Accounting Information System Essay Example | Topics and Well Written Essays - 1250 words

Accounting Information System - Essay Example The company is in the process of determining the advantages and drawbacks of automatic feeding of data by the use of the GL software. Data extraction- this is the act of mining raw data from the accounting information system in use by the company. The question is whether the data can sustain numerous needs of the company and the accuracy of the data. Data analysis- after data has been extracted from the system, the company has to enter into the analysis stage where the details of the customers are identified to determine how they could be applied to increase the sales volume of the company. Taking Orders- customers who demand the company’s products were to make order manually, either via telephone or online ordering. This means that the activity of ordering had to be undertaken by the company staff that had to capture the requirements of the customers. Processing orders- after taking the orders, the customer service personnel of top burger had to process the customer orders to ensure that the customers received exactly what they required at the nearest stores. Delivery- burger undertook the activity of delivering their goods to the customers at their doorsteps as a way of gaining competitive edge against their competitors and as a means of increasing their sales volume. Preparation of daily sales- restaurant managers were charged with the obligation of preparing daily sales summaries that were to be electronically submitted to the company administrators the next day. The activity assists in determining the level of sales of the company. System management activities- these are the activities, which involve the maintenance of the IT system to ensure it, worked effectively and smoothly. It also ensured customer integration in the transaction processing system. Promotion activities- promotion is one of the strategies that businesses use in marketing their products to the target market. To achieve this, top burger promoted amateur athletics and other communit y groups to win the loyalty of the community. Question 2 I think that Top Burger business strategy drives the development of its information system. Top burger has the growth strategy that entails increasing their customer base and their lines of products. Growth strategy also involves the improvement of their number of processes. This can be justified as follows: Bob is worried on whether the increasing number of orders could be sustained by the company current systems. This means that the developments in the system is compelled by the increased number of customers i.e. the high activity during peak season require enhancement of information system. The growth in the number of sales propelled the acquisition of new equipment. Bob argued that a new machine would make automatic feeding of data possible and this would increase the pace and quality of service delivery. The multiple activities also made the management review the ability of the information system to facilitate the diverse needs (Roman, 2008). The growth in the number of activities therefore called on the need to develop IT infrastructure. A look at the companies timeline also shows that it was because of the growth record that the company decided to begin making online ordering through the internet in 2003. The first prototype was also opened as the growth continued. Moreover, Bob’s worry is on the ability of the IT system in supporting the business strategy. He is concerned with the limitation of the current system to provide sufficient internal controls, improvements because of the integration and the use of software in supporting

Property Law Essay Example | Topics and Well Written Essays - 2000 words - 1

Property Law - Essay Example †¦Ã¢â‚¬ ¦broadly, I think what must be shown as constituting factual possession is that the alleged possessor has been dealing with the land as an occupying owner might have been expected to deal with it and that no-one else has done so.†6 Another condition that may invoke such possession right includes an intention to possess the land7 without the consent of the actual owner8. One of the salient points noted by Browne Wilkinson in allowing the judgment for the defendant was the issue of whether the defendant squatter had â€Å"dispossessed the paper owner by going into ordinary possession of the land for the requisite period without the consent of the owner.†9 Adverse possession will not be valid if the illegal occupant gives written notice of the true owners10 or if the actual owners physically occupy the land. After twelve years of occupation, the squatter on a land may apply for registration of title as proprietor of a new or registered estate11. Therefore, by w ay of these limitations, the granting of title to the squatter was provisional and the required time period had to elapse before a squatter could legally claim the title to the land. However, the issue of ownership did not rise in this case. But according to the provisions of the new Land registration Act12, which came into force on 13 Oct 2003, squatters may directly put in an application for ownership. The law relating to adverse possession has undergone several changes, whereby the issue at stake is not title to the land, so much as direct ownership of the land, either by the squatter or the paper owner. In a recent case, the Court suspended the possession order against a widowed mother who had enjoyed possession of land for thirteen years13. However the Land registration Act of 200214 clarifies that no matter how long the possession of the land, it will not bar the paper owner’s title to the registered land. Adverse possession in itself will not give the

Addressing International Legal and Ethical Issues Simulation Summary Essay

Addressing International Legal and Ethical Issues Simulation Summary - Essay Example While taking legal action against a foreign business partner based in another country, there often exists certain practical considerations that need to be taken into account. Contextually, the laws prevailing in the host country may not be the same as in the domestic environment and may vary drastically. Moreover, the culture of the host nation as well as the ethical values possessed by foreign business partner may not place the same degree of importance to the aspects such as trust and belief (Aliment, 2009). Consequently, it is necessary for the guest business partner to consider and get acquainted with local values, norms and ethics for seeking legal action against a foreign business partner. With respect to CadMex Pharma company, its partnering company i.e. Gentura is located in Candor country. There are certain factors that may work against CadMex’s decisions to grant sublicensing agreements. It should be noted that a company having a number of sublicensing agreements is vulnerable to face lawsuits, if the third parties such as workers are involved in any kind of property damages or related activities. In other words, CadMex will be liable for any damages occurring due to any third party negligence. When, the local customs and laws conflict with the customs and laws of an organization operating abroad, essentially the local customs and laws of the particular country in which the company is located prevails. A company operating abroad has to abide by the laws, value the ethics and respect the culture of the host nation, failure to do so may render the company to attract legal actions against for not abiding by the rules and the regulations of the host country. Such legal actions may hurt the business relationship immensely and create misunderstanding against each other. I believe that local laws, ethics and cultural aspects are closely related with each other. If the

Innovation, Ethics & Change-Hypocrisy Essay Example | Topics and Well Written Essays - 3000 words

Innovation, Ethics & Change-Hypocrisy - Essay Example The resistance change is human nature and the employees should undergo certain training to accept change. The managers are also encouraged to ensure the employees’ opinions with regard to certain changes, whether positive or negative is not held against them. This discourse is about guidance to help employees accept change within organizations. There are several reasons why various employees resist change. Some employees could be perfect in their areas of duties. For instance, an employee could choose to associate himself with other races or tribes as this could seem as a let down to his personality and even the family. Such employees will always address his peers sarcastically to discourage them from further participation. The managers should identify such employees and guide them or counsel them since such attitude could limit the overall productivity of an organization ( Kegan & Lahey 2001). Mistakes in any working organizations are to both the employees and even the managers. The global financial crisis was a mistake of the topmost leaders in the financial institutions as well as the legislators. The legislators involved despised opinions of various experts and their decisions still have an impact on the global economy to the current date. We may have the urge to accept change but there are factors called the sidetracks that may limit our abilities to embrace such changes. One of the sidetracks is the ‘forces within’ and these are our personal thoughts that encourage resistance to change. This could also be attributed to the people surrounding us. The emotional attachment could also hinder us from accepting changes, for instance, Yahoo boss turned down deal termed as one of its kind, when Microsoft was ready to buy its search engine at $22 B, in 2008.

How Drones Challenge Our Political System Essay Example | Topics and Well Written Essays - 750 words

How Drones Challenge Our Political System - Essay Example The writer mainly focuses on the negative impact posed by the drones. He acknowledges that drones have been a significant improvement from the B52s that were earlier used. He provides an example whereby B52s were used in World War 2. This resulted in major civilian casualties. However, the American public did not raise a finger to this. The writer goes on to explain how the smart bombs came into play. He directs the reader to consider the fact that these bombs were purported to be highly accurate. This, however, was not the case. These bombs resulted in unavoidable â€Å"collateral damage†. He continues to give the example of the attempt on Saddam Hussein. The reader may be inclined to think that the writer is either against technology or the use of drones precisely. Drones are, as have been proved, more accurate than the B52s that were earlier used. Therefore, why is the writer so against them? The writer is not concerned with the technology or the drones themselves, he is co ncerned with the moral impact that its use posses. He paints a picture of the future whereby drones will be available even to the terrorists. In this article, Allen proposes that the use of the drone technology should be regulated. Otherwise, the repercussions in the future will be unfathomable. The use drones pose an ethical and moral dilemma. In order to make his point clear, Allen views the targeting of individuals as synonymous to assassination. He introduces a comic relief to the article by claiming that the infamous Borgia would have been pleased by the new and effective way of murdering people. (Allen 5). Aside from the moral aspect associated with drones, the writer raises the issue of drone regulation. The writer wishes the readers to understand that the government of US has not monopolized the technology used in drones. This technology is fast spreading to other countries (Allen 5). There is a great possibility that, with time, this technology may find its way in the hands of the terrorists. Compared to the conventional means used by terrorists currently, the use of drone technology will be a big boost to them. Any persons who are considered to be enemies of the terrorists will have a cause to worry. The writer reiterates that drones employ artificial intelligence. Thus, they can be used to adapt to situations that humans may find difficult. The size of the drones is also decreasing significantly (Allen 5). It is expected that in the future drones will be the size of insects, thus, allowing them to penetrate into areas that humans cannot penetrate stealthily. Finally, Allen concludes that drones, given their technological impact, undermine the US political system. He points out that private firms, may venture into the business of drone technology. This will make it even difficult to institute a ban on this technology. The article hopes to inform the public of the ramifications of the use of drone technology. The moral questions posed by the targeting of individuals and its regulation. The writer appeals to the readers to understand the political decay caused by potential profits in the use of drone

Evolution of the Nation & the Civil War Essay Example for Free

Evolution of the Nation the Civil War Essay The end of the Civil War brought about political and economic reform to the United States. Reforms in the Reconstruction Period were passed to foster and maintain economic activity, creating industries and expanding businesses, contributing to the boom of Industrial Revolution in the country. Years of political clout and debate remarkably laundered constitutional amendments for the black man’s rights (Oberholtze, 1917). Inventions paved the way to the development of new industries such as telecommunication, transportation, electricity and construction. The discovery, access to and processing of raw materials -facilitated by technology- allowed products to be transported from manufacturing factories to populated areas for distribution. Such enterprise development measures were supported by the improvement of the transport and communication system via paved roads, bridges, canals, railroads and the telegraph. New jobs were created to cater to the needs of the fast growing economy and population. Enormous infrastructures were built to accommodate the growing city dwellers and workers. Increase in profits of manufacturing industries encouraged a steady influx of immigrants working in the production lines (Engerman, 2000). Competition and increasing market goals forced companies to expand trade and operations in other countries, imperialism’s objective. Altogether, technological advancement, cheap labor and availability of capital led to America’s heavy industrialization. This period of rise and fall largely contributed to America’s development into a superpower. However, as a young nation, America was set back with undesirable impacts of industrialization. As production staff volume increased, conflict between workers and management grew. Industrial achievements due to aggressive marketing of manufactured goods and increased foreign trade introduced more white collar jobs facilitating corporate operations. Management and administrative work were better rewarded than assembly-line work, providing better opportunities for educated and powerful who came to enjoy lucrative lifestyles; while creating inequalities of wealth, discontent and rousing uprisings from laborers who formed unions to pursue their rights. Marxist leaders helped reshape capitalist thought and corporate laws. Growing market demand meant increasing supply needs leading to resource exploitation in some areas leaving irreparable damage to the environment. Concern for increased production overshadowed the seemingly abundant resources. Even large corporations who came to have leverage in policy-making used their power to generate more profit. Developments in transportation made it easy for people to move about creating a melting pot of US residents and migrants, gave way to explosion of population in urban areas, forming cities. The attraction of immigrants made uprisings in social injustices -competition and aggression- between old American settlers versus the new immigrants. Urban legislators targeted services to win the votes of increasing foreign workers maligning the democratic electoral process. Too aggressive amassing and building of wealth and power triggered the economic crash of 1873 when the government’s major investment bank, financer of government reconstruction loans and the Northern Pacific Railroad, crashed (Bancroft, 1902). For many years, new business management styles were practiced by corporations, every time cleaning up failed ventures with hopefully better alternatives. Many times, the finance and investment sector failed but lessons were dealt with outmost concern for capital and development-oriented sources. As population grew in cities, people moved to occupy the western territories. Development gradually followed increasing the number of states joining the union. At the same time, America had growing concern for the rapid growth of the British Empire. Following British example, America colonized territories and expanded the home base and also the market for its produce. American industrialists pushed for westward progression, integrating the continent-wide unified market reducing production cost and increasing value per output of production enabling American working class to earn higher than counterparts across the globe. Higher wage was initially purported by higher bid of wage levels experienced in the pre-industrialization era using slave-labor and eventually sustained by capitalism during the reconstruction period despite the increase in laborers and economic conflict many years after. Nevertheless, the labor force and farmers suffered greatly being dependent on businesses that supported their sources of income (McElvaine, 1993). The years of depression was a roller coaster ride for most corporate giants who still reap the most benefit even during economic downfall. During World War I, America tapped the international market (McElvaine, 1993). They penetrated into countries that did not have resources for food production. US production increased to cater to the needs of incapacitated economies. With its strong capitalist foundation, the US took advantage of World War I devastation across Europe and Japan (Olson, 1988). Forced to reduce trade barriers, Europe, Japan and their colonies were opened to globalization, with efforts initiated by American powers. Development of farm machinery automated farming practices and increased US production. The US became the world’s foremost producer of agricultural products in power economies like Europe and Japan were destroyed. The downside, farmers became all the more dependent on new businesses offering loans, transportation, equipment manufacturers and middle men who facilitated crop entry into international markets. Though, when the war ended, competition became stiff forcing America to make internal changes to beef up industrial efforts. This led to the development of advertising and marketing strategies encouraging people to consume. People wanted to get away from the prolonged depression and rode with the bandwagon, buying what advertisers offered. A mass culture of consumerism proliferated. This was made possible even for people who would not afford through the credit system, promoting instant access to commodities and luxuries and deferring payment with terms (McElvaine, 1993). Many US bankers reached across the globe and lent European countries for post war reconstruction. Economic instability after war did not go along US banks’ objectives and increased the risk of non-payment of loans. This would eventually lead to the Great Depression affecting businesses and communities worldwide. The entire financial industry suffered leading to property and business closures (Olson, 1988). The imposition of higher taxes on imports caused local markets to patronize locally manufactured goods. However, other countries retaliated by imposing high taxes on US exports, resulting in less foreign trade profits and eventually less power in international market penetration (McElvaine, 1993). The growing rate in stocks investment of major industrial companies caught the attention of many. Stock buying became a trend, relying in the belief that this will make people rich. The Stock Market Crash of 1929 marked the domino economic disruption made vulnerable by unequal distribution of wealth and banking problems. Renewed global strategy in achieving international trade through humanitarian and democratic efforts became America’s initiative to promote global security. Priority was also given to secure citizens through provision of social welfare. State-governed economic planning organized nationwide industrial regulations to propel the rise from the Depression. Powerful American businesses have lobbied for rights in exploring resources in other countries despite conservative and anti-colonialism proponents in US government limited US economic expansion. However, open trading between economies leveled the playing field in production and markets. The Progressive Era marked a turning point of US imperial power into a more humanist and democratic torch bearer in an effort to resolve the problems and issues brought about by industrialization and urbanization. Leaders focused on long-term goals, core values and implementation of development programs. Reformists, including President Franklin Roosevelt in the New Deal, sought to end monopolies, destroy political corruption and lessen the gap between the affluent and poor. Through the New Deal, authorized nationwide assistance to socio-economic development of individuals. Agencies were set up to provide employment, regulate mortgage and housing conditions, administered social security, consumer rights and raised funding for education, food and drug safety. Concerns of the working and business class were brought together (Mintz, 2006). Progressive ideologies affected political, social and cultural movements in the local and eventually made impacts on international human rights revolution and the initiation of international governing bodies to secure international relations in politics and economics. Radical changes in international standards and relationships were fostered. The UN and the NATO was founded in 1945 and 1948, respectively. The US became stronger despite its diversity. Operation Breadbasket was launched to increase employment of cultural minorities. From its indistinguishable character, America’s economic, social and technological transformation continues to awe the world. Today, its mandate for democracy and freedom still thrive and inspire other nationalities. References Bancroft, H. (1902). The Financial Panic of 1837. The Great Republic By the Master Historians Vol. III. Retrieved 11-5-2008 from http://www. publicbookshelf. com/public_html/The_Great_Republic_By_the_Master_Historians_Vol_III/thepanic_ce. html Engerman, S. and K. Sokoloff. (2000). Technology and Industrialization, 1790-1914. In The Cambridge Economic History of the United States, Vol. II. Cambridge: Cambridge University Press. McElvaine, R. S. (1993). The Great Depression: America 1929-1941. Three Rivers Press. Mintz, S. (2006). Learn About the Progressive Era. Digital History. Retrieved 11-5-2008 from http://www. digitalhistory. uh. edu/modules/progressivism/index. cfm. Oberholtze, E. (1917). A History of the United States since the Civil War, Vol. 1. Macmillan. Olson, J. (1988). from World War I to the New Deal, 1919-1933. Historical dictionary of the 1920s. New York : Greenwood Press.

Compressive Sensing: Performance Comparison of Measurement

Compressive Sensing: Performance Comparison of Measurement Compressive Sensing: A Performance Comparison of Measurement Matrices Y. Arjoune, N. Kaabouch, H. El Ghazi, and A. Tamtaoui AbstractCompressive sensing paradigm involves three main processes: sparse representation, measurement, and sparse recovery process. This theory deals with sparse signals using the fact that most of the real world signals are sparse. Thus, it uses a measurement matrix to sample only the components that best represent the sparse signal. The choice of the measurement matrix affects the success of the sparse recovery process. Hence, the design of an accurate measurement matrix is an important process in compressive sensing. Over the last decades, several measurement matrices have been proposed. Therefore, a detailed review of these measurement matrices and a comparison of their performances is needed. This paper gives an overview on compressive sensing and highlights the process of measurement. Then, proposes a three-level measurement matrix classification and compares the performance of eight measurement matrices after presenting the mathematical model of each matrix. Several experimen ts are performed to compare these measurement matrices using four evaluation metrics which are sparse recovery error, processing time, covariance, and phase transition diagram. Results show that Circulant, Toeplitz, and Partial Hadamard measurement matrices allow fast reconstruction of sparse signals with small recovery errors. Index Terms Compressive sensing, sparse representation, measurement matrix, random matrix, deterministic matrix, sparse recovery. TRADITIONAL data acquisition techniques acquire N samples of a given signal sampled at a rate at least twice the Nyquist rate in order to guarantee perfect signal reconstruction. After data acquisition, data compression is needed to reduce the high number of samples because most of the signals are sparse and need few samples to be represented. This process is time consuming because of the large number of samples acquired. In addition, devices are often not able to store the amount of data generated. Therefore, compressing sensing is necessary to reduce the processing time and the number of samples to be stored. This sensing technique includes data acquisition and data compression in one process. It exploits the sparsity of the signal to recover the original sparse signal from a small set of measurements [1]. A signal is sparse if only a few components of this signal are nonzero.   Compressive sensing has proven itself as a promising solution for high-density signals and has major a pplications ranging from image processing [2] to wireless sensor networks [3-4], spectrum sensing in cognitive radio [5-8], and channel estimation [9-10].   As shown in Fig. 1. compressive sensing involves three main processes: sparse representation, measurement, and sparse recovery process. If signals are not sparse, sparse representation projects the signal on a suitable basis so the signal can be sparse. Examples of sparse representation techniques are Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), and Discrete Cosine Transform (DCT) [11]. The measurement process consists of selecting a few measurements,   from the sparse signal that best represents the signal where. Mathematically, this process consists of multiplying the sparse signal by a measurement matrix. This matrix has to have a small mutual coherence or satisfy the Restricted Isometry Property. The sparse recovery process aims at recovering the sparse signal from the few measurements selected in the measurement process given the measurement matrix ÃŽ ¦. Thus, the sparse recovery problem is an undetermined system of linear equations, which has an inf inite number of solutions. However, sparsity of the signal and the small mutual coherence of the measurement matrix ensure a unique solution to this problem, which can be formulated as a linear optimization problem. Several algorithms have been proposed to solve this sparse recovery problem. These algorithms can be classified into three main categories: Convex and Relaxation category [12-14], Greedy category [15-20], and Bayesian category [21-23]. Techniques under the Convex and Relaxation category solve the sparse recovery problem through optimization algorithms such as Gradient Descent and Basis Pursuit. These techniques are complex and have a high recovery time. As an alternative solution to reduce the processing time and speed up the recovery, Greedy techniques have been proposed which build the solution iteratively. Examples of these techniques include Orthogonal Matching Pursuit (OMP) and its derivatives. These Greedy techniques are faster but sometimes inefficient. Bayesian b ased techniques which use a prior knowledge of the sparse signal to recover the original sparse signal can be a good approach to solve sparse recovery problem. Examples of these techniques include Bayesian via Laplace Prior (BSC-LP), Bayesian via Relevance Vector Machine (BSC-RVM), and Bayesian via Belief Propagation (BSC-BP). In general, the existence and the uniqueness of the solution are guaranteed as soon as the measurement matrix used to sample the sparse signal satisfies some criteria. The two well-known criteria are the Mutual Coherence Property (MIP) and the Restricted Isometry Property (RIP) [24]. Therefore, the design of measurement matrices is an important process in compressive sensing. It involves two fundamental steps: 1) selection of a measurement matrix and 2) determination of the number of measurements necessary to sample the sparse signal without losing the information stored in it. A number of measurement matrices have been proposed. These matrices can be classified into two main categories: random and deterministic. Random matrices are generated by identical or independent distributions such as Gaussian, Bernoulli, and random Fourier ensembles. These matrices are of two types: unstructured and structured.  Ã‚   Unstructured type matrices are generated randomly following a given distribution. Example of these matrices include Gaussian, Bernoulli, and Uniform. These matrices are easy to construct and satisfy the RIP with high probability [26]; however, because of the randomness, they present some drawbacks such as high computation and costly hardware implementation [27]. Structured type matrices are generated following a given structure. Examples of matrices of this type include the random partial Fourier and the random partial Hadamard. On the other hand, deterministic matrices are constructed deterministically to have a small mutual coherence or satisfy the RIP. Matrices of this category are of two types: semi-deterministic and full-deterministic. Semi-deterministic type matrices have a deterministic construction that involves the randomness in the process of construction. Example of semi-deterministic type matrices are Toeplitz and Circulant matrices [31]. Full-deterministic type matrices have a pure deterministic construction. Examples of this type measurement matrices include second-order Reed-Muller codes [28], Chirp sensing matrices [29], binary Bose-Chaudhuri-Hocquenghem (BCH) codes [30], and quasi-cyclic low-density parity-check code (QC-LDPC) matrix [32]. Several papers that provide a performance comparison of deterministic and random matrices have been published. For instance, Monajemi et al. [43] describe some semi-deterministic matrices such as Toeplitz and Circulant and show that their phase transition diagrams are similar as those of the random Gaussian matrices. In [11], the authors provide a survey on the applications of compressive sensing, highlight the drawbacks of unstructured random measurement matrices, and they present the advantages of some full-deterministic measurement matrices. In [27], the authors provide a survey on full-deterministic matrices (Chirp, second order Reed-Muller matrices, and Binary BCH matrices) and their comparison with unstructured random matrices (Gaussian, Bernoulli, Uniform matrices). All these papers provide comparisons between two types of matrices of the same category or from two types of two different categories. However, to the best of knowledge, no previous work compared the performances of measurement matrices from the two categories and all types: random unstructured, random structured, semi-deterministic, and full-deterministic. Thus, this paper addresses this gap of knowledge by providing an in depth overview of the measurement process and comparing the performances of eight measurement matrices, two from each type. The rest of this paper is organized as follows. In Section 2, we give the mathematical model behind compressive sensing. In Section 3, we provide a three-level classification of measurement matrices. Section 4 gives the mathematical model of each of the eight measurement matrices. Section 5 describes the experiment setup, defines the evaluation metrics used for the performance comparison, and discusses the experimental results. In section 6, conclusions and perspectives are given. Compressive sensing exploits the sparsity and compresses a k-sparse signal by multiplying it by a measurement matrix where. The resulting vector    is called the measurement vector. If the signal is not sparse, a simple projection of this signal on a suitable basis, can make it sparse i.e. where. The sparse recovery process aims at recovering the sparse signal given the measurement matrix and the vector of measurements. Thus, the sparse recovery problem, which is an undetermined system of linear equations, can be stated as: (1) Where is the, is a sparse signal in the basis , is the measurement matrix, and   is the set of measurements. For the next of this paper, we consider that the signals are sparse i.e. and . The problem (1) then can be written as: (2) This problem is an NP-hard problem; it cannot be solved in practice. Instead, its convex relaxation is considered by replacing the by the . Thus, this sparse recovery problem can be stated as: (3) Where is the -norm, is the k-parse signal, the measurement matrix and is the set of measurements. Having the solution of problem (3) is guaranteed as soon as the measurement matrix has a small mutual coherence or satisfies RIP of order. Definition 1: The coherence measures the maximum correlation between any two columns of the measurement matrix . If is a matrix with normalized column vector , each is of unit length. Then the mutual coherence Constant (MIC) is defined as: (4) Compressive sensing is concerned with matrices that have low coherence, which means that a few samples are required for a perfect recovery of the sparse signal. Definition 2: A measurement matrix satisfies the Restricted Isometry Property if there exist a constant such as: (5) Where is the and is called the Restricted Isometry Constant (RIC) of which should be much smaller than 1. As shown in the Fig .2, measurement matrices can be classified into two main categories: random and deterministic. Matrices of the first category are generated at random, easy to construct, and satisfy the RIP with a high probability. Random matrices are of two types: unstructured and structured. Matrices of the unstructured random type are generated at random following a given distribution. For example, Gaussian, Bernoulli, and Uniform are unstructured random type matrices that are generated following Gaussian, Bernoulli, and Uniform distribution, respectively. Matrices of the second type, structured random, their entries are generated following a given function or specific structure. Then the randomness comes into play by selecting random rows from the generated matrix. Examples of structured random matrices are the Random Partial Fourier and the Random Partial Hadamard matrices. Matrices of the second category, deterministic, are highly desirable because they are constructed deter ministically to satisfy the RIP or to have a small mutual coherence. Deterministic matrices are also of two types: semi-deterministic and full-deterministic. The generation of semi-deterministic type matrices are done in two steps: the first step consists of the generation of the entries of the first column randomly and the second step generates the entries of the rest of the columns of this matrix based on the first column by applying a simple transformation on it such as shifting the element of the first columns. Examples of these matrices include Circulant and Toeplitz matrices [24]. Full-deterministic matrices have a pure deterministic construction. Binary BCH, second-order Reed-Solomon, Chirp sensing, and quasi-cyclic low-density parity-check code (QC-LDPC) matrices are examples of full-deterministic type matrices. Based on the classification provided in the previous section, eight measurement matrices were implemented: two from each category with two from each type. The following matrices were implemented: Gaussian and Bernoulli measurement matrices from the structured random type, random partial Fourier and Hadamard measurement matrices from the unstructured random type, Toeplitz and Circulant measurement matrices from the semi-deterministic type, and finally Chirp and Binary BCH measurement matrices from the full-deterministic type. In the following, the mathematical model of each of these eight measurement matrices is described. A. Random Measurement Matrices Random matrices are generated by identical or independent distributions such as normal, Bernoulli, and random Fourier ensembles. These random matrices are of two types: unstructured and structured measurement random matrices. 1) Unstructured random type matrices Unstructured random type measurement matrices are generated randomly following a given distribution. The generated matrix is of size . Then M rows is randomly selected from N. Examples of this type of matrices include Gaussian, Bernoulli, and Uniform. In this work, we selected the Random Gaussian and Random Bernoulli matrix for the implementation. The mathematical model of each of these two measurement matrices is given below. a) Random Gaussian matrix The entries of a Gaussian matrix are independent and follow a normal distribution with expectation 0 and variance. The probability density function of a normal distribution is: (6) Where is the mean or the expectation of the distribution, is the standard deviation, and is the variance. This random Gaussian matrix satisfies the RIP with probability at least given that the sparsity satisfy the following formula: (7) Where is the sparsity of the signal, is the number of measurements, and is the length of the sparse signal [36]. b) Random Bernoulli matrix A random Bernoulli matrix is a matrix whose entries take the value or with equal probabilities. It, therefore, follows a Bernoulli distribution which has two possible outcomes labeled by n=0 and n=1.   The outcome n=1 occurs with the probability p=1/2 and n=0 occurs with the probability q=1-p=1/2. Thus, the probability density function is: (8) The Random Bernoulli matrix satisfies the RIP with the same probability as the Random Gaussian matrix [36]. 2) Structured Random Type matrices The Gaussian or other unstructured matrices have the disadvantage of being slow; thus, large-scale problems are not practicable with Gaussian or Bernoulli matrices. Even the implementation in term of hardware of an unstructured matrix is more difficult and requires significant space memory space. On the other hand, random structured matrices are generated following a given structure, which reduce the randomness, memory storage, and processing time. Two structured matrices are selected to be implemented in this work: Random Partial Fourier and Partial Hadamard matrix. The mathematical model of each of these two measurement matrices is described below: a) Random Partial Fourier matrix The Discrete Fourier matrix is a matrix whose entry is given by the equation: (9) Where. Random Partial Fourier matrix which consists of choosing random M rows of the Discrete Fourier matrix satisfies the RIP with a probability of at least , if: (10) Where M is the number of measurements, K is the sparsity, and N is the length of the sparse signal [36]. b) Random Partial Hadamard matrix The Hadamard measurement matrix is a matrix whose entries are 1 and -1. The columns of this matrix are orthogonal. Given a matrix H of order n, H is said to be a Hadamard matrix if the transpose of the matrix H is closely related to its inverse. This can be expressed by: (11) Where is the identity matrix, is the transpose of the matrix. The Random Partial Hadamard matrix consists of taking random rows from the Hadamard matrix. This measurement matrix satisfies the RIP with probability at least provided    with and as positive constants, K is the sparsity of the signal, N is its length and M is the number of measurements [35]. B. Deterministic measurement matrices Deterministic measurement matrices are matrices that are designed following a deterministic construction to satisfy the RIP or to have a low mutual coherence. Several deterministic measurement matrices have been proposed to solve the problems of the random matrices. These matrices are of two types as mentioned in the previous section: semi-deterministic and full-deterministic. In the following, we investigate and present matrices from both types in terms of coherence and RIP. 1) Semi-deterministic type matrices To generate a semi-deterministic type measurement matrix, two steps are required. The first step is randomly generating the first columns and the second step is generating the full matrix by applying a simple transformation on the first column such as a rotation to generate each row of the matrix. Examples of matrices of this type are the Circulant and Toeplitz matrices. In the following, the mathematical models of these two measurement matrices are given. a) Circulant matrix For a given vector, its associated circulant matrix whose entry is given by: (11) Where. Thus, Circulant matrix has the following form: C= If we choose a random subset of cardinality, then the partial circulant submatrix that consists of the rows indexed by achieves the RIP with high probability given that: (12) Where is the length of the sparse signal and its sparsity [34]. b) Toeplitz matrix The Toeplitz matrix, which is associated to a vector    whose entry is given by: (13) Where. The Toeplitz matrix is a Circulant matrix with a constant diagonal i.e. .   Thus, the Toeplitz matrix has the following form: T= If we randomly select a subset of cardinality , the Restricted Isometry Constant of the Toeplitz matrix restricted to the rows indexed by the set S satisfies with a high probability provided (14) Where is the sparsity of the signal and is its length [34]. 2) Full-deterministic type matrices Full-deterministic type matrices are matrices that have pure deterministic constructions based on the mutual coherence or on the RIP property. In the following, two examples of deterministic construction of measurements matrices are given which are the Chirp and Binary Bose-Chaudhuri-Hocquenghem (BCH) codes matrices. a) Chirp Sensing Matrices The Chirp Sensing matrices are matrices their columns are given by the chirp signal. A discrete chirp signal of length à °Ã‚ Ã¢â‚¬ËœÃ… ¡ has the form:   Ã‚  Ã‚  Ã‚  Ã‚   (15) The full chirp measurement matrix can be written as: (16) Where is an matrix with columns are given by the chirp signals with a fixed and base frequency   values that vary from 0 to m-1. To illustrate this process, let us assume that and Given , The full chirp matrix is as follows: In order to calculate, the matrices and should be calculated. Using the chirp signal, the entries of these matrices are calculated and given as: ; Thus, we get the chirp measurement matrix as: Given that is a -sparse signal with chirp code measurements and is the length of the chirp code. If (17) then is the unique solution to the sparse recovery algorithms. The complexity of the computation of the chirp measurement matrix is. The main limitation of this matrix is the restriction of the number of measurements to    [29]. b) Binary BCH matrices Let denote as a divisor of for some integer an