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

Waste management is the collection, transport, treatment and disposal of waste. Waste segregation at source is the crux of waste management solution as it improves efficiency in processing of waste and resource recovery. The strategies for waste segregation differ from one region to another depending on the financial and technological resources available. Here in this paper, we design and discuss a simple prototype that segregates the generated waste into three categories: wet waste, metal waste and other waste using different type of sensor. The prototype further notifies the user using GSM, to empty the bin

VIT Unitversity

Electronics

School of Electronics Engineering

Chennai Campus

International Journal of Engineering and Advanced Technology Regular Issue

Garbage Segregating and Alerting Nexus

Farming has now become a job that is of less interest, farm lands are being filled up and the new generation do not want to take up that job because of reasons such as higher risk, low returns etc. Due to this reasons we need to find an alternative to human work done in farms. This can be done by automation which can reduce human intervention and also providing security to the farmland which reduces the damages created by the animals or other conditions that may affect the proper growth of the crop. This paper concentrates to improve the security of the farmland as well as provides assistive technology that can guide the farmer to get the best result out from their fields. © 2006-2019 Asian Research Publishing Network (ARPN).

World University root node

ARPN Journal of Engineering and Applied Sciences

Iot enabled farming assist and security using machine learning

In today’s digital era, the device size, speed and power consumption are regarded as the most sought-after design constraints of battery powered embedded gadgets. The emerging computing paradigm called “approximate computing” can be applied to fulfil the said constraints by compromising the accuracy through inexact hardware design techniques. Inherent error resilient applications viz., image, media processing etc., are well suited for approximate computing. Nevertheless, certain niche applications may demand better accuracy levels than the fully approximated output. Since adders are the basic building blocks of all arithmetic operations involving the said applications, the specific architecture involving these blocks greatly influence the efficacy of the system design. Accordingly, a new architecture is proposed in which a fusion of accurate-approximate 8-bit adder and subtractor design is developed employing memristors as both logic elements and memory components. Besides the system level validation involving the designed architecture, the standard performance metrics are deduced by employing MATLABtools and the new accurate-approximate 8-bit architecture thus designed yields better accuracy as expected. © BEIESP.

International Journal of Recent Technology and Engineering

Memristor employed accurate-approximate ripple carry adder and ripple borrow subtractor architectures

Approximate computing aims at reducing circuit complexity and delay, by allowing leverages in the output for a certain combination of inputs. The reduction in circuit complexity is achieved by minimizing the number of components in the circuit. This paper proposes a plausible approach towards approximate computing with memristors for designing memristor based approximate full adder and subtractor with logic minimization technique. The validation of our proposed approximate adder is verified by designing an 8-bit Ripple Carry Adder (RCA) to perform bitwise pixel addition of two gray scale images of the same size and compare the design with images obtained by exact addition method. Similarly, we have affirmed that the designed 8-bit Ripple Borrow Subtractor (RBS) is verified on foreground detection. Furthermore, to corroborate the above observation, performance metrics like Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Content (SC), Mean Absolute Error (MAE), Average Distance (AD), Mean Average Distance (MAD) and Normalized Absolute Error (NAE) are deduced in MATLAB®. Owing to the logic minimization technique, for a certain combination of inputs, the MSE has been found to be predominant and its impact on PSNR is studied. © 2018 Elsevier GmbH

AEU - International Journal of Electronics and Communications

Memristor augmented approximate adders and subtractors for image processing applications: An approach

Background: The proposed system describes a surveillance system developed using Raspberry Pi and a camera, which keeps monitoring a certain highly secured region continuously. When the system recognizes a change in motion (human motion) compared to its previous frame, the system starts recording video and stores it primarily in its memory and also in the cloud (for the reason that even if the burglar tries to destroy the system his image/video will be saved in the cloud storage), and the user receives alert mail from the system stating “human motion detected” along with the captured image attached with the alert mail. The system contains database of face patterns of local suspects which is compared with the face pattern of the person triggering the system, and image processing is done in real time to correctly identify the detected face; the system also keeps tracking the face throughout the region even if the person moves out of the frame by a camera mounted over a servo motor. The system turns on a buzzer alarm when the burglar attempts to cause damage to the system. The system allows the user to remotely access the camera to monitor live streaming video output and control the rotation of the camera. Methods/Statistical analysis: In this project, different types of surveillance systems which already exist are analysed, and the methods of having a portable surveillance system were developed using Raspberry Pi. Image processing methods for facial identification and face recognition is used. Findings: A study based on various image processing techniques is done; it is found that Haar-cascade and linear binary pattern are the suitable algorithm for performing image processing in real time. Application/Improvements: For better surveillance, face tracking in introduced, which can track the detected face throughout the region even if the person goes out of the camera frame, and remote accessing with control of the camera through IoT is introduced. © Springer Nature Singapore Pte Ltd. 2018.

Lecture Notes in Electrical Engineering Intelligent Embedded Systems

Raspberry Pi-Based Surveillance System with IoT

Image processing is a type of memory-access-intensive application that requires to access memory for a longer duration thereby lowering the overall throughput of the system by limiting the speed of memory access and bandwidth. This profound memory wall problem can be overcome by using memristor-based Computation-In-Memory (Memory-driven) architecture that can be simultaneously utilized as memory and processing element. To further enhance the computational speed and to perform energy/power-efficient operations, approximate computing is employed by allowing leverages in output for certain combination of inputs. In image processing applications, the computing blocks consist of multipliers and delays whose working is determined by suitable combination of adders. In this work, memristor-based approximate full adder is designed by random incorporation of errors in SUM and CARRY, respectively, and the resulting truth table is verified. The capability of proposed approximate adder is validated by designing (in Cadence Virtuoso) a 4-bit ripple carry adder (RCA) to perform bit-wise pixel addition of two grayscale images of same size and compared with an image obtained by exact addition method. © 2018, Springer Nature Singapore Pte Ltd.

Lecture Notes in Electrical Engineering

Memristor-Based Approximate Adders for Error Resilient Applications

In the nanoscale era, enhancing the performance of digital circuits and systems becomes increasingly cumbersome. As a result, computing becomes increasingly heavy with multimedia processing. Approximate computing is regarded as one of the emerging technologies that could produce less-than-optimal results which are sufficient enough in user's perspective. This is accomplished by minimizing energy consumption as well as hardware area. The slight compromise in output quality is acceptable for inherent error resilient applications. This paper reviews different types of existing CMOS based approximate circuits designed by various functional approximation techniques. Further, this survey focuses on analyzing the existing approximate arithmetic circuits based on various error metrics such as error rate, error distance, mean error distance, normalized error distance, and minimum acceptable accuracy. The impact of approximation on circuit characteristics for instance, delay, power consumption and throughput are also presented. This paper critically reviews the progress made in the context of approximate computing by using CMOS architecture and its impact on replacing with RRAMs (Memristor). The latter is reportedly found to be energy efficient due to its nano-scale dimension. Although memristor based approximation is still in its infancy, it is high time to understand its emerging importance in nanoscale computing in general and approximate computing in particular. © 2006-2018 Asian Research Publishing Network (ARPN).

A critical survey on approximate computing with CMOS and memristors

Objectives: To design a visualization of a network containing wireless meters installed with RF modules which uses RPL protocol for routing. Methods/Statistical analysis: Meters are linked wirelessly for obtaining the packets containing its details and other parameters, by using a Constrained Application Protocol (CoAP). Thus, details collected from each meter are used for the construction of the visualization, by dynamically assigning the position of each node. Findings: RPL, routing protocol, is used for the construction of the network. The advantage of its self-healing nature makes the whole system stable by removing the irresponsive meters, when detected, from the network and reconstructing it from the starting. Application/Improvements: Maintenance of such a network which switches its neighbors at different instances may be complicated. In such cases, these visualization methods can be included. In future, the firmware update options for each meter can be implemented along with visualization. © Springer Nature Singapore Pte Ltd. 2018.

Local Visualization for Troubleshooting the RF Mesh Network in a Wireless Metering System

Objectives: With the fast increment in the quality of client of the internet over the previous decade has made the internet as a feature of the life and IoT is the most recent and developing innovation. Internet of Things (IoT) is developing systems of ordinary item from customer merchandise to mechanical machine that can share data and complete and while you are occupied with different exercises. This system is planned minimal effort and expandable permitting a variety of device to be monitored. In this project only sensors has been monitored for better result they has to be controlled wirelessly through IoT. Methods/Statistical analysis: All the sensors that are connected to the Atmega are sensing the values at different conditions and displaying the values in LCD as well as the values are stored in the cloud. Whenever there is a change in the measured parameter that changed values are being updated. This ensures the correctness of the system at every instant of time the values are automatically updated in the cloud. Findings: Global industrial process monitoring through IoT is a system that uses computer or mobile devices to monitor functions in industry. It is intended to spare electric force and human vitality. The sensors that can be checked are temperature, light intensity, water level, current and voltage. These sensors are associated with Atmega and interface with Raspberry pi and observed qualities are put away in cloud. The modern procedure monitoring system varied from other system by permitting the client to work the system from any place around the globe through internet association. The system will consequently change on the base of the sensors information. Application: Engineers and scientists around the world use embedded systems to prototype and diagnostics, monitoring, and control applications within a variety of demanding industry environments. Rugged and modular hardware provides the flexibility to meet your specific embedded system application needs today and in the future. © 2017 IEEE.

2017 International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2)

Global industrial process monitoring through IoT using Raspberry pi

Electrical appliances that operate at high voltages require customized tests based on the appliance rating and insulation range to ensure the safety and wastage of current. This system will focus on a custom Virtual Instrumentation technique for testing key parameters and validating the machine based on its specifications/name plate details. Hardware section of this instrument will operate in tandem with a user configured Field Programmable Gate Array (FPGA) and provide documented results from LabVIEW about the health of the device under test. The Virtual Instrument used in this system is National Instrument Single-Board RIO (NI sbRIO) which provides real time limelight on unique feature of an application. This system focus on efficient usage of High Voltage for the specified applications, improves the time and reduces the complexity of system using the latest advancement in Virtual Instrumentation. © 2017 IEEE.

FPGA based hand-held high voltage testing equipment

Objectives: Post hospitalization care of a patient treated for critical illness, for instance cardiac arrest has been regarded as imperative. Nevertheless, it has become inevitable to skip follow-ups by the patients themselves. Method: This paper aims at designing a system which continuously monitors the health of the patient despite the patient being busy with his routine. An application is developed on android platform combined with Internet of Things (IoT) and sensor devices. Health of patient will be monitored by sensors and data from sensors will be sent to cloud which can be accessed anytime by the doctor and/or caretaker both in the cloud and application for necessary follow-ups in real time. Findings: In order to find the health of a patient more parameters are needed. But the main parameter for identifying the health of a patient are heart rate and temperatures. More parameters can be added to the system developed. Improvements/Applications: More sensors can be added to the system and the system has many application scenarios such as family health care. © 2017 IEEE.

IoT augmented health monitoring system

Objectives: This project focuses on monitoring of use of water, consider, by one block of house in a flat system, where at the partition of pipeline from where the water gets diverted to various part of a block. Methods/Statistical analysis: Water places a vital role for living beings in their day to day lives. The earth's 71% is covered by water is a ubiquitous fact. Among which Oceans has approximately 96.50% and 3% is considered to be freshwater, again out of which only 0.08% is accessible direct to human use and rest is preserved in tundra regions and in different form on and in the earth surface which is very difficult to abstract for the human purposes. From this it states that only 0.08% is available as fresh water for human being to make use for drinking, domestic purposes, sanitation, manufacturing, leisure, agriculture etc which gets recharged by rain and snowfall 1. Findings: According to scientists and organizations as IPCC (Intergovernmental Panel on Climate Change), state has come, since a long time, where water management as such implies to maximizing use of water and minimizing the wastage of water and thus preventing the domino effect cycle arises as wastage of water. The sensors will sense the flow of water to each pipe which ultimately tells the usage of water at one block ideally. This water usage data would be sent to cloud using the IOT (Internet of things) space. This cloud data would be sent to the concern resident's person's mobile app (application) reporting the water used and alerting the user to limit the water use if it gets extended to the limit usage set by municipal government or corporation. If the limit gets extended the user have to pay accordingly. This will be real time operation. The objective of doing so is for limiting and minimizing the usage of water for an average of per person. And secondly, the cloud data will be used as statistic data for use of water at every seasons that is winter, summer and monsoon so that measuring steps for water management can be taken with the appropriate statistics, yielding an avenue for predictive measure. Improvements/Applications: To appraise the IOT based water management, it can be ramified as diligent, frugal for water management in a symbiotic parity way, which will constrict the water resource evenly according to the in situ factors. © 2017 IEEE.

IoT based water management

Supporting Public Protection and Disaster Relief (PPDR) services are mandatory in modern wireless access networks. The sub-1GHz band is particularly suited to provide PPDR services due to the better propagation characteristics and attendant increase in link budget. In this work, we describe a novel transmit mode under a multi-operator non-orthogonal spectrum sharing framework, which can provide robust PPDR waveforms. This Ultra-Reliable Broadcast (URB) solution is based on the Licensed Shared Simultaneous Access (LSSA) broadband wireless access system which has been designed earlier by our group to provide much higher sum-rates in each cell when compared to a single-operator network, by enabling synchronization and different levels of co-operation across multiple TDD operators simultaneously using the same air-interface technology in the same spectrum. Using 19-cell system simulation with appropriate fractional frequency reuse scenarios, we compare the post-processing SINR CDFs of the 4 operator LSSA network and a single operator network, when both of these broadcast a single network-wide PPDR message. Their performances are also compared under disaster conditions where with a probability p, any of the eNodeBs can suddenly fail. We develop and use a simple 3-D stochastic path-loss model to account for users in multi-storey buildings, where depending on the user location the mean value of the shadow loss is varied. Our simulation results in the UHF band (500MHz carrier frequency) indicate that at the 10th percentile of the CDF, under normal operating conditions, the proposed URB solution with four LSSA operators can provide more than 10dB gain in SINR, and for p=0.5, this gain increases to 14dB, when compared to the single operator system. © 2017 IEEE.

GENERAL

Department of Electrical Engineering

Department of Automotive Engineering

School of Mechanical Engineering

Vellore Campus

2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017

Ultra-Reliable PPDR Broadcast using Licensed Simultaneously Shared Access networks

Background: Speaker recognition systems plays a pivotal role in the field of forensics, security and biometric authentication for verifying or identifying the speaker from the group of speakers. Methods: This paper gives a brief introduction about developing a hardware based speaker recognition system using Mel Frequency Cepstral Coefficients (MFCC) which are extracted from input speech signal to linearize the frequency scale at higher frequencies and Perceptron Neural Networks to provide layer weights for verifying the speaker identity to compare the output in the database of stored speaker identities. Findings: The input speech features are extracted using blocking and windowing to reduce noise and get the audio samples to store in the RAM where sampled data is converted into frequency domain using FFT to get the Cepstral Coefficients which are normalised and fed to neural network tool box present in the MATLAB to obtain layer weights for given set of data and the output is compared with the saved speaker identities to find a match. The decision making logic is written in NIOS II processor of FPGA where the taken input features are compared to the existing database of speaker identities with the help of perceptron neural network layer weights which gives the nearest possibility of the match in the database of the group of speakers. The designed system has been tested using two speakers as reference where the vowels spoken by them are taken into account to compare with the database of speakers already stored in FPGA. Conclusion/Improvements: The probability of detection of the speakers is 80% and verifying the speaker is quite accurate in hardware based systems than in software based systems where performance factor is less. The given performance in the designed system can be increased by retraining the neural networks which can provide nearly 90% in detecting the speaker.