Biblio

In this work, an IF(intermediate frequency) module of a hyperspectral microwave radiometer based on a polyphase filter bank (PFB) and Discrete Fourier Transformation (DFT)is introduced. The IF module is designed with an 800MSPS sampling-rate ADC and a Xilinx Virtex-7 FPGA. The module can achieve 512 channels and a bandwidth of 400M and process all the sampled data in real-time. The test results of this module are given and analyzed, such as linearity, accuracy, etc. It can be used in various applications of microwave remote sensing. The system has strong expandability.

Nowadays most of our data is stored on an electronic device. The risk of that device getting infected by Viruses, Malware, Worms, Trojan, Ransomware, or any unwanted invader has increased a lot these days. This is mainly because of easy access to the internet. Viruses and malware have evolved over time so identification of these files has become difficult. Not only by viruses and malware your device can be attacked by a click on forged URLs. Our proposed solution for this problem uses machine learning techniques and signature matching techniques. The main aim of our solution is to identify the malicious programs/URLs and act upon them. The core idea in identifying the malware is selecting the key features from the Portable Executable file headers using these features we trained a random forest model. This RF model will be used for scanning a file and determining if that file is malicious or not. For identification of the virus, we are using the signature matching technique which is used to match the MD5 hash of the file with the virus signature database containing the MD5 hash of the identified viruses and their families. To distinguish between benign and illegitimate URLs there is a logistic regression model used. The regression model uses a tokenizer for feature extraction from the URL that is to be classified. The tokenizer separates all the domains, sub-domains and separates the URLs on every `/'. Then a TfidfVectorizer (Term Frequency - Inverse Document Frequency) is used to convert the text into a weighted value. These values are used to predict if the URL is safe to visit or not. On the integration of all three modules, the final application will provide full system protection against malicious software.

One of the many methods for identifying malware is to disassemble the malware files and obtain the opcodes from them. Since malware have predominantly been found to contain specific opcode sequences in them, the presence of the same sequences in any incoming file or network content can be taken up as a possible malware identification scheme. Malware detection systems help us to understand more about ways on how malware attack a system and how it can be prevented. The proposed method analyses malware executable files with the help of opcode information by converting the incoming executable files to assembly language thereby extracting opcode information (opcode count) from the same. The opcode count is then converted into opcode frequency which is stored in a CSV file format. The CSV file is passed to various machine learning algorithms like Decision Tree Classifier, Random Forest Classifier and Naive Bayes Classifier. Random Forest Classifier produced the highest accuracy and hence the same model was used to predict whether an incoming file contains a potential malware or not.

Aiming at the problems of poor stability and low accuracy of current communication data informatization processing methods, this paper proposes a research on nonlinear frequency hopping communication data informatization under the framework of big data security evaluation. By adding a frequency hopping mediation module to the frequency hopping communication safety evaluation framework, the communication interference information is discretely processed, and the data parameters of the nonlinear frequency hopping communication data are corrected and converted by combining a fast clustering analysis algorithm, so that the informatization processing of the nonlinear frequency hopping communication data under the big data safety evaluation framework is completed. Finally, experiments prove that the research on data informatization of nonlinear frequency hopping communication under the framework of big data security evaluation could effectively improve the accuracy and stability.

The proposed combination of statistical methods has proved efficient for authorship attribution. The complex analysis method based on the proposed combination of statistical methods has made it possible to minimize the number of phoneme groups by which the authorial differentiation of texts has been done.

Wireless transmission of power has been in research for over a century. Our project aims at transmitting electric power over a distance of room. Various methods using microwaves, lasers, inductive coupling, capacitive coupling and acoustic medium have been used. In our project, we are majorly focusing on acoustic method of transferring power. Previous attempts of transferring power using acoustic methods have employed the usage of ultrasonic sound. In our project, we are using infrasonic sound as a medium to transfer electrical power. For this purpose, we are using suitable transducers and converters to transmit electric power from the 220V AC power supply to a load over a considerable distance. This technology can be used to wirelessly charge various devices more effectively.

Hardware Trojans that can be easily embedded in synchronous clock generation circuits typical of what are used in large digital systems are discussed. These Trojans are both visible and transparent. Since they are visible, they will penetrate split-lot manufacturing security methods and their transparency will render existing detection methods ineffective.

This paper presents a model calibration algorithm for the modulated wideband converter (MWC) with non-ideal analog lowpass filter (LPF). The presented technique uses a test signal to estimate the finite impulse response (FIR) of the practical non-ideal LPF, and then a digital compensation filter is designed to calibrate the approximated FIR filter in the digital domain. At the cost of a moderate oversampling rate, the calibrated filter performs as an ideal LPF. The calibrated model uses the MWC system with non-ideal LPF to capture the samples of underlying signal, and then the samples are filtered by the digital compensation filter. Experimental results indicate that, without making any changes to the architecture of MWC, the proposed algorithm can obtain the samples as that of standard MWC with ideal LPF, and the signal can be reconstructed with overwhelming probability.

With the growing demand for increased spectral efficiencies, there has been renewed interest in enabling full-duplex communications. However, existing approaches to enable full-duplex require a clean-slate approach to address the key challenge in full-duplex, namely self-interference suppression. This serves as a big deterrent to enabling full-duplex in existing cellular networks. Towards our vision of enabling full-duplex in legacy cellular, specifically LTE networks, with no modifications to existing hardware at BS and client as well as technology specific industry standards, we present the design of our experimental system FD-LTE, that incorporates a combination of passive SI cancellation schemes, with legacy LTE half-duplex BS and client devices. We build a prototype of FD-LTE, integrate it with LTE's evolved packet core and conduct over-the-air experiments to explore the feasibility and potential for full-duplex with legacy LTE networks. We report promising experimental results from FD-LTE, which currently applies to scenarios with limited ranges that is typical of small cells.