The phase-sensitive detection (PSD), popularly referred to as lock-in amplifier (LIA) has been around for quite a long time. Lock-in amplifier has been quite a versatile measurement instrument for extracting low-level signals buried in noise. In the recent past, the importance of LIA has been realized as a sensor interfacing platform and a number of portable implementations targeting sensing applications have been reported. This review surveys LIA design, implementation and application as portable sensor interfaces. The paper put forward a brief history of phase-sensitive detection leading up to the state-of-the-art implementation in various fields of application. Different designs and implementation techniques for LIA have been categorized and introduced as per the design topologies. The main category being analog, digital, FPGA and CMOS integrated design. Finally, the application of LIA in resistive, capacitive, impedance, optical and Nano-bio sensors is categorized and investigated in detail.

Analog signal conditioning circuit based on dual differential subtractor for wide range resistive measurement of single element resistive sensor is presented in this paper. The differential output from the sensor and a voltage-controlled reference resistor is followed by an auto-nulling technique, which helps to achieve high accuracy and wide dynamic range by generating a control voltage proportional to the sensor resistance. The presented automatic approach not only enhances the auto-nulling range but also ensures constant sensitivity. The interface design provides high accuracy, fast and linear response at the same time avoids drawbacks of lead wire variations. A prototype PCB of the proposed circuit is fabricated and tested. The experimental result shows that the circuit can measure a wide range of resistance (20 kΩ-1.1 M) with relative error less than 0.78%, compared to other bridge-based circuits which are capable of measuring the maximum resistance of up-to 10 kΩ for the same accuracy. Multiple variations of the proposed circuit are designed and experimentally tested for verifying the tunability of the circuit in terms of the sensitivity and the range of resistance measurement. The prototype circuit is also interfaced with an in-house fabricated MEMS-based ammonia sensor, proving its efficacy in resistive sensor system development.

We report on a selective hydrogen sulfide gas sensor based on zinc ferrite film which is obtained by a microwave-assisted solvothermal deposition route. The response of the chemiresistive device is found to be in the range of 1872% - 90% for 5.6 ppm – 0.3 ppm of H ₂ S gas at an operating temperature of 250°C. The density functional theory studies suggest physisorption of the H ₂ S molecules at the partially-inverted ZnFe ₂ O ₄ surface as the reason behind the fast rise (of the order of ~40 sec) and fall time (of the order of ~70 sec) with complete recovery of the device. Finally, a dual-differential subtractor based auto-balancing interface circuit is proposed to drive this sensor which is advantageous in terms of accuracy and particularly suitable for a device such as ours, which has a wide dynamic range.

This paper presents an online local path planning approach for autonomous drone navigating a 2-D plane in an unknown, indoor corridor-like environment. The proposed method utilizes a reinforcement learning approach for training a local path planner for navigation in the said environment. With a Continuous Actor-Critic Learning Automaton (CACLA) applied for continuous action spaces, the proposed algorithm uses a reward structure that formulates a balancing function that gives reward based on balancing the vehicle between potential artificial hills. The drone thereby learns steering control and obstacle avoidance while maintaining a central aligned position with respect to the unknown hallways or corridors. A novel CACLA algorithm and incorporation of a special experience replay memory for the better converging tendency of drone towards the balancing point have been introduced in the paper. The proposed reinforcement learning-based online local path planner has been tested on a simulated drone in Gazebo environment.