Rajeev Kumar Pandey

This study addresses the limitations of conventional lighting systems, which often fail to adapt to dynamic changes in occupancy, daylight availability, and user preferences, resulting in significant energy waste and compromised user comfort. To address these shortcomings, this study presents a cost-effective & highly efficient real-time IoT-enabled adaptive lighting control model that incorporates a time-varying gain controller, passive infrared (PIR) sensor, Pulse width modulated (PWM) LED light sensors, and an adaptive processing unit within a feedback loop to optimize lighting for energy efficiency and user comfort. In addition to this a data-driven approach has been used for zone division, reference generation and occupancy mapping which helps to enhance adaptability across diverse environments. The proposed model dynamically adjusts the lighting levels by integrating real-time user input, daylight harvesting, and occupancy mapping. The real-time experimental validation demonstrates a 35% reduction in energy consumption compared to traditional methods, with a discounted payback period of 3.68 years. The figure of merit (efficiency/cost) of 33.3%, demonstrating comparability to current state-of-the-art systems and highlighting the potential for substantial cost reductions and energy savings. Future research will address sensor calibration challenges, investigate the effects of extended LED dimming, and explore advancements in scalability and predictive control integration.

A next-generation 0.5V based ultra-low voltage and low power (ULV-ULP) readout for long-time continuous monitoring of PPG signals is designed by this effort for flexible hybrid OLED-μLED-OPD patches to detect heart rate (HR) in real-time. The designed readout consists of a transimpedance amplifier, a programmable continuous DC cancellation loop, and an auto-zero-offset cancellation. Towards ULV-ULP, a 0.5V body-driven operational amplifier with multipath miller zero compensation and PPG-Gated pulse density modulation (PDM) to drive OLED are employed as core designs that achieve ultra-low power. Also, this design incorporates a band-pass filter, a delta-sigma modulator, and a comparator. The aforementioned designs are implemented in an IC via the TSMC 0.18 μm process in a chip area of 1.6 mm ² . The sensing range of the readout circuit reaches from 20 nA to 200 μA. The measured integrated noise over 0.1-25 Hz is 0.8 nA _rms , while the achievable dynamic range of the PPG readout is 101.9 dB. The total power consumption of the readout system is as low as only 6 μW, while the power of OLED-μLED ranges from 33μW-3.8mW.

A low-power and low-noise photoplethysmography (PPG) readout for a flexible circular organic light emitting diode (OLED)- organic photodiode (OPD) patch to estimate blood pressure (BP) is designed by this effort. The designed system consists of a current-to-voltage converter, a low-pass filter, a high-pass filter, a programmable gain amplifier (PGA), a window-based external OLED driver, and a micro-controller unit. The front-end circuit of readout is designed to switch automatically between ambient Light and OLEDs to be used as a light source for PPG, that is, to reduce or even switch off the OLED during high contrast light to minimize power consumption. The analog front-end circuit is implemented in the integrated chip with an area of 1.44mm ² and fabricated via the TSMC T18 process. With the standard 1.8V, the experimental result shows that the measured linear current sensing range is 50nA-11μA. The measured signal-to-noise ratio with the OLED-OPD PPG sensing system is over 40 dB (including the sensor). The total power consumption of the designed chip is 54 μW for the readout, while the power consumption of the OLED driver varies from 3.6 μW to 5.3 mW@3.3V.

With the rapid rise of online applications in the contemporary digital world, security concerns are also developing day by day. A web application is a collection of several web pages that are kept on a server. It is stated that as the economy grows, so will the security risks. There are numerous current web vulnerabilities that are still a significant concern, and with time, there will be much more new vulnerability that emerges as technology advances. In this paper, There is a detailed study of various web threats i.e., vulnerabilities that are emerging and proposed a system which detects those vulnerabilities, resolves them for the user and generates a user log for the administrator to monitor the type of vulnerabilities. In the proposed model we have generated the system in the format of API. In the testing phase we’ve detected various web vulnerabilities such as SQL Injections, Cross Site Scripting, Session Hijacking, Unauthorized access, Middle man attack etc.

An adaptive PPG (Photoplethysmography) readout system for a dual-channel OLED-OPD flexible sensor is designed and developed with motion artifact (90dB (1nA to 100 µA) and input-referred noise of 0.26 nA/√H, with power consumption of 460µW. The DC drift is successfully reduced to 1% of its average. The accuracy for heart rate is 96%.

This study presents a low-power analog front-end readout for the organic sensor to sense the Photoplethysmography (PPG) signal from the radial wrist artery. The system consists of a current-to-voltage converter, a low pass filter, a high pass filter, a programmable gain amplifier (PGA), an OLED Driver, and a micro-controller unit. In addition, an ambient light channel assisted, a window-based external OLED driver, is proposed for the low power consumption. The analog front-end circuit is implemented in the integrated chip with an area of 1.6 mm ² and fabricated via TSMC 0.18µm process. With the standard 1.8V, the experimental result shows that the measured linear current sensing range is 50 nA-25 µA. The measured signal-to-noise ratio (SNR) with OLED-OPD PPG sensor is >40dB (at the wrist), and the measured integrated input-referred noise is 36 pA/Hz ^½ . The total power consumption of the designed chip with the senor while measuring PPG at the wrist is 54 µW (readout) + 1.62 mW (OLED driver worst-case power)/ 0.18 mW (best-case depends on the subject and skin), presenting the least power consumption compared to all the other studies.