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Editor-in-Chief: Ghazi I. Alkhatib, Princess Sumaya U. for Technology, JordanErnesto Damiani, U. of Milan, Italy

Associate Editors: Farid Meziane, U. of Salford, UKAndres Iglesias Prieto, U. of CantabriaDavid Taniar, Monash UniversityZakaria Maamar, Zayed UniversityMichael Berger, Siemens Corporate Technology, Intelligent Autonomous SystemsSchahram Dustdar, Vienna U. of TechnologyNanjangud C. Narendra, IBM Research IndiaWael Toghuj, Isra UniversityWalter Binder, U. of LauganoHesham A. Ali, Mansoura U. Egypt

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Special Issue on the Role of Information and Communications Technology in Developing Renewable Energy Processes and Systems

Guest Editorial PrefaceV Hussein Al-Bahadili, University of Petra, Amman, Jordan

Research Articles

1 Requirements and Design a Small Wind Rotor for a Small House in GuildfordTriada Vlasakoudi, Faculty of Engineering, University of Surrey, Guildford, UKMohammed Sanduk, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK

13 Design of an Embedded Solar Tracking System Based on GPS and Astronomical EquationsFawzi M. Al-Naima, Al-Nahrain University, Baghdad, IraqRamzy S. Ali, Basrah University, Basrah, IraqAhmed J. Abid, Foundation of Technical Education, Baghdad, Iraq

33 A Novel Approach to Build a Generic Model of Photovoltaic Solar System Using Sound Biometric TechniquesKhalid T. Al-Sarayreh, Department of Software Engineering, Hashemite University, Zarqa, JordanKenza Meridji, Department of Software Engineering, Petra University, Amman, JordanEbaa Fayyoumi, Department Computer science and Applications, Hashemite University, Zarqa, JordanSahar Idwan, Department Computer science and Applications, Hashemite University, Zarqa, Jordan

48 Demand-Driven Algorithm for Sharing and Distribution of Photovoltaic Power in a Small Local Area GridMohammad Abu-Arqoub, Faculty of Information Technology, University of Petra, Amman, JordanGhassan F. Issa, Faculty of Information Technology, University of Petra, Amman, JordanAhmad F. Shubita, Faculty of Information Technology, University of Petra, Amman, JordanAbed Alkarim Banna, Faculty of Information Technology, University of Petra, Amman, Jordan

62 A Layered Communication Architecture for Power Routing in the Smart GridF. Bouhafs, School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UKM. Merabti, School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK

74 Energy-Aware Manufacturing Using Information Technology Tools: A Knowledge Based System ApproachMohammed A. Omar, Masdar Institute of Science and Technology, Masdar City, UAEAhmad Mayyas, Lawrence Berkley National Laboratory, Berkley, CA, USAQilun Zhou, Department of Automotive Engineering, Clemson University, Greenville, SC, USA

Table of Contents

January-March 2014, Vol. 9, No. 1

International Journal of Information Technology

and Web Engineering

International Journal of Information Technology and Web Engineering, 9(1), 33-47, January-March 2014 33

Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

ABSTRACTThis chapter presents the proposed model of combination between Photovoltaic solar system resources and sound biometric techniques, to generate power energy from the sunlight using the PVS controlled by a sound biometric technique. This work contributes to research knowledge by proposing and validating a sound biometric technique for allowing to reduce the consumption of the generated power energy by turn the lights on for the public roads only when there are vehicles on the way and only for some period of time to make the driving out of harm’s way and trouble-free. The proposed and combination models between the PVS and biometric sound chip is used for generating electric power by using solar cells to convert energy from the sun light into a flow of direct current electricity, which can be used to power equipment or to recharge a bat-tery. In addition the Sound biometric techniques can enable PVS to listen and understand their surrounding auditory environment since turning the lights on all the time will get through a lot of energy which it might be used in other significant concerns.

A Novel Approach to Build a Generic Model of Photovoltaic

Solar System Using Sound Biometric Techniques

Khalid T. Al-Sarayreh, Department of Software Engineering, Hashemite University, Zarqa, Jordan

Kenza Meridji, Department of Software Engineering, Petra University, Amman, Jordan

Ebaa Fayyoumi, Department Computer science and Applications, Hashemite University, Zarqa, Jordan

Sahar Idwan, Department Computer science and Applications, Hashemite University, Zarqa, Jordan

Keywords: Electricity Consumption, Energy Conversion, Energy Storage Device, Photovoltaic Solar System (PVS), Sound Recognition Techniques

1. INTRODUCTION

The lights are accessible for the national public

roads in Jordan to keep away from accidents and to make the driving secure and uncom-plicated, but turning the lights on all hours of darkness will consume a lot of energy which it might be used in one more important issues.

DOI: 10.4018/ijitwe.2014010103


34 International Journal of Information Technology and Web Engineering, 9(1), 33-47, January-March 2014

This chapter will introduce a photovoltaic solar system using an embedded sound recognition chip to control the lighting of street lights in public road. The system will turn the lights on only if there is a vehicle in the highway for a pre-defined period of time, and will keep the lights off for any other sound.

There are two approaches for using pho-tovoltaic solar system using a sound biometric chip: stand alone system that requires batteries to store power for the times when the sun is not shining, this approach can be used in a public road that also has utility power as long as they are completely separated. Grid interface system by using the power from the central utility when needed and supplies extra generated power for a highway as a parallel system by the util-ity (Meridji et al., 2013). In this Chapter one philosophy is to use the first approach in the view of the second approach to reduce the loss of the transferred power. In addition the word photovoltaic combines two terms: photo means light and voltaic means voltage.

A photovoltaic system in this chapter uses photovoltaic cells to directly convert sunlight into electricity controlled by a sound biometric technique including the algorithms that define the Conserving Energy of Street Lights System use the Database which consists of 200 sounds of vehicles and a lot of sounds from other do-mains. The proposed system can be adopted for other uses to supply power lights, televisions, pumps and other appliances at our homes. The advantages of used such systems with the low-maintenance, safe and pollution free.

While one target in the next future is to build a Grid connected interface for the PV power systems for a decentralized electrical network. Power is generated closer to where it is needed such system will reduce the need to increase the capacity of transportation and distribution lines. In our view the grid connected interface system generates its own electricity and feeds its surplus power into the utility grid for later use such as a battery bank. The battery banks can be used to provide backup power when the grid goes down using a large inverter to convert

DC power output into AC power which can handle many panels as in a standalone system.

This chapter is organized as follows. Section 2 presents an overview of the related techniques for combination systems. Section 3 describes the theoretical part of the proposed generic model of integration Section 4 presents a practical part of the proposed generic model of integration. A conclusion is described in Section 5.

2. OVERVIEW OF THE RELATED TECHNIQUES

Generating and Conserving Energy is one of the most important concerns in several coun-tries in view of the piece of evidence that they have inadequate resources of energy to depend on, and they may be import all their needs of energy from other countries. Consequently, many conventions have been held push for the public to demeanor the consumption of energy. Conserving energy of public road lights using photovoltaic solar system could be used to trim down the power invoice by generating and controlling the lights of street lamps in the public road and will save a lot of energy (Meridji et al., 2013).

This section presents an overview of the related techniques used for integrated our pro-posed system of photovoltaic solar system and sound biometric techniques on what say called system on chip.

2.1 Photovoltaic Cells

Semiconductor material normally composed of the silicon and is used in slim wafers or ribbons in most commercially existing cells. One side of the semiconductor material has a positive charge and the other side is negatively charged. Sunlight hitting the positive side will activate the negative side electrons and produce an electrical current (Sakai et al., 2006)



2.2 Types of Cells

Crystalline cells (Hsin-Hsin Hsieh, Wen-Kuei Lee, Tao-Chih Chang, & Chi-Shiung his, 2010) have been in service the longest and exhibit outstanding longevity. Cells developed almost 40 years ago are still operating and most manu-facturers offer 10-year or longer warranties on crystalline cells. There are two sub-categories of “crystalline cells – single crystal and poly-crystalline”. They both perform similarly.

The efficiency of crystalline cells is around 13%. Amorphous (Hsin-Hsin Hsieh, Wen-Kuei Lee, Tao-Chih Chang, & Chi-Shiung his, 2010) silicon is a recent technology for solar cells. It is cheaper to produce and offers greater elasticity, but their competence is half of the crystalline cells and they will disgrace with use. These types of cells will produce power in low light situations. This technology is expected to get better application possibilities far exceeding crystalline technology.

Currently, the best choice for solar cells will be the crystalline selection.

2.3 A Voltage Regulator

A voltage regulator (IEEE Standard Require-ments, Terminology, and Test Code for Step-Voltage Regulators - Redline, 2009) is designed to automatically maintain a constant voltage level of the generated current of electricity in the photovoltaic.

A voltage regulator may be a simple “feed-forward” design or may include nega-tive feedback control loops. It may use an electromechanical mechanism, or electronic components. Depending on the design, it may be used to normalize one or more AC or DC voltages.

2.4 Energy Storage

Energy storage (Steiner et al., 2007) is skilled by devices or physical media that store energy to vehiclery out useful operation at a later time. A device that stores energy is sometimes called an accumulator. An early solution to the problem of storing energy for electrical purposes was the

development of the battery as an electrochemi-cal storage device.

Batteries have previously been of limited use in electric power systems due to their rela-tively small capacity and high cost. However, since about the last decade of the 21st century, newer battery have been developed that can now provide important utility scale load-leveling capabilities.

2.5 Shunt

A shunt (Kabir, M.A., & Mahbub, 2011) is a device which allows electric current to pass about a new point in the circuit by creating a low resistance path. The term is also extensively used in photovoltaics to describe a surplus short circuit between the fronts and back outside contacts of a solar cell, typically caused by wafer damage. The origin of the term is in the verb ‘to shunt’ meaning to turn away or follow a different path.

2.6 Conditioning Circuit

A conditioning circuit (Salem, M. S., Salem, M. S., Zekry, A. A., & Ragai, H. F., 2005) includes a list of sensor types to modify in an electrical property to indicate in its environment. The electrical properties sheltered the voltage and its change from the analog to digital, current, resistance, capacities and charge.

2.7 Microcontroller

There are four types o the microcon-troller devices (Azzouzi, 2011) (PIC16F873, PIC16F874, PIC16F876 and PIC16F877). The PIC16F876/873 devices come in 28-pin pack-ages and the PIC16F877/874 devices come in 40-pin packages. The Parallel Slave Port is not implemented on the 28-pin devices. In our proposed model we advice to use PIC 16F876.

2.8 Voice Recognition

Voice recognition (Al-Sarayreh, Khalid T. et al., 2009) consists of two main tasks, that is, Feature Extraction system and model Recog-



nition system. Feature extraction attempts to determine character of the sound signal, while model recognition refers to the analogous of features in such a way as to determine, within probabilistic limits, whether two sets of features are from the same or dissimilar domain (Peeters, S., Marpu, P. R., Benediktsson, J. A., & Dalla Mura, M., 2011). In general, orator recognition can be subdivided into speaker identification, and speaker verification. Speaker verification will be used in this paper to recognize the sound of vehicles.

2.9 Linear Predictive Coding (LPC)

Linear predictive coding (LPC) (Al-Sarayreh, Khalid T. et al., 2009) is defined as a digital method for programming an analogue signal in which exacting value is predicted by a lin-ear function of the past values of the signal. It was first proposed as a method for encoding person speech by the United States Depart-ment of Defense (DoD) in federal standard, published in 1984 (Feng-Lian Li, & Xue-ying Zhang, 2009). The LPC model is based on a mathematical approximation of the vocal tract. The most important phase of LPC is the linear predictive filter which allows determining the current sample by a linear combination of the previous samples. Where, the linear combina-tion weights are the linear prediction coefficient.

The LPC based feature extraction (Feng-Lian Li, & Xue-ying Zhang, 2009) is the most widely used method by developers of speech detection. The main reason is that speech pro-duction can be modelled completely by using linear predictive analysis, beside, LPC based feature extraction can also be used in speaker recognition system where the main purpose is to extract the vocal tract.

2.10 Vector Quantization (VQ)

The quantization is the process of representing a large set of values with a much smaller set (Martinez, J., Perez, H., Escamilla, E., Suzuki, M.M. 2012) . While, the Vector Quantization (VQ) is the process of taking a large set of feature vectors, and producing a smaller set

of feature vectors, that represent the centroids of the distribution, i.e. points spaced so as to minimize the average distance to every other point (Al-Sarayreh, Khalid T. et al., 2009).

However, optimization of the system is achieved by using vector quantization (Jianke Li, Baojun Zhao, Linbo Tang, & Xiaoxia Zhao, 2012) to compress and subsequently reduce the variability amongst the feature vectors de-rived from the frames. In vector quantization, a reproduction vector in a pre-designed set of K vectors approximates each feature vector of the input signal. The feature vector space is divided into K regions, and all subsequent feature vectors are classified into one of the corresponding codebook-elements, according to the least distance criterion (Euclidian dis-tance) (Jianke Li, Baojun Zhao, Linbo Tang, & Xiaoxia Zhao, 2012).

2.11 Digital Signal Processing (DSP)

The Digital Signal Processing (DSP) (Kim, & Namyong, 2010) is the study of signals in a digi-tal representation and the processing methods of these signals. The DSP and analogue signal processing are subfields of signal processing. Furthermore, the DSP includes subfields such as audio signal processing, control engineering, digital image processing, and speech processing (Huijun Ding, Ing Yann Soon, Soo Ngee Koh, & Chai Kiat Yeo, 2009).

2.12 Frequency Domain

The signals are converted from time or space domain to the frequency domain typically through the Fourier transform. The Fourier transform (Kuech, F., & Kellermann, 2005) converts the signal information to a magni-tude and stage component of each frequency. Normally the Fourier transform is converted to the power spectrum, which is the magnitude of each frequency component squared. This is one of the features that we have depended on in our analysis.



2.13 Time Domain

The most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. Filtering (Hendricks, K. J., Heggemeier, J., & Greenwood, 2004) usually consists of some transformation of a number of surrounding samples around the current sample of the input or output signal. There are various ways to characterize filters. Most filters (Al-Sarayreh, Khalid T. et al., 2009) can be described in Z-domain (a superset of the frequency domain) by their transfer functions. A filter may also be described as a difference equation, a collection of zeroes and poles or, if it is an FIR filter, an impulse response or step response. The output of an FIR filter (Soni, V., Shukla, P., & Kumar, M., 2011) to any given input may be calculated by convolving the input signal with the impulse response. Filters can also be represented by block diagrams which can then be used to derive a sample processing algorithm to implement the filter using hardware instructions.

3. THEORETICAL PART OF THE PROPOSED GENERIC MODEL OF INTEGRATION

A photovoltaic system in this paper uses pho-tovoltaic cells to directly convert sunlight into electricity controlled by a sound biometric tech-nique including the algorithms that defines the Conserving Energy of street lights system use the Database which consists of 200 sounds of vehicles and a lot of sounds from other domains.

In this paper one philosophy is to use the first approach in the view of the second approach to reduce the loss of the transferred power. The energy generated by the proposed model can be used immediately or stored in batteries for later use (See Figure 1). In general, the surplus energy generated in autonomous PV systems during sunny periods is stored in batteries. The batteries then provide electricity at night during overcast periods or when there is not enough

solar radiation controlled by the sound technique used in the system

In addition, many samples were gathered for different types of vehicles sound from several areas at the side of the national public road. These samples were taken after the mid night to give surety that one has taken the un-contaminated sound of the vehicles with the smallest probable amount of noise. Next the feature extraction and template based compari-son; were applied on the collected sounds. The collected sounds were passed through high and low-pass filters to eliminate the noise or clut-ters. The extraction of the LPC coefficient, the magnitude of the signals, and the pitch of the signals were concluded in our proposed model. These features were normalized and clustered using vector quantization algorithm for clus-tering which based on the k-mean algorithm, for more details of these steps (See Figure 2).

Figure 2 illustrates the detailed steps in the proposed and integrated model.

3.1 Database

The database that is used in this system was built from the recorded vehicles sound which was recorded from different places, also from sounds of the rain, thunder, and plane that we have brought them from internet, also from different human sounds for more details see (Al-Sarayreh, Khalid T. et al., 2009). For the vehicles, rain, and aircraft groups, vector quan-tization method is used for clustering based on LBG algorithm and k-mean algorithm, and the Euclidian distance for matching.

Numerical analyses were used for the per-son groups, since the sounds of the human are very different and cannot be bounded. Statistical analyses were based on the power scale of the sound then the mean and standard deviation was taken to make the comparison.

3.2 Collecting Samples

About 200 samples of vehicles sound were col-lected from different places beside the public roads. These samples were taken on the night to assure that we have taken the wholesome



sound of the vehicle with the smallest quan-tity possible noise. A microphone connected to a laptop was used; it was at a far above the ground place to assure to collect all the sound, since the proposed hardware should be beside the light of the highway, which is about 1 to 30 meters above vehicles, this technique used before from the same author in Al-Sarayreh, Khalid T. et al. (2009).

A program called Sound Forge was used for recording the sounds. Most of the sounds were recorded at a sample frequency of 60 KHz to make sure that the sound has a high quality, and all the part of the sound will be shown when converting the sound to the frequency domain see Figures 3 and 4.

3.3 Feature Extraction

For identify the sound of the vehicles among other sounds we need to extract the factors from the sound signal, these factors help us to differentiate the sounds domain from others (vehicle, plane, weather, and human sounds). Feature extraction consists of choosing those features which are most effective for preserv-ing class discretely. The main features that we have chosen which most effectively expressed the sounds are LPC analysis, magnitude of the signal, and pitch of the signal – See Figures 5, 6 and you can see also (Al-Sarayreh, Khalid T. et al., 2009).

3.4 Pitch Extraction

The vocal peak based method (Shahnaz, C., Zhu, W.-P., & Ahmad, M. O., 2008) has been

Figure 1. The proposed system of integrated PVS and biometric sound chip



used to extract pitch from the wave sound. Since harmonic peaks occur at integer multiples of

Figure 2. Block diagram of the sound biometric system



the pitch frequency, then we compared peak frequencies at each time (t) to locate the fun-damental frequency to find the highest three magnitude peaks for each frame. Consequently, the differences between them computed. Since

the peaks should be found at multiples of the fundamental, we know that their differences should represent multiples as well.

As a result, the differences should be integer multiples of one another. Using the differences,

Figure 3. Vehicles sound wave

Figure 4. Aircraft sound wave



we can get our estimate for the fundamental frequency. While, the largest five peaks in each frame are identified; then we have established the spectrogram of the signal; spectrogram computes the windowed discrete-time Fourier transform of a signal using a sliding window.

The spectrogram is the degree of this func-tion which shows the areas where the energy is mostly appear, after that we have take the largest five peaks in each frame. A major advantage to this method is its very noise-resistive. Even as noise increases, the peak frequencies should still be detectable above the noise.

3.5 Feature Comparison

Subsequent to feature extraction, the similar-ity between the parameters derived from the collected sound and the reference scale need to be computed. The three most commonly encountered algorithms in the literature are Hidden Markov Modeling (HMM), Dynamic

Time Warping (DTW), and Vector Quantization (VQ). In this paper, we use the VQ to compare the parameter matrices.

3.6 Decision Function

There are usually three approaches to construct the decision rules [19], that is: Topological or Probabilistic or Geometric rules. In this paper, we will discuss two types of decision rules, which are based either on linear functions or on more complex functions such as Support Vector Machines (SVM).

3.7 Microcontroller

The decision function take a decision if the input sound is matched with our constraints or not, if the sound matched our conditions then the sound electronic circuit chip send order to the program that found in the microcontroller

Figure 5. LPC analysis of the vehicles sound wave



to open the photo voltaic circuit to turn the light on for some predefined period of time.

The type of microcontroller used in the proposed model is PIC 16F887. The PIC16F887 is used in a wide range of applications, high qual-ity and easy availability; it is an ideal solution in applications such as: the control of different processes, machine control devices, measure-ment of different values. The PIC 16F887 in the proposed model of integration is used as interface between the sound chip circuit and photo voltaic solar system circuit.

4. PRACTICAL PART OF THE PROPOSED GENERIC MODEL

The initial consideration for this implementa-tion was to simplify the matched filters of the input wave forms of two signals as opposed to certain characteristics of these wave forms. During the step; we pass a collected sound

throughout a high-pass filtered. Then we split each signal into set of frames, each frame about 30ms long, this technique is used before by the same author in (Al-Sarayreh, & Khalid T. et al., 2009). As a result of breaking the signal into frames, we fairly accurate these discrete sounds in our analysis. For each frame we calculate the LPC coefficient. We also calculated the Magnitude and the pitch of the sound. These coefficients characterize the envelope of each sound domain by low pass filtering, for more details see (Al-Sarayreh, & Khalid T. et al., 2009), see Figure 7.

The second step is to map these data by using vector quantization (VQ) and is accom-plished by a clustering algorithm. However, the clustering algorithm takes a number of random vectors and condenses the vectors that are nearest to it, iterating until the least mean error between the vectors is reached. One clustered the data into vectors, and each of these vectors is

Figure 6. LPC analysis of the planes sound wave



called a codeword. This set of vectors, or code-words is created for each sound (Al-Sarayreh, Khalid T. et al., 2009). The codewords for a given sound are then stored together in a codebook for that sound domain, see Figure 8. Then, each sounds’ codebook is then stored together in a master codebook which is compared to the test sample during the testing phase to determine the sound domain, see Figure 8.

Suppose there is a region of space where codeword vectors from several different sounds were laid (Al-Sarayreh, Khalid T. et al., 2009). If a test vector also falls in this region, the codewords do not help determine the identity of the sound domain because the errors between the test vector and the various codewords will be roughly equal.

The sound biometric system that one built identifies three main sound domains which are planes, vehicles, and weather. The detection for these domains was 100% according to the vector quantization method but the vector quantization method divided all the sound into three mainly regions (Al-Sarayreh, Khalid T. et al., 2009).

Approximated into one of these regions, so to get more accuracy and avoid any similar sound to the vehicle sound. If you have de-veloped a new code called feature extraction based on numerical analysis to any sound that is similar to the vehicle sound but really not vehicle sound this method with the vector quantization method get accuracy 100% and we assure to make energy conserving with prob-ability reached 100% since the lights will turn only for vehicles .the feature extraction based on statistical analysis method gain accuracy of 94% when work alone, see also (Al-Sarayreh, Khalid T. et al., 2009).

However, the results of applying this method were as the following three choices (Al-Sarayreh, Khalid T. et al., 2009):

• When executing the code as it is (with acceptable interval [av-2*std, av+2*std]), the results are shown in Table 1.

• When executing the code and taking the acceptable interval to be [av-sd, av+sd], the results are shown in Table 2.

Figure 7. All sounds in all domains before clustering



• When executing the code and taking the FFT (sound, 12000) and summing the area under the curve from 0-5000 and interval to be [av-2*sd, av+2*sd] the results are shown in Table 3.

Based on the results of this paper, we can note that the choice number one is the best.

5. CONCLUSION

The lights is available for public roads to avoid accidents and to make the driving more safely and more easy, but turning the lights on all the time will consume a lot of energy.

This paper proposed a new and integrated system to generate electricity using a proposed photovoltaic system and to reduce electricity

Figure 8. All sounds in all domains after clustering

Table 1. The results of code implementation for all sounds in all domains after clustering with interval 1

No. Code Execution Results Percentage

1 The probability of data in this case 95%

2 The correct recognition of vehicle sound 94%.

3 Recognition sound of planes as vehicle 4%

4 Recognition sound of rain as vehicle 2%

5 Recognition sound of animals as vehicle 0%

6 The average 0.835

7 STD 0.114



consumptions for the electricity in Jordanian public road by using the sound recognition techniques in order to turn the lights on only when there are vehicles on the highway and only for some period of time. However, this paper made the following contributions: Designing a new system for conserving energy based on the voice recognition of the vehicle sound, this system is the first application of this type that concern the street lights and this paper also dem-onstrates that the weighted Euclidian distance with the LBG algorithm was very helpful and achieved high accuracy as well as a proposed model of using a photovoltaic system with the biometric sound chip.

This paper shows that the feature of the sound biometrics technique that one have extracted is very precious and really can make a distinction between different sounds, so it is make different sounds from other or which we

can call that it makes speaker identification (ID) with high precision.

The system also built and stores an IDs for all the previous domains of sound to retrieve the reference sound print for vehicles, the newly input sound is converted into sound print and compared to the reference sound print tem-plate, the results of the comparison process are quantified to an acceptance/ rejection threshold to determine whether the two sound print are similar enough for the system to accepts the identity claim.

Finally, this paper proposed a model for generating electricity by using solar cells to convert energy from the sun light into a flow of direct current electricity, which can be used to power equipment or to recharge a battery. In addition the embedded sound biometric chip with the PVS can enable to listen and understand



1 The probability of data in this case 67%





6 The average 0.835

7 STD 0.114







6 The average 0.835

7 STD 0.115



their surrounding auditory environment since turning the lights on and off.

REFERENCES

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