Sunil B. Somani

Bio: Sunil B. Somani is an academic researcher from College of Engineering, Pune. The author has contributed to research in topics: Ankle & Orthotic device. The author has an hindex of 5, co-authored 18 publications receiving 81 citations. Previous affiliations of Sunil B. Somani include Massachusetts Institute of Technology & Savitribai Phule Pune University.

The reduction of PAPR in OFDM systems using clipping and SLM method

Abstract: Orthogonal Frequency-division multiplexing is an attractive technique for high-bit-rate communication systems. It has been widely used in modern wireless communication because of its high data rate, immunity to delay spread and frequency spectral efficiency and other advantages.

Biometric user authentication using brain waves

Abstract: Authentication has become an essential part of our everyday lives which is used at almost every place from banks to experimental labs, from car automation to home automation. This authentication is generally provided through systems like passwords, PIN codes, card readers. At some places biometrics like fingerprint and retina scans are used. All designed with one purpose; to confirm a person's identity. Brain wave based authentication is another addition to the wide range of authentication systems, which has many advantages over other authentication systems. With a standard password someone can watch or ‘shoulder-surf’ what others type, but no one ca n watch thoughts. Cards and keys can be lost, but the brain wave is always present. Differently abled persons can't use systems which uses fingerprints or retina scans but they can use system using brain-waves. This clears that using brain waves as biometric to provide authentication is very beneficial. A system is designed and implemented which allows user to set a pattern of brain waves which must be provided as an unlock pattern to get the access. This pattern can be any combination of eye blink, attention and various brain rhythms like Alpha, Beta, Theta and Delta. The system described in this paper provides two-level authentication. First level of which is brain waves. Once the correct pattern of brain signal is provide the system will ask for a pass key as a second level of authentication. This paper describes the design and implementation of the system.

Design and comparison of multiplier using vedic mathematics

Abstract: Digital signal processors (DSPS) fundamentally contains multipliers as its core element. The speed of the multipliers affects the speed of the DSPs. The execution of most DSPs is dependent on its multipliers, and hence need for high-speed multipliers arises. In this digitalization era, it becomes necessary to increase the speed of the digital circuits while reducing on-chip area and memory consumption. Latency and throughput are the basic parameters associated with multiplication algorithms where latency is a total delay in computing a function while throughput is the measure of computations performed in a given stipulated time. For increasing multiplication speed and reducing delay there is more and more emphasis on designing faster multipliers. There are many algorithms like standard modified booth algorithm, Wallace tree methods and several new techniques are worked on to enhance the speed of the multiplier. Among this, algorithms based on Vedic mathematics are under focused as they can be used to design faster and low power multipliers. Vedic mathematics is based on sixteen sutras, out of them “Urdhva Tiryakbhyam” and “Nikhilam Navatashcaramam Dashatah” are noticed most. In this paper 32 bit implementation of “Urdhva Tiryakbhyam” and “Nikhilam Navatashcaramam Dashatah”. Multipliers in this paper are coded using Verilog language, it is synthesised and simulated using Xilinx ISE 14.5. In this paper, Urdhva Tiryakbhyam and Nikhilam sutra both algorithms are compared in terms of propagation delay and found that Urdhva Tiryakbhyam sutra performs faster for less bit input while Nikhilam sutra is faster for larger inputs.

Instinctive Traffic Control and Vehicle Detection Techniques

Abstract: Traffic congestion genuinely affects our living quality and environment in modern cities. Primary requirement is to increase the efficiency and capacity of the exiting traffic monitoring network due to continues expansion in traffic quantity and limited construction of new highways in rural and urban areas. Traffic congestion is a rising problem in major cities as it leads the fuel wastage in volume of billion gallons per year. In this paper, Concept of Intelligent Transportation System (ITS) is proposed and to solve Traffic problem different alternatives are explored as well as survey has been carried out in field of vehicle and traffic control system.

Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition.

Abstract: The emergence of the digital world has greatly increased the number of accounts and passwords that users must remember. It has also increased the need for secure access to personal information in the cloud. Biometrics is one approach to person recognition, which can be used in identification as well as authentication. Among the various modalities that have been developed, electroencephalography (EEG)-based biometrics features unparalleled universality, distinctiveness and collectability, while minimizing the risk of circumvention. However, commercializing EEG-based person recognition poses a number of challenges. This article reviews the various systems proposed over the past few years with a focus on the shortcomings that have prevented wide-scale implementation, including issues pertaining to temporal stability, psychological and physiological changes, protocol design, equipment and performance evaluation. We also examine several directions for the further development of usable EEG-based recognition systems as well as the niche markets to which they could be applied. It is expected that rapid advancements in EEG instrumentation, on-device processing and machine learning techniques will lead to the emergence of commercialized person recognition systems in the near future.

Analysis, Optimization, and Demonstration of a Vehicular Detection System Intended for Dynamic Wireless Charging Applications

Abstract: To overcome the range limitation associated with electric vehicles (EVs), the emerging technology of dynamic wireless power transfer (DWPT) can be employed. Electrified road infrastructure and wirelessly charged vehicle constitute a complex dynamic system whose successful operation requires coordination between the two subsystems and a certain level of knowledge regarding the EV position and speed on the road. A comprehensive vehicular detection system (DWPT-VDS) operating on magnetic principle and intended for DWPT applications is proposed in this paper. The following functionalities are integrated into the DWPT-VDS: a vehicle detection mechanism, the measurement of the vehicle lateral misalignment, vehicle speed measurement, driver information system (DIS), as well as the wireless communication between a roadside power controller and the DIS. When integrated with a DWPT charging system, the DWPT-VDS allows some critical functions, such as correction of the lateral position of the vehicle by the driver, an extended range of full-power reception for a misaligned vehicle, as well as the smooth transition between adjacent pads. This paper presents a comprehensive study of different coil structures and algorithms for speed and misalignment estimation in a DWPT-VDS, along with their comparison. The objective is to maximize the misalignment detection range for a given size of test coils and provide a robust solution for vehicles with different ground clearances. A three-coil system for vehicle misalignment and speed detection is selected as part of a proof-of-concept design. Part of the system is embedded in the road, and the rest is mounted on a wirelessly charged electric bus. The implemented system has been successfully tested in an outdoor environment. A DIS visualizing speed and misalignment information is also developed and tested to help the driver align the vehicle with the road-embedded primary pads.

EEG signal classification using Principal Component Analysis and Wavelet Transform with Neural Network

Abstract: The Brain-Computer Interface (BCI) is the technology that enables direct communication between the human brain and the external devices. Electroencephalography (EEG) proves to be the most studied measure of recording brain activity in BCI design. The paper is intended to analyze and extract the features of EEG signal and to classify the signal so that human emotions can be discriminated and serve as the control signal for BCI. The proposed method involves EEG data acquisition and processing which is done by feature extraction and classification of features at different frequency levels for Beta, Alpha, Theta and Delta waves. The Principal Component Analysis(PCA ), and the Wavelet Transform(WT) can be used for dimensionality reduction and feature extraction. The Artificial Neural Network (ANN) which is a computationally powerful model, is used as the classifier. The paper presents the comparison between the two approaches PCA and WT applied on the ANN Classifier.