An Electronic System to Combat Drifting and Traffic Noises on Saudi

Roads

Imed Ben Dhaou

College of Engineering

Al Jouf University

P.O.Box 2014, Sakakah

Kingdom of Saudi Arabia

Email: imed.bendhaou@ju.edu.sa

Abstract— This paper proposes an electronic system to com-bat drifting and traffic noises in the urban area of Saudi Arabia.The proposed solution can be integrated into a smart cityplatform. The system comprises a sound processing hardware,a CCTV camera, and a GPRS module for wireless IP access. Analgorithm to address drifting for noise and accidents is derivedand tested over a range of audible traffic noises in Sakakahtown. Hardware implementation of the algorithm using Radix-8, 64-point FFT algorithm, and a semiconductor intellectualproperty is elaborated. The results show that the algorithmproduces no false alarm.

I. INTRODUCTION

According to the latest statistics, Saudi Arabia has the

highest road death roll in the world. In 2008, on average 18

persons die per day [1]. Statistics collected from 7 December

2010 to 2 June 2011 by the Saudi ministry of interior show

that 54% of road accidents are caused by the driver, 3% are

due to the road condition, 15% are attributed to the vehicle

anomalies, and 3% are caused by bad weather condition. The

remaining 25% of road accidents are the resulting effects

from other factors. Tab.I shows the distribution of accidents

in the 13 Saudi provinces as published in a report by the

Saudi authorities.

TABLE I

ROAD ACCIDENTS IN SAUDI PROVINCES RECORDED IN 2008

Province Accident Fatal Accident Percentage

Riyadh 143466 1405 29.52

Makkah 107932 8477 22.21

East 127460 3965 26.23

Madinah 20956 1557 4.31

Qassim 17645 1687 3.63

Tabuk 11634 1672 2.4

Asir 22929 816 4.72

Al-Baha 4245 946 0.87

N. Borders 6799 417 1.4

Jouf 7284 941 1.5

Hail 5714 1132 1.18

Najran 4215 848 0.87

Jizan 5652 1778 1.16

To reduce road fatalities, the Saudi government has intro-

duced a new automated traffic control and management sys-

tem, named SAHER[2]. SAHER uses electronic equipments

(mobile/fixed radar, CCTV camera, and wireless nodes) and

software to, among others, manage traffic, avoid accidents,

and study the efficiency of the infrastructure. SAHER com-

prises the following systems: (i) Traffic management system

that automatically and intelligently controls traffic lights so

as to allow for continuous traffic flow (green wave), (ii)

automatic vehicle location that tracks and directs police car to

solve traffic anomalies, (iii) license plate recognition system

to automatically recognizes blacklisted vehicle or bill the

owner for traffic violation, and (iv) variable message sign to

avoid traffic congestion.

Drifting is a dangerous driving style practiced in a great

number by Saudi youth. In Saudi society, drifting is known

as Tafheet, Hajwalah, or Farfarh. According to [3], drifting

is banned by the Saudi authorities as it was responsible for

serious road accidents. Drifting is a threat not only to the road

users but also to the well being of the society as it increases

traffic noise. Tafheet can be done at lower and higher speeds.

In the latter case, the drifter forces the car to spin out at

terrifying speed (180-200kmph). Addressing this particular

case is possible with the existing SAHER system.

Lower speed Tafheet (50-70kmph) occurs for instance

at roundabouts, crossings, or U-turns. A special electronic

system to address this particular case is needed to (1) reduce

road fatalities and (2) combat noise.

The rest of the paper is organized as follows. Section

II analyses sources of road traffic noise, reviews existing

methods and regulations to reduce road noise in urban

environment, and it explores published techniques to estimate

road traffic noise. Section III derives algorithm to combat

drifting and noise. Section IV proposes hardware architecture

for the proposed electronic system. Section V validates the

efficiency of the proposed algorithm. Finally, Section VI

concludes the paper and proposes further direction in this

research topic.

II. ROAD TRAFFIC NOISE

Smart or ubiquitous city is widely defined as a city that

has modern ICT infrastructure and a sustainable quality of

life[4].

The European Union has identified six characteristics,

thirty one factors, and seventy four indicators to measure

the smartness of medium-sized cities [5]. Smart mobility

is one important feature for the EU smart city model.

Intelligent transportation system (ITS) can increase mobility

2012 Intelligent Vehicles SymposiumAlcalá de Henares, Spain, June 3-7, 2012

978-1-4673-2118-1/$31.00 ©2012 IEEE 217

as it enables sustainable, innovative, green and safe transport

systems.

Smart city platform aims to develop all needed logistics

(hardware and software) to render current cities ubiquitous.

Contemporary urban areas suffer from traffic noise. In the

Netherlands, it has been reported that 14% of the residents

in cities are exposed to traffic noise[6].

Road traffic noise can cause various health problems such

as hypertension, somatic health disorders, the release of

stress hormones, and decreases cognitive performance[6].

Theoratical solutions of traffic noise aimed at solving

Euler equation for sound intensity which is shown in Eq.1

[7].

∇ ~I(t) +∂w(t)

∂t= 0, (1)

where ~I(t) is the instantaneous intensity vector, and w(t) the

instantaneous value of the total energy. The relationship be-

tween the instantaneous pressure p(t) and the instantaneous

value for the particle velocity ~u(t) is given in Eq.2.

~I(t) = p(t) ~u(t). (2)

In [8] the author has analyzed the source of noise in urban

areas and proposed a countermeasures to reduce the effect of

traffic noise. For road traffic noise, the author has proposed

the deployment of low-noise asphalt, the standardization of

stringent requirements on noise emitted by vehicle engine,

and the installation of noise barrier.

Car noise has been identified in [9] as a sum of engine,

aerodynamic and tire noises. The noise produced by engines

has a stationary and non-stationary component. The former

depends on the engine rotation and the latter is proportional

to the engine size and shape. Aerodynamic and tire noises

were termed as friction noise and has been shown to be white

noise with ranging power.

In this work, traffic noise is divided into three sources: (1)

noise generated by vehicles, (2) noise caused by bad driving

habits, and (3) noise emitted due to bad infrastructures.

The noise caused by vehicles has been the subject of

intensive research. This type of noise can be addressed

for instance by automotive industries and at the vehicle

inspection stations. In various countries, new regulations for

the engine and tire noises have been put forward. In Europe,

the tire manufacturers have been asked to label tires in

compliance with the EU regulation number 1222/2009 [10].

In the EU regulation, tires should have labels that indicate

the energy efficiency, wet grip, and exterior rolling noise.

The EU regulation number 661/2009 reports tight limit on

the exterior rolling noise for various classes of tires [11].

To investigate the cause of engine noise in the idle status,

the sound of passenger’s car engine has been recorded.

The noise has been sampled at a sampling frequency of

44.100kHz. To measure the impact of noise on human health,

the A-weighted filter that mimics human ear has been used.

The frequency response of the A-weighted filter used in this

work is shown in Eq.3.

H(f) =122002f4

(f2 + 20.62)√

(f2 + 107.72)(f2 + 737.92)αf

,

(3)

where f is the frequency, and αf = f2 + 122002.

0 1 2 3 4 5 6 7 8 9 10

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

Elapsed time (sec)

Am

plit

ude (

V)

Noise of a gasoline engine in the idel status

−2 −1.5 −1 −0.5 0 0.5 1 1.5 2

x 104

−100

−95

−90

−85

−80

−75

−70

−65

−60

Frequency (Hz)

Am

plit

ude d

b

Noise Spectrum of a passenger car engine

Fig. 1. Measured noise of an idle gasoline engine.

Fig.1 shows measured engine noise in time domain and

its amplitude spectrum. Using the A-weighted filter reported

in Eq.3, the signal level has been found equals 68.48dBA.

In the idle status, the engine noise is caused primarily by

the ensemble pulley-belt system. On road, the engine noise

increases due to, for instance, cooling fan , intake and exhaust

system.

To reduce traffic noise, several countries and international

organizations have set-up a limit on the tolerable level

of noise generated by moving vehicle. In June 2007, the

commission of the European communities adapted tolerable

noise limits for moving vehicle that had been published in

the regulation number 51 by the United Nation Commission

for Europe (UNCE). The noise limits ranges between 74dBA

and 80dBA. The former limit is for passenger car that has a

maximum of nine seats including the driver. The latter is a

limit for vehicle that has an engine power less than 150kW

[12].

0 1 2 3 4 5 6 7 8 9 10

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

Elapsed time (sec)

Am

plit

ude (

V)

Noise of a gasoline engine in the idel status

−2 −1.5 −1 −0.5 0 0.5 1 1.5 2

x 104

−100

−90

−80

−70

−60

−50

−40

Frequency (Hz)

Am

plit

ude d

b

Noise Spectrum of a passenger car engine

Fig. 2. Measured noise of a gasoline engine rotating at 2200RPM.

To investigate the behavior of engine noise in the non-idle

status, the same vehicle used to obtain Fig.1 has been used

to record engine noise at an engine speed of 2200 rotations

per minute (RPM). Fig.2 shows the noise in both time and

frequency domain. The noise level has been found equals

85.14dBA. This value is higher than the limit setup by the

218

EU because the data was recorded very close to the engine.

Due to resource and facilities limitations, it was not possible

to comply with the technical requirements to measure sound

noise as reported in the ISO standard ISO/CD 362 (part 1 and

part 2) [13]. Theoratically, traffic noise drops off as the point

of interest is moved from a location d1 to a new location d2.

The drop off factor in dBA is given in Eq.4 [14].

η = 10 log10[(d1

d2)1+α], (4)

where α is the ground attenuation coefficient. For hard

ground α = 0.

Engine exhaust noise occurs at lower frequencies as de-

picted in Eq.5 [15].

f = Zη

30τ, (5)

where Z is the number of cylinder, η the engine RPM, and

τ the engine cycle.

In a typical 4-stroke, gasoline, sedan vehicle, exhaust noise

occurs at the frequency range 0-200Hz (engine RPM=6000).

Consequently, exhaust noise depends on the engine size as

well as the engine RPM.

The traffic noise caused by vehicle can be further aggra-

vated by bad tire/pavement interactions. Fig.3 depicts time

domain and amplitude spectrum of a tire drifting noise.

The measurement has been taken at the second floor of an

apartment hotel located near a busy traffic-light crossing.

0 0.5 1 1.5 2 2.5 3 3.5−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

Elapsed time (sec)

Am

plit

ude (

V)

Tire drifting noise

−2 −1.5 −1 −0.5 0 0.5 1 1.5 2

x 104

−100

−90

−80

−70

−60

−50

−40

−30

Frequency (Hz)

Am

plit

ude d

b

Noise Spectrum

Fig. 3. Measured drifting noise.

Noise generated by tires and tire/road interactions has been

extensively researched in the past years [16]. It has been

found that the tire/pavement noise is boradband in nature

with peaks in the range 700-1300Hz . The peak value is the

resulting effects of many factors such as (1) pitches of tyre

threads , (2) the horn effect caused by tyre/road interactions,

and the (3) shape of road texture [16] [17] [16].

Although reducing tyre/pavement noise is an important

step towards ECO driving and sustainable society, this work

deals primarily with tire/pavement noise caused by bad and

offensive driving behavior. Next section details the proposed

algorithm to combat drifting.

III. ALGORITHM AND NETWORK ARCHITECTURE TO

COMBAT TRAFFIC NOISE AND DRIFTING

The algorithm to combat drifting and traffic noise uses as

input a sampled noise captured by a microphone. Silicon

implementation for capacitive microphone is discussed in

Section IV.

The algorithm analyses the signal amplitude in frequency

domain to decide whether or not the driver/ vehicle violated

anti-drifting or anti-noise laws. The frequencies at which

potential traffic noise violation occurs are inputed to the

algorithm. These frequencies are referred to as harming

frequencies.

The harming frequencies can be determined in various

ways, such as statistical means, training methods, or the-

oratical studies. After the transformation of a time domain

window to the frequency domain using a Fast Fourier Trans-

form (FFT) algorithm, the proceeding step is to compare

the amplitude of the Fourier domain signal at the harming

frequencies to the tolerable signal level (denoted by Ai in

Algo.1). The time domain windows do not overlap as stated

in the instruction i ← i+NFFT of Alg.1.

The algorithm alerts the detection of violation if the

amplitude spectrum exceeds the values of Ai. The number

of violating cases in the given signal spectrum are recorded

and then compared to a threshold τ . The threshold can be

adjusted so as to prevent false alarms. The pseudo-code for

the algorithm is sketched in Alg.1.

Algorithm 1 Algorithm to combat drifting and traffic noise

Inputs: frequency vector (fi), tolerable amplitude (Ai),

sampling frequency (Fs), size of the FFT window

(NFFT ), threshold value for binary classifier τ .

Output: vehicle license’s plate(sv), GPS coordinate, viola-

tion type.

Sample the input signal at Fs and store Nsamples in the

input buffer (BufI ). { The sample size should be power

of two}i = 0while i ≤ log2(Nsamples) do

x ← BufI(i : i+NFFT − 1) {Read a data window of

size NFFT }XF ← FFT (x) { Windowed FFT}{Initialize the detect variable} {Compare the amplitude

of XF with tolerable amplitude Ai}if XF (fi) ≥ Ai then

detect ← true{Compute the number of times a noise

is detected.}Ndetect ← Ndetect + 1

end if

i ← i + NFFT {Point to the next coming NFFT

samples}end while

if detect is true& Ndetect ≥ τ then

return GPS coordinates and vehicle’s license plate.

end if

The system to resolve drifting and traffic pollution can be

installed on top of lighting column as shown in Fig.4

219

IV. HARDWARE ARCHITECTURE OF THE PROPOSED

ALGORITHM

Hardware implementation of the proposed system can be

implemented using FPGA, ASIC, DSP, or PIC. Fig.5 shows

the block diagram of the proposed system. The sound is

electrically converted by a microphone, filtered and finally

sampled by an ADC converter. The digital input is then

stored at a buffer of size NFFT named BufI in Algo.1.

The digital block starts by computing the N-point FFT of the

time domain samples presented at BufI . The amplitude of

the obtained samples at the output of the FFT processor are

then compared with those presented at the register file. The

latter contains frequencies fi and the corresponding tolerable

amplitude spectrum Ai. The commanding unit instructs the

CCTV camera to take a picture of the violating vehicle.

Finally, it ensures wireless transmission of all relevant data

to the national database.

The main part of the system is the FFT processor.

Hardware implementation of the Fourier transform using

embedded circuit, FPGA or ASIC has been studied exten-

sively in previous years. In [18] the authors have analyzed

various Radix-R architecture and a synthesis procedure has

been reported. In [19], a free soft IP for Radix-8 64 point

FFT/IFFT is reported. In this work, 64-point FFT algorithm

has been assumed as demonstrated in the next section. The

bit-widths for the input samples and the FFT coefficients are

respectively 8 and 10. This low number of bit-width has been

verified with experiments. High-level power consumption

models for the data-path of the FFT processor is proportional

to the bit-width of the data and the bit-width of the FFT

coefficients. As a result, a substantial power savings and

area reduction can be achieved as compared to high bit-

width. Using the model elaborated in [20], and neglecting

the power consumed by the adders, data-path consumption

Fig. 4. Illustration of the anti-drifting and anti-noise system.

Fig. 5. Block diagram for the anti-drifting system.

can then be estimated using Eq.6.

Pmult. ≈ NdataNcoeff.fmult., (6)

where Ndata and Ncoeff. are respectively the bit-widths

for the data and FFT coefficients. The frequency of the

multiplication, fmult can be estimated using the closed-form

expression presented in [18].

As an example, if the unoptimized bit-width is 10, then

reducing this number to 8 bits leads to 25% of power savings.

Analog front-end of the system (microphone, amplifiers,

and DAC) can be implemented using CMOS technology. In

[21] the authors have presented a single chip implementation

of a capacitive microphone, operational amplifier and sigma-

delta modulator. The proposed capacitive microphone needs

no charge-pump circuits. The reported figure-of-merit of the

sigma delta modulator is 163.85dB. The sampling frequency

and DAC’s resolution are higher than these required by the

system proposed in this work. Nevertheless the digitized con-

denser microphone is a potential candidate for the hardware

realization of Fig.5.

Wireless access to the national data-base is insured via

wireless TCP. There are a number of wireless packet network

standards for instance GPRS (General Packet Radio Service),

EDGE (Emhanced Data rate for GSM Evolution), HSPA

(High-Speed Packet Access), WiMAX(Worldwide Interop-

erability for Microwave Access), and LTE (Long Term

Evolution). There are a number of a software defined radio

chips that implement these standards. In [22], the authors

reported a multi-standard base band processor that imple-

ments the physical layer of GSM, EDGE, GPRS, UMTS,

HSPA, GMR1-3G, and LTE. This solution is expensive for

our system as it realizes complex functions not needed by

the system. The data rate of the latter depends on the number

of events leading to fire the CCD camera. Furthermore,

the response to the traffic violation is not needed to be

instantaneous. Hence, GPRS packet switched technology

offers the throughput and latency required by the system

depicted in Fig.5.

The number of time slots defines the GPRS class. Higher

classes have higher number of slots. The supported time slots

are 3,4 and 5. There are six available classes (2,4,6,8,10 and

12).

Many semiconductor companies (Siemens, Intel, Sierra

wireless, and Telit) offer GPRS modules. The wireless mod-

ule MC55/56 from Siemens implements GPRS class 10 and

has a built-in TCP/IP stack [23]. The features of MC55/56

are in compliance with the requirements of the proposed

system.

V. EXPERIMENTAL RESULTS

To validate the efficiency Alg.1, several test signals have

been recorded in Sakakah, Saudi Arabia. Signals with sig-

nificant presence of drifting noise is recorded at a crossing

shown in Fig.6. The circle shows the area of interest to

combat drifting noise. The recorded signals are tabulated

in Tab.II. These signals have been chosen to reflect typical

traffic noise. Signal labeled S2 in Tab.II has been collected

220

Fig. 6. Location of the experiments.

at the parking of a busy supermarket. The other signals are

traffic noises generated by vehicles during stopping, starting-

up or turning. Drifting noises are caused by bad driving habit

during start-up or turning.

TABLE II

RECORDED TRAFFIC SIGNALS.

Signal Length Annoyance Cause

S1 26 sec. No -

S2 3 min. No -

S3 5 sec. Yes Drifting

S4 3 sec. Yes Drifting

S5 10 min. Yes Horn and Drifting

S6 4 sec. Yes Horn

The detected instances (Ndetect), and frequencies for drift-

ing and horn noises, using Alg.1 are summarized in Tab.III.

The range for Ndetect depends on the noise duration, the

sampling frequency and NFFT , so it is inappropriate to put

it in a percentage form. In the experiments, the sizes of the

FFT algorithm, referred to as NFFT in Alg.1, are 64, 256

and 1024.

TABLE III

PERFORMANCE OF ALG.1 TO DETECT DRIFTING AND TRAFFIC NOISES.

FFT size instance Ndetect Frequencies (kHz)

S1 0 -S2 0 -

64 S3 142 2.067, 2.75, 3.44S4 199 2.067S5 42 2.067, 2.75, 3.44, 4.13, 5.512S6 12 2.067

S1 0 -S2 0 -

256 S3 30 2.067, 2.23, 2.411, 2.58S4 45 2.067S5 10 2.067, 2.92S6 0 -

S1 0 -S2 0 -

1024 S3 3 2.45S4 8 2.067, 2.11S5 0 -S6 0 -

Results tabulated in Tab.III clearly show the impact of

NFFT on both the detected instances and frequencies of the

traffic noises. In theory, the size of the FFT algorithm should

be computed using Eq.7.

NFFT = Fs∆t, (7)

where Fs and ∆t are respectively the sampling frequency and

sample size. The sampling frequency in our experiments has

been set to 44.1kHz. Theoretical values for the observation

time (∆t) is reported in Tab.IV. Lower FFT size leads to

better time resolution at the expense of frequency resolution.

From the interpretation of the results shown in Tab.III the

64-point FFT algorithm was able to detect both horn and

drifting noises for the following signals S3, S4, S5 and S6.

The 256-point FFT algorithm failed to detect horn noise. In

mixed noise scenario (horn and drifting) or in the presence

of horn noise only, the 1024-point FFT algorithm was unable

to report a noise violation.

TABLE IV

OBSERVATION INTERVAL FOR DIFFERENT NFFT VALUES.

FFT size (NFFT ) 64 256 1024

∆t (ms) 1.45 5.8 23.26

Leakage occurs during spectral analysis because of the

time-domain multiplication of the observed noise with a

rectangular window with a finite duration. The time-duration

of the window equals NFFT . To reduce leakage, the sampled

time-domain noise is first multiplied by a window function.

The widely used window functions are Hamming, Hanning,

Barlett, Gaussian, Dolph-Chebychev, and Blackman. Com-

parison of various popular windowing functions is reported

in [24]. The impact of various data windowing techniques

for harmonic analysis in the presence of broadband noise

and harmonic interferences is studied in [25].

TABLE V

IMPACT OF THE HANNING WINDOW TO DETECT DRIFTING AND TRAFFIC

NOISES.

FFT size instance Ndetect Frequencies (kHz)

S1 0 -S2 0 -

64 S3 16 2.067, 2.75S4 20 2.067S5 4 2.067S6 0 -

S1 0 -S2 0 -

256 S3 4 2.41S4 6 2.067S5 0 -S6 0 -

S1 0 -S2 0 -

1024 S3 0 -S4 2 2.11S5 0 -S6 0 -

To study the effect of windowing to detect drifting noise,

Hanning and Hamming filters are considered.

Results of the windowed FFT reported in Tab.V and

Tab.VI reveal that rectangular window is more efficient than

221

TABLE VI

IMPACT OF THE HAMMING WINDOW TO DETECT DRIFTING AND TRAFFIC

NOISES.

FFT size instance Ndetect Frequencies (kHz)

S1 0 -S2 0 -

64 S3 26 2.067, 2.75S4 29 2.067S5 4 2.067S6 0 -

S1 0 -S2 0 -

256 S3 6 2.23, 2.41, 2.58S4 8 2.067S5 0 -S6 0 -

S1 0 -S2 0 -

1024 S3 0 -S4 2 2.11S5 0 -S6 0 -

Hamming and Hanning windows at detecting broadband

noise. The equivalent noise bandwidth for the rectangular

window is less than those of the Hamming and Hanning

windows [25].

The threshold value (τ ) in Alg.1 can be used to deal with

the false alarm. Fig.7 plots the receiver operating charac-

teristics using 64-FFT algorithm and rectangular window.

The RoC shows that the classifier has no false alarm and

it operates on the Y-axis. Lower values for τ lead to liberal

classifications.

Fig. 7. RoC curve for 64-point FFT and rectangular window.

VI. CONCLUSION

This paper a system for combating drifting and traffic

noises has been elaborated. An architecture and hardware so-

lution has been proposed. The architecture comprises an FFT

algorithm of size 64 and a GPRS transceiver to communicate

with a data-base that manages traffic violations. Experiments

show that 64-point FFT algorithm at a sampling frequency

of 44.1kHz is able to detect the presence of horn and drifting

noises. Spectrum leakage has also been examined using

Hamming and Hanning data windowing techniques.

To further optimize the architecture of the system (area,

power and throughput), it has been shown that sampled data

can be represented using 8 bits. Further work should address

the detections and interpretations of other form of traffic

noises such as sirens for emergency service vehicles.

REFERENCES

[1] Ministry of Interior, Saudi Arabia. Annual Statistical Report for 2008(1429 hijri). Available online in Arabic.

[2] Saher website, General Department of Traffic, Ministry of Interior,Saudi Arabia. Internet:www.saher.gov.sa, [20-11-2011].

[3] R. F. Worth and B. Denton. ”Saudis Race All Night, Fueled byBoredom.” The New York Times (March 8, 2009 ), p.A1.

[4] J. Lee, S. Baik, C. Lee. ” Building an Integrated Service ManagementPlatform for Ubiquitous Cities.” IEEE Computer Society , vol.44, issue6, pp.56-63, June 2011.

[5] European Union (2007, October). ”Smart cities Ranking of Europeanmedium-sized cities.” [Online]. Available http://www.smart-cities.eu/[Dec.21, 2011]

[6] World Health Organization (2009). Night Noise Guidelines for Europe.ISBN 978 92 890 4173 7. [online]. Available: http://www.euro.who.int/

[7] F. J. Fahy. Sound Intensity, 2nd ed. London, 1995.[8] Y.Shi.” Urban traffic noise analysis and control measures,” in Proc.

WMSVM, 2010, pp.11-14.[9] Y. Ban, H.Banno, K.Takeda, F.Itakura.”Synthesis of car noise based on

a composition of engine noise and friction noise,”, in Proc. ICASSP,2002, pp.II-2105 - II-2108.

[10] European Union (2009, Nov.).”On the labelling of tyres with respectto fuel efficiency and other essential parameters,”, official journal of theEuropean Union. Available: http://eur-lex.europa.eu [Nov. 21, 2011]

[11] European Union (2009, Jul.).”Concerning type-approval requirementsfor the general safety of motor vehicles, their trailers and systems,components and separate technical units intended therefor,”, officialjournal of the European Union. Available: http://eur-lex.europa.eu [Nov.21, 2011]

[12] European Union (2007, June).”Amending for the purposes of its adap-tation to technical progress, Council Directive 70/157/EEC concerningthe permissible sound level and the exhaust system for motor vehicles,”Commission directive 2007/34/EC, official journal of the EuropeanUnion. Available: http://eur-lex.europa.eu [Nov. 27, 2011]

[13] International Organization for Standardization (2007). ” Measurementof noise emitted by accelerating road vehicles, engineering method,”ISO standards. Available: http://www.iso.org/ [Nov.27, 2010]

[14] Federal Highway Administration (1980, Sept.). ” Highway Noise Fun-damentals, Noise Fundamental Training Document.”, US department ofTransportation, 1980.

[15] X.-y. Lei G.-z. Cao. ”Development of a novel instrument based onDSP for measuring vehicle noise combined with engine rotating speed,” in Proc. PESA, 2009, pp.1-4.

[16] U. Sandberg and J. A-Ejsmont. Tyre/Road Noise Reference Book.

INFORMEX, Kista, Sweden, 2002.[17] R. A. G. Graf, C.-Y. Kuo, A.-P. Dowling and W.R. Graham.” On

the Horn Effect of a Tyre/Road Interface, Part I: Experiment andcomputation.”,J Sound Vibr , 256(3), pp.417-431, 2002.

[18] I.B.Dhaou and H.Tenhunen.”Combinatorial architectural level poweroptimization for a class of orthogonal transforms,” in Proc. ISCAS,1999, vol.1, pp.70-75.

[19] Unicore Systems Ltd. ”Pipelined FFT/IFFT 64 points processor”.Internet opencores.org/project,pipelined fft 64, Jan 27, 2010 [Dec. 06,2011]

[20] S. R. Powell and P. M. Chau. ”A Model for Estimating PowerDissipation in a Class of DSP VLSI Chips.” IEEE Trans. On Circuits

and Systems, vol. 38, No. 6, pp.646 -650, June 1991.[21] C.-T. Chiang and C.-Yu Wu. ” A CMOS Digitized Silicon Condenser

Microphone for Acoustic Applications.” IEEE Sensors Journal, Vol. 11,No.2, pp.296 - 304, Feb. 2011.

[22] U. Ramacher et al. ”Architecture and implementation of a software-defined radio baseband processor,” in Proc. ISCAS, 2011, pp.2193 -2196.

[23] Siemens inc .”Wireless Modules MC55/56 Reduce the size and maxi-mize the connectivity.” Internet: www.siemens.com/wm, 2006[12 Dec.2011]

[24] J. Arrillaga, N.R. Watson. Power System Harmonics. Wiley, pp.35-58,2003.

[25] F. J. Harris. ”On the Use of Windows for Harmonic Analysis withthe Discrete Fourier Transform.” in Proc. of the IEEE, vol.66, N0.1,pp.51-83, Jan. 1978.

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