Multirotor Unmanned Aerial Vehicles (UAVs) and their ability to hover and maneuver in the air make them the best vehicle for applications requiring quick package deliveries. Our assumed example system is a collaboration of a UAV and a land vehicle capable of storing multiple packages and charging the UAV’s batteries. Another example is quickly exploring some unknown region where the UAV scans the unexplored region and returns to the moving vehicle when required knowledge is gathered. An active research problem in such systems is about takeoff and landing the UAV on the moving vehicle. The landing problem, however, is quite challenging without unique markers like visual tags, hard-coded trajectory targets, etc. We present a markerless controller that uses only computer vision and calculates the optimum trajectory to land the UAV on the moving platform. This paper presents the work in a two-stage implementation process. First, the detection of a moving vehicle and finding the centroid by applying optical flow and contour technique on image feed from an RGB camera, and secondly, following the ground vehicle by matching the velocity by utilizing a visual servoing process followed by simple PID controller to descend in the smooth trajectory towards the landing point.

Severe TBI survivors face long-term brain damage requiring accurate diagnosis and early treatment as the therapeutic time window typically ranges from minutes to hours before the secondary injury. In this work, we report design aspects and development of an ultrasensitive GaN HEMT-based biosensing platform for IL-6 detection along with novel design correlation equations for sensitivity analysis. Design analysis reveals that the lower value of Lsd is desirable for enhanced sensitivity as the biosensor with Lsd of 10 mu m offers a peak sensitivity of 129.20 mu A pg-1 ml-1 as compared to 25 and 50 mu m with a sensitivity of 70.76 and 35.60 mu A pg-1 ml-1 respectively. Furthermore, the peak sensitivity reaches as high as 158.01 mu A pg-1 ml-1 when the Wg was increased from 100 to 250 mu m for biosensors with Lsd of 10 mu m using conventional gate design. An increase in sensitivity from 70.76 to 88.40 mu A pg-1 ml-1 was observed when a meander gate biosensor was used. An analysis of the EDC-NHS coupling process reveals an enhancement in sensitivity from 78.60 to 129.20 mu A pg-1 ml-1 for the EDC-NHS coupled biosensor. The peak response of the biosensor for IL-6 detection was observed at 5 min after the drop cast of antigens over the sensing areas.

In this paper, we propose a gallium nitride (GaN) high-electron-mobility transistor-based (HEMT) sensor and developed circuit for point of care detection of mercury contamination in water. The platform, GaN HEMT, has been fabricated for three different epitaxial structures and four different device designs. The drain current of GaN HEMTs for various designs for a specific epitaxial structure has been recorded at a $$V_{\text {ds}}$$ of + 5 V where a 2x50-25ID device design performs better than the other three. The sensor is developed on the 2x50-25ID design and tested in continuous running mode that is able to detect nM level of concentrations of mercury in water. The sensor exhibits a drain current change of 6.66% at a $$V_{\text {ds}}$$ of + 5 V when immersed into 1 nano-molar solution of the mercury with reference to the drain current obtained in normal DI water. The developed handheld system also shows a good response to the mercury contaminated water, and the the performance is reasonably close to the on chip detection. The packaged sensor and circuit is portable and can work stand alone for the detection of the mercury.

Landing a UAV precisely to its target location with challenges like external disturbance and inaccurate GPS localization makes it more difficult. This paper proposes a simplified pure pursuit-based method to approach the target landing location using virtual targets. Firstly the Aruco marker placed on the landing pad is identified using an OpenCV framework which helps the UAV for self-localization. In the next step, drone way-points are generated using the proposed algorithm where the 3D space problem is converted to a 2D cartesian system followed by classical pure-pursuit. This method generates a smooth arc for the Drone to follow and avoid crashing while landing. A PID controller ensures that the Drone follows the generated trajectory within a reliable margin of error. This method delivers an efficient and computationally less expensive landing approach for autonomous drones.

This article presents an online local path planning approach for autonomous drone navigating a 2D 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 artificial potential 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 toward the balancing point have been introduced in this article. The proposed reinforcement learning-based online local path planner has been tested on a simulated drone in Gazebo environment.

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 H2S gas at an operating temperature of 250°C. The density functional theory studies suggest physisorption of the H2S molecules at the partially-inverted ZnFe2O4 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.

A novel approach for signal enhancement of electrochemical biosensors by incorporating mechanical vibrations has been developed. We report the electrochemical study of the ferricyanide and ferrocyanide Fe CN 6 − 3 / − 4 a redox couple, at room temperature (∼25 °C), under the effect of mechanical vibrations of different frequencies applied to the sensor, for sensitivity enhancement. The experimental results showed a sensitivity enhancement of ∼332% (from 3.125 nA μ M − 1 to 13.5 nA μ M − 1 ) at 88.75 Hz of vibration. This novel approach of signal sensitivity enhancement is also validated with antibody immobilization-based Vi antigen detection for typhoid assessment. The sensitivity enhancement up to ∼15% is achieved for Vi antigen detection under the mechanical vibration of 120 Hz. The sensor is fabricated using microfabrication technology. The vibration-assisted ultrasensitive biosensing platform is also prototyped as a portable and Internet of Thing (IoT) enabled device, suitable for point-of-care applications. Detailed features of the prototype along with the test results are elaborated in the paper.

Two symmetric countries compete over two-period under a non-preferential taxation regime to attract multiple investors where investors are strategic and investments are sunk once invested. Contrary to the existing results, we find that tax holidays do not arise during the initial period. Equilibria in mixed strategies arise in both periods where competing countries set strictly positive tax rates during the initial period. Strategic interaction between large investors reduces competition and increases tax rates during the initial period. We provide full characterization and uniqueness of equilibria in mixed strategies.JEL classification: F21, H21, H25, H87

In the present work a Neuro-Evolution based approach has been used to train a neural network for control of some sample systems. This method makes use of Genetic algorithm, here it is generating a population of neural networks and introduces mutation for producing better off-springs for the next generation. The approach is kind of black box optimization and do not require any back propagation for training. It makes use of fitness function to evaluate performance of off-springs, this fitness function is based on a novel reward function which allows for quick and smooth settling of the sample system towards set point. In order to address dynamics of the system's time sequenced error has been taken as exogenous input for the neural network. The method has been tested on a linear first order system and a system having non linearity.

In a dynamic two-period model of tax competition, where competing countries strategically choose foreign investment restrictions which increases the sunk cost of investments, we show that choosing a higher level of restriction is beneficial for the competing countries. A higher level of restriction reduces competition and increases tax revenue in the later period, which allows the government to offer large tax holidays during the initial period of investment. The result is counter-intuitive as it is widely believed that sunk cost reduces foreign direct investments. Moreover, even though competing countries are ex-ante symmetric, the equilibrium choice of the level of restrictions may not be equal.

Abstract A country has an incentive to unilaterally commit to a non-preferential taxation regime even though the competitor adopts a preferential taxation regime. We show that a mixed taxation regime arises in a dynamic two-period model of tax competition between two symmetric countries where an investor has home-bias for the country where he/she invests in the initial period. A scenario where competing countries jointly adopt non-preferential taxation regimes is also a subgame-perfect equilibrium. The tax revenue of the country which adopts a preferential taxation regime in a mixed taxation regime is equal to the tax revenue a country receives when competing countries jointly adopt a non-preferential taxation regime.JEL classification: F21; H21; H25; H87

We analyze the taxation regimes that may emerge in a two-period dynamic tax competition game where a country that attracts investments during the initial period has agglomeration advantages during the later period. When competing countries choose taxation regimes simultaneously, mixed taxation may arise in an equilibrium where one country adopts a non-preferential and the other adopts a preferential taxation regime. Equilibrium tax revenues of competing countries decrease with the increase in agglomeration effects. Whether a country with a non-preferential or a preferential taxation regime obtains a higher tax revenue depends critically on the extent of agglomeration effects. Moreover, whether a country with a non-preferential or a preferential regime attracts investments during the initial period and in turn will have agglomeration advantages during the later period also depends on the extent of agglomeration effects. When competing countries choose taxation regimes sequentially, a mixed taxation regime arises, and the first mover chooses a non-preferential taxation regime when the agglomeration effect is not very large. On the other hand, when the agglomeration effect is very large, a mixed taxation regime arises where the first mover chooses a preferential and the second mover chooses a non-preferential regime. We provide the complete characterization and proof of the uniqueness of the equilibrium in mixed strategies.JEL classification: F21, H21, H25, H87

In a dynamic two-period game between two symmetric countries, we show that a unique subgame-perfect equilibrium arises during the initial stage of the game. A mixed taxation regime arises in the equilibrium where one country adopts a non-preferential taxation regime while its competitor adopts a preferential taxation regime. The country with a non-preferential taxation regime earns a higher tax revenue compared to the country with a preferential taxation regime. A tax holiday does not arise during the initial stage of the game when the size of the mobile capital base that enters during the later stage is considerably larger than the size of the mobile capital base that enters the economy during the initial stage. We provide the complete characterization and proof of the uniqueness of the mixed strategy Nash equilibrium.JEL classification: F21, H21, H25, H87

The paper describes an Internet of Vehicle (IoV) communication technology to control and monitor in-vehicle prototype-based devices as connected sensors and actuators. In addition to providing an Internet of Things (IoT) platform over the mobile application system, the proposed solution brings a fundamental change in reducing human energy as well as time. The IoV communication technology delivers real-time controlling, monitoring, and gathering of information on the vehicular network. Moreover, it provides a key solution for the processing, computing, sharing, and safe release of information onto the vehicular information platform. The ideal purpose of the work is to develop a signal generating kit which locks/unlocks the car doors anywhere through a tablet or smartphone. Moreover, we can monitor parameters through a smartphone application, the monitoring of tire air pressure, and fuel level through respective sensors network. For the purpose of the above-cited subject, the article aimed at developing a smart vehicle management technology.

The paper reports a tunable interface electronics for High Electron Mobility Transistors (HEMTs) based Field Effect Transistor (FET) sensor. The approach presented in the work can estimate both the ‘on resistance’ and drain current of the FET sensor while maintaining a constant drain-source voltage (VDS). The interface electronics consist of a full Wilson current mirror with a floating voltage-controlled resistance which is controlled by the output of an integrator forming a closed-loop feedback control. The bias point for the sensor is set by the external voltage, Vbias and the difference between the sensor drain-source voltage, VDS and Vbias is fed to the integrator as an error signal. The output of the integrator, Vc varies the tunable resistance which in turn varies the current in the current mirror branch. This current is mirrored to the sensor branch and continues to vary until the drain-source voltage, VDS matches the applied external bias voltage, Vbias. At this point, the circuit is at the balanced state and the corresponding control voltage, Vc represents the sensor signal. The circuit is evaluated with SPICE simulation and experimentally verified using a prototype PCB. Experimental results revealed that the circuit can work for a range of 200Ω to 2000Ω with an absolute relative error of less than ±1%. The circuit can also be tuned for the desired working range and allows the user to apply external bias potential based on the sensor’s requirement. The proposed circuit has great potential as a bio-chemical interface circuit for FET based sensors.

SummaryIn this paper, we present an improved full Wilson current mirror‐based interface system, which utilizes an auto‐balancing technique, for High Electron Mobility Transistor (HEMT)‐based biochemical sensors. The auto‐tuning technique maintains a constant voltage across the sensor and simultaneously provides an output voltage proportional to the measurand. The proposed circuit is particularly useful for HEMT‐based sensors with large offset current since the design performs base current compensation. The design and analysis of the proposed circuit are verified experimentally on a prototype Printed Circuit Board (PCB). The effectiveness of the circuit is proven by carrying out repeated experiments for evaluating linearity, accuracy, and standard deviation. The obtained result of the prototype shows that the proposed circuit is able to work from 300 Ω to 2.1 kΩ with a relative error of less than ±2%. The prototype is finally tested with an in‐house fabricated HEMT‐based heavy metal sensor. The measurement range of the proposed circuit is tunable according to the sensor operating range, which enables the potential use of the proposed circuit for a wide range of HEMT‐based sensors.

In this paper, a modified heuristic guided Artificial Potential Field (APF) based algorithm has been proposed to find a practical trajectory for an Autonomous Unmanned Aerial Vehicle (UAV) path planning. The local minima are encountered in the conventional APF algorithm due to the cancellation of attractive and repulsive potential while avoiding unknown obstacles within the desired path, which results in the trapping of the agent before reaching the goal. Consequently, the traditional APF technique is therefore no longer advantageous in such cases. So in this proposed perpendicular approach based on APF helps to avoid such local minima. The advantage of the newly proposed method is the low computing time that lines up with the standard global path planner method. The proposed algorithm is tested and validated against existing general potential field techniques for different simulation scenarios in a 3D simulated environment using ROS and Gazebo supported PX4-SITL. The results have been presented for drone navigation and obstacle avoidance for the different scenarios in a simulated environment.

This work demonstrates a MEMS piezoresistive pressure sensor and its interface circuit based on a versatile current conveyor based current signal processing. To exploit the effectiveness of current mode design in VLSI, the proposed interface electronics utilizes a differential current signal and performs trans-impedance conversion through a single-ended grounded gain resistor, thus ensuring high sensitivity and linearity. The current mode approach utilizes minimal active components with just two positive current conveyors (CCII+) in a closed loop architecture, thus eliminating the need for a negative current conveyor (CCII-) or an extra CCII+ to implement the same. This approach delivers linearity and offers ease of offset balancing through external control voltages, one each for incremental and decremental cancellation. The circuit performance is investigated in simulation using AD844, which represents a functional current conveyor. Further, the proposed interface is integrated with the fabricated MEMS piezoresistive pressure sensor to obtain a sensor-electronics module. Experimental evaluation of the module reveals a sensitivity of 13.7 V/Bar for low pressure input (0-100mBar).

A robust signal conditioning circuit for capacitive sensors, based on the dual-slope and phase-sensitive-detection technique, is presented in this article. The circuit is designed to measure the capacitance of the leaky capacitive sensors. The phase-sensitive-detection technique mitigates the effect of the shunting conductance. In addition, the circuit is designed in such a way that the error due to the parasitic/stray capacitance on the measurement of the sensor capacitance is negligible. Furthermore, an autotuning frequency-independent quadrature phase-shifter circuit is designed for an accurate 90° phase generation. A prototype of the circuit is built and tested. The experimental results show that the designed circuit can measure the sensor capacitance in the range from 65 to 528 pF with accuracy within ±1%. The robustness of the proposed circuit is tested against the variation in the amplitude and frequency of input sinusoidal excitation signal. The results confirm that the proposed circuit is well suited for sensors, such as capacitive humidity sensors, water level detection sensors, and fingerprint sensors.