Published by
Department of Computer Science and Engineering

Vision of the Department

  To Make the Department of Computer Science and Engineering the unique of its kind in the field of Research and Development activities in this part of world.


Mission of the Department

  To impart highly innovative and technical knowledge to the urban and unreachable rural student folks in Computer Science and Engineering through "Total Quality Education".


SERVER 5 NODE 1
AUGUST 2K18


ADVISORY BOARD

Dr. M.Indra Devi, HOD/CSE
Mr. L.Prabahar, Assistant Professor/CSE




MEMBERS

S. Hariharan IV Year/CSE
S.Sri Aravindan, IV Year/CSE
Herwin Selvaraj.H, IV Year/CSE
R.Sivagurunathan,IV Year/CSE



CONTENTS

1) Quantum Computers
2) Blue Brain Project
3) Wi-Vi Technology
4) Blue Eyes
5) Events

                                                Quantum Computers

    Quantum computers will be particularly suited to factoring large numbers , solving complex optimization problems, and executing machine-learning algorithms.

     At the heart of quantum computing is the quantum bit, or qubit, a basic unit of information analogous to the 0s and 1s represented by transistors in your computer.

    Qubits have much more power than classical bits because of two unique properties: they can represent both 1 and 0 at the same time, and they can affect other qubits via a phenomenon known as quantum entanglement.

 


     That lets quantum computers take shortcuts to the right answers in certain types of calculations.

  The academic and corporate quantum researchers says that somewhere between 30 and 100 qubits—particularly qubits stable enough to perform a wide range of computations for longer durations—is where quantum computers start to have commercial value.

Applications :
• The deployment of 100,000-qubit systems in the future will disrupt the materials, chemistry, and drug industries by making accurate molecular-scale models possible for the discovery of new materials and drugs
• And a million-physical-qubit system, whose general computing applications are still difficult to even fathom.




                                                                                                         JEGANATHAN.A
                                                                                                         IV Year CSE-B

                                                Blue Brain Project

        The Blue Brain, a Swiss national brain initiative, aims to create a digital reconstruction of the brain by reverse-engineering mammalian brain circuitry. The mission of the project, founded in May 2005 by the Brain and Mind Institute of the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, is to use biologically-detailed digital reconstructions and simulations of the mammalian brain (brain simulation) to identify the fundamental principles of brain structure and function in health and disease.

       The project is headed by the founding director Henry Markram, who also heads the European Human Brain Project, and co-directed by Felix Schürmann and Sean Hill. Using a Blue Gene supercomputer running Michael Hines's NEURON software, the simulation does not consist simply of an artificial neural network, but involves a biologically realistic model of neurons.It is hoped that it will eventually shed light on the nature of consciousness. There are a number of sub-projects, including the Cajal Blue Brain, coordinated by the Supercomputing and Visualization Center of Madrid(CeSViMa), and others run by universities and independent laboratories



Neocortical column modelling
        The initial goal of the project, completed in December 2006, was the simulation of a rat neocortical column, which is considered by some researchers to be the smallest functional unit of the neocortex (the part of the brain thought to be responsible for higher functions such as conscious thought). In humans, each column is about 2 mm in length, has adiameter of 0.5 mm and contains about 60,000 neurons neocortical columns are very similar in structure but contain only 10,000 neurons (and 108 synapses). Between 1995 and 2005, Markram mapped the types of neurons and their connections in such a column.

Progress
        In November 2007, the project reported the end of the first phase, delivering a data-driven process for creating, validating, and researching the neocortical column.By 2005, the first single cellular model was completed. The first artificial cellular neocortical column of 10,000 cells was built by 2008. By July 2011, a cellular mesocircuit of 100 neocortical columns with a million cells in total was built.

   A cellular rat brain is planned for 2014 with 100 mesocircuits totalling a hundred million cells. Finally a cellular human brain is predicted possible by 2023 equivalent to 1000 rat brains with a total of a hundred billion cells.Now that the column is finished, the project is currently busying itself with the publishing of initial results in scientific literature, and pursuing two separate goals:

  1.Construction of a simulation on the molecular level, which is desirable since it allows studying the effects of gene expression;
  2.Simplification of the column simulation to allow for parallel simulation of large numbers of connected columns, with the ultimate goal of simulating a whole neocortex(which in humans consists of about 1 million corticakle columns).

    In 2015,scientists at EcolePolytechniqueFederale de Lausanne(EPFL) developed a quantiative model of the previously unknown relationship between the glial cell astrocytes and neurons.This model describes the energy management of the brain through the function of the neuro-glial vascular uni (NGC).The additional layer of neuron-glial cells is being added to Blue Brain project models to improve functionality of the system.

Finding:
  The project is funded primarily by the Swiss government and the Future and Emerging Technologies (FET) Flagship grant from the European Commission, and secondarily by grants and some donations from private individuals. The EPFL bought the Blue Gene computer at a reduced cost because at that stage it was still a prototype and IBM was interested in exploring how different applications would perform on the machine. BBP was viewed a validation of the Blue Gene super computer concept.








                                                                                                            S.HANISHKUMAR
                                                                                                             IV Year CSE B

                                                Wi-Vi technology

    Wi-Vi technology helps a person to “see through walls” using the Wi-Fi signalsCatching hold of criminals hiding behind walls has been made easy by the passive radar which can read the Doppler shifts in ambient radio signals which are emitted by Wi-Fi or mobile communications towers.Gone are the days when criminals could easily shoot out the lights and try hiding in dark when congregated by police authorities. Already the creation of night vision goggles was helping the army and police authorities to catch hold of the criminals and now the hooligans have to worry about one more creation by the researchers and that is the passive radars which can catch hold of them on the basis of soft radio glow of wireless routers or mobile communication towers.

    WiFi Signals to Passively See Through Walls Using NI USRP and LabVIEW:
    Researchers at the University College London (UCL) have designed a novel technology which can track the Doppler shifts of the ubiquitous Wi-Fi and smartphone signals, thus helping authorities to “see” people who are moving even behind walls that are 25 centimeters in thickness.

    

Researchers believe that this novel technology can be used to detect the indoor moving targets for security purposes such as hostage situations.Apart from using it in public defenses the technology also finds it application in various scenarios such as crowd and traffic monitoring and human-machine interfacing.

    At the National Instruments’ NI Week 2015 conference held in Austin, Texas from 3-9 August, this innovative technology has won the Engineering Impact Award under the category of RF and Communications.

    In case of the UCL technology, it uses only passive radiation which it receives from the Wi-Fi routers hence it is much more reliable and cannot betray the surveillance. Different kinds of wireless signals are applied under different scenarios, for instance, by acquiring IEEE 802.11x (b, g, n, ac) signals this technology can help to track the indoor moving objects for security purposes as in case of hostage situations. On the contrary, the same system can also help to monitor cellular signals, such as Global System for Mobile Communications (GSM) or Long-Term Evolution (LTE) and this can help in determining the direction as well as the velocity of moving vehicles.



  For calculating the position of the hidden target, the UCL system would compare the two signals:

    Reference channel : This receives the baseline signal from the Wi-Fi access point or from other RF source.

   Surveillance channel: This is the one which picks up the Doppler shifted waves that gets reflected from a moving human.

    UCL technology works more or less like the traditional radar systems and it does rely on detecting the Doppler shifts in radio waves which are reflected by the moving objects. However, in case of traditional radar systems they transmit the radio waves whereas in the UCL technology the passive system relies on the ubiquitous Wi-Fi signals which are already present in the surrounding air waves. Due to the passive system, there is complete lack of spectrum occupation and power emission and this makes the UCL technology far better because the radar is undetectable.

Wi-Vi: See through Walls with W-Fi Signals:
        Another group of researchers, Dina Katabi and Fadel Adib of MIT have developed a technology (Wi-Vi) which helps a person to “see through walls” using the Wi-Fi signals.

    Basically, Wi-Vi captures the signals emitted by a person moving behind walls and in closed room. The Wi-Vi device comprises of active radars which are built through the wall and it can transmit as well as receive the signals

Indoor target tracking using high doppler resolution passive Wi-Fi radar:
    Another study comprises of a group of engineers who have published their papers which describes two Doppler only indoor passive Wi-Fi tracking methods based on “high Doppler resolution passive Wi-Fi radar”. Extended Kalman filter and the Sequential Importance Resampling (SIR) particle filters are the two filters which engineers have used in their study. This system basically works by comparing the reference and surveillance signals and it interprets even a very small frequency shifts and thus reveals the location and motion of hidden subjects.

    Tan and his team, used a software defined passive Wi-Fi radar using a standard 802.11 access point as an illuminator to present the experimental results for these two tracking filters. Based on the experiment, the engineers concluded that of the two filters, SIR particle filter is the one which should be used for indoor tracking on the basis of Wi-Fi signals as it gives a much accurate and better performance. In their paper, the engineers have also given suggestions for simplifying the SIR particle and discussed various applications to multiple target tracking

    The engineers tweaked the processing parameters such as increasing the signal integration time and lowering the sensitivity thresholds; ultimately the engineers were able to make the passive radar “see through” the subtle movements which included even the hand gestures. The device was able to give some radar-style scatter plot along with some varied signals.





                                                                                                      S.vijay prasath
                                                                                                       IV Year CSE B


                                                               BLUE EYES

                                    

     Aims at Creating Computational Machines that have perceptual and sensor ability Uses Camera and Microphone to Identify User Actions and Emotions the term “BLUE” refers as the Bluetooth which enables to obtain a lot of Interesting and Information. The needs to build a Machine that can understand emotions verify your Identity, feels the presence and Interacts with the user a pc that can listen, talk or scream. The Technologies used are Emotion Mouse Manual and Gaze Input Cascaded (MAGIC) Artificial Intelligent Speech Recognition.

     The Applications are Automobile Industry, Video Games, Power Station, Flight Control Centers and Operating Theatres. The Blue Eyes technology system is a combination of a set of hardware and software systems. The hardware consists of a central system unit (CSU) and data acquisition unit (DAU). The DAU used in the Blue Eyes technology is the mobile component of the system. The main function of DAU is to gather the physiological information from sensors and forward to the CSU for processing and verification purposes.









                                                                                                                        M.Rajasekaran
                                                                                                                        AP/CSE

                                                Fog Computing

What is fog computing?
    Fog computing is an extension of cloud computing that provides computation, storage and networking devices between end devices and traditional cloud servers. It is an architecture where a large number of heterogeneous and decentralized devices communicate and cooperate among them and with the network to carry out a substantial amount of storage, communication, control, configuration, measurement and management.

Why cog computing?
    It is very crucial to place the contents and application services closer to the consumers. Fog computing provides a solution for this. Location awareness i.e. ability to determine their location by the devices is not addressed by cloud computing whereas fog computing is able to address this.

                                                                                                                               Fig 1. Downloading the flyer of a nearby store

    Let us consider a mobile user who is inside a shopping center. He/She has to download the localized store flyers within the shopping center. For this, the flyers are to be uploaded in the remote cloud server. Though the store and the user are physically close, the user has to retrieve the contents through a long-distance link from the cloud. This results in delay and increases the overhead of cloud server. A fog server can be used in the vicinity of the user and devices and the contents can be pre cached. The user can be connected quickly without the need to search over cloud. The scenario is shown in Fig 1.

    From the above example, it is understood that the amount of data transported to the cloud for processing, analysis and storage is reduced and thereby the efficiency is improved.

Characteristics of fog computing:
The following is the list of unique characteristics of fog computing:
  •Edge location: Latency-sensitive applications that require real-time data processing are supported.
  •Location awareness: The fog nodes are geographically distributed which makes the end users to derive their locations and track their devices.
 •Real-time interactions: Real-time interactions are supported by fog applications.

 •Real-time interactions: Real-time interactions are supported by fog applications.
 •Edge analytics: Fog computing analyzes sensitive data locally instead of sending it to the cloud for analysis.
 •Scalability: Increase in the number of end devices in IOT becomes a bottleneck for cloud which can be overcome by Fog computing.


                  The architecture of fog computing is given in Fig 2.

How fog computing works?
 Fog computing has three-layer Mobile-Fog-Cloud hierarchy. An intermediate fog layer consists of geo-distributed Fog servers which are deployed at the local premises of mobile users. Fog server is a virtualized device with build-in data storage, computing and communication facility. A Fog server can be adapted from existing network components, e.g., a cellular base station. The role of Fog servers is to bridge the mobile users and cloud.

Advantages
    Fog computing reduces the amount of data sent to the cloud thereby conserves bandwidth. System response time is improved. It improvese security and supports mobility.

Application areas
    Fog Computing is used in the following application areas:
    • Autonomous vehicle
    • Smart Grid
    • Smart Traffic Lights
    • Smart building
    • Wireless Sensor and Actuator Networks

References
1.https://en.wikipedia.org/wiki/Fog_computing
2.”Fog Computing: Focusing on Mobile Users at the Edge”, Tom H. Luan, Longxiang Gao, Yang Xiang School of Information Technology, Deakin University , Zhi Li, Limin Sun Institute of Information Engineering, Chinese Academy of Sciences, China,aRXiv:502.01815v1 [cs.NI] 6 Feb 2015.









                                                Dr.R.Muthuselvi, Prof/CSE                                                                                      Mrs.V.Sutha Jebakumari, AP/CSE



WORKSHOP

1. 30 III CSE students attended the 10 days Summer Internship workshop from 19.06.2018 to 30.06.2018 organized by Quality Improvement Cell of CSE Department of our college. The workshops include 09 days program on "Application Development using Python" and one day on "Environment Monitoring using IOT"

                                             


S.NOParticipants NameDate
1 16UCSE004 SUNIL KRISHNAN P
2 16UCSE011 MUTHU SANKAR.K
3 16UCSE014 SANJAY.S
4 16UCSE017 SONALI.B
5 16UCSE019 PAVITHRA R.V.
6 16UCSE020 RINITHA.R
7 16UCSE021 ILAKKIYA.V
8 16UCSE022 SANTHA RUBAN.R.P
9 16UCSE025 ARUNACHALAM.M
10 16UCSE026 KAMATCHI KUMARAN.A
11 16UCSE027 JANANI.C
12 16UCSE028 GURUPRASHATH.R
13 16UCSE034 PUJA DEVI K
14 16UCSE042 SIVASRI SARANYA.R
15 16UCSE047 ABHIRAAMI.N
16 16UCSE049 KRISHNAPRIYA.R
17 16UCSE053 VIDHYA.S
18 16UCSE054 NAGARJUNAKUMARAN.P
S.NOParticipants NameDate
19 16UCSE055 SNEHA.V.P
20 16UCSE056 NANDHINIPRIYA.T
21 16UCSE057 GIRTHANA.K
22 16UCSE062 MARISANGAVI.V
23 16UCSE072 GOGAL VIJAY.B
24 16UCSE077 DEVI NANDHINI.A
25 16UCSE080 MANOJ KUMAR.K
26 16UCSE081 THARANI.S
27 16UCSE083 MOHAMMED AFROSE.J
28 16UCSE086 KAVITHA.K
29 16UCSE087 RANGEELA.S
30 16UCSE090 KARAN.B



Contributions and feedbacks can be sent to following mail:
cseplugin@kamarajengg.edu.in

VALUE ADDED COURSES

2. 63 III CSE students attended the 10 days FP 5.0 campus connect program from 19.06.2018 to 30.06.2018.

S.NOParticipants NameDate
1 16UCSE001 NANDHINI T
2 16UCSE006 SHARANDEEP.S
3 16UCSE007 AADHI GOAURISH.K
4 16UCSE009 JEYA GANESH.J
5 16UCSE013 BALAPRADEEP.R
6 16UCSE015 IRENE SHARON.P
7 16UCSE018 SIVA BALAN.R
8 16UCSE023 KEERTHANA.S
9 16UCSE029 NANDHINI.S.R
10 16UCSE030 HARSHATH KANNAN.S
11 16UCSE036 MUTHU ESHWARAN R
12 16UCSE038 HARISHA PRABHA.S.P
13 16UCSE039 YUVARAJ.S
14 16UCSE040 RAMKAMAL.S
15 16UCSE044 NIVETHA.V
16 16UCSE046 SANGEETHA.M
17 16UCSE048 JASMINE REENA WINSY.E
18 16UCSE050 SATHISHKUMAR.C
19 16UCSE051 KARTHIGAI SELVI.B
20 16UCSE052 HARAN.A.R
S.NOParticipants NameDate
21 16UCSE058 AISHWARYA.M
22 16UCSE059 ISWARYA.P
23 16UCSE060 VEERASOLAIYAPPAN.V
24 16UCSE061 ISHWARYA.K
25 16UCSE065 BALAMURUGAN.A
26 16UCSE066 JOHNSY.R
27 16UCSE069 DEEPAK.M
28 16UCSE071 ALLWIN SANTHOSH.M
29 16UCSE073 ISWARYA.A
30 16UCSE079 MUTHU GOKILA.P
31 16UCSE082 SHOBANA.S
32 16UCSE089 NILOFER.B
33 16UCSE091 PAVITHRA.R
34 16UCSE092 KAVYA.S
35 16UCSE093 SANGEETHA.K
36 16UCSE094 AAFRIN BANU.M
37 16UCSE095 SWARNAMALIYA.M
38 16UCSE096 NIVETHA.S
39 16UCSE097 HARIVIGNESH.V
40 16UCSE098 ABINAYA.S
S.NOParticipants NameDate
41 16UCSE099 SELVA MANIKANDAN.K
42 16UCSE101 GOMATHI VINAYAGAM
43 16UCSE002 CHITHRA MALA K
44 16UCSE003 ROSHINI A
45 16UCSE005 KARKUVELAYYANAR.T
46 16UCSE010 SAANMUGAKUMAAR K
47 16UCSE016 NANDHA KISHORE.V
48 16UCSE024 INDHU MATHI.S
49 16UCSE031 RAMYADEVI.P
50 16UCSE041 SILO MIRACLIN EDWARD.P.S
51 16UCSE063 MUTHU PANDI.S
52 16UCSE064 LAKSHMI PRIYA.R
53 16UCSE067 VANITHA.P
54 16UCSE068 GURU PRIYA.R
55 16UCSE070 AJITH KUMAR N.S
56 16UCSE074 ATSHAYA.P.S
57 16UCSE075 ARAVINDH.K
58 16UCSE076 RAJA SUSHMITHA.T
59 16UCSE078 SOUNDARYA.P
60 16UCSE084 RAKSHANADEVI.V
S.NOParticipants NameDate
61 16UCSE085 MADHUVANTHI.P
62 16UCSE088 MUTHU PRIYA.K
63 16UCSE102 MUTHU SUDHA DEVI.M