Mohammad Y Abualhoul
email: mohammad.abualhoul@inria.fr

Pierre Merdrignac
email: pierre.merdrignac@inria.fr

Oyunchimeg Shagdar
email: oyunchimeg.shagdar@inria.fr

Fawzi Nashashibi
email: fawzi.nashashibi@inria.fr

Study and Evaluation of Laser-based Perception and Light Communication for a Platoon of Autonomous Vehicles

Keywords: VLC, Laser sensors, Platoon, ITS, Road safety applications

  L'archive ouverte pluridisciplinaire

I. INTRODUCTION

Intelligent transport systems (ITS) can provide more safe and secure traffic by properly utilizing the information technology. Such information can be acquired using the vehicular embedded sensors, which provides the necessary data to perceive the vehicle's surrounding environment. Furthermore, inter-vehicle communication and the Cooperative ITS (C-ITS) mode can also play a significant role in improving road traffic safety and comfort [START_REF] Tsugawa | Inter-vehicle communications and their applications to intelligent vehicles: an overview[END_REF]. In such context, communication between vehicles (V2V), vehicles sensors range and viewing capability are one of the critical issues for C-ITS.

As proposed solutions for traffic congestion, automobile manufacturers have developed advanced driver assistant systems such as Adaptive Cruise Control (ACC), which intend to improve driving comfort. ACC uses sensors such as cameras, laser or radars to estimate the relative distance and relative velocity of two nearby vehicles [START_REF] Hosseinnia | Experimental application of hybrid fractional-order adaptive cruise control at low speed[END_REF]. Vehicles with ACC are capable of changing acceleration automatically to adapt desired distance and sustain a safe distance from the preceding vehicle. Moreover, the Cooperative Adaptive Cruise Control (CACC) systems are introduced as an improvement to the ACC by exchanging information between vehicles using radio communication. Beside improve driving comfort, the main objective of CACC is to improve the traffic flow and save energy in congested scenarios. Compared with ACC, CACC can keep relatively smaller headway time, which can enhance traffic flow dramatically, and improves the system stability by providing the assessment of approaching future situations and also result in better decision-making and prediction.

Due to the accelerated increase of the wireless-based communication devices numbers, well-known wireless communication technologies such as the standardized IEEE 802.11p technology for ITS [START_REF] Jiang | Ieee 802.11p: Towards an international standard for wireless access in vehicular environments[END_REF] are pushed toward an insatiable demand for wireless networks data access, with a remarkable increase in both latency and channel congestion levels. This instability introduced more usage constraints for ITS applications [START_REF] Wang | Reliability evaluation of ieee 802.11p-based vehicle-to-vehicle communication in an urban expressway[END_REF], especially for the hard-safety use, such as platooning. Different communication technologies using another communication medium such as Visible light communication (VLC) has become an outstanding intriguing supportive technology lately. Realizing both low-cost implementation and dual functionality, VLC has also the advantage of using the vehicular existed infrastructure, (e.g. rear and front lamps of the vehicle) as transmitters. The main benefits of using VLC in such configuration is to activate the CACC mode between platoon members without introducing any load to the radio communication channel. Although that VLC is sensitive to the vehicular orientation, holding a reliable backup perception sensing able to drive the platoon in ACC mode with a sufficient hand-over can overcome this limitation. Fig. 1 illustrates the field of view (FOV) of embedded sensors (laser) and VLC communication range. Such configuration intends to utilize both technologies to ensure platoon application safety requirements.

Laser Range Finders (LRF) can have wide FOV with a sensing range up to 100 m, which is sufficient to track the preceding vehicle in a platoon. On the other hand, VLC operates in relatively narrower FOV, and has communication range up to 15m.

In this paper, we study and evaluate the advantage of using VLC and LRF for platooning. By combining both solutions, the vehicular status and GPS positions are exchanged among the platoon members using the vehicular rear lighting system. Further, the LRF is operating in redundant mode as a backup for the VLC blackout. We concentrate on observing the overall platoon behaviour for different trajectory scenarios, intended to drive the VLC link to its FOV limits by introducing communication failure and evaluate the limits of our system. In this particular scenes, our study highlights the switching modes between the presence of VLC and disconnections cases, in other words, the availability of CACC and ACC, recognising the platoon minimum safety requirements. In this paper, we make the following contributions:

• Suggesting a new architecture intend to improve the platoon performance by utilizing both LRF and VLC. • Studying the limitations and performance of both technologies for various car-following scenarios (straightline, lane-change and curve). • Proposing hand-over algorithm to check the platoon safety requirements and the availability of ACC and CACC modes. • Evaluating the system performance and the hand-over mechanism between cooperative and LRF-only platooning modes. The remainder of this paper is organized as follows. Section II discusses the previous literature and related work. The proposed system to drive platoon using VLC and LRF including the used models is discussed in Section III. Section IV details the hand-over algorithm proposal. After describing the system structure in Section V, we report the system performance and our findings in Section VI. Conclusion and planned future work are given in Section VII.

II. RELATED WORKS

Over the last thirty year, platooning concept has been proposed to improve the traffic throughput and increase roads capacity. PATH project in California [START_REF] Shladover | Path at 20 -history and major milestones[END_REF] and PRAXITELE project in France [START_REF] Daviet | Longitudinal and lateral servoing of vehicles in a platoon[END_REF] were one of the first pioneering projects demonstrating the platoon. Later proceeding, Auto21 CDS [START_REF] Hall | Car platoons simulated as a multiagent system[END_REF] project was dedicated to advance the smooth merging/leaving for a platooning-enabled system. Also, the SARTRE project [START_REF] Tom Robinson | Operating platoons on public motorways: An introduction to the sartre platooning programme[END_REF] demonstrated the fully autonomous platoon excluding the manually-driven leader, where all following vehicles are free to merge and/or leave the platoon in a fully autonomous mode.

By mapping the environment using infrared beams [START_REF] Schwarz | LIDAR: Mapping the world in 3-D[END_REF], LRF can detect and precisely localize obstacle with up to 360 • FOV [START_REF] Douillard | On the segmentation of 3D LIDAR point clouds[END_REF], [START_REF] Nashashibi | Laser-based vehicles tracking and classification using occlusion reasoning and confidence estimation[END_REF]. Thus, they can be mounted on every platoon member to estimate the inter-vehicle distance. However, using such sensors might introduce some imprecision when estimating the preceding vehicle velocity, especially when vehicles follow sharp road curvatures. The vehicular status exchange between platoon members (V2V) communication was the straight-forward proposal to overcome these types of limitations [START_REF] Fernandes | Platooning with dsrc-based ivc-enabled autonomous vehicles: Adding infrared communications for ivc reliability improvement[END_REF]. In this paper, we introduce an area of interest using the vehicle motion model in order to identify more exactly the vehicle in front and to estimate its position.

Authors in [START_REF] Fernandes | Platooning with dsrc-based ivc-enabled autonomous vehicles: Adding infrared communications for ivc reliability improvement[END_REF] represented vehicular platoon model based on constant inter-vehicle spacing. In this model, the platoon members receive broadcasted event-triggered messages over Dedicated Short Range Communications (DSRC), while a periodic vehicle control-based messages between platoon members utilize an infra-red (IR) spectrum to communicate in unicast mode. Although that IR is a popular medium for optical LOS communications, still compared to VLC, the use of IR spectrum considered costly with strict safety requirements. We have also contributed in theoretically studying the VLC channel characteristics and the technology feasibility for ITS applications [START_REF] Abualhoul | Enhancing the field of view limitation of visible light communication-based platoon[END_REF], [START_REF] Abualhoul | Platooning control using visible light communications: A feasibility study[END_REF].

Experimental studies on VLC for outdoor applications are conducted as presented in [START_REF] Cailean | Visible light communications: Application to cooperation between vehicles and road infrastructures[END_REF]. The studies show that VLC can satisfy the stringent reachability requirements for Inter-Vehicle Communication (IVC) in dense vehicle traffic conditions. The authors also presented a prototype of vehicular system proposing a VLC down link between road infrastructures and vehicles, aiming to enhance traffic security.

The main requirement of platoon controller concerns the longitudinal control [START_REF] Oncu | Cooperative adaptive cruise control: Network-aware analysis of string stability[END_REF], [START_REF] Milanés | Cooperative adaptive cruise control in real traffic situations[END_REF]. ACC with a constant time headway policy has been a common solution to maintain a safe distance between platoon members [START_REF] Milanés | Cooperative adaptive cruise control in real traffic situations[END_REF]. Then, it has been shown that one of the main advantages of using both communication and sensors in a Cooperative ACC (CACC) mode over only sensors in an ACC mode, is the decrease the inter-vehicle time headway from 1.1 s to 0.6 s while satisfying the safety requirement and maintaining the string stability within the platoon [START_REF] Milanés | Cooperative adaptive cruise control in real traffic situations[END_REF]. In our paper, we study and evaluate the conditions for relying on either ACC or CACC modes in a platoon using VLC communications and LRF.

In addition, we propose a hand-over method to ensure the safety with ACC mode when IVC is not available.

III. PLATOON DRIVING USING VLC AND LRF

The proposed system differs from the earlier discussed related research in the way that we demonstrate the deployment of two different technologies ensuring stable platoon behavior. Benefiting from the adaptability of the laser sensors for detection and tracking in the ACC mode, and using the vehicle commercial lighting system to establish a low-cost communication link between platoon members, we can rely on the CACC mode as long as the VLC is active.

This approach allows us to overcome the introduced errors when using laser sensors to estimate speed and inter-vehicle distance, at the same time, keeping a backup when the VLC blackout occurs due to the FOV limitations. In our study, we propose to use VLC to exchange vehicular status (e.g., speed, orientation, acceleration) and relative locations within platoon members, redundantly with the laser sensors. In this way, the proposed system can rely on two independent information sources for taking the best decision for platooning. To our best knowledge, there is no published research concerning the combination of both technologies for platooning.

Figure 2 shows our proposed platooning system, where each platoon member is equipped with LRF to measure the distance with its preceding vehicle and ensure a safe platoon by means of ACC longitudinal control. Vehicles are also equipped with a VLC transmitters and receivers such that the platoon members can rely on a CACC longitudinal control. LRF has FOV of 180 • with a sensing range up to 100 m, where VLC operates in relatively narrower FOV, and has a communication range of 30 m, suitable to establish Inter-Vehicle Communication (IVC). The FOV for LRF and VLC in our system depicted in Fig. 2 and defined as the following:

• Laser-Range-Finder (LRF) is mounted on the follower vehicle, allowing the follower vehicle to detect reflected beams from the preceding one. Thus, the LRF-FOV is defined to be from the follower vehicle end toward the preceding one. Among 75 ITS services recognized by the department of US transportation, eight services were classified as hard safety requirements [START_REF]Vehicle safety communications project task 3[END_REF]. Regardless the technology-in-use, the highest priority safety services such as crash avoidance and lane change warning target 90-100% of data update Packet Delivery Ratio (PDR), and maximum end-to-end latency of 10 to 100 ms. In our study, we investigate the VLC and LRF models and the system implementation by considering a platoon application with hard safety requirements.

A. Laser Based Detection Model

By using laser sensors, obstacles are detected, classified and their position regarding the sensor is estimated [START_REF] Merdrignac | Study on Perception and Communication Systems for Safety of Vulnerable Road Users[END_REF]. In a platoon formation, as the preceding vehicle is close enough, we can rely on laser based detection algorithm to estimate its distance d obs with the platoon member [START_REF] Nashashibi | Laser-based vehicles tracking and classification using occlusion reasoning and confidence estimation[END_REF]. Such algorithm also provides a confidence value C on the detected vehicle that can reach 100% after a certain number of successful observations. However, many obstacles fall within LRF-FOV and the preceding vehicle has to be correctly identified. Since this particular obstacle is supposed to be in the near future trajectory of the platoon member, it is searched in an area of interest of width W Area around the predicted vehicle trajectory (x p (t), y p (t)) as illustrated in Fig. 3. In our implementation, a bicycle model is used to predict the vehicle trajectory points from its current velocity v p and steering angle ε p , using the following equation:

x p (t) = L ε p sin( L×v p ε p t) y p (t) = L ε p (1 -cos( L×v p ε p t)), (1)
where L represent the vehicle length.

B. Visible Light Communication Model

VLC channel characteristics can be defined mainly by the LED lambertian emission pattern and both transmitter and receiver physical characteristics [START_REF] Kahn | Wireless infrared communications[END_REF], where the path-loss can be presented as:

P loss = (m+1)A ph 2πd n cos m (ϕ)cos (ψ) , 0 < ψ, ϕ < θ c 0 elsewhere . (2)
For VLC path-loss theoretical model, together with an experimental results obtained from the implemented prototype [START_REF] Abualhoul | Visible Light Inter-Vehicle Communication for Platooning of Autonomous Vehicles[END_REF], the path-loss exponent n is equal to 2, where d is the separation distance between transmitter and receiver, θ c is the maximum incidence and irradiance angles max(ψ, ϕ) , which is also our system VLC-FOV limits as depicted in Fig. 3. A ph is the physical area of the PD and m = -ln 2 ln(cos φ ) is the lambertian emission order, which is a key parameter specifying the directivity of the transmitter. The LED halfpower angle φ has a remarkable influence on the coverage range and pattern shape of the lambertian light source. By choosing narrower φ or mounting the LED with an optical lens, we can directly change the VLC-FOV and increase the communication range.

IV. HAND-OVER BETWEEN VLC AND LRF

In our system, the cooperative platooning mode can be possible only when both LRF and VLC are available and satisfy the hard safety requirements. Initially, the minimum configuration considers LRF for estimating the inter-vehicle distance in order to rely on an ACC mode. The ideal configuration is when VLC is meeting the same requirements and starts to exchange the preceding vehicle velocity, orientation and position. For this configuration, the system can rely on a CACC mode. Thus, a hand-over is required between these two modes based on the availability of LRF and VLC. Our proposed hand-over is influenced by three main metrics that characterise and track the limitations of LRF and VLC as the following:

1) The laser based detection confidence C is the main LRF metric, this value is found after certain number of detections. Once the leader vehicle is correctly detected with C ≥ 90%, the leader vehicle identifier L ID is passed and ACC mode is active.

2) The maximum of ϕ, ψ and θ , which represent the VLC transmitter and receiver angular difference with respect to the relative orientation and the lane change. If any of the tracked angles exceeds ζ =80% of the critical FOV limit (θ c ), the decision then handing the LRF output before reaching the VLC disconnection. 3) End-to-end delay τ, this condition continuously checks the safety requirement, where the delay must remain under the 100ms limit. Also, the exceeded delay can indicate VLC disconnection due to an obstacle presence even though transmitter and receiver might be perfectly aligned. Our proposed hand-over has two main input and three output as illustrated in Fig. 4. Both VLC and LRF provides data update simultaneously where the algorithm output is the availability of either ACC, CACC or STOP modes that satisfy the platoon safety requirements.

VLC LRF

⇣  ✓ c ? is ⇣  ✓ c ? is ⌧  75ms ? is ⌧  75ms ?
As LRF is the minimum and initial configuration for our system, the algorithm is activated with laser data update. The preceding vehicle has to be detected and its identifier L ID is tracked over time while it remains in the area of interest. At each time instant, the LRF confidence of detecting the leader vehicle C is compared to a given threshold to establish if a cooperative platoon can be considered. When C ≤ 90%, the track of the leader vehicle is considered lost, and the inter-vehicle distance d t is predicted based on the previous estimated inter-vehicle distance d t-1 using eq. ( 3), where the sensor update period T 0 and current vehicle velocity v p .

d t = d t-1 -(T 0 × v p ).
(

) 3
The prediction stage used here to maintain the ACC mode as long as possible to recover the track of the leader vehicle while the distance d is above the vehicle length L. If the predicted distance is too short, then the STOP is called to exit the platooning mode. Otherwise, the vehicle can continue to rely on ACC mode.

While the preceding vehicle is correctly detected by the LRF system (C ≥ 90%), the updated VLC data are analyzed to evaluate whether the conditions are sufficient or not to activate a cooperative mode. The first criteria checks if all of the angles in eq. ( 4) satisfies the safety requirement of being less than the critical angle ζ ≤ θ c .

θ c = 0.8 × max(ϕ, ψ, θ ). (4)
The second criteria checks in real time if the delay τ between any two consecutive received packets is below the maximum allowed time delay (75ms). When these two criteria are satisfied, the algorithm outputs that it is safe to activate the CACC mode. However, when one of these conditions fails, only the ACC mode is allowed, if this is followed by any problems with the LRF requirements, the STOP state is called immediately to exit the platooning mode.

V. PLATFORMS ARCHITECTURE

Our system is evaluated using real VLC devices and a simulation environment for the LRF, vehicular controllers and car-following trajectories. Such experiment meant to validate the feasibility of our system for the platooning application before implementing the system in our experimental platforms.

A. VLC Platform

Based on our previous work [START_REF] Abualhoul | Visible Light Inter-Vehicle Communication for Platooning of Autonomous Vehicles[END_REF] to realize the VLC dual functionality of both data transmission and vehicular lighting, 6 LEDs array with an emission wavelength of 635 nm were deployed to imitate vehicular rear-light as transmitter. As for the receiver end, the main components are the silicon Photo-Diode (PD) with a trans-impedance switchable gain PDA100A, combined with an optical band-pass filter and a direct detection decision circuit. Fig. 5 pictures the VLC principal components used in our study. Moreover, a low-cost Mini-pro Arduino boards based on ATmega328P microcontroller has been used at both transmitter and receiver ends to perform modulation/demodulation, line coding and interfacing with vehicles controller. The exchanged vehicular status are packed into a particular packet format as shown in Fig. 6. These data are then adapt Manchester line coding in the micro-controller before modulating the LED array.

B. Pro-SiVIC Platform

Our experiments intend to validate the uses cases and feasibility of the proposed approach for different road curvatures. This validation allow us to test the system over-all performance before the final implementation and integration into a real platforms. Hence, Pro-SiVIC, a simulation platform for vehicles, sensors, and environment is used to simulate two autonomous vehicles in three scenarios:

• Straight, the leader vehicle is driving in a straight line,

• Lane change, the leader vehicle is changing the lane at certain time instants. • Curve, the leader vehicle reaches 90 • of orientation at crossroad during the simulation. I.

VI. PERFORMANCE EVALUATION

The performance of the proposed platoon system deploying VLC and LRF is evaluated by driving platoon in the Pro-SiVIC simulator together with using an actual VLC prototype. The three main simulation scenarios represent different road curvature and vehicular maneuvers. The scenarios were meant to introduce communication and/or sensor failure and compare the platoon behavior with and without using VLC. For each of the listed scenarios, the VLC-FOV was set to 5 • ,15 • ,30 • and 45 • , the LRF-FOV was set to 180 • with width of interest W Area = 9 m.

All the depicted results in Fig. [START_REF] Tom Robinson | Operating platoons on public motorways: An introduction to the sartre platooning programme[END_REF][START_REF] Schwarz | LIDAR: Mapping the world in 3-D[END_REF][START_REF] Nashashibi | Laser-based vehicles tracking and classification using occlusion reasoning and confidence estimation[END_REF] compare the leader vehicle speed (Leader Speed ) with the received speed over VLC link at the follower side (RX Speed ). Regardless the technology-in-use, either VLC or LRF, the results compare these speeds with the actual applied speed on the follower vehicle (Follower Speed ), where the Speed di f f erence for each VLC-FOV case is calculated by: Speed di f f erence = Leader Speed -Follower Speed .

(5)

A. Straight driving Scenario

Figure 8 shows a sample of the simulation results for straight curvature scenario. The results manifest an agreement between Leader Speed , RX Speed and Follower Speed , where none of the tracking angles(θ , φ , ψ) exceeded the minimum 5 • limit, and the LRF confidence C = 100% allover the trajectory. Thus, follower vehicle always uses the received Leader Speed over VLC link and drive in the CACC mode.

B. Lane-Change Scenario

The VLC link is more subjected to FOV misalignment when leader vehicle changes the lane. The results depicted in Fig. 9 show the smooth performance of the Follower Speed till the simulation time window 30-43s. The period when the leader vehicle began to change the lane until the follower vehicle reach aligned state. While the max(θ , ψ, φ ) exceed the θ c = 0.80 * 5 • , the disconnection prediction occur, and since C = 100%, LRF will manage to backup and the driving mode switch from CACC to the ACC as described in the hand-over algorithm. For any VLC-FOV ≥ 30, the system will behave as in the straight-lane scenario.

C. Curve Scenario

Compared to the other previous scenarios, the 90 • curve trajectory subjects VLC link to the maximum FOV misalignment and drives the follower vehicle close to the edge of LRF detection area. As depicted in Fig. 10, while the leader vehicle is turning at the boundary of the laser area of interest, the track continuity is not always maintained, and the LRF confidence parameter C will fluctuate between 0 and 100% for the first time compared with the previous other scenarios. Thus, the distance to the preceding vehicle predicted is based on the latest vehicle displacement. In this particular case, the Follower Speed slows down while the leader vehicle is not detected with C ≥ 90% over the time window 39.5-40.5s for VLC-FOV=5 • in Fig. 11. Better performance is observed for a VLC-FOV≥ 45 • , but the communication disconnection still occurs for shorter time period.

When C is falling below the 90% limit, a non-smooth behaviour of the Follower Speed is observed due to the fluctuations of the estimated inter-vehicle distance. Since VLC is not enabled here, the system will try to predict the distance and maintains the ACC mode as long as possible as described in the hand-over algorithm. After simulation time 40.5s for VLC-FOV≥ 45 • , the leader vehicle is already detected with C≥ 90% and the VLC is enabled, which allows the system to activate the CACC mode again providing smoother Follower Speed response. Figure 12 addresses the laser detection problem when the leader vehicle leaves the laser detection area of the following one. The results examine the narrowest VLC-FOV over the sharpest curvature (90 • ). Case 1 indicates the first occurrence of VLC disconnection due to the orientation limitation. During this period, the LRF is capable of substituting VLC and activate the ACC mode ensuring the safety requirements.

In case 2, the disconnection occurs again, but this time, the LRF will not be able to backup within a time delay satisfies

VII. CONCLUSIONS

In this work, we studied a combined solution using laserbased perception and visible light communication for platooning, where we proposed a hand-over algorithm profiting from both technologies. Although that LRF and VLC are independent of each other and provide a different type of information, the trajectory shape and vehicles orientation has a direct influence on both technologies. Indeed, our results show a perfect availability of cooperative platooning mode for low curve scenarios with 100% of PDR for VLC and 100% of confidence for LRC. However, these performances drop over extreme curves (≥ 90 • ) due to discontinuity in the VLC system, combined with fluctuations in LRF confidence value. Concerning LRF based perception, the main limitation comes from the difficulty to correctly identify the preceding vehicle. For such cases, using cameras to improve the vehicle recognition and tracking could tackle this limitation and improve the system performance.

Our future work focus on 1). Using the GPS data to improve the searching area of interest, 2). Efficient implementation of the hand-over algorithm by introducing radio communication to handle the system unmanaged failures by both technologies, 3). The tests of the overall system on real platforms, and finally 4). Establishing the proposed system performance using the state of the art of both ACC and CACC techniques in different conditions.

Fig. 1 .

 1 Fig. 1. VLC and LRF Systems for platooning application.

Fig. 2 .

 2 Fig. 2. Two autonomous vehicles platoon using LRF and VLC.

Fig. 3 .

 3 Fig. 3. Laser based preceding vehicle detection and VLC for two platoon members. L represent the vehicle length and W Area is width of the laser detection area of interest. ψ and ϕ are VLC incidence and irradiance angles.

Fig. 4 .

 4 Fig. 4. Flow chart of hand-over between VLC and LRF.

Fig. 5 .

 5 Fig. 5. VLC transmitter and receiver ends.

Fig. 6 .

 6 Fig. 6. VLC packet content.

Fig. 7 .

 7 Fig. 7. Pro-SiVIC simulation platform.

Figure 7

 7 Figure 7 illustrates the Pro-SiVIC simulation environment with the two vehicles, where the black one is the leader

Fig. 8 .

 8 Fig. 8. Simulation results of Straight driving platoon scenario. laser based detection confidence C=100%.

Fig. 9 .

 9 Fig. 9. Simulation results of Lane-change driving platoon scenario. laser based detection confidence C=100%.

Fig. 10 .

 10 Fig. 10. Pro-SiVIC simulation case for 90 • curvature scenario, the platoon leader vehicle locate at the edge of the LRF of the follower vehicle.

Fig. 11 .

 11 Fig. 11. Simulation results of Curve driving platoon scenario.

Fig. 12 .

 12 Fig. 12. PDR and C Simulation result of Curve driving platoon scenario.

  • , 35 • , 45 •

	PD active area	100 mm 2
	Transmitter & Receiver heights	85 cm
	Gain levels	30 dB, 40 dB
	Pro-SiVIC Parameters	Value
	Vehicle mass	1500 kg
	Vehicle length (L)	4.2 m
	LRF range	100 m
	LRF angular aperture	180•
	LRF angular resolution	0.25•
	W Area	9 m

ACKNOWLEDGMENT

This study was supported in part by the research grant SI-NETIC of the French "Fonds Unique Interministriel" (FUI).


Mélissa Dali
email: melissa.dali@inria.fr

Olivier Rossel
email: olivier.rossel@lirmm.fr

David Guiraud
email: david.guiraud@inria.fr

Tool chain to explore selective neural stimulation

Keywords: spatial selectivity, cuff electrode, quasi-static approximation

In functional electrical stimulation, selective stimulation of axons is desirable to activate a specific target, in particular muscular function. This implies to simulate a fascicule without activating neighboring ones i.e. to be spatially selective. Spatial selectivity is achieved by the use of multicontact cuff electrodes over which the stimulation current is distributed. Because of the large number of parameters involved, numerical simulations provide a way to find and optimize electrode configuration. The present work offers a computation effective scheme and associated tool chain capable of simulating electrode-nerve interface and find the best spread of current to achieve spatial selectivity.

Implanted electrical stimulation is an approach used for many years with success to restore motor or organic function. The main objective of FES [e.g. Functional Electrical Stimulation (FES)] is to provide efficient recruitment of nervous fiber as the natural behavior would achieve. In distal regions [START_REF] Sunderland | Nerves and Nerve Injuries[END_REF][START_REF] Jabaley | Internal topography of major nerves of the forearm and hand: a current view[END_REF][START_REF] Gustafson | Fascicular anatomy of human femoral nerve: Implications for neural prostheses using nerve cuff electrodes[END_REF] it exists a spatial organization of fascicles that target specific pools of motor neurons. To independently activate muscles, a control of activation by spatial selectivity of axons is required. Selectivity as defined in [START_REF] Grill | Stimulus waveforms for selective neural stimulation[END_REF] is "the ability to activate one population of neurons without activating neighboring ones". A way to focus activation profile is the use of multicontacts cuff electrode with multipolar configuration of currents. [START_REF] Deurloo | Transverse tripolar stimulation of peripheral nerve: a modelling study of spatial selectivity[END_REF][START_REF] Sweeney | A nerve cuff technique for selective excitation of peripheral nerve trunk regions[END_REF][START_REF] Ordelman | Selectivity for Specific Cardiovascular Effects of Vagal Nerve Stimulation With a Multi-Contact Electrode Cuff[END_REF] . Number of contacts, their size and the current distribution have a large influence on axons regional activation. Our technology 8 is able to provide multipolar current configuration and complex waveforms profile. Since the number of parameters is very huge, testing all the current profiles in experiments is not realistic, in particular for clinical applications. Numerical simulations can help to find optimal configurations by modeling interface between electrode and nerve. A 2-part process is commonly used to simulate electrical stimulation. First, computation of the electric field generated by the electrode within the nerve, assuming that nerve is a volume conductor. Second, determination of axons excitation within the nerve based on the results of the previous computations. These numerical investigations are quite long, and need for experts to be carried out. All-in-one fast tool chain was created to overcome these issues. This software is divided in two parts: Computation of electric field performed by OpenMEEG [START_REF] Gramfort | OpenMEEG: opensource software for quasistatic bioelectromagnetics[END_REF] and activation / optimization of current spreading computed through Matlab scripts.

Materials and Methods

The software MOS2SENS (from Model Optimization and Simulation To Selective Electrical Neural Stimulation) is an adjustment support tool for neuroprosthetic devices. It models and optimizes the current injected by cuff electrodes inside the nerve in order to activate selective fiber targets in terms of spatial criterion. There are two software that perform the following functions:

-Generation of 3D geometric model of nerve and cuff electrode -Mathematical description of the link between stimulation currents and extracellular voltage inside the nerve -Nerve fiber activation prediction based on the current stimulation -Optimization of the current injected according to the chosen target and cost function The electrical potential i.e. extracellular voltage is computed with OpenMEEG open source software, using the BEM [e.g. Boundary Element Method (BEM)] to solve Laplace equation, under quasi-static approximation. [START_REF] Plonsey | Considerations of quasistationarity in electrophysiological systems[END_REF][START_REF] Bossetti | Analysis of the quasi-static approximation for calculating potentials generated by neural stimulation[END_REF] Determination of activation was based on activating function. [START_REF] Rattay | Analysis of models for external stimulation of axons[END_REF] Optimization is performed in term of selectivity, efficiency and robustness. The software was implemented in Matlab with graphical user interface (Figure 1).

Results

A. 3D Model settings

User can set the following geometrical parameters to simulate 3D model of the nerve: number of layers (minimum an epineurium and a fascicle), diameter of each layer, number of nervous fibers inside a fascicle (Nbfib). It's possible to set conductivity for each layer, but only isotropic. The 3D nerve model is made up of nested cylinders which surfaces are represented by bounded triangular meshes. The inner cylinder represents a fascicle that contains axons. The outer cylinder represents the connective tissue that surrounds the fascicle. It's possible to simulate perineurium between fascicle and epineurium. The conductivities of the media are supposed piece-wise constant. Default values are taken from litterature [START_REF] Choi | Selectivity of multiple-contact nerve cuff electrodes: a simulation analysis[END_REF][START_REF] Geddes | The specific resistance of biological material -A compendium of data for the biomedical engineer and physiologist[END_REF] and user have the possibility to modify them. Only myelinated fibers are considered, therefore, we only need to know value of potential field at the Ranvier Nodes. The Ranvier nodes are represented as a grid of point in space (Nbfib, 160 nodes per fiber). With regards to the electrode, user can choose number, size and position of contacts around the nerve. By default, number of contact is set to 12 but it's possible to increase or decrease this number, and simulate the case where the electrode's span does not fit exactly to the nerve diameter. Once parameters are set, OpenMEEG computes a lead field matrix describing the mathematical link between electrode and node of Ranvier.

B. Activation and optimization settings

Once computation of lead field matrix is achieved, user can visualize fibers activation related to the distribution of currents. Activation is shown across a 2D cross section of nerve by taking the maximum of activating function along each fiber. First, user chooses a target area within the nerve. He can manually test different spreads of the currents, i.e., multipolar configurations, and visualize performances in terms of spatial selectivity, efficiency and robustness indexes. Then, user can choose weights for indexes and find the optimized solution linked to the cost function. The results are expressed in terms of current distribution and total injected charges.

Discussion

We created software to evaluate quickly the effects of multipolar configurations on spatial selectivity and optimize spread of currents through the contacts of a CUFF electrode. Computation of electric field within the nerve represented by two layer (endoneurium and epineurium) is achieved in 25 min for 333 fibers with 160 node of Ranvier, this is quite short compared to other method for instance FEM. [START_REF] Jacquir | Computation of the electrical potential inside the nerve induced by an electrical stimulus[END_REF] Computation of activation takes less than 1s by using predictive model of activating function. [START_REF] Rattay | Analysis of models for external stimulation of axons[END_REF] Optimization of current spreading is obtained instantaneously (less than 1s). Computation of potential was made under quasi-static approximation: propagation effects and inductive effects are negligible, capacitive effects are reasonably ignored. [START_REF] Plonsey | Considerations of quasistationarity in electrophysiological systems[END_REF][START_REF] Bossetti | Analysis of the quasi-static approximation for calculating potentials generated by neural stimulation[END_REF] However, the last assumption is correct if capacitive current of the media is negligible compared to resistive current, which are frequency dependent.

Fig 1. Graphical user interface

User must consider this criterion before to use the program. The model was built to study currentcontrolled stimulation electrode. In this case, electrode capacitance could be ignored and electrode-tissue interface was not represented. In conclusion, MOS2SENS is a powerful tool to scan optimal spatial configurations of 12-pole electrode. MOS2SENS is filed in the APP [e.g. Agency for the Protection of Programs (APP)] under the identifier: IDDN.FR.001.490036.000.S.P.2014.000.31230. Future work will provide the possibility to model anisotropy, and more complex geometry such as multifascicles. Results of simulations will be shown and a demo would be provided. Finally, experimental work is under design, to validate the results of simulations.

Acknowledgements

This work was supported by BPI France, Grant "Intense" Number #6354. Thanks to Maureen Clerc for her help on the use of OpenMEEG. Paper prepared for the 2016IFESS Conference.

Contributions

MD Software development, publication writing; OR Software development, publication writing; DG Supervision of software development, publication drafting, publication review.

Conflict of Interest

The authors declare no potential conflict of interests.


Anne Dalbiès-Dulout

Thierry Doré
email: dore@inapg.inra.fr.

Management of inflorescence and viable seed production of blackgrass (Alopecurus myosuroides) on set-aside in France

Keywords: Blackgrass, viable seed production, set-aside

Two experiments were carried out, one in a glasshouse and the other in the field, to assess the effect of weeding management (type and date) on blackgrass seed production during set-aside. The results of the field experiment were used to establish relationships between head length and spikelet number per head. Head number per plant was not reduced by one mowing in glasshouse, but it was reduced by double mowing. Early (beginning of anthesis) or intermediate (hal-anthesis) glyphosate reduced head number by 72 or 50 % respectively compared to a control, but later (during seed ripening) application did not. A first mowing reduced length of heads elongated after the mowing by 25 % compared to a control, but less than two successive mowings (by 50 %). Seed viability was greatly reduced by early glyphosate (by 91 %) compared to a control, less by medium date spraying and two mowings (by 17 % and 47 % respectively), and not by one mowing or late spraying. These results were checked at two densities in the field experiment. The results of head length for low density were consistent with those in glasshouse, but head number did not differ between treatments.

Head number per plant and head length at the highest density were lower (from 50 to 76 % according to the treatments for head number and from 3 % to 16 % according to the treatments for head length) than for individual plants. These preliminary results give indications, which should be checked in farmers' fields conditions, for better control of blackgrass in set-aside fields.

Introduction

The reforms made to the European Union Common Agricultural Policy in 1992 to regulate crop production imposed set-aside (i.e. no crop production on part of the arable land) on European farmers. Rotational set-aside has a variety of not always quantified effects on insect and disease epidemiology [START_REF] Hancock | The effects of shortand long-term set-aside on cereal pests[END_REF][START_REF] Yarham | Effect of set-aside on diseases of cereals[END_REF][START_REF] Dulout | Effects of wheat volunteers and blackgrass in set-aside following a winter wheat crop on soil infectivity and soil conduciveness to take-all[END_REF], and on weed seed production [START_REF] Jones | Significance of the seed rain from setaside[END_REF][START_REF] Lechner | Effect of rotational fallow on weed infestation[END_REF][START_REF] Rew | Changes in vegetation composition and distribution within set-aside land[END_REF][START_REF] Connolly | The effect of five years set-aside on the weed seed bank in the soil[END_REF] and dissemination [START_REF] Wright | The influence of field boundaries on recolonisation of set-aside[END_REF]. The evolution of flora under and after set-aside has been studied by several authors (for example [START_REF] Aquilina | Effect of timing and frequency of cutting on perennial broad-leaved weeds[END_REF][START_REF] Brodie | Spatial and temporal variation in the vegetation in set-aside fields at Connington, Cambridgeshire[END_REF][START_REF] Clarke | Vegetation changes and weed levels in setaside and subsequent crops[END_REF][START_REF] Zwerger | Does rotational fallow cause weed problems in subsequent crops[END_REF][START_REF] Boberfeld | Effects of rotational fallows on subsequent winter wheat[END_REF], and [START_REF] Rew | Changes in vegetation composition and distribution within set-aside land[END_REF], [START_REF] Lawson | Shortterm effects of set-aside management on the soil seedbank of an arable field in south-east Scotland[END_REF] and [START_REF] Jones | Significance of the seed rain from setaside[END_REF] have assessed the seed production of weeds during set-aside. They observed that seed production could occur, and that the management of the set-aside field greatly affected this seed production. It would therefore be useful to be able to predict the effects of set-aside management on weed seed production, in order to propose set-aside management limiting weed seed production, and an increase in the size of the weed seed bank in the soil.

Alopecurus myosuroides Huds (blackgrass) is a common weed in cereal rotations in Europe [START_REF] Froud-Williams | A survey of grass weeds in cereals in central southern England[END_REF][START_REF] Orson | The technical and financial impact of herbicide resistant blackgrass (Alopecurus myosuroides) on individual farm business in England[END_REF][START_REF] Jouy | Influence des pratiques culturales sur l'évolution de la flore adventice en grandes cultures[END_REF], and is frequently found in set-aside fields [START_REF] Lechner | Effect of rotational fallow on weed infestation[END_REF]Shield & Godwin, 1992;[START_REF] Zwerger | Does rotational fallow cause weed problems in subsequent crops[END_REF][START_REF] Clotuche | L'entretien des jachères rotationnelles[END_REF], where it can be the dominant weed [START_REF] Chauvel | Développement de populations adventices en situation de jachère annuelle[END_REF]. Blackgrass is one of the most difficult annual weeds for eliminate from fields in a cereal succession. Farmers frequently plan their timing of weed control practices to prevent blackgrass reproduction in set-aside areas. There are several references on the demographic parameters of blackgrass (e. g. [START_REF] Chauvel | Polymorphisme génétique et sélection de la résistance aux urées substituées chez Alopecurus myosuroides Huds[END_REF][START_REF] Melander | Impact of drilling date on Apera spica-venti L. and Alopecurus myosuroides Huds. in winter cereals[END_REF], among which the studies of [START_REF] Naylor | The prediction of blackgrass infestations[END_REF][START_REF] Naylor | Aspects of the population dynamics of the weed Alopecurus myosuroides Huds. in winter cereal crops[END_REF] and [START_REF] Moss | The influence of tillage and method of straw disposal on the survival and growth of blackgrass, Alopecurus myosuroides, and its control by chlortoluron and isoproturon[END_REF][START_REF] Moss | Some effects of burning cereal straw on seed viability, seedling establishment and control of Alopecurus myosuroides Huds[END_REF][START_REF] Moss | The production and shedding of Alopecurus myosuroides Huds. seeds in winter cereals crops[END_REF][START_REF] Moss | The survival of Alopecurus myosuroides Huds seeds in soil[END_REF][START_REF] Moss | Influence of tillage, straw disposal system and seed return on the population dynamics of Alopecurus myosuroides Huds. in winter wheat[END_REF][START_REF] Moss | The seed cycle of Alopecurus myosuroides in winter cereals: a quantitative model[END_REF] provide the most comprehensive information. [START_REF] Moss | The seed cycle of Alopecurus myosuroides in winter cereals: a quantitative model[END_REF] and [START_REF] Chauvel | Influence des facteurs culturaux sur la dynamique du vulpin (Alopecurus myosuroides Huds[END_REF] proposed quantitative models for predicting the development of blackgrass populations. However, they cannot be used to predict blackgrass seed production in set-aside fields because the values were developed using plants in competition with crops. Shield and Godwin (1992) showed that blackgrass occurence is sensitive to cutting frequency and [START_REF] Clarke | Vegetation changes and weed levels in setaside and subsequent crops[END_REF] found that frequent cutting progressively reduced the number of heads regrowing after each cut, but provided no quantitative data. Better knowledge of the effect of weeding on seed production of blackgrass during set-aside is necessary in order to establish appropriate set-aside management. It would also be useful to make weeding in wheat crop more appropriate if it is impossible to completely avoid blackgrass seed production.

The purpose of this research was to determine head and seed production of blackgrass in set-aside as affected by management practice and plant density in glasshouse and field experiments. The viability of the seed produced was also determined. Management practices included simulated mowing or an application of a non selective herbicide (glyphosate), which are the most common weed management practices used in set-aside (Dalbiès-Dulout, 1999).

Materials and Methods

Two experiments were carried out at Grignon (1°58'E, 48°51'N) in the Paris basin (France), in a glasshouse (Experiment 1, 1996) or in loamy soil (Experiment 2, 1995). Blackgrass seed used in both experiments was collected from a population grown on a fallow land near Dijon (Eastern France, 5°02'E, 47°20'N), and stored under dry conditions until used. The growth stages were measured according to the Zadoks scale (Zadocks et al., 1974). A treatment growth stage was a median stage, i.e. the stage of half the plants on the recording date. Zadoks growth stages 50 and after were determined on the plant head population: a growth stage was attained when half of the heads had attained this stage. A head is considered to be mature when spikelets at the top of the inflorescence begin to fall. Mowing was simulated by cutting the plants with shears to a height of 100 mm. Glyphosate (Roundup®, 360 g a.i. l -1 , 0.75 l.ha -1 , Monsanto) was sprayed with a hand-sprayer at 270 g a.i. ha -1 .

Glasshouse experiment (Experiment 1)

Seeds were kept for two weeks in the dark at 4°C before sowing in March unfertilised ovules or aborted seeds, E) and full seeds were sorted and counted until 200 full ones were attained. These 200 seeds were germinated on filter paper sprayed with a solution of giberellic acid (100 mg.l -1 ). The room temperature was about 20°C. The germinating seeds were counted after 10 and 15 days. The number of germinating seeds on the second date (N) was always the same as on the first date. The seed viability ratio (SV) was thus calculated as:

SV = N/(E+200) (1)
The seed viability was measured on three replicates (plants) for all treatments, except MM1 (twice) and T1 (four times). Analysis of variance was performed with STATGRAPHICS software for number of heads per plant and head length.

Field experiment (Experiment 2)

The seeds used in experiment 2 were sown directly in the experimental field on The final number of heads and head length were measured for each plant of each treatment of the low density. Only the mean head number per plant was measured for the high density treatments, and the head length of all plants. The number of spikelets per head were counted on about 50 heads from each of the three following treatments: 49 for T2h, 46 for MM2h, and 51 for 2MM2h.

Results

Head Length and Spikelet Production

Head length (HL, expressed in mm) and spikelet number per head (SNH) measured on heads of different treatments of high density in experiment 2 were used to establish relationships. The heads obtained from control plants (unmown T2h) were distinguished from those obtained after one mowing (MM2h), and from those obtained after two mowings (2MM2h). Linear fits calculated for each of these three head samples gave the following results (Cf figure 1 -9 -

Head Number and Length, Seed Viability

Experiment 1

The results of the management treatments in terms of final head number, head length, seed viability and seed production per head in experiment 1 are shown in Differences between treatments were more marked for head length. Double mowing (2MM1) gave the lowest mean value (44 mm), half the control value.

One early mowing at the end of inflorescence emergence (EM1), or at halfanthesis (MM1) gave the same result (66 mm), longer than 2MM1, but different from the control, whereas later mowing (LM1) during seed ripening reduced the head length to an intermediate value (48 mm), close to the value of 2MM1.

Mowing always resulted in heads that were 50-75 % of the control head length.

Glyphosate spraying (EG1, MG1, LG1) gave head lengths that were always at least equal to the control value. This could be due to the fact that the heads remaining after spraying were the ones that appeared earlier on the plants, which are also probably the longest.

The mean seed viability ratio for the control (T1) was 42.6 %. It was higher for all the treatments with one mowing, and nearly halved by double mowing. SV ranged from 4.0 % for early spraying (EG1) to 61.2 % for late spraying (LG1). A small difference in the date of spraying (EG1 vs MG1) gave large differences in the seed viability ratio (4.0 vs 35.4 %).

The final estimated number of viable seeds by plant was highly decreased by double mowing and early glyphosate spraying, and at a lower extent by intermediate date of glyphosate spraying and late mowing.

Experiment 2

The mean number of heads per plant and the head length for both densities are

shown in Table 2

Discussion

The relationships between head length and spikelet number are close to that given by [START_REF] Chauvel | Influence des facteurs culturaux sur la dynamique du vulpin (Alopecurus myosuroides Huds[END_REF]. A medium head length of 70 mm resulted in about 90 spikelets per head, which is close to the 100 value given by [START_REF] Moss | The seed cycle of Alopecurus myosuroides in winter cereals: a quantitative model[END_REF], lower than the average of 120 in [START_REF] Naylor | Aspects of the population dynamics of the weed Alopecurus myosuroides Huds. in winter cereal crops[END_REF], and far less than the 150 value found by [START_REF] Stryckers | Biologie et propagation du vulpin des champs, Alopecurus myosuroides Huds[END_REF]. In our experiment the relationship did not differ a lot whether the heads were from plants mown or not. This seems to mean that the inflorescence structure is not very affected by plant management, and that it is mainly the head length which supports the reported [START_REF] Menck | Die Verbreitung des Ackerfuchsschwanzes (Alopecurus myosuroides Huds.) in Schleswig-Holstein[END_REF]) differences obtained when plants are in different growth conditions.

The differences for head number and head length between experiment 1 and 2 in both absolute and relative values might well be due to differences in genetics or growth conditions, but as the seeds were all from the same batch, genetic differences must be lowered. Nevertheless the predicted spikelet number of 8 plants from a single UK field which had a significant relationship of spikelet number and head length (Naylor, 1973) ranged from 104 to 140. The plant density in experiment 2 (low density treatment) was very low, and there was no competition for light. The blackgrass plants in the second experiment in the field were given water, but no mineral fertilizer was added, as was the case in the glasshouse (experiment 1). The difference in head length of the controls between the two experiments might also be due to differences in the plant growth conditions in the two experiments. The results from experiment 1 (increased head length due to glyphosate spraying during flowering) were not confirmed in the field, maybe due to greater between-plant differences within each treatment in the field.

Our results showed that cutting the blackgrass plants decreased the number of seeds more by decreasing the head length that the number of heads per plant, though [START_REF] Lechner | Effect of rotational fallow on weed infestation[END_REF] showed differences in head number also. These differences between our results and those of [START_REF] Lechner | Effect of rotational fallow on weed infestation[END_REF] might be due to the differences in growth conditions in experiments: new growth after cutting is probably far more difficult in highly competitive conditions with other plants (e.g. within a crop, or a set-aside field with a lot of weed species) than in individual plant conditions [START_REF] Dulout | Risques de production de semences du vulpin en jachère[END_REF]. Nevertheless the effect of cutting was more marked after two mowings. But large differences in head length between mowing treatments were observed in both experiments. These differences probably reflect the differences in growth conditions during inflorescence development, and the effect of mowing on the reduction of the green leaf area, which resulted in a large decrease in the number of seeds per plant, especially after late mowing or double mowing. This preliminary result on the effect of mowing on head length should be checked in farmers' field conditions.

The effect of mowing on the viability of seeds from new heads had never been studied, and we found no clear effect. The seed viability ratio we observed for the control in experiment 1 (42.6 %) was low, but consistent with the 49 % obtained by [START_REF] Naylor | Aspects of the population dynamics of the weed Alopecurus myosuroides Huds. in winter cereal crops[END_REF], the 43-76 % reported by [START_REF] Moss | The production and shedding of Alopecurus myosuroides Huds. seeds in winter cereals crops[END_REF], and the 49-87 % reported by [START_REF] Chauvel | Variabilité de la production de semences chez le vulpin (Alopecurus myosuroides Huds.) en fonction de la culture[END_REF]. One mowing did not affect the seed viability ratio;

two mowings decreased it. This could be due to a poorer pollen flow and a subsequent lower fertilisation ratio, to worse seed growth conditions for later flowering and ripening, or one more time to reduced assimilates available for inflorescence growth due to the decrease of green leaf area due to mowing. The increase in density from low to high greatly decreased the number of heads per plant, and was more than three times as severe as the effect reported by Moss -14 -(1990) for a plant density 0-100 per m². This could be due to the fact that [START_REF] Moss | The seed cycle of Alopecurus myosuroides in winter cereals: a quantitative model[END_REF] reported results for blackgrass in competition with a wheat crop, where the intra-species competition effect was probably biased by the inter-species effect at low densities. Our results confirm that blackgrass is sensitive to competition and seem to indicate that it adapts its head production to the available growth factors.

But the differences in head length between high and low density treatments were far smaller, although significant. This suggests that the number of heads per plant is the major variable that is influenced by growth conditions for the blackgrass plant.

Glyphosate treatment gave highly date-dependant results. Early spraying resulted in a very low viable seed production, whereas late spraying did not reduce the seed below that of the control. The number of heads per plant was decreased after an early spraying (EG1), but it was mainly the seed viability ratio which led to the differences from the control and from the spraying six days later (MG1). [START_REF] Shuma | Germination of seeds from plants of Avena fatua L treated with glyphosate[END_REF]

1996.

  They were germinated on a potting mix in the glasshouse and when seedlings had reached Zadoks stage 13 transplanted into a 4-liter pot (one plant per pot) filled with a mixture of earth:sand:peat (15:4:1 by volume). Plants were watered as necessary and fertilised with a drip irrigation system with automatic release. Treatments consisted of a non-treated control (T1); a single mowing at Zadoks stage 59 (EM1), 65 (MM1), or 80 (LM1); a single application of glyphosate at Zadoks stage 61 (EG1), 65 (MG1) or 79 (LG1); and an initial mowing at Zadoks stage 67 followed by a second mowing when new heads were at the same stage (2MM1). Treatments were replicated 10 times. Each replicate consisted of an individual plant. Growth stages of individual plants were determined and heads per plant were counted every 3-4 days. The number of heads before mowing or spraying was also recorded. The lengths of 10 randomly chosen heads of each plant from each treatment were measured at the end of the experiment (100 heads per treatment). The seed viability was assessed by performing germination tests at least 2 months after the harvest. Empty (i.e.

  March 21, 1995. The experiment was conducted at low and high population densities of blackgrass. Low density consisted of single plants separated by a distance of at least 200 mm from other blackgrass plants, obtained by planting seeds at a depth of 30 mm on a row. Treatments in the low density were a nontreated control (T2), a single mowing at Zadoks stage 69 (MM2), a single application of glyphosate at Zadoks stage 65 (MG2), and an initial mowing at Zadoks stage 69 followed by a second mowing when new heads were at the same stage (2MM2). Each plant within a treatment was considered as a replication. The number of replications per treatment varied due to poor emergence of the blackgrass. The number of replications for T2, MM2, MG2 and 2MM2 were 19, 27, 13 and 20, respectively. Plots for high population density treatments were established by planting five blackgrass seeds at a depth of 30 mm at 70 x 70 mm intervals in squares measuring 1 x 1 m. Two squares were established for each treatment. Treatments in the high density were a non-treated control (T2h), a single mowing at Zadocks stage 69 (MM2h), and an initial mowing at Zadoks stage 69 followed by a second mowing when new heads were at the same stage (2MM2h). Each plant within a treatment was considered as a replication. The establishment rate was low : plant number ranged 74-112 plants m -² in the 6 squares, with a mean of 87.5 plants m -².

  ): T2h: SNH = 1.74*HL -35.97 (n = 49, R² = 0.86) (2) MM2h: SNH = 1.61*HL -20.17 (n = 46, R² = 0.67) (3) 2MM2h: SNH = 1.40*HL -4.18 (n = 51, R² = 0.77) (4) Though comparison of the slopes showed no significant difference at a 5 % level between the three samples, the three different relationships were used in the other experiment to evaluate the spikelet number per head. Equation (2) was used for treatments T1, EG1, MG1 and LG1, equation (3) for treatments EM1, MM1 and LM1, and equation (4) for the treatment 2MM1.

Table 1 .

 1 Variance analysis showed that the number of heads before mowing or spraying was not different (P>0.05) at this stage, whereas the final number of heads per plant and head length were significantly different (P<0.01). A single mowing (EM1, MM1, LM1) always gave the same final number of heads per plant as the control and as LG1. Both glyphosate application timings EG1 and MG1 reduced final head number (less than half the control). EG1 gave a slightly

lower final number of heads than the head number before spraying, which means that some developing heads have been killed by the glyphosate after spraying at an early flowering stage (stage 61). The earlier the spraying, the greater the decrease in the final head number compared to the control. Double mowing led to a final head number intermediate between those for early glyphosate spraying and the control or a single mowing.

Table 1 .

 1 had already shown a similar importance of the spraying date on seed viability for Avena fatua. If confirmed in field trials, this is an important practical result for deciding when to treat blackgrass in set-aside fields. Farmers often use the blackgrass head appearance as a signal for deciding the date of treatment in set-aside(Dalbiès-Dulout, 1999), and our results show that this decision rule provides high risk of seed production if the treatment is delayed even by a short time. More accurate studies about the mechanisms (herbicide translocation in the plants) which are responsible for the date of application effect are needed.Finally, our results show that the demographic parameters of blackgrass we have studied are very different in set-aside compared to measurements in competition in winter cereals crops reported by different authors. Our results also show that single mean values for demographic parameters cannot be used for modelling blackgrass dynamics in set-aside, because head number, head length and seed viability all seem to depend on the type of weeding used and application date. The current results could be used to adapt the existing demographic models to the case of set-aside. Number of heads per plant before management, final number per plant, head length, seed viability ratio and calculated number of viable seeds per plant for the treatments in experiment 1. Means values within columns followed by the same letter are not significantly different at the 0.05 level of probability as determined by the Newman-Keuls test.

		Head number	Final head	Head length	Seed	Estimated
		before	number per	(mm)	viability	viable seed
	Treatment	treatment	plant		Ratio	number per
					(%)	plant
	T1	18.5 a	53.5 d	88 d	42.6	2670
	EM1	21.9 a	58.3 d	66 c	66.5	3338
	MM1	18.4 a	47.8 cd	66 c	63.7	2621
	LM1	19.7 a	57.5 d	48 b	66.6	2187
	2MM1	18.5 a	35.8 bc	44 a	22.7	467
	EG1	17.4 a	15.2 a	100 e	4.0	84
	MG1	20.6 a	26.9 ab	106 f	35.4	1414
	LG1	18.6 a	47.7 cd	89 d	61.2	3471

Acknowledgements: This study was supported by the Institut National de la Recherche Agronomique and the Ministère de l'Environnement (comité EGPN).

We thank D. Le Floch, M. Tremblay and J. Troizier for technical assistance, B.

Chauvel for useful suggestions and for providing the seeds, and O. Parkes for revising the English text.


Koki Mizutani

Takumi Miyoshi

Olivier Fourmaux

Traffic Analysis in Concurrent Multi-Channel Viewing on P2PTV

Keywords: P2PTV, traffic analysis, traffic flow, multi-channel

In recent years, peer-to-peer (P2P) video streaming services (P2PTV) have attracted much attention because of the ability to decrease the load on the content servers by distributing data delivery function to peers. On the other hand, P2P overlay networks are oblivious to the physical network topology and thus may cause undesirable traffic straddling on some Internet service providers (ISPs). To optimize P2PTV traffic, several traffic measurements have been studied and revealed the characteristics of P2PTV traffic. However, these studies did not focus on users' behavior and traffic flow of each user. In this paper, we focus on PPTV that is one of the most famous P2PTV services and collect traffic data when multiple channels are viewed at the same time. Through this measurement, we observed the changes of the number of peers, traffic flows, and packet arrival time. As a result, we found the new characteristics of PPTV such as the transmission state of PPTV by monitoring the variation in the number of new arrival peers. Moreover, we could detect video servers by simultaneously analyzing multi-channel PPTV traffic.

Introduction

In recent years, video delivery servers and Internet service providers (ISPs) have been suffering from the increase of traffic load due to the huge demand of multimedia contents. Cisco Systems, Inc. stated that video traffic will be on the increase accounting for 86 percent of all Internet traffic in 2016 [START_REF]Cisco visual networking index: forecast and methodology[END_REF]. As shown in Fig. 1, peer-to-peer (P2P) video streaming services (P2PTV) have attracted attention as a solution for decreasing the traffic load on content servers due to the nature of P2P communication that can distribute data delivery function to peers. Currently, P2P streaming applications have become getting popular such as SopCast [2], PPTV [START_REF] Pptv | [END_REF], and PPStream [4]. In the future, P2P traffic will still increase since video streaming services will shift from the client/server model to the P2P model. To distribute the load on the content servers, P2P communication implements a specific peer selection strategy. In P2PTV communication, a host first selects some peers randomly from the peer list that is offered by the server, and then sends request packets to them. As a result, P2PTV may download the video data from farther peers even when there exist some neighbor peers that have the desired video data, and thus establishes inefficient P2P overlay networks. Moreover, it is difficult for ISPs to deduce P2PTV traffic because of the distribution of peers around the world. Therefore, P2P traffic optimization is one of the biggest problems for ISPs.

To optimize P2PTV traffic, several traffic measurements have been studied and revealed its characteristics [START_REF] Hei | A measurement study of a largescale p2p iptv system[END_REF]- [START_REF] Silverston | On fairness and locality in p2p-tv through large-scale measurement experiment[END_REF]. However, these studies are not enough to reveal users' behavior since they only inspected a steady-state traffic in a long duration of time on the same channel. To study P2PTV traffic sufficiently, we need to inspect the behavior of peers and to compare the characteristics of P2PTV among multiple channels. This paper focuses on analyzing traffic characteristics and users' behavior among multiple channels on PPTV, which is one of popular P2PTV applications. We run PPTV applications on multiple PCs to view multiple channels and collect traffic data at the same time. As a result, we show the following characteristics: (1) frequencies in the use of the same peers among multiple channels, (2) the transmission state of PPTV by comparison between the variation in the number of new arrival peers and long duration flows, and (3) the possibility to find super peers that provide stable video streaming by analyzing traffic flows among multiple channels.

Related Work

Several conventional studies have investigated the characteristics of P2PTV applications including PPStream, PPTV, SopCast, etc. Hei et al. analyzed the steady state of P2PTV traffic on PPLive (former name of PPTV) with a crawler that can automatically send and receive control packets [START_REF] Hei | A measurement study of a largescale p2p iptv system[END_REF]. They discovered the variation in the characteristics of throughput, users' behavior, and the number of connections on PPLive. They also concluded that users tend to change their channels to others on the desired channel selection. This behavior is similar to the channel zapping on analog TV. Jia et al. analyzed the characteristics of the traffic on PPStream with crawler [START_REF] Jia | Characterizing ppstream across internet[END_REF]. They clarified the geographical characteristics, the variation in the number of peers, and the ratio of sent and received packets. They concluded that PPStream has a hierarchical mesh network topology including super nodes eager to send the video data to many users. Silverston et al. analyzed P2PTV traffic on PPStream and SopCast measuring on multiple points in different locations of the world [START_REF] Silverston | On fairness and locality in p2p-tv through large-scale measurement experiment[END_REF]. They clarified the number of AS hops and the ratio of download and upload data from the viewpoint of measurement locations. They concluded that the P2PTV applications have not been equipped with any traffic localization mechanism because many peers received video data from not own countries but others. In addition, they indicated that P2PTV also has no fairness in sharing the video data because of the differential ratio of download and upload data among peers.

The above studies analyzed P2PTV traffic in a long duration of time and revealed the characteristics of P2PTV traffic, the geographical location of peers, the overlay topology, etc. However, they are not enough to study the characteristic of P2PTV since they did not focus on the characteristics of users' behavior such as traffic per each user. In addition, these studies were measured only on single channel. To reveal and generalize the characteristic of P2PTV traffic, it is essential to investigate P2PTV traffic on multiple channels.

Objective of P2PTV Traffic Measurement

These days, various analyses of P2PTV traffic have been performed. However, these studies are not enough to reveal the P2PTV traffic thoroughly considering users' behavior and the extraction of P2PTV characteristics among multiple channels.

In this paper, we present a novel measurement experiment for multi-channel viewing by using multiple measurement PCs. We collect traffic data among multiple channels on P2PTV and analyze the traffic per each user. Based on the collected traffic, we firstly determine single-channel peers and multi-channel peers among peers that the P2PTV application connects to. A single-channel peer is defined as the peer that appears only in one channel, i.e., the peer is observed on a single channel. A multi-channel peer is defined as the peer that appears in two or more channels, i.e., the peer is observed on multiple channels. On P2PTV, it is not sure that measurement PCs can observe the same peers among multiple channels due to the nature of random peer selection. Therefore, we examine frequencies in the use of the same peers and define peers observed on various channels as multi-channel peers. In our second analysis, we examine the transmission state of P2PTV on a specific channel. This can be done by analyzing the variation in the number of new arrival peers and long duration flows. In particular, if many new arrival peers are intensively found on a channel, we can assume that the measurement PC is trying to search some stable peers at that time. Finally, we specify which peers could be a video server by analyzing traffic from multi-channel peers on each measurement PC. If some measurement PCs can simultaneously observe peers with the same IP address among multiple channels, we can decide that these peers are video servers deployed for providing stable P2PTV services.

Experimental Setting

This section describes our experimental environment. We focused on PPTV that is one of the most famous P2PTV services. We measured traffic for twelve hours (from 12:00 JST to 23:59) on 18th December 2013 on PPTV using ten measurement PCs including six virtual machines. As shown in table 1, we prepared three types of measurement PCs including high-, medium-, and low-performance PCs assuming actual users' environment. Every PC has an Internet connection provided by FLET'S HIKARI NEXT, 100 Mbps optical access service on next generation network (NGN) via Plala HIKARI Mate as an ISP in Japan. For capturing and monitoring traffic, Wireshark [START_REF]Wireshark[END_REF], a well-known packet sniffer, is installed on every measurement PC. As shown in Fig. 2, we viewed different live streaming contents through 10 popular channels based on the official popularity ranking on PPTV.

In the measurement PCs, we extracted only packets with not less than 1000 Bytes as video packets to cut off control packets and then analyzed only video traffic flows. Note that a flow represents a group of packets consecutively received from the same IP address. If the interval between two consecutive packets from the same IP address is more than 60 seconds, however, we regard them as different flows. Moreover, some flows may continue to exist even after our measurement. Therefore, we excluded some flows that maintained connections within the last 60 seconds of the measurement.

Analysis of P2PTV Traffic

In this section, we analyze PPTV traffic measured in our experiment.

Ratio of multi-channel peers

First, we extracted multi-channel peers that appeared in multiple channels. Then, we calculated the ratio of multi-channel peers to the total number of peers appearing in each channel. As shown in table 2, for instance on channel 1, a measurement PC observed 94 multi-channel peers and 793 peers in total, and therefore the ratio of multi-channel peers accounted for 11.9 percent. Moreover, we categorized channels considering the total number of peers into two groups of channels; popular channel group that has 1000 or more peers and unpopular channel group that has less than 1000 peers. In this experiment, channels 1, 3, 4, 5, 6, and 10 are categorized into unpopular channel group while channels 2, 7, 8, and 9 are categorized into popular channel group. The number of multi-channel peers accounted for 5 to 12 percent of the total number of peers on each channel.

Analysis of traffic flows

We analyzed traffic flows on channel 1 and 9, which represent the unpopular and popular channel groups, respectively. From Figs. 3 to 6 show the variation in peer numbers, the order of peers arrival, for 45000 seconds. In this experiment, we extracted long duration flows that connected to the same peers for not less than 1200 seconds (20 minutes). Firstly, Figs. 3 and5 show all traffic flows on channel 1 and 9, respectively. In addition, Figs. 4 and 6 pick up long duration flows from Figs. 3 and5, respectively. In each of figures, orange lines show traffic flows of single-channel peers, and blue lines show those of multi-channel peers. At the beginning of each measurement, as shown in Figs. 3 and5, measurement PCs drastically connected to multi-channel peers. Moreover, we found a relationship between the number of new arrival peers and long duration flows. In particular, the comparison between Figs. 3 and4 indicates that a huge increase of new arrival peers is observed on the disappearance of long duration flows from 23000 to 25000 seconds.

To clear the above relationship, we focused on the variation in the number of new arrival peers and long duration flows for 45000 seconds on channel 1 and 9, shown in Figs. 7 and8, respectively. In each of figures, red lines show the number of new arrival peers, and blue lines show the number of long duration flows. In Fig. 7, a huge increase of new arrival peers is observed while long duration flows disappear from 23000 to 25000 seconds. We conclude that the variation in the number of new arrival peers is a very important point to understand the transmission state of PPTV. We assume that PPTV lost stable peers to download the video data due to the immense decrease of long duration flows and then started searching new stable peers. According to this assumption, we can deduce the transmission state of PPTV by monitoring the variation in the number of new arrival peers. We found the similar tendency in Fig. 8 regardless of the number of peers in channels.

Behavior of multi-channel peers

Finally, we try to find the super peer among multiple channel. Fig. 9 shows the flows from a multi-channel peer on each channel viewed by each measurement PC. In Fig. 9 (a), measurement PCs simultaneously observed the same peer among multiple channels for a long duration. Thus, we assume that this peer was a video server called super peer, which provides stable video streaming services dedicating itself to sending video packets. On the contrary, in Fig. 9 (b), measurement PCs observed a channel transition of another multi-channel peer. This multi-channel peer was not observed simultaneously by several measurement PCs and disappeared shortly. We assume that this kind of behavior is users because user peers do not have intension to provide stable video streaming services and just select their preferred channels by themselves. According to these analyses, we can simply detect video servers with video traffic measurements on multiple channels.

Conclusion

In this paper, we discovered new characteristics of PPTV traffic with measurement experiment among multiple channels. Firstly, we calculated the ratio of multi-channel peers to the total number of peers appearing in each channel and found that multi-channel peers accounted for 5 to 12 percent of the total number of peers observed on each channel. Secondly, we found that huge increases of new arrival peers were observed on the disappearance of long duration flows. According to this characteristic, we can deduce the transmission state of PPTV by monitoring the variation in the number of new arrival peers. Finally, we can also detect super peers by monitoring the same peer observed among multiple channels simultaneously.

For the future works, we are going to analyze traffic characteristics on other P2PTV applications with measurement experiment among multiple channels simultaneously. Moreover, we will configure a traffic optimization system that controls P2PTV traffic considering the transmission state of P2PTV.

Fig. 1 .

 1 Fig. 1. Network model of video streaming services.

Fig. 2 .

 2 Fig. 2. Experimental environment.

Fig. 3 .Fig. 4 .Fig. 5 .Fig. 6 .Fig. 7 .Fig. 8 .

 345678 Fig. 3. All traffic flows on channel 1.

Fig. 9 .

 9 Fig. 9. Flows from specific peers appearing on each measurement PC.

Table 1 .

 1 Performance of measurement PCs.

	Performance	CPU	# of cores Memory (GB)	OS
	High	Intel Core i5-3470S 2.90GHz	4	12	Windows 7
	Medium	Intel Core i3-3217U 1.80GHz	2	8	Windows 7
	Low	Intel Core i5-3470S 2.90GHz	1	2	Windows 7

Table 2 .

 2 Statistics of peers and multi-channel peers.

	CH# Total peers Multi-channel peers Ratio of multi-channel peers (%)
	1	793	94	11.9
	3	838	66	7.9
	4	680	70	10.3
	5	514	56	10.9
	6	665	62	9.3
	10	544	64	11.8
	2	1882	149	7.9
	7	1810	94	5.2
	8	1516	88	5.8
	9	1440	119	8.3

Acknowledgment

This study was partly supported by JSPS KAKENHI Grant Number 26420370.