Nicolas Lafitte
email: lafitte@iis.u-tokyo.ac.jp

Yassine Haddab

Yann Le Gorrec

Hervé Guillou

Momoko Kumemura

Laurent Jalabert

Hiroyuki Fujita

Dominique Collard

Closed-loop Control of Silicon Nanotweezers for Improvement of Sensitivity to Mechanical Stiffness Measurement and Bio-Sensing on DNA Molecules

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. INTRODUCTION

In the field of molecular biology, a variety of remarkable techniques such as fluidic, electric, magnetic and optical traps have been developed to trap and directly interrogate molecules [START_REF] Bustamante | Grabbing the cat by the tail: manipulating molecules one by one[END_REF]. These techniques interact mechanically with the molecule and measure the forces related to the structural configuration of the molecule. These have proven the relevance of mechanical characterizations in biology and we have thus developed a MEMS (Micro Electro Mechanical Systems) device which enables these mechanical assays in a systematic manner for diagnostic applications. MEMS are appropriate because they are integrated, monolithic, cheap and can be engineered for specific applications.

The paper is organized as follows. The silicon nanotweezers (SNT), the platform and the sensing method are introduced in the next Section. In Section III, the theory of the closed loop control is developed followed by simulations and experimental results. Especially Section III-B demonstrates experimentally the enhancement of the sensitivity for DNA molecules stiffness. The paper concludes with a discussion on the limitations of the moment (Section IV), and summarizing the capabilities of the developed platform and perspectives (Section V).

II. BIO-CHARACTERIZATIONS ON MOLECULES WITH SILICON NANOTWEEZERS (SNT)

A short description of the device and its operating principle are presented in this Section. The mechanical design, the instrumentation and the sensing method are deeply detailed in [START_REF] Lafitte | Modeling and control of MEMS tweezers for the characterizations of enzymatic reactions on DNA molecules[END_REF]. The Figure 1 shows a three-dimensional illustration of the device. It consists of two sharp tips that act as electrodes for both DNA trapping by dielectrophoresis and conductivity measurement of DNA molecules [START_REF] Yamahata | Silicon Nanotweezers With Subnanometer Resolution for the Micromanipulation of Biomolecules[END_REF], [START_REF] Yamahata | Humidity dependence of charge transport through DNA revealed by silicon-based nanotweezers manipulation[END_REF]. One tip is fixed and the other one moves with an electrostatic actuator. The motion of the electrode is measured using two capacitances with gaps that vary in proportion to the electrode displacement. Therefore with an appropriate electronic read-out (2 current amplifiers and 1 lock-in amplifier), the displacement or the velocity of the moving tip is inquired in real-time and continuously [START_REF] Yamahata | Silicon Nanotweezers With Subnanometer Resolution for the Micromanipulation of Biomolecules[END_REF].

A. Silicon nanotweezers (SNT)

B. Sensing method

The SNT is driven at its main mechanical resonance which variations are monitored in real-time and enable the quantitative detection of the molecules rigidity. The Figure 2 demonstrates the frequency response of the SNT through the sensor and actuated with a voltage of 1 V rms .

1) Model: Considering one degree of freedom, we assume that the SNT can be identified as a 2 nd -order oscillator with M the mass of the moving tip, k the mechanical stiffness of the suspensions and ν the Stokes viscous losses mainly due to the medium, i.e. air and liquid when tips are immersed in solution. Other modes of resonance arise at higher frequencies which are irrelevant for this study (e.g. at 2.4 and 3 kHz in the Figure 2-a).

2) Characterizations of DNA molecules: The mechanical characteristics of the trapped molecules (rigidity and viscous losses) are measured in real-time by monitoring the resonance frequency of the SNT. When a bundle is trapped in between the tips, the resonance curves change according to the added rigidity k bundle and the added losses ν bundle (Figure 2-b). The mechanical contribution of the sample are extracted from the resonance frequency and amplitude shifts according to the original model of the SNT (Figure 2-c).

3) Resolution: The resolution of the measurements is based on the precision we can measure the peak of the resonance and discriminate shifts in frequency and in amplitude. Resolutions achieved are as low as 10 mHz in frequency and 0.01 in quality factor changes, which corresponds to resolutions in stiffness and in viscous losses of respectively 0.5 mN • m -1 and 0.05 µN • s • m -1 . Such resolutions correspond to the sensing of 10 λ-DNA molecules 1 [START_REF] Lafitte | An open microfluidic device with active valves for accurate trapping of DNA by silicon nanotweezers[END_REF], [START_REF] Lafitte | Real-Time sensing of molecule binding on DNA with silicon nanotweezers[END_REF].

These numerical values are obtained after differentiation of the 2 nd -order oscillator resonance equations:

f R = 1 2π k M and Q = √ kM ν (1)
and from the model parameters (i.e. M , ν and k). f R being the frequency and Q the quality factor of the resonance. The derivative ∂f R /∂k is:

∂f R ∂k = 1 2π 1 2 √ kM or 1 8π 2 M f R (2)
such this sensitivity of the resonance frequency to stiffness variations is 20 Hz • N -1 • m. These resolutions and this sensitivity limit the method for more relevant experiments at the single-molecule level.

Currently the device is limited by its mechanical structure, especially its stiffness. k is around 30 N.m -1 and can not be lowered more because of fabrication and manipulation concerns. However the device can be controlled such as to change the characteristics of the system. In the Equation 2, it appears that the variation of f R with respect to k is even more pronounced when f R is small. Sensitivity to stiffness variation can be enhanced by designing a low resonance frequency sensor.

III. CLOSED-LOOP CONTROL: THEORY, SIMULATIONS AND EXPERIMENTAL RESULTS

The Section describes the adopted state feedback method, shows simulations and ends with experimental results for the validation.

A. Theory and simulations a) Model based control loop method:

As aforementioned, the system is represented by an equivalent massspring-damper system shown in Figure 2-c. From Newton's second law we can write:

M d 2 x dt 2 + ν dx dt + kx = F es ( 3
)
where M is the mass of the movable part of the device, ν is the equivalent viscosity of the system, k is the stiffness of the suspensions and F es is the electrostatic force applied to the tip through the comb-drive actuator. k bundle and ν bundle are the rigidity and the equivalent of the bundle of molecules are omitted here. The Equation 3 is so recasted under an equivalent controllable canonical state space representation (A, B, C) where 2) and X is the state vector

A ∈ R 2 , B ∈ R (2,1) , C ∈ R (1,
(displacement & velocity) X = x ẋ . Ẋ = 0 1 - k M - ν M A X+ 0 1 M B F es (4)
and

y = 0 1 C X (5)
y is the output vector, i.e. the measurement.

b) State feedback design:

The control design strategy is depicted in the flow chart of Figure 3. Accordingly to the motivation, we use a state feedback eigenstructure assignment approach and reduce the resonance frequency of the system through the placement of the poles of the closed-loop system. The drawback of this method is that it is required the implementation of an observer for the reconstruction of the state vector.

From the system under its controllable canonical state space representation (A, B, C) given by Equations 4 and 5, The state feedback gain vector L = (l 1 , l 2 ) ∈ R (1,2) is designed to assign the desired closed-loop poles. The closedloop system model becomes:

Ẋ = 0 1 - k + l 1 M - ν + l 2 M X + 0 1 M F es (6)
Thus, the closed loop resonance frequency and sensitivity become:

f R-cl = 1 2π (k + l 1 ) M (7) ∂f R-cl ∂k = 1 4π (k + l 1 ) M = 1 8π 2 M f R-cl (8)
such as both values can be controlled by the feedback parameter l 1 .

c) Identification of the model parameters:

For the development of the feedback method, the model of the device is identified around the operating point over all the experiments are performed. Differences have been pointed out between the theoretical and the final geometrical dimensions of the device after fabrication, leading to parameter uncertainties. Tiny structures such as the mechanical suspensions are more delicate to overetching, changing significantly related parameters as the device stiffness.

The identification is achieved through standard recursive approach with a method of least squares. Responses to small signals around 9 V offset (i.e. 110 nm offset) are recorded in open-loop and identified. During the experimentations 2 SNT from the same wafer of fabrication have been used and the identified model parameters for both are summarized in the Table I

d) Simulations:

With the parameters identified in the Table I, the feedback system has been first implemented and tested under Matlab/Simulink without observer (i.e. all the state variables are supposed to be measured).

The Figure 4 shows the resonance frequency shifts of open-loop and closed-loop systems undergoing a stiffness variations from -1 to 1 N/m. The closed-loop systems with a resonance frequency reduction factor set at n = 1.2 and n = 2 have respectively 1.2 and 2 times more important shifts than the open-loop system. Indeed, as theoretically expected, the sensitivities of the closed-loop system are n times enhance.

In the second instance, we design and simulate a Luen- berger observer for the real implementation of the feedback. The design of the observer is not discussed in this paper but the main objective is to reconstruct the system state without altering the closed-loop sensitivity or amplifying the measurement noise. More simulations demonstrate that the overall parametric sensitivity of the system depends on the observer dynamics. In the following experimental part, the observer is specifically designed in such a way that the closed-loop performances are not degraded.

B. Experimental sensing results

In order to validate the theoretical results, here we aim to conduct different experiments :

• with an added stiffness by contact with a cantilever,

• with an added mass by coating an additional layer of photoresist on the SNT's tip,

• and, finally, characterizing DNA molecules rigidity.

The feedback controller and the observer are implemented in a dSPACE's prototyping board. The Figure 5 shows the results of the three experiment cases. All the responses are fitted with a 2 nd -order oscillator resonance curve in order to extract the resonance frequency.

1) Added stiffness: First measurements are performed causing variations of the stiffness parameters (Figure 5-A). In order to change the stiffness of the system, a cantilever of calibrated stiffness is put in contact with the SNT's tip. Two experiments are performed with two cantilevers adding different stiffness, respectively 0.3 and 0.42 N/m (with ±0.15 N.m -1 manufacturer tolerance).

In Figure 5-A1, three curves are plotted standing for the experiments 1) without cantilever contact, 2) with the first cantilever of 0.3 N/m and 3) with the second cantilever of 0.42 N/m. For each curve/configuration, 7 points plot the resonance frequency of the open loop system (n = 0) and for 6 closed-loop systems with different n from 1.1 to 2.1. As expected, it appears that in all the cases the frequency increases due to the stiffness of the cantilevers.

For comparison, Figure 5-A4 plots the shifts in frequency between configurations with and without cantilever contact, for the two cantilevers and for the different closed loop systems. Both curves show a linear increase of the shift in frequency with the reduction factor n, expressing an increase of the sensitivity to stiffness variations However the improvements are more important than expected. First in open-loop, the shifts of frequency are +9.2 and +15.3 Hz respectively for stiffness of 0.3 and 0.42 N/m, instead of +6.0 and +8.4 Hz according to Equation 2. Moreover the improvement of the sensitivity is, for example, ×3.5 instead of ×2.1 in the case of n = 2.1. The shifts and the improvement are 1.7 times more important than predicted.

2) With mass variations: Another evaluation of the closed loop control is to test the effect when the mass changes (Figure 5-B). In order to add mass to the system, a small layer of photoresist is deposited on the mobile tip. We dip it into a droplet of photoresist (Shipley S1805) prepared on a glass, retrieved and wait until all solvent has evaporated (Figure 5-B3). The remaining dried layer of photoresist increase the inertial mass of the system. The operation is repeated in a second time for a second experiment. This method has the advantage to be simple and efficient but the quantity of photoresist is unknown.

As for the cantilevers, the performances are compared between open loop and closed loop systems with different reduction factor n. The Figure 5-B1 shows the resonance frequency of the systems for experiment without additional mass and with the two added layers. As expected in openloop configuration, the resonance frequency decreased first of -2.3 Hz and then of -7.7 Hz because of the increased inertia. From Equation 1, the added mass is deduced to be 1.1 µg after the 1 st deposition and 3.7 µg after the 2 nd deposition.

The Figure 5-B4 compares the shifts in frequency between the systems with and without additional mass and for both added mass. It appears in both cases that the variations are first negative and tend to 0, then becomes positive and increases for large reduction of the resonance frequency. By theory, it is expected that the negative variation shifts decrease until 0. Furthermore, it is hard to conceive that an increased of the mass tend to cause positive variation of the frequency such as demonstrated. This leads us to understand the cause of this behavior and probably to improve the model and its parameters. This point is considered in the following discussion Section.

(C)-1 1 1 .2 1 .4 1 .6 1 .8 2 2 .2 2
3) DNA characterizations: Finally characterizations of DNA molecule bundle have been performed (Figure 5-C). The goal being to characterize, as for the cantilevers, the rigidity of DNA molecules. λ-DNA molecules (which are DNA molecules of 16 µm length of the bacteriophage lambda) are trapped from a DNA solution droplet combining dielectrophoresis [START_REF] Lafitte | An open microfluidic device with active valves for accurate trapping of DNA by silicon nanotweezers[END_REF], [START_REF] Kumemura | Single DNA molecule isolation and trapping in a microfluidic device[END_REF] and combing methods [START_REF] Van Mameren | See me, feel me: methods to concurrently visualize and manipulate single DNA molecules and associated proteins[END_REF].

The bundle is first characterized in air under biological room conditions (at 22 • C and with 30% of humidity). Afterwards the bundle is softened by bringing the bundle at less than 100 µm of a 50 µL water droplet (Figure 5-C3), and the bundle is tested again. The humidity at this level is not measured. The Figure 5-C1 shows the characterizations of the bundle rigidity in open loop and closed-loop configurations. In openloop configuration, the resonance frequency increased of +69.2 Hz in dry condition and of +7.1 Hz in wet condition; such as, from Equation 2, the rigidity of the DNA bundle are deduced to be respectively 2.77 and 0.28 N/m.

Finally the last Figure 5-C4 demonstrates the shifts in frequency caused by the rigidity of the bundle. The sensi-tivity of the resonance frequency to stiffness variations are significantly improved by reducing the resonance frequency in closed loop. Once again the method shows a linear enhancement of the sensitivity upon the reduction factor n. The enhancement is again more important than theory predicts and is discussed in the next Section.

IV. DISCUSSION

In the previous Section, experimental results demonstrate the fulfillment of the initial motivation for this work by the control of the SNT. The enhancement of the sensitivity for more relevant experimentations has been achieved. However, as it has been pointed out, the improvement is more important than theory and simulations predict. Moreover the method is currently limited to a reduction factor of n = 2.2; further, the closed loop system goes to an unstable behavior (i.e. to saturation or to oscillations). Especially two points are under investigation and discussed here: the relevance of the model and the identification of the model parameters.

The identification of the parameters is a delicate and essential step for the modeling and the development of the feedback. On the one hand, the dimensions and the shape of the smallest parts of the device such as the width of the mechanical suspensions (which are 12 µm wide, 30 µm thick, and 1 mm long) are highly dependent on the fabrication. On the other hand, parameters are many and hard to characterize precisely and independently one after one. In this work, the mass M of the movable part have been considered to be a reference being close to the theoretical value. However according to the experiment on sensitivity to added mass, the set of parameters is not yet accurately identified and parameter identification should be improved.

Besides, limitations in the performances may arise from the model of the system. The mechanical motion of the SNT fits accurately with a 2 nd -order oscillator model and other modes of motion are out of the bandwidth. Nevertheless, in the current instrumentation, the current pre-amplifiers show a low-pass filter behavior with a cut-off frequency (∼ 2 kHz) close to the dynamic of the SNT. Moreover Analog-to-Digital and Digital-to-Analog Converters associated with the dSPACE prototyping board bring non-negligible delays to the loop. Both behaviors has been for the moment, considered as a delay and approximated with a Padé model [START_REF] Lafitte | Modeling and control of MEMS tweezers for the characterizations of enzymatic reactions on DNA molecules[END_REF].

Work on a more accurate model is on the way on and may lead to a complete validation of the method.

V. CONCLUSIONS

A state feedback control has been applied to silicon nanotweezers in order to improve characterizations of biological molecules. Sensing with micromachined tweezers enables systematic and routine experimentations on biological samples such as DNA molecules but, in addition, the sensitivity has been improved by control strategy toward single molecule resolution. The implementation of a feedback control achieved to reduce the resonance frequency and emulate a more compliant (i.e. low stiffness) sensor which increase the sensitivity of the tool to detect rigidity of the molecules.

The method have been illustrated with three cases of experiments: 1) characterizing the rigidity of silicon cantilevers, 2) characterizing the mass of extra deposited layer and finally 3) characterizing DNA molecules bundle rigidity. Before the work, the resolution was of 10 molecules of λ-DNA. This work allowed experimentally to improve this value by 3.5 and bring the resolution close to the single molecule.

This approach paves the way for diagnostic analysis with micromachined silicon nanotweezers and with single molecule resolution.

Fig. 1 .

 1 Fig. 1. Silicon nanotweezers (SNT) for bio-electro-mechanical characterizations of molecules bundle. (a) 3D schematic view: the mobile electrode is electrostatically actuated by Vact and the motion displacement ∆x changes the capacitances C 1 and C 2 . Chip dimensions: 4.5 × 4.5 mm. (b) Close view on the electrostatic comb-drive actuator (SEM img). (c) Sharp tips in between a bundle of DNA molecules is trapped (optical microscope img). (d) Close view on the capacitive sensor (SEM img).

FrequencyFig. 2 .

 2 Fig. 2. Model and sensing principle of the SNT. (a) SNT frequency response driven with 1 Vrms actuation. (b) Variations of the resonance response in air with 2 different DNA bundles. (c) Mechanical model of the system SNT + DNA molecules bundle.

Fig. 3 .

 3 Fig. 3. Feedback control scheme. The Tweezers box includes the mechanical structure of the SNT. The dSPACE implementation box shows the closed-loop elements numerically implemented in the dSPACE prototyping board.

Fig. 4 .

 4 Fig. 4. Simulation results. In black line, the tweezer transfer function. In blue and red lines, the implemented closed-loop system transfer function with respectively n = 1.2 and n = 2.0. Both systems undergo a variation of k the system stiffness. k varies from -1 to 1 N/m by 0.1 N/m steps.

2 ( 2 (

 22 photoresist resin

2 (Fig. 5 .

 25 Fig. 5. Experimental results comparing the sensitivities of different closed-loop systems (A) when a stiffness is added, (B) when a mass is added and (C) for the mechanical characterizations of DNA molecules bundle. Resonance frequencies of closed-loop systems are designed with different reduction factor n of the original resonance frequency (f R ≃ 1480 Hz). n = 1.0 corresponds to the open-loop configuration. (A) Cantilever experiments: (A)-1 Resonance frequencies of systems with different reduction factor n from 1.1 until 2.1 with 0.2 steps. The black curve is the bare tweezers systems without cantilever contact. The blue curve is with the cantilever of 0.3 N/m stiffness in contact with the tweezers tips (Figure (A)-3). The green curve is with a shorter and stiffer cantilever (0.42 N/m). (A)-2 & -3 Microscopy images of the cantilever chip respectively in approach of the tweezer's tip and in contact with the tweezer's tip. (A)-4 Resonance frequency shifts due to the added stiffness. (B) Added mass experiments: (B)-1 Resonance frequencies of systems with different reduction factor n from 1.1 until 2.2 with 0.1 steps. The black curve is the bare tweezers system. The blue curve is with an added layer of photoresist on the tweezers tips (Figure (B)-3). The green curve is with one more additional layer of photoresist coated at the end of tip. (B)-2 & -3 Microscopy image of the immersion of the tip in S1818 photoresist and of the resulting remaining photoresist layer on the tip. (B)-4 Resonance frequency shifts due to the added mass. (C) λ-DNA characterization experiments: (C)-1 Resonance frequencies of systems with different reduction factor n from 1.1 until 2.1 with 0.2 steps. The black curve is the bare tweezers systems. The blue curve is with an small trapped λ-DNA molecule bundle characterized in air (Figure (C)-2). The green curve is with the same molecule bundle characterized very close (< 100 µm) to a humidity source in order to soften the bundle (Figure (C)-3). (C)-2 Photo of the bundle of λ-DNA molecules. (C)-3 Photo of the setup softening the DNA bundle because of the close vicinity of water. (C)-4 Resonance frequency shifts due to the DNA bundle.

  .

	Parameters	TwN13.A4	TwN13.B4
	Mechanical parameters
	M (kg)	344 × 10 -9	344 × 10 -9
	k (N/m)	29.83	28.71
	ν (N.s/m)	80.9 × 10 -6	87.1 × 10 -6
	Comb-drive actuator
	αes N/V 2	53.25 × 10 -9	57.51 × 10 -9
	Capacitive sensor
	β C (F/m)	227.5 × 10 -9	227.5 × 10 -9

λ-DNA molecules are a type of DNA molecules we use for our experiments, see Section III-B.3.

ACKNOWLEDGMENT

The photolithography masks were fabricated with the 8inch EB writer F5112+VD01 donated by Advantest Corporation to the VLSI Design and Education Center (VDEC, the University of Tokyo).


ACTIVE CONTROL OF SILICON NANOTWEEZERS DETECTS ENZYMATIC

REACTION AT THE MOLECULAR LEVEL N. Lafitte 1,3* , Y. Haddab 2 , Y. Le Gorrec 2 , H. Guillou 1 , M. Kumemura 1,[START_REF] Kumemura | Proc. of IEEE MEMS[END_REF] , L. Jalabert 1 , D. Collard 1,[START_REF] Kumemura | Proc. of IEEE MEMS[END_REF] and H. Fujita 3 1 LIMMS/CNRS-IIS (UMI-2820), The University of Tokyo, Tokyo, JAPAN [START_REF] Lafitte | Proc. of MicroTAS'10[END_REF] FEMTO-ST/UFC-ENSMM-UTBM-CNRS, Besançon, FRANCE [START_REF] Kumemura | Proc. of IEEE MEMS[END_REF] The University of Tokyo, Tokyo, JAPAN This work achieved the control of micromachined tweezers for the enhancement of the sensing of DNA molecules and related enzymatic reactions. The mechanical stiffness of the silicon nanotweezers is decreased by feedback design and the sensitivity of the system is drastically improved. Biological characterizations on DNA molecules have been performed monitoring the mechanical resonance frequency of the system [1]. DNA molecules are trapped by dielectrophoresis in between the nanotips of the tweezers and form a bundle of several hundreds of DNA molecules. The MEMS integrates electrostatic comb-drive actuator and capacitive sensor with sub-nanometer resolution. The in-situ real-time monitoring of the mechanical parameters allowed the characterizations of molecules trapping [2] or digestion kinetics of DNA by restriction enzymes [3].

However the resolution of the current system is limited by the resolution of the electronic circuit, while the sensitivity is limited by the mechanical design of the tweezers. In order to improve the overall system sensitivity to force gradient (N/m) characterizations, one may decrease the tweezers stiffness. Unfortunately such a design would result in a system too fragile for fabrication and experimentation. As it remains problematic to design a new all-integrated MEMS device with extremely low stiffness (< 1N/m), we propose a new method to emulate a compliant system. Based on a model of the system, a state feedback gain is computed in order to obtain a new system with a lowered frequency of resonance with respect to the natural frequency of the tweezers (Figure 1). Theory predicts that the shift of the resonance upon molecular stiffness variations is increased for such system.

We demonstrate the enhancement by the monitoring of reactions on DNA with HindIII restriction enzymes in open-loop and in closed-loop. The feedback is implemented on a dSPACE prototyping board, carrying out the sensor signal integration and the device control generation. After trapping of DNA molecules, the probes of the tweezers with trapped bundle are introduced into the reaction cell filled with the enzyme solution (Figure 2). Frequency response of the tweezers is continuously recorded with 50ms-time resolution using a Phase-Locked-Loop (PLL) algorithm.

Figure 3 depicts the change of the resonance frequency from the open loop driven tweezers and the closed loop system while the DNA is digested by the enzymes. During time in both situations the resonance frequency of the system decreases and the amplitude increases due to the reduction of the bundle rigidity and of the viscous losses brought by the bundle. Both curves show the same time reaction that depends on the enzymatic activity and the enzyme concentration. This time reaction is actually in adequacy with what we have previously published in [3] with a set of different enzyme concentrations. However for a same concentration, in the case of the closed-loop driven system, Figures 4 and5 demonstrates that the variations of the resonance frequency and amplitude during the digestion and the sensitivity Δf/Δk are larger than in open-loop.

An enhancement of the sensitivity to parameter variations was demonstrated using closed-loop control. The sensing limit is currently the detection of 30 λ-DNA molecules stiffness (i.e. 1mN/m) in liquid (Figure 6). Approaching the sensitivity of one DNA molecule clears the way for biological experiments on single molecule with MEMS tweezers, like with complex systems such as AFM or optical tweezers. This novel method opens up the possibility to achieve this objective and can be applied to other MEMS actuators/sensors where low stiffness is required.

Figure 2 :

 2 Figure 2: Experimental set-up for the monitoring of enzymatic reaction on DNA. (a) Photo top-view of the SNT and the fluidic. (b) Scheme side-view of the immersion of the tweezers tips into enzyme solution. (c) Microscopic image of the tips and the trapped DNA bundle before digestion by the enzymes.

Figure 3 :

 3 Figure 3: Real-time monitoring of reactions of HindIII restriction enzymes on λ-DNA molecules. Experiments have been performed with (A) open-loop driven tweezers and (B) implementing the closed-loop control.

Figure 4 :

 4 Figure 4: Comparison of the performances of the openloop driven system and the closed-loop system. Resonance frequency and amplitude have been normalized with their model values.

Figure 5 :

 5 Figure 5: Sensitivity Δf/Δk comparison between openloop system and closed-loop system. The closed-loop curve shows a larger sensitivity and a better resolution.

Figure 6 :

 6 Figure 6: Noise comparison of the open-loop and the closed-loop systems. The SNR is not degraded.

Figure 1 .

 1 Figure 1. Feedback control and PLL schematic. Silicon nanotweezers (SNT) integrate electrostatic comb-drive actuator and capacitive sensor allowing feedback implementation. State feedback implementation requires in addition the design of an observer for system state reconstruction.

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Florian Dittmann

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Use of Diffusion Tensor Images in Glioma Growth Modeling for Radiotherapy Target Delineation

Keywords: glioma, tumor growth model, target delineation, diffusion tensor imaging, radiotherapy

In radiotherapy of gliomas, a precise definition of the treatment volume is problematic, because current imaging modalities reveal only the central part of the tumor with a high cellular density, but fail to detect all regions of microscopic tumor cell spread in the adjacent brain parenchyma. Mathematical models can be used to integrate known growth characteristics of gliomas into the target delineation process. In this paper, we demonstrate the use of diffusion tensor imaging (DTI) for simulating anisotropic cell migration in a glioma growth model that is based on the Fisher-Kolmogorov equation. For a clinical application of the model, it is crucial to develop a detailed understanding of its behavior, capabilities, and limitations. For that purpose, we perform a retrospective analysis of glioblastoma patients treated at our institution. We analyze the impact of diffusion anisotropy on model-derived target volumes, and interpret the results in the context of the underlying images. It was found that, depending on the location of the tumor relative to major fiber tracts, DTI can have significant influence on the shape of the radiotherapy target volume.

Introduction

Gliomas, the most common primary brain tumors, grow infiltratively into the brain parenchyma. Unfortunately, conventional imaging modalities reveal only the central, highly cellular part of the tumor, whereas areas of low tumor cell density appear normal. In standard radiation treatments of high-grade gliomas, the clinical target volume (CTV) is defined by applying an isotropic 1-3 cm margin around the abnormality visible in MRI, to account for potential tumor infiltration in normal appearing brain [START_REF] Becker | Status quo-standard-of-care medical and radiation therapy for glioblastoma[END_REF]. However, this process hardly accounts for the growth characteristics of gliomas that are known from histopathological findings [START_REF] Coons | Anatomy and growth patterns of diffuse gliomas[END_REF][START_REF] Matsukado | The growth of glioblastoma multiforme (astrocytomas, grades 3 and 4) in neurosurgical practice[END_REF]:

1. preferential spread of tumor cells along the white matter fiber tracts 2. reduced infiltration of gray matter 3. anatomical boundaries that prevent tumor cell migration, e.g. ventricles, falx cerebri, and tentorium cerebelli Accounting consistently for these growth patterns can result in a more personalized definition of the CTV, and therefore potentially improve the current treatment procedure. We use a phenomenological tumor growth model, which estimates a spatial tumor cell distribution by formalizing the growth characteristics. The model, which is based on the Fisher-Kolmogorov equation [START_REF] Harpold | The evolution of mathematical modeling of glioma proliferation and invasion[END_REF][START_REF] Konukoglu | Extrapolating glioma invasion margin in brain magnetic resonance images: suggesting new irradiation margins[END_REF], is personalized via the patient's MRI imaging data [START_REF] Menze | A generative model for brain tumor segmentation in multi-modal images[END_REF]. For radiotherapy planning, the CTV is defined as an isoline of the estimated tumor cell density obtained from the model. For a clinical application, the model is used as a tool that automatically suggests a CTV to the physician, thus making target delineation semi-automatic and more objective. To that end, it is crucial to understand the model's behavior, its capabilities, and its limitations. Previous work has conceptually introduced the Fisher-Kolmogorov model to describe glioma growth [START_REF] Harpold | The evolution of mathematical modeling of glioma proliferation and invasion[END_REF], including the integration of DTI [START_REF] Jbabdi | Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging[END_REF]. However, the specific problems related to radiotherapy planning applications have not been addressed in detail.

In this paper, we evaluate the potential of the Fisher-Kolmogorov model for target delineation. By retrospective analysis of glioblastoma multiforme (GBM) patients treated at our institution, we identify the best clinical use-cases. We determine tumor locations for which the model yields target contours that are more consistent with glioma growth patterns, compared to the manually drawn contours used for treatment. The paper focuses on the use of DTI to model preferential tumor spread along white matter fiber tracts within the glioma growth model. In particular, we characterize anatomical situations in which the additional use of DTI (on top of modeling anatomical barriers and reduced gray matter infiltration) suggests different target volumes.

In section 2, we briefly summarize the underlying tumor growth model and its personalization. Section 3 discusses how DTI is incorporated to model preferential cell migration along white matter fiber tracts. We suggest a new parameterized construction of the cell diffusion tensor from the DTI data, using a single quantitatively interpretable anisotropy parameter. In section 4, we present results for 3 patients. For one case, we present a detailed case study to explain the impact of DTI on CTV delineation. The results are interpreted in the context of the patient anatomy and the imaging data.

Tumor Growth Model

The glioma growth model formalizes the two processes that describe tumor growth: local proliferation of tumor cells and diffusion into surrounding brain tissue. It is based on the Fisher-Kolmogorov equation, a reaction-diffusion equation for the normalized tumor cell density u = u(x, t):

∂u ∂t = ∇ • (D(x)∇u) + ρu (1 -u) (1)
The proliferation rate ρ is assumed to be spatially constant, but the 3×3 diffusion tensor D(x) depends on the location x. The diffusion term ∇ • (D(x)∇u) represents the migration of tumor cells into the surrounding tissue, and the reaction term ρu(1 -u) describes the cell proliferation as logistic growth.

Conversion into a static model: A direct integration of ( 1) is impractical, because the initial condition that corresponds to the tumor appearance in MRI at the time of treatment planning is unknown. To circumvent this problem, we use the method suggested in [START_REF] Konukoglu | Extrapolating glioma invasion margin in brain magnetic resonance images: suggesting new irradiation margins[END_REF], which is based on the assumption that the visible tumor is an isoline of the cell density, and the fact that the asymptotic solution of (1) is a traveling wave. Thus, (1) can be approximated by an anisotropic Eikonal equation, which is solved by the anisotropic fast marching algorithm presented in [START_REF] Konukoglu | A recursive anisotropic fast marching approach to reaction diffusion equation: application to tumor growth modeling[END_REF]. The result is similar to the geodesic distance model in [START_REF] Cobzas | Tumor invasion margin on a Riemannian space of brain fibers[END_REF].

Personalization of the model: To apply the model to the patient at hand, an initial segmentation of the brain into tumor, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is performed, using co-registered diagnostic MR images (T1, T2, FLAIR, and T1 post contrast) as input. We utilize Expectation-Maximization-based segmentation similar to the generative tumor segmentation model of [START_REF] Menze | A generative model for brain tumor segmentation in multi-modal images[END_REF]. However, the initial segmentation result is enhanced as suggested in [START_REF] Unkelbach | Glioblastoma growth modeling for radiotherapy target delineation[END_REF], in order to reliably incorporate the most prominent anatomical boundaries falx cerebri and tentorium cerebelli. The propagation of infiltration values is restricted to the WM and GM area, and CSF is used to model anatomical boundaries.

Parameterization of Anisotropic Cell Diffusion

The diffusion tensor D(x) for a voxel x is constructed as

D(x) = D w T (x), x ∈ W M D g I, x ∈ GM ( 2
)
where I is the identity matrix, and D w and D g are diffusion rates in WM and GM, respectively. By choosing D w > D g , we model reduced infiltration of GM.

In this paper, we set D w /D g = 100. By constructing T (x) from the water diffusion tensor DT I(x) obtained from DTI, we can describe anisotropic diffusion in WM. The general idea behind the parameterization of T (x) is based on the orthonormal eigenvector matrix E(x) of DT I(x) :

T (x) = E(x)   ξ 1 0 0 0 ξ 2 0 0 0 ξ 3   E T (x) (3)
The eigenvectors e i in E(x) are the eigenvectors of DT I(x) with associated eigenvalues λ i . ξ 1 , ξ 2 , and ξ 3 control the cell diffusion along these eigenvectors, where ξ 1 corresponds to the principle eigenvector e 1 .

Weighting of diffusion anisotropy:

Setting ξ i = λ i leads to T (x) = DT I(x). For this naive construction, one cannot control the degree of anisotropy. Since this paper focuses on the question how diffusion anisotropy can potentially impact target volumes, a parameterization is needed that matches the following criteria: (1) one single parameter controls the amount of anisotropy, (2) the parameter is interpretable quantitatively, (3) the parameterization results in an intuitive behavior of the model. The main idea of our parameterization is to increase the cell diffusion along the principle diffusion axis, depending on the water diffusion anisotropy and a free constant weighting parameter. To that end, ξ 2 and ξ 3 are set to ξ 2 = ξ 3 = 1, whereas ξ 1 is chosen to be larger than 1. To construct ξ 1 , we first normalize λ i by using the apparent diffusion coefficient (ADC):

λ 1 = 3λ 1 / i λ i .
With λ 1 being independent from the overall water diffusion at the location x, we define ξ 1 as:

ξ 1 = 1 + γ(λ 1 -1), γ ≥ 0 (4)
The anisotropy weighting parameter γ controls the amount of diffusion that is added to the principal axis. Furthermore, the parameterization has the following desirable properties:

1. For γ = 0 the diffusion is isotropic and independent from DT I(x).

2. For γ = 1 the diffusion along the principal axis is equal to the ADC normalized water diffusion. 3. An isotropic water diffusion results in an isotropic T (x). 4. Arbitrary high anisotropy can be introduced through increasing the diffusion along the principal axis via γ. 5. Because only the cell diffusion along the principal axis is modified, it is easier to trace the influence of anisotropy on the model and on the estimated tumor cell distribution. 6. The diffusion ξ 1 along the principal axis scales linearly with λ 1 and γ, which makes γ quantitatively interpretable.

Model-Based Target Delineation

We performed a retrospective study involving six glioblastoma multiforme patients with varying tumor location treated at our institution. In this section, we report general observations from this group of patients, and illustrate relevant findings. Section 4.1 presents an in-depth analysis of one case to explain the anatomical features (major fiber tracts) that cause difference in model-derived targets when incorporated via DTI. Sections 4.2 and 4.3 show the impact of incorporating DTI data for two additional cases.

Case I

For case I, the tumor is located in the left parietal lobe (right side of the image) close to the corpus callosum (CC). Figure 1a displays the FLAIR image showing peritumoral edema, as well as the segmentation of the contrast enhancing tumor core (green), and the manually delineated CTV (red) from the actually applied treatment plan. The manually delineated CTV covers most of the left hemisphere except for the anterior part of the frontal lobe. Figure 1c visualizes the principal eigenvector e 1 of the diffusion tensor weighted with the normalized first eigenvalue λ 1 . As shown, the major fiber tracts adjacent to the tumor core are the CC with a dominant left-right fiber orientation (red), and the tracts with a strong superior-inferior directionality (blue), such as the corticopontine tract and corticospinal tract. The edema surrounding the tumor core leads to a low diffusion anisotropy in this area, visible as low intensity in fig. 1c and in the λ 1 images (fig. 1d to 1f).

Illustration of the growth model without DTI

Figure 1b illustrates the result of the tumor growth model. It shows the isolines of the tumor cell density throughout the brain for isotropic diffusion in WM (γ = 0). The orange contour in fig. 1a represents the isoline that encompasses the same total volume as the manually drawn CTV (red). The model accounts for the falx cerebri and the ventricles as anatomical barriers, but models tumor cell migration through the CC into the contralateral hemisphere. Here, the growth model suggests an expansion of the target volume into the contralateral hemisphere. This is not accounted for in the manual delineation, which incorporates the falx cerebri as boundary but does not consistently account for the CC. In addition, the model describes the reduced infiltration of gray matter, resulting in a sharp falloff of the tumor cell density within the cortical gray matter surrounding the sulci.

Influence of DTI on target volumes

We now discuss the additional influence of DTI. For that purpose, we calculate the tumor cell densities for γ = 0, γ = 5, and γ = 20. In fig. 1d to 1f, the λ 1 images are overlaid with five selected isolines. For the sake of comparison, the same color of isoline corresponds to the same size of the enclosed volume. 3 In particular, the volume of the orange isolines matches the size of the manually delineated CTV. The red and green contours enclose smaller volumes, which can be used to define radiotherapy boost volumes. The contours in fig. 1d (γ = 0), serving as reference, are extracted isolines from the cell density distribution shown in fig. 1b.

To show the major differences between targets derived from the isotropic and anisotropic cell diffusion in more detail, figure 2 illustrates the contours, matching the size of the manual CTV, for γ = 0 (yellow) and γ = 20 (red) for two axial slices. Frontal lobe: We now consider the region anterior to the tumor core (pink box area in fig. 1f). For the isotropic case (γ = 0, fig. 1d), the target extension into the frontal lobe is characterized by a smooth contour (mostly defined by the geometric distance in WM from the core). In contrast, in the anisotropic case with high diffusion weighting (γ = 20, fig. 1f), the isolines have a jagged shape since they incorporate the varying fiber tract orientations in this region.

Especially the yellow and the blue lines tend to follow the anterior-posterior oriented fibers, i.e. the anterior part of the internal capsule, and the superior part of the fronto-occipital fascicilus (green fiber tracts in fig. 1c). This effect is also apparent in fig. 2. For a lower anisotropy weighting (γ = 5, fig. 1e), this effect is less pronounced.

Superior part of the corpus callosum: We now compare the target volumes in the superior part of the CC (top circle in fig. 2a). In the anterior direction, the red contour (γ = 20) does not extend beyond the yellow contour (γ = 0); the yellow contour is expanded even further than red contour. This is because the dominant fiber direction (left-right) is perpendicular to the primary direction of cell migration. Since we compare contours that encompass the same volume size, and the red contour includes larger areas in the contralateral hemisphere, the target volume is to be trimmed in other regions (e.g. in the superior part of the CC).

Case II

For case II, the tumor core is located in the left temporal lobe adjacient to the brain boundary. Figure 3a shows the FLAIR image in the superior part of the lesion (the green contour corresponds to the contrast enhancing core). The manually delineated CTV (red) covers large areas in the left temporal, occipital, and parietal lobes.

Figure 3b compares the model derived target volumes for the isotropic case (yellow: γ = 0) and the anisotropic case (red: γ = 20). Both contours account for the ventricles as anatomical barrier, as well as the effect of reduced gray matter near major sulci. In this case, this becomes most apparent near the lateral sulcus. The additional impact of DTI on the target volume is visible in the CC and the inferior fronto-occipital fasciculus. Similar to case I, the assumption of preferential spread of tumor cells along major fiber tracts modeled via DTI suggests a further expansion of the target into the CC. However, compared to case I, the effect is reduced because the tumor core is located further away from the CC.

Case III

For case III, the tumor core is located within the left superior frontoparietal region. The FLAIR image shows extensive peritumoral edema around the contrast enhancing core (green contour in fig. 4a). The manually delineated CTV (red) covers most of the left parietal lobe and is partially extended into the left frontal lobe.

Figure 4b shows the location of major fiber tracts and compares of the modelderived target volumes (matching the size of the manual CTV) for γ = 0 (yellow) and γ = 20 (red). The findings for this patient are consistent with case I and II: it is illustrated that the integration of anisotropic cell diffusion leads to further extension of the isolines into the CC and the contralateral side. The magnitude of this effect depends on the distance of the tumor from the CC. Case III also illustrates a limitation of the use of DTI to model preferential tumor cell migration along fiber tracts: within the edematous region the anisotropy signal obtained from DTI is reduced (visible as the hypointense region in fig. 4b). In this region, the information about the dominant fiber orientation is compromised. This reduces the differences between the isotropic and the anisotropic model.

Application to Low-Grade Gliomas

Model-based CTV definition for low-grade gliomas was not explicitly addressed in this paper. However, the presented methodology would be equally applicable to low-grade gliomas. In current clinical practice, the margin for the manual CTV definition in low-grade cases (1-2 cm, [START_REF] Grier | Low-grade gliomas in adults[END_REF]) is smaller than in high-grade cases, resulting in a smaller volume. Therefore, for low grade-gliomas, only high anisotropy of the DTI signal in the immediate neighborhood of the MRI abnormality can have an impact on model-derived CTVs. On the other hand, accurate target delineation and optimal sparing of brain tissue is of particular interest in low-grade gliomas due to the longer life expectancy of patients.

Conclusion

The spatial growth patterns of gliomas are influenced by the preferential spread of tumor cells along the white matter fiber tracts. This can be formalized via a spatially varying, anisotropic cell diffusion tensor in a reaction-diffusion equation. In this work, we formulated a construction of the cell diffusion tensor from the DTI derived water diffusion tensor. We introduce a single anisotropy parameter γ, which controls the increased diffusion along the principle axis, i.e. the primary fiber direction. Based on this comprehensive tensor parameterization, we investigate the influence of DTI on target delineation for radiotherapy. Six patients were analyzed. One of the main findings is that, for tumors located in proximity to the corpus callosum, an anisotropic diffusion parameterization indicates a further expansion into the contralateral side. This shows that the integration of DTI can have significant impact on the model-derived target volume for radiation therapy. Current research addresses the validation of the glioma growth model based on follow-up MR imaging and clinical outcome data.

Fig. 1 :Fig. 2 :

 12 Fig. 1: Comparison of multiple isolines for γ = 0, 5, 20 (γ = 0 corresponds to an isotropic growth model without DTI; lines of the same color correspond to the same volume; orange line corresponds to the manual CTV size)

  (a) FLAIR image with tumor core (green) and manual CTV (red) (b) Target volumes for γ = 0 (yellow) and γ = 20 (red) overlaid on the visualization of e1 with λ1 weighting

Fig. 3 :

 3 Fig. 3: Case II: Comparison of the target volumes

  (a) FLAIR image with tumor core (green) and manual CTV (red) (b) Target volumes for γ = 0 (yellow) and γ = 20 (red) overlaid on the visualization of e1 with λ1 weighting

Fig. 4 :

 4 Fig. 4: Case III: Comparison of the target volumes

Instead of isolines with the same infiltration value, it is indicated to compare isolines enclosing a volume of the same size, because the parameterization presented in section 3 introduces higher diffusion rates, resulting in higher infiltration values per se.


ABSTRACT

Every visualization researcher and practitioner knows the painful experience of a beautifully designed network layout breaking down once the input graph scales up to realistic node and edge counts.

The resulting "hairball" suffers from cluttering and overplotting to an extreme that renders it unusable for any practical purposes.

Since researchers have had this experience for decades, various approaches have been developed on all stages of the visualization pipeline to alleviate this problem. They range from filtering and clustering techniques on the data level to modern GPU-based techniques on the image level. This tutorial gives an overview of these techniques and discusses their applicability and interplay in different application scenarios. By doing so, it provides a unique problem-oriented perspective on the field of scalable network visualization, which is an area of active research today more than ever.

The tutorial serves mainly to further the understanding of network visualization beyond the point of creating an initial layout. It thus caters to an intermediate level audience with some basic knowledge on graph layout and visualization, but it will certainly present an interesting cross-section through the larger domains of network visualization and graph drawing for established researchers as well.

Index Terms:

I.3.

TUTORIAL CONTENTS

Since network-structured data has become a mainstream concept that every user of online social networks is familiar with, the interest in the visualization of such data has grown -and so has the realization that this is a challenging and computationally complex task in particular for larger networks. The proposed 1/2 day tutorial for intermediate audiences discusses the different existing approaches that address this challenge of producing or refining network visualizations to scale to realistically sized data sets while maintaining readability. It covers the topic from the two perspectives of node-set-based methods (Part I) and edge-set-based methods (Part II), which are each further subdivided into methods working on the three levels of the visualization pipeline: the data level, the geometry level, and the image level. A third part then brings the former two parts together by discussing their interplay and application in various domain-specific scenarios (Part III). The proposed three parts of this tutorial are outlined in the following.

PART I: Methods Working on the Node Set

Methods that aim to reduce or refine the node set are probably the most commonly used approaches to get a grip on large networks. The reason is that they automatically serve the reduction of the edge set as well, as all reduction on the nodes is reflected on their incident edges. In this part, a selected number of such approaches is covered that are deemed characteristic for the particular level and widely used in practice.

Data Level Methods. On data level, two main directions can be taken that aim to reduce the input graph before it is laid out and thus generates the problems mentioned above. These directions are abstraction (e.g., clustering or partitioning) and selection (e.g., filtering or contraction). Abstraction basically coarsens the level of detail, but keeps the full extent of the network, whereas selection cuts down on the extent, but keeps the remainder of the network at fullest detail. This part highlights some fundamental graph clustering approaches and graph partitioning approaches, and then it shows how partitioning can be used in conjunction with clustering, as it is done in [START_REF] Abello | ASK-GraphView: A large scale graph visualization system[END_REF][START_REF] Tominski | CGV -an interactive graph visualization system[END_REF]. The latter is a means to alleviate some of the problematic aspects of graph clustering, such as its long runtime and the poor interpretability of its results. The selection part briefly covers how it is actually performed and then focuses on the underlying question of how to distinguish between salient nodes and those of lesser importance to be filtered out. To this end, different importance metrics [START_REF] Herman | Skeletal images as visual cues in graph visualization[END_REF][START_REF] Karloff | Maximum entropy summary trees[END_REF] and different Degree of Interest methods [START_REF] Van Ham | Search, show context, expand on demand: Supporting large graph exploration with degree-of-interest[END_REF][START_REF] May | Using signposts for navigation in large graphs[END_REF] are introduced and discussed.

Geometry Level Methods. On geometry level, one has to distinguish between methods that merely reflect a prior reduction on data level (e.g., layouts for clustered graphs) and those that perform a genuine reduction on the geometry level of a previously unreduced graph. As examples for layout methods reflecting a reduction, techniques are presented that tie in with a hierarchical clustering by using force-directed layouts [START_REF] Eades | Navigating clustered graphs using forcedirected methods[END_REF][START_REF] Muelder | Rapid graph layout using space filling curves[END_REF], as well as with navigation techniques that support switching between different clustering layers [START_REF] Elmqvist | Hierarchical aggregation for information visualization: Overview, techniques, and design guidelines[END_REF]. Methods that perform the desired reduction of the number of nodes on a purely geometric basis are rare. But approaches, such as GraphDice [START_REF] Bezerianos | Graphdice: A system for exploring multivariate social networks[END_REF], which position nodes with the same numerical attributes on the same position of a scatterplot-like layout, are a good example of how this can nevertheless be achieved.

Image Level Methods. On image level, the methods described in the literature aim to render nodes, which are placed close together as one larger blob or splat. This does not only reduce the clutter, but also indicates where many nodes have been aggregated in this way, thus giving visual cues for zooming in to investigate these dense regions in more detail. Existing techniques range from those that operate globally on the entire given node-link diagram, such as Graph Splatting [START_REF] Van Liere | GraphSplatting: Visualizing graphs as continuous fields[END_REF], to those that operate more locally only in those regions where it is necessary. Example for the latter are the density-based node aggregation [START_REF] Zinsmaier | Interactive level-of-detail rendering of large graphs[END_REF] and the adaptive coloring by content-aware scaling [START_REF] Schulz | Point-based visualization for large hierarchies[END_REF]. These examples will be covered in this last part of the node-related reduction techniques, before switching the perspective to the edge-related methods in the following part.

PART II: Methods Working on the Edge Set

In this part of the tutorial, we will discuss algorithms and interactive techniques to reduce edge clutter in visualizations. These techniques mainly rely on edge-bundling algorithms which have been subject to an increased interest in active research and a number of improvements and enhancement in recent years. In the same way as the first part of the tutorial, this part is divided into the three levels on which edge simplification can occur: the data level, the geometry level, and the image level.

Data Level Methods. On data level, edges can be filtered or aggregated. Filtering techniques remove edges with given criteria, whereas aggregation techniques merge edges having similar semantics.

Aggregation and edge clustering methods are given in [START_REF] Ellis | A taxonomy of clutter reduction for information visualisation[END_REF]. Interactive systems, such as Node Trix, compact dense subgraphs into matrix representations [START_REF] Henry | NodeTrix: A hybrid visualization of social networks[END_REF][START_REF] Hadlak | In situ exploration of large dynamic networks[END_REF]. In Ploceus [START_REF] Liu | Network-based visual analysis of tabular data[END_REF], one can display networks from different perspectives, at different levels of abstraction, and with different edge semantics.

Regarding filtering techniques, direct queries can filter out edges with multivariate criteria. Edges can be filtered with Centrality Based Visualization of Small World Graphs [START_REF] Van Ham | Centrality based visualization of small world graphs[END_REF], or explored with spanning tree as used in TreePlus [START_REF] Lee | TreePlus: Interactive exploration of networks with enhanced tree layouts[END_REF].

Geometry Level Methods. On geometry level, dense edge visualizations can be uncluttered by using edge bundling techniques. Edge bundling techniques trade clutter for overdraw by routing geometrically and semantically related edges along similar paths. This improves readability in terms of finding groups of nodes related to each other by tracing groups of edges (the bundles) which are separated by whitespace [START_REF] Gansner | Multilevel agglomerative edge bundling for visualizing large graphs[END_REF]. Dickerson et al. merge edges by reducing non-planar graphs to planar ones [START_REF] Dickerson | Confluent drawings: Visualizing non-planar diagrams in a planar way[END_REF]. The first edge bundling technique was the flow map visualizations which produce a binary clustering of nodes in a directed graph representing flows to route curved edges along [START_REF] Phan | Flow map layout[END_REF]. Flow maps' control meshes are used by several authors to route curved edges, e.g., [START_REF] Qu | Controllable and progressive edge clustering for large networks[END_REF][START_REF] Zhou | Energy-based hierarchical edge clustering of graphs[END_REF].

These techniques were later generalized into edge bundling approaches that use a graph structure to route curved edges. Holten pioneered edge bundling for compound graphs by routing edges along the hierarchy layout using B-splines [START_REF] Holten | Hierarchical edge bundles: Visualization of adjacency relations in hierarchical data[END_REF]. Gansner and Koren bundle edges in a circular node layout similar to [START_REF] Holten | Hierarchical edge bundles: Visualization of adjacency relations in hierarchical data[END_REF] by area optimization metrics [START_REF] Gansner | Improved circular layouts[END_REF]. Control meshes can also be used for edge clustering in graphs, e.g., [START_REF] Qu | Controllable and progressive edge clustering for large networks[END_REF][START_REF] Zhou | Energy-based hierarchical edge clustering of graphs[END_REF]; a Delaunay-based extension called geometric-based edge bundling (GBEB) [START_REF] Cui | Geometry-based edge clustering for graph visualization[END_REF]; and "winding roads" (WR) that use Voronoi diagrams for 2D and 3D layouts [START_REF] Lambert | Winding roads: Routing edges into bundles[END_REF][START_REF] Lambert | 3D edge bundling for geographical data visualization[END_REF].

The most popular technique is the force-directed edge layout technique which use curved edges to minimize crossings, and implicitly creates bundle-like shapes [START_REF] Dwyer | Integrating edge routing into force-directed layout[END_REF]. Force-directed edge bundling (FDEB) creates bundles by attracting control points on edges close to each other [START_REF] Holten | Force-directed edge bundling for graph visualization[END_REF], and was adapted to separate bundles running in opposite directions [START_REF] Selassie | Divided edge bundling for directional network data[END_REF]. The MINGLE method uses multilevel clustering to significantly accelerate the bundling process [START_REF] Gansner | Multilevel agglomerative edge bundling for visualizing large graphs[END_REF].

Computation times for larger graph struggle with the algorithmic complexity of the edge bundling problem. This makes scalability the major issue when using edge bundling techniques. The latest techniques use therefore the image level to bundle edges.

Image Level Methods. Thanks to the recent improvements regarding graphic hardware and its flexible usage, image level methods are nowadays very popular. Graphic cards can be used to improve rendering aesthetics and to address scalability issues.

Several techniques exist for rendering and exploring bundled layouts, e.g., edge color interpolation for edge directions [START_REF] Holten | Hierarchical edge bundles: Visualization of adjacency relations in hierarchical data[END_REF][START_REF] Cui | Geometry-based edge clustering for graph visualization[END_REF]; transparency or hue for local edge density, i.e., the importance of a bundle, or for edge lengths [START_REF] Lambert | Winding roads: Routing edges into bundles[END_REF]. Bundles can be drawn as compact shapes whose structure is emphasized by shaded cushions [START_REF] Telea | Image-based edge bundles: Simplified visualization of large graphs[END_REF][START_REF] Scheepens | Composite density maps for multivariate trajectories[END_REF]. Graph splatting visualizes node-link diagrams as continuous scalar fields using color and/or height maps [START_REF] Van Liere | GraphSplatting: Visualizing graphs as continuous fields[END_REF][START_REF] Hurter | FromDaDy: Spreading data across views to support iterative exploration of aircraft trajectories[END_REF].

Several techniques exists to improve scalability based on image level. Skeleton-based edge bundling (SBEB) use the skeletons or medial axes of the graph drawing's thresholded distance transform as bundling cues to produce strongly ramified bundles [START_REF] Ersoy | Skeleton-based edge bundles for graph visualization[END_REF]. To explore crowded areas where several bundles overlap, bundled layouts can be interactively deformed using semantic lenses [START_REF] Hurter | MoleView: An attribute and structure-based semantic lens for large element-based plots[END_REF].

Hurter et al. use a pixel based bundling method to explore dynamic graphs [START_REF] Hurter | Smooth bundling of large streaming and sequence graphs[END_REF].

PART III: Application Scenarios and Summary

In this final part, we will bring the methods of the first two parts together by discussing which of them to apply and how to go about it for a few selected application cases. In particular, this part will highlight how to combine edge and node simplification under the different constraints imposed by the application domains. For this part, we rely mostly on our own experience in applying these techniques to trajectory exploration, software revision analysis, and stream graph exploration [START_REF] Hurter | FromDaDy: Spreading data across views to support iterative exploration of aircraft trajectories[END_REF][START_REF] Hurter | MoleView: An attribute and structure-based semantic lens for large element-based plots[END_REF][START_REF] Hurter | Smooth bundling of large streaming and sequence graphs[END_REF], as well as to social networking [START_REF] Van Ham | HoneyComb: Visual analysis of large scale social networks[END_REF].

At the end of this part, we will briefly summarize the key points of our tutorial and also emphasize on the open challenges in this area, so that starting graduate students will have some pointers in which direction to look to make a contribution. Among other points, these challenges include scalability issues w.r.t. screen space, computation time, but also interaction, as well as compatibility issues among the individual approaches, which currently limit their concerted use.

TUTORIAL ORGANIZATION AND COURSE MATERIAL

The half-day tutorial will be held as a presentation using pictures, videos, and live demos from literature as well as the presenters' own work. We are going to spend about 75 minutes for each of Part I and Part II, and the remaining 1 hour for Part III and answering further questions. The participants will be provided with tutorial notes including an extensive literature list on the subject.

After completing this tutorial, participants can expect to have gained a comprehensive overview of existing approaches for making visualizations more expressive and more effective for communicating larger networks. Through the selected application scenarios, the participants will also get a grasp on when and how to utilize these techniques in practice. In addition, concrete suggestions on issues of realizing and implementing the discussed algorithms from the experience of the presenters will be given.

INSTRUCTOR INFORMATION

Hans-Jörg Schulz received his PhD from the University of Rostock in 2010, where he is now a post-doctoral researcher. With his expertise in graph visualization, his perspective on showing relationships in data stems from a graph drawing point of view. His main interest lies in visualizing hierarchical relationships in data [START_REF] Schulz | .net: A tree visualization reference[END_REF][START_REF] Schulz | A generative layout approach for rooted tree drawings[END_REF]. This naturally includes compact tree visualizations for reducing clutter and overplotting [START_REF] Schulz | Point-based visualization for large hierarchies[END_REF] and hierarchically clustered graphs [START_REF] Van Ham | HoneyComb: Visual analysis of large scale social networks[END_REF], but also extends to the recursive refinement of dynamic network visualization [START_REF] Hadlak | In situ exploration of large dynamic networks[END_REF]. More on his work can be found at http://hjschulz.net Christophe Hurter received his PhD from the University of Toulouse in 2010. He is associate professor at the Interactive computing laboratory at the French national aviation university (ENAC, Ecole Nationale de l'Aviation Civile) in Toulouse France. His research interests lie in the research areas of information visualization and Human-Computer Interaction, particularly including the visualization of multivariate data in space and time, the design of scalable visual interfaces and the development of pixel based rendering techniques. He published several papers on new scalable edge bundling techniques [START_REF] Hurter | Graph bundling by kernel density estimation[END_REF][START_REF] Ersoy | Skeleton-based edge bundling for graph visualization[END_REF], and real-time interaction techniques with edge bundling [START_REF] Hurter | MoleView: An attribute and structure-based semantic lens for large element-based plots[END_REF][START_REF] Hurter | Smooth bundling of large streaming and sequence graphs[END_REF]. More on his work can be found at http://www.recherche.enac.fr/ ˜hurter/

1/2-Day Tutorial Proposal: Grooming the Hairball - How to Tidy up Network Visualizations?

	Hans-J örg Schulz *	Christophe Hurter †
	University of Rostock, Germany	ENAC/University of Toulouse, France

ACKNOWLEDGEMENTS

Hans-Jörg Schulz's research is funded through an individual grant by the German Research Foundation (DFG).


Nathalie Haziza
email: nathalie.haziza@thalesgroup.com

Mohamed Kassab
email: mohamed.kassab@ifsttar.fr

Raymond Knopp
email: raymond.knopp@eurecom.fr

Jérôme Härri
email: jerome.haerri@eurecom.fr

Florian Kaltenberger
email: florian.kaltenberger@eurecom.fr

Philippe Agostini
email: philippe.agostini@thalesgroup.com

Marion Berbineau
email: marion.berbineau@ifsttar.fr

Christophe Gransart
email: christophe.gransart@ifsttar.fr

Joëlle Besnier
email: joelle.besnier@ifsttar.fr

Jacques Ehrlich
email: jacques.ehrlich@ifsttar.fr

Hasnaa Aniss
email: hasnaa.aniss@ifsttar.fr

Multi-technology vehicular cooperative system based on Software Defined Radio (SDR)

Keywords: Software-Defined Radio, vehicular communication, V2V, V2I, driver assistance application, proof of concept, network simulation, IEEE 802.11p

Introduction

Nowadays, the integration of cooperative functions is the main focus of advanced driver assistance systems (ADAS). Following the European policy for Intelligent Transport System (ITS), the cooperative functions are used to enhance road safety, to optimize the traffic and to reduce the impact of transports on the environment. A key potential is the combination of local environment data (exchanged between neighbor vehicles or between vehicles and Road Side Units) and regional traffic data broadcast. In other words, the drivers can get detailed information from immediate surroundings and longer-term forecasts and alerts controlled from a central infrastructure. The communication system associated to such functions must support vehicle-to-vehicle (V2V), vehicle-toinfrastructure (V2I) and infrastructure-to-vehicle (broadcasting) communications. Several wireless technologies are able to support these communications e.g. WLAN technologies for V2V and V2I and technologies such as Digital Audio Broadcasting (DAB), Digital Multimedia Broadcasting (DMB) and Digital Video Broadcasting (DVB) for broadcasting from the infrastructure. The technologies used for cooperative systems will depend on communication standards and infrastructures defined in each country. Several working groups are active in the field of communications for ADAS. The International Organization for Standardization (ISO) TC204 Working Group 16 [1] produced specifications for the Communications Access for Land Mobile (CALM) [START_REF]Intelligent transport systems " communications access for land mobiles (CALM) " architecture. Technical report[END_REF], providing a reference framework for future implementations. Harmonization at the European level has been provided with C2C-CC (with the European Telecommunications Standards Institute-ETSI [2]), and with the network working group for the IETF and WAVE for IEEE [START_REF]IEEE Standard for Information technology-Local and metropolitan area networks-Specific requirements-Part 11: Wireless[END_REF]. Among them, the architecture proposed by CALM is particularly interesting regarding interoperability and flexibility. It is based on heterogeneous cooperative communication framework to provide continuous communications to users. The CALM concept is based on the juxtaposition of several communication modems in the vehicles [START_REF]Intelligent transport systems " communications access for land mobiles (CALM) " architecture. Technical report[END_REF]. The need of such heterogeneity is justified by several characteristics of vehicular communications.

As an alternative to several modems to answer to the need of multiple standards, the Software-Defined Radio (SDR) is a wireless system implemented by software routines so that various wireless radios can be supported by the same hardware based on software changes. SDR hardware allows to modify easily the communication technology supported by a terminal in accordance with its environment. The use of SDR allows vehicle manufacturers to adapt communication technologies supported by vehicles to fit to the local standards.

Despite the prospective advantage of interoperability and reconfigurability, several studies to optimize the SDR signal processing chain have shown implementation issues impacting performances that have to be solved. The first main argument concerns the processing speed, needing an optimal sharing in the baseband processor (implementation choices between DSP and FPGA). The second argument deals with the power consumption in the digital signal processing, becoming of major influence for terminal batteries and handset devices. Nevertheless, interest for SDR implementations (vehicular communications, payload systems) kept growing since the 90's, starting with military applications and spreading to civilian areas, thanks to the recent progress in chip development. Semiconductors and automotive manufacturers perceived the combination of telematics and SDR as a promising area. [5][6] On the other hand, the adequation of the IEEE 802.11p/WAVE standard for vehicular context is now well-known: the average needed data rate stays low, and the WAVE protocol mechanims simplify V2V communications in a restricted area. Comparison with other systems has been performed (LTE in [START_REF] Vinel | 3GPP LTE versus IEEE 802.11p/WAVE: Which technology is able to support cooperative vehicular safety applications?[END_REF] giving advantage to the WAVE system). However, performance assessment for the IEEE 802.11p/WAVE standard performed in LOS and NLOS scenarios (straight roads, urban context with intersections and shadowing buildings) have shown a need to combine two systems, for local information and regional broadcast.

The PROTON-PLATA [START_REF]PROTON-PLATA project web site[END_REF] project lies on a German-French collaboration (Deufrako/ANR agreement) for the development of a common platform, able to deal with various standards, emerging in each country; industrial and academic partners interested in ITS and particularly in vehicular systems were involved. The goal of this project is to show the feasibility and the interest of a cooperative ADAS making use of the SDR technology to enhance communication performances. The main idea is to propose a multi-technology communication infrastructure and to equip On-Board Units by SDR devices that support simultaneously V2V communications, V2I communications and broadcasting (infrastructure-to-vehicle). The scope of the project includes "proof of concept" part and a performance study part. The first one proposes the development of a prototype, including SDR device and driver-assistance applications. The second is a performance evaluation based on network simulation tools.

This paper is organized as follows. In section 2, we propose an overview of research studies related to cooperative ADAS. Section 3 introduces the multitechnology cooperative system designed within the PLATA-PROTON project. In section 4, we describe the prototype development and related experiments. Section 5 propose an overview of results obtained with the simulation based performance study. Concluding remarks are presented in section 6.

Related work

Several researches deal with to the enhancement of ADAS to vehicular cooperative systems. These works vary from specification frameworks proposed by international standardization institutes, e.g. ETSI, ISO, research projects managed by academic and industrial consortiums and academic works. The research scope is very large from PHY layer to applications, protocols, security, mobility management, communication architecture, etc. In this section, we propose a survey of the most interesting works in this area.

Regarding international-wide, three main groups are working on this subject: CALM for the ISO, WAVE for the IEEE and C2C-CC for the ETSI [2].

At the European level, we have identified several key projects related to the enhancement of vehicular cooperative systems.

The Cooperative Vehicle Infrastructure System (CVIS) [8] (2006-2010) is a very well known European research project related to the enhancement of cooperative services based on interaction between vehicles and centralized infrastructure. The goal of the project was to develop an open application framework that enables a wide range of potential cooperative services to operate in vehicles, roadside equipments and centralized management centers. It assumes some diversity in communication technology (802.11p, WiMAX, UMTS, DVB / DAB, etc.) and is based on IP network mechanisms (IPv6, MIPv6, NEMO, etc.).

The EVITA (E-safety Vehicle Intrusion proTected Application) [START_REF]EVITA project[END_REF] (2008-2011) focused on the design of an on-board automotive network architecture, to ensure protection against unexpected or unauthorized manipulation, for sensitive data transmission. The CO-OPerative SystEms for Intelligent Road Safety (COOPERS) [START_REF]COOPERS project[END_REF] (2006-2011) concern was about the development of Co-operative Traffic Management applications on the road infrastructure with an interaction between vehicles and infrastructure. The approach aims to extend the concepts of in-vehicle autonomous systems and vehicle-to-vehicle communication (V2V) with tactical and strategic traffic information which can only be provided by the infrastructure operator in real time (traffic jam warning and guidance, in-car display and alert of area-specific speed limits, car breakdown/emergency services, etc). The SAFESPOT project [START_REF]SAFESPOT project[END_REF] (2006-2010) considered the combination of the information from vehicles and from the infrastructure, to improve the driver perception of the surrounding vehicles. It focuses on the identification of cooperative solutions that will first be applied to the critical areas, such as the so called black spots. Sensing systems on the infrastructure side are proposed in combination with the information coming from vehicles to detect critical conditions and events.

All these projects have contributed to the definition of new applications and services for driving assistance and the specification of cooperative architecture models. The PROTON-PLATA project is a follow up of these works as it focuses on the use of multi-technology architecture and SDR devices to enhance the communication capabilities of ADAS.

Multi-technology cooperative system

In this section, we present the main components of the vehicular cooperative ADAS proposed in PROTON-PLATA. We detail the drivers assistance applications and the communication architecture.

Driver assistance applications

We have defined four applications that cover several types of services in vehicular environment.

-GPS position exchange: This application allows vehicles to exchange their GPS data. Each vehicle broadcasts a message containing its own information: vehicle category (car, truck, motorbike, bike, etc.), geographical position, speed, course and geolocation time. Geolocation time is the time given by the geo-location system. -Local weather condition: This application informs the driver about the weather condition where he/she is and according to the travel direction. A centralized server broadcasts information over a given geographic area using a wireless technology. -Incident hazard warning: This application keeps drivers aware of traffic hazards based on information provided by central server, roadside units, road equipment and other vehicles. The hazards that can be supported by this application are multiple such as wrong way driver also called Ghost driver, vehicles at a standstill, obstacles on the road, black ice, etc. When a vehicle detects a hazard on its way, it sends a message to its environment to share the information with other vehicles. Two versions of this application are defined: V2V hazard warning and V2I hazard warning. V2V hazard warning is fully decentralized, a vehicle that detects a hazard sends messages to neighboring vehicles via peer-to-peer communications. With V2I hazard warning, a vehicle that detects a hazard sends messages to a server located in the management network via a Road Side Unit (RSU). The server forwards the message to appropriate areas using broadcast communications. -Speed limit: This application defines a database for speed limits in a road network. A reference database is located in a centralized server and speed limits are stored on-board the vehicles in a local database. A new edition of the reference database is published at regular intervals. Vehicle on-board systems have to update their local databases periodically. This application defines dynamic and static versions of updating mechanisms. The Static speed limit detects and reports any obsolescence of the embedded database by comparing its version number with the reference database. The dynamic speed limit is executed when dynamic changes are decided in the center.

Updates are sent to vehicle by means of broadcasting transmissions into the related area.

Communication system

PROTON-PLATA project proposes a communication infrastructure that allows to fully exploit the SDR capacity of the devices in vehicles. The communication infrastructure manages the centralized part of the cooperative system, namely Network-Head (NH). This architecture connects the road traffic management center to two wireless networks. The first one is a wireless access network based on Road Side Units (RSU) along the roads. The second one is a broadcast network based on Base Stations (BS) with large coverage. Figure 1 shows an example of a deployment. The uplink communications between On Board Unit (OBU) and the Network-Head (NH) are supported by the RSUs network, while the downlink communications are supported by both the BS and the RSU.

The key innovation of the project lies in the design of the multi-technology OBU, which role is to exchange information with the NH (through the RSUs or the BS) and with other OBUs. The OBUs implement different protocol stacks on a Software Defined Radio (SDR) device. The SDR is a wireless system implemented by software routines so that various wireless radios can be supported by the same platform based on software changes. SDR hardware allows to modify easily the communication technology supported by a terminal in accordance with its environment and/or needs. The PLATA-PROTON specifications suppose that OBU are equipped with SDR devices that manage V2V communications with other OBUs, V2I communications with RSUs, I2V broadcast reception from BS.

SDR Prototype

For the SDR protoype design, the choice of the open-source hardware/SDR DSRC prototype on the OpenAirInterface platform [14] has been identified as a real asset, as it answers to the overall aim of the cooperative PROTON-PLATA system, precisely to adapt to standards specific to each country. The standards for the PROTON-PLATA have thus been set to the promising LTE wireless standard associated to the 802.11p vehicular technology.

Figure 2 depicts the software architecture of the OpenAirInterface SDR platform. It is segmented into three parts, the User Space, the Kernel Space and the SDR Express-MIMO board. The user-space contains the code regarding the 3GPP LTE protocol stack and the IEEE 802.11p soft-modem from the Ope-nAirInterface. It also contains the ITS safety applications that sends IP packets to the PROTON-PLATA virtual interface on the linux subsystem. The kernelspace contains deep linux routines and kernel extensions modules required by PROTON-PLATA. In particular, it contains a hook from the linux subsystem either to the linux wireless subsystem modified to support the IEEE 802.11p (OCB mode) and to a IEEE 802.11p driver, or to the LTE NASMesh device. Finally, the PCI-Express MIMO SDR board is connected via a PCI-Express and an OpenAirInterface firmware to the OpenAirInterface main driver. The salient aspect is that PROTON-PLATA designed it so to be able to support both LTE and 802.11p signals on the same board, and as such shall access to the PCI-Express MIMO board through the same OpenAirInterface driver. Both protocol stacks are capable to work at the same time using different RF chains of the PCI-Express MIMO board. Following Figure 2, the SDR architecture of the IEEE 802.11p protocol stack is composed of three blocks. The upper block contains an extension for IEEE 802.11p of the Linux kernel 802.11 subsystem. This subsystem is composed of nl80211, a netlink configuration interface for user-space applications, cfg80211 which is the Linux wireless configuration interface bridging user-space and drivers and mac80211, which offers a framework for driver developers writing soft-MAC wireless devices. The mac80211 subsystem is the Linux stack for IEEE 802.11.

The second block is the IEEE 802.11p driver, which bridges the Linux 802.11 subsystem and the hardware. One major difference with standard architecture for 802.11 systems, is that the IEEE 802.11p driver does not link to the SDR chipset directly, but rather to a soft-modem via netlink sockets. We chose this architecture for flexibility in the development and configuration of the low layer functionalities of the IEEE 802.11p stack and to ease the reconfigurability of our radio. The soft-modem is the placeholder of all the functionalities of IEEE 802.11p physical layer. Being located out of the chipset, it is totally accessible and reconfigurable. The soft-modem is finally connected to the SDR board via an IOCTL link and a dedicated OpenAirInterface driver, which composes the last block.

3GPP LTE protocol stack

As for the IEEE 802.11p, the software architecture of the 3GPP LTE rel. 8/9 is composed of three blocks. The upper block is located in the Linux user-space and contains an open-source implementation of the 3GPP LTE rel-8/9 protocol stack, including PDCP, RLC, MAC and the upper PHY. It contains 3GPP LTE rel.8/9 compliant functionalities and a LTE soft-modem. Located in user-space, they are easily extensible to future LTE rel. 10 and beyond features. The second block is the LTE NASMesh device, which is located in the kernel space, due to realtime constraints for low PHY operations. The last block is the OpenAirInterface driver, which acts as interface with the Express-MIMO board.

SDR Express-MIMO Board

The prototype is based on an Agile Radio Front-end (RF) and ExpressMIMO SDR board. They provide a fully reconfiguration RF and baseband DSP, and include "developper-friendly" tools for real-time hard-modem development and validation. The Express-MIMO board is a FPGA platform on PCI-Express with up to 8 MIMO capacity and providing full SDR descriptions of three air interfaces: LTE, 802.11p, and DAB/DMB.

Experimentation

As a "proof-of-concept", the major goal within the scope of the project was the demonstration of communications with the defined scenarios. It opens further work on more detailed measurements and field-test characterization.

We performed in-situ tests on a dedicated IFSTTAR test-tracks dedicated to road-safety experimentation (Versailles-Satory, France). We equipped two vehicles with the previously described 3GPP LTE/IEEE 802.11p OBU prototype, including an on-board GPS (Figure 4b), and one RSU equipped only with the 3GPP LTE technology. As a test case, we evaluated a contextual speed warning application as well as a ghost driver applications as specified by the French SCOREF project [START_REF]SCOREF project[END_REF], using V2I communication over 3GPP LTE for the former, and V2V over IEEE 802.11p for the latter. Figure 5 is a screen-shot of the contextual speed warning application, which also depicts the IFSTTAR test track. On that test case, using the 3GPP LTE RSU, vehicles provides to the NH their instantaneous speed and in return the NH provides contextual speed limitations that are displayed on the car HMI. In this performance study, we are interested in the communication efficiency of multi-technology cooperative system, and particularly the communication performances experienced by driver assistance applications. This cooperative system is able to balance the traffic generated by V2I communications and I2V Broadcast over two different wireless networks in order to increase the capacity of the system and provide better performances to the applications. To model communications in the multi-technology cooperative system, we consider the IEEE 802.11 technology for V2V and V2I communications and the DVB-T technology for I2V broadcast. The performance study is based on a simulation framework, developed in the OPNET Modeler network simulator, that includes realistic vehicular mobility models [START_REF] Kaisser | Simulation of vanet scenarios with opnet and sumo[END_REF] and SDR device modeling [START_REF] Kassab | A software defined radio module for vehicular communications[END_REF]. Evaluation context As an evaluation context, we consider a three ways straight road. Two hundred vehicles travel on this road. Vehicle mobility is based on the SuMo tool [START_REF] Kaisser | Simulation of vanet scenarios with opnet and sumo[END_REF]. The Vehicles are equipped with SDR devices that manage IEEE 802.11 and DVB-T radios [START_REF] Kassab | A software defined radio module for vehicular communications[END_REF]. We model the network architectures conforming to the PLATA cooperative system specification 3. We deploy IEEE 802.11 access points along the roadside as RSUs and a DVB-T base station that cover all the road. These wireless networks are connected to a wired network that hosts the application server. Figure 6 shows this architecture. In this architecture, we deploy 35 access points over the roadside, one access point every 300 meters. The access points manage an 802.11 network with a throughput of 54 Mbps and a transmission power of 100 mW. This wireless network is connected to a wired network over a router. Vehicles are equipped with IEEE 802.11 hardwares with a transmission power of 20 mW. The DVB-T base station is broadcasting over the 800 MHz frequency on a 8 kHz bandwidth and a 32 Mbps throughput.

We modeled the driver assistance applications that define V2I communications (V2I hazard warning and Static speed limit) and I2V broadcasting (Local weather condition and Dynamic speed limit). In this configuration, data exchanges, defined by the Static speed limit and V2I Incident Hazard Warning, are carried through the RSUs. Broadcasts, defined by the Local weather condition and Dynamic speed limit, are sent through the broadcast base stations.

Simulation results

The results show that the driver assistance application operates with good performances. 2. Detailed results show the mean response time is about 1.026s with a standard deviation of 3.38. The disparity of response time values is due to the message exchange which consists of four 2-way-handshakes. The obtained values for response time are acceptable, even the highest, considering the nature of this application that aims to synchronize the speed database of the vehicles with the database of the management center. In addition, there is no packet loss for these applications as shown in table 1.

For V2I Incident hazard warning, results shows that values vary between 0.001 ms and 9.50s as shown in table 2. The mean response time is about 0.06s with a standard deviation of 0.93. In the case of a vehicle velocity equal to 50 km/h, a delay equal to 3.5 ms enables the information to reach a neighbor vehicle after a move of less than one meter. This value is more than sufficient to get the driver or the onboard system informed in time.

We look to the state of communications on IEEE 802.11 cells (cell load, data dropped and medium access delay) for both architecture. Results in table 3 show that the association of IEEE 802.11 with DVB-T, thanks to SDR in the vehicles, enables an increase of the available bandwidth in each cell. These results open the way for the specification of additional applications that will make use of this bandwidth without disturbing the good operation of the system.

Conclusion

Vehicular cooperative systems rely on information obtained from immediate surroundings and longer-term forecasts and alerts controlled from a central infrastructure to offer assistance to drivers. The enhancement of these systems depends closely on wireless communication performances that enable cooperation between vehicles and between vehicles and infrastructure. The communication systems associated to cooperative systems have to support vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and infrastructure-to-vehicle (broadcasting) communications. The use of SDR represents an asset to ensure both a wide area coverage and the use of complementary applications to answer to various events on a travel. In this context, the PLATA-PROTON project proposes a multi-technology cooperative Advanced Driver Assistance System (ADAS), based on the integration of Software-Defined Radio (SDR) devices in vehicles. In this paper, we have shown the feasibility and interest of such ADAS through the development of a proof of concept that included SDR and driver assistance application prototypes and a performance study of communications performances, experienced by applications and based on network simulation tools.

Fig. 1 :

 1 Fig. 1: Communication architecture

Fig. 2 :

 2 Fig. 2: OpenAirInterface software architecture

Fig. 3 :

 3 Fig. 3: Express-MIMO SDR Board

Fig. 4 :Fig. 5 :

 45 Fig. 4: Illustration of the experimental test

Fig. 6 :

 6 Fig. 6: Simulation scenario for the IEEE 802.11 and DVB-T based architecture

Table 1 :

 1 Packet Loss

		Sent data (Pkt) Received data (Pkt) Packet loss rate
	Local weather condition	109654	109654	0
	Dynamic speed limit	14	14	0
	V2I Incident hazard warning	37438.5	43959.66	0,14
	Static speed limit	40536.833	40595.5	0.001
	For static speed limit response time values vary between 0.11 s and 21.62 s as
	shown in table

Table 2 :

 2 Response Time

		Mean (sec) Min (sec) Max (sec) Stdev (sec)
	Static Speed Limit	1.026	0.11	21.62	3.38
	V2I Incident Hazard Warning	0.06	0.001	9.50	0.93

Table 3 :

 3 IEEE 802.11 cell metrics

		Mean	Min	Max	Stdev
	Media Access Delay (sec) 0.0002	0.0003	0.0001 0.00002
	Cell load (bps)	128354.82 201554.44	0	25930.79
	Dropped data (bps)	1055.91	0	26449.99 2142.32

Acknowledgment. The PROTON-PLATA research leading to these results has received funding as a Deufrako collaboration from the German Federal Ministry of Economics and Technology and the French Agence Nationale de la Recherche, with reference ANR-08-VTT-010-01.