Saraju P. Mohanty

Sibi C. Sethuraman, Gaurav R. Tadkapally, Saraju P. Mohanty et al.

Individuals with visual impairments often face a multitude of challenging obstacles in their daily lives. Vision impairment can severely impair a person's ability to work, navigate, and retain independence. This can result in educational limits, a higher risk of accidents, and a plethora of other issues. To address these challenges, we present MagicEye, a state-of-the-art intelligent wearable device designed to assist visually impaired individuals. MagicEye employs a custom-trained CNN-based object detection model, capable of recognizing a wide range of indoor and outdoor objects frequently encountered in daily life. With a total of 35 classes, the neural network employed by MagicEye has been specifically designed to achieve high levels of efficiency and precision in object detection. The device is also equipped with facial recognition and currency identification modules, providing invaluable assistance to the visually impaired. In addition, MagicEye features a GPS sensor for navigation, allowing users to move about with ease, as well as a proximity sensor for detecting nearby objects without physical contact. In summary, MagicEye is an innovative and highly advanced wearable device that has been designed to address the many challenges faced by individuals with visual impairments. It is equipped with state-of-the-art object detection and navigation capabilities that are tailored to the needs of the visually impaired, making it one of the most promising solutions to assist those who are struggling with visual impairments.

Abhishek Sharma, Arpit Jain, Shubhangi Sharma et al.

ADHD is a prevalent disorder among the younger population. Standard evaluation techniques currently use evaluation forms, interviews with the patient, and more. However, its symptoms are similar to those of many other disorders like depression, conduct disorder, and oppositional defiant disorder, and these current diagnosis techniques are not very effective. Thus, a sophisticated computing model holds the potential to provide a promising diagnosis solution to this problem. This work attempts to explore methods to diagnose ADHD using combinations of multiple established machine learning techniques like neural networks and SVM models on the ADHD200 dataset and explore the field of neuroscience. In this work, multiclass classification is performed on phenotypic data using an SVM model. The better results have been analyzed on the phenotypic data compared to other supervised learning techniques like Logistic regression, KNN, AdaBoost, etc. In addition, neural networks have been implemented on functional connectivity from the MRI data of a sample of 40 subjects provided to achieve high accuracy without prior knowledge of neuroscience. It is combined with the phenotypic classifier using the ensemble technique to get a binary classifier. It is further trained and tested on 400 out of 824 subjects from the ADHD200 data set and achieved an accuracy of 92.5% for binary classification The training and testing accuracy has been achieved upto 99% using ensemble classifier.

Sibi Chakkaravarthy Sethuraman, Gaurav Reddy Tadkapally, Athresh Kiran et al.

The utilization of consumer electronics, such as televisions, set-top boxes, home theaters, and air conditioners, has become increasingly prevalent in modern society as technology continues to evolve. As new devices enter our homes each year, the accumulation of multiple infrared remote controls to operate them not only results in a waste of energy and resources, but also creates a cumbersome and cluttered environment for the user. This paper presents a novel system, named SimplyMime, which aims to eliminate the need for multiple remote controls for consumer electronics and provide the user with intuitive control without the need for additional devices. SimplyMime leverages a dynamic hand gesture recognition architecture, incorporating Artificial Intelligence and Human-Computer Interaction, to create a sophisticated system that enables users to interact with a vast majority of consumer electronics with ease. Additionally, SimplyMime has a security aspect where it can verify and authenticate the user utilising the palmprint, which ensures that only authorized users can control the devices. The performance of the proposed method for detecting and recognizing gestures in a stream of motion was thoroughly tested and validated using multiple benchmark datasets, resulting in commendable accuracy levels. One of the distinct advantages of the proposed method is its minimal computational power requirements, making it highly adaptable and reliable in a wide range of circumstances. The paper proposes incorporating this technology into all consumer electronic devices that currently require a secondary remote for operation, thus promoting a more efficient and sustainable living environment.

Jayakrishna Amathi, Venkata Prasanth Yanambaka, Saraju P. Mohanty et al.

Despite the success of deep neural networks in vision, medical diagnosis, and IoT scenarios, their deployment on resource-limited platforms poses serious challenges due to their high storage requirements, computational complexity, and large footprint. In particular, fully connected layers require a large number of weights, making it difficult for edge devices to accommodate them. To overcome these challenges associated with limited platforms, this paper proposes the Two-Valued Symmetric Circulant Matrix (TVSCM), a very sparse architecture that employs just two weights per layer to keep it circulant and symmetric. The extreme form of structured sparse architecture provides negligible storage costs compared to traditional full-weight storage. Instead of hardware and additional stages of other traditional sparse learning techniques, such as low-rank approximation and pruning approaches, this architecture provides an extreme form of sparsity, achieving very minimal storage requirements. The simulation study demonstrates more than 80$\times$ reduction in model parameters, reducing parameters from 623,290 to 7,852 on MNIST and from 24,709 to 942 on the MIT-BIH arrhythmia dataset, while maintaining comparable accuracy from 97.6% to 93.5% on MNIST and from 97.6% to 93.1% on MIT-BIH. Due to its minimal architectural requirements and very low power consumption, this architecture would be ideal for edge computing platforms, tiny-ML platforms, IoMT systems, and battery-powered systems.

Alakananda Mitra, Saraju P. Mohanty, Elias Kougianos

We live in the era of Generative Artificial Intelligence (GenAI). Deepfakes and Large Language Models (LLMs) are two examples of GenAI. Deepfakes, in particular, pose an alarming threat to society as they are capable of spreading misinformation and changing the truth. LLMs are powerful language models that generate general-purpose language. However due to its generative aspect, it can also be a risk for people if used with ill intentions. The ethical use of these technologies is a big concern. This short article tries to find out the interrelationship between them.

Abdulrahman Alkinani, Alakananda Mitra, Saraju P. Mohanty et al.

Fruits are rich sources of essential vitamins and nutrients that are vital for human health. This study introduces two fully automated devices, FruitPAL and its updated version, FruitPAL 2.0, which aim to promote safe fruit consumption while reducing health risks. Both devices leverage a high-quality dataset of fifteen fruit types and use advanced models- YOLOv8 and YOLOv5 V6.0- to enhance detection accuracy. The original FruitPAL device can identify various fruit types and notify caregivers if an allergic reaction is detected, thanks to YOLOv8's improved accuracy and rapid response time. Notifications are transmitted via the cloud to mobile devices, ensuring real-time updates and immediate accessibility. FruitPAL 2.0 builds upon this by not only detecting fruit but also estimating its nutritional value, thereby encouraging healthy consumption. Trained on the YOLOv5 V6.0 model, FruitPAL 2.0 analyzes fruit intake to provide users with valuable dietary insights. This study aims to promote fruit consumption by helping individuals make informed choices, balancing health benefits with allergy awareness. By alerting users to potential allergens while encouraging the consumption of nutrient-rich fruits, these devices support both health maintenance and dietary awareness.

Anand K. Bapatla, Saraju P. Mohanty, Elias Kougianos

Access to essential medication is a primary right of every individual in all developed, developing and underdeveloped countries. This can be fulfilled by pharmaceutical supply chains (PSC) in place which will eliminate the boundaries between different organizations and will equip them to work collectively to make medicines reach even the remote corners of the globe. Due to multiple entities, which are geographically widespread, being involved and very complex goods and economic flows, PSC is very difficult to audit and resolve any issues involved. This has given rise to many issues, including increased threats of counterfeiting, inaccurate information propagation throughout the network because of data fragmentation, lack of customer confidence and delays in distribution of medication to the place in need. Hence, there is a strong need for robust PSC which is transparent to all parties involved and in which the whole journey of medicine from manufacturer to consumer can be tracked and traced easily. This will not only build safety for the consumers, but will also help manufacturers to build confidence among consumers and increase sales. In this article, a novel Distributed Ledger Technology (DLT) based transparent supply chain architecture is proposed and a proof-of-concept is implemented. Efficiency and scalability of the proposed architecture is evaluated and compared with existing solutions.

Debanjan Das, Chirag Samal, Deewanshu Ukey et al.

The pandemic of novel Coronavirus Disease 2019 (COVID-19) is widespread all over the world causing serious health problems as well as serious impact on the global economy. Reliable and fast testing of the COVID-19 has been a challenge for researchers and healthcare practitioners. In this work we present a novel machine learning (ML) integrated X-ray device in Healthcare Cyber-Physical System (H-CPS) or smart healthcare framework (called CoviLearn) to allow healthcare practitioners to perform automatic initial screening of COVID-19 patients. We propose convolutional neural network (CNN) models of X-ray images integrated into an X-ray device for automatic COVID-19 detection. The proposed CoviLearn device will be useful in detecting if a person is COVID-19 positive or negative by considering the chest X-ray image of individuals. CoviLearn will be useful tool doctors to detect potential COVID-19 infections instantaneously without taking more intrusive healthcare data samples, such as saliva and blood. COVID-19 attacks the endothelium tissues that support respiratory tract, X-rays images can be used to analyze the health of a patient lungs. As all healthcare centers have X-ray machines, it could be possible to use proposed CoviLearn X-rays to test for COVID-19 without the especial test kits. Our proposed automated analysis system CoviLearn which has 99% accuracy will be able to save valuable time of medical professionals as the X-ray machines come with a drawback as it needed a radiology expert.

Carson Labrado, Himanshu Thapliyal, Saraju P. Mohanty

Within vehicles, the Controller Area Network (CAN) allows efficient communication between the electronic control units (ECUs) responsible for controlling the various subsystems. The CAN protocol was not designed to include much support for secure communication. The fact that so many critical systems can be accessed through an insecure communication network presents a major security concern. Adding security features to CAN is difficult due to the limited resources available to the individual ECUs and the costs that would be associated with adding the necessary hardware to support any additional security operations without overly degrading the performance of standard communication. Replacing the protocol is another option, but it is subject to many of the same problems. The lack of security becomes even more concerning as vehicles continue to adopt smart features. Smart vehicles have a multitude of communication interfaces would an attacker could exploit to gain access to the networks. In this work we propose a security framework that is based on physically unclonable functions (PUFs) and lightweight cryptography (LWC). The framework does not require any modification to the standard CAN protocol while also minimizing the amount of additional message overhead required for its operation. The improvements in our proposed framework results in major reduction in the number of CAN frames that must be sent during operation. For a system with 20 ECUs for example, our proposed framework only requires 6.5% of the number of CAN frames that is required by the existing approach to successfully authenticate every ECU.

Sibi C. Sethuraman, Pranav Kompally, Saraju P. Mohanty et al.

Smart healthcare which is built as healthcare Cyber-Physical System (H-CPS) from Internet-of-Medical-Things (IoMT) is becoming more important than before. Medical devices and their connectivity through Internet with alongwith the electronics health record (EHR) and AI analytics making H-CPS possible. IoMT-end devices like wearables and implantables are key for H-CPS based smart healthcare. Smart garment is a specific wearable which can be used for smart healthcare. There are various smart garments that help users to monitor their body vitals in real-time. Many commercially available garments collect the vital data and transmit it to the mobile application for visualization. However, these don't perform real-time analysis for the user to comprehend their health conditions. Also, such garments are not included with an alert system to alert users and contacts in case of emergency. In MyWear, we propose a wearable body vital monitoring garment that captures physiological data and automatically analyses such heart rate, stress level, muscle activity to detect abnormalities. A copy of the physiological data is transmitted to the cloud for detecting any abnormalities in heart beats and predict any potential heart failure in future. We also propose a deep neural network (DNN) model that automatically classifies abnormal heart beat and potential heart failure. For immediate assistance in such a situation, we propose an alert system that sends an alert message to nearby medical officials. The proposed MyWear has an average accuracy of 96.9% and precision of 97.3% for detection of the abnormalities.

Deepak Puthal, Saraju P. Mohanty, Venkata P. Yanambaka et al.

In today's connected world, resource constrained devices are deployed for sensing and decision making applications, ranging from smart cities to environmental monitoring. Those recourse constrained devices are connected to create real-time distributed networks popularly known as the Internet of Things (IoT), fog computing and edge computing. The blockchain is gaining a lot of interest in these domains to secure the system by ignoring centralized dependencies, where proof-of-work (PoW) plays a vital role to make the whole security solution decentralized. Due to the resource limitations of the devices, PoW is not suitable for blockchain-based security solutions. This paper presents a novel consensus algorithm called Proof-of-Authentication (PoAh), which introduces a cryptographic authentication mechanism to replace PoW for resource constrained devices, and to make the blockchain application-specific. PoAh is thus suitable for private as well as permissioned blockchains. Further, PoAh not only secures the systems, but also maintains system sustainability and scalability. The proposed consensus algorithm is evaluated theoretically in simulation scenarios, and in real-time hardware testbeds to validate its performance. Finally, PoAh and its integration with the blockchain in the IoT and edge computing scenarios is discussed. The proposed PoAh, while running in limited computer resources (e.g. single-board computing devices like the Raspberry Pi) has a latency in the order of 3 secs.

Saraju P. Mohanty, Venkata P. Yanambaka, Elias Kougianos et al.

This article presents the first-ever blockchain which can simultaneously handle device and data security, which is important for the emerging Internet-of-Everything (IoE). This article presents a unique concept of blockchain that integrates hardware security primitives called Physical Unclonable Functions (PUFs) to solve scalability, latency, and energy requirement challenges and is called PUFchain. Data management and security (and privacy) of data, devices, and individuals, are some of the issues in the IoE architectures that need to be resolved. Integrating the blockchain into the IoE environment can help solve these issues and helps in the aspects of data storage and security. This article introduces a new blockchain architecture called PUFchain and introduces a new consensus algorithm called "Proof of PUF-Enabled Authentication" (PoP) for deployment in PUFchain. The proposed PoP is the PUF integration into our previously proposed Proof-of-Authentication (PoAh) consensus algorithm and can be called "Hardware-Assisted Proof-of-Authentication (HA-PoAh)". However, PUF integration is possible in the existing and new consensus algorithms. PoP utilizes PUFs which are responsible for generating a unique key that cannot be cloned and hence provide the highest level of security. A PUF uses the nanoelectronic manufacturing variations that are introduced during the fabrication of an integrated circuit to generate the keys. Hence, once generated from a PUF module, the keys cannot be cloned or generated from any other module. PUFchain uses a PUF and Hashing module which performs the necessary cryptographic functions. Hence the mining process is offloaded to the hardware module which reduces the processing times. PoP is approximately 1,000X faster than the well-established Proof-of-Work (PoW) and 5X faster than Proof-of-Authentication (PoAh).

Saraju P. Mohanty

With the explosive growth of internet technology, easy transfer of digital multimedia is feasible. However, this kind of convenience with which authorized users can access information, turns out to be a mixed blessing due to information piracy. The emerging field of Digital Rights Management (DRM) systems addresses issues related to the intellectual property rights of digital content. In this paper, an object-oriented (OO) DRM system, called "Imaging System with Watermarking and Attack Resilience" (ISWAR), is presented that generates and authenticates color images with embedded mechanisms for protection against infringement of ownership rights as well as security attacks. In addition to the methods, in the object-oriented sense, for performing traditional encryption and decryption, the system implements methods for visible and invisible watermarking. This paper presents one visible and one invisible watermarking algorithm that have been integrated in the system. The qualitative and quantitative results obtained for these two watermarking algorithms with several benchmark images indicate that high-quality watermarked images are produced by the algorithms. With the help of experimental results it is demonstrated that the presented invisible watermarking techniques are resilient to the well known benchmark attacks and hence a fail-safe method for providing constant protection to ownership rights.