DESIGN OF MEDIAN FILTER IN QUANTUM-DOT CELLULAR AUTOMATA FOR IMAGE PROCESSING APPLICATIONS

Advances and Applications in Mathematical Sciences
Volume 18, Issue 9, July 2019, Pages 893-900
© 2019 Mili Publications
2010 Mathematics Subject Classification: 81V65, 03D15, 13Pxx.
Keywords: Median filter; Quantum-dot cellular automata; Nanotechnology; QCA Designer.
Received November 13, 2018; Accepted January 7, 2019
DESIGN OF MEDIAN FILTER IN QUANTUM-DOT
CELLULAR AUTOMATA FOR IMAGE PROCESSING
APPLICATIONS
BANDAN KUMAR BHOI1, NEERAJ KUMAR MISRA2,
IPSITA DASH1, SONALI PANDA1 and ANKITA PATRA1
1Department of Electronics and Telecommunication Engineering
Veer Surendra Sai University of Technology
Burla, 768018, Odisha, India
E-mail: bkbhoi_etc@vssut.ac.in
2Department of Electronics and Communication Engineering
Bharat Institute of Engineering and Technology
Hyderabad 501510, India
E-mail: neeraj.mishra3@gmail.com
Abstract
In this paper, a new layout of the median filter by using Quantum-dot cellular automata
(QCA) is designed. This filter is designed using one hot encoding technique. A new algorithm
for designing median filter is proposed using majority logic. The 1-bit median finding
architecture occupies area of 0.05 µm2, with a delay of 0.5 clk. The proposed median finding
architecture is also extended to higher bit sizes with optimized performance. The uniqueness of
our n-bit median finding architecture is that for any bit size the delay for finding the median
value is same which is 0.5 clk because every majority gates are working independently.
Therefore for faster operation, this majority logic-based technology is highly congruous. All the
QCA circuits are implemented in QCA Designer software.
1. Introduction
Median filtering is basically used in image processing applications which
are basically used for the noise reduction in the image. The pixels of the
image are sorted, and the middle position of that sorted value is known as the
median. These pixels of the image are replaced by its median value [1]. There
are different types of sorting algorithms like quick sort, bubble sort, etc. are

B. K. BHOI, N. K. MISRA, I. DASH, S. PANDA and A. PATRA
Advances and Applications in Mathematical Sciences, Volume 18, Issue 9, July 2019
894
present. But these sorting algorithms are not very much efficient in designing
hardware. Here we proposed a new algorithm using majority logic for finding
the median which is very much efficient and fast in operation. The basics of
QCA and an algorithm for the median filter using QCA are described in
section 2 and section 3 respectively. Finally, the conclusion is presented in
section 4.
2. QCA Basics
Quantum-dot cellular automata (QCA) is a technique which consists of an
array of cells & each cell contains four quantum dots which are arranged in a
square pattern. The cell consists of two mobile electrons that can tunnel
between the dots [2][3]. Electrons cannot be tunneled out due to the potential
barriers between the cells. Here data are moved from one point to another of
the cells by transferring its polarization value. In this polarization state ‘-1’
refers to logic ‘0’ and ‘’+1’ refers to logic ‘1’. Some universal gates like majority
gate & inverter have been made by using QCA [4-8]. If one value among the
inputs of the majority gate is given by ‘0’ then it will behave like an AND
gate. Similarly, if one value among the inputs of the majority gate is given by
‘1’, then it will behave like an ‘OR’ gate. The QCA inverter can be
implemented by different types of cell configuration. The QCA wire can be
constructed by connecting the QCA cells back to back. Figure 1a shows QCA
cells, Figure 1b shows majority gate, Figure 1c shows inverter and Figure 1c
shows binary wire [9-12].
Figure 1. QCA basics (a) QCA cell (b) majority gate (c) inverter (d) binary
wire.
3. Proposed Median Filter Architecture
Here a new architecture for finding the median is proposed. Figure 2 shows
the median finding and encoding diagrams. Here, for example, 3×3 size

DESIGN OF MEDIAN FILTER IN QUANTUM-DOT 
895
matrix is considered with nine numbers present. The maximum size of each
number is considered as a maximum of 4-bit in size. Here the numbers are in
the ascending order as 2, 3, 4, 6, 7, 8, 10, 12, and 15. By using one-hot
encoding method, a median filter has been designed. By using this method,
the input data can be decoded without using the decoder.
Figure 2. The median finding of a 3×3 matrix.
Figure 3. Median finding one hot encoding.

896
Figure 4. Median Filter architecture for each bit using Majority Gates.
Here every number is represented by one hot encoding, for example, 2 digit
can be written as “0000000000000011”. Here 16 numbers digits are present in
encoded data, for making of all possible combinations of 4-bit size number.
Similarly, 8 digits can be represented as “0000000011111111”. Similarly, all
the numbers are entered in one hot encoding format. Now in each column,
nine numbers of bits are present. In each column, the majority number, i.e., 1
or 0 is selected. The majority value of each column will be the median value of
the input nine numbers. This circuit can be suitably designed with majority-
logic based circuits such as Quantum-dot cellular automata. Figure 4 shows
the majority gates based circuit to find out a lower bit of final median (i.e., Y
[0] in this case). Figure 5 shows the QCA layout of the median filter
architecture for a single column of Figure 4. For this column, four numbers of
majority gates are needed. Therefore designing 4-bit numbers median
architecture needs 144 (i.e., 16×9) numbers of majority gate. The advantage
of this architecture is that all columns can find its value independently.
Therefore the Median value can be generated, with a clock delay of single
column gates. Here I[0] to I[8] are input numbers of any single column, and
Y[0] is an output value of that column.

897
Figure 5. QCA layout of Majority gate based Median finding architecture.
4. Result Analysis
The result of the median filter layout simulation is displayed in Figure 6.
From the simulation result, it can be verified that there is a delay or 0.5
clocks. Figure 7 shows the QCA layout of the 4-bit numbers median finding
architecture. The cell complexity, area and delay analysis of the proposed
architecture are shown in below Table 1.
In this table for 1-bit architecture only 4 numbers of majority gates
needed, with a delay of 0.5clk. For designing 2-bit architecture 16 numbers of
majority gates needed with a delay of 0.5 clk. The 4-bit architecture needed
64 numbers of majority gates with a delay of 0.5 clk. Similarly n-bit
architecture have 4×2n number of majority gates with a constant delay of 0.5
clk. From the result of Table 1, it can be concluded that proposed architecture
has achieved greater performance in speed, compared to other parameters
when the size of the architectures are increasing, because the delay is
constant irrespective of the size.

898
Figure 6. Simulation result of a majority output of a single column.
Table 1. Result analysis of the median finding QCA layouts.
Median
filter
Number of
Majority gates
Number of
Cells
Total Area Delay
1-bit 04 21 0.05µm2 0.5 clk
2-bit 16 84 0.2 µm2 0.5 clk
4-bit 64 336 0.8 µm2 0.5clk
8-bit 1024 5376 12.80 µm2 0.5 clk

899
Figure 7. QCA layout of 4-bit numbers median finding architecture.
5. Conclusion
In this chapter, a new median filter is designed using majority logic-based
technology. One hot encoding technique is used in the proposed architecture,
which consumes low power compared to a binary encoding technique. The
proposed n-bit median filter layout requires 4×2n numbers of majority gates.
This filter can be used in image processing and any median finding related
applications.

900
References
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