Person: Mishra, Biswajit
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Name
Biswajit Mishra
Job Title
Faculty
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079-68261561
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Specialization
Ultra Low Power and Sub-threshold Circuit Methodologies, Very Low Voltage Circuits for Wireless Sensor Networks, Digital IC Design, Power Management for Energy Harvesters, Signal Processing Hardware for Color Image Processing, Geometric Algebra and Novel Hardware
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Publication Metadata only Customized IoT Enabled Wireless Sensing and Monitoring Platform for Smart Buildings(Elsevier, 01-04-2017) Shah, Jalpa; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; DA-IICT, Gandhinagar; Shah, Jalpa (201121001)In this paper we present a customized Internet of Things (IoT) enabled Wireless Sensing and Monitoring Platform to monitor the temperature, relative humidity and light in the context of building automation. In developed system, data is sent from the transmitter node to the receiver node through a customized hopping method. The data received at the receiver node is monitored and recorded in an excel sheet in a personal computer (PC) through a Graphical User Interface (GUI), made in LabVIEW. An Android application has also been developed through which data is transferred from LabVIEW to a smartphone through which data is remotely monitored.Publication Metadata only Analytical equations based prediction approach for PM2.5 using artificial neural network(Springer, 01-09-2020) Mishra, Biswajit; Shah, Jalpa; DA-IICT, GandhinagarThe worldwide, particulate matter pollution is considered one of the deadliest types of air pollution due to its significant impact on the global environment and human health. The particulate matter (PM2.5) plays a key role in evaluating the air quality index. However, the conventional PM2.5 monitoring instruments used by the air quality monitoring stations are costly, bulkier, time-consuming, and power-hungry. Furthermore, due to limited data availability and non-scalability, it is challenging to provide high spatial and temporal resolution in real-time. To overcome these challenges, we present analytical equations based prediction approach for PM2.5 using an artificial neural network. Moreover, we contribute the correlation study between PM2.5 and other pollutants using a large authenticate data set of Central Pollution Control Board online station, India. The correlation study reveals the strong correlation of eight pollutants with PM2.5, which found useful for the proposed prediction model and future research work. The computation of the proposed analytical equation using a low-cost processing tool (excel sheet) demonstrates a good match between predicted and actual results. Additionally, the derived analytical equation for the prediction can be computed using a wireless sensor node which ultimately eliminates the need for costly propriety tools. The performance of proposed analytical equation for prediction show root mean square error and coefficient of determination (R2) 1.80�\upmug/m3�and 0.99 respectively using eight correlated predictors. The recalibrated prediction model with three correlated predictors show RMSE of 7.54�\upmug/m3�and�R2�of 0.97 and proves the effectiveness of the proposed approach in implementation using minimum power-hungry gas sensors on the WSN. Therefore, obtained results demonstrate that the proposed approach is one of the promising approaches for monitoring PM2.5 without power-hungry gas sensors and bulkier analyzers.Publication Metadata only Detection and classification of atrial and ventricular cardiovascular diseases to improve the cardiac health literacy for resource constrained regions(The Institution of Engineering and Technology(IET), 01-06-2023) Arora, Neha; Mishra, Biswajit; DA-IICT, Gandhinagar; Arora, Neha (201721007)ECG is a non-invasive way of determining cardiac health by measuring the electrical activity of the heart. A novel detection technique for feature points P, QRS and T is investigated to diagnose various atrial and ventricular cardiovascular anomalies with ECG signals for ambulatory monitoring. Before the system is worthy of field trials, it is validated with several databases and recorded their response. The QRS complex detection is based on the Pan Tompkins algorithm and difference operation method that provides positive predictivity, sensitivity and false detection rate of 99.29%, 99.49% and 1.29%, respectively. Proposed novel T wave detection provides sensitivity of 97.78%. Also, proposed P wave detection provides positive predictivity, sensitivity and false detection rate of 99.43%, 99.4% and 1.15% for the control study (normal subjects) and 82.68%, 94.3% and 25.4% for the case (patients with cardiac anomalies) study, respectively. Disease detection such as arrhythmia is based on standard R-R intervals while myocardial infarction is based on the ST-T deviations where the positive predictivity, sensitivity and accuracy are observed to be 94.6%, 84.2% and 85%, respectively. It should be noted that, since the frontal leads are only used, the anterior myocardial infarction cases are detected with the injury pattern in lead�avl�and ST depression in reciprocal leads. Detection of atrial fibrillation is done for both short and long duration signals using statistical methods using interquartile range and standard deviations, giving very high accuracy, 100% in most cases. The system hardware for obtaining the 2 lead ECG signal is designed using commercially available off the shelf components. Small field validation of the designed system is performed at a Public Health Centre in Gujarat, India with 42 patients (both cases and controls). 78.5% accuracy was achieved during the field validation. It is thus concluded that the proposed method is ideal for improvisation in cardiac health monitoring outreach in resource constrained regions.Publication Metadata only Light-weight configurable architecture for QRS detection(IET, 01-03-2019) Jain, Nupur; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; DA-IICT, Gandhinagar; Jain, Nupur (201221008)In this study, the authors present a configurable architecture having gate count of ?3.2??? and on the fly reconfigurability for low-power biomedical applications such as QRS detection, ExG processing etc. The proposed architecture is a light-weight co-processor that supports on-node digital signal and image processing functions potentially eliminating the power consumed by radios in wireless sensor node and body sensor network. The architecture consists of a 3?�?3 array of register units along with adaptive memory with configurable data path. The architecture can be configured on-the-fly for seven functions with the current memory structure. However, more number of functions can be targeted with increased memory. They demonstrate the realisation of Pan�Tompkins algorithm commonly used for QRS detection on the proposed architecture using the reconfigurability. This work offers ?4� reduced area and 2.3� increase in performance with respect to the existing contemporary literature.Publication Metadata only Ultra-low power digital front-end for single lead ECG acquisition integrated with a time-to-digital converter(IET, 16-07-2019) Mishra, Biswajit; Thakkar, Sanket; Jain, Nupur; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; DA-IICT, Gandhinagar; Thakkar, Sanket (201611046); Jain, Nupur (201221008)A low power single lead electrocardiogram front-end acquisition system in 0.18 ?m CMOS operating at 0.5 V is presented here. The analogue blocks in low noise amplifier (LNA), filters and passive elements that perform amplification and DC offset cancellation are replaced by a moving average voltage to time converter (MA-VTC) to get amplification and anti-aliasing in the time domain. A digital feedback algorithm is used to cancel out the DC offset. The front-end structure is designed in the sub-threshold region of MOS to reduce the power consumption in the circuit. The proposed architecture consumes 50 nW of power with a gain of 670 ?s/V. The output of the front-end is fed to an all digital time-to-digital converter (TDC) that operates in the near threshold region with a resolution of 586.4 ps and 32.5 ?W power consumption.Publication Metadata only IoT-enabled Low Power Environment Monitoring System for of PM2.5(Elsevier, 01-09-2020) Shah, Jalpa; Mishra, Biswajit; DA-IICT, GandhinagarAir pollution is a major concern worldwide due to its significant impacts on the global environment and human health. The conventional instruments used by the�air quality monitoring�stations are costly, bulkier, time-consuming, and power-hungry. Furthermore, due to limited data availability and non-scalability, these stations cannot provide high spatial and temporal resolution in real-time. Although energy-efficient,�wireless sensor network�with the high spatio-temporal resolution is one of the potential solutions, real-time remote monitoring of all significant air quality parameters with�low power consumption�is challenging. To address this challenge, we propose internet of things-enabled low power environment�monitoring system�for real-time monitoring of ten significant air quality parameters. Moreover, the proposed system enables remote monitoring and storage of data for future analysis. Unlike earlier�research work, further expansion of the proposed system is easily possible, as the proposed�Wireless Sensor Node�(WSN) can interface a higher number of sensors with the same number of interfacing pins. We did an in-depth analysis through calibration, experiments, and deployment which confirms the power efficiency, flexibility, reliability and accuracy of the proposed system. Results illustrate the low power consumption of 25.67mW,�data transmission�reliability of 97.4%, and battery life of approximately 31 months for a sampling time of 60�min. The study of the correlation between�Particulate Matter�2.5 (PM2.5) and other pollutants is performed using Central�Pollution Control�Board data of 41 months. The initial study related to correlation is performed for the future work of developing a prediction model of PM2.5 using highly correlated pollutants. The future approach for developing a prediction model in the form of analytical equations with the help of�artificial neural network�is demonstrated. This approach can be implemented using the proposed WSN or low-cost processing tool for evaluating PM2.5 from precursor gases. Therefore, this approach can be one of the promising approaches in the future for monitoring PM2.5 without power-hungry gas sensors and bulkier analyzers.Publication Metadata only A Low gate count reconfigurable architecture for biomedical signal processing applications(Springer, 08-03-2021) Jain, Nupur; Mishra, Biswajit; Wilson, Peter; DA-IICT, Gandhinagar; Jain, Nupur (201221008)A new reconfigurable architecture for biomedical applications is presented in this paper. The architecture targets frequently encountered functions in biomedical signal processing algorithms thereby replacing multiple dedicated accelerators and reports low gate count. An optimized implementation is achieved by mapping methodologies to functions and limiting the required memory leading directly to an overall minimization of gate count. The proposed architecture has a simple configuration scheme with special provision for handling feedback. The effectiveness of the architecture is demonstrated on an FPGA to show implementation schemes for multiple DSP functions. The architecture has gate count of�25k and an operating frequency of 46.9 MHz.Publication Metadata only CMOS Power Management Unit Along with Load Regulation Using Switched Capacitor Converters(American Scientific Publishers, 01-06-2019) Patel, Purvi; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; DA-IICT, Gandhinagar; Patel, Purvi (201521008)In this paper, a power management unit (PMU) with battery backup and output load regulation using switched capacitor converters using 0.18 m Complimentary Metal Oxide Semiconductor technology is presented. Low power consumption is obtained by employing subthreshold design methods. The main building blocks of the PMU are a voltage regulator, a voltage monitor, a battery backup and switched capacitor (SC) converters. The voltage regulator has an output voltage VOUT at 0.95 V and 0.968 V at input voltages of 0.98 V and 1.33 V, respectively. Perpetual operation at the load side is confirmed by implementing a battery backup module, which provides the battery voltage to the load circuit when the output voltage of the energy harvesting (EH) source is insufficient. The presented PMU consumes 46 nW at 0.98 V and 3.919 W with SC converters at 1.33 V. Different voltages can be tapped from the PMU from the 3:2, 2:1, 3:1, 4:1 and 5:1 down converters that provide output voltages of 0.67 V, 0.5 V, 0.33 V, 0.25 V and 0.2 V to the load, respectively. The maximum current that can be delivered by the PMU is 1 mA at an input voltage of 1.33 V and is adequate for many low power applications.Publication Metadata only A novel signal processing coprocessor for n-Dimensional geometric algebra applications(SCIRP, 01-11-2014) Mishra, Biswajit; Kochery, Mittu; Wilson, Peter; Wilcock, Reuben; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; DA-IICT, GandhinagarThis paper provides an implementation of a novel signal processing co-processor using a Geome-tric Algebra technique tailored for fast and complex geometric calculations in multiple dimensions. This is the first hardware implementation of Geometric Algebra to specifically address the issue of scalability to multiple (1 -8) dimensions. This paper presents a detailed description of the imple-mentation, with a particular focus on the techniques of optimization used to improve performance. Results are presented which demonstrate at least 3x performance improvements compared to previously published work.Publication Metadata only A Low Power Wearable Device for Real-Time Electrocardiogram Monitoring and Cardiovascular Arrhythmia Detection for Resource Constrained Regions(American Scientific Publishers, 01-06-2019) Arora, Neha; Mishra, Biswajit; Vora, Yash; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; Mishra, Biswajit; DA-IICT, Gandhinagar; Arora, Neha (201721007); Vora, Yash (201711043)Electrocardiography is a non-invasive technique for obtaining the electrical activity of heart by placing electrodes on various thoracic points of human body. The obtained electrical signals are then used to detect various cardiovascular abnormalities i.e., arrhythmias, Myocardial Infarction (MI) conditions, hypercalcemia and hypocalcemia etc. by comparing critical features of normal ECG signals with the abnormal one. In this work, we propose a low power wearable device with two transmission channels that runs an intuitive algorithm and processes the data from a single lead ECG front end (extended upto 3 Leads) and reports various arrhythmic conditions based on R�R interval. Pan Tompkins Algorithm is used as the basic R peak detection scheme and extending detections of P wave, T wave, Q, S and J points of the signal. To verify the proposed algorithm, the MIT/BIH arrhythmia database is used as a source of the ECG signals and the reference for R peak annotations. The R peak detection algorithm provides metric of False Detection Rate to be 1.289%, Sensitivity to be 99.492% and Positive Predictivity to be 99.293%. The energy requirement for ISM band enabled wearable device is 1.4165 J and bluetooth enabled wearable device is 2.7548 J respectively and the device can operate for 12.3 days on nRF and 4.2 days on bluetooth on a coin cell and can prove to be effective for remote monitoring applications. Results for arrhythmia, I degree atrioventricular block (AV block) and atrial fibrillation (AF) have been obtained. The probable cases of bundle branch block (BBB) are also categorized by the algorithm. It is concluded that the proposed low power wearable device design can be a useful tool in the resource constraint regions in Asia and Africa where health care is a major concern and remote monitoring can prove to be a useful alternative.