Matlab

The PCA toolbox for MATLAB is a collection of modules freely available via Internet.The toolbox calculates PCA, Cluster Analysis and Multidimensional Scaling.An easy-to-use graphical user interface (GUI) environment is available.Theory of methods, toolbox features, and an example of application are described.Principal Component Analysis is a multivariate method to project data in a reduced hyperspace, defined by orthogonal principal components, which are linear combinations of the original variables. In this way, data dimension can be reduced, noise can be excluded from the subsequent analysis, and therefore, data interpretation is extremely facilitated. For these reasons, Principal Component Analysis is nowadays the most common chemometric strategy for unsupervised exploratory data analysis.In this paper, the PCA toolbox for MATLAB is described. This is a collection of modules for calculating Principal Component Analysis, as well as Cluster Analysis and Multidimensional Scaling, which are two other well-known multivariate methods for unsupervised data exploration. The toolbox is freely available via Internet and comprises a graphical user interface (GUI), which allows the calculation in an easy-to-use graphical environment. It aims to be useful for both beginners and advanced users. The use of the toolbox is discussed here with an appropriate practical example.

This report reviews our basic knowledge on tornadoes and questions that have not been yet answered. We describe the life cycle of a tornado, its genesis and explain how to recognize a mesocyclonic tornado from a non-mesocyclonic one. Prevention and detection systems such as Doppler radars are also discussed in the report. In a second part, we follow an inertial particle’s trajectory through the velocity field of a Burgers’ vortex model, where gravity, inertia and Stokes drag are applied. Thanks to the equations of motion’s adimensionnalization and the introduction of adimensionnal numbers (Stokes, Froude, Reynolds), we analyze the different equilibrium points and scenarios of particle’s motion for defined values of Stokes, Froude and Reynolds

An increasing number of schools are teaching programming to their pupils and there is also an increase in programming in Higher Education with recent reports recommending this approach. At Manchester Metropolitan University (MMU) we wanted to attract and retain mathematics students and prepare them for careers upon graduation. By integrating Mathematics/Statistics/Operational Research packages across the curriculum and by solving real world problems we have managed to make the course highly desirable and loved by our students. In this case study, we show how it is possible to integrate programming and mathematical/computational modelling across the curriculum.

Infrared (IR) technology is widely adopted and dominantly used for wireless communication in short-range distance. Computer can easily communicate and transfer data & files by IR technology. A protocol has been researched to transfer data between computers by using IR technology and an apparatus has been developed also according to the protocol. MATLAB manipulates the data in a computer and transfer it via serial port to the micro-controller of the apparatus. Mathematical and logical operations are performed in the controller according to programmed instructions in order to send the data as a signal of IR light beam which modulates at a frequency of 38 kHz. An IR receiver picks up the signal and the receiving module converts the modulated light beam to corresponding electric signal which is decoded & defragmented by an another micro-controller to generate corresponding data. Then the accrued data is sent to another computer through serial port where it is received by MATLAB. This apparatus works perfectly within 8 meters with viewing angle between 15 to 30 degrees

Skin diseases are among the most common health problems worldwide. In this article we proposed a method that uses computer vision based techniques to detect various kinds of dermatological skin diseases. We have used different types of image processing algorithms for feature extraction and feed forward artificial neural network for training and testing purpose. The system works on two phases- first pre-process the colour skin images to extract significant features and later identifies the diseases. The system successfully detects 9 different types of dermatological skin diseases with an accuracy rate of 90%.

This paper describes a generalized model of the three-phase induction motor and its computer simulation using MATLAB/SIMULINK. Constructional details of various sub-models for the induction motor are given and their implementation in SIMULINK is outlined. Direct-online starting of a 7.5-kW induction motor is studied using the simulation model developed.

The conventional method for detecting blood abnormality is time consuming and lacks the high level of accuracy. In this paper a MATLAB based solution has been suggested to tackle the problem of time consumption and accuracy. Three types of blood abnormality have been covered here, namely, anemia which is characterized by low count of red blood cells (RBCs), Leukemia which is depicted by increasing the number of white blood cells (WBCs), and sickle cell blood disorder which is caused by a deformation in the shape of red cells. The algorithm has been tested on different images of blood smears and noticed to give an acceptable level of accuracy. Image processing techniques has been used here to detect the different types of blood constituents. Unlike many other researches, this research includes the blood sickling disorder which is epidemic in certain regions of the world, and offers a more accuracy than other algorithms through the use of detaching overlapped cells strategy.

Illumination invariance remains the most researched, yet the most challenging aspect of automatic face recognition. In this paper we investigate the discriminative power of colour-based invariants in the presence of large illumination changes between training and test data, when appearance changes due to cast shadows and non-Lambertian effects are significant. Specifically, there are three main contributions: (i) we employ a more sophisticated photometric model of the camera and show how its parameters can be estimated, (ii) we derive several novel colour-based face invariants, and (iii) on a large database of video sequences we examine and evaluate the largest number of colour-based representations in the literature. Our results suggest that colour invariants do have a substantial discriminative power which may increase the robustness and accuracy of recognition from low resolution images.

We use the framework implicit in the model of inflation by Shone (1997) to address the analytical properties of a simple dynamic aggregate supply and aggregate demand (AS-AD) model and solve it numerically. The model undergoes a bifurcation as its steady state smoothly interchanges stability depending on the relation between the sensitivity of the demand for liquidity to variations in the interest rate and the way expectations on inflation are formed based on real output fluctuations. Using code embedded into a unique function in Matlab, we plot the numerical solutions of the model and simulate different dynamic adjustments using different parameter values. The same function also accommodates for the implementation of different policy shocks: monetary policy shocks through changes in the growth rate of money supply, fiscal policy shocks due to variations in public spending and in the exogenous tax rate, and supply side shocks as given by changes in the level of natural output.

Internet of Things (IoT) and services is an interesting topic with a wide range of potential applications like smart home systems, health care, telemedicine, and intelligent transportation. Traditionally, key agreement schemes have been evaluated to access IoT services which are highly susceptible to security. Recently, Biometric-based authentication is also used to access IoT services and devices. They are involving a larger amount of memory with increased running time and found to be computationally infeasible. To provide robust authentication for IoT services, Histogram of Neighborhood Tripartite Authentication with Fingerprint Biometrics (HNTA-FB) for IoT services is proposed in this paper. This proposed HNTA-FB method uses binary patterns and a histogram of features to extract the region of interest. To reduce the memory requirements while providing access to IoT services, Histogram of Neighborhood Binary Pattern Pre-processing (HNBPP) model is proposed. The discriminative power of Neighbourhood Binary Pattern Registration (NBPR) is integrated with the normalized sparse representation based on the histogram. Additionally, this work presents a new Tripartite User Authentication model for fingerprint biometric template matching process. When compared with different state-of-the-art methods, the proposed method depicts significantly improved performance in terms of matching accuracy, computational overhead and execution speed and is highly effective in delivering smart home services.

The purpose of this paper is to develop a particular portfolio investment strategy limiting its downside risk, assuming low risk and maximizing its performance. This
kind of portfolio is suggested for Pension Funds or Insurance companies where their objective is to ensure a minimum annual return with a limited risk assumed or build portfolios with low risk assumed. The core financial asset of a pension fund and of an Insurance company is represented by fix income asset class. The idea is to develop a Dynamic Core-Satellite Portfolio using particular financial instruments called ETFs.
The Core component of this strategy is constituted Fix Income asset, instead the Satellite component might be constituting by equity, commodities or by fix income asset (but riskier and with different period of time). Building this portfolio, I will impose some constraint to the strategy that permit to safeguard the performance of the investor.

Recognition is a very effective area of research in regard of security with the involvement of biometric analysis, human computer interface and digital image processing. Humans inherently use faces to recognize individuals and similarly gestures are used in non-verbal communication to efficiently and effectively express thoughts. So, in order to migrate the natural means of communication by gesture into computer can setup a good move in making systems more interactive.

A simply supported bridge is modelled to investigate the effect of a strain rate dependent modulus of elasticity on the dynamic response of the structure to a moving load. The bridge is modelled as a one-dimensional discretized finite element beam and the moving load is represented by a point force. A constant modulus of elasticity is traditionally employed when simulating the dynamic response of structures under moving loads. In this paper, a time-variant modulus is used to calculate strains and displacements and compare them to the traditional approach for different speeds and bridge spans. The time-variant modulus is obtained from the strain rate of the structure which is used in turn to update the strain. The results show significant changes in the modulus and in the resulting load effect as load magnitude and speed increase.

Abstract Modeling and simulation for different parabolic dish Stirling engine designs have been carried out using Matlab®. The effect of solar dish design features and factors such as material of the reflector concentrators, the shape of the reflector concentrators and the receiver, solar radiation at the concentrator, diameter of the parabolic dish concentrator, sizing the aperture area of concentrator, focal Length of the parabolic dish, the focal point diameter, sizing the aperture area of receiver, geometric concentration ratio, and rim angle have been studied. The study provides a theoretical guidance for designing and operating solar parabolic dish Stirling engines system. At Zewail city of Science and Technology, Egypt, for a 10 kW Stirling engine; The maximum solar dish Stirling engine output power estimation is 9707 W at 12:00 PM where the maximum beam solar radiation applied in solar dish concentrator is 990 W/m 2 at 12:00 PM. The performance of engine can be improved by increasing the precision of the engine parts and the heat source efficiency. The engine performance could be further increased if a better receiver working fluid is used. We can conclude that where the best time for heating the fluid and fasting the processing, the time required to heat the receiver to reach the minimum temperature for operating the Solar-powered Stirling engine for different heat transfer fluids; this will lead to more economic solar dish systems. Power output of the solar dish system is one of the most important targets in the design that show effectiveness of the system, and this has achieved when we take into account many factors in the design of the solar dish system. One of these factors is the reflector material of the concentrator and using the results from the Matlab simulation program; where the Polymeric Film, Non Metal reflectors, with a net conversion power of more than 97.07%, still holds the conversion record than the Anod Aluminum reflectors, which has a net conversion power 85.97% with respect to the polished stainless reflectors with a net conversion power 49.52% from the 10 kW Stirling engine. Where the power output differ as 9707, 4952, 8597 W, respectively from the 10 kW Stirling engine. It is shown that there are changes in Stirling power output for different materials, which guide us to select the optimum material, based on the targeted power output and cost. Our target to reach the optimum power that we need it in the design 10 kW power output design as an example from the solar dish Stirling engine.

Described in this paper is a method for the analysis and comparison of multi-speaker surround sound algorithms using HRTF data. Using Matlab and Simulink [1] a number of surround sound systems were modeled, both over multiple speakers (for listening tests) and using the MIT Media Labs HRTF set (for analysis)[2]. The systems under test were 1 Order Ambisonics over eight and five speakers, 2 Order Ambisonics over eight speakers and Amplitude panned 5.0 over five speakers. The listening test results were then compared to the HRTF analysis with favourable results.

Following type-1 fractional fuzzy inference systems presented recently as the new generation of fuzzy inference systems, interval type-2 fractional fuzzy inference systems (IT2FFISs) as a leap further ahead in the evolution of fuzzy inference systems (FISs) are introduced in this article. The IT2FFISs, which are outlined in this article, add to the armamentarium of FISs some particular concepts such as interval type-2 fractional membership functions, type-2 fractional translation rule, type-2 fracture index, the concept of switching, the entanglement, the degeneracy concept, and so forth. An IT2FFIS exploits not only the tolerance for the uncertainty in the interpretation of the meaning of a word, but also the relevance between the quality and quantity levels of the given information to infer an answer to an inference query. The IT2FFISs make an increase in machine intelligence quotient possible by an increase in the range of FISs order rather than their type. Moreover, the synergy of the concepts coming with various modes of IT2FFISs such as the aggressive mode opens a gate to a space of fuzzy systems outputs which used to be indiscoverable. Furthermore, it is demonstrated that as the type-2 fracture index approaches zero, the space of IT2FFISs outputs contracts and eventually it coincides the space of IT2FISs output when the fracture index is equal to zero. It is also proved that, provided a particular order of the IT2FFIS is taken into account, independent of the problem in question, a typical IT2FIS never leads to results which are more satisfactory than those obtained by the IT2FFIS corresponding to the typical IT2FIS.

This paper focuses on an essential task of the enhanced NodeB eNodeB element in LTE architecture, the Radio Resource Manager RRM, which aims to accept or reject requests for connection to the network based on some constraints and ensuring optimal distribution of radio resources between Users Equipments UEs. Its main functionalities include Admission Control AC and Packet Scheduling PS. This paper will center mainly on the PS part of the RRM task, which performs the radio resource allocation in both uplink and downlink directions. Several approaches and algorithms have been proposed in the literature to address this need (allocate resources efficiently), the diversity and multitude of algorithms is related to the factors considered for the optimal management of radio resource, specifically, the traffic type and the QoS (Quality of Service) requested by the UE. In this article, an art’s state of the radio resource allocation strategies and a detailed study of several scheduling algorithms proposed for LTE (uplink and downlink) are made. Therefore, we offer our evaluation and criticism.

Dynamically changing background ("dynamic background") still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: that of swaying tree branches and their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new dataset suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras. The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected dataset, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art in the dynamic background literature.

Diseases in fruit cause problems in economic losses and production in agricultural industry worldwide. Modern food industries work on the quality and safety of the products. Fruits such as oranges and apple are imported and exported on large scale. Identifying the defect manually has become very much time consuming process. The combined study of image processing and clustering technique gave a turning point to the defected part segmentation in fruits to calculate percentage of infection in fruit. The increasing awareness towards quality of food has opened new opportunities of research in this area. In this research work, Marker Controlled Watershed algorithm for segmentation of fruit's infected parts as been implemented. Implementation time for the code has also been calculated. The affected area of fruit will also be computed in terms of percentage.

As a result of the limitations of human cognitive skills, judgment, decision-making, and tactical understanding in the use of Unmanned Aerial Vehicles (UAV), there is a need to redesign the current human-computer interface (HCI) for Autopilot control to improve the interaction and communication links between operators and the UAVs. This system displays the information to increase situational awareness; the operator will see everything, interpret it, make appropriate decisions, and have the ability to implement the decision. Multiple interfaces are developed using C’s Graphical User Interface (GUI) capabilities as a simulation environment. Both alternatives will combine buttons and place them in sequential order according to the steps needed to initialize UAVs and flight paths. Usability tests with participants are conducted to measure their performance based on previously determined metrics: the time it takes to train a participant on how to use the interface, the time to complete a...

—The paper describes about the design and performance of transceiver for the software defined radio (SDR).The sensible solution for reconfiguring radio is the SDR. It uses identical hardware platform to perform various tasks.The design is simulated using MATLABSimulink and performance is analyzed based on the error rate against signal to noise ratio.The design is investigated with 40MHz intermediate frequency transmission.

Analytic Hierarchy Process (AHP): Matlab computation software

WenJun Zhang
School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China; International Academy of Ecology and Environmental Sciences, Hong Kong

Received 10 July 2017;Accepted 23 December 2018;Published 1 March 2019
IAEES

Abstract
How to determine relative importance (e.g., weight) of nodes or links in a network is one of the focus in network biology. Analytic Hierarchy Process (AHP) can be used to determine weights of edges (i.e., links) and vertices (i.e., nodes) of a network, which is suitable for the hierarchical structure problems hard to be quantified. In present article, the Matlab computation software of AHP was presented for practical uses.

http://www.iaees.org/publications/journals/nb/articles/2019-9(1)/e-suppl/Zhang-Supplementary-Material.rar

This project addresses a cost effective, flexible solution of underground mine workers’ safety. A module of MEMS based sensors are used for underground environment monitoring and automating progression of measurement data through digital wireless communication technique is proposed with high accuracy, smooth control and reliability. A microcontroller is used to collecting data and making decision, based on which the mine worker is informed through alarm as well as voice system. ZigBee, based on IEEE 802.15.4 standard is used for this short distance transmission between the hardware fitted with the mine worker and the ground control Centre.
Industrial safety is one of the main aspects of industry specially mining industry. In the mining industry safety is a very vital factor. To avoid any types of unwanted phenomena all mining industry follows some basic precaution and phenomena. Communication is the main key factor for any industry today to monitor different parameters and take necessary actions accordingly to avoid any types of hazards. To avoid loss of material and damaging of human health, protection system as well as faithful communication system is necessary inside the underground mines. To increase both safety and productivity in mines, a reliable communication must be established between workers, moving in the mine, and a fixed base station.
Inside mines, the wired communication system is not so effective. Due to uncomfortable situation inside mines the installation cost as well as maintenance cost is high for wired communication networks. It is very difficult to reinstall the wired communication system inside mines after a landslide or damage due to any reason. To monitor other parameters during this condition it is very much necessary to maintain the communication system as usual. Accordingly, development of mine monitoring system to accurately detect temperature, pressure, flammable and poisonous gas and to track underground miners has significant meaning to safety production and rescue of coal mine disaster.

معادلات دیفرانسیل در علوم پایه نظیر ریاضی، فیزیک، شیمی، زیست شناسی و ستاره شناسی و همچنین علوم مهندسی نظیر مکانیک، برق، مواد و مهندسی شیمی کاربردی گسترده و حضوری چشمگیر دارند. معادله دیفرانسیل یک دسته از معادلات ریاضی است که بیانگر رابطه بین یک تابع مجهول از یک یا چند متغیر مستقل و مشتق های مرتبه های مختلف آن نسبت به متغیرهای مستقل است. بسیاری از قوانین عمومی طبیعت(در فیزیک، شیمی، زیست شناسی و ستاره شناسی) طبیعی ترین بیان ریاضی خود را در زبان معادلات دیفرانسیل می یابند. با توجه به اینکه اغلب معادلات دیفرانسیل که به واقعیت پدیده ها نزدیک هستند، دارای ترم های غیرخطی و پیچیده می باشند و یافتن جواب تحلیلی برای آنها امری دشوار یا غیرممکن است. عدم وجود پاسخ تحلیلی برای این گونه معادلات پیچیده و غیرخطی، منجر به ایجاد و گسترش روش های حل عددی شده است. مهمترین پارامترهای ارزیابی روش های حل عددی، سرعت، دقت و صحت حل معادله است. با توجه به ارتقاء چشمگیر سرعت و ظرفیت پردازش اطلاعات در پردازنده ها و کامپیوترها در اواخر قرن نوزدهم، روش¬های حل عددی نیز گسترده تر شدند و همچنان نیز این ارتقاء و گستردگی ادامه دارد. در این کتاب علاوه بر آشنایی با معادلات دیفرانسیل مختلف، روش های حل عددی آن ها با استفاده از یکی از بهترین نرم-افزارهای محاسباتی یعنی متلب آموزش داده می شود. این کتاب در تابستان سال 97 تالیف شد و تا ابتدای پاییز سال 98 مورد ویرایش و تصحیح قرار گرفت. مطالعه این کتاب به تمامی دانشجویان علوم پایه وهمچنین علوم مهندسی نظیر مکانیک، برق، مواد، مهندسی شیمی، مهندسی
عمران و مهندسی هوافضا اکیدا توصیه می شود

The popularity of Wireless Sensor Network
(WSN) has increased tremendously in recent times. WSN
connects the physical world with the virtual world by
forming a network of sensor nodes. In a sensor network
the sensor nodes sense the data within their sensing
range. Each node has limited energy which is used in
transmitting and receiving the data thus energy
preservation is most important for the survival of sensor
networks for longer period time. Many routing protocols
in WSN are used for the same purpose. In WSN,
Geographic Adaptive Fidelity (GAF) is a location based
routing protocol which transmits data on the basis of
location information of destination node. It works in
three phases i.e. discovery phase, sleeping phase and
active phase. This paper proposes a mechanism to
improve the discovery phase and reduces the energy used
by nodes in discovery phase and increases the lifetime of
network. Implementation of proposed Optimized GAF
protocol is done using MATLAB.

Skin diseases are among the most common health
problems worldwide. In this article we proposed a method that
uses computer vision based techniques to detect various kinds of dermatological skin diseases. We have used different types of image processing algorithms for feature extraction and feed
forward artificial neural network for training and testing purpose. The system works on two phases- first pre-process the
colour skin images to extract significant features and later
identifies the diseases. The system successfully detects 9 different types of dermatological skin diseases with an accuracy rate of 90%.

The assignment – selection problem used to find one-to- one match of given “Users” to “Laptops”, the main objective is to minimize the cost as per user requirement. This paper presents satisfactory solution for real assignment – Laptop selection problem using MATLAB coding.

Manufacturers generally share datasheet values of photovoltaic (PV) modules at only standard test conditions (STC). These conditions enable PV modules to generate high power but are rarely encountered in the real environment. Therefore, accurate modeling of PV modules is very important in terms of estimating the energy that can be obtained under all operating conditions. Many studies have been conducted in this field in the literature. In this study, a new method is proposed for the implementation of the commonly used five-parameter model. This new method uses a bisection search algorithm for calculating the value of the series resistance, which is one of the five parameters, and thus extracting the other parameters. The datasheet values provided by the manufacturers are sufficient for obtaining the series resistance and therefore other parameters. The accuracy of the method was first tested by comparing the datasheet values of the three different PV modules with the outputs of the proposed method. Finally, the simulation accuracy of the proposed method for different operating conditions was tested by comparing the real measurement data collected by the National Renewable Energy Laboratory (NREL) with the outputs of the method under the same operating conditions. The results show that the proposed method demonstrates good agreement with both datasheet values and real measurement data. The method offers a good balance of simplicity-accuracy.

A disaster is a sudden occurring dreadful event which can disrupt the functioning of community or society and causes great loss of life and property. There are two types of disaster – Natural disaster and Man-made disaster. When disasters occur due to natural forces they are called natural disasters, over which man has hardly any control. Some common natural disasters are earthquakes, landslides floods, droughts, cyclones, etc. These disasters cause enormous loss of life and property. Floods are one of the most common natural disasters occurring in many parts of the world every year. Floods have been a recurrent phenomenon in India and cause huge losses to lives, properties, livelihood systems, infrastructure and public utilities. India's high risk and vulnerability are highlighted by the fact that 40 million hectares out of a geographical area of 3290 lakh hectares are prone to floods (Prafulla Kumar Panda, 2014). Floods occur due to heavy rainfall within a short duration of time in a particular region which causes the rivers and streams to overflow. But Floods are not always caused by heavy rainfall. They can result from other phenomena, like snowmelt, steep slopes, impermeable rock, too much wet and saturated soil, or compacted or dry soil. And, floods near to the coastal areas are increasing because of increase in global warming.

Pattern classification and pattern association are the two powerful tools in neural network, these networks has a
capability to use the features of the brain and compute some of its strengths for the competitions. Adaline, is
found to use bi-polar activations for its input signal and target output. When adaline is to be used for pattern
classification, after training, a threshold function is applied to the input to obtain the activation and can solve
the problem with linear separability technique. In this paper, has simulated one of the methods of pattern
classification carried out using Adaline network. The object of the paper is to estimate the length of the stay of a
patient in a hospital by monitoring the symptoms of a patient’s diseases. The factor “Number of days to stay”
has a direct relationship to the severity of illness of the patient. Here, all the proposed algorithms has
implemented in Matlab 2011a. Matlab uses statistical data technique, called abalone data, of symptoms of the
patient for the purpose of training and testing. The main advantage of this system is to provide the statistical
data of patient in the view of finance required for further stay, no of days require to stay and patient recovery
condition etc.

Shallow water simulation is a prominent area of research in virtual reality systems. It is used for physics simulations as well as rendering for games and virtual environments. The simulation in important because we need to show the wave nature and also the buoyancy to make the environment more realistic. In essence it adds as a new dimension to the whole setting. These models can also be used to predict nature of tides, tsunami's and ocean surfaces. We have used a GUI in MATLAB R and for the physical simulation and Unity and Blender for a realistic virtual environment development.

Many pupils studying A-Level Mathematics will be wondering if it is worth learning a programming language, and whether this can help with their studies and their future employment. Most people have now heard of Artificial Intelligence (AI) and the Internet of Things (IoT), and with the proliferation of connected devices and ever-faster computer processing, there has been an explosion of data collection and analysis. AI, analytics, automation, data science and robotics are all changing the way we work and that’s why more and more employers are seeking mathematicians who can program.