The Electrochemical Society Transactions (ECST) is the official conference proceedings publication of The Electrochemical Society. Recently, a research paper was published in ECST by  Mr Vasudeva Bevara, a PhD scholar of the Department of Electronics and Communication Engineering, under the supervision of Assistant professor Dr Pradyut Kumar Sanki. The paper is titled VLSI Architecture of Decision Based Adaptive Denoising Filter for Removing Salt & Pepper Noise and proposes an innovative concept to restore a highly corrupted digital image.

Abstract

A new Decision Based Adaptive Denoising Filter (DBADF) algorithm and hardware architecture are proposed for restoring the digital image that is highly corrupted with impulse noise. The proposed DBADF detects only the corrupted pixels, and that pixel is restored by the noise-free median value or previous value based upon the noise density in the image. The proposed DBADF uses a 3×3 window initially and adaptively goes up to a 7×7 window based on the noise corruption of more than 50% by impulse noise in the current processing window. The proposed architecture was found to exhibit better visual qualitative and quantitative evaluation based on PSNR, IEF, EKI, SSIM, FOM, and error rate. The DBAMF architecture also preserves the original information of digital image with a high density of salt and pepper noise compared to many standard conventional algorithms. The proposed architecture has been simulated using the VIRTEX7 FPGA device, and the reported maximum post place and route frequency are 149.995MHz, and the dynamic power consumption is 179mW.

Wireless Sensor Networks (WSNs) and their derivatives such as Internet of Things (IoT) and the Internet of Industrial Things (IIOT) are no longer confined to traditional applications such as smart homes and transportation. It has already marked its presence in Industrial applications and extended even to wildlife conservation. The impending concerns associated with such wireless networks are their privacy and security. One such menace afflicting wildlife is cyber poaching. Taking this into consideration, Dr Manjula R, Assistant Professor, and her student Mr Tejodbhav Koduru, from the Department of Computer Science and Engineering, have published a paper, “Position-independent and Section-based Source Location Privacy Protection in WSN” in the journal, ‘IEEE Transactions on Industrial Informatics’ having an Impact Factor of 10.215. The article is published in collaboration with Ms Florence Mukamanzi from the University of Rwanda, Rwanda, Africa and Prof Raja Datta from IIT Kharagpur, West Bengal, India.

The sensors collect data about these endangered animals and report it to the central controller which is connected to the Internet. Over the period, the hunters have also evolved and are equipped with smart devices that help them to easily locate the animal with minimal effort. In the simplest form, the attacker or the hunter just eavesdrops on the communication links to know the message’s origin and backtrack to the source of information. Once the source of information i.e., the location is identified then the endangered animal is captured. To overcome such backtracking issues, their work aims at delaying the information disclosure to the attacker through traffic obfuscation.

Although it may not act as an ultimate solution, the research work focuses on contextual privacy, unlike traditional content privacy. The attacker collects only contextual information such as packet rate, traffic intensities, routing paths, time correlations etc., to determine the source of information. The work focuses on mitigating traffic correlation i.e., hop-by-hop backtrack attacks and protecting the assets that are monitored using WSNs. The performance metrics include safety period and network lifetime amongst other metrics. The proposed random-walk-based routing solution achieves an improved safety period and network lifetime compared to the existing schemes. The work was simulated using a custom-designed simulation tool and was validated with the numerical results obtained using mathematical models.

The proposed solutions could be seamlessly used in monitoring endangered animals such as rhinoceros or in military applications to track soldiers. In addition, the routing algorithm could also be used in delaying tolerant networks to improve the efficiency and lifetime of the network, in designing the random trajectories of bio-nano bots for intrabody monitoring etc. Their future research plan includes developing improved source location privacy preservation techniques for terrestrial and underwater wireless sensor networks using the benefits of Artificial Intelligence and Machine Learning. In addition, they also aims at the development of data collection and routing protocols for intrabody nanonetwork operating at tera hertz frequencies— next-generation networks, envisioned networks.

Continue reading →

The Department of Electronics and Communication Engineering is glad to announce that Dr V Udaya Sankar, Assistant Professor has published the patent (App no. 202141056542), ‘A system and method with Matrix enabled Road distress classification with reduced computational complexity and reduced memory requirements’, in collaboration with Dr Siva Sankar Yellampalli and Ms Gayathri Lakshmi Chinthakrindi.

This work has applications related to visual inspection systems. While this research considers road crack detection application, the same can be extended to various applications such as leaf disease prediction, covid prediction etc. This invention provides an alternative approach instead of using traditional machine learning algorithms that has less computational complexity as opposed to deep neural networks that take more complex operations. This method will also lead to further research in matrix-based machine learning applications related to image processing and image classification.

The research team is planning to collaborate with Efftronics Systems Pvt ltd. for PCB defect detection and discussions are initiated with some start-ups for visual inspection applications. Their future research plan is to look deeper into these algorithms in combination with some of the deep neural networks to reduce computational complexity. In addition, Dr Udaya Sankar is also looking forward to establishing his own start-up in the incubation centre soon.

Abstract of the Research

A method for image classification is provided, wherein, the pre-processed gray scale image is first sent to the feature extraction block, and the said feature extraction block considers every image as a matrix and computes the metrics for features, viz., 1) EMD distance which is popularly known as Wassertain distance/Earth movers distance and is computed with respect to block image and 2) Frobenius Norm which is the square root of the sum of the absolute squares of its elements and finally, 3) Condition Number, which measures the ratio of the maximum relative stretching to the maximum relative shrinking that matrix does to any non-zero vectors. This method is preferred over the existing methods due to the drastic reduction in computational complexities and, utilizing lesser memory. Also, with this method and system, the communicational complexities too are significantly reduced and also, and the results yielded are far more significantly accurate.

Continue reading →