Research Projects Executed

1. Artificial Intelligence for Energy Efficiency in 5G Wireless Networks

5G wireless technology requires significant power in order to search for the best available beam pairs prior to establishing communications, leading to rapid power drain in the equipment. Dr. Memon researched the application of artificial intelligence (AI) techniques to optimize beam searching. He developed an AI-based discontinuous reception (AI-DRX) heuristic that predicts the next packet’s arrival time. This enables the radio circuitry to be turned off until the arrival of the packet, thereby saving energy. Dr. Memon’s algorithm was shown to enable a 70% improvement in energy efficiency for certain types of packets.

1) “Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks,” Electronics, 2019 

2) “Deep‐DRX: A framework for deep learning–based discontinuous reception in 5G wireless networks,” Transactions on Emerging Telecommunications Technologies, 2019.

Some notable citations from prominent researchers around the globe

The publication of Dr. Memon’s work in such authoritative venues, and the substantial attention this work has received, serves as a testament to the value of his research on artificial intelligence heuristics for improved 5G power efficiencies. Dr. Memon’s research has already driven additional important advances in the field. Some examples of his impact are detailed below:

• Ari et al., in their development of a resource allocation scheme for 5G radio access networks (C-RAN) (Computer Networks, 2019), cite Dr. Memon’s finding that 15-20% of tower sites in current C-RANs consume more than 50% of the power. The authors reference Dr. Memon’s results, stating that “[i]n traditional architectures only about 15-20% of BSs operating in the current RAN architecture are loaded more than 50% [19, 20].”

•  In their review article (Electronics, 2020), Maruta and Falcone highlight Dr. Memon’s work on energy efficiency in massive multiple-input multiple-output (MIMO) technology for 5G. The authors especially call out his use of AI as a mechanism for energy use regulation. They note that “[d]iscontinuous reception can also contribute to improving the energy efficiency. Authors in [23] introduced an artificial intelligence (AI) approach, i.e., recurrent neural network (RNN), to adapt sleep cycles of user terminals.”

• Gonçalves et al., in their analysis of methods to reduce the carbon footprint of 5G (Electronics, 2020), cite Dr. Memon’s research into improving the energy efficiency of 5G technologies. The authors call out their findings on energy efficiency for mobile devices. They also propose that his work on energy efficiency be extended to device-to-device communication. The authors note, “[c]onsidering, for example, the data exchange between two users which are geographically close to each other, which represents an ever-increasing reality, the possibility of having such data exchange directly between the devices in a D2D fashion can represent several enhancements in power consumption reduction. D2D can be used as a mechanism to decrease power consumption [59,60], by reducing the network hops to only one with the immediate advantage of lower latency; better quality of service and experience; and, from the core network perspective, decrease signaling and overall backbone traffic.”

It is evident from these citations that Dr. Memon’s research into artificial intelligence heuristics for improved 5G power efficiencies is actively advancing his field.

Furthermore, Dr. Memon’s study has received funding from the National Research Foundation of Korea. This funding supports projects that contribute to the advancement of knowledge and the improvement of quality of life through supporting creative research. Therefore, Dr. Memon’s research clearly advances these goals through the reduction in the carbon footprint of rapidly growing 5G technology.

In sum, Dr. Memon's work on artificial intelligence heuristics for improved 5G power efficiencies serves as evidence that he has already made progress toward his larger effort to advance the design of green wireless networks and preventive healthcare systems to create energy-efficient 5G wireless networks that support various healthcare systems.

2. Green and Emerging Wireless Networks

In this research, Dr. Memon investigated the use of radio-frequency (RF) signals as an energy source for battery-free communications. He specifically studied the use of backscatter communication (BackCom) to harvest energy from incident RF. Dr. Memon detailed various types of BackCom, including ambient BackCom (Amb-BackCom), its evolution and architecture, and its modes, including half-duplex and full-duplex. He then examined range extension and security considerations. Dr. Memon’s research provides a comprehensive guide to BackCom, enabling researchers to evaluate its suitability within the context of their particular investigations and designs.

Dr. Memon has described his findings related to his analysis of backscatter communication and emerging wireless networks in the following three peer-reviewed papers

1) “Backscatter communications: Inception of the battery-free era—A comprehensive survey,” Electronics, 2019.

2) “Ambient Backscatter Communications to Energize IoT Devices,” IETE Technical Review, 2020.

3) “Femtocell: What, Why, and How?” IJCSNS International Journal of Computer Science and Network Security, 2019.

Some notable citations from prominent researchers around the globe

Dr. Memon’s successful publication of his work demonstrates its value in the field. Dr. Memon’s peers have been significantly influenced in their own work by his prior findings regarding his analysis of backscatter communication and emerging wireless networks. Some examples include:

• Maraqa et al., in their survey of optimal power domains for future wireless networks (IEEE Communications Surveys & Tutorials, 2020), cite Dr. Memon’s work on backscatter communication. The authors discuss his research in the context of their own focus, the rate-optimal power domain non-orthogonal multiple access (NOMA). They note that, “[s]ome other surveys identified in Table I(b) [including Dr. Memon’s] discuss rate-optimal Non-orthogonal multiple access (NOMA). Scattered discussion (i.e., rate-optimal NOMA works were mentioned alongside the NOMA works that considered optimizing other metrics, such as power minimization and energy efficiency maximization as well as the NOMA works that investigated performance analysis metrics, such as BER, SER, and outage probability. Hence, rate-optimal NOMA works were discussed in a scattered fashion within those survey papers.”

•  In their analysis of the applicability of artificial intelligence to 6G communication networks (Computer Communications, 2020), Sheth et al. cited Dr. Memon’s research on battery-free communications. The authors posit that, as per Dr. Memon's findings regarding device-to-device communications via backscatter communication, the same is applicable to 6G technologies. The authors state that “Enhanced Energy Efficiency: It is the most important service provided by the 6G network. Energy consumption plays a vital role in sustainable development. 6G also has productive communication strategies for enhancing energy efficiency [45]. The vision is to accomplish without battery communication wherever conceivable, focusing on communication efficiency on the request of 1 pJ/b [46].”

•  Guo et al., in their research into resource allocation for symbiotic radio systems (IEEE Access, 2019), cite Dr. Memon’s research into backscatter communication to harvest energy. The authors highlight Dr. Memon’s analysis of harvesting incident radio signals to provide power to the Internet of Things (IoT) devices. The authors note, “[i]n particular, the IoT device in the symbiotic radio system, also referred to as the backscatter device (BD), transmits information over the incident primary signal via backscatter modulation without requiring active radio-frequency (RF) components [4]–[7].”

As demonstrated by these uses of his findings regarding his analysis of backscatter communication and emerging wireless networks, Dr. Memon has served as an active contributor to the field.

Dr. Memon’s study has been supported by funding from the National Research Foundation of Korea. The National Research Foundation of Korea funds projects that promote the advancement of knowledge and improvement of quality of life through supporting creative research. This support of Dr. Memon’s research, therefore, serves as evidence that it provides for greener sources of energy in communication devices.

Dr. Memon’s analysis of backscatter communication and emerging wireless networks has advanced the field and the work of his peers. Consequently, this work represents progress toward his larger goal of advancing the design of green wireless networks and preventive healthcare systems to create energy-efficient 5G wireless networks that support various healthcare systems.

3. Intelligent Solutions for Preventive Healthcare Systems

In this research, Dr. Memon investigated several unique, artificial intelligence-based solutions to be used for preventive healthcare, including a machine learning (ML) based architecture for a personalized glucose monitoring system (PGMS). Using both samples acquired through invasive and non-invasive methods, the ML model was trained and repeatedly refined using a unique adaptive boosting algorithm. Once trained, the model was personalized for individual patient's characteristics. Dr. Memon then analyzed the performance of the system, finding that his PGMS significantly reduced the error rate as measured against previously measured data using non-invasive glucose values. Continuing his research into ML-based solutions in preventive healthcare, Dr. Memon developed a wearable system for health monitoring using a unique event similarity search algorithm. Dr. Memon’s research has provided several intelligent solutions for use in preventive healthcare.

Dr. Memon’s research has resulted in a total of five peer-reviewed papers. These include:

1) “Adaptive Boosting Based Personalized Glucose Monitoring System (PGMS) for Non-Invasive Blood Glucose Prediction with Improved Accuracy,” Diagnostics, 2020.

2) “Personalized Non-Invasive Blood Glucose Monitor Using Machine Learning Models,” Test Engineering and Management, 2020.

3) “A CNN-based Automated Activity and Food Recognition using Wearable Sensor For Preventive Health Care,” Electronics, 2019.

4) “Accelerated Reliability Growth Test for Magnetic Resonance Imaging System Using Time-of-Flight Three-Dimensional Pulse Sequence,” Diagnostics, 2019.

5) “MRI Gradient Subsystem Accelerated Reliability Test Using Nominal Day Usages,” Test Engineering and Management, 2020.

Some notable citations from prominent researchers around the globe

This publication record serves as clear evidence of the relevance of Dr. Memon’s work on AI-based solutions in preventive healthcare in the field. Many other researchers have been significantly influenced in their own work by Dr. Memon’s prior findings, as exemplified below:

•  Ali et al., in their research applying social media data to a framework for intelligent healthcare monitoring (Future Generation Computer Systems, 2021), cite Dr. Memon’s research on the algorithm he used to train his healthcare solutions. The authors note that “[c]onvolution neural network (CNN): CNN has been applied for various classification tasks using sensors and textual data [86–88].”

•  In their research into ML-based algorithms for use in classifying personal data (International Journal of Environmental Research and Public Health, 2020), Park and Kim cite Dr. Memon’s work on ML-applied activity recognition. Regarding Dr. Memon’s research, the authors note, “[l]ike most human activity-pattern research, this paper attempted to recognize the activity patterns of children in very limited scenarios, where the classification was done only for the data collected in advance. Unlike most related research associated with acquiring training data for activity-pattern recognition [5,35], we aim to relieve the inconvenience of manual handwriting work.”

•  Bahador et al., in their investigation into ML techniques for use in wearable sensors (JMIR mHealth and uHealth, 2021), cite Dr. Memon’s research into classification techniques for use in wearables. The authors refer to his results derived from the use of trainable neural networks, noting that “[t]he obtained results showed an overall validation accuracy comparable to the approaches proposed earlier in the literature (Table 4) ([38] uses Piezoelectricity and Convolutional neural network with accuracy 91.9%).”

These citations reflect Dr. Memon’s status as a key driver of progress in the field of electrical engineering.

• The National Research Foundation of Korea has supported Dr. Memon’s study. This organization is dedicated to funding projects that promote the advancement of knowledge and improvement of quality of life through supporting creative research, so their endorsement of Dr. Memon’s work is a reflection of its clear importance in advancing technologies that improve preventive healthcare

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• Dr. Memon’s research on AI-based solutions in preventive healthcare stands out in the field due to its innovative application of ML techniques and neural networks to improve the performance of these systems. Furthermore, his success here serves as evidence that Dr. Memon has already made progress toward advancing his proposed endeavor.

4. Image Recognition and AI for Real-Life Applications

In this research, Dr. Memon explored the use of convolutional neural networks (CNNs) for two image recognition applications: satellite detection of ships and automated recognition of human actions. The first, he trained a CNN to recognize ships from satellite data that improved the accuracy of existing systems, particularly when operating on noisy data due to weather conditions or the presence of high waves. Dr. Memon validated this performance against open-source datasets that provided exhaustive scenarios. The second topic to which Dr. Memon applied CNNs was the recognition of human actions. After training the CNN, he measured its performance using the stanford40 dataset, obtaining an 87.3% accuracy

Dr. Memon has published three peer-reviewed papers based on his work on machine-learning techniques to improve automated image recognition. These are:

1) “Ship Detection in Satellite Imagery by Multiple Classifier Network,” IJCSNS International Journal of Computer Science and Network Security, 2019.

2) “Feature Fusion Based Human Action Recognition in Still Images,” IJCSNS International Journal of Computer Science and Network Security, 2019.

3) “Finger-vein Image Dual Contrast Enhancement and Edge Detection,” IJCSNS International Journal of Computer Science and Network Security, 2019.

4) "Multi-Path Deep CNN with Residual Inception Network for Single Image Super-Resolution" Electronics, 2021. 

Some notable citations from prominent researchers around the globe

Dr. Memon’s successful publication of his work makes it clear that his discoveries are relevant in the field. Others in the field have directly benefited from these discoveries:

• Cañas et al., in their research into image recognition (Proceedings of the 16th International Conference on Computer Vision Theory and Applications, 2021), cite Dr. Memon’s research into the use of CNN-based approaches. The authors reference his approach, noting that “[s]ome of the strategies involve human pose estimation, human and/or object detection to find human-object interactions and combinations with general scene understanding (Chan et al., 2019).”

Dr. Memon’s work on machine learning techniques to improve automated image recognition is unique in the field due to its application of neural network approaches to image recognition. His success here also demonstrates that he has the necessary experience in AI and ML technologies to advance the design of green wireless networks and preventive healthcare systems to create energy-efficient 5G wireless networks that support various healthcare systems and serves as further evidence of his success in electrical engineering to date.

While these specific research contributions represent only a subset of Dr. Memon’s most successful endeavors, these projects are indicative of the overall quality of his research and illustrate his particular expertise and ability to continue contributing significantly to his field and to advancing the proposed endeavor.

Thus, as the above shows, the significance of Dr. Memon’s research in his field is corroborated by the evidence of peer interest in his research. Dr. Memon’s education, experience, and expertise in his field, the significance of his contributions, and his past record of success position him well to continue to advance his proposed endeavor of the design of green wireless networks and preventive healthcare systems to create energy-efficient 5G wireless networks that support various healthcare systems. Dr. Memon, therefore, satisfies this prong.

Technical Projects Executed