The radar has been found to have broader utility and its utility has now matured for observing weather phenomena. In effect, the “clutter background” that atmospheric phenomena represent for a primary aircraft surveillance radar application becomes the “signal” interpreted in meteorological applications of radar. The meteorological radar relies on meteorological targets distributed in space and occupies a large fraction of the spatial resolution cells observed by the radar.Moreover, it is necessary to make quantitative measurements of the received signal’s characteristics in order to estimate such parameters as precipitation rate, precipitation type, air motion, turbulence, and wind shear. In addition, because so many radar resolution cells contain useful information, meteorological radars require high-data rate recording systems and effective means for real-time display Understanding the nature of sea clutter is crucial to the successful modeling of sea clutter as well as to facilitate target detection within sea clutter.To this end, an important question to ask is whether sea clutter is stochastic or deterministic. Since the complicated sea clutter signals are functions of complex (sometimes turbulent) wave motions on the sea surface, while wave motions on the sea surface clearly have their own dynamical features that are not readily described by simple statistical features, it is thus very appealing to understand sea clutter by considering some of their dynamical features.In the past decade, Haykin have carried out analysis of some sea clutter data using chaos theory, and concluded that sea clutter was generated by an underlying chaotic process. Recently, their conclusion has been questioned by a number of researchers. In particular, Unsworth have demonstrated that the two main invariants used by Haykin, namely the “maximum likelihood of the correlation dimension estimate” and the “false nearest neighbors” are problematic in the analysis of measured sea clutter data, since both invariants may interpret stochastic processes as chaos.They have also tried an improved method, which is based on the correlation integral of Grassberger and Procaccia and has been found effective in distinguishing stochastic processes from chaos. Still, no evidence of determinism or chaos has been found in sea clutter data. To reconcile ever growing evidence of stochasticity in sea clutter with their chaos hypothesis, recently, Haykin et al. have suggested that the non-chaotic feature of sea clutter could be due to many types of noise sources in the data.To test this possibility, McDonald and Damini have tried a series of low-pass filters to remove noise; but again they have failed to find any chaotic features. Furthermore, they have found that the commonly used chaotic invariant measures of correlation dimension and Lyapunov exponent, computed by conventional ways, produce similar results for measured sea clutter returns and simulated stochastic processes, while a nonlinear predictor shows little improvement over linear prediction. While these recent studies highly suggest that sea clutter is unlikely to be truly chaotic, a number of fundamental questions are still unknown.For example, most of these studies are conducted by comparing measured sea clutter data with simulated stochastic processes. We can ask: can the non-chaotic nature of sea clutter be directly demonstrated without resorting to simulated stochastic processes? Recognizing that simple low-pass filtering does not correspond to any definite scales in phase space, can we design a more effective method to separate scales in phase space and to test whether sea clutter can be decomposed as signals plus noise?Finally, will studies along this line be of any help for target detection within sea clutter? In this paper, we employ the direct dynamical test for deterministic chaos developed by Gao and Zheng to analyze 280 sea clutter data measured under various sea and weather conditions. The method offers a more stringent criterion for detecting low-dimensional chaos, and can simultaneously monitor motions in phase space at different scales. However, no chaotic feature is observed from any of these scales.But very interestingly, we find that sea clutter can be conveniently characterized by the new concept of power-law sensitivity to initial conditions (PSIC), which generalizes the defining property for chaotic dynamics, the exponential sensitivity to initial conditions (ESIC). There is currently a debate in the radar community as to whether sea clutter is stochastic or deterministic in nature. Conventionally, high-resolution radar sea clutter has been modelled by a stochastic compound k-distribution [I]. Haykin et al.[2, 31 performed an analysis on sea clutter data sets using nonlinear techniqucs and concluded that sea clutter was generated by an underlying chaotic process. This analysis relied on two main chaotic invariants, the ‘maximum likelihood estimation of the correlation dimension’ (DML value)  and ‘false nearest neighhours’ (FNN) . The Lyapunov exponents were also measured, where the number of exponents to be measured was determined from the FNN calculation. In  the results of surrogate data tests were also reported and it was concluded that sea clutter was a nonlinear process.From this finding a nonlinear predictor function was then applied to predict the sea clutter which was claimed to improve the prediction performance of maritime surveillance radar. However, the surrogate test  performed in  was based on the Fourier transfonn (FT). Several problems have been identified with the FT surrogate test (Le. spurious detection of nonlinearity for random systems with strong periodic components [7, 81). Recently, the surrogate statistic in  has been shown to imply chaotic behaviour for white and correlated noise signals  and replaced by a more robust statistic.More recently, a new surrogate test was developed specifically to measure the [lo]. The nonlinear prediction network used in  was not compared with the performance of a linear prediction network. Recently this comparison has been performed in [I I ] where it was shown that the clutter prediction functions were well approximated by a linear function and that nonlinear predictor network functions provided little or no further improvement in performance. In  it was reported that sea clutter had fractional DML values in the range 4. 1-4. 5 and FNN global dimension in the range 5-6.It could be inferred from these results that the system is low-dimensional and fractal which is symptomatic of chaos. From the FNN result reported in , 5-6 Lyapunov exponents were calculated, resulting is positive and negative values, indicative of chaos. More recently, in [I21 the Kolmogorov-Sinai entropy was derived from the values of the Lyapunov exponents and found to be positive which is also a signature of chaos. In this paper it is shown that two of the invariants that the nonlinear analysis of  and  relied upon cannot be employed to determine the nature of an unknown system.Each invariant is taken in turn and its robustness tested. It is shown that each invariant can lead to the false detection of chaos for known stochastic time series. An alternative method is then proposed to test the ature of sea clutter for stochastic or deterministic behaviour. The examination of the behaviour of the correlation dimension d, drawn from the correlation integral C(r) of Grassherger et al. For increasing embedding dimension dE is robust at distinguishing white and correlated stochastic times series from deterministic ones. Therefore it is suggested that such a method is used first to determine the nature of an unknown system.
Insert surname2 Professor’s name Student’s name Course title Date White Collar Crimes
White Collar Crimes
White collar crimes can be defined as the offenses designed to produce financial gains through some form of deception. This type of crime commonly is committed by people within the business world who, as an outcome of their position in their job, they can or are able to easily access to large amounts of other people’s funds. The white collar crimes do not involve drug-related, violent, or overtly illegal activities. In fact most of the offenders typically are involved in otherwise legal businesses and may even be holding high positions in their community prior to their fraudulent schemes discovery. Such fraudulent crimes may involve; falsification of financial information likes false accounting entries, fraudulent trades and illicit transactions, self dealing with corporate insiders, and fraud in connection with an otherwise legitimately operated mutual hedge fund.
Most of these crimes are investigated and prosecuted by the federal authorities. Federal conviction rates are too much high hence this may not be good news for the accused individuals. The District Attorney’s offices in the United States of America are provided with substantial resources which enables them to pursue the wrongdoers. There has been a certain misconception in the general public eye that the convicted persons are always treated with leniency, but the fact is that, the sentences purely involving financial crimes may even be longer than other sentences which are handed down in cases involving drugs or violence.
Sutherland’s White Collar Crime Definition
According to Sutherland, his definition shows that only a specific type of individuals commit white collar crimes, and they are the people holding respectably and high social statuses. He also specified that such acts must arise out of the course of the occupation of the actor. His distinguishing feature in his approach was the willingness to involve the administrative and civil violations as being part of the white collar crimes.
For Sutherland and most of his fellow sociologists, such crimes are not “crimes” in the legal sense of the term. There is no criminal sanction subject, or, if it was, rarely it could be prosecuted as such. He dismissed the common principle of criminal law to follow his values that there is unfair stigmization by the law to the unfortunate, while it spears the rich and those who are powerful enough, claiming the two should be equal in the eyes of the law. He never sought to get rid of stigma of crime; rather he sought to develop it (Horsley & Mark, 2014).
Example of White-Collar Crime Committed by an Individual
Bernard Madoff was convicted in the year 2009, in the massive fraud scandal which cost investors $65 billion. The court sentenced him to 150 years in prison. He ran an elaborate scheme named Ponzi scheme, which had promised huge investment returns. He used funds from the new investors to pay the previous investors without actually investing the money. Madoff’s scheme got in to trouble when most of his investors demanded to be paid their money back, and he was not able to pay them.
This is a fraudulent operation where Madoff generated returns for older investors through the revenue which was being paid by his new investors, rather than investing the money. His scheme required initial investment while promising well-above-average returns (Levi & Michael, 2013).
Horsley, Mark. “White-collar crime.” Shades of Deviance: A Primer on Crime, Deviance and Social Harm (2014): 139.
Levi, Michael. Regulating Fraud (Routledge Revivals): White-Collar Crime and the Criminal Process. Routledge, 2013.