V. P. Jayachitra | Anna University (original) (raw)

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Papers by V. P. Jayachitra

2019 11th International Conference on Advanced Computing (ICoAC)

Due to large development in multimedia information processing and fashion styling in commercial a... more Due to large development in multimedia information processing and fashion styling in commercial and social applications, cloth image recognition creates a huge impact. However, the large variations due to blurring in cloth image appearances and their complicated style formation conditions create challenges in image recognition. Moreover, Super Resolution (SR) technique raises the high frequency components that are used to generate a high-resolution image with good perceptual quality from a low-resolution image. A novel residual deep neural network called SuperNet approach that converts a low-resolution image to a high-resolution image by providing more advanced features for better characterization of clothing genre is introduced in this work. Furthermore, the proposed framework reduces the complexity of the network without content loss of the original image.

Biomedical Signal Processing and Control, 2021

The COVID-19 emerged at the end of 2019 and has become a global pandemic. There are many methods ... more The COVID-19 emerged at the end of 2019 and has become a global pandemic. There are many methods for COVID-19 prediction using a single modality. However, none of them predicts with 100% accuracy, as each individual exhibits varied symptoms for the disease. To decrease the rate of misdiagnosis, multiple modalities can be used for prediction. Besides, there is also a need for a self-diagnosis system to narrow down the risk of virus spread in testing centres. Therefore, we propose a robust IoT and deep learning-based multi-modal data classification method for the accurate prediction of COVID-19. Generally, highly accurate models require deep architectures. In this work, we introduce two lightweight models, namely CovParaNet for audio (cough, speech, breathing) classification and CovTinyNet for image (X-rays, CT scans) classification. These two models were identified as the best unimodal models after comparative analysis with the existing benchmark models. Finally, the obtained results of the five independently trained unimodal models are integrated by a novel dynamic multimodal Random Forest classifier. The lightweight CovParaNet and CovTinyNet models attain a maximum accuracy of 97.45% and 99.19% respectively even with a small dataset. The proposed dynamic multimodal fusion model predicts the final result with 100% accuracy, precision, and recall, and the online retraining mechanism enables it to extend its support even in a noisy environment. Furthermore, the computational complexity of all the unimodal models is minimized tremendously and the system functions effectively with 100% reliability even in the absence of any one of the input modalities during testing.

Circuits and Systems, 2016

Designing a multi-constrained QoS (Quality of service) communication protocol for mission-critica... more Designing a multi-constrained QoS (Quality of service) communication protocol for mission-critical applications that seeks a path connecting source node and destination node that satisfies multiple QoS constrains such as energy cost, delay, and reliability imposes a great challenge in Wireless Sensor Networks (WSNs). In such challenging dynamic environment, traditional routing and layered infrastructure are inefficient and sometimes even infeasible. In recent research works, the opportunistic routing paradigm which delays the forwarding decision until reception of packets in forwarders by utilizing the broadcast nature of the wireless medium has been exploited to overcome the limitations of traditional routing. However, to guarantee the balance between the energy, delay and reliability requires the refinement of opportunistic routing through interaction between underlying layers known as cross-layer opportunistic routing. Indeed, these schemes fail to achieve optimal performance and hence require a new method to facilitate the adoption of the routing protocol to the dynamic challenging environments. In this paper, we propose a universal cross-layered opportunistic based communication protocol for WSNs for guaranteeing the user set constraints on multi-constrained QoS in low-duty-cycle WSN. Extensive simulation results show that the proposed work, Multi-Constrained QoS Opportunistic routing by optimal Power Tuning (MOR-PT) effectively achieves the feasible QoS trade-off constraints set by user by jointly considering the power control and selection diversity over established algorithms like DSF [1] and DTPC [2].

Circuits and Systems, 2016

One of the most important challenges in the Wireless Sensor Networks is to improve the performanc... more One of the most important challenges in the Wireless Sensor Networks is to improve the performance of the network by extending the lifetime of the sensor nodes. So the focus is on obtaining a trade-off between minimizing the delay involved and reducing the energy consumption of the sensor nodes which directly translate to an extended lifetime of the sensor nodes. An effective Sleep-wake scheduling mechanism can prolong the lifetime of the sensors by eliminating idle power listening, which could result in substantial delays. To counter this, an anycast forwarding scheme that could forward the packet opportunistically to the first awaken node may result in retransmissions as if the chosen node falls in resource constraints. The algorithm, namely Prim's-Dual is proposed to solve the said problem. The algorithm considers five crucial parameters, namely the residual energy of the nodes, transmission power, receiving power, packet loss rate, interference from which the next hop is determined to extend the lifetime of the sensor node. Since the proposed work is framed keeping critical event monitoring in mind, the sleep-wake scheduling is modified as low-power, high-power scheduling where all nodes are in low-power and the nodes needed for data transmission are respectively turned on to high-power mode. The integrated framework provides several opportunities for performance enhancement for conflict-free transmissions. The aim of our algorithm is to show reliable, energy efficient transfer without compromising on lifetime and delay. The further effectiveness of the protocol is verified. The results demonstrate that the proposed protocol can efficiently handle network scalability with acceptable latency and overhead.

Asian Journal of Research in Social Sciences and Humanities, 2016

This paper proposes a cost-effective design for remote diagnosis yet imposes Quality Of Service r... more This paper proposes a cost-effective design for remote diagnosis yet imposes Quality Of Service requirements to meet stringent services in medical facilities of under-developed or developing countries. This paper proposes a novel method to record and auscultate heart sounds using a piezoelectric disk and an 3.5 mm audio jack plugged into Raspberry Pi module with Bluetooth/WIFI support. Signal conditioning is performed using a pre-amplifier and filter circuit at the input and is then transmitted through Bluetooth/WIFI to a close range Computer or Smart Phone. Power amplification is then performed at this output stage and sent to an appropriate hearing device such as speaker or an earphone. Alernatively, Open source tools such as Audacity can be used to record the heartbeat sounds to a Computer using the audio jack. After an average of 10 trials with the recording system, the peak frequency was found at 32.3 Hz. This figure is a measure of accuracy as heart sounds lies below 80 Hz. The total harmonic distortion is 48.0339%, Signal to Noise Ratio is -2.9 dB and Inter modulation distortion is 100.09% (minimum 100%) thereby indicating that very low noise nature of the recorded signal.

2011 International Conference on Recent Trends in Information Technology (ICRTIT), 2011

In this work, a novel clustering and aggregating algorithm is proposed to reduce energy consumpti... more In this work, a novel clustering and aggregating algorithm is proposed to reduce energy consumption in wireless sensor networks (WSN) by focusing on regulating the intercluster traffic and data aggregation at the cluster heads. Previous works have aimed at reducing the intra-cluster traffic by choosing the node at the mean position inside as cluster head. But intra-cluster distance is small compared with inter-cluster distance. Hence reducing inter-cluster traffic will have a greater impact in power optimization. In our approach, inter-cluster traffic is reduced by clustering where cluster heads are chosen by outlier analysis. To reduce energy consumption further, we have implemented data aggregation using pattern mining at the cluster heads. Aggregation techniques proposed so far are based on probability and they arrive at implementing a static timer for data aggregation. Because of varying network traffic, a static timer results in constant delay. Implementing a dynamic timer is a possible solution for improvising the performance of data aggregation. Outlier analysis together with aggregation using pattern mining helps to reduce energy consumption.

Advances in Intelligent and Soft Computing, 2012

In the recent decade the incidence of animal fatalities involving trains has remained high in the... more In the recent decade the incidence of animal fatalities involving trains has remained high in the country. According to recent survey by Wildlife Trust of India (WTI), 72 animals are dying each year due to collision with speeding trains. Its high time we protect the lives of endangered species of animals. Though railway authorities ordered the drivers to reduce the speed of the trains inside forest areas, it does not have any fruitful results so far. We need a mechanism to alert the animals from crossing railway tracks when the train is approaching near. This paper proposes a simple and efficient technique which alerts animals about speeding trains. Unlike other techniques, our proposed mechanism does not need human intervention for operation.

2019 11th International Conference on Advanced Computing (ICoAC)

Due to large development in multimedia information processing and fashion styling in commercial a... more Due to large development in multimedia information processing and fashion styling in commercial and social applications, cloth image recognition creates a huge impact. However, the large variations due to blurring in cloth image appearances and their complicated style formation conditions create challenges in image recognition. Moreover, Super Resolution (SR) technique raises the high frequency components that are used to generate a high-resolution image with good perceptual quality from a low-resolution image. A novel residual deep neural network called SuperNet approach that converts a low-resolution image to a high-resolution image by providing more advanced features for better characterization of clothing genre is introduced in this work. Furthermore, the proposed framework reduces the complexity of the network without content loss of the original image.

Biomedical Signal Processing and Control, 2021

The COVID-19 emerged at the end of 2019 and has become a global pandemic. There are many methods ... more The COVID-19 emerged at the end of 2019 and has become a global pandemic. There are many methods for COVID-19 prediction using a single modality. However, none of them predicts with 100% accuracy, as each individual exhibits varied symptoms for the disease. To decrease the rate of misdiagnosis, multiple modalities can be used for prediction. Besides, there is also a need for a self-diagnosis system to narrow down the risk of virus spread in testing centres. Therefore, we propose a robust IoT and deep learning-based multi-modal data classification method for the accurate prediction of COVID-19. Generally, highly accurate models require deep architectures. In this work, we introduce two lightweight models, namely CovParaNet for audio (cough, speech, breathing) classification and CovTinyNet for image (X-rays, CT scans) classification. These two models were identified as the best unimodal models after comparative analysis with the existing benchmark models. Finally, the obtained results of the five independently trained unimodal models are integrated by a novel dynamic multimodal Random Forest classifier. The lightweight CovParaNet and CovTinyNet models attain a maximum accuracy of 97.45% and 99.19% respectively even with a small dataset. The proposed dynamic multimodal fusion model predicts the final result with 100% accuracy, precision, and recall, and the online retraining mechanism enables it to extend its support even in a noisy environment. Furthermore, the computational complexity of all the unimodal models is minimized tremendously and the system functions effectively with 100% reliability even in the absence of any one of the input modalities during testing.

Circuits and Systems, 2016

Designing a multi-constrained QoS (Quality of service) communication protocol for mission-critica... more Designing a multi-constrained QoS (Quality of service) communication protocol for mission-critical applications that seeks a path connecting source node and destination node that satisfies multiple QoS constrains such as energy cost, delay, and reliability imposes a great challenge in Wireless Sensor Networks (WSNs). In such challenging dynamic environment, traditional routing and layered infrastructure are inefficient and sometimes even infeasible. In recent research works, the opportunistic routing paradigm which delays the forwarding decision until reception of packets in forwarders by utilizing the broadcast nature of the wireless medium has been exploited to overcome the limitations of traditional routing. However, to guarantee the balance between the energy, delay and reliability requires the refinement of opportunistic routing through interaction between underlying layers known as cross-layer opportunistic routing. Indeed, these schemes fail to achieve optimal performance and hence require a new method to facilitate the adoption of the routing protocol to the dynamic challenging environments. In this paper, we propose a universal cross-layered opportunistic based communication protocol for WSNs for guaranteeing the user set constraints on multi-constrained QoS in low-duty-cycle WSN. Extensive simulation results show that the proposed work, Multi-Constrained QoS Opportunistic routing by optimal Power Tuning (MOR-PT) effectively achieves the feasible QoS trade-off constraints set by user by jointly considering the power control and selection diversity over established algorithms like DSF [1] and DTPC [2].

Circuits and Systems, 2016

One of the most important challenges in the Wireless Sensor Networks is to improve the performanc... more One of the most important challenges in the Wireless Sensor Networks is to improve the performance of the network by extending the lifetime of the sensor nodes. So the focus is on obtaining a trade-off between minimizing the delay involved and reducing the energy consumption of the sensor nodes which directly translate to an extended lifetime of the sensor nodes. An effective Sleep-wake scheduling mechanism can prolong the lifetime of the sensors by eliminating idle power listening, which could result in substantial delays. To counter this, an anycast forwarding scheme that could forward the packet opportunistically to the first awaken node may result in retransmissions as if the chosen node falls in resource constraints. The algorithm, namely Prim's-Dual is proposed to solve the said problem. The algorithm considers five crucial parameters, namely the residual energy of the nodes, transmission power, receiving power, packet loss rate, interference from which the next hop is determined to extend the lifetime of the sensor node. Since the proposed work is framed keeping critical event monitoring in mind, the sleep-wake scheduling is modified as low-power, high-power scheduling where all nodes are in low-power and the nodes needed for data transmission are respectively turned on to high-power mode. The integrated framework provides several opportunities for performance enhancement for conflict-free transmissions. The aim of our algorithm is to show reliable, energy efficient transfer without compromising on lifetime and delay. The further effectiveness of the protocol is verified. The results demonstrate that the proposed protocol can efficiently handle network scalability with acceptable latency and overhead.

Asian Journal of Research in Social Sciences and Humanities, 2016

This paper proposes a cost-effective design for remote diagnosis yet imposes Quality Of Service r... more This paper proposes a cost-effective design for remote diagnosis yet imposes Quality Of Service requirements to meet stringent services in medical facilities of under-developed or developing countries. This paper proposes a novel method to record and auscultate heart sounds using a piezoelectric disk and an 3.5 mm audio jack plugged into Raspberry Pi module with Bluetooth/WIFI support. Signal conditioning is performed using a pre-amplifier and filter circuit at the input and is then transmitted through Bluetooth/WIFI to a close range Computer or Smart Phone. Power amplification is then performed at this output stage and sent to an appropriate hearing device such as speaker or an earphone. Alernatively, Open source tools such as Audacity can be used to record the heartbeat sounds to a Computer using the audio jack. After an average of 10 trials with the recording system, the peak frequency was found at 32.3 Hz. This figure is a measure of accuracy as heart sounds lies below 80 Hz. The total harmonic distortion is 48.0339%, Signal to Noise Ratio is -2.9 dB and Inter modulation distortion is 100.09% (minimum 100%) thereby indicating that very low noise nature of the recorded signal.

2011 International Conference on Recent Trends in Information Technology (ICRTIT), 2011

In this work, a novel clustering and aggregating algorithm is proposed to reduce energy consumpti... more In this work, a novel clustering and aggregating algorithm is proposed to reduce energy consumption in wireless sensor networks (WSN) by focusing on regulating the intercluster traffic and data aggregation at the cluster heads. Previous works have aimed at reducing the intra-cluster traffic by choosing the node at the mean position inside as cluster head. But intra-cluster distance is small compared with inter-cluster distance. Hence reducing inter-cluster traffic will have a greater impact in power optimization. In our approach, inter-cluster traffic is reduced by clustering where cluster heads are chosen by outlier analysis. To reduce energy consumption further, we have implemented data aggregation using pattern mining at the cluster heads. Aggregation techniques proposed so far are based on probability and they arrive at implementing a static timer for data aggregation. Because of varying network traffic, a static timer results in constant delay. Implementing a dynamic timer is a possible solution for improvising the performance of data aggregation. Outlier analysis together with aggregation using pattern mining helps to reduce energy consumption.

Advances in Intelligent and Soft Computing, 2012

In the recent decade the incidence of animal fatalities involving trains has remained high in the... more In the recent decade the incidence of animal fatalities involving trains has remained high in the country. According to recent survey by Wildlife Trust of India (WTI), 72 animals are dying each year due to collision with speeding trains. Its high time we protect the lives of endangered species of animals. Though railway authorities ordered the drivers to reduce the speed of the trains inside forest areas, it does not have any fruitful results so far. We need a mechanism to alert the animals from crossing railway tracks when the train is approaching near. This paper proposes a simple and efficient technique which alerts animals about speeding trains. Unlike other techniques, our proposed mechanism does not need human intervention for operation.