teams generally spend 8-12 months designing, building, and preparing their vehicles for competition.

Clarence Michael
English Editor Specialist
5 July 2021

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���ճԹ� researchers find a way to use convolutional neural networks to identify cancer in tissue samples, which could speed up diagnosis and improve outcomes in patients with colorectal cancer.

Colorectal cancer is the third most common cancer among men and women worldwide, and the second most common cause of cancer-related mortality. Most colorectal cancers are due to old age and lifestyle factors, with only a small number of cases due to underlying genetic disorders. It typically starts as a benign tumor, such as a polyp, which over time becomes cancerous. Like all forms of cancer, early diagnosis and differentiation of the tumor are crucial for a patient’s survival and wellbeing.

Colorectal cancer may be diagnosed by obtaining a sample of the colon and using histopathology – the study of changes in tissues caused by diseases – to determine the characteristics of the tumour tissue at the microscopic level.

Histology is the study of the microanatomy of cells, tissues and organs as seen through a microscope. The structure of each tissue in the body is directly related to its function and diseases affect tissues in distinctive ways. Studying the histology of a tissue can be very useful in making a diagnosis and determining the severity and progress of a condition.

Because of the great variety of tests that are available, and the high level of skill needed to carry out and interpret them, researchers are beginning to turn to computational pathology and artificial intelligence techniques to identify in tissue samples diseases like cancer.

Dr. Sajid Javed, Assistant Professor, and Dr. Naoufel Werghi, Associate Professor of Electrical Engineering and Computer Science, have collaborated with researchers from around the world to develop algorithms to identify samples of colorectal cancer tissue. A paper based on this research has been published in.

“Computational pathology is a fast-growing research area in cancer diagnosis and can play an instrumental role in helping medical professionals detect and classify tumors,” said Dr. Javed.

Cancer histology reveals underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes.

The phenotype is the set of observable characteristics or traits of an organism or a tissue. Image-based phenotyping aims to develop the computer vision techniques and tools needed to recover quantitative data from a wide range of images. But phenotyping presents challenging problems, particularly in images from colorectal cancer tissues.

Aided by advances in slide scanning microscopes and computing, convolutional neural networks (CNNs) have emerged as an important image analysis tool. CNNs use a network of interconnected layers of filters that highlight important patterns in the images and can continue to learn from previous results.

“Manual examination of tissue samples is time-consuming, highly subjective, and often affected by the observer,” explained Dr. Javed. “Meanwhile, algorithms analyzing digitized Whole Slide Images (WSIs) can examine hundreds of thousands of cells and billions of pixels to differentiate seven distinct tissue phenotypes.”

Deep learning methods require large amounts of annotated histology data for training, which may be tedious to obtain. Additionally, while these methods may be effective in determining tumor tissue, the tissues in colorectal cancer also contain a rich mix of several other types of tissue, including smooth muscle, inflammatory, necrotic, and benign tissue. Any algorithm must be taught to distinguish between these tissue types to be effective.

Texture analysis is a commonly used approach for tissue phenotyping, where texture features are computed to train classifiers, which are then used to predict distinct tissue types.

“Texture analysis may be attractive due to its simplicity but it does not fully capture the biological diversity of tissue components,” explained Dr. Javed.

“Recent methods have proposed integrating cellular connectivity features, which are used as a proxy to cellular interaction features. The notion of cellular connectivity features is based on the fact that spatially adjacent cells have a higher probability of receiving inter-cellular signals from each other than from cells that are farther away. It has also been shown that inter-cellular signals between various types of cells can influence the progression of cancer. However, a dynamic network of tumor growth cannot be adequately modelled by a single type of interaction. Our technique uses a multiplex network model to represent the intricate relationships between cell populations. We propose four different types of cellular networks integrating a variety of features representing tissue characteristics at different levels.”

In the researchers’ model, cells from a WSI are detected and classified into five distinct categories using a deep neural network. Then, four different types of cellular interaction features are computed and used to construct a four-layer multiplex graph. Since each slide contains thousands of cells, the slides are segmented into tiles or patches, which helps the algorithm determine the distribution of different types of tissues across the cells.

“There are many directions in which this work can be further extended,” added Dr. Javed. “Further cellular types such as blood cells could improve performance and also reveal more micro-level tissue communities. Additionally, our framework could be adapted to WSIs of different types of cancer. Of course, in clinical practice, our work can help medical practitioners understand the contents of the WSI and make more accurate and timely diagnoses.”

This work was funded by the Khalifa University of Science and Technology and the UK Medical Research Council. The collaborators were also supported by the PathLAKE digital pathology consortium, which is funded from the Data to Early Diagnosis and Precision Medicine strand of the UK government’s Industrial Strategy Challenge Fund, managed and delivered by UK Research and Innovation.��

Jade Sterling
Science Writer
5 July 2021

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‘CovidSense’ App Will Collect Metadata and Self-Reported Health Status data, Along with Breathing Sounds, Cough, Heart Rate, and GPS Location from Smartphones ��

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A team of researchers at Khalifa University of Science and Technology launched an app to collect data from smartphone users to identify, through machine intelligence, whether they are in the CoVid-19 ‘high risk’ category.��

The app named ‘CovidSense’ will target all mobile phone users. It will also help those users under quarantine to monitor their symptoms and location, while assisting them with their health control measures. The app will record metadata and self-reported health status data, along with breathing sounds, cough, heart rate, the GPS location from a smartphone, as well as details of those who the user has interacted with.

The data collected from the smartphones can be used to monitor the evolution of the health status over a period of time, informing the status of the Covid-19 patients to the connected physicians. At the same time, it will allow researchers to form ‘Deep Learning’ models in order to come up with a ‘reliable predictive high-risk index’ in an updated version at a future date. This update will also help minimize spreading by alerting or helping healthcare workers to act at the correct time and place.

The development of ‘CovidSense’ app is being led by Dr. Leontios Hadjileontiadis, Professor, Electrical Engineering and Computer Science, Acting Chair, Department of Biomedical Engineering, Khalifa University, along with Dr Herbert F. Jelinek, Associate Professor, Dr. Ahsan Khandoker, Associate Professor, and Dr. Kinda Khalaf, Associate Professor and Associate Chair, Department of Biomedical Engineering, Khalifa University, for the metadata and physiological signal analysis, when they will be obtained from the users.

���ճԹ� is also collaborating with the research Lab, Signal Processing and Biomedical Technology Unit, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece, involving the development team of Tsoumalis George, Zafiris Bampos, and Iakovakis Dimitrios, for implementing the functional versions of CovidSense in both operational systems of Android and iPhone. For more information about the app please visit

Clarence Michael
English Editor Specialist
17 January 2021

The post ���ճԹ� Researchers Launching App to Identify CoVid-19 ‘High Risk’ Category Users from Smartphone Data appeared first on ���ճԹ�.