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

The aim of this work is to study the suitability of two newly introduced bio-inspired algorithms, namely the dragonfly algorithm (DA) and the salp swarm algorithm (SSA) for thinning a linear antenna array. In array thinning, a fully populated array is chosen as a starting point, and a thinned array is obtained through careful deactivation of select sensors such that the residual active sensors enable the array to achieve a desired side-lobe performance. In this paper, we apply the binary versions of DA and SSA, namely the binary dragonfly algorithm (BDA), and the binary salp swarm algorithm (BSSA) to thin a symmetric linear array with uniform inter-element spacing of half wavelength. Extensive simulations were performed in MATLAB by considering arrays of different sizes. The results obtained from BDA and BSSA were compared against those obtained from the binary versions of two benchmark algorithms, namely the genetic algorithm (GA) and the gray wolf optimizer (GWO). Relative side-lobe level (RSLL) and filling percentage were used as performance comparison metrics. It has been observed that both BDA and BSSA offer promising results in line with BGA and BGWO. More specifically, BDA was found to be faster than BSSA. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Department of Communication Engineering

School of Electronics Engineering

Vellore Campus

World University root node

Lecture Notes in Electrical Engineering

On–off Thinning in Linear Antenna Arrays Using Binary Dragonfly Algorithm

This paper explains the design and implementation of a safety system for heavy vehicles. A small-scale embedded system based on Node MicroController Unit (NodeMCU) is proposed and prototyped here. Peripherals such as Global Positioning System (GPS) receiver (for location updates), tilt sensor (for accident detection), gas sensor (for alcohol activity detection), RFID tags and reader (to monitor entry and exit of vehicles in restricted areas), a touch sensor (to indicate an emergency) etc., have been used in this system. A major improvement over existing accident alert systems is that the proposed design makes use of ultrasonic sensors to indicate over-height of the vehicles approaching underpasses (tunnels or rail over road bridges) on highways. A sound alarm is triggered if the vehicle’s height is more than the entry point of the underpass so that the driver can slow down the vehicle. A working prototype of the overall system has been implemented and tested successfully. The data gathered by all sensors is sent to the cloud using the Message Queuing Telemetry Transport (MQTT) protocol and the user can monitor all the updates using his/her android smartphone. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

International Journal of Emerging Trends in Engineering Research

NodeMCU and IoT-based safety and security ecosystem for heavy vehicles

Automotive radars make use of angle information obtained from antenna arrays to distinguish objects that lie in the same range-doppler cell (relative to the ego vehicle). This paper proposes novel ways of using presently known minimum redundancy arrays (MRAs) in single-input multiple-output (SIMO) and multiple-input multiple-output (MIMO) automotive radars. Firstly, an MRA-based sparse MIMO array is proposed as a novel modification to the nested MIMO array. The proposed sparse MIMO array uses MRAs as the transmitting and receiving modules, unlike the nested MIMO array, which uses two-level nested arrays (TLNAs) at the transmitting and receiving blocks. Upper bounds for the sum co-array aperture and the overall attainable degrees of freedom (DOF) offered by the MIMO radar have been derived in terms of the number of sensors. Secondly, the suitability of large Low-Redundancy Linear Arrays (LRLAs) in SIMO automotive radars is also studied. A long-range automotive radar driving scenario was assumed for DOA estimation and simulations were carried out in MATLAB using the co-array MUltiple SIgnal Classification (co-array MUSIC) algorithm. Simulation results confirm that the proposed MRA-based MIMO array provides better angular resolutions than the nested MIMO array for the same number of sensors and that LRLAs can serve as a handy replacement for ULAs in SIMO radars owing to their acceptable performance. As MIMO and SIMO radars designed from currently known MRAs were sufficient to satisfy the angular resolution requirements of modern automotive radars, a need to synthesize new MRAs did not arise. © 2020 Electromagnetics Academy.

Progress In Electromagnetics Research B

Novel MRA-based sparse MIMO and SIMO antenna array for automotive radar applications

The agriculture sector has always been a crucial factor in India’s growth. It is utmost important to bring efficiency and precision in farming. This paper proposes the use of a Wireless Sensor Network (WSN) to develop an automated and intelligent irrigation system. The SENSEnuts embedded platform is used to create and maintain the WSN. The proposed system uses soil moisture data from different nodes which are deployed at various locations in the crop field. The nodes form a WSN. Each node (consisting of NXP JN5168, a 32-bit microcontroller) collects the data regarding the soil moisture and transmits it to the coordinating station (through Zigbee) which then takes the decision on whether or not to activate the sprinkler/motor pump. The decision is communicated to the corresponding node for further action. This enables precise watering only in the dry portion of the cultivation field and reduces the wastage of water or overwatering other moist areas. Apart from the soil moisture data, readings from the temperature, light, and humidity sensors are also transmitted to the coordinator. Such data is useful for indoor, underground or nursery crop monitoring. A working prototype of the proposed WSN is made and the functionality is verified. The data from various sensors is uploaded to the Thing Speak Internet of Things (IoT) platform for remote monitoring and further data analytics. ©BEIESP.

International Journal of Innovative Technology and Exploring Engineering

WSN hardware prototype for irrigation control and multi-parameter plant growth monitoring using iot

This work applies existing array processing principles to devise a new area of application. The properties of minimum redundancy linear arrays (MRLAs) and flexible arrays are studied, keeping in mind the possibility of using them in flexible 5G smartphones of the future. Millimeter frequencies for 5G communications enabled the use of a decent number of array elements, even at the user equipment (UE). MRLAs possess attractive properties among linear sparse arrays and flexible conformal arrays (flexible arrays) operate satisfactorily even when the surface they are built into changes shape. To the best of our knowledge, MRLAs were not applied to smartphones previously. In this work, a 16-element uniform linear array (ULA) and a 7-element MRLA (with the same aperture) are considered for simulations. Array factors of both the arrays in flat and bent positions have been computed using MATLAB. The effect of phase compensation and bending radii on the array pattern were verified. That phase compensation using the projection method (PM) restores the array pattern even for a bent MRLA is a major finding. Possible array processing modes have been suggested for a 5G smartphone in which the array could be made to operate in any of the four configurations: a flat ULA, a bent ULA, a flat MRLA, and a bent MRLA.

Fulltext

International Journal of Antennas and Propagation

A Conceptual Framework for the Use of Minimum Redundancy Linear Arrays and Flexible Arrays in Future Smartphones

Spatial smoothing is a pre-processing technique and is a prerequisite to be able to perform accurate direction-of-arrival (DOA) estimation of coherent sources if subspace-based DOA methods are employed. In this work, a large-scale 5G uniform linear array (ULA) of sixty elements is considered. Signals from five sources, which are uniformly separated in space by an angle of 2°, impinge upon the array. The first two sources are coherent to each other whereas the rest are uncorrelated. Root-MUSIC and ESPRIT were considered for analysis. MATLAB simulations reinforce the fact that spatial smoothing enables the array to accurately estimate coherent sources. The root mean square error (RMSE) between the true and estimated DOAs was used as a measure of performance. RMSE decreased with an increase in signal-to-noise ratio (SNR), array size, and separation between the DOAs. ESPRIT was faster than Root-MUSIC in execution, and therefore, its output was fed, as an input, to the Least Mean square (LMS) null steering algorithm to achieve smart antenna functionality. The aperture lost due to spatial smoothing is negligible in 5G arrays since it accounts only for a small fraction of the array’s total aperture. © IAEME Publication Scopus Indexed.

International Journal of Mechanical Engineering and Technology

DOA estimation and adaptive nulling in 5G smart antenna arrays for coherent arrivals using spatial smoothing

Wireless communications and electromagnetic applications make use of a sensor array which is made up of antenna elements. Direction of arrival (DOA) estimation refers to the process of determining at a receiving antenna array, the angles from which the different source signals impinge the array. Root MUSIC and Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) are two such algorithms which do not use the spatial spectrum to estimate the DOAs. Root MUSIC finds the roots of a polynomial which correspond to the DOAs and ESPRIT makes use of sub-arrays (named as doublets) within the main array to find the DOAs. In this paper, we have simulated these two algorithms at a 5G frequency of 30 GHz (wavelength is 0.01 m) considering a uniform linear array (ULA) of 72 elements. Twelve (12) sources were considered for DOA estimation. At the millimeter wave (mmWave) frequencies, it is possible to have dense arrays which can accommodate a large number of antenna elements within a small space. We have evaluated the Root mean square error (RMSE) for the two algorithms by varying the number of snapshots, inter-element spacing and number of elements; respectively. It is seen that the Root MUSIC algorithm outperformed the ESPRIT under good radio channel conditions.

2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

1D direction of arrival estimation using root-MUSIC and ESPRIT for dense uniform linear arrays

Array signal processing has attracted the interest of the scientific community for the past several decades. An Array of sensor elements (be it microphones, hydrophones, antenna elements, piezoelectric sensors) achieves better performance than a single element would. Antenna arrays are made up of antenna elements which can be arranged in a variety of configurations (with respect to the geometry, inter-element spacing, etc.). The Direction of Arrival (DOA) estimation is a signal processing technique that can be used at a receiving array to find the directions of the incoming signals that impinge on the antenna array. Beamforming is a technique that can be used to focus the transmit energy towards or to collect the received energy from certain desired directions. A smart antenna system is one which can perform DOA estimation as well as beamforming. The correctness of DOA estimation algorithms is a major contributing factor in the performance of smart antenna systems. Since beamforming is a key enabler for Millimeter Wave (mmWave) and fifth-generation cellular (5G); DOA estimation assumes much importance in future wireless communications. MmWave allows the use of large arrays owing to the small wavelengths. In this paper, we have studied the suitability of DOA estimation algorithms using Eigen decomposition methods which include the Pisarenko Harmonic Decomposition (PHD), Multiple Signal Classification (MUSIC), Modified MUSIC and Root-MUSIC at 30 GHz, which is a proposed frequency in 5G. MATLAB simulations throw light upon the various factors affecting DOA estimation accuracy and it is found that the above conventional methods are very much suited for the mmWave frequencies. © Research India Publications.

International Journal of Applied Engineering Research

Suitability of conventional 1D noise subspace algorithms for DOA estimation using large arrays at millimeter wave band

Microphone arrays are commonly used for Direction of Arrival (DOA) estimation of audio sources. This paper presents the design and implementation of a real-time embedded system (RTES) for audio source localization.A commercially available microphone array assembly (consisting of four microphones) has been interfaced to Raspberry Pi3Bfor estimating the arrival angles of audio/sound/acoustic sources.The acoustic signalsreceived ateach of the microphones are used for DOA estimationusingtheGeneralizedCross Correlation Phase Transform (GCC-PHAT)algorithm. The estimatedDOA is then displayed on the serial monitor. Servomotors are interfaced to the Raspberry Piand are programmed to point towardsthe estimated direction of the audio source(on the basis of the estimated DOA value). The estimatedDOA valueis also updatedto the cloud-based server Firebaseusing the inbuilt Wi-Fi onRaspberry Pi, and can be remotely accessed through a smartphone running on android platform. The proposed system has been designed and tested successfully and it is observed that it provides accurate DOA estimation and audio source tracking in real-time. This has various uses in applications like voice assistant robots,machine audition, hearing aids, source remixing, augmented reality headsets,security cameras, sound tracking (sonar sensor) devices, birdidentification, mammal localization and so on.