Abstract— outside and also ensure that the movement


Abstract— Human machine interface is used to accept the
input/instructions from the user and pass it to the computer/machine for
processing. With the advent of interfacing technology it is possible to use
voice command for operating the machines. Display technology also had undergone
a great revolution and it is possible to move the display along the direction
of movement of user. In this work we propose a model that uses voice interface
for command and holographic visual projection for display. The model on
implementation will allow the user to control the machine via physical device
or voice command. It will also allow the user to convey the voice message in
the form of encrypted text message through 
mail or as SMS. As the user turns display will also rotate so that user
will be able to see the display in front of him.


Human machine interface; Display
technology; Holographic
visual projection ; Encryption.

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I.     Introduction 


Combat vehicles are usually integrated with
 complex and multiple display screens within  such a small space. The
display technology here follows a series of technologies compatible with each
other to provide smart virtual display as human machine interface. The display
technology includes 3D holographic virtual display interfaced with sensor for
head movement detection. The 3D holographic virtual display is a mid-air
holographic screen which enables the display of various control panels without
a physical screen.


vehicle’s display need to be smart enough to enable the user give instructions orally.
Since the crew need to monitor the outside and also ensure that the movement of
vehicle also happens in the manner in which he wants the vehicle to move. Considering
the fact that crew need to do multitasking and has to keep moving in different
angles .It is required that the display should be smart enough to detect his
direction of movement(head) and show the display accordingly.


    Thus the sensors for head
movement detection is interfaced that senses the head movement of the user and
enables the screen to move as the user turns or moves.There are various
different algorithms for head movement detection.One such method recovers 3-D
head pose video with a 3-D cross model in order to track continuous head
movement. The model is projected to an initial template to approximate the head
and is used as a reference. Full head motion can then be detected in video
frames using the optical flow method. It uses a camera that is not
head-mounted. It can be considered a high complexity algorithm.


  One of the easiest interfacing is
voice recognition interface when compared to feeding the input by
typing.Now-a-days voice machine interfacing has become common. The crew station
human machine interface we propose uses voice command of the user as one of the
interface. The entire display and the respective functions works on voice
commands.Voice recognition software captures and converts your speech via a
microphone. Voice recognition applications can transcribe recordings from a
number of formats. Everyone’s voice and phrasing sounds slightly different, so
the most effective programmes use a simple, one-off process called ‘enrolment’
for the software to determine how you speak.Most voice recognition software
gives you the ability to start, navigate and control your computer programmes
through spoken commands.


other important feature is speech to text conversion for communicating with the
main base or the neighbor co-soldier.This helps to contact them in the form or
text or email.The encryption algorithms are used for end user display of
messages.Text can be edited very easily. You can highlight the text to be
changed by using commands such as “Select line”  or “Select paragraph” and
then saying the changes you want to make to the selected text.In case of email,
as the text is dictate,it formats the text in an email format.In case of SMS
the texts just transmits in the form of a simple text. End-to-end encryption
(E2EE) is a system of communicating  where
only the communicating users can read the messages.The end-to-end encryption
paradigm does not directly address risks at the communications endpoints
themselves. Even the most perfectly encrypted communication pipe is only as
secure as the mailbox on the other end. Thus various encryption algorithms are
used to secure these messages.


this paper we have discussed various technologies and algorithms used for crew
station human machine interface and display technologies along with end- to-
end communication with encryption.


II.    Literature Review

Based on
the various earlier research papers on our project, some of the papers appears
to be relevant.


In the paper “Automatic speech recognition:
A review” 1,
they have proposed that The process of speech recognition begins with a speaker
creating an utterance which consists of the sound waves. These sound waves are
then captured by a microphone and converted into electrical signals. These
electrical signals are then converted into digital form to make them
understandable by the speech-system. Speech signal is then converted into
discrete sequence of feature vectors, which is assumed to contain only the
relevant information about given utterance that is important for its correct
recognition. An important property of feature extraction is the suppression of
information irrelevant for correct classification such as information about
speaker (e.g. fundamental frequency) and information about transmission channel
(e.g. characteristic of a microphone). Finally recognition component finds the
best match in the knowledge base, for the incoming feature vectors. Sometimes,
however the information conveyed by these feature vectors may be correlated and
less discriminative which may slow down the further processing. Feature
extraction methods like Mel frequency cepstral coefficient (MFCC) provides some
way to get uncorrelated vectors by means of discrete cosine transforms (DCT).

Ramakrishnan  have proposed that Feature extraction is a process that
extracts data from the voice signal that is unique for each speaker. Mel
Frequency Cepstral Coefficient (MFCC) technique is often used to create the
fingerprint of the sound files.  These extracted features are Vector
quantized using Vector Quantization algorithm. Vector Quantization (VQ) is used
for feature extraction in both the training and testing phases. After feature
extraction, feature matching involves the actual procedure to identify the
unknown speaker by comparing extracted features with the database using the
DISTMIN algorithm. Also, Hidden Markov Processes are the statistical models in
which one tries to characterize the statistical properties of the signal with
the underlying assumption that a signal can be characterized as a random
parametric signal of which the parameters can be estimated in a precise and
well-defined manner. In order to implement an isolated word recognition system
using HMM, the following steps must be taken:
(1) For each uttered word, a Markov model must be built using parameters that
optimize the
observations of the word.
(2) Maximum likelihood model is calculated for the uttered word.

In the paper ,”  Speech Recognition as Emerging Revolutionary Technology”
3,the authors
have proposed that Speech recognition is the translation of spoken words into
text. It is also known as “automatic speech recognition”,
“ASR”, “computer speech recognition”, “speech to
text”, or just “STT”. Speech Recognition is technology that can
translate spoken words into text. Some SR systems use “training”
where an individual speaker reads sections of text into the SR system. These
systems analyze the person’s specific voice and use it to fine tune the
recognition of that person’s speech, resulting in more accurate transcription.
Also, Both acoustic modelling and language modelling are important parts of
modern statistically-based speech recognition algorithms. Hidden Markov models
(HMMs) are widely used in many systems. Language modelling has many other
applications such as smart keyboard and document classification.


The paper, “End-to-end Encrypted Messaging
Protocols: An Overview” 4, aims at giving an overview of the different core protocols
used for decentralized chat and email-oriented services. This work is part of a
survey of 30 projects focused on decentralized and/or end-to-end encrypted
internet messaging, currently conducted in the early stages of the H2020 CAPS
project NEXTLEAP. They have used various email and chat protocols such as SMTP
and XMPP for their work.


Also in the paper, “Messengr: End-to-End
Encrypted Messaging Application With Server-Side Search Capabilities” 5
,they have
implemented a proof-of-concept app that provides strong end-to-end encryption
for chats and allows users to search through their encrypted messages without
the server learning the contents any message nor what keyword the user searched


that the algorithm used to encrypt data is one of the most important steps, as
this is what determines how difficult it is for the publicly transmitted
encrypted data to be decrypted by an unwanted party. One of the most commonly
used cipher algorithms is the Advanced Encryption Standard (AES). A National
Institute of Standards and Technology (NIST) publication released on November
26, 2001, outlined AES to be the standard for securing sensitive information
within Federal departments and agencies. According to this publication, AES is
a specified form of the Rijndael algorithm, which is a block cipher that can
process data blocks of 128 bits using cipher keys with lengths of 128,192, or
256 bits. These cipher keys are typically obtained from the Diffie-Hellman key
exchange mention earlier. Next, the text is converted into hexadecimal format
and stored in arrays, so it can be interpreted by the computer. From there, the
data that is to be encrypted undergoes four transformations for every round.
The number of rounds is decided by the length of the cipher key mention
earlier, and are 10, 12, and 14 in order of increasing key length. All of these
steps work to scramble the data as much as possible, making a user’s data even
more difficult to intercept


In the paper,”Full-Parallax Holographic
Light-Field 3-D Displays and Interactive 3-D Touch” 7,

Masahiro Yamaguchi have explained that
Human–computer visual interfaces are evolving toward more natural and intuitive
interactions, e.g., from multitouch to gesture, and from 2-D to 3-D. Combining
a 3-D display and a gesture interface allows direct interaction with an image
reproduced in 3-D space, which makes interaction easier and more enjoyable.In
this system, the reproduced 3-D images and the 3-D touch detection are
associated with each other, and thus, we do not have to worry about the
complicated registration between them. The identification of the user’s
interaction is simple, because the color information of the 3-D image can be used
for this purpose. Some experimental results of the 3-D touch-sensing display
are introduced, and possible applications of this technology are discussed as


In the paper,”Holographic 3D Touch Sensing
Display “8,Masahiro
Yamaguchi and Ryo Higashida have proposed that a 3D image floating in the air
is reproduced by a 3D light-field display using a holographic screen, and a 3D
touch interface is implemented by detecting the touch to the reproduced real
image. A simple 3D touch sensing experiment based on the proposed method is
demonstrated.The holographic screen consists of a 2D array of small elementary
holograms that reproduce diverging light . It is produced by the optical system
of holographic 3D printer  without exposing 3D image. The holographic
screen is illuminated by the light from a projector, where the projected image
contains the light-ray information similar to the one used in integral imaging.
The holographic screen reconstructs a 2D array of diverging light-rays, which
are modulated by the projected image, and it works as a full-parallax
light-field 3D display.The system for 3D touch sensing display is shown in fig.
2. A 3D real image is reproduced by the display presented in the previous
section, and a user can touch the floating image. Then the fingertip that
touches the real image is colored by the image, and it is detected by the
camera behind the holographic screen. If the color of the detected light agrees
with the image reproduced by the 3D display, then the fingertip is detected. By
monitoring the difference between the successive frames, the background pattern
can be almost removed in the images captured by the camera.


In a paper named,”Head motion controlled
power wheelchair” 9,Kupetz et al. implemented a head movement tracking system
using an IR camera and IR LEDs. It tracks a 2×2 infrared LED array attached to
the back of the head. LED motion is processed using light tracking based on a
video analysis technique in which each frame is segmented into regions of
interest and the movement of key feature points is tracked between frames . The
system was used to control a power wheelchair. The system needs a power supply
for the LEDs which could be wired or use batteries. The system can be improved
to detect the maximum possible unique head movements to be used in different
applications. It needs more experiments to prove its accuracy in addition to
more theoretical proofs.


In the paper,”Head movement
recognition based on LK algorithm and Gentleboost” 10,Jian-zheng and Zheng  presented a mathematical approach using
image processing techniques to trace head movements. They suggested that the
pattern of the head movements can be determined by


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