A continuous time adc and digital signal processing system for smart dust and wireless sensor applications

IJRET: International Journal of Research in Engineering and Technology ISSN: 2319-1163
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Volume: 02 Issue: 06 | Jun-2013, Available @ http://www.ijret.org 941
A CONTINUOUS-TIME ADC AND DIGITAL SIGNAL PROCESSING
SYSTEM FOR SMART DUST AND WIRELESS SENSOR APPLICATIONS
B. Janardhana Rao1
, O.Venkata Krishna2
, V.Silpa Kesav3
1, 3
Asst.Prof, ECE Dept., 2
Sr. Asst.Prof, CVR College of Engineering, Hyderabad, A.P., India,
janardhan.bitra@gmail.com, venkatakrishna.odugu@gmail.com, shilpakesav@gmail.com
Abstract
In this paper an event-driven (ED) digital signal processing system (DSP), Analog-to-digital converter (ADC) and Digital-to-analog
converter (DAC) operating in continuous-time (CT) with smart dust as the target application is presented. The benefits of the CT
system compared to its conventional counterpart are lower in-band quantization noise and no requirement of a clock generator and
anti-aliasing filter, which makes it suitable for processing burst-type data signals.
A clock less EDADC system based on a CT delta modulation (DM) technique is used. The ADC output is digital data, continuous in
time, known as “data token”. The ADC used an un buffered, area efficient, segmented resistor string (R-string) feedback DAC. This
DAC in component reduction with prior art shown nearly 87.5% reduction of resistors and switches in the DAC and the D flip-flops in
the bidirectional shift registers for an 8-bit ADC, utilizing the proposed segmented DAC architecture. The obtained Signal to noise
distortion ratio (SNDR) for the 8-bit ADC system is 55.73 dB, with the band of interest as 220 kHz and Effective number of bits
(ENOB) of more than 9 bits.
Index Terms: smart dust, continuous-time (CT), Delta modulation, Analog-to-digital converter (ADC), Digital-to –analog
converter (DAC)
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1. INTRODUCTION
The target of this paper is to study and design a mixed-signal
processing system consisting of an Analog-to-Digital Converter
(ADC), Digital Signal Processor (DSP) and Digital-to-Analog
Converter (DAC), for the smart dust application, in a top-down,
test-driven design methodology. The complete system has to be
operated in a CT mode without any sampling. The focus is to
achieve low power consumption and area, without sacrificing the
overall system performance.
There has been a new phenomenon emerging out in recent past
called “smart dust” also known as “motes” or “wireless sensor
networks” or “swarming”. Smart dust is a tiny (size and density
of a sand particle), ultra-low- power electronic sensory
autonomous device packed with various sensors, intelligence and
even wireless communication capability. A typical scenario
could be smart dust motes deployed in large numbers to form an
ad-hoc network, called as swarm, detects environment variables
such as light, temperature, pressure, vibration, magnetic flux
density or chemical levels. This network, formed by few tens or
even millions of motes, can communicate with neighboring
motes and pass on the data collected from one mote to another in
a systematic way, which can be collected and processed further
[1]. Smart dust can derive the energy necessary for its
functioning either from batteries or from the environment itself,
or from both. The power consumption of the smart dust
decreases with the shrinkage in the size of the mote. It has to be
in the order of few microwatts. This imposes a stringent
constraint on the design, and has to be addressed by following
various low power design techniques.
Fig- 1: Block diagram of a typical smart dust mote.
Fig-1 shows the block level details of a single autonomous smart
dust supply regulator, control, high-speed sensor interface, low-
speed sensor interface, DSP and radio transceiver section.
Energy harvester generates energy from the environment such as
solar radiation, wind or even vibration. The generated energy is
stored for future usage, using an energy storage element. The

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available energy is supplied efficiently, in the form of fixed
voltages and currents, to the various circuits of the system with
the help of the supply regulator. The control circuitry is
responsible for the various control mechanisms involved in a
mote, which decides the operation of the mote. The sensors
collect the environmental variations of interest and convert them
to an equivalent electrical signal, analog in nature. The sensory
interfaces are required to perform initial signal conditioning and
analog to digital conversion of the sensed data. The data is
processed digitally using a DSP. DSPs can essentially perform
various kinds of operations on the digital signals, such as
filtering, domain transformations, compression, encryption, etc.
The processed data is reconstructed back to analog equivalent
necessary for transmission, using a DAC (not shown in figure).
The transceiver section up-converts the baseband signal to Radio
Frequency (RF) signals and transmits them. It also receives the
RF signals and down-converts them to the baseband signal. This
paper targets the design of the low speed sensor interface and the
DSP, as highlighted in fig- 1.
The specifications of the CTDSP system, that is finally going to
be deployed in the smart dust motes are shown in table 1. The
major constraint imposed on the system is that all the blocks in
the design should be clockless. Further, the system should be
implemented in 90 nm advanced Complementary Metal Oxide
Semiconductor (CMOS) process. The specifications of the ADC,
DAC and the transversal direct-form Finite-Impulse-Response
(FIR) Low Pass Filter (LPF) is as shown in table 1.
Table- 1 SPECIFICATIONS OF THE CT-DSP SYSTEM FOR
SMART DUST
Continuous time processing
A new emerging field of digital signal processing in CT has been
presented by Prof. Yannis Tsividis of Columbia University [2,
3]. It is opposed to the conventional sampled systems where the
processing is carried out in discrete time. This CTDSP system
processes the data sensed by the analog Interface block of the
sensor module and produce the output to the RF front-end. As
the input data is of burst nature, occurring occasionally, it is
highly desirable that the CTDSP system remains idle most of the
time and operates only when there is a significant data to
process. This is achieved by using level crossing technique,
where the ADC operates only if there is a significant variation
observed in the input signal. It doesn’t produce a sample if there
is a long silence or no data available to process.
A conventional DSP system operates with a global reference or
clock signal. The input signal, x(t), is sampled-and-quantized at
the rising or falling edge of the clock signal for an edge triggered
system. The quantized signal is encoded digitally and processed
by the subsequent DSP section and the reconstruction of the
digital signal to the analog equivalent by the output DAC.. The
overall system is completely synchronized with respect to the
global clock. The drawbacks of the conventional DSP systems
are, A sampled system requires clock generator, which has a
large fan-out, since it has to drive gates of many transistors.
Hence, making it costly to implement both in terms of the area
and power consumption, the sampling frequency is decided
based on the highest frequency component of the input signal. It
is therefore necessary to implement a band-limiting filter known
as an anti-aliasing filter, to avoid aliasing, whose implementation
might also be a costly affair, the sampling results in the aliasing
of higher frequency terms to the band of interest, even if the
Nyquist theorem is followed and the deviation from the precise
sample timing instances due to the clock jitter and the clock
skew can cripple down the system performance to undesired
levels, if not designed properly.
The signal to be processed by the smart dust module is of burst
nature, which occurs occasionally. The clocked systems,
although equipped with many advantages, are not an ideal choice
for processing the burst-like signals. The drawbacks listed for
the clocked system are more dominating compared to the
advantages for the smart dust application. Hence in this paper the
event-driven CT digital signal processing systems are discussed,
which operates on the principle of level-crossing and are very
suitable for burst-type data processing.
2. CONTINUOUS-TIME OR EVENT-DRIVEN DSP SYSTEMS
The CT or event-driven Digital Signal Processor (DSP) systems,
as the name suggests, operates in continuous time mode [2,6].
The input signal, x(t), is processed by the CTDSP system to
produce output, y(t). There is no clock involved anywhere in the
system. A CT system can also be divided into three basic sub-
systems: the CTADC, CTDSP and CTDAC. As pointed out
above, there is no global clock involved anywhere in CTDSP
system. Hence, all the three blocks operates in a CT mode and
are represented with a prefix CT [5]. Figure 2 shows the block
diagram of the CTDSP system with the three sub-systems.
CTADC is the first block in the CTDSP system which quantizes
and digitizes the input signal, x(t), to a CT output, bi(t). Unlike
conventional system, the input signal is not sampled before
quantization. The CT data is then processed by the CTDSP sub-
system to produce digital data, dj(t), which is reconstructed to
analog signal, y(t), by the CTDAC block. Note that the time
domain is represented in “t” as it is always continuous in nature.
Item Parameter Value
ADC Resolution 8 bits
Maximum input signal
frequency
20 KHz
LPF Sampling frequency 44 KHZ
Passband edge frequency 5.2 KHZ
Stopband edge frequency 10KHz
Pass band ripple 1 dB
Stopband attenuation 50dB
DAC Resolution 8
Generic Supply voltage 1 V
Technology 90nm

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Fig- 2: CTDSP system with the basic building blocks CTADC-
CTDSP-CTDAC.
The CTDSP system adopts non-uniform sampling approach,
where samples are generated only when the event crosses
predefined quantization reference level. The operation of the
CTDSP system can be classified as quantization by the CTADC
block and the processing of the digital (continuous) data by the
CTDSP subsystem.
2.1 CTDSP QUANTIZATION TECHNIQUE: A LEVEL-
CROSSING APPROACH.
A conventional DSP system adopts uniform quantization
technique, where samples are taken and processed at every clock
cycle. The maximum frequency of the input signal dictates the
minimum possible clock frequency of sample generation.
However, it turns out that, for signals with burst-like nature and
long period of silence, exhaustive sampling is not required as
done in conventional systems. The samples generated either
contains no information or repetitive information. Further,
additional dynamic power is required for the processing of these
samples by the DSP section. A different class of the quantization
technique, called a level-crossing sampling scheme, suggested
by Sayiner et al., and further explained by Tsividis in [2, 3, 5]. It
is a non-uniform sampling approach, where the samples are
generated only when the event crosses the predefined
quantization reference level. Since the quantization is based on
the event and is continuous in time, it is called as an EDCT
quantization [3].
The quantization steps can be uniform or non-uniform and can
be customized further based on the application. To understand
the CT quantization technique, let’s consider figure.3. As shown,
the input signal, x(t), when crosses the predefined quantization
reference level, get quantized to signal, xq(t), and produces
samples (ti, x(ti)). This is a non-uniform sampling operation,
where the signals with faster slope generate samples more
frequently (“faster”) compared to signals with lesser slopes.
Hence, signal-dependent sampling is achieved simply by
utilizing the built-in feature of the quantizer. With this approach,
the signal with less variation produces lesser number of samples
to be processed by the subsequent DSP section; hence resulting
in a signal dependant power consumption. This is a clear
advantage of the CT systems, making it very suitable for
processing burst type signals.
The continuous-time digital output, bi(t), of the CTADC block is
processed by the CTDSP sub-system. Since the ADC output is
digital in nature, the advantages of the digital signal processing
can be utilized in the CT system as well. The design approach
for the CTDSP system is described in [6]. Based on the transfer
function, a conventional DSP system can be realized using
multipliers, adders and delay taps as basic building blocks. A
digital signal processor operating in the CT mode can be mapped
from the discrete counterpart by replacing these blocks by the
one operating in the CT domain. For Example, asynchronous
multipliers and adders, which operates without clock, can be
used in the CT system. One method of implementing the delay
taps is by using chain of inverters. A better approach is presented
in [9,10], where collection of delay cells connected in series are
used to delay and hold the data. Handshaking is used between
delay cells to ensure proper data transmission. The usage of
these delay blocks are experimentally confirmed in [4, 11]. The
CTDSP sub-system produces output, dj(t), as shown in fig- 2,
which is reconstructed back to the analog equivalent, y(t), by the
CTDAC module. The CTDAC can be realized using
architectures and circuit implementation techniques as
mentioned in section V for the clocked counterpart. The only
difference is that the DAC should operate without clock.
Fig-3: Level-crossing quantization technique in CTDSP system
3. EVENT-DRIVEN ADC
The first module in the ED DSP system is the CTADC or the ED
ADC. It converts the analog input signal, x(t), provided by the
Analog Interface (AI) to CT digital binary output signal, bi(t). A
block diagram of the AI and the CTADC is shown in figure 4.
The AI section consists of a sensor network and an analog filter.
The sensor network detects the variation in the input signal
(temperature, pressure, humidity , etc. based on the application)
and converts it to the electrical signal. The analog filter limits the
frequency of the input signal to the operating range of the ADC.
Fig- 4: Block diagram of AI and CTADC.
The AI is designed based on the overall specification defined for
the smart dust module, for e.g., the sensor interface is selected
based on the application and the cutoff frequency of the analog
filter is selected based on the frequency of operation of the
system. As illustrated in figure 4, the AI provides a CT signal,

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x(t), to the CTADC block. The CTADC consists of a CT
quantizer and an encoder, which digitize the signal to, bi(t). The
digital output, bi(t), is continuous in time and can be represented
in many ways depending on the deployed ADC architecture [3].
3.1 LEVEL-CROSSING SAMPLING FOR THE CTADC
The CTADC adopts a level-crossing sampling technique. In this
section a detailed description of the level-crossing quantization
technique is presented. Figure. shows the quantization
procedure in the CTADC. When the input signal, x(t), crosses a
quantization reference level, it gets quantized to the nearest
quantization level based on the direction of the input signal. The
quantized signal, xq(t), as shown in figure 5, is continuous in
time and the samples are represented as (ti, x(ti)), where ti−1 and
ti represent the time instances of the sample generation,
respectively. The quantized signal, xq(t), can be digitized in
many ways [3], like the CT binary bits in a flash ADC or by
using data token consisting of change and direction
(increment/decrement) as in DM [15]. The former approach is
illustrated in figure.5, where b0, b1 and b2 are the CT binary
signals. The level-crossing sampling is a non-uniform sampling
operation, where the faster signals with higher slopes generate
samples more frequently (“faster”) compared to signals with
lower slopes. Hence, a signal-dependent sampling is achieved
simply by utilizing the built-in feature of the quantizer. It means
that an input signal with lower variation generates lower number
of samples to be processed by the subsequent DSP section, hence
achieving lower dynamic power dissipation.
Fig- 5: Level-crossing quantization in the CTADC with 3-bit CT
digital output representation; b0, b1 and b2.
The frequency spectrum of an 8-bit CT quantizer as well as the
sampled quantizer is shown in figure 6 and fig-7, respectively.
The CT quantized signal spectrum has only the signal and its
harmonic components. Whereas, the output spectra of the
conventional sampled quantizer contains not only the signal and
its harmonics, but also other undesirable tones, which are spread
across the entire spectrum, and are not harmonically related to
the signal frequency. This is an artifact due to aliasing of the
higher frequency harmonic distortion components into the lower
frequency band of interest and can be attributed to the sampling
of the CT quantized output, which is just same as the
conventional technique of quantization after sampling. Hence the
difference between fig-6 and fig-7 is the noise floor added in the
latter case due to aliasing, which is an inherent feature of
sampling.
Fig- 6: Frequency spectrum of an 8-bit CT quantizer with input
amplitude A = xmax.
Fig-7: Frequency spectrum of an 8-bit sampled quantizer with
frequency of sampling, fs = 44 kHz.

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3.2 PROPOSED ARCHITECTURE OF AN EVENT DRIVEN
DM ADC
An ED DM ADC system consists of two comparators, a
feedback DAC, a bidirectional Shift Register (SR) and an
asynchronous digital control logic. The block diagram of the ED
DMADC system is shown in figure.8. The DAC generates two
reference signals: VTop(t) and VBot(t) for the upper and lower
comparator, respectively. The upper (lower) comparator tracks
the positive (negative) going signal and produces output Inc
(Dec), which is high, when input signal is greater (lesser) than
VTop(t) (VBot(t)) and zero when the signal is in between the
two reference levels. Based on the value of Inc/Dec, the digital
asynchronous control logic produces signals: change and
direction, collectively called as “data token ”. The data token is
provided to the bi-directional SR which increments (decrements)
the DAC outputs, VTop(t) and VBot(t), by _ (voltage resolution
of the ADC), if Inc (Dec) is high. The data token is also provided
to the subsequent DSP section, which processes the samples. The
DSP section sends the acknowledge signal Ack back to the
control logic.
Fig-8: Event-driven ADC based on delta modulation technique.
4. CONTINUOUS TIME DSP
After the CT quantization of the input signal picked up by the
sensor, to infer the information content of the quantized and
digitized tokens, one has to process it. The processing is done
using a digital signal processor, before the data transmission.
This can be performed either in CT or discrete-time. The CTDSP
is preferred over the conventional sampled DSP, to avoid
sampling clock and the drawbacks associated with it. CTFIR
filter is considered for designing the CTDSP. The CT processing
of the signals in digital domain can be achieved by using basic
blocks such as adders, multipliers and delay elements, all
operating in continuous mode. The subsequent discussion
provides detail about the design of
the DSPs system operating in CT.
4.1 EVENT-DRIVEN FIR FILTER WITH DELTA
MODULATION TECHNIQUE
An approach is to use an asynchronous DMADC. The two
signals which collectively form the output data token from the
DMADC are change and the direction. The change represents
the time instance of the sample generation and the direction
represents the sample value which can be “high” or “low”. Note
that, these two binary signal fully represents an N-bit signal and
can be reconstructed back to the quantized signal, q(t).
Henceforth, change and direction are collectively called as a data
token ([change, direction]). As shown in fig- 9, the data token is
passed to the up-down counter, which counts up or down by 1
LSB if the data token is [1,1] or [1,0], respectively. Counting up
or down can simply be performed by adding or subtracting 1
LSB from the previous value stored in the counter. The output
from the counter is q(t − k.TD), which is multiplied with the tap
coefficient, bk, to produce output, mk(t), as given by
mk(t) = bk · q(t − k · TD). (1)
Fig-9: Event-driven FIR filter with DM encoding technique.
And the final output, y(t), which is the weighted sum of mk(t), is
given by
(2)
4.2 DESIGN CONSIDERATION OF CTDSP SYSTEM
In this section a few basic design parameters are discussed that
are required for designing the CTDSP system. A transfer
function of the CTDSP system is the filter transfer function,
B(s), can be determined by taking the Laplace transform of
output, y(t), as given by equation (2), where y(t) is represented
as the linear combination of the quantized input signal, q(t). The
output Y (s) of a system with frequency response, B(s), and a
quantized input signal Q(s) is given by

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Y (s) = Q(s) · B(s). (3)
The expression for Y (s) can be written as
(4)
The data token generated by the CTADC depends on the slope of
the input signal. The total number of data tokens generated and
the minimum possible distance between the two tokens are of
primary importance, as it defines the functional requirement on
the DSP section. Both, the number and the distance between the
two data tokens has to be preserved, to achieve the correct
filtering operation by the DSP block. Any missing data token or
deviation in the distance between the two tokens, may result in
undesirable variation in the frequency response of the filter.
The CTADC should be able to operate at the rate of 1/Tmin,
which for the above case of fb= 20 kHz is approximately equal
to 16 MHz. This is equivalent to oversampling the ADC by a
rate of 400. Hence, this system is suitable only for low frequency
operation, because, for the high input frequency and considering
the same oversampling ratio, the data token rate goes drastically
high, which can be impractical for implementation with the
mainstream CMOS process. Based on the above discussion and
design parameters, fb and N of the ADC, it is possible to roughly
define the power consumption of the CTDSP system.
5. CONTINUOUS TIME DAC
The CTDAC is the final block of the CTDSP system. It converts
the digitally processed CTDSP sub-system output to an analog
equivalent. In the previous sections, it is concluded that the DM-
based ADC and DSP are preferred architectures for the smart
dust application. Hence, the DAC architecture selection is based
on the assumption that it is capable of accepting the 8-bit output
word from the DM operated DSP sub-system. Also, as shown in
fig-8, one feedback DAC is required inside the CTADC block.
Hence, there are two DACs in the complete system. Target of the
design is to reuse the same architecture in both the places. Any
DAC which work asynchronously can be used for this purpose.
Few important points to take care, while selecting the DAC
architecture, which is suitable for the feedback as well as the
output DAC, are: The CTDAC shall generate two reference
levels VTop(t) and VBot(t) required as a reference for the top
and bottom comparators shown in fig-8. The two reference
levels are separated by ∆LSB given by equation (2)
VTop(t) − VBot(t) = ∆LSB. (2)
Note that this constraint is not applicable for the output DAC,
where only one output is required. The output shall increment or
decrement by only one bit at a time to track the input signal
continuously in the DMADC. It shall operate in an asynchronous
mode without any clock, as no clock is used in the other sub-
modules. It shall occupy very less area and it shall consume as
less power as possible.
There are many possible architectures which can be used for
realizing the required DAC structure with the constraints given
above, e.g., resistor-string (R-string), current steering DAC,
charge accumulation DAC and segmented DAC [7]. It has been
shown in [11], that a resistor-string DAC architecture occupies
lesser area, consumes less power, simple in design and can
generate the two reference levels as described above. Due to
these advantages a resistor-string DAC architecture is selected in
this design.
5.1 SEGMENTED RESISTOR-STRING DAC
It is described that the resistor-string DAC architecture is
preferred over the other DAC architectures. However, the simple
resistor-string DAC may not be a cost effective solution for
higher ADC is to use segmented resistor-string architecture,
which produces same resolution as resistor-string architecture,
with less hardware. Various possible segmented DAC
architectures are available with the advantages and drawbacks. A
final DAC architecture is presented, which is used in the design
and block diagram is shown in fig- 10.
Fig- 10: Block diagram of the m × n segmented resistor-string
DAC with the m × n segmented bi-directional Shift registers.
The segmented m × n resistor-string DAC consists of two
segments, where the first string with m resistors, resolves the
Most Significant Bits (MSBs) and the second substring with n
resistors, resolves the Least Significant Bits (LSBs) [7].
As shown in fig-10, two bi-directional SRs are required in this
architecture, which based on the data token, produces m and n
bit control signals for the MSB and LSB string, respectively.
One possible selection of m and n is given by

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(5)
Fig-11 presents the segmented resistor-string DAC architecture
with the MSB and LSB strings. Vref,high and Vref,low are the
highest and lowest output reference voltages, respectively, from
the DAC and are applied across the MSB string. The control
signals, m and n from the bi-directional SR, are applied to the
switches used to tap the output of the MSB and LSB string,
respectively. Only one output is high at a time for each m and n
bits. The total number of resistors in the segmented DAC and the
total number of flip-flops in the bi-directional SR are now given
by (m+n) and the total switches in the DAC are given by (2) ·
(m+n). The voltage step, ∆MSB, resolved by each resistor in the
MSB string is given by
(6)
which is further resolved by the LSB string to voltage step, ∆,
given by
(7)
where m·n = 2N. Hence, the segmented architecture produces
the same resolution as obtained by an N-bit resistor-string DAC,
with reduced hardware count.
This architecture, however, suffers from the loading effect of the
LSB string to the tapped MSB resistor as explained next. Let
RMSB and RLSB be the respective resistances of each resistor
in the MSB and the LSB string. The total series resistance of
LSB string, RLSB,T otal, is given by
RLSB,T otal = n.RLSB. (8)
The equivalent resistance, RMSB, Equivalent, of the tapped
MSB resistor in parallel to RLSB,T otal is given by
RMSB,Equivalent = RMSB || (n.RLSB), (9)
which is different from RMSB. Hence, the voltage drop across
the tapped MSB resistor is not exactly equal to ∆MSB, as given
by equation (6). It results in the generation of incorrect reference
voltages at the output of the DAC. The loading effect of the LSB
string to the MSB string can be avoided simply by adding
buffers between the MSB and the LSB string then
RMSB,Equivalent = RMSB. (10)
It means, that effectively there are m resistors in series in the
MSB string, with each resistor generating ∆MSB as given by
equation (6), which is exactly the same as required. Hence, with
the proper choice of LSB string resistors, the loading effect can
be avoided.
6. SIMULATION RESULTS
Systematic top-down test-driven methodology is employed
throughout the project. Initially, MATLAB models are used to
compare the CT systems with the sampled systems. The
complete CTDSP system is implemented in Cadence design
environment. The architecture of the DM ADC system ,as shown
in Fig.8. is used to realize a high level working model of an 8-
bit ADC system employing the proposed segmented DAC
architecture.
Fig- 11: Resistor-string segmented DAC architecture.
Fig- 12: Output frequency spectrum of an 8-bit DM ADC with
16×16 shared MSB resistor DAC, 20.416 kHz input frequency
and 220 kHz band of interest.

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Table II. Comparision of components, SNDR and ENOB
obtained for 8-bit system for different types of DACs.
Parameter
8-bit ADC with
Resister string
DAC
8-bit ADC
with
Segmented-
Resister-string
DAC
Frequency
band
220 100
SNDR(dB) 56.5 67.5
ENOB 9.1 10.9
Resisters in
DAC
512 64
Switches in
DAC
256 33
D Flip-flop in
DAC
256 32
The noise immunity to the system is provided by adding an
offset of ∆/4 to the lower comparator of the CTADC. The system
is tested with an input sinusoid of frequency 20.416 kHz, the
SNDR obtained for the 8-bit system is around 56.5 dB when
considering the band of interest as 220 kHz. For the smart dust
application, a resolution as high as 8-bit is not required. It is
possible to reduce the number of bits to 3-bits and still be able to
detect the signal at the input and process it.
The fig-12 presents the spectrum of an 8-bit ADC system plotted
. It can be observed that the odd order harmonics are dominating
in the spectrum and even order harmonics are suppressed by
more than 80 dB. Hence no noise floor.
Table II presents a comparison of the SNDR and ENOB for an
8- bit ADC using resister string DAC and 8- bit ADC using
segmented resister string DAC. The SNDR improvement can be
observed when the band of interest is reduced from 220 kHz to
100 kHz and improvement of ENOB is also clearly observed.
It has been shown that the sensitive feedback DAC or resistor
string DAC used in the ADC can be modified further to a
segmented structure, which helps in reducing the overall
components( resistors, switches, and D Flip-flops) observed is
approximately 87.5%. in the design. The savings are doubled for
systems utilizing similar DAC at the DSP output.
The hardware overhead for ADC system utilizing the proposed
segmented architecture reduces drastically compared to the ADC
using resistor string DAC. Hence, it is possible to design an N-
bit ADC with a segmented DAC architecture which utilizes
lesser hardware compared to its resistor string counterpart and
provides better performance.
The transient response of the 8-bit system with 8-bit ADC, 8-bit
DSP and 8-bit output DAC with 20.4 kHz input frequency. The
overall output of the system is shown on the right side of the
figure, OutChange and outDirection are the tokens generated by
the digital delta modulator.
Fig-13: Transient response of the 8-bit system with a 8-bit
ADC,8-bit DSP and 8-bit output DAC and 20.4 kHz sinusoidal
input frequency.
CONCLUSIONS
This paper presents Continuous time ADC and DSP system for
smart dust and wireless applications it is concluded that the CT
processing is very well suited for the smart dust application. The
noise immunity to the system is provided by adding an offset of
∆/4 to the lower comparator of the CTADC.
To validate the presented theory, an 8-bit ED ADC system is
designed. It has been shown that the sensitive feedback DAC
used in the ADC can be modified further to a segmented
structure, which helps in reducing the overall components in the
design. The Lower SNDR obtained for the 8-bit system as there
is no noise present in the output spectrum.
The presented system CTADC or the ED ADC and CT DSP
section can be potential area and energy saver and its suitability
for smart dust and wireless applications is shown.
REFERENCES
[1] B.Warneke, M. Last, B. Liebowitz, and K. S. J. Pister,
“Smart Dust: communicating with a cubic-millimeter
computer,” Computer, vol. 34, no. 1, pp. 44– 51, 2001.
[2] Y. Tsividis, “Continuous-time digital signal processing,”
Electronics Letters, vol. 39, no. 21, pp. 1551–1552, 2003.
[3] Y. Tsividis, “Event-Driven Data Acquisition and Digital
Signal Processing_A Tutorial,” Circuits and Systems II:
Express Briefs, IEEE Transactions on, vol. 57, no. 8, pp.
577–581, 2010.
[4] B. Schell and Y. Tsividis, “A Continuous-Time
ADC/DSP/DAC System With No Clock and With
Activity-Dependent Power Dissipation,” Solid-State
Circuits, IEEE Journal of, vol. 43, no. 11, pp. 2472–2481,
2008.
[5] Y. Tsividis, “Digital signal processing in continuous time:
a possibility for avoiding aliasing and reducing
quantization error,” in Acoustics, Speech, and Signal

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BIOGRAPHIES
Bitra Janardhana Rao was born in
1984.He received his B.Tech in ECE from
Nagarjuna University ,Guntur in 2006. He
has completed his Master of Technology in
Communication and Radar Systems from
KL University in 2010.He is currently
working as an Assistant professor in CVR
College Of Engineering, Hyderabad. His research interest
includes Signal Processing and Image processing. .
O. Venaka Krishna was born in 1983. He
received his B.Tech in ECE from Nagarjuna
University, Guntur in 2004.He has
completed his Master of Technology in
VLSI System Design from JNTU,
Hyderabad in 2009.He is currently working
as an Sr. Assistant professor in CVR
College Of Engineering, Hyderabad. His research interest
includes VLSI Signal Processing.
V. Silpa Kesav was born in 1986. She
received her B.Tech in ECE from JNTU,
Hyderabad in 2007. She has completed her
Master of Technology in VLSI Design NIT,
Rourkela 2009. She is pursuing her Ph.D in
JNTU, Hyderabad. She is currently working
as an Assistant professor in CVR College Of
Engineering, Hyderabad. Her research interest includes VLSI
Signal Processing .

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