Large size high resolution (HR) satellite image matching is a challenging task due to local distortion, repetitive structures, intensity changes and low efficiency. In this paper, a novel matching approach is proposed for the large size HR satellite image registration, which is based on coarse-to-fine strategy and geometric scale-invariant feature transform (SIFT). In the coarse matching step, a robust matching method scale restrict (SR) SIFT is implemented at low resolution level. The matching results provide geometric constraints which are then used to guide block division and geometric SIFT in the fine matching step. The block matching method can overcome the memory problem. In geometric SIFT, with area constraints, it is beneficial for validating the candidate matches and decreasing searching complexity. To further improve the matching efficiency, the proposed matching method is parallelized using OpenMP. Finally, the sensing image is rectified to the coordinate of reference image via Triangulated Irregular Network (TIN) transformation. Experiments are designed to test the performance of the proposed matching method. The experimental results show that the proposed method can decrease the matching time and increase the number of matching points while maintaining high registration accuracy. Image matching is a vital process that acquires the correct matched points between two images of the same scene, which may have been acquired at the same time or at different times by one or more sensors from the same viewpoint or different viewpoints . Image mCurrent matching methods can be broadly classified into two categories: area-based methods and feature-based methods . In areaThe majority of SIFT-based approaches are designed for small size images, and approaches related to large size HR image registration are less reported. Gong et al. proposed a coarse-to-fine registration scheme where the coarse step is implemented by SIFT and fine step is implemented by mutual information . Huo proIn this paper, we focus on addressing the matching difficulties caused by the overwhelming increase in image size. A geometric constraint SIFT method with a coarse-to-fine scheme is proposed to improve the matching performance and decrease the computing time. In the coarse procedure, the images are downsampled to reduce the memory burden. The SR-SIFT is used to match CPs between the downsampled images. The coarse transformation between images is then estimated. In the fine procedure, the reference image is divided into blocks, and the corresponding blocks in the sensing image are determined by the coarse transformation. In each block, a geometric SIFT is proposed to match more CPs. Finally, the sensing image is rectified to the coordinate of reference image via TIN transformation. The rest of this paper is organized as follows: The proposed large size HR image registration method is a coarse-to-fine matching method. In the coarse matching stage, the image is downsampled to lower resolution, and the images are matched by a robustness matching method (SR-SIFT). The coarse geometric relation between images is estimated according to the matching points. In the fine matching stage, the reference image is divided into blocks, and the corresponding blocks in the sensing image are detected by coarse geometric relation. In each block, the proposed geometric SIFT is used for matching. After all blocks are matched, the sensing image is resampled to the reference image coordinate by TIN transformation. The flow chart of the proposed method is shown in N is the minimum of the width and height of the reference image and sensing image. M is the minimized size of the image after down-sampling. In this paper, we set the value of M to 800.The purpose of the coarse matching is to find the coarse geometric transformation between images. We use SIFT as it can be implemented efficiently for images of low resolution. The down-sampling level The image pairs are downsampled to low resolution leading to the loss of information. With information loss and noise, the acquisition of the correct match points from the incorrect points is difficult. The random sample consensus (RANSAC) is used to remove the outliers. RANSAC will fail if the correct matching is low, and the SIFT matching will fail. The SR-SIFT algorithm is a robust matching method which can raise the correct match rate in remote sensing images matching application .The SR-SIFT is used to improve the correct matching rate. Given a match pair SD can be larger than those of mismatches. The peak in the histogram is noted as A histogram of SDs is formed. According to the initial scale difference between images, the correct matches’ The matches that satisfy the scale restriction criteria are selected as tie points. Then RANSAC is used to further remove outliers and obtain CPs. Finally, the coarse transformation between images is estimated using CPs. The affine transformation model is as follows:The direct application of SIFT to these large size HR images will produce an “out of memory” message. To overcome this problem, block-based methods for large size HR, in which the entire image is divided into small-size image blocks, have been proposed . A smallThe entire reference image The feature-matching algorithm adopts minimum Euclidean distance on the vector for each key point in one image to find the nearest neighbor as its corresponding key point in the other image. The feature description vector of the sensing image can be expressed as There are two main methods for feature retrieval. One is the linear scan or the exhaustive search. By calculating the Euclidean distance one by one to find the nearest and second nearest key points, the exhaustive search is slow. The other method is based on constructing the data index, dividing the search space hierarchically and realizing fast matching. An example is the k-d tree, which consumes less time than the former method. However, when the dimension of the data is greater than 20, the performance of the k-d tree decreases dramatically. Lowe proposed a BBF (Best Bin First) search method, which is a modified version of the k-d tree method. However, the runtime is still large. (1)Extract the SIFT keypoints: the sensing image keypoint set (2)Obtain the matching keypoint set (3)Search each the key point The current feature matching method searched all of the key points on the image. Here, considering the coarse registration relationship between images is obtained at coarse matching step, the paper proposes a search matching method which just searches the key points in the predict area. As shown in The proposed geometric matching method reduces the key point searching area. If the key points are evenly distributed around the image, the time complexity is Recently, computer engineering has provided the development of multi-core processors, which are composed of four or more independent cores in a single physical package . OpenMP  			 for 			       Geometric_SIFT;     With OpenMP, this code can be parallelized as:		   	 #pragma omp parallel for			     for 				  Geometric_SIFT ;		  In this algorithm, the matching blocks are independent and are considered to be loop-level matching. The loop-level matching can be parallelized using OpenMP. The flowchart of the multi-thread matching method is shown in After the block is matched, the matching points are sent to the master thread. After all blocks are matched, the master thread performs outliner removal to acquire the correct matching point.Local distortion and relief displacement related to landscape height occur in large size HR satellite images. Therefore, global transformation such as affine transformation, polynomial transformation and TPS transformation cannot be used to express the relationship between HR satellite images. Here, a local deformation TIN model using piecewise mapping based on transformation generated from obtained CPs. After the CPs are obtained, TINs are generated. The TIN generation is realized by the GDALTriangulationCreateDelaunay function in Geospatial Data Abstraction Library (GDAL) . Each TIIn this section, the proposed approach is evaluated in terms of matching performance and efficiency. Three sets of experiments are designed. The first set of experiments is to test the performance of the proposed geometric SIFT. The second experiment tests computation time to demonstrate the advantages of the multi-thread matching method using OpenMP. The third experiment compares the proposed method to other HR matching methods and shows the improvement of the proposed registration method. Three datasets which composed of multispectral and multitemporal pairs are employed for experiments, as shown in In this section, we will test the performance of the proposed geometric SIFT and demonstrate the advantage of using geometric constraint. To reduce the computational complexity, the total image is divided into blocks, the corresponding blocks in the sensing image are determined by the affine transformation. In each block pair matching, the blocks are match by the proposed geometric SIFT. In block matching, we compare the proposed method with the SIFT. The geometric SIFT is matching the keypoints on the predicting area, the SIFT is matching the keypoints on the total block. To quantitatively evaluate the proposed method, the total number of extracted match points (TMP), the number of correct match points (CMP), the correct match rate (CMR), the number of false match points (FMP) and runtime are the measures to evaluate the proposed geometric SIFT. The definitions of CMR is as follows:The proposed geometric SIFT can obtain more matching points and higher CMR than SIFT . The runp. In this section, to demonstrate the advantages of the multi-thread matching method using OpenMP, the runtime and increase in speed were measured for the different number of cores that were used for processing. We perform the experiment using Dataset 3. p = 12 threads. The times of increase in speed are reduced with more than p = 12 threads. The first factor behind these results is that the threads are locked in the data reading procedure, so the communication among threads requires additional time. The second factor is load imbalance, since block matching may have different workloads. This experiment shows that multi-thread processing can improve the running efficiency with the highest increase in speed to 12.1 times, which shows the advantage of the multi-thread matching method using OpenMP.The parallelized block-based method shows increase in speed compared with one-core processing. It achieves the highest increase in speed, 12.1 times with In this section, we compare the proposed HR registration method with the method proposed by Lee and the method proposed by Li. Lee uses an adaptive block processing matching strategy to matching the HR satellite images . Li matcn represents the number of match points. If the reference image and sensing image are registered accurately, point To compare the algorithms quantitatively, the CMP, CMR, runtime, registration accuracy are compared. The RMSE is used to measure the registration accuracy and is defined as: From An effective and efficient matching method is proposed in this paper for the registration of HR images. The coarse-to-fine matching strategy, the block matching method, geometric SIFT, multi-threads processing and TIN transformation are proposed in the HR image registration method. The block matching method and geometric SIFT benefits from the coarse registration at low resolution images. The block matching method can overcome the memory problem. The geometric SIFT match the keypoints in the prediction area. With area constraint, the search complexity is decreased and the false matches may be eliminated before matching. The efficiency is improved, and our parallelized approach can achieve the highest increase in speed to 12.1 times. The TIN transformation is a local transformation which is used for HR images registration. Experimental results show that the proposed method can match more CPs at a shorter runtime while maintaining a high registration accuracy.


In wireless sensor networks, in order to satisfy the requirement of long working time of energy-limited nodes, we need to design an energy-efficient and lifetime-extended medium access control (MAC) protocol. In this paper, a node cooperation mechanism that one or multiple nodes with higher channel gain and sufficient residual energy help a sender relay its data packets to its recipient is employed to achieve this objective. We first propose a transmission power optimization algorithm to prolong network lifetime by optimizing the transmission powers of the sender and its cooperative nodes to maximize their minimum residual energy after their data packet transmissions. Based on it, we propose a corresponding power-optimized cooperative MAC protocol. A cooperative node contention mechanism is designed to ensure that the sender can effectively select a group of cooperative nodes with the lowest energy consumption and the best channel quality for cooperative transmissions, thus further improving the energy efficiency. Simulation results show that compared to typical MAC protocol with direct transmissions and energy-efficient cooperative MAC protocol, the proposed cooperative MAC protocol can efficiently improve the energy efficiency and extend the network lifetime. Wireless sensor networks (WSNs) are widely used in many applications, such as environmental monitoring, traffic control, product quality monitoring, mineral survey and disaster relief . One of To reduce the energy consumption and improve energy efficiency on multiple access, most MAC protocols use duty cycle mechanism . Duty cyIn recent years, both multi-input multi-output (MIMO) and cooperative communication techniques can efficiently improve energy efficiency at a certain throughput and bit error rate (BER), by taking advantage of the broadcasting nature of wireless communication and independent fading characteristic of wireless channel to obtain spatial diversity. The MIMO technique employs multiple transmitting and receiving antennas to achieve spatial diversity and thus improves the system capacity and reduces energy consumption. However, in WSNs, due to the limitation of size and cost, it is difficult to install multiple antennas on each node. Therefore, it cannot be widely used in WSNs. In this case, the cooperative communication technique can transform the direct transmission link to the cooperative transmission link with better channel quality by exploiting cooperative nodes to help the sender forward data packets to its recipient, which constructs a virtual MIMO system and reduces energy consumption of data packet transmissions. Moreover, it can optimize local nodes’ transmission power and energy consumption as a whole and reasonably allocate transmission energy consumption among the sender and its cooperative node(s), thereby balancing local nodes’ energy consumption. Therefore, the cooperative communication technique can be used in WSNs to improve energy efficiency and extend network lifetime.Early research work on cooperative communication techniques primarily focuses on the physical (PHY) layer ,8. In orMotivated by this, we proposed a power-optimized cooperative MAC (PO-CMAC) protocol for WSNs in this paper. The main work includes two aspects. First, to prolong the lifetime of the WSNs with limited energy, we propose the lifetime-extended transmission power optimization (LETPO) algorithm based on the greedy algorithm and linear programming algorithm. This algorithm balances the energy consumption of the local network by maximizing the minimum residual energy of all the nodes participating in each data packet transmission. Second, based on the algorithm, we propose the PO-CMAC protocol resolving the problem that coordinates multiple nodes to access and use the wireless channel. With the help of a proposed cooperative node selection mechanism, the protocol can choose the nodes with the best channel quality to take part in the cooperative transmissions, thereby reducing the energy consumption of data packet transmissions and improving the nodes’ energy efficiency.We consider both energy efficiency  and energy utilization  to extend network lifetime and transmit more data packets in our proposed PO-CMAC protocol. With the help of the proposed LETPO algorithm, the protocol can balance the energy consumption of different nodes and improve the network energy utilization. Additionally, by using the proposed cooperative node selection mechanism, the protocol can select the cooperative nodes with the highest channel gain to participate in each cooperative transmission to reduce the average energy consumption of data packet transmissions and improve the energy efficiency of every node.Compared to the single node cooperation mode which is widely used in the current cooperative MAC protocols, we consider the node cooperation mode with multiple optimal cooperative nodes. The number of cooperative nodes taking part in the node cooperation can be changed in this protocol according to the actual optimization results of the proposed LETPO algorithm, thereby taking full advantage of the multiple best cooperative nodes in the network to achieve better effect than the single node cooperation mode.We propose a cooperative node selection mechanism to guarantee multiple cooperative nodes to access wireless channel orderly according to their channel gains, which can avoid contention collisions and reduce extra cooperation overhead. According to the contention results, it can also optimize the transmission powers of the cooperative nodes to balance the energy consumption of the local network, which further transfers the cooperative gain and energy utility improved by the LETPO algorithm from the PHY layer to the MAC layer.The main contributions of this paper are summarized as follows:The remainder of the paper is organized as follows. In Cooperative MAC protocols tackle two key problems based on MAC protocols, i.e., when to cooperate and how to select the cooperative node(s). To resolve the first problem, it is critical to assess whether the node cooperation could improve the network performance and which kind of network performance is improved. The existing research work mainly focuses on two aspects of network performance, i.e., the network throughput and the transmission reliability . AccordiIn the former cooperative MAC protocols, the sender or the cooperative nodes need to determine whether the cooperative transmission is adopted before the transmission of data packet. If at least one cooperative node can improve throughput or decrease the transmission delay, the cooperative transmission is initiated. Otherwise, the data packet is directly transmitted from the sender to the recipient. Typical protocols include the cooperative MAC (CoopMAC) protocol  and the The latter cooperative MAC protocols adopt the concept of cooperative automatic repeat request (ARQ). If the direct transmission failed and there existed cooperative nodes to successfully receive the data packets, the cooperative transmission is employed to improve successful transmission probability. Typical protocols are the cooperative diversity MAC (CD-MAC) protocol , the difAccording to the method to select cooperative nodes, cooperative MAC protocols can be divided into three categories: the sender-specified method, the recipient-specified method, and the cooperative node contention method. In early cooperative MAC protocols, such as the CoopMAC protocol  and the From the above, we can see that most current cooperative MAC protocols mainly focus on improving the throughput and transmission reliability other than improving the energy efficiency. Considering that saving energy is the main challenge in WSNs, the cooperative MAC protocols for the purpose of improving the energy efficiency and extending the network lifetime are more and more proposed.The objective of energy-efficient cooperative MAC protocols is twofold: optimize energy consumption for each data packet transmission and optimize the entire network lifetime. The first takes advantage of diversity gain generated by node cooperation to reduce the energy consumption for each data packet transmission by choosing the best cooperative node and thus improves the energy efficiency of entire network at a certain throughput and packet error rate (PER) ,32,33,34The second category of cooperative MAC protocols takes advantage of cooperation among nodes to balance local network energy consumption, avoids some nodes consuming their energy too quickly and makes full use of each node’s energy in the whole network, thereby improving network energy utilization and extending network lifetime ,36,37. GBoth the wireless sensor network cooperative MAC (WcoopMAC) protocol  and the In summary, the existing research work on improving energy efficiency and extending network lifetime can be improved in the following aspects. Firstly, the existing cooperative MAC protocols mainly use single cooperative node and do not consider the cooperation of multiple cooperative nodes. By exploiting the potential multiple cooperative nodes, the cooperative transmissions can improve the energy efficiency and extend network lifetime more effectively. Furthermore, with the application of randomized DSTC (RDSTC) technique  to coopeTherefore, this paper proposes a power-optimized cooperative MAC (PO-CMAC) protocol based on multiple cooperative nodes and considering both the energy consumption of each data packet transmission and network energy utilization. Each node located in one-hop transmission range of both the sender and its recipient contends to become a cooperative node and participate in cooperative transmission according to the instantaneous channel quality between itself and the sender/recipient, thus reducing the transmission energy consumption and improving energy efficiency. Meanwhile, the sender can optimize its transmission power and each cooperative node’s transmission power according to the proposed LETPO algorithm, thus balancing the energy consumption of the local nodes. Through the above two points, the PO-CMAC protocol can efficiently extend entire network lifetime.In WSNs, all nodes share a wireless channel to exchange control packets and data packets. Each node can estimate the channel gain between itself and other nodes based on the signal-to-noise ratio (SNR) of received control packets. For simplicity, we assume that the communication channels between any two nodes are bidirectionally symmetrical, i.e., they have the same channel gain, and the channel is invariant during the transmission session of each data packet. The recipient can decode data packets sent from the sender and its cooperative nodes by maximum ratio combining (MRC) technique.Pmax. Its transmission power can be continuously adjusted within the range of Pmax according to the actual requirement. All nodes have the same initial energy, and their batteries cannot be charged if exhausted. If the residual battery energy of a node is insufficient to send the current packet, we consider that its battery energy is exhausted. For simplicity, this paper only considers the energy efficiency of WSNs in the transmission of information, and ignores the energy consumed in sensing and data processing.Each node in WSNs has a half-duplex transceiver with the maximum transmission power 1, R2 and R3) of both the sender S and its recipient D, and the current energy of these nodes are ES, ED, hSD, and can be obtained by the recipient from receiving cooperative RTS (CRTS) packet and specified in its cooperative CTS (CCTS) packet for the sender and cooperative nodes. In the cooperative transmission model, the sender S transmits its data packets with a lower transmission power than that in the direct transmission model, and the recipient cannot decode the data packets but receive and save the data packets. Some common neighbor nodes of S and D , respectively. Channel gain hSR can be obtained by the cooperative node from receiving CRTS packet and specified in its help-to-send (HTS) packet for the sender and recipient. Channel gain hRD is assumed to be equal to channel gain hDR, which can be obtained by the cooperative node from receiving CCTS packet. For asymmetric wireless channels, the sender and cooperative node can obtain channel gain hRD by this way: the recipient obtained channel gain hRD from recently received control packets or data packets from the cooperative node and specifies this value in its CCTS packet. The recipient can use the MRC technique to decode the data packets from the sender and these cooperative nodes. This two-hop transmission method is the cooperative transmission model of multiple nodes used in this paper.In order to guarantee the recipient successfully decoding the data packets, improve the node energy efficiency and extend the network lifetime, the key issues in this cooperative transmission model include how much transmission power the sender should use to transmit data packets, which common neighbor nodes should take part in the cooperative transmission, and how much transmission power each cooperative node should use to forward the data packets. To resolve these issues, we propose the LETPO algorithm and PO-CMAC protocol.In this paper, the network lifetime is defined as the duration from the beginning time of the network operation to the time when the first node exhausts its energy ,43. BaseNetwork lifetime is a global variable, which is related to the data packet generation of each node and network traffic load. However, network traffic load distribution is determined by actual application, and each node cannot predict its future data packet arrival rate. In this case, in order to extend network lifetime, we can use the local optimization to replace the global optimization according to the greedy algorithm. That is to say, we should maximize the minimum residual energy among the sender and cooperative nodes during the current data packet transmission ,43. Thise of the sender and cooperative nodes after the current data packet transmission can be expressed as:ES is the residual energy of the sender before the current data packet transmission, PS is the transmission power of the sender to be used during the current data packet transmission, and T is the duration of the data packet transmission. Then, ES − PST represents the residual energy of the sender after the current data packet transmission. Likewise, i  after the current cooperative transmission using a transmission power PS and e to be maximum after the current data packet transmission, i.e., the ultimate objective of the LETPO algorithm is:The minimum residual energy PS and Pmax.The optimal transmission powers R is the bandwidth utilization between S and D, i, and N0 is the power of Gaussian white noise. In the cooperative transmission, each data packet transmission is composed of two–hop transmissions, i.e., from the sender to the cooperative node and then from the cooperative node to the recipient, and each one-hop transmission only occupies a half of the channel resource. Therefore, there is a factor 1/2 in Equation (3).According to Shannon theorem, to ensure cooperative nodes to receive data packets correctly, the transmission power of the sender should satisfy the following constraint condition:Deduced from Equation (3), the received SNR of a cooperative node should satisfy the following condition:In the cooperative transmission, the recipient receives the signals from the sender and cooperative nodes, and decodes the signals by the MRC technique. Therefore, the transmission powers of the sender and cooperative nodes should satisfy the following condition:hSD is the channel gain between the sender S and its recipient D, and i and the recipient D.That is to say, the received SNR at the recipient should satisfy the condition:Meanwhile, all nodes in the network should adjust their transmission powers in range of the maximum transmission power as follows:Ps is always positive because the sender always needs to transmit data packets. However, the transmission power of a cooperative node The transmission power of the sender A cooperative node can take part in the cooperative transmission and then achieve the objective of improving the system capacity and extending the network lifetime only when its residual energy and channel gain between itself and the sender/recipient satisfy certain conditions.PD is the transmission power of the sender in direct transmission, which can be deduced from the Shannon theorem as follows:Firstly, in order to extend network lifetime, the cooperative node should have enough residual energy to participate in the cooperative transmission and forward the data packets. That is to say, the residual energy of the cooperative node before the cooperative transmission should be more than that of the sender after transmitting the data packets directly, i.e.,Then we get:Secondly, the cooperative node transforms the packet transmission between the sender and its recipient from one-hop to two-hop. In order to improve the system capacity, the channel gain of the cooperative node between itself and the corresponding sender/recipient should be higher than that of the direct link between the sender and its recipient, i.e.,L is the length of data packets, and B is the bandwidth of the channel.Moreover, the energy consumption of the cooperative transmission should be constrained. In the cooperative transmission, the energy consumption of the sender should be less than that in the direct transmission, i.e.,i participates in the cooperative transmission, according to Equation (3), in order to guarantee its correct packet decoding, the transmission power of the sender should satisfy the following condition:If a cooperative node RSubstituting Equations (11) and (15) into Equation (14), we have:Compared to Equation (12), Equation (15) is a strict constraint. Furthermore, the channel gains of all potential cooperative nodes’ links should satisfy Equations (12), (13) and (16).PS and e, and thus extend the network lifetime.Equations (9), (12), (13) and (16) restrict the residual energy and the channel gains of all cooperative nodes’ links. Equations (3)–(8) give the feasible region of the transmission powers of the sender and its multiple cooperative nodes. The optimal values of PS and each cooperative node Ri relays the data packets using the optimized transmission power The above LETPO algorithm achieves the objective of optimizing the transmission powers of both the sender and its multiple cooperative nodes theoretically. However, during the execution of the algorithm, each node should transmit its channel gain and residual energy to the sender through a control packet. Then the sender performs the transmission power optimization algorithm with these parameters and declares the optimization results to the cooperative nodes with another control packet. Finally, the sender and cooperative nodes transmit the data packets by using their optimized transmission powers, i.e., the sender transmits the data packets using the optimized transmission power To perform the LETPO algorithm efficiently, there are still two issues to be addressed. Firstly, if there are many potential cooperative nodes satisfying the constraint condition shown in Equations (9), (12), (13) and (16), how to select parts of them to transmit control packets to the sender, thus avoiding too much delay and energy overhead. Secondly, we should determine the transmission sequence of these control packets to avoid packet collisions.To address the above issues, execute the LETPO algorithm more efficiently and extend the network lifetime for WSNs, we propose the PO-CMAC protocol. As shown in hSD by the received SNR of the CRTS and CCTS. The node Ri receiving both CRTS and CCTS knows that it is the common neighbor node of S and D and estimates the channel gain ES, and the CCTS packet contains the channel gain hSD. With the information in control packets, the common neighbor nodes Ri of S and D can determine whether its corresponding parameters satisfy Equations (9), (12), (13) and (16). If satisfied, the node Ri can take part in the process of cooperative node contention.When the sender S has a data packet to send and senses the channel idle, it sends the cooperative RTS (CRTS) packet to its recipient D like IEEE 802.11 distributed coordination function (DCF) protocol. On receiving the CRTS packet correctly, the recipient replies with a cooperative CTS (CCTS) packet if the channel is available from its perspective. With the help of the CRTS/CCTS handshake, other nodes in the network know the data packet transmission between S and D and set their own network allocation vectors (NAVs) to avoid packet transmission collisions. Meanwhile, S and D can estimate the channel gain In the cooperative node selection phase, the nodes satisfying the constraint conditions of cooperation contend to be cooperative nodes by sending help-to-send (HTS) packet to the sender. There are two issues to be addressed in this process, i.e., the contention access delay of the HTS packet transmission and the method to determine the optimal group of cooperative nodes.i satisfying the constraint conditions of cooperation send their HTS packets to the sender, there will be too much unnecessary delay, energy overhead and HTS packet collisions. To avoid this case, the maximum number of HTS packets sent to the sender during the cooperative node selection phase is set to be M in the PO-CMAC protocol. The value of M can be set in advance based on the network scale, the node density and the traffic load distribution of the network.If all the common neighbor nodes RMoreover, a mechanism is needed to evaluate the ability of all common neighbor nodes to take part in the cooperative transmissions, in order to guarantee the nodes with higher cooperative gain to access the channel and send the HTS packet earlier to participate in the cooperative transmissions, thus extending the network lifetime.n during the whole network lifetime. For simplicity, it can be formulated as follows:E0 is the initial energy of the network, η is the energy utilization of the network, and EPKT is the average energy consumption of each data packet transmission.The length of the network lifetime can be reflected by the total number of transmitted data packets Equation (17) shows that, with a certain initial energy, the total number of data packet transmissions is proportional to the energy utilization of the network and inversely proportional to the energy consumption of each data packet transmission. The LETPO algorithm balances the local network energy consumption by maximizing the minimum residual energy of all related nodes according to the greedy algorithm. In this process, the energy utilization is fully considered and all nodes in the network drain out their residual energy as evenly as possible, thus extending the network lifetime. However, it overlooks the energy consumption during each data packet transmission. In other words, the LETPO algorithm concerns the energy utilization more than the energy efficiency. Therefore, in the cooperative node selection phase, we should focus on the energy consumption of each data packet transmission cooperated by the selected cooperative nodes through the corresponding cooperative links. That is to say, we also consider the energy efficiency improvement in the cooperative node selection phase.i can be expressed as:PSRi need to satisfy Equation (3), and the transmission power PRiD of the cooperative node Ri needs to satisfy the following condition:The energy consumption of each data packet transmission cooperated by a single cooperative node RhSRi and hRiD in Equations (12), (13) and (16), to minimize EPKT-Ri in Equation (18), PSRi and PRiD should be set to the following value:Considering the constraint conditions of the channel gain Substituting Equations (20) and (21) into Equation (18), we get:i through its cooperative link is i and the corresponding sender/recipient. Namely, The above equation shows the minimum energy consumption of one data packet transmission cooperated by single cooperative node RtRi as follows:TW is the maximum waiting time allowed for cooperative nodes to contend to access the channel. Each cooperative node calculates its access delay t1, t2, …, ti, …, and tM, the first HTS packet is transmitted at t1 after the beginning of contention phase, and the second HTS packet is transmitted at t2 − t1 after the end of the first HTS packet transmission. In the same way, the M-th HTS packet is transmitted at tM − tM-1 after the end of the (M − 1)-th HTS packet. This method can avoid the HTS packet collisions from different cooperative nodes.For this reason, in the cooperative node selection phase, each node needs to wait a certain access delay (1)M. As shown in M-th HTS packet, the sender calculates the optimal transmission powers of itself and all the cooperative nodes based on the information of M HTS packets  and the channel gain estimated by itself. Then it broadcasts the optimization results to the cooperative nodes through an optimized-power-declaration (OPD) packet. Meanwhile, all the other cooperative nodes that have not transmitted their HTS packets stop their predefined HTS packet transmissions because they sensed M HTS packet transmissions and received the OPD packet.The first case is that the number of HTS packets transmitted to the sender reaches the predefined maximum number of selected cooperative nodes (2)TE after a certain HTS packet transmission. When the cooperative nodes that prepare to transmit their HTS packets sense this phenomenon, they will not transmit their HTS packets anymore. Because the sequence of HTS packet transmissions reflects the cooperative gains of the nodes to take part in the cooperative transmission, if the sender has received a few HTS packets, it indicates that some cooperative nodes with higher cooperative gain can participate in the cooperative transmission. In this case, the waiting time for the subsequent HTS packet transmissions should decrease gradually in order to avoid wasting too much time on waiting for the HTS packets of the cooperative nodes with lower cooperative gain and thus leading to more overhead than their incurred cooperative gain. TE can be set as follows:k is the number of already-transmitted HTS packets in the network. The value of TE decreases as k increases, which illustrates that the more HTS packets have been received by the sender, the shorter the time of waiting for the subsequent HTS packet transmissions is. If there are no HTS packet transmissions during an interval of TE, the cooperative contention phase prematurely ends. Furthermore, based on k HTS packets, the sender calculates the optimal transmission powers of itself and its k selected cooperative nodes, and declares the results through an OPD packet.As shown in (3)TR) as the new access delay of the HTS packets and begin counting. Meanwhile, these cooperative nodes keep sensing the channel. When a cooperative node senses the HTS packet transmission from another cooperative node, it pauses the counting and continues the counting after the current HTS transmission. It transmits its HTS packet at the end of its access delay counting. If the waiting time exceeds TR, the cooperative contention phase ends because there will be no more HTS packet transmissions. The sender calculates the optimal transmission powers based on the received HTS packets and declares the results through an OPD packet. The reason for this HTS retransmission strategy is that the nodes with higher cooperative gain should always have priority to be employed than the nodes with lower cooperative gain.All the cooperative nodes that transmit their HTS packet successfully to the sender from the beginning of the cooperative node contention phase to its end form the optimalcooperative node group to participate in the power-optimized cooperative transmission. According to different ending manners of the cooperative contention phase, there are three cases to determine the optimal cooperative node group.On receiving the OPD packet, each cooperative node knows whether it will take part in the following data packet transmission phase and the value of its corresponding transmission power. Then the PO-CMAC protocol enters into the data packet transmission phase.Combining this cooperative node selection strategy with the LETPO algorithm, we can consider both the average energy consumption of each data packet transmission  and the energy utilization of the network, thus extending the network lifetime efficiently.PS calculated during the cooperative node selection phase. Since the optimal transmission power PS is lower than PD, which is a threshold of correctly decoding in direct transmission, the recipient cannot decode the data packet except receiving and saving it. The cooperative nodes forward the data packet to the recipient using the optimal transmission powers declared in the OPD packet. Then the recipient decodes the data packet based on the data packet sent from the sender and those forwarded from the cooperative nodes by using MRC technique. If the recipient successfully decodes the data packet, it will reply with an acknowledgment (ACK) packet to the sender to confirm its successful data packet reception.In the data packet transmission phase, the data packets are transmitted from the sender to its recipient through two-hop manner, i.e., from the sender to the cooperative nodes and then from the cooperative nodes to the recipient. As shown in If the recipient cannot decode the data packet successfully, the data packet needs to be retransmitted. Because the recipient has saved the data packets from the sender and the cooperative nodes, it only needs an extra data packet transmission in a certain power in order to correctly decode the packet without wasting too much energy. In the cooperative node selection phase, the cooperative nodes transmit their HTS packets to the sender in an order of their cooperative ability to participate in the cooperative transmission. Therefore, we should choose the cooperative node with the highest cooperative gain to retransmit the data packet.PD.As shown in In this part, we employ C programming language for simulation and give performance comparison among the lifetime-extended PO-CMAC protocol, direct transmission (DT) protocol and energy efficiency cooperative relay (EE-CR) protocol proposed in . To measλ. The default value of λ is 1 packet/s. The recipient of each data packet is randomly selected from the neighbor nodes of the sender. Each node has initial energy of 1 J. The wireless channel is a Rayleigh fading channel and the path loss exponent is 3. All protocols operate in a single rate mode. In order to guarantee that the transmission rates from the sender to its recipient are the same in different protocols, if the direct transmission is used, the bandwidth utilization is R, and if the cooperative transmission is used, the bandwidth utilization is 2R. The default value of R is 2 bit/s/Hz. In the simulation, the energy consumption of each data packet transmission includes not only the energy to transmit the data packet but also the energy to transmit the corresponding control packets. Let LPHY, LMAC, LCRTS, LNRTS, LCCTS, LHTS, LACK, LNACK, LOPD and LDATA be the lengths of PHY header, MAC header, CRTS, NRTS, CCTS, HTS, ACK, NACK, OPD and data packets, respectively. All nodes in the network are randomly distributed in a rectangle area of 100 m × 100 m. Each node generates data packets with a fixed length according to a Poisson distribution with the arrival rate In WSNs, the traffic load distribution is an important influence factor on network lifetime. Therefore, we investigate the network performance at both uniform traffic load and nonuniform traffic load.N in the network changes from 50 to 150. As shown in M = 1 has the longest network lifetime and the most data packets transmitted by each node. When N = 150 and M = 1, the network lifetime and the number of data packets transmitted by each node of the PO-CMAC protocol are 56.17% and 39.24% higher than those of the EE-CR protocol, respectively. The advantage of the PO-CMAC protocol mainly originates from the following two aspects.(1)The cooperative transmission obtains spatial diversity gain by considering transmission link quality and transforming the direct transmission link from the sender to its recipient to the two-hop cooperative transmission links, i.e., from the sender to the cooperative nodes and from the cooperative nodes to the recipient. If the direct transmission link between the sender and its recipient is in deep fading, in traditional direct transmission, the sender can only increase the transmission power to guarantee the transmission reliability. However, in the cooperative transmission, considering the independent fading channel, the sender can select the cooperative nodes with the best cooperative transmission link to cooperate. Therefore, it can obtain diversity gain and guarantee the transmission reliability with low transmission power. As shown in Equations (22) and (23), in the cooperative node selection phase of the PO-CMAC protocol, the cooperative nodes with higher channel gain is chosen with higher priority, which ensures that the cooperative transmission fully explores the spatial diversity gain obtained by node cooperation and consumes less energy than the direct transmission.(2)Meanwhile, the PO-CMAC protocol considers the impact of the residual energy balance of the local network on the network lifetime. By balancing the energy consumption of nodes in local network, it avoids decreasing network lifetime due to the early energy exhaustion of some nodes. Usually when the cooperative transmission achieves spatial diversity, it also leads to extra energy consumption of some nodes due to their cooperative transmissions for other nodes, which aggravates the non-uniform energy consumption of the nodes in local network. However, in the PO-CMAC protocol, the LETPO algorithm makes the nodes with little residual energy consume energy as less as possible in the current data packet transmission by maximizing the minimum residual energy of each associated node, thus avoiding part of nodes consuming their energy too soon and balancing the energy consumption of the local network. Therefore, the PO-CMAC protocol outperforms the EE-CR protocol, which also adopts cooperative transmission manner.The results in M = 1, the network lifetime is the longest and the number of data packets transmitted by each node is the largest. Furthermore, as M increases, both of them decrease. This is because when the network traffic load is uniform, different nodes have the same arrival rates and then consume the energy in a similar way. In this case, it only needs to select one cooperative node with the best channel quality in the cooperative node selection phase and then balances the residual energy of the sender and this cooperative node through the LETPO algorithm. By this mechanism, both high energy utilization of the network and high energy efficiency of the nodes are guaranteed, and thus the network lifetime is further extended. M = 1, the energy utilization of the PO-CMAC protocol is nearly 1 and there is no more room for performance improvement. Moreover, when M = 1, the overhead is the least and the energy efficiency is the highest. Therefore, the network lifetime is the longest.From N = 150 and M = 1, the network lifetime and the number of packets transmitted by each node of the PO-CMAC protocol are 41.11% and 25.40% higher than those of the EE-CR protocol, respectively. Furthermore, when N = 150 and M = 2, they are 88.53% and 59.98% higher than those of the EE-CR protocol, respectively. This can be explained as follows. The non-uniform traffic load intensifies the energy consumption difference of different nodes and results in that the nodes with higher packet arrival rate exhausted their energy too early due to more data packet transmissions, thus reducing the network energy utilization and decreasing the network lifetime. However, the PO-CMAC protocol not only improves node energy efficiency through diversity gain obtained by node cooperation but also balances the local network energy consumption through the LETPO algorithm. Any nodes with low packet arrival rate can use more energy to cooperate the nodes with high packet arrival rate or other nodes. Therefore, the PO-CMAC protocol can reduce the difference of energy consumption caused by different packet arrival rates, make full use of the network energy and extend the network lifetime.M = 1 at non-uniform traffic load is also lower than that at uniform traffic load. According to Equations (22) and (23), when M = 1, the PO-CMAC protocol chooses only one cooperative node with the best link quality in cooperative node selection phase, and then the sender and the cooperative node transmit data packets in the optimal transmission powers calculated by the LETPO algorithm. However, at non-uniform traffic load, the residual energy of nodes differs greatly from each other. The selected cooperative node with the highest channel gain in cooperative node selection phase probably has consumed too much energy due to too many data packet transmissions. In this case, if the node still takes part in the cooperative transmission, it will speed up its energy consumption and will not balance the energy consumption of the local network, thus reducing the network lifetime. As seen from M = 1 drops to between 0.6 and 0.7.In addition, there are still some differences between the performance at uniform network traffic load and that at non-uniform network traffic load. Compared to the results at uniform network traffic load in M = 1, 2 and 3 outperforms other protocols because the performance advantage of the PO-CMAC protocol is derived not only from the diversity gain obtained by cooperative transmissions, but also from the effect of the LETPO algorithm on balancing the local network energy consumption. The LETPO algorithm balances the energy consumption of different senders and cooperative nodes in the process of each data packet transmission, which finally balances the energy consumption of the whole network. For M = 1, the LETPO algorithm only balances the residual energy of the sender and a single cooperative node. Therefore, from the global viewpoint, the effect of network energy balance cannot compensate the residual energy differences caused by non-uniform traffic transmissions. For M = 2, the LETPO algorithm can balance the residual energy of the sender and two cooperative nodes during each data packet transmission, thereby accelerating the effect of balancing the whole network energy consumption. More specifically, it is more likely to select the cooperative nodes with both good channel quality and sufficient residual energy for cooperation transmissions, which takes full advantage of both diversity gain and the cooperative nodes with more residual energy. Therefore, it resolves the unbalance problem of energy consumption due to non-uniform traffic transmissions and extends the network lifetime efficiently. As shown in M = 2, the energy utilization of the PO-CMAC protocol reaches 0.95. If M is further increased, there is no room for improving energy utilization, and instead, there will be more energy overhead for each data packet transmission. Therefore, when M = 3, although the energy utilization of the PO-CMAC protocol nearly reaches 1, its network lifetime and average number of data packets transmitted by each node become lower than those when M = 2.As shown in M can be changed according to the traffic load to achieve more satisfactory performance. Generally, the PO-CMAC protocol with M = 1 is the best in the presence of uniform traffic load, and the PO-CMAC protocol with M = 2 is suitable for non-uniform traffic load.From the above results, it can be seen that the PO-CMAC protocol outperforms the DT and EE-CR protocols in terms of the network lifetime, the average number of data packets transmitted by each node and energy utilization at both uniform and non-uniform traffic load. In the PO-CMAC protocol, the parameter M on the performance of the PO-CMAC protocol. Herein, the number of nodes N is 150. As shown in M = 1, and then decreases as M increases. Furthermore, at non-uniform network traffic load, they increase at first and then decrease. All their maximum values are obtained at M = 2. This is the tradeoff results between the less energy consumption as M decreases and the better effect on balancing network energy consumption as M increases. According to Equation (17), with a certain initial energy, the total number of data packet transmissions of the network is proportional to the network energy utilization and inversely proportional to the average energy consumption of each data packet transmission. For this reason, M.M = 1) can obtain cooperative diversity gain, and meanwhile the LETPO algorithm can balance the network energy consumption efficiently, which guarantees high energy utilization. At this time, if M is further increased, there is no room for improving energy utilization. At the non-uniform network traffic load, the network energy utilization increases significantly with the increase of M and then approach to 1 smoothly. This is because the different packet arrival rates lead to the differences in the energy consumption and residual energy of each node. For M = 1, the LETPO algorithm can only balance the residual energy of the sender and a single cooperative node. Furthermore, it is possible that both these two nodes have little residual energy. Therefore, it might fail to balance the local network energy consumption when the network traffic load is non-uniform, which leads to low network energy utilization. With the increase of M, it is more possible for the PO-CMAC protocol to choose cooperative nodes with both higher quality link and sufficient residual energy in cooperative node selection phase. Meanwhile, the LETPO algorithm can balance the residual energy of nodes within a larger local network, thus improving the network energy utilization. M is changed from 1 to 2, the improvement of the energy utilization is the largest.As shown in M at both uniform traffic load and non-uniform traffic load. There are two reasons for this phenomenon. Firstly, the increase of M leads to more HTS packet transmissions in cooperative node selection phase, which results in extra energy consumption overhead. Secondly, as M increases, it is more likely for the sender to choose the cooperative nodes with poor channel quality but sufficient residual energy to take part in cooperative transmission, which also increases the energy consumption of each data packet transmission.Furthermore, as shown in M is better for balancing the residual energy of the local network and improving network energy utilization. On the other hand, it increases the average energy consumption of each data packet transmission, thus reducing the energy efficiency. Therefore, according to Equation (17), in different cases, in order to transmit more data packets and extend network lifetime, we should select the optimal value of M to guarantee high energy utilization and avoid low energy efficiency.Considering both M is large, the average number of data packets transmitted by each node at non-uniform traffic load is a little higher than that at uniform traffic load. The reason is that, when the packet arrival rates of nodes in the network are different, their residual energy is different from each other and the nodes with more residual energy are more likely to take part in cooperative transmissions. Namely, more energy is used for cooperative transmissions and thus more cooperative gain is obtained, which leads to less energy consumption of each data packet transmission. The results in Moreover, in M. It can be seen that in any case, the throughput decreases with the increase of M because more HTS packet transmissions lead to more transmission delay, thus reducing the throughput to some extent. Therefore, comprehensively considering M anymore to avoid reducing the throughput.R = 2 bit/s/Hz, and the PO-CMAC protocol with R = 2 and 4 bit/s/Hz, respectively.From the above simulation results, the PO-CMAC protocol has a significant improvement on energy performance. However, in order to coordinate the contention and access of cooperative nodes, it introduces extra overhead, which unavoidably increases the transmission delay and reduces the network throughput. To resolve this decrease in throughput, for a high throughput requirement of the network, we can improve the data transmission rate between the sender and the recipient in the PO-CMAC protocol . The increase of the data transmission rate will increase energy consumption of each data packet transmission, thus leading to the reduction in network lifetime. However, compared to the DT protocol with a low data transmission rate, the PO-CMAC protocol still extends the network lifetime efficiently in this case. To illustrate this case, we give the results of the DT protocol with R = 2 bit/s/Hz, the throughput of the PO-CMAC protocol is 0.6 and lower than that of the DT protocol  due to extra control packet transmissions. However, when R = 4 bit/s/Hz, its throughput is 1.25, which is distinctly higher than that of the DT protocol.R = 2 bit/s/Hz, the PO-CMAC protocol has much higher average number of data packets transmitted by each node than the DT protocol. When R = 4 bit/s/Hz, the throughput of the PO-CMAC protocol increases, but its average energy consumption of each data packet transmission also increases, which leads to a decrease of the average number of data packets transmitted by each node. However, in this case, it is still higher than that of the DT protocol with R = 2 bit/s/Hz.M, thus extending the network lifetime efficiently. In general, the PO-CMAC protocol with M = 1 is the best for uniform traffic load, and the PO-CMAC protocol with M = 2 is suitable for non-uniform traffic load.In this paper, we proposed a network lifetime-extended power-optimized cooperative MAC protocol for WSNs. In the protocol, the LETPO algorithm is used to calculate optimal transmission powers of the sender and its cooperative nodes under certain constraint conditions based on linear programming and greedy algorithm, which obtains diversity gain and transfers part of energy consumption of one data packet transmission from the sender to the cooperative nodes, thus balancing the local network energy consumption and extending the network lifetime. Furthermore, a cooperative node selection scheme is used to efficiently choose cooperative nodes with high channel gain and avoid HTS transmission collisions by limiting the maximum number of cooperative nodes to transmit HTS packets and coordinating their access delay. Simulation results show that compared to the DT and EE-CR protocols, the proposed PO-CMAC protocol can efficiently improve the network lifetime, average number of data packets transmitted by each node and network energy utilization at both uniform and non-uniform network traffic load. Furthermore, it can adapt to different network traffic load by adjusting the parameter In the future work, we will improve our protocol by optimizing both throughput and energy consumption performance in a multiple data rate network. We also plan to embed a duty cycling mechanism into our protocol to further extend network lifetime and improve throughput.


In the QTL analysis of multi-parent populations, the inclusion of QTLs with various types of effects can lead to a better description of the phenotypic variation and increased power.For the type of QTL effect in QTL models for multi-parent populations (MPPs), various options exist to define them with respect to their origin. They can be modelled as referring to close parental lines or to further away ancestral founder lines. QTL models for MPPs can also be characterized by the homo- or heterogeneity of variance for polygenic effects. The most suitable model for the origin of the QTL effect and the homo- or heterogeneity of polygenic effects may be a function of the genetic distance distribution between the parents of MPPs. We investigated the statistical properties of various QTL detection models for MPPs taking into account the genetic distances between the parents of the MPP. We evaluated models with different assumptions about the QTL effect and the form of the residual term using cross validation. For the EU-NAM data, we showed that it can be useful to mix in the same model QTLs with different types of effects . The benefit of using cross-specific residual terms to handle the heterogeneity of variance was less obvious for this particular data set.The online version of this article (doi:10.1007/s00122-017-2923-3) contains supplementary material, which is available to authorized users. The papers by Rebaï and Goffinet  and MuraTo structure our reasoning, we present some assumptions on the genetic properties of specific MPPs and make plausible how these properties can affect the choice of a statistical model for QTL mapping. In MPPs, the use of more than two parents potentially increases the allelic diversity in the MPP as a whole and so, increases the chance of segregation at any particular genomic position Xu . MPPs alThe allelic diversity of the population may be related to the genetic distance among the parents of the population. When parents are genetically distant, the expected number of segregating alleles at a particular locus increases with the probability that these alleles are unique to a parental line. On the other hand, when the parents are genetically closer, one expects a reduced number of alleles segregating at a particular locus and that the alleles are shared throughout the population.It seems beneficial to QTL detection and estimation that the statistical model for the phenotypic variation in an MPP takes into account the genetic properties  of the MPP. The genetic diversity contained in the MPP can be translated to properties of the statistical model via the form of the QTL effects and the structure of the polygenic variation. This latter variation is the natural variation against which to test QTL effects and determines statistical quantities, such as power and false-positive rate.Models for the QTL effect If the number of segregating alleles at a particular QTL increases , the statistical model can capture that diversity by allowing more parameters for the QTL effect. In crossing schemes starting from pure lines, it implies estimating a maximum of one effect per parental line . On the other hand, when MPP genetic diversity is lower, parental relatedness can be used to infer a reduced number of ancestral segregating alleles that need to be estimated, thereby increasing model parsimony and probably also QTL detection power . The lower bound will be reached when only two alleles are segregating in the totality of the MPP . Within fixed QTL effect models, the reduction of QTL parameters to be estimated to improve QTL detection power has been a central objective  model that allows various types of QTL effects at individual loci, where loci can differ in the most suitable type of QTL effect. We also relax the assumption of constant variance for the residual genetic term using a cross-specific residual term (CSRT) model. We performed QTL detection in three subsets of the EU-NAM Dent population characterized by different degrees of genetic relatedness between the parents. The different models were evaluated using cross validation (CV).https://github.com/vincentgarin/MPP_EUNAM. This material makes it possible to reproduce all steps of the analysis, tables, and figures of the article and of the supplemental material. To test the various models, we used the maize EU-NAM Dent panel  lines originating from ten crosses between the central line F353 and ten peripheral parents. This population was developed to represent the maize diversity in Northern Europe and also included the central parent of the US-NAM population, and it has been described in detail in Bauer et al.  and Lehed et al.  availablIn the absence of pedigree information, genetic relatedness between parental lines can be estimated by similarity of molecular marker scores. We used the genetic similarity coefficient defined by Nei and Li  Supplem involvinTo assure that all subsets were of equal size, we randomly selected 361 lines from the crosses of the short and heterogeneous subsets to make their size equal to that of the long subset. For QTL analyses, we removed markers that were not segregating in any of the MPPs and that showed a minor allele frequency <0.01 or missing values >10% across the entire MPP. When multiple SNPs mapped at a single chromosome position, we selected the most polymorphic locus. After pre-processing, 5737, 5934, and 6212 SNPs were used for the short, heterogeneous, and long subsets, respectively, 3348 of which were common between the MPPs .http://www.genetics.org/content/198/1/3/suppl/DC1 and calculated the adjusted means and variance components and heritability following their procedure  information. In this model, The Wald test was used to test the global null hypothesis of all allele QTL effects equal to 0  model The parental, ancestral, and bi-allelic models have already been used in other papers  algorithm to compute approximate solutions. To calculate such an approximation, we estimated first the residual term The combination of the four QTL effects  with the two residual terms (HRT and CSRT) gives eight possible models for QTL detection. For the parental, ancestral, and bi-allelic HRT models, the significance thresholds were determined by 1000 genome-wide permutations taking the For the QTL detection methods based on QTL models with a single effect, a first run of simple interval mapping (SIM) was followed by two runs of composite interval mapping (CIM) by adding markers as cofactors Zeng , 1994. Wn et al.  to compuFor the CSRT model, we used the likelihood nd Snell : \documen et al. , we estiz et al. : \documeTo build the multi-locus MQE models, we used a forward selection approach, where at each step, a new QTL was added that was allowed to have either a parental, an ancestral, or a bi-allelic effect. To identify a new QTL, we computed three genome-wide profiles using the same type of QTL effect for the tested position . Then, we selected in each profile the most significant position based on the https://github.com/vincentgarin/MPP_EUNAM/software/mppR_1.0.tar.gz).HRT models were fitted by least-squares (lm function in R), and CSRT by restricted maximum likelihood (REML) using the asreml-R package  was often reduced for the more parsimonious models especially for the bi-allelic model. This is, for example, the case in the short subset for both traits.SM coefficients between the parents of the crosses , the bi-allelic model gave the largest pVS. From a general point of view, more parsimonious models, especially the bi-allelic model, gave results with a reduced bias (difference between pTS and pVS). The improvement of QTL detection in MPP using more parsimonious models based on shared polymorphism has been an important topic in MPP QTL analysis. While simulation studies have supported this idea . We expected the difference between the HRT and CSRT models to be largest in the heterogeneous subset. The CV results Fig. , we coulThe absence of difference between the HRT and CSRT models for the CV results can be caused by the use of the f-CSRT approximation. Alternatively, the sample sizes for the crosses may have been too small to allow HRT and CSRT to be tested as being different. The f-CSRT seems to have less QTL detection power than the exact solution, because the correction for heterogeneous residual term only reflects the general level of heterogeneity. In the exact solution, however, the residual genetic variance is calculated at each genomic position conditional on the estimated QTL effects. In that case, the Wald statistics can truly benefit from the within cross-variance reductions following from the included QTL effects. Therefore, given the full subsets results Table , we stilThe result presented in this study illustrates the potential of using different types of QTL effect models and also represents different ways to model genetic relatedness in an MPP. We demonstrated that it can be interesting to integrate multiple assumptions about the origin of the QTLs in the same multi-locus QTL model. The question of genetic relatedness definition is one of the most important ones in genetics Fisher . We knowA first way to improve the estimation of genetic relatedness modelling is using SNP markers in place of pedigree information. According to Powell et al. , this reA second, more challenging option is to use methods for IBD computation using ancestral lines higher up in the MPP pedigree as a reference like in Zheng et al. . FinallyFrom a general point of view, we would like to emphasize the main motivation for our QTL mapping approach: try to make as explicit as possible the connection between the biological assumptions and the properties of the statistical model that is used. A constant dialogue between these two dimensions is certainly a promising way to make progress in both the understanding of biological processes occurring in MPPs and their statistical modelling. All authors developed the theoretical framework and the hypotheses, edited the manuscript, and read and approved the final version. VG wrote the software used for data analysis, analysed data, and wrote the manuscript. VW reviewed the software. FvE coordinated the research.Supplementary material 1 (PDF 1351 kb)Below is the link to the electronic supplementary material.


In this paper, the washability of wearable textronic (textile-electronic) devices has been studied. Two different approaches aiming at designing, producing, and testing robust washable and reliable smart textile systems are presented. The common point of the two approaches is the use of flexible conductive PCB in order to interface the miniaturized rigid  electronic devices to conductive threads and tracks within the textile flexible fabric and to connect them to antenna, textile electrodes, sensors, actuators, etc. The first approach consists in the use of TPU films (thermoplastic polyurethane) that are deposited by the press under controlled temperature and pressure parameters in order to protect the conductive thread and electrical contacts. The washability of conductive threads and contact resistances between flexible PCB and conductive threads are tested. The second approach is focused on the protection of the whole system—composed of a rigid electronic device, flexible PCB, and textile substrate—by a barrier made of latex. Three types of prototypes were realized and washed. Their reliabilities are studied. The “Wearable Technology industry”, as an important sector of the overall “Smart Textile industry”, may be perceived as a newly-defined industrial segment derived from the convergence of many incumbent sectors such as flexible and miniaturized electronics, technical textiles, membranes and barrier insulation, etc. This new industrial sector has numerous fields of applications capturing and emphasizing several key trends such as sports and leisure, healthcare, military and security apparel, fashion, consumer electronics, etc.According to the 2016 ID Tech Exhibition Report, the Compound Annual Growth Rate (CAGR) will be 37% for smart watches, 34% for medical devices, 146% for smart sports apparel, and 585% for AR&VR  eyewear devices from 2015 to 2018. In most wearable technology industries, textronic (textile-electronic) structures play an important role as a substrate for sensors and actuators, user-end devices, and communication platforms.With the development of material science and electronic engineering, wearable textronic devices are tremendously miniaturized and their form moves from bulky and rigid to thin and flexible. More and more devices become invisible and ubiquitous for end-users. The user-machine interfaces become more friendly ,2,3,4 anHowever, the washability issue is always an obstacle in terms of application, reducing the reliability of textronic devices and making them not robust enough, and therefore not ready for the market. Many of experimental wearable textronic devices cannot be used in real life because of the washability problem. Due to the capillary effect, even the hydrophobic textile substrate can still absorb the water in the textile bulk and make electronic devices fail. Besides, the mechanical stresses provoked by the washing process may destroy the electrical contacts between the conductive thread and the electronic wearable device. As a result, the electric impedance becomes uncontrollable after several cycles of the washing process and the wearable device becomes unstable and, in some cases, stops functioning.In this article, two different approaches aiming at designing, producing, and testing robust washable and reliable textronic systems are presented. The common point of both approaches is the novel method consisting in the use of flexible conductive PCB (printed circuit board), in order to interface the miniaturized rigid  electronic devices to conductive threads and tracks within the textile flexible fabric and to connect them to antenna, textile electrodes, sensors, etc.For conductive threads, the polymer based barrier, TPU film (thermoplastic polyurethane), is deposited by the press under controlled temperature and pressure parameters in order to protect the electrical contacts against water and mechanical stresses. The washability of protected conductive thread and the contact resistance between the flexible PCB and conductive thread are studied.As for the whole textronic device, the latex-based barrier is involved to protect the whole system composed of a rigid electronic device, flexible PCB, and textile substrate. Several prototypes have been designed, realized, and tested to verify the washability and reliability of the protected systems.Three kinds of prototypes , LED array, and ECG (ElectroCardioGraphy) textronic devices) have been designed, realized, and tested to verify the washability and reliability of the protected systems. Up to 50 standardized washing cycles have been performed to verify the quality and robustness of the barrier.® LF9120R , is employed for electrode parts to make the interconnection with the conducting yarn.Three types of conductive thread have been involved in this study. The silver-plated polyamide thread  is sewn into fabrics as transmission wire. Its linear resistance is less than 100 Ω/m, which is acceptable for sensing the ECG signal in low energy consumption system. This two-ply structure allows the thread to be adaptable in the sewing machine or embroidery machine. In order to make electronic components compatible with the textile structure, the flexible PCB, PyraluxThe interconnection technology between polyamide conductive thread and flexible PCB is realized by using a professional sewing machine . The conductive thread is used as needle and as spool thread and results in low resistance. The distance between each stitch is 2.5 mm. The electric resistance model of interconnect is shown in 2), The electric impedance between the conductive thread and PCB is composed of three parts: thread resistance e theory , the facpressure .Two other threads with low resistance (less than 5 Ω/m), the nickel-plated copper wire  and silver-plated silver copper tinsel , are used to realize LED application, because the LED application needs a higher current than ECG sensing. These two conductive threads are woven directly into the fabric because of their lack of flexible mechanical properties.The thin film of TPU is used to protect the conductive thread and the connection with the flexible PCB. It is welded to textiles by the heat press transfer molding machine . The thickness of TPU was 30 µm. The pressure is 3 bars. The temperature was 180 °C and the pressure time was 20 s. Both sides of conductive thread were protected by TPU film. The electric resistance was measured by the four-point method.Two kinds of silicon were employed to encapsulation the whole textronic devices: Silicon 1  and silicon 2 . The process of encapsulation is shown in The washing test widely used as a reliability test for electronic textiles. For this purpose, ISO 6330 standard was applied to the washability test . It was The LEDs were integrated on textile structures by two different types of connection, soldering with tin and gluing with silver adhesive  and two types of silicone. Two kinds of conductive thread were employed in the textile structure. The silver-plated polyamide thread was sewn in a zig-zag pattern in order to link the flexible PCB with textile structures a and theThe final samples are shown in A large-scale sample with 16 × 23 LED array was investigated on a woven structure with silver-plated silver copper tinsel. The LEDs were connected with the warp conductive line and the power lines were connected with the four weft conductive lines. The tin solder paste  was used to glue the LEDs and warp conductive lines and the textile with LEDs array was heated under 230 °C for about 20 s for soldering . After tECG wearable textronic devices are designed to realize smart clothing for monitoring issues. The ECG controller device is made on a rigid substrate. It is connected with the flexible and washable textiles based knitted electrodes, used as skin sensors, realized by TTRI (Taiwan Textile Research Institute) by silver-plated polyamide thread, zig-zag sewing, and flexible PCB. This device is able to monitor heart rate and ECG. It also contains an accelerometer in order to detect the movements. It is equipped with a Bluetooth communication module in order to send data to a smart watch, smart phone, or other communication devices. Two types of samples were designed: battery aside a and batTwenty samples were tested after every cycle of washing tests. The absolute values of linear resistance are shown in The effective numbers of samples are shown in From the previous studies , it is pIt should be mentioned that, even when the high temperature 180 °C was applied during the heat press transfer process, the plated silver on the filament surface was not damaged because of the short operation time.The effective numbers of samples of the nickel-plated copper wire samples are shown in The images for the nickel-plated copper wire samples during wash testing are shown in Comparing to the silver polyamide thread and nickel-plated copper wire, the silver-plated silver copper tinsel has the best resistance performance. Even when there is not any protection, the resistance value is still almost the same as the initial value. However, it is also less flexible than the other two, it is not suitable for the knitted structure, but it can be used in the woven structure for high current applications.The absolute contact resistance values are shown in Contact resistance with finite values is shown in In order to asses in depth the flexible protective barriers three categories of textile samples have been set up and tested: samples with three LEDs, textiles with LED array and textiles with ECG electronic devices and battery. All the samples have been protected using latex-based barrier (silicon 1 and 2) for smart textile systems in order to make them washable.Compared with Samples 5, 6, and 10, glued LEDs with conductive lines appear to have the better performance than the soldering. This is because the conductive thread is nickel-plated copper wire and the solder joint is brittle for nickel.Regardless of the woven textile with conductive line or flexible PCB, none of them had a broken LED. This suggests that the flexible PCB combined with the sewing thread is more stable, robust, and could be used to embed the flexible circuit into the textile.The result for the textiles with an ECG monitoring device and battery is shown in In this study, the main problem and the most important barrier to market readiness for smart and electronic connected clothing related to its washability has been investigated. The lack of reliability is often due to bad contacts or to an important increase of contact and thread resistance. For the conductive thread, the shedding of conducting material occurs because of the mechanical movement during the washing process. Similarly, an important increase in contact resistance between the conductive thread and the flexible PCB is provoked by the shedding of conductive material on conductive threads.With thermoplastic polymer film, the conductive threads and their contacts with the PCB can be protected from the mechanical stresses during the washing process. After 50 washing cycles, most of samples were still in operation with an acceptable resistance value. Their conductivity declination became less intensive compared to the samples without film protection.The flexible PCB with the thermoplastic protection is a promising approach to electronics-in-textile research, which provides a reliable method to integrate electronic components into textile structures.As for the latex-based barrier in smart textile application, the encapsulation method for LEDs and ECG monitoring devices were found to perform well, which is due to a reliable method to embed the LEDs, flexible PCB, and hardware circuits into woven and knitted structures. The traditional button battery was still not suitable for the washing test. A new kind of textile battery which can endure the washing test should be investigated for a better integration. For e-textile applications in daily life, two approaches (the thermoplastic polymer for the conductive thread and flexible PCB and the latex-based barrier for the rigid PCB or electronic components) can also be combined together for better washability.


This paper describes a flexible camera calibration method using refined vanishing points without prior information. Vanishing points are estimated from human-made features like parallel lines and repeated patterns. With the vanishing points extracted from the three mutually orthogonal directions, the interior and exterior orientation parameters can be further calculated using collinearity condition equations. A vanishing point refinement process is proposed to reduce the uncertainty caused by vanishing point localization errors. The fine-tuning algorithm is based on the divergence of grouped feature points projected onto the reference plane, minimizing the standard deviation of each of the grouped collinear points with an O(1) computational complexity. This paper also presents an automated vanishing point estimation approach based on the cascade Hough transform. The experiment results indicate that the vanishing point refinement process can significantly improve camera calibration parameters and the root mean square error (RMSE) of the constructed 3D model can be reduced by about 30%. Camera calibration is an important step in both photogrammetry and computer vision in order to extract metric information from two-dimensional 2D) images. Calibration includes interior orientation parameters (IOPs), e.g., focal length, principal point, lens distortion, skew, and aspect ratio, as well as exterior orientation parameters (EOPs), e.g., camera orientation and position. Various camera calibration methods have been developed using three-dimensional 3D) reference objects, 2D planes, or even lines . TraditiD images.D referenUsing vanishing points is an efficient process to obtain the camera pose directly in the scene by extracting parallel and perpendicular lines . The geoThis study developed an effective and flexible camera calibration method, which is particularly useful for on-site calibration, based on vanishing point extraction and refinement. The geometric relations between vanishing points and the camera system are defined according to collinearity condition equations. The developed algorithms require no prior camera parameters, nor internal or external parameters for onsite calibration. The proposed vanishing point refinement algorithm can reduce the uncertainty of vanishing point localization errors.Projecting detected line segments in the image plane onto the Gaussian sphere is one of the classic approaches ,9 for deAn iterated Hough transform method was proposed to help find vanishing points and lines ,18. The Instead of using double transformation, another approach was to work directly on the first Hough polar plane . By searBesides using transformation-based parameter estimation, the grouping together of features that satisfy a geometric relationship can also be used to detect and estimate vanishing points and lines. For example, McLean and Kotturi  integratThere are three common types of geometric grouping , which aFor real-time vanishing point detection, the local dominant orientation signature (LDOS) descriptor was introduced  to extraThe proposed vanishing point estimation method consists of three parts. They are: image pre-processing, feature detection, and vanishing point localization.The objective of pre-processing is to extract enough line segments for initial vanishing point detection. It is also useful for line-based radial distortion correction . FirstlyAn improved cascade Hough transformation approach is proposed to extract line segments from the edge pixels and to classify them to the probable vanishing point candidates. The two steps of Hough transform are illustrated in The first Hough transform is commonly used to detect line segments in the image by keeping the dominant peaks in the normal-distance and normal-angle  on the image. The local maximum peaks in the first Hough space appear to be collinear because that specific pixel (possible vanishing point) in the image contributes to all of the Two conditions for obtaining stable vanishing points are considered. The first is the number of line segments in each direction, i.e., adjusting line group number threshold to prevent most detected lines from pointing to a certain direction. The other is that the representative vanishing points should be stable under different Afterward, similarity rectification is applied to identify different line segments. Then, a least-squares method is employed to trace line groups interactively by adjusting the threshold of histogram peaks until the number of line groups is satisfied. Finally, vanishing points are calculated according to the grouped line segments and optimized with iterative calculation.If necessary, a third Hough transform can be applied to the peaks of the second one to detect collinear vanishing points. These kinds of features can be used to construct vanishing lines.f is the principal distance; M, consisting of three rotation angles: Vanishing point-based calibration is considered as one of the most practical calibration methods. The collinearity condition equations utilize the geometric position of a perspective center, an image point, and its corresponding object point as follows, (swing) :(3)M=cosThe camera and object coordinate systems are illustrated with geometric relations between vanishing points, the image plane, and the center of the camera in Under the assumption of well-calibrated lens distortion of the perspective system, ectively . TherefoEquation  can be rSimilarly, assuming From Equation  to EquatA pair of vanishing points can be used to define a vector (vanishing line), and thus, three vectors can be found from the combination of three vanishing points:m) of these three vanishing lines can be calculated asThe slope ω and ϕ aquations  and 13)ω and ϕ aEach vanishing point and the principle point can also be used to define a vector, therefore, three vectors can be found from three vanishing points:The orthocenter of the triangle formed from the three vanishing points of the three mutually orthogonal directions identifies the principal point through the inner product of the segments of the triangle and its heights. For instance, the inner product of f, can be computed afterwards as the square root of the product of the distances from the principal point to any of the triangle’s vertices and the opposite side:The focal length, According to the derivation above, the standard procedure of the three vanishing point-based camera calibration starts from the rotation angle estimation (Equations –11)), f), f11)),If and only if the image is un-cropped and captured by a pinhole camera, the lens distortion calibration can also be achieved using vanishing points. The most commonly encountered lens distortion is radial distortion, including barrel and pincushion distortion. The standard model is formulated as:To find the distortion parameters Include the symmetric radial distortion parameters Equation , and it When The vanishing points are imaginary points an infinite distance away from the projection center. Therefore, no direct measurement can be achieved to locate the exact locations of the vanishing points. It is difficult to extract vanishing points without random or systematic errors, especially in the cases of images with weak perspective geometry . Consequently, increasing the reliability of the vanishing points’ positions is an important task for vanishing point-based camera calibration. The proposed vanishing point refinement process described here minimizes both random and systematic errors based on the constraints derived from common geometric properties of man-made structures. For instance, feature points pertaining to the same (flat) roof, building base, or floor etc. should have the same height or planar coordinates, however, biases may occur because of computational errors. The systematic error from the perspective of projection consistency thus provides an indication for fine-tuning the best positions of the vanishing points.O) is normally formulated as,To estimate the perspective projection consistency using vanishing points, it is necessary to find sufficient feature points and their corresponding base points on the reference plane. The most common features of artificial structures are corner points at the intersected edges, planes, or boundaries. Thus, detection of feature points from the extracted long edges is more reliable than from the raw image. Short segments and small closed polygons from detected segments can be ignored because most of them are windows, patterns, or minor structures. The candidate feature points are then detected using Harris corner detector . Some ofThe reference origin is defined as the intersection point from the bottom edges along the X and Y axes of the main structure. Candidate feature points below the reference plane or collinear to others can also be removed. However, the proposed procedure requires user interaction to make the final selection. The next task is to estimate base points. A base point is the vertical projection of a feature point onto the reference plane. It is a necessary element to estimate the consistency of perspective projection constructed from vanishing points. However, most base points are hidden in the image because of self-occlusion; only a few of them may have the potential to be extracted directly from the raw image. For estimating the corresponding base points, the proposed process is based on the characteristics of vanishing point constraints, and is an automatic and robust solution. The estimation process can be generalized into three steps. First, all feature points are projected onto the Y–Z plane according to The error of each set of grouped projection point during the base points estimation process can be minimized by fine-tuning the positions of vanishing points. The vanishing point localization errors will cause the displacement during the projection process; therefore, the calibration results normally include systematic errors. Feature points with the same height and Y coordinate should be perfectly projected onto the same projection point as shown in The divergences in the first and second projection steps provide useful information for vanishing point refinement. The more precisely the vanishing point positions are estimated, the fewer the divergences that may occur during the projection process.k with a mean value of N is the number of moving pixels along horizontal and vertical axes. The proposed moving pixel pyramid is a coarse-to-fine approach, fine-tuning the vanishing points from large pixel spans to the sub-pixel level with To decide the fine-tuning values and orders, a moving pixel pyramid and half-and-half adjustment strategy was developed in the proposed algorithm. The objective is to minimize the standard deviation of each of the clustered projection points,The fine-tuning process is based on statistic estimations, and it is difficult to determine which vanishing point localization displacement affects the overall errors most. In case of weak perspective geometry, one of the vanishing points may contain larger error than the others, but the fine-tuning process may reduce the error caused by the vanishing point which should not be adjusted. The proposed half-and-half adjustment strategy can reduce the error caused by a large vanishing point displacement. This strategy first adjusts the half-distance of each vanishing point from the original position to the optimized position. Which adjustment provides the greatest contribution should be determined and then that specific vanishing point should be fully adjusted towards the optimized position.Geometrically, modifying Equation .A computer-simulated model test case a was useY axis, marked as green triangles  or global (green triangles) divergences. Reducing the standard deviation for red triangle groups will decrease the local error of each point group. Minimizing the standard deviation for red triangles, however, may increase the global error. For instance, two red triangle groups at different heights should be in the same green triangle group. Fine-tuning the vanishing points may lead to divergences when projecting these two red triangle groups on the To validate the robustness of the proposed refinement process, IOPs and EOPs were calculated with several additional offset errors manually put on to the initial vanishing point Vx . The lisa to g) of the targeted building. This case assumed the back side of the buildings have the same a, b and c).Z) direction is far away from the center  applications in the human-made environment. Future improvements will focus on the integration into the SLAM system as a real-time camera pose tracking attribute. The developed calibration strategy also has a great potential for implementation with panoramic and omnidirectional cameras.