The emerging Modular Multilevel Converter (MMC) is considered as one of the promising topologies. Balancing of the capacitor voltage of the MMC Submodules (SMs) plays a critical role for safe operation of MMC. This paper proposes a balancing approach based on space vector PWM (SVPWM). The proposed method uses only one SVPWM to generate the switching vectors for the upper arm of MMC. The switching vectors of the lower arm are obtained by finding the complement of the upper arm switching vectors, which in turn eliminates the requirement of using another SVPWM for the lower arm. It also minimizes the inner difference current that results from the voltage unbalanced between the arms. To verify the proposed method a simulation of MMC was carried out in MATLAB/ SIMULINK/SIMPOWER. The simulation results demonstrate the capability of the proposed strategy in balancing the SMs capacitors voltages and in reducing the inner difference current.
This paper presents analytical synthesis of the transfer function as new technique for realizing high order filters, by translating equations into active blocks. A 4th order low pass filter (LPF) was designed using this technique and using the differential difference current conveyor (DDCC) as its active block. The proposed filter was used in the baseband of the multistandard receivers, with different cutoff frequencies to support different standards. LT Spice simulation results are presented using 90nm technology, with ?0.5V supply. This filter has a total power consumption of 1mW and a DC gain of 1 dB
This paper deals with identification of fractional nonlinear systems? Hammerstein Controlled AutoRegression (HCAR) models are considered. Different identification models can be derived for fractional HCAR system based on identification principles such as the Overparametrization principle and the Keyterm separation principle. The LevenbergMarquardt algorithm combined with each of these principles is used to identify the fractional HCAR system. Various simulations test the efficiency of the optimization method based on these principles.
Without a doubt, cyberspace has many countless benefits since most of our activities are online. Ever since the dawn of cyberspace, the cyberattacks have been emerged. Unlike tradition crimes, cybercrimes make the investigation processes very difficult for the investigators, since the criminals' identity can be hidden or fraud. As a result, the forensics science has been expanded to the network to provide the evidence of the criminal activity. Also, it enhances the network security using the collected information. Until now most of the proposed network forensics frameworks are struggling in determining the methodology of collecting, preserving, and analyzing data. Therefore, this paper aims to propose an enhanced framework for network forensics to assist the network security and the ability to prove the criminal activity besides helping investigators to analyze data for investigation purposes by using free of cost tools.
Privacy requirements and the need for collaborative analysis has motivated a significant amount of research on anonymization techniques and privacyaware analysis. Anonymization techniques are typically applied to data in order to preserve certain distances and properties of the original data points without revealing compromising information about it. A popular family of approaches in this field are distancepreserving hashes. These techniques allow data owners to share private information safely while retaining properties that enable analytics. However, typical anonymization techniques require a lot of expertise and domain knowledge in order to be applied effectively because they alter certain properties of the data. In this paper we discuss the types of distancepreserving hashing in order to give insight on how they operate.
In this paper, we present a Forensic Finite State Log Analyzer (FFSLA) framework to analyze cloud based web service composition process behaviors and classify the normal from nonconventional attack behavior. Also, we will discuss preliminary results of the proposed framework tests and evaluation. Stakeholders may consider the proposed framework to redesign business process execution in order to mitigate risks associated with process misbehavior.
Unstructured server logs datasets are increasing geometrically. The complexity in processing and analyzing threats poses a challenge to security data experts and research community. This paper proposes intelligent data abstraction technique, called FLUKES, to process unstructured server logs and generate a visualization of the attack threat using opensource D3.js modules. FLUKES has been tested experimentally with server log events, specifically FTP server logs, and produced a new signature pattern of Bruteforce attack. FLUKES accepts input log files in the format of .JSON and .CSV, and generates representation summary, which is processed and visualized throw a programmable dashboard. The ultimate outcome is to forensically correlate then visualize logs and detect threats of successful access into the network without altering the original log evidence.
In digital forensics, file carving of video files is an important process to recover evidence of several criminal cases. The traditional carving techniques recover video files based on their file structure. However, these techniques fail in the cases if the file is split into several fragments over storage media and some of its parts were overwritten. In this paper, we present an overview of an advanced forensics video file carving framework to recover and reassemble fragmented video files into playable video files. We provide experimental results showing that the video can be recovered based on the proposed framework. The overall accuracy rate can produce forensically sound evidence and play a critical role in the process of recovery of digital evidence in many criminal cases.
The present paper investigates the effects of inphase/ quadraturephase imbalance (IQI), which are known to degrade the performance of wireless communication systems. Specifically, we evaluate the effects of IQI on the bit error rate (BER) performance of differential quadrature phase shift keying (DQPSK) for ideal receiver (RX) with transmitter (TX) IQI, ideal TX with RX IQI and joint TX/RX IQI. Explicit analytic expressions are derived for the BER of singlecarrier systems suffering from IQI at the TX and/or RX. Extensive MonteCarlo simulation offered analytic results which show that realistic TX/RX IQI impairments can degrade the corresponding BER by over 12%. Likewise, it is shown that the detrimental effects of IQI are more considerable on DQPSK than on QPSK.
This work introduces an efficient blind channel estimation technique using a hybrid frame structure for OFDM systems. In particular, the hybrid frame contains Amplitude shift keying (ASK), phase shift keying (PSK) and quadrature amplitude modulation (QAM) modulated symbols, where the ASK carriers are considered as data carriers as well as pilots, which enhances the spectral efficiency. Closedform expression for the symbol error rate was derived and the results are corroborated by respective results from Monte Carlo simulations.
