Electronics and Communication Engineering

Biodegradable plastic composites designed with various natural and biobased material can potentially satisfy biodegradability specifications as per ASTM, CEN, ISO and OECD guidelines. A laboratory scale simulated aerobic composting facility (as per ASTM D 5338) was designed and utilized to determine and evaluate the extent of degradation of various components of compostable plastic composites. The materials of natural origin components like starch, microcrystalline cellulose, cellulose fibre, wheat straw, soy straw, distiller’s dried grains with solubles (DDGS) and soy meal demonstrated ready degradability with conversion of 60% of the organic carbon to carbon dioxide. The individual polymers polylactic acid (PLA) and polycaprolactone (PCL) showed inherent degradability of 60% of the organic carbon to carbon dioxide within 180 days. The composites of polymers PLA with wheat straw and soy straw and PCL with DDGS and soy meal showed inherent degradability of 60% of the organic carbon to carbon dioxide within 180 days. Enhanced compostability of plastic is observed with inclusion of natural, ready degradable components in the composite in comparison to the individual polymer.

In this paper, we present a deep neural network (DNN) based transceiver architecture for delay-Doppler (DD) channel training and detection of orthogonal time frequency space (OTFS) modulation signals along with IQ imbalance (IQI) compensation. The proposed transceiver learns the DD channel over a spatial coherence interval and detects the information symbols using a single DNN trained for this purpose at the receiver. The proposed transceiver also learns the IQ imbalances present in the transmitter and receiver and effectively compensates them. The transmit IQI compensation is realized using a single DNN at the transmitter which learns and provides a compensating modulation alphabet (to pre-rotate the modulation symbols before sending through the transmitter) without explicitly estimating the transmit gain and phase imbalances. The receive IQI imbalance compensation is realized using two DNNs at the receiver, one DNN for explicit estimation of receive gain and phase imbalances and a...

In this paper, we consider the problem of low-complexity detection of orthogonal time frequency space (OTFS) modulation signals using deep neural networks (DNN). We consider a DNN architecture in which each symbol multiplexed in the delay-Doppler grid is associated with a separate DNN. The considered symbol-level DNN has fewer parameters to learn compared to a full DNN that takes into account all symbols in an OTFS frame jointly, and therefore has less complexity. Under the assumption of static multipath channel with i.i.d. Gaussian noise, our simulation results show that the performance of the symbol-DNN detection is quite close to that of the full-DNN detection as well as the maximum-likelihood (ML) detection. Further, when the noise model deviates from the standard i.i.d. Gaussian model (e.g., non-Gaussian noise with t-distribution), because of its ability to learn the distribution, the symbol-DNN detection is found to perform better than the ML detection. A similar performance a...

This paper presents a simple two-branch transmit diversity scheme. Using two transmit antennas and one receive antenna the scheme provides the same diversity order as maximal-ratio receiver combining (MRRC) with one transmit antenna, and two receive antennas. It is also shown that the scheme may easily be generalized to two transmit antennas and M M M receive antennas to provide a diversity order of 2M M M. The new scheme does not require any bandwidth expansion any feedback from the receiver to the transmitter and its computation complexity is similar to MRRC.

To date, little research has been done in emotion classification and recognition in speech. Therefore, there is a need to discuss why this topic is interesting and present a system for classifying and recognizing emotions through speech using neural networks through this article. The proposed system will be speaker independent since a database of speech samples will be used. Various classifiers will used to differentiate emotions such as neutral, anger, happy, sad, etc. The database will consist of emotional speech samples. Prosodic features like pitch, energy, formant frequencies and spectral features like mel frequency cepstral coefficients will be used in the system. Further the classifiers will be trained by using these features for classifying emotions accurately. Following classification, these features will be used to recognize the emotion of the speech sample. Thus, many components like pre-processing of speech, MFCC features, classifiers, prosodic features come together in the implementation of emotion recognition system using speech.

The Inductance-Capacitance (LC) meter is an electronic instrument that can be used for the measurement of the value of unknown inductor or capacitor of a simple coil. This paper aims to discuss the concept and the technique of measuring the unknown inductance and capacitance with the help of a microcontroller board. This meter makes use of the resonance frequency of a tank circuit which consists of a capacitor and an inductor and an unknown inductor or capacitor value connected in parallel. This changes the natural frequency at which the tank circuit resonates. The new frequency depends on the value of inductance or capacitance. The new frequency is determined and the value is calculated with the help of simple mathematical equations. AVR Microcontroller Board has been used to develop this instrument. It consists of an ATmega328p micro-controller chip that facilitates the operation.

The major objective of the present study is to develop water quality prediction models after evaluation of water quality to predict water pollution of two lakes situated inside Tezpur University. The water quality parameters were analyzed using linear regression analysis and artificial neural network to predict the water quality. Correlation studies suggested a highly positive correlation between TS with turbidity and EC for both lakes. Modeling of TS and BOD by regression analysis suggests a good correlation between actual and predicted TS but a very poor correlation between actual and predicted BOD. Modeling of TS and BOD by ANN shows a very good correlation between the actual and predicted values for both TS and BOD for both the lakes studied. The error between the experimental and estimated ANN model is less than regression model, suggesting suitability of ANN model for prediction of studied parameters.

The need for skilled financial advisors is more than ever in the current scenario when there are in-numerous moneymaking strategies and at the same time, the global economy might be on the verge of collapse. Also there is a pure lack of good financial advisors and even if you find one, you will end up paying a hefty amount. The current proposed application fulfils the above-stated demand in a cost-effective and reliable way. The proposed system automates the job of a financial advisor using Artificial Intelligence. It provides the user with a simple and easy to use interface where every individual will have their own account handled by Google’s firebase platform. The application uses 'Plaid' API which allows app to send a request to the corresponding bank server and fetch the account details of an individual. Logged in user is shown a very comprehensive representation of their account details which also includes category-wise expenditure, their investment, and the savings. O...

Road Transport in India is an extremely extensive system, it involves different categories of vehicles like motorize and non-motorized vehicles, animal or human powered vehicles. India has 159.5 million vehicles at present and 16.7 million vehicles added every year. Traffic volume on Indian road increase at a Compound Annual Growth Rate of 10.5% per annum. This paper focuses on traffic forecasting on a multilane highway in India and development of simulation model to study the speed-flow relationship. For this purpose, a strip based approach method is used. A section of 50-meter length was marked as study stretch on National highway-06 (Nagpur to Amravati). A video recording of this study stretch was done for morning and evening normal weekdays. Traffic volume, space mean speed, vehicle category, vehicle frequency etc were extracted from the video. A speed-flow relation established to evaluate capacity. All the traffic data obtain from the field are put in the simulation model VISSIM. The simulated speed-flow curve is then correlated with the field speed-flow curve. It was found that simulated capacity 3688 PCU/hr/dir and field capacity 3733 PCU/hr/dir. A general equation is established for prediction of traffic volume at National Highway-06 by using previous traffic data. Also, a relationship between capacity and width of the carriageway is obtained follow the second-degree curve.

With the continuous increase in vehicle on urban roads the congestion also increases and it became the major concern that how to maintain free flow speed of traffic. The traffic in India is highly heterogeneous with different classes of vehicle and no lane discipline. To avoid the congestion we must require proper and channelized traffic and it can be achieved by developing a microscopic model by using computer program such as simulation. Simulation is the mirroring of the operation of a real world process or system. To simulate something firstly we require to develop model and this model represent the behavior or function of the selected process. Traffic flow phenomenon consist of a wide range of complex activity, speed of the travel, lane discipline, overtaking and crossing logic, gap acceptance, acceleration and deceleration etc. The characteristics of traffic flow depend upon various factor like road features, vehicle performance characteristic and road user behaviour. To understand the phenomena of traffic flow behaviour, it can be achieved by observing movement of vehicle in traffic stream and collect data and synthesise the flow characteristic through analytical or mathematical model. Simulation model can be developing to understand the phenomena of traffic flow and to reduce the problem commonly met with in road traffic and transportation. Some of these are: development of speed flow relationship, prediction of fuel consumption of vehicle, gap acceptance problem in the design of intersection, queuing problem, bus route schedule, accident occurrence etc. This study can contribute to an improved understanding of traffic flow on Indian expressway.

Sign language is used all over the world by the hearing-impaired and disabled to communicate with each other and the rest of the world. Sign language is more than just moving fingers or hands; it is a viable and visible language in which gestures and facial expressions play a very important role. These signs can be effectively used as far as communication with humans is concerned but with respect to communication with machines better methodologies and algorithms have to be developed. This paper presents a system which can be used by disabled people (who can communicate only with sign languages) to communicate with machines in order to employ it to their daily life. The system focuses on pose estimation of gestures used by physically disabled people to give suitable signals to the machines. Pose is generated using silhouettes followed by gesture recognition using Mahalanobis distance metric. The system further was to control a wireless robot from different gestures used by disabled people. The basic American sign language symbols I Hand, II Hand, Aboard, All Gone, etc were recognized and a specific signal with respect to each gesture is transmitted which controls the robot. The robot is made to perform forward, backward, left, right and stop actions for each gesture presented to the system by the disabled person. The present system can also be modeled to send wireless SMS or emails in worse situations. The system demonstrated potency in the estimation of motion and pose for interpreting the sign language by using the silhouettes of the pose. The proposed system recognizes sign language, thus providing disabled people a medium to communicate with machines, leading to simplicity in their day to day work.

This study examines the factors affecting the share price of Nepalese non-life insurance companies. The knowledge of the factors and their possible impact on share prices is highly appreciable as it would help investors make wise investment decisions and enable firms to enhance their market value. This study is based on secondary data of 15 non-life insurance companies which are listed in Nepal stock exchange. The study covers seven years period from the fiscal year 2011/12 to 2017/18. The result shows that firm size is positively related to market price of share and price earnings ratio. It indicates that larger firm size leads to increase in market price of share and price earnings ratio. However, the study shows that inflation is negatively related to market price of share and price earnings ratio. The study also shows that dividend per share and return on assets are negatively related to the market price of share and price earnings ratio. Similarly, earnings per share have negat...

The primary objective is to teach a machine about human emotions, which has become an essential requirement in the field of social intelligence, also expedites the progress of human-machine interactions. The ability of a machine to understand human emotion and act accordingly has been a choice of great interest in today's world. The future generations of computers thus must be able to interact with a human being just like another. For example, people who have Autism often find it difficult to talk to someone about their state of mind. This model explicitly targets the userbase who are troubled and fail to express it. Also, this model's speech processing techniques provide an estimate of the emotion in the case of poor video quality and viceversa.

A regulation scheme to linearize the tuning curve of CMOS ring oscillators is proposed in this paper. The scheme uses the current consumption of the CMOS ring elements, which is proportional to the output frequency to the first order. The design of the feedback loop is presented on system level in conjunction with performance enhancements made on the implementation level. A weakly non-linear ring oscillator model is developed to simplify the control loop design. Model based linearity simulations proof a good agreement with measurements, while the simulation time is significantly reduced. A proof of concept implementation of the proposed linearization technique using CMOS ring oscillators is presented. Operating at 50 MHz sampling frequency, a maximum SFDR of 83.8 dB in a narrow band configuration verifies the effectiveness of the linearization. Implemented in a 180 nm process, the active area of the circuit is only 0.03 , which enables the use of this technique for multi-channel, multi-parameter measurement applications. Index Terms-Analog to digital converter (ADC), linearization technique, noise shaping, oscillator based ADC, oversampling, ring oscillator, time-domain signal processing, voltage-controlled oscil-lator (VCO).

A bias-compensated normalized subband adaptive filter (BC-NSAF) algorithm is proposed for system identification. In the proposed algorithm, a bias-compensation vector is derived to eliminate the bias caused by the noisy input signals. To estimate the input noise variance, a new estimation method is proposed, which does not require the input-output variance ratio in advance. Simulation results show that the proposed algorithm obtains better convergence performance than the existing algorithms in the presence of noisy input signals.