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Abstract Radar signal generation and processing techniques are developing with an aim of reducing interference and signal interception. Signal jamming and signal detection have become emerging areas of interest in the field of radar engineering. So there is a need for design and development of new waveforms which are less detectable by the hostile receiver and less susceptible to jamming. In the work mentioned here, Stepped Frequency Waveform (SFW) is modulated with phase modulation techniques such as Barker codes and polyphase codes to improve the complexity and to reduce the detectability of waveform thus increasing the bandwidth of waveform and limiting the transmitted power of the waveform by using pulse compression techniques. The SFW considered here is generated having varying step size. Pulse compression technique is employed here in order to generate a pulse having a range resolution of shorter pulse and transmitted power of longer pulse. The modulated waveform characteristics have been studied and analyzed using ambiguity functions of the waveform. [ABSTRACT FROM AUTHOR]

Abstract In recent times, the domain of high speed information transfer has seen a quantum jump in performance with the advent of novel RADAR techniques. The spectrum available for programmable, pulsed radar systems is limited; hence, devising different multicarrier techniques to meet the demands for high data throughput becomes imperative. At a radar base station the amplifiers placed in the RF front end are devices that consume most of the power. It creates distortions in the transmitted signal due to its non-linear characteristics. Here, the performance of OFDM and FBMC multicarrier signals transmitted over additive white Gaussian noise (AWGN), Rayleigh fading and Rician fading channels along with the influence of the high power amplifier (HPA) are analyzed. The different types of amplifier models used for comparison are of the solid state power amplifier (SSPA) and traveling wave tube amplifier (TWTA) type. The results are compared for evident degradation in the quality metrics such as spectral efficiency and PAPR. [ABSTRACT FROM AUTHOR]

Abstract In a RADAR system, the introduction of a jamming signal in the received signal path makes the distinguishing of targets a challenging task. The most frequently used signals in radar are of the continuous and pulsed wave types. When using pulsed radar signals, modification of the waveform is required in the radar operations for improved target sensitivity and range resolution. In addition, for the monostatic radar case, two antennas are used, one each at the transmitter and receiver sides. In the work discussed here, along with the monostatic model radar signals such as the stepped frequency waveform (SFW) and the 4-level pulse amplitude modulation (PAM4) waveform are considered, and they are observed to be powerful modulation techniques capable of avoiding jamming. SFW is used to obtain high range resolution, while PAM4 is used to obtain lower bandwidth. Combination of SFW and PAM4 provides a compound pulsed radar modulation technique which shows efficient results in target detection. To examine the performance of SFW and PAM4, simulation is done using a monostatic radar model under different conditions such as multipath propagation, fading and jamming. The compound pulsed radar modulation techniques are shown as being able to detect targets in the presence of a jamming signal. The results are analyzed in terms of the critical radar parameters such as range-speed response, burn-through range, cross-over range and signal-jammer noise ratio. [ABSTRACT FROM AUTHOR]

Abstract Scene classification is considered as an imperative issue for computer vision and has got extensive consideration in the recent past. Due to recent developments in high performance computing units such as GPUs, popularly known deep learning algorithm namely, convolutional neural networks (CNNs), exploits huge datasets to give powerful models. The paper proposes the use of transfer learning technique, by which a pre-trained model known as Places-CNN is used to generate feature vectors for each scene image of the dataset. The scene-classification experiments are conducted on the Oliva Torralba (OT) scene dataset, which consists of eight outdoor scene categories. The features were extracted from the fully connected layer of the pre-trained Places CNN architecture. The deep features were extracted from the input color images and the grayscale images converted using two different techniques based on singular value decomposition (SVD). The results obtained from classification experiments show that, models trained on SVD-Decolorized and Modified-SVD decolorized images give comparable performance to the input color images. Unlike the color images, which use three planes (RGB) of information, the grayscale images use only one plane of information. The grayscale images were able to retain the required shape and texture information from the original RGB images and, thus sufficient to categorize the classes of scene images. [ABSTRACT FROM AUTHOR]

Tree adjoining grammars (TAGs) are mildly context sensitive formalisms used mainly in modelling natural languages. Usage and research on these psycho linguistic formalisms have been erratic in the past decade, due to its demanding construction and difficulty to parse. However, they represent promising future for formalism based NLP in multilingual scenarios. In this paper we demonstrate basic synchronous Tree adjoining grammar for English-Tamil language pair that can be used readily for machine translation. We have also developed a multithreaded chart parser that gives ambiguous deep structures and a par dependency structure known as TAG derivation. Furthermore we then focus on a model for training this TAG for each language using a large corpus of text through a map reduce frequency count model in spark and estimation of various probabilistic parameters for the grammar trees thereafter; these parameters can be used to perform statistical parsing on the trained grammar. [ABSTRACT FROM AUTHOR]

Work on Malayalam WordNet was initiated in Amrita Vishvavidya Peetam, Coimbatore in December, 2011 as a part of the project entitled "Development of Dravidian WordNet: An Integrated WordNet for Telugu, Tamil, Kannada and Malayalam", funded by Department of Information Technology, MCIT, Govt. of India. The main objective of the project is to build WordNets for Dravidian languages by making use of the already built Hindi WordNet under the project scheme IndowordNet. Hindi WordNet has been built based on Princeton English WordNet which is a component of EuroWordNet. The main objective of EuroWordNet is to develop an extensive and high quality of multilingual database with WordNets for several languages (mainly European Languages such as French, German, Czech, Italian, etc.) in a cost-effective manner. On similar line, IndoWordNet is being built for Indian languages. Malayalam WordNet is a component of Dravidian WordNet which in turn is the component of IndoWordNet. Malayalam WordNet is an online lexical database. It is useful for many applications of Natural Language Processing. [ABSTRACT FROM AUTHOR]

NLP and Machine Translation tools are upcoming areas of study in the field of Computational Linguistics. The development of Language technology and its growth leads to the need for the detailed study of computational aspect of Language and especially for those who mastered the field of Technology. This paper tends to create a basic reference tools for those students and researchers who are interested in Computational Linguistics and also give some basic idea of every tool used to develop such software tools. [ABSTRACT FROM AUTHOR]