FPGA and ASIC implementation of robust invisible binary image watermarking algorithm using connectivity preserving criteria

Digital watermarking is the process of hiding information into a digital signal to authenticate the contents of digital data. There are number of watermarking algorithm implemented in software and few in hardware. This paper discusses the implementation of robust invisible binary image watermarking algorithm in Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuits (ASIC) using connectivity preserving criteria. The algorithm is processed in spatial domain. The algorithm is prototyped in (i) XILINX FPGA (ii) 130 nm ASIC. The algorithm is tested in Virtex-E (xcv50e-8-cs144) FPGA and implemented in an ASIC.     

Convolutional neural network based early fire detection

The detection of manmade disasters particularly fire is valuable because it causes many damages in terms of human lives. Research on fire detection using wireless sensor network and video-based methods is a very hot research topic. However, the WSN based detection model need fire happens and a lot of smoke and fire for detection. Similarly, video-based models also have some drawbacks because conventional algorithms need feature vectors and high rule-based models for detection. In this paper, we proposed a fire detection method which is based on powerful machine learning and deep learning algorithms. We used both sensors data as well as images data for fire prevention. Our proposed model has three main deep neural networks i.e. a hybrid model which consists of Adaboost and many MLP neural networks, Adaboost-LBP model and finally convolutional neural network. We used Adaboost-MLP model to predict the fire. After the prediction, we proposed two neural networks i.e. Adaboost-LBP model and convolutional neural network for detection of fire using the videos and images taken from the cameras installed for the surveillance. Adaboost-LBP model is to generate the ROIs from the image where emergencies exist Our proposed model results are quite good, and the accuracy is almost 99%. The false alarming rate is very low and can be reduced more using further training.

     

Multi-retinal disease classification by reduced deep learning features

This paper presents the retina-based disease diagnosis through deep learning-based feature extraction method. This process helps in developing automated screening system, which is capable of diagnosing retina for diseases such as age-related molecular degeneration, diabetic retinopathy, macular bunker, retinoblastoma, retinal detachment, and retinitis pigmentosa. Some of these diseases share a common characteristic, which makes the classification difficult. In order to overcome the above-mentioned problem, deep learning feature extraction and a multi-class SVM classifier are used. The main contribution of this work is the reducing the dimension of the features required to classify the retinal disease, which enhances the process of reducing the system requirement as well as good performance.

     

Implementation of Interior Noise Control System Using Digital Adaptive Filter for On-Road Car Applications

Wireless Personal Communications - There is a tremendous progress in last 3 decades in Automotive engineering especially in fuel-injection, exhaust Gas treatment, and safety and comfort systems....     

FPGA based audio watermarking—Covert communication

The rapid growth of internet has greatly facilitated the unauthorized distribution and hacking of digital media, which has become easier due to the availability of processing platforms. Watermarking help us to enforce the intellectual property rights and protect digital media from tampering. There are numerous software based watermarking available and it has attained certain degree of maturity. But hardware based watermarking is still at its infancy. It helps to achieve real time performance by integrating a watermarking chip with existing electronic devices. This paper is an extension of [1]. In [1], the technique was tested only for pulse signal, whereas in this paper FPGA implementation of audio watermarking using chirp spread spectrum (CSS) technique is analyzed with different test patterns and real time audio signal (.wav) and it is observed that the proposed technique provides good imperceptible, robust and low power consumption. Hence this technique helps to achieve covert communication using chirp signal as a secret key between the transmitter and the receiver. The proposed chirp spread spectrum audio watermarking technique is implemented in Vertex 4-XC4VLX25-10FF668 FPGA device. To our knowledge this is the first FPGA based audio watermarking chip, which helps to provide covert communication.