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

The Department of Electronics and Communication Engineering is delighted to announce that Assistant Professor Dr Anirban Ghosh, PhD scholars Mr Naga Srinivasarao and Ms Manasa Santhi, and BTech student Mr Sk Abdul Hakeem have filed and published their patent, “A System and a Method for Low Transmission Delay and Energy Efficiency,” with Application No: 202441045389. The research cohort has demonstrated groundbreaking research on integrating Small-World Characteristics (SWC) into Low-Power Wide-Area Networks (LPWANs) through Reinforcement Learning.

Abstract

To support the rapid growth of Internet of Things (IoT) applications, networking technologies like Low-Power Wide-Area Networks (LPWANs) are evolving to provide extended network lifespan and broader coverage for Internet of Things Devices (IoDs). These technologies are highly effective when devices remain stationary under static conditions. However, practical IoT applications, ranging from smart cities to mobile health monitoring systems, involve heterogeneous IoDs that move dynamically, leading to changing network topologies. Typically, dynamic networks use multi-hop data transmission schemes for communication, but this method presents challenges such as increased data latency and energy imbalances. To address these issues, this patent introduces a novel approach that integrates recent advancements in social networks, specifically Small-World Characteristics (SWC), into LPWANs using Reinforcement Learning. Specifically, the SWCs are embedded into heterogeneous LPWANs through the Q-learning technique. The performance of the developed heterogeneous Small-World LPWANs is then evaluated in terms of energy efficiency (including the number of alive and dead IoDs, as well as network residual energy) and data transmission delay within the network.

Explanation of Research in Layperson’s Terms

The existing or the present technology moves around the applications that are either static or dynamic in nature, but the current invention considers a realistic IoT application that contains both static and dynamic nodes in the network. However, maintaining low data transmission delay and high network longevity over such a heterogeneous network is a challenge. By integrating SWCs over the developed heterogeneous networks using Q-learning technique helps in minimizing the data transmission delay and improves the network lifetime (energy efficient data transmission).

Practical Implementation of the Research

Applications that contain both static and dynamic nodes, such as smart health care systems, smart environmental monitoring systems, real-time traffic monitoring systems, and smart cities and homes, require less data transmission delay and high network longevity.

Collaborations

1. Dr Om Jee Pandey – Assistant Professor, Department of Electronics Engineering, Indian Institute of Technology (BHU) Varanasi
2. Dr Satish Kumar Tiwari – Assistant Professor, IIITDM Jabalpur, India

In the next phase of research, the reserach team will work towards investigating how the energy efficiency and other quality of service of smart devices in an IoT setting can be improved if they are completely mobile.

We are thrilled to announce that Dr Sunil Chinnadurai, Associate Professor in the Department of Electronics and Communication Engineering has published a significant research paper titled “Ethereum Blockchain Framework Enabling Banks to Know Their Customers” in the esteemed journal IEEE Access. In his paper, Dr Chinnadurai explores the innovative applications of Ethereum blockchain technology in enhancing customer verification processes within the banking sector. His research addresses the growing need for robust and secure methods for banks to comply with Know Your Customer (KYC) regulations while ensuring customer privacy and data integrity.
This pioneering work contributes to the ongoing discourse on digital transformation in the banking industry and presents a framework that could potentially revolutionise customer onboarding and identity verification practices.
We extend our congratulations to Dr Chinnadurai for this remarkable achievement and look forward to his continued contributions to the field of electrical and electronics engineering. His research not only enhances the academic reputation of SRM University-AP but also paves the way for innovative solutions in the financial sector.

Abstract of the Research
This paper looks at how blockchain technology can improve the Know Your Customer (KYC) process. It aims to make things more open, secure, and unchangeable. Banks can use the Ethereum blockchain to get and keep customer information, which saves time and money. The solution tries to solve problems with KYC procedures making sure banks follow the rules and stop fraud. The central bank will keep a list of all banks and check if they’re doing KYC right. This spread-out approach gives banks a good long-lasting way to bring in new customers.

Explanation of the Research in Layperson’s Terms
Our study seeks to cause a revolution in the Know Your Customer (KYC) process for banks using Ethereum blockchain technology. Current KYC methods take too long, cost too much, and leave room for cheating. Blockchain offers a clear, safe, and unchangeable platform to store customer data letting banks check and confirm identities. This spread-out approach means customers only need to complete the KYC process one time, which saves a lot of time and money for both banks and customers. Also, blockchain’s safety features make sure that private data stays unchanged and safe from people who shouldn’t see it. Our planned system involves the central bank keeping a full list of all banks and watching to make sure they follow KYC rules. In the future, we plan to put our solution on the real Ethereum network and build a working decentralized app. This system promises to make KYC processes faster, safer, and cheaper, giving a strong answer for banks all over the world.

Practical Implementation or the Social Implications associated
Our research puts blockchain tech to work to improve how banks verify customers. This decentralized system gives everyone access to the same current info through a shared record. This cuts down on middlemen and their costs. Smart contracts that run on their own speed up checks with less human involvement. This lowers the chance of data getting out. It makes transactions faster and keeps data safe from changes it shouldn’t have. This new way of checking customers can save money, make customers happier, and follow rules better. It can make people trust banks more by keeping data safer and being more open. It also means banks don’t have to do the same checks over and over, which is better for them and their customers. In the end, our blockchain answer for customer checks aims to make banking safer, smoother, and cheaper. It should also help build more trust in banks overall.

FIGURE 1. Implementation of a blockchain-based KYC process

FIGURE 2. Sequential flow diagram illustrating the proposed KYC process using blockchain technology

Future Research Plans

We’re planning to test our idea a lot on the Ethereum network to make sure it works well. We want to build a working DApp that shows our KYC system is doable. We’ll check if people might use it and look at how safe and private it is. By doing this, we hope to make a strong and reliable DApp that’s easy to use, open, safe, and quick. In the end, we want to create something that makes KYC better and sets a new bar for money stuff making banking safer and faster for everyone. Our main goal is to make a system that does not improve how KYC works but also changes how money moves around, making sure banks are safer and work better for people.

The link to the article

In a groundbreaking collaboration, Dr Banee Bandana Das, Assistant Professor in the Department of Computer Science and Engineering, and Dr Saswat Kumar Ram, Assistant Professor in the Department of Electronics and Communication Engineering, have joined forces with Btech-CSE students Mr Rohit Kumar Jupalle, Mr Dinesh Sai Sandeep Desu, and Mr Nikhil Kethavath to develop and patent an innovative invention.,” The team’s invention, titled “A SYSTEM FOR VISION-BASED INTELLIGENT SHELF MANAGEMENT SYSTEM AND A METHOD THEREOF,” has been officially filed and published with Application Number 202441039394 in the Patent Office Journal. This invention showcases the academic excellence and collaborative spirit within the institution, as faculty members and students work together to push the boundaries of technology and create solutions with real-world impact.
This significant achievement not only highlights the creativity and dedication of the individuals involved but also underscores the institution’s commitment to fostering a culture of innovation and research. The publication of this invention paves the way for further exploration and development in the field of intelligent shelf management systems, demonstrating the potential for transformative contributions to the industry.

Abstract
This research offers the best solution to improve the business in the retail realm, maintaining On- Shelf Availability (OSA) is vital for customer satisfaction and profitability. Traditional OSA methods face accuracy challenges, prompting a shift to deep learning models like YOLO and CNN. However, data quality remains a hurdle. This research introduces OSA, a novel semi-supervised approach merging ’semi-supervised learning’ and ’on-shelf availability’ with YOLO. It reduces human effort and computation time, focusing on efficient empty-shelf detection. Implementing a Vision-Based Intelligent Shelf Management System empowers retailers with real-time insights, revolutionizing decision-making. The model is optimized for diverse devices and provides practical solutions for efficient retail operations. Balancing model complexity, size, latency, and accuracy, the research paves the way for an advanced, data-driven shelf management approach, contributing to improved shopping experiences and business profitability

Practical Implementation and the Social Implications Associated
1. The present invention is a time-saving method in maintaining the stocks.
2. The use Vision-Based Intelligent Shelf-Management System provide a well alternative in reducing the labor efforts.
3. The system will help in terms of self-management system using machine learning techniques to optimize restocking decisions.
The present invention can be used in shopping malls and business areas for enhancing customer experiences and business and few application areas are:
• Smart City and smart Village
This technique and system can reduce the human efforts in identifying vacant slots for items in business areas and provides necessary inputs to fill the same within a time frame.
• Automobile Industry
The system can be easily integrated with the showrooms to identify the empty spaces and inform to get it fill with products.

Collaborations
SRM AP Faculties and UROP Students

Future research plan

In the future, different deep learning and machine learning methods can be merged to explore better performance in identifying overlapping objects.