Research

Dr. Pal, Arpan

Principal Scientist and Head of Research, TCS Innovation Labs- Kolkata

 

Education:

  • Ph.D from Dept. of Electronic Systems, Aalborg University Denmark, in 2013
  • M.Tech in Telecommunication systems Engg. from IIT Kharagpur in 1993
  • B.Tech. in Electronics and Electrical Communication Engg. from IIT Kharagpur in 1990.

Research Interests:

  • Physiological sensing for healthcare and people context
  • Mobile Phone based Sensing, Camera based 2D/3D Sensing
  • Sensor Signal Processing and Informatics, Speech/Audio/Video/Image Processing
  • Internet-of-Things, Ubiquitous Computing, Intelligent Infrastructure
  • Grid Computing, Semantic Sensor Web, Protocols, Security and Privacy
  • Embedded Systems, Set-top Boxes, Television as a Ubiquitous Device


List of Publications (PDF, 226 KB)

Select Publications

Internet of Things: Making the Hype a Reality
Author: Arpan Pal
Editorial, IT Professional Magazine, Issue No. 03 - May-June (2015 vol.17), pp: 2-4, IEEE Computer Society

The Internet democratized information. The Internet of Things (IoT) will democratize knowledge. As industrial researchers who work with businesses, we at Tata Consultancy Services see our customers in every industry looking for ways to create active knowledge and insight from IoT data.

Read more

Noise Cleaning and Gaussian Modeling Of Smart Phone Photoplethysmogram To Improve Blood Pressure Estimation
Authors: Rohan Banerjee, Anirban Dutta Choudhury, Arpan Pal, Avik Ghose, Aniruddha Sinha
Proceedings of ICASSP, Brisbane Australia, April 2015

Abstract:
Photoplethysmography (PPG) signals, captured using smart phones are generally noisy in nature. Although they have been successfully used to determine heart rate from frequency domain analysis, further indirect markers like blood pressure (BP) require time domain analysis for which the signal needs to be substantially cleaned. In this paper we propose a methodology to clean such noisy PPG signals. Apart from filtering, the proposed approach reduces the baseline drift of PPG signal to near zero. Furthermore it models each cycle of PPG signal as a sum of 2 Gaussian functions which is a novel contribution of the method. We show that, the noise cleaning effect produces better accuracy and consistency in estimating BP, compared to the state of the art method that uses the 2-element Windkessel model on features derived from raw PPG signal, captured from an Android phone.

Adaptive Sensor Data Compression in Iot Systems: Sensor Data Analytics Based Approach
Authors: Soma Bandyopadhyay, Arpan Pal, Arijit Ukil
Proceedings of ICASSP, Brisbane Australia, April 2015
Abstract:
Sensor nodes are embodiment of IoT systems in microscopic level. As the volume of sensor data increases exponentially, data compression is essential for storage, transmission and in-network processing. The compression performance to realize significant gain in processing high volume sensor data cannot be attained by conventional lossy compression methods. In this paper, we propose ASDC (Adaptive Sensor Data Compression), an adaptive compression scheme that caters various sensor applications and achieve high performance gain. Our approach is to exhaustively analyze the sensor data and adapt the parameters of compression scheme to maximize compression gain while optimizing information loss.

IoT Data Compression: Sensor-agnostic Approach
Authors: Arijit Ukil, Soma Bandyopadhyay, Arpan Pal
Proceedings of Data Compression Conference (DCC), Utah, USA, April 2015
Abstract:
Management of bulk sensor data is one of the challenging problems in the development of Internet of Things (IoT) applications. High volume of sensor data induces for optimal implementation of appropriate sensor data compression technique to deal with the problem of energy-efficient transmission, storage space optimization for tiny sensor devices, and cost-effective sensor analytics. The compression performance to realize significant gain in processing high volume sensor data cannot be attained by conventional lossy compression methods, which are less likely to exploit the intrinsic unique contextual characteristics of sensor data. In this paper, we propose SensCompr, a dynamic lossy compression method specific for sensor datasets and it is easily realizable with standard compression methods.

Read more

Why Not Keep Your Personal Data Secure Yet Private in IoT?: Our Lightweight Approach
Authors: Tulika Bose, Soma Bandyopadhyay, Arpan Pal, Abhijan Bhattacharyya, Arijit Ukil
Proceedings of ISSNIP, Singapore, April 2015
Abstract:
IoT (Internet of Things) systems are resource constrained and primarily depend on sensors for contextual, physiological and behavioral information. Sensitive nature of sensor data incurs high probability of privacy breaching risk due to intended or malicious disclosure. Uncertainty about privacy cost while sharing sensitive sensor data through Internet would mostly result in overprovisioning of security mechanisms and it is detrimental for IoT scalability. In this paper, we propose a novel method of optimizing the need for IoT security enablement, which is based on the estimated privacy risk of shareable sensor data. Particularly, our scheme serves two objectives, viz. privacy risk assessment and optimizing the secure transmission based on that assessment.

Read more

HeartSense: Estimating Heart Rate from Smartphone Photoplethysmogram using Adaptive Filter and Interpolation
Authors: Anirban Dutta Choudhury, Aditi Misra, Arpan Pal, Rohan Banerjee, Avik Ghose, and Aishwarya Visvanathan
Proceedings of HealthyIoT 2014, IOT 360, Rome, Oct 2014

Abstract:
In recent days, physiological sensing using smartphones is gaining attention everywhere for preventive health-care. In this paper, we propose a 2-stage approach for robust heart rate (HR) calculation from photoplethysmogram (PPG) signal, captured using smartphones. Firstly, Normalized Least Mean Square (NLMS) based adaptive filter is used to clean up the noisy PPG signal. Then, heart rate is calculated from the frequency spectrum, which is further fine-tuned using different interpolation techniques. Experimental results, show that the overall HR calculation improves significantly due to the proposed 2-stage approach.

A robust heart rate detection using smart-phone video
Authors: A Pal, A Sinha, A Dutta Choudhury, T Chattopadyay, A Visvanathan, Proceedings of the 3rd ACM MobiHoc workshop on Pervasive wireless healthcare , 2013

Abstract:
In this paper, the authors have presented a smartphone based robust heart rate measurement system. The system requires the user to place the tip of his/her index finger on the lens of a smart phone camera, while the flash is on. The captured video signal often contains noise generated due to (i) improper finger placement, (ii) imparting excessive pressure, which subsequently blocks normal blood circulation and (iii) movement of the fingertip. To mitigate the above issues, a two stage approach has been proposed. Firstly, the onset of good video signal is detected by formulating a finite state machine, which employs multiple window short time fast fourier transform. Only upon receiving sufficient acceptable video signal, the heart rate is computed. Results indicate that the proposed method has successfully identified and rejected noisy video signal, resulting in avoidance of erroneous output.

Read more

Historical Data based Real Time Prediction of Vehicle Arrival Time
Authors: Tanushyam Chattopadhyay, Santa Maiti, Arindam Pal, Arijit Mukherjee, Arpan Pal
Proceedings of Intelligent Transportation Systems Conference (ITSC) 2014, Qingdao, Oct 2014
Abstract:
In recent times, most of the industries provide transportation facility for their employees from scheduled pick-up and drop points. In order to reduce longer waiting time, it is important to accurately predict the vehicle arrival in real time. This paper proposes a simple, lightweight yet powerful historical data based vehicle arrival time prediction model. Unlike previous work, the proposed model uses very limited input features namely vehicle trajectory and timestamp considering the scarcity and unavailability of data in the developing countries regarding traffic congestion, weather, scheduled arrival time, leg time, dwell time etc. Our proposed model is evaluated against standard Artificial Neural Network (ANN) and Support Vector Machine (SVM) regression models using real bus data of an industry campus at Siruseri, Chennai collected over four months of time period. The result shows that proposed historical data based model can predict two and half (approx.) times faster than ANN model and two (approx.) times faster than SVM model while it also achieves a comparable accuracy (75.56%) with respect to ANN model (76%) and SVM model (71.3%). Hence, the proposed historical data based model is capable of providing a real time system by balancing the trade-off between prediction time and prediction accuracy.

Model-driven Development for Internet of Things: Towards easing the concerns of Application Developers
Authors: Arpan Pal, Arijit Mukherjee, Balamuralidhar P
Proceedings of IoTaaS 2014, IOT 360, Rome, Oct 2014
Abstract:
Internet-of-Things (IoT) is asset to trigger disruptive growth in near future with wide and easy deployments of sensors connected to Internet. Horizontal service platforms for IoT are increasingly gaining prominence for quick development and deployment of IoT applications. However, IoT application development needs diverse skill and knowledge from domain, analytics, infrastructure and programming, which is difficult to find in one application developer. In this paper we introduce a Model-driven-development (MDD) framework that tries to address the above issue by separating out the concern of different stakeholders through models and knowledge bases.

Read more

Software Platforms for Internet of Things and M2M,
Authors:  P. Balamuralidhar, P Misra, A Pal
Journal of the Indian Institute of Science 93 (3), 487-498, 2013

Abstract:
The technologies and applications consolidated under the vision of Internet of Things (IoT) and Machine to Machine Communications (M2M) are attracting the interest of businesses and poised to trigger next disruption in Information & Communication Technology (ICT) applications. The domain specific solutions involving sensors, mobile phones and other devices are maturing to integrate in a generic ICT services paradigm. Architecture for a unified horizontal services platform is emerging. This paper surveys important trends, key requirements, evolving technologies and emerging solutions for such a software platform for IoT and M2M services.

Read more

Adapting protocol characteristics of CoAP using sensed indication for vehicular analytics
Authors: S Bandyopadhyay, A Bhattacharyya, A Pal
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, 2013

Abstract:
In this paper we present a unique approach to make use of CoAP (Constrained Application Protocol) from IETF (Internet Engineering Task Force) in a situation aware mode. The protocol adapts its characteristic for resource optimization depending on the indication inferred from sensed data. We consider a use-case for vehicular telemetry using a constrained in-vehicle sensor gateway which posts the vehicular information (accelerometer, GPS, device-identifier, time). In this use-case bandwidth usage is the main concern for the sensor gateway whereas usage of power which is directly impacted by overall bandwidth consumption is a key factor in case of mobile phone used as sensor gateway. We have reduced communication cost and optimized resource usage in terms of energy and bandwidth which are essential for any constrained sensor gateway by adapting characteristics of CoAP as mentioned above. The improvements are established by analyzing the data captured in real field.

Read more

Road condition monitoring and alert application: Using in-vehicle Smartphone as Internet-connected sensor
Authors: A Ghose, P Biswas, C Bhaumik, M Sharma, A Pal, A Jha
Pervasive Computing and Communications Workshops (PERCOM Workshops), 2012

Abstract:
The proposal describes a road condition monitoring and alert application using the in-vehicle Smartphone as connected sensors, which are connected to an Internet-of-Things platform over the Internet. In addition to providing a generic Internet-of-Things based platform, the proposed solution brings in novel energy-efficient phone-orientation-agnostic accelerometer analytics in phone, reduces the data volume that needs be communicated between phone and the back-end over Internet, brings in multi-user fusion concepts to create authentic road condition maps and addresses privacy concerns for the phone user for sharing the required data.

Read more

 

Reach Us.

Share

   

Other

Blog Post: Predictive Analytics for Man and Machine

The author discusses the importance of predictive analytics for the well being of man and for machine maintenance.