Dr Anirban Ghosh and his BTech students, Mr Taraka Sai Tanishq Chebrolu and Mr V.M.V.S. Aditya from his department, have come up with a pathbreaking innovation where a Smart Face Shield (SMAFS) helps detect a virus and reminds the wearer to maintain a safe distance. This innovation, patented under the Indian Patent Office Journal, with application number-202241000990 , marks a milestone step towards public health and safety.

Abstract:

The recent spurt of corona virus has wreaked havoc across the globe and led to huge loss of human lives. An intelligent system with innovative technologies can be implemented to address the rapid spread of the deadly virus. The wearable face shield that can not only help to maintain appropriate social distancing in a crowded place but also to identify a person with preliminary symptoms of corona virus. It is designed as a technically improved face shield to maintain social distancing by appropriate use of proximity sensor and to measure temperature of the wearer by using contact temperature sensor. LED’s and buzzer are placed strategically to alert people via visual and audio signals respectively. Such precautionary detection and proximity alert prototype can prove instrumental in early diagnosis and isolation aiding in crowd management and free movement in places of social gathering.

Practical Implementation of the Patent:

Such precautionary detection and proximity alert prototype can prove instrumental in early diagnosis and isolation aiding in crowd management and free movement in places of social gathering. Hence, wearable face shield ensures adequate separation between persons and facilitates temperature monitoring and early disease detection.

Future Research Plans:

Future research plans are to further improve the capability of the existing prototype for example integration of oxygen saturation measurement, Heartbeat, Blood pleasure, Temperature, Location, etc of the user. In the event of an emergency or critical drop in any of the vitals, the system can automatically alert the local hospital, ambulance service, and relatives.

The Department of Electronics and Communication Engineering, SRM University-AP, is pleased to announce that Assistant Professor Dr Durga Prakash has published a noteworthy research paper titled “A Novel LG=40 nm AlN-GDC-HEMT on SiC Wafer with fT/IDS,peak of 400 GHz/3.18 mA/mm for Future RF Power Amplifiers.” This accomplishment reflects Dr Durga Prakash’s expertise and dedication to advancing research in the field and further enriching the academic contributions of the varsity.

Abstract:

This study presents the initial RF/DC performance of innovative AlN/GaN/Graded-AlGaN/GaN double-channel HEMT (AlN-GDC-HEMT) on SiC wafer. Traditional AlGaN/GaN/Graded-AlGaN/GaN double-channel HEMTs (AlGaN-GDC-HEMT) and the AlN-GDC-HEMT are compared. Both devices form two quantum wells, resulting in prominent double peaks in transconductance and cut-off frequency graphs, demonstrating efficient inter-channel communication. AlN-GDC-HEMT and AlGaN-GDC-HEMT are compared based on gate recess length (LR) and top barrier thickness. Gate lengths (LG) are also used to study HEMT scaling. Additionally, gate engineering and lateral scaling affect both devices’ DC/RF behaviour. Based on rigorous comparison investigation, the AlN-GDC-HEMT outperforms the AlGaN-GDC-HEMT due to its higher polarization (spontaneous) density and larger bandgap. The optimized AlN-GDC-HEMT with LG = 40 nm, LGS = 250 nm, and LGD = 400 nm has high performance, with transconductance (GM) values of 203.1 and 787.5 mS/mm at two peaks, IDS_peak of 1.97 A/mm, IDS_sat of 3.18 A/mm, and the highest fT of 285.1 and 416.8 GHz from the left and right peaks First-stage results suggest AlN-GDC-HEMTs could be used in future RF power amplifiers.

Practical & Social Implications of the Research:

It can be concluded that the AlN-GDC-HEMT that has been proposed is extremely promising, as it possesses remarkable performance and is appealing for power microwave GaN-based HEMT production. This highlights the fact that it is suitable for a broad variety of high-performance applications.

Collaborations:

Department of ECE, Faculty of Science and Technology (IcfaiTech), ICFAI Foundation for Higher Education Hyderabad, Hyderabad-501203, India.

Future Research Plans:

Novel semiconductor device development

In a groundbreaking development, Dr Rupesh Kumar, a Professor in the Department of Electronics and Communication Engineering, has been awarded a significant project titled “Mapping the Canopy of the Amazon Forest Using an Aerial Drone Platform Coupled with Radar Sensors.” The initiative, funded by the International Peruvian National Research Institute, boasts an impressive outlay of Rs. 1.11 Crores and is set to span over a two-year period.

The project is spearheaded by Principal Investigator Dr Mark Donny Clemente Arenas, an Associate Professor at the National Technological University of South Lima in Peru. This collaboration aims to enhance the understanding of the Amazon’s intricate canopy structure and promote conservation efforts through innovative technology.

In recognition of this notable achievement, SRM University-AP proudly congratulated Dr Kumar and highlighted the significant impact this project could have on environmental research and sustainability. The university’s support underscores its commitment to fostering research initiatives that address global challenges, encouraging faculty members to pursue innovative solutions through collaboration and the application of cutting-edge technology.

This initiative marks a significant milestone in international research collaboration, leveraging technology to address critical environmental challenges in one of the world’s most vital ecosystems.

A Brief Description of the Project

This project facilitates the mapping of the Amazon forest in Peru. An integrated approach of advanced sensors such as LiDAR, Millimeter-Wave Radar, Camera, etc. and UAV will achieve this.
This will help assess the Amazon forest’s health in real time by leveraging the ML/AI approaches.

Figure 1: Scheme for height estimation

Explanation of the Research in Layperson’s Terms

The plant/tree generally reflects radio waves and other signals, and this reflection depends on the density of the forest. If a suitable signal processing is applied to the reflected signals, it will provide insight information about the forest profile. Nevertheless, this will help in the quantification of land covered by trees, identifying the location of those trees. Consequently, the tree canopy assessments help in determining the amount and location of impervious cover.

Funding Agency and Amount Sanctioned

National Scientific Research and Advanced Studies Program (PROCIENCIA) of the National Council for Science, Technology and Technological Innovation (CONCYTEC), Peru.

In Spanish: “ Programa Nacional de Investigación Cientifica y Estudios Avanzados (PROCIENCIA), del Consejo Nacional de Ciencia, Tecnología e Innovación
Tecnológica (CONCYTEC), Perú”.

Practical Implementation of the Research or the Social Implications Associated with it

The proposed research work help will help in the assessment of deforestation as well as its impact on climate change and global warming. Not only this, but the research will also contribute to achieving carbon neutrality by 2050!

Collaborations

Universidad Nacional Tecnológica de Lima Sur
Collaborator: Prof. Mark Clement Arenas

Future Research Plans

In future, this work will be extended for infrastructure monitoring. With the boom in real estate, a continuous monitoring system is desired for proper maintenance.

Dr Anirban Ghosh, an esteemed Assistant Professor in the Department of Electronics and Communication Engineering, has recently published a significant research paper titled “Channel Modeling and Characterization of Access, D2D, and Backhaul Links in a Corridor Environment at 300 GHz.” This paper has been featured in the prestigious Q1 Journal, IEEE Transactions on Antenna and Propagation, with an impressive impact factor of 4.6.
Dr Ghosh’s research delves into the intricate aspects of channel modelling and characterisation, focusing on access, device-to-device (D2D), and backhaul links within a corridor environment at a high frequency of 300 GHz. This study is poised to make substantial contributions to the field of wireless communication, particularly in enhancing the understanding and development of next-generation communication systems.
The publication in such a renowned journal underscores the quality and impact of Dr. Ghosh’s work, reflecting the cutting-edge research being conducted at SRM University – AP. The university community extends its heartfelt congratulations to Dr. Ghosh for this remarkable achievement and looks forward to his continued contributions to the field of electronics and communication engineering.

Abstract:

This paper presents comprehensive double-directional channel measurements at 300 GHz across various corridor scenarios, including Access, Device-to-Device (D2D), and Backhaul, using an in-house developed channel sounder. The measurements, validated by ray tracing simulations, reveal that while 300 GHz quasi-optical propagation in corridors can be modeled using ray optics, non-trivial propagation phenomena, such as quadruple-bounce reflections, also occur. To accurately model these mechanisms, a quasi-deterministic (QD) channel model combining deterministic and random components is proposed. The QD model results align well with observations, highlighting similar propagation mechanisms for Access and D2D scenarios, while Backhaul scenarios show Line-of-Sight (LoS) impacts from ceiling reflections. These findings are crucial for designing next-generation THz communication systems.

Explanation of Research in Layperson’s Terms

This research contributes to building the next generation of communication networks, which will significantly impact society by improving connectivity, supporting technological advancements, and promoting economic development, and bringing forth several futuristic applications.

Practical Implementation

The results align with the design of high-frequency ultra-high speed, low-latency, reliable communication envisioned for several futuristic applications using beyond 5G and 6G networks.

The measurement scenarios explored in the paper.

Collaborations

Prof. Minseok Kim
Professor, Faculty of Engineering, Course of Electrical and Electronics Engineering
Niigata University, Japan.
e-mail: mskim@eng.niigata-u.ac.jp

Future Research Plans

The efforts would be extended to other communication scenarios for a similar study. Additionally, generating appropriate channel models, coverage design, link budget, etc for the explored and unexplored scenarios would also encompass an interesting study.

Link to the Article

Dr Sibendu Samanta, Assistant Professor in the Department of Electronics and Communication Engineering, and Ms Radha Abburi, a PhD Scholar, have made significant strides in the field of fetal health monitoring. Their paper, titled “Adopting Artificial Intelligence Algorithms for Remote Fetal Heart Rate Monitoring and Classification using Wearable Fetal Phonocardiography,” has been published in the prestigious Q1 Journal, Applied Soft Computing, which boasts an impressive impact factor of 7.2.

This pioneering study addresses the critical gaps in the analysis of Fetal Heart Rate (FHR) recordings by leveraging wearable Phonocardiography (PCG) signals and advanced AI algorithms. The primary goal of the research is to achieve accurate classification results through the remote monitoring of fetal heartbeats. Additionally, the study tackles complex issues related to data quantity and the inherent complexity of FHR analysis. Dr Samanta and Ms Abburi’s work represents a significant advancement in the field, promising to enhance the accuracy and reliability of fetal health monitoring, ultimately contributing to better prenatal care.

Abstract of the Research:

Fetal phonocardiography (FPCG) is a non-invasive Fetal Heart Rate (FHR) monitoring technique that can detect vibrations and murmurs in heart sounds. However, acquiring fetal heart sounds from a wearable FPCG device is challenging due to noise and artefacts. This research contributes a resilient solution to overcome the conventional issues by adopting Artificial Intelligence (AI) with FPCG for automated FHR monitoring in an end-to-end manner, named (AI-FHR). Four sequential methodologies were used to ensure reliable and accurate FHR monitoring. The proposed method removes low-frequency noises and high-frequency noises by using Chebyshev II high-pass filters and Enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ECEEMDAN) in combination with Phase Shifted Maximal Overlap Discrete Wavelet Transform (PS-MODWT) filters, respectively.

The denoised signals are segmented to reduce complexity, and the segmentation is performed using multi-agent deep Q-learning (MA-DQL). The segmented signal is provided to reduce the redundancies in cardiac cycles using the Artificial Hummingbird Optimization (AHBO) algorithm. The segmented and non-redundant signals are converted into 3D spectrograms using a machine learning algorithm called variational auto-encoder-general adversarial networks (VAE-GAN). The feature extraction and classification are carried out by adopting a hybrid of the bidirectional gated recurrent unit (BiGRU) and the multi-boosted capsule network (MBCapsNet). The proposed method was implemented and simulated using MATLAB R2020a and validated by adopting effective validation metrics.

The results demonstrate that the proposed method performed better than the current method with accuracy (81.34%), sensitivity (72%), F1-score (83%), Energy (0.808 J), and complexity index (13.34). Like other optimization methods, AHO needs precise parameter adjustment in order to function well. Its performance may be greatly impacted by the selection of parameters, including population size, exploration rate, and learning rate.

The title of the Research Paper in the Citation Format:
R. Abburi, I. Hatai, R. Jaros, R. Martinek, T. A. Babu, S. A. Babu, S. Samanta, “Adopting artificial intelligence algorithms for remote fetal heart rate monitoring and classification using wearable fetal phonocardiography”, Applied Soft Computing, vol. 165, pp. 112049, 2024, ISSN 1568-4946.

Practical Implementation or the Social Implications Associated with the Research

• Chebyshev filter and EC2EMDAN-PS-MODWT reduce low and high frequency noises.
• MA-DRL and optimization algorithms reduce complexity during classification.
• Machine learning spectrogram conversion to capture time, frequency, and spectral variations.
• Hybrid deep learning algorithms can be used to reduce positive rates.

Collaborations:

• Dr. Indranil Hatai (Signal Processing and FPGA, Mathworks, Bangalore, India)
• Dr. T. Arun Babu (HoD, Dept. of Pediatrics, All India Institute of Medical Sciences (AIIMS), Andhra Pradesh, India)
• Dr. Sharmila Arun Babu, MBBS, MS (HoD, Dept. of Obstetrics and Gynecology, All India Institute of Medical Sciences (AIIMS), Andhra Pradesh, India)
• Dr. Rene Jaros (Dept. of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, 708 00, Ostrava, Czechia)
• Prof. Radek Martinek (Dept. of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, 708 00, Ostrava, Czechia)

Future Research Plans:

• Design a low cost for continuous fetal heart rate (FHR) monitoring system
• Develop a proper deep learning algorithm to get a proper understanding of fetal’s abnormality.

Link to the article