Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Department of Sensor and Biomedical Technology

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Department of Communication Engineering

School of Electronics Engineering

Electronics

Chennai Campus

Journal of Information and Knowledge Management

Preface

Purpose: This paper aims to review the shape sensing techniques using large area flexible electronics (LAFE). Shape perception of humanoid robots using tactile data is mainly focused. Design/methodology/approach: Research papers on different shape sensing methodologies of objects with large area, published in the past 15 years, are reviewed with emphasis on contact-based shape sensors. Fiber optics based shape sensing methodology is discussed for comparison purpose. Findings: LAFE-based shape sensors of humanoid robots incorporating advanced computational data handling techniques such as neural networks and machine learning (ML) algorithms are observed to give results with best resolution in 3D shape reconstruction. Research limitations/implications: The literature review is limited to shape sensing application either two- or three-dimensional (3D) LAFE. Optical shape sensing is briefly discussed which is widely used for small area. Optical scanners provide the best 3D shape reconstruction in the noncontact-based shape sensing; here this paper focuses only on contact-based shape sensing. Practical implications: Contact-based shape sensing using polymer nanocomposites is a very economical solution as compared to optical 3D scanners. Although optical 3D scanners can provide a high resolution and fast scan of the 3D shape of the object, they require line of sight and complex image reconstruction algorithms. Using LAFE larger objects can be scanned with ML and basic electronic circuitory, which reduces the price hugely. Social implications: LAFE can be used as a wearable sensor to monitor critical biological parameters. They can be used to detect shape of large body parts and aid in designing prosthetic devices. Tactile sensing in humanoid robots is accomplished by electronic skin of the robot which is a prime example of human–machine interface at workplace. Originality/value: This paper reviews a unique feature of LAFE in shape sensing of large area objects. It provides insights from mechanical, electrical, hardware and software perspective in the sensor design. The most suitable approach for large object shape sensing using LAFE is also suggested. © 2021, Emerald Publishing Limited.

World University root node

Industrial Robot

Recent trends and role of large area flexible electronics in shape sensing application – a review

Many mental disorders are caused due to improper regulation of the brain and depression is one such. It affects both children and adults and is very common among teenagers. There are many challenges clinicians face regarding the management of this disease. These challenges have prompted the development of various neuroimaging techniques that effectively diagnose the condition. The main techniques are Magnetic resonance imaging (MRI) and Positron emission tomography (PET) which have gained momentum over the years. Advanced MRI techniques help study certain regions of the brain such as hippocampus and amygdala. Effective treatments for depression include antidepressant medications and brain stimulation techniques. Although treatments are effective for a lot of people, there is still room for improvement. This article (1) presents background on depression, its types, symptoms and risk factors; (2) elaborates the neuroimaging techniques used and reviews the various techniques adopted over the years to study depression; and (3) discusses the treatment methods that can be practised to cure depression. © 2021 North Atlantic University Union NAUN. All rights reserved.

International Journal of Biology and Biomedical Engineering

Assessment of major depressive disorders through neuroimaging studies and their treatment methods

Backward walking is used increasingly as a rehabilitation exercise for stroke and diabetic peripheral neuropathy patients to improve strength and balance. However, it is unclear how visual referencing affects backward and forward walking. In this study, we evaluated spatiotemporal gait characteristics changes due to visual referencing while backward/forward walking. Sixteen healthy young participants were recruited in this study. All participants walked for 2 min with and without visual referencing in the virtual reality environment. While walking backward participants faced the virtual reality screen similar to forward walking, but their treadmill belt direction of movement was reversed. All participants walked at their preferred speed. We found that backward walking with visual reference affected symmetry in step length (p < 0.05) and step width (p < 0.001). Backward walking increased variability in step length (p < 0.001) and COM side excursions (p < 0.01) but also increased base of support through increased step width (p < 0.02). We also found backward walking with visual reference had significantly increased double support time (p < 0.001) and reduced swing time (p < 0.001). We also found that backward walking does not predispose to slip and trip risk, thereby, reduced foot contact velocity (p < 0.0001) and increased foot clearance (p < 0.0001). The findings of this study will help understand the effects of visual reference in backward and forward walking enables clinicians to design patient-centered rehabilitation exercises. © 2020 Elsevier B.V.

Fulltext

Displays

Effects of visual referencing on backward and forward treadmill walking in VR environments

Water waves are a promising source of renewable energy. Their kinetic energy can potentially drive energy harvesters. Herein, we describe a fully packed spheroidal smart buoy hybrid generator (SB-HG) composed of triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG) for scavenging water-wave energy. Each of the energy harvesting components generated electrical outputs from the same wave motion. A position-tracking long-range (LoRa) device placed in the buoy enabled identification of the buoy location when it was placed in the sea for energy harvesting, navigation, and fishnet tracking purposes. A solar cell placed on the top of the buoy powered the device under calm wave conditions. The TENG and EMG components generated maximum electrical outputs of 100 V/2 μA and 20 V/15 mA, respectively. Combining the devices efficiently converted the kinetic energy of the waves into useful electrical energy, which was used to charge a commercial capacitor and lithium-ion battery. The charged battery drove the position-tracking LoRa device for a positioning application and demonstrated that the SB-HG device is an inexpensive and reliable candidate for ocean navigation systems. Our water-wave energy harvester is highly promising as a clean energy power source and as a self-powered sensor for various environmental monitoring systems. © 2019 Elsevier Ltd

Nano Energy

A fully packed spheroidal hybrid generator for water wave energy harvesting and self-powered position tracking

In the digitized world, there is an increased demand for high-speed Time to Digital converters (TDC) in many fields like Nuclear physics, Time of Flight measurements (TOF), Space sciences, medical diagnosis, and imaging. This paper majorly focuses on establishing a Field Programmable Gate Array (FPGA) based Area Efficient and high-performance TDC architectures using KINTEX-7(28 nm) FPGA. Here, we have proposed an area-efficient shift register-based TDC whose output is a thermometer code. Later, this code is passed to an encoder to produce the relative binary code as an output. We have also proposed two encoding techniques, namely Multiplexer (MUX) based encoder with fourth-order bubble error correction and Scalable encoder to generate the output binary code. Our test results using Vivado Design Suite Software have shown that single channel TDC consumes less logical resources with decreased critical path delay and it will be suitable for multichannel architectures. Kintex-7 FPGA has a maximum clock frequency of 450 MHZ, with which we can achieve a resolution of 2.2 ns. To obtain the picoseconds resolution, we can use our TDC in multiple stages, connected in a ring configuration, which can provide fine interpolation and wider dynamic range. In this paper, finally we implemented all the encoders in 180 nm CMOS technologies to estimate the chip layout area and power. © 2019 Elsevier B.V.

Microprocessors and Microsystems

Area efficient high-performance time to digital converters

Fabrication and characterization of fiber optic volatile organic compounds (VOCs) sensor using pristine and copper (Cu) functionalized zinc oxide (ZnO) nanoflakes have been reported. Pristine ZnO nanoflakes were synthesized by co-precipitation method and were functionalized with Cu nanoparticles. Further, the synthesized nanoflakes were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and X-ray photoelectron spectroscopy. The characterization results reveal that the nanoparticles are in flakes shape with wurtzite structure and also confirm the Cu functionalization. Cladding modification technology was adopted to fabricate the sensor probe followed by dip coating of Cu functionalized ZnO nanoflakes over un-cladded part of an optical fiber. Different VOC concentrations of 0−350 ppm (such as acetone, ammonia, ethanol, methanol, hexane and chloroform) were prepared and their spectral characteristics were recorded at ambient temperature. The sensing analysis revealed that Cu functionalized ZnO nanoflakes can significantly improve the sensitivity towards all VOCs than pristine ZnO and exhibited highest sensitivity (∼ 47.4 at 350 ppm) towards ammonia with the limit of detection 10 ppm, being 6 times higher than pristine ZnO. The response towards ammonia was linear (R2 = 0.9912) over the concentration range of 10 ppm–50 ppm. Further, the sensor showed shorter response/recovery time of 11 s and 13 s, stable repeatability along with the long-term stability. The remarkable sensing property advocates that Cu functionalized ZnO nanoflakes are propitious candidate for ammonia sensor. © 2020

Sensors and Actuators, A: Physical

Clad-modified fiber optic ammonia sensor based on Cu functionalized ZnO nanoflakes

Visual odometry is a popular technique used to estimate motion in GPS-challenged environment, whose accuracy depends on the features extracted from the images. In past attempts to improved feature distinctiveness, these features have become complex and lengthier, requiring more storage space and computational power for matching. In this paper, an attempt is made toward reducing the length of these feature descriptors while maintaining a similar accuracy in pose estimation. Elimination of feature indices based on variance analysis on feature column sets is proposed and experimented in this paper. The features with reduced descriptor length are applied over the 3D-2D visual odometry pipeline and experimented on KITTI dataset for evaluating its efficacy. The proposed scheme of variance-based descriptor length reduction is found to reduce the overall time taken by the motion estimation framework while estimating the transformation with similar accuracy as that with full-length feature vector. © 2020, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

Descriptor-Length Reduction Using Low-Variance Filter for Visual Odometry

Attitude estimation is one of the core frameworks for a vehicle navigating with the help of inertial sensors such as accelerometer, gyroscope and magnetometer. Measurements obtained by these sensors are fused together to obtain vehicle attitude in the form of roll, pitch and yaw angles. Several state estimation frameworks have been proposed in the literature of which the extended Kalman filter and the complementary filtering based schemes are most popular. In this paper, the Fractional Order Extended Kalman Filter (FKF) approach is designed for estimating attitude with the help of inertial sensors in the attitude heading and reference system architecture. The FKF scheme is applied on the sensor data captured from commercial navigation units and compared with reference attitude for analysis. The simulations are carried out for varying fractional orders of different states and the corresponding results depict the dependency of estimation accuracy on system order. © Springer Nature Switzerland AG 2020.

VIT Unitversity

Advances in Intelligent Systems and Computing Intelligent Systems Design and Applications

Fractional Order Extended Kalman Filter for Attitude Estimation

The fast urbanization and speedy growth of automobiles throughout the globe requires the development of a highly reliable vehicle security system to protect against vehicle crime. Similarly, the interest in space exploration increases rapidly in many of the countries, and the rover used in space exploration operates on solar power with 6 wheels for its movement. So, it is highly desirable to find an energy harvester as well as motion tracker in space exploration vehicles. Herein, a vehicle security alert system and tire motion signaling and harvesting system are developed using a triboelectric nanogenerator (TENG) with good reliability and cheap manufacturing cost. The TENG device is made by reusing the waste plastic bottle made of PET, which serves as the substrate for housing the triboelectric active layers. The active layers composed of sandpaper made roughness created PBAT (Polybutylene adipate terephthalate, commercially available as Ecoflex-0030) film as a negative layer, copper film as a positive layer, and electrodes. A Kapton layer is introduced at the bottom of the Ecoflex film as a charge accumulation layer. The entire layers with the reused plastic substrate made the device as an eye-shaped TENG device (EYE-TENG). The device generates a maximum electrical output of voltage and current ~250 V and ~25 µA respectively with a maximum instantaneous peak power density of ~16 µW/Cm2. Furthermore, the device exhibits long term stability, durability and has potential to light up 100 LEDs. The device also demonstrates its capability is scavenging the biomechanical energy by hand and leg tapping at fast and slow motions. After that, the device was used to perform a vehicle security alarming system by placing into the vehicle tire and alarming with the help of an Arduino controller and Bluetooth module. The studies and results prove that the EYE-TENG is capable of acting as a sustainable power source as well as an alarming system and can be used as potential device in the future robotics, automobile industries, tire motion monitoring in rovers and aerospace electronics applications. © 2020