Lane changing is considered as one of the most dangerous driving behaviors because drivers have to deal with the traffic conflicts on both the current and target lanes. This study aimed to propose a method of predicting the driving risks during the lane-changing process using drivers’ physiology measurement data and vehicle dynamic data. All the data used in the proposed model were obtained by portable sensors with the capability of recording data in the actual driving process. A hidden Markov model (HMM) was proposed to link driving risk with drivers’ physiology information and vehicle dynamic data. The two-factor indicators were established to evaluate the performances of eye movement, heart rate variability, and vehicle dynamic parameters on driving risk. The standard deviation of normal to normal R–R intervals of the heart rate (SDNN), fixation duration, saccade range, and average speed were then selected as the input of the HMM. The HMM was trained and tested using field-observed data collected in Xi’an City. The proposed model using the data from the physiology measurement sensor can identify dangerous driving state from normal driving state and predict the transition probability between these two states. The results match the perceptions of the tested drivers with an accuracy rate of 90.67%. The proposed model can be used to develop proactive crash prevention strategies. During the lane-changing process, drivers have to deal with complex traffic conflicts between current and target lanes, leading to increased driving workload . If drivThe influencing factors on driving tasks can be divided into the driver’s status and external environment factors . Both phThe data processing methods fall into three categories. The first category treats the normalized parameters of a specific driver as an eigenvector. The clustering methods  can thenExisting literature shows that the physiological indicators can be utilized to describe the driver’s status in a specific driving environment using various sensors. The driving risk is a comprehensive result of the driver’s physiological status, vehicle dynamic data, and external environment. Although single or several influence factors were considered to represent the driver’s status, little research focuses on the prediction on driving risk with the considerations of all the factors. These gaps are summarized as follows: ① the existing prediction models mainly based on one type of performance measurement factor, such as the Time-To-Collision (TTC) extracted from the surveillance video or the driver’s physiological indicators (such as the driving fatigue degree). A driving risk prediction model is required with the consideration of both vehicle dynamic and driver’s physiological information; ② the prediction models should have the capability of extracting the potential connections between the driving risk and the performance indicators of the driver’s physiology and vehicle dynamic status. The objective of this study was to develop a compound model for the prediction of the driving risk during one of the most dangerous driving stages, the lane-changing process, with the consideration of both the driver’s physiological information and vehicle dynamic information. For the purpose of collecting comprehensive data for driving risk prediction during the lane-changing process, a field experiment was designed to collect the driver’s eye movement information, an electrocardiogram, and vehicle dynamic information. With the description using the two-factor indicator for processed data, a hidden Markov Model (HMM) was used to link the experimental performance indicators with the driving risk during the lane-changing process.The major contributions of this study are twofold: (1) the driver’s physiological information is added along with the vehicle dynamic information in the driving risk prediction model during the lane-changing process. All the major influence factors are considered in the driving risk prediction model; (2) the potential connections between the major influence factors and driving risk are linked using the hidden Markov model, which can predict the driving risk using the state transmit probabilities.The remainder of this paper is organized as follows: The field experiment method was selected to collect the driver’s physiology data and vehicle dynamic data for the purpose of estimating the driving risk during the lane-changing process. Data used in this study were collected in Xi’an City. As shown in Both the diver’s physiological data and vehicle dynamic data are critical to our research. In order to improve the accuracy of the data, we chose some commercial sensors, which will be more reliable in the experiment process. The BIOPAC MP 160 physiological data acquisition system, SMI ETG 2w eye-tracking glasses, AOZER Hi-Drive 10 vehicle dynamic data acquisition system, and the OBD (on-board diagnostics) were selected in the experiment. The equipment installations on the test vehicle are illustrated in The BIOPAC MP 160 physiological data acquisition system is a flexible, proven modular data acquisition and analysis system for life science research. This system has 16 channels with the capabilities of recording reproducible ECG, EEG, EMG, blood pressure, and more data. Each kind of data can be measured by an independent detachable module. The ECG data can represent both basic physiological characteristics and driver reactions to real-time incidents with adequate robustness. Thus, we selected the ECG data as the measurement indicator for the purpose of minimizing the influence on the drivers. The MP 160 system uses micro-electrodes to record the ECG data with the sampling rate at a speed of 400 kHz (aggregated). To measure the heart’s electrical activity accurately, proper electrode placement is crucial. Three electrodes are required to be pasted on the chest by the MP 160 system. The placement of the electrodes is shown in The eye movement information was collected using the SMI Eye-Tracking Glasses 2 Wireless (SMI ETG 2w) system. The SMI ETG 2w system can record a driver’s natural gaze behavior in real time with adequate robustness, mobility, and ease of use. As shown in The vehicle dynamic information, including the real-time speed, position, and gap between the leading vehicles, was collected by the Aozer Hi-Drive 10 system b. The HiEven with the support of these commercial sensors, there were still some failures during the experiment. We had to park the vehicle to the roadside and re-calibrated the experimental equipment, which had a strong influence on the drivers. Meanwhile, one of the most important points of the field experiment is that we should minimize the influences of the equipment on the drivers. Although a training section and many measures (such as wearing the contact lenses) were carried out, there were still certain influences on the drivers. The indicators of the first ten to fifteen minutes usually had significant volatility. In this way, these data were removed to ensure the data utilized in this research can represent the drivers’ characteristics. In our experiments, if the drivers wore the sweater, which may discharge static electricity, the ECG data would have lots of noise. These data were carefully processed manually and used as a reference. Loose clothes are recommended in the experiment that collects ECG data. Meantime, portable devices with the capabilities of collecting heart rate, such as heart rate belt, are potential devices for this experiment, which can be utilized along with the ECG recording devices.According to the characteristics of the overtaking process, it can be divided into four stages: car following, perception, intention, and execution . During The lane changing action usually requires higher driving skills than other driving tasks, such as car following. The drivers should deal with the interactions between the front and rear vehicles in both the current lane and target lane during the lane-changing process. The drivers should maintain the vehicle speed while judging the real-time gaps between the leading vehicles and rear vehicles. Because the driver focuses on the lane-changing task, they may miss other turbulences, which will raise the driving risk. In this way, the driving risk in heavy-flow conditions will be higher. Based on the characteristics of the lane-changing process and the incentives of driving risk, the levels of service and actions of the lane-changing process are the critical influence factors on the driving risk.I1 can be calculated using Equation (1). If I1 is greater than the threshold It, the samples affected by the selected influence factor are significantly different, and vice versa.iS and jS are the variances of sample datasets i and j; there are m and n elements in sample datasets i and j, and the samples can be expressed as imx and jnx.Two-factor indicators were proposed to evaluate the performances of selected indicators on driving risk. The two-factor indicators should be capable of evaluating the probability of the events and the differences between the selected two factors. In this way, the ratio between the variances of two sets of sample data can indicate the degree of deviation of the samples. The indicator I2 can represent the probability caused by sampling error if a specific measure is applied. This indicator can reflect the overall influence of the sampling error. If the value of I2 is less than the pre-set significance level α, we can reject the hypothesis, i.e., there are significant differences between the sets of sample data. Based on the significance test method, there will be some influence if I2 is less than 0.05, whereas the influence is significant when I2 is less than 0.01, and the influence is extremely significant when I2 is less than 0.001.The indicator As we summarized in the literature review section, the driving risk is mainly affected by the driver’s physiological status and external roadway environment. The driver’s workload will be significantly different under different levels of service (LOS), which makes it the most critical influence factor among the external roadway environment factors . When thThe Highway Capacity Manual (HCM) divides the LOS into six levels: LOS A as the free flow, LOS B as the reasonably free flow, LOS C as the stable flow, LOS D as the approaching unstable flow, LOS E as the unstable flow, and LOS F as the forced or breakdown flow. To reduce the complexity of analysis, we combined the LOS with a similar pattern of the indicators into free flow , stable flow , unstable flow (LOS III or LOS E in the HCM), and forced or breakdown flow (LOS IV or LOS F in the HCM). Literature shows that the safe limit for monotonous highway driving is 80 min  and the I2 is less than the pre-set significance level, the variance obtained from the ANOVA method can be utilized to calculate the indicator I1. The evaluation of indicator I1 should be analyzed based on the characteristics of the selected indicator. To evaluate the influence of selected factors on the driving risk, one-way ANOVA  for the influence factors of each overtaking stage with related driving duration interval and LOS was processed. When the indicator The fixation duration, saccade range, and variability of pupil diameter, which were obtained from the SMI ETG 2w eye-tracking glasses, were selected as the influence factors on driving risk. The fixation duration is the time of maintaining visual gaze on a single location. In medicine, the fixation duration represents the difficulty of capturing and processing visual information by the driver, which indirectly reflects driver fatigue. The saccade is a quick, simultaneous movement of both eyes between two or more phases of fixation in the same direction. In the driving process, the saccade range represents a driver’s visual angle. The driver is considered as being nervous when he/she has a large saccade range, which will lead to poor recognition of the road environment . The diaThe measurements of the selected parameters in each lane-changing stage are summarized in The distributions of these two indicators under each LOS and driving duration are shown in Heart rate variability (HRV) is the fluctuation in the time interval between adjacent heartbeats . The heaThe ECG signal was extracted from AcqKnowledge software. As shown in The distributions of SDNN under each LOS and driving duration are shown in The average vehicle speed and acceleration, which were obtained from the AOZER Hi-Drive 10 vehicle dynamic recording system, were selected as the influence factors on driving risk in the field of vehicle dynamics. The variations of the vehicle speed and acceleration during one specific lane-changing process are shown in The hidden Markov model (HMM) assumes that the modeling system is a Markov process. However, the states of the Markov process cannot be observed, i.e., they represent hidden states. Some variables that are related to the hidden states can be observed with no Markov characteristics. In this way, we can estimate the Markov transition process of the HMM using the observed data. A, whose structure is shown in Π can be either 1 (i = 1) or 0 (i ≠ 1).B is the probability density function of the outputs.The structure of the HMM can be represented by Equation (2). The driving process is a continuous time-dependent process, whose state can be transferred to the next state or can stay with the current state. In this way, the left to right model was selected to model the state transition probability matrix The observed eye movement, ECG, and vehicle dynamic parameters were selected as the inputs to predict the driving risk during the lane-changing process. Based on the influence of these parameters on the driving risk, the fixation duration, saccade range, SDNN, and average speed were selected as the observed inputs. The prediction result can be represented as the driving state being safe or dangerous. N was two (safe or dangerous); the number of observable parameters M was 4; the initial state transition probability matrix A0 could be obtained by the averaging method shown in Equation (4); the observed probability matrix kB could be calculated from the kth observation parameters; the initial probability distribution of the lth driver lΠ could be obtained by the averaging method in Equation (5).The initial settings of the HMM are listed as follows: the number of hidden states Step 1.jmC. The vector of the mean values jmμ and covariance matrix jmU should be the mean value and covariance of the entire sample (M = 1) or part of the sample (M ≥ 2).Convert the state transition probability matrix into a random matrix using the mixed weight vector Step 2.αt(i) and backward probability iβ(j) using the current sample set. Calculate the forward frequency Step 3.iε and estimate the value of Calculate the probability of auxiliary variable Step 4.Calculate the likelihood probability Step 5. If the The HMM-based driving risk prediction can be accomplished using the following procedure:The proposed model was tested using MATLAB software. In total, 500 sets of data samples of the physiological parameters during the lane-changing process were collected. The drivers were asked to review the video of their driving events. Then, they were asked to evaluate the degree of safety of their actions. The HMM was trained with 425 samples. The rest samples were selected to evaluate the performance of the prediction.The training results are shown in Equation (6). The results indicate that 85.84% of driving actions during the lane-changing process were deemed safe and the rest of them were deemed dangerous. The probability of keeping a safe driving state was 75.29%, and the probability of continuing a dangerous state was 74.63%. The probability of transferring a safe state to a dangerous state was 24.71%, and the opposite probability was 25.37%. The emission probabilities of the safe states were 13.69% of the fixation duration, 25.36% of the saccade range, 43.59% of the SDNN, and 17.36% of the average speed. Meantime, the emission probabilities of the dangerous states were 37.26% of the fixation duration, 21.08% of the saccade range, 19.03% of the SDNN, and 22.63% of the average speed.We found that 68 out of 75 predictions successfully matched the evaluations of the drivers. In this way, the accuracy rate of the prediction was 90.67%. When we reviewed the four events with divergences between our prediction and the driver’s perception, we found that it was difficult to distinguish the driving risk of these events. Most driving actions fell into an intermediate range between safe driving and aggressive driving. In this way, the proposed model can predict the driving risk with high accuracy.In order to evaluate the driving risk of the overtaking process, we used the same training set but changed the model input to the subsets of the original test dataset, which corresponded to the four stages of the lane-changing process. The prediction results using the four subsets as the input are shown in Based on the evaluations of influence factors, the fixation duration, saccade range, the SDNN of R–R interval, and the average speed were selected as the input of the HMM. However, some other performance indicators also had a significant influence on certain lane-changing stages. Some studies indicated that the frequency-domain HRV indicators have precise descriptions on the driver’s physiological status . HoweverThe results of the experiments indicate that the SDNN is suitable for estimating safety status, and the fixation duration is suitable for estimating danger status. The experimental results also indicate that the car following stage is the safest stage, and the intention stage and execution stage are relatively dangerous. In this way, the more general Bayesian network models may also be suitable to estimate the weight of each influence factor in the common car following stage or a specific lane-changing stage, which may improve the accuracy and efficiency of the proposed model.Existing methods for estimating the driving risk are twofold: estimating the driving risk by evaluating the drivers’ physiological indicators or TTC. There are many physiological indicators to describe the driver’s state. However, it is difficult to distinguish which parameter has the best performance to estimate the driving risk. Some of these indicators may even have contradictory values in describing the same event. Thus, the difficulty of using the physiological indicators is the selection of the appropriate indicator and the establishment of the connection between the selected indicators and driving risk. The TTC is a frequently-used indicator to describe the driving risk. However, computing the TTC is not trivial, which usually be calculated based on the current or prior vehicle dynamic information. It is difficult to predict the driver’s behaviors in the near future, which may have a critical influence on the driving risk. A rough calculation of the driving risk based on TTC using our test data set found that 63 out of 75 predictions were consistent with the drivers’ perceptions. The results of verifying the 12 incorrect samples indicate that 9 drivers changed their operation maneuvers (like a sudden acceleration/deceleration), which lead to a significant change of the TTC. The rest 4 samples fell into the situation that is hard to distinguish the driving risk. Thus, the proposed method can locate the appropriate indicator to predict the driving risk, and combine all the possible indicators to predict the driving risk with reasonable accuracy.The limitations of this research are listed as follows: ① the performance of the HMM is based on the training dataset. If the training set covers all possible patterns, the predictions are reliable. The HMM cannot process the data out of the range of the training data set; ② it is difficult to verify the proposed model in real time. One of the possible usages of the proposed model is proactive crash prevention. However, the performance of the proposed model can only be verified using post evaluation, which is based on a review of the driving process; ③ we only tested the driving risk prediction during the lane-changing process. More scenes of the driving process should be tested using the proposed method. In this study, an HMM-based model was proposed to predict the driving risk during the lane-changing process using the driver’s physiological data and vehicle dynamic data from related sensors. The following conclusions could be obtained based on the research process: (1) Sensors that can collect the driver’s physiological information and vehicle dynamic information can be utilized to predict the driving risk during the lane-changing process. Some of the eye movement data, the heart rate variability data, and the vehicle dynamic data were proven to have an influence on the driving risk. During the lane-changing process, the drivers’ HRV parameter became active firstly in the perception stage. Then, the eye movement became active in the intention stage. The implementation of operations can only be identified in the final execution stage, (2) A two-factor indicator analysis method was proposed to evaluate the influence of each parameter on the driving risk during the lane-changing process. The results indicate that the fixation duration, saccade range, the SDNN of R–R interval, and the average speed were proven to have a significant influence on the driving risk under different levels of service and driving duration.(3) An HMM-based model was proposed to predict the driving risk during the lane-changing process. Based on the evaluation of the influence of eye movement, ECG, and vehicle dynamic data, the fixation duration, saccade range, SDNN, and average speed had significant influences on the driving risk of the lane-changing process and were selected as the observed parameters of the HMM. The proposed model was trained using 500 data samples from the lane-changing process of 25 drivers. The results indicated that 85.84% driving actions were safe and 14.16% driving actions were dangerous. The probability of maintaining a safe state was 90.67% and the probability of maintaining a dangerous state was 86.42%. The probabilities of transferring a safe state to a dangerous state and the opposite transferring process were 9.33% and 13.58%, respectively. The results matched the perceptions of the tested drivers with an accuracy rate of 90.67%.(4) The proposed method can be utilized in the proactive crash prevention system with the related sensors and communication devices. Such sensors can be portable eye tracker, portable heart rate/ECG monitor, and OBD. With enough training data, the proposed method can estimate the probabilities of each driving state in the next few intervals. When the predicted value exceeds the threshold, the system can remind the driver, which will improve the driving safety condition. (5) Further research can focus on the mechanism of the interactions between the psychological indicators and TTC. With this information, the TTC can be adjusted considering the driver’s psychological status, which will make the predictions of driving risk given by TTC more accurate. Further discoveries on the relationship between physiological indicators and driving behaviors are also needed to acquire the driver’s decision-making mechanism.


The efficient and safe management of air conditioner (AC), Piped Natural Gas (PNG) and water pipelines in large buildings is a major challenge for the safety of these buildings. In recent years, Linear Wireless Sensor Networks (LWSN) are being used extensively for monitoring of long natural gas, water, and oil pipelines. LWSNs can also be used for efficient and safe management of AC, PNG and water pipelines in large buildings. In this paper, a scheme for optimal placement of sensors and base stations in a linear fashion to monitor the various pipelines used in large buildings has been proposed. The proposed scheme utilizes the Lion Optimization Algorithm (LOA) and has been compared with three strategies, namely Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Greedy Approach with respect to throughput, lifetime and end-to-end delay. The simulation results show that the proposed scheme exhibits better performance in comparison to the other three considered techniques for all the three parameters. The most striking feature of the proposed approach is that optimization is more effective when the length of the pipeline is more as far as end-to-end delay is concerned. The lifetime of the network is significantly improved using the proposed approach, especially when the length of the pipeline is of medium size, which makes the proposed scheme suitable for energy efficient buildings. In recent years, the reduction in the cost of sensor nodes has opened various new areas where Wireless Sensor Networks (WSNs) can be used. Some of the applications of WSNs include environmental, AC/oil/gas/water pipeline, river, border and health monitoring . UnderwaIn certain applications, a one-dimensional linear wireless sensor network must be established. The example of such applications includes border, oil/gas pipeline and sea/river shore monitoring. For such applications, a new subclass of sensor networks, named Linear Wireless Sensor Networks (LWSNs) has been coined by the researchers . The monWater, gas and oil pipelines cross states, countries and sometimes also continents and their length varies between a few kilometers to thousands of kilometers. Modern large smart buildings also use various types of pipelines such as AC, water and Piped Natural Gas (PNG) pipelines, which may cross each other. The length of such pipelines varies from a few hundred meters to a few kilometers. Moreover, the network of pipelines in a smart building is a combination of various pipelines, which cross each other at different points. The number of skyscrapers is bound to increase due to the scarcity of premium space and increasing populations. Moreover, buildings are getting smarter day by day. The reduced cost of sensors has made it possible to extensively use sensors for monitoring and safety purposes in smart buildings. Recently, Kumar et al.  publisheIn LWSNs, the nodes are placed in a strictly linear or semi-linear form. Due to their special topology and application requirements, solutions used for ordinary sensor networks cannot be directly applied to LWSNs. The LWSNs can be classified either based upon topology (thick/thin/very thin) or based upon hierarchy (one level/two level/three level) .Once an LWSN is deployed, it may not be feasible or cost effective to change the power source in sensor nodes frequently. Thus, while deploying the LWSN one of the most critical and challenging tasks is to deploy sensor nodes effectively so that the deployed network must be long-lived with minimum energy consumption. The major research challenges in LWSNs include node placement, coverage, topology, routing, lifetime optimization, and energy efficiency . Some ofNode placement, routing, and lifetime optimization are important research problems in the LWSN field. In ,21,22,23In , Guo et Meta-heuristic optimization techniques are turning out to be useful in designing applications using LWSNs. Some of the major meta-heuristic techniques are single solution-based meta-heuristics and population-based meta-heuristics . Nature-Rabei et al. , used naThe solutions proposed for very long oil/gas pipelines crossing states, countries, and continents cannot be directly applied effectively for managing pipelines inside a building. Moreover, the greedy approach is heuristic in nature, and ACO follows a strictly linear path, which does not consider the crossing nature of pipelines inside a building.The paper proposes a solution to manage various pipelines inside a large building effectively.Lion Optimization Algorithm (LOA) has been used to optimize the node placement. To the best of our knowledge, LOA has not been used in the literature for efficient node placement for LWSN.An efficient mechanism for data transfer for such pipeline has been designed.The performance comparison of the proposed scheme with three techniques namely greedy approach, ACO and GA  with resIn , YazdaniWe consider a straight section of pipeline (AC/water/gas/oil) which is enclosed with two stations, one at each end of the section of the pipeline. The pipeline (channel) can run for a few meters to a few kilometers. This channel should be effectively checked; at the end of the day, the entire length of the channel should be secured, and the detected data should be transmitted to a specific node named sink or base station. The sink node should be at one of the stations. In this manner, it is accepted that there is a pipeline with length L, with sensor nodes SN  and two stations are disseminated at the closures of the channel. A sink node S should be placed at one of the channel′s closures near one of the stations. Sensors and also the sink node are expected to have restricted abilities including the communication range and the size of the buffer. Here, two scenarios exist, one is to establish a new pipeline to connect two stations efficiently, and the other is to efficiently manage the connection between existing stations. The problem can be generalized in that there are multiple channels, which are interconnected with each other in some pre-specified manner. This paper discusses the establishment of a network of pipelines or channels with minimum number of nodes such that the entire area is covered in a secure, robust manner and data is transferred to the BS with a minimum number of nodes. The energy efficiency and lifetime of the network are also considered while designing the solution of the proposed problem. For setting up a pipeline, there are multiple straight pipelines connected in a manner to common BS as shown in In recent years, researchers have used evolutionary approaches such as Genetic Algorithm and Ant Colony Optimization (ACO)  for solvThe Lion Optimization Algorithm, first described by Yazdani and Jolai  is a popThe behavior of a wanderer that moves independently describes the exploration search while the pride behavior represents the exploitation search. The use of both identifies a balance between the exploitation and exploration search, which suggest better convergence towards global optima.P are the wanderers and rest are separated into c bunches of prides. The %f in every pride is females and the remainder are the males. The tertiary of the pride is characterized by the best gone-by position of every lion Here, rand(4)Initialize the position of the base station(5)Transfer the data from nodes to BS and calculate dist, delay and drop_ratio as:(6)Calculate(7)Transmit a one-hop hello signal from the BS then:(8)Else:ii.Here, end ifIf (no_of_leader > 1)(9)Transfer the data from nodes to BS and calculate dist, delay and drop_ratio as:(10)Calculate:(11)F′ < F)Then:and end ifIf((12)Then exitElse go to step 3If (The systematic algorithm is presented below:The above algorithm first estimates the number of nodes in the network based on the length of the pipeline. Initially, the position of nodes is generated randomly on the pipeline. After that, the Lion Optimization meta-heuristic is used to get the optimized position of the nodes. The mathematical details have been discussed in the previous subsection. While optimizing the position of nodes the drop ratio and delay are considered, i.e., the position having optimized delay and drop ratio is selected.The algorithm described in the previous section has been implemented using MATLAB. The proposed approach has been compared with different existing techniques such as ACO, GA and Greedy Approach. The parameters considered for comparison are delay, throughput and the normalized lifetime. The length of pipeline has been varied from 500 m to 3500 m. The initial energy of each sensor node has been considered to be 1 joule, the energy consumed in communication is directly proportional to communication distance. The parameters considered are similar to the closest work . The simThe comparison of end-to-end delay between proposed technique and other three techniques namely ACO, GA and greedy approach has been shown in the The simulation results shown in It is evident from the simulation results that the delay in the greedy approach is increasing with the length of the pipeline whereas this trend is not consistent in the case of the other three meta-heuristic techniques. A possible reason for this phenomenon may be that the number of nodes also increases when the length of the pipeline is increased. In the case of meta-heuristic techniques, when the number of nodes in the system is higher, the meta-heuristic techniques can be applied more efficiently which may offset the effect of increased length of the pipeline on the end to end delay in some cases.The proposed algorithm outperforms greedy approach as far as throughput is concerned. Additionally, the throughput of our technique is slightly better than the other two considered techniques namely GA and ACO. In the presented approach, the location has been optimized based upon drop ratio and delay whereas the authors in  try to oThe comparison of the throughput is shown in The normalized lifetime of the proposed techniques signifies that lifetime of the network in each scenario with different pipeline length is better than the other techniques under consideration. The reason behind this phenomenon is like the nature of pipelines inside a building; LOA also considers crossing paths for optimization. The performance is significantly better than all other three techniques under consideration when the length of the pipeline is 1500 m, which makes it suitable for pipelines inside a large building.When we increase the communication range of the sensor nodes the lifetime of the network increases slightly for all the algorithms. Although, the energy consumed per data packet is proportional to the communication range of sensor nodes, however, due to reduced number of intermediate nodes, the total energy requirement to transfer a data packet is less. Therefore, the normalized lifetime shows improvement upon increasing the communication range of the sensor nodes.The paper presents an algorithm to set up a network of straight pipeline segments crossing each other and efficient deployment of sensor nodes by using the Lion Optimization Algorithm. The pipeline setup using the proposed algorithm exhibits better delay, lifetime and throughput of the network as compared to the ant colony optimization and genetic algorithm for varying length of pipeline. The proposed approach is particularly suitable for the pipelines used inside a large building as clear from the simulation results. Although the proposed approach has been validated using simulation experiments, the field study of the proposed approach is left as future work. One may explore the suitability of other meta-heuristic techniques and hybrid meta-heuristic techniques for node placement in crossing pipelines inside a large building.


Neurological impairments such as stroke cause damage to the functional mobility of survivors and affect their ability to perform activities of daily living. Recently, robotic treatment for upper limb stroke rehabilitation has received significant attention because it can provide high-intensity and repetitive movement therapy. In this review, the current status of upper limb rehabilitation robots is explored. Firstly, an overview of mechanical design of robotics for upper-limb rehabilitation and clinical effects of part robots are provided. Then, the comparisons of human-machine interactions, control strategies, driving modes, and training modes are described. Finally, the development and the possible future directions of the upper limb rehabilitation robot are discussed. Stroke is one of the leading causes for disability. In China, there are more than 2 million new cases every year. More than 1.5 million people die from stroke each year, and three quarters of the survivors have varying degrees of sequelae [With the development of robot technology, the application of robot in rehabilitation has aroused wide concern in the international community. A series of intelligent rehabilitation robots including artificial prosthesis and external mechanical auxiliary system have successfully developed to help patients to achieve functional recovery or compensation for the loss of motor function –5.There are two types of rehabilitation robot for upper extremity: one is end-effector upper limb rehabilitation robots, another is exoskeleton rehabilitation robot . These rThis paper gives a systematic review of research status in an upper limb rehabilitation robot. In order to review the development of the robot in more detail, we divide following chapters to present. Firstly, we provide a classification of typical mechanisms and clinical effects of part robots. The next section introduces the comparisons of human-machine interactions, control strategies, driving modes, and training modes. Then, the third section gives an overview of development and the possible future directions of the upper limb rehabilitation robot.A wide literature search from 1985 until December 2017 has been conducted resorting to the main databases, such as Web of Science, Google Scholar, and IEEE Xplore databases. The keywords used for the electronic search were upper limb, exoskeleton, end-effector type, and rehabilitation robotics. The studies that satisfied these criteria were (1) technology of part of the rehabilitation robot system, (2) robot that is able to assist the stroke patients to exercise upper limb movement, (3) focus on upper-limb functional abilities, and (4) control strategy and man-machine interaction. A total of 230 papers have been gathered by using the aforementioned search method; 117 papers have been excluded since they did not meet the inclusion criteria.The rehabilitation training robot was used to assist patients to complete the rehabilitation training. It has different design requirements of general industrial robots because of special working objects and environment. Usually, rehabilitation robots can be divided into end-effector type and exoskeleton according to the robot's different ways of supporting and pulling the limb. Specifically, the end-effector rehabilitation robot can be divided into rigid rod traction type and rope traction type , 12. ComThe end-effector rehabilitation robot system consists of ordinary connecting rod and series robot mechanism. In the working state, the robot drives the movement of the upper limbs by connecting with the patient's arm to achieve the rehabilitation training. The robot system is relatively independent of the patient, which only connects to the end of the robot.The upper limb rehabilitation robot firstly appeared in 1993. Lum et al. developed a “hand-object-hand” system used in the upper limbs of hemiplegic stroke patients after a period of time of rehabilitation . The patIn 1995, Lum et al. developed a hand-lifting recovery machine  to train patients with both hands [P ≤ 0.20) and motor power scores (P ≤ 0.10), motor status score (P ≤ 0.05)) [Since 1995, Krebs et al. of the United States have developed MIT rehabilitation robots and expanded their function. So far, the mechanical parts of the robot have three modules: graphic module, wrist module, and hand module. The robot assists patients to complete the drawing of the elbow, forearm, and wrist joint movement and hand grasping training. Twenty hemiparetic stroke patients were divided into a control group and an experimental group in the clinical trial. The latter were required to execute traction movement of the shoulder and elbow with a machine. Results indicated that patients in the experimental group improved further and faster  technology with electrical stimulation technology to complete the training on the shoulder, elbow, and wrist, achieving good healing properties. Eleven healthy subjects and five stroke patients participated in the experiment. The results confirmed that striking improvement has been seen for the anterior deltoid .The Arizona University at United States developed 5-DOF upper limb rehabilitation robot RUPERT and artificial 4-DOF robot driven by pneumatic muscle (PM). Movement driven by the pneumatic muscle is more flexible, because of similar muscle function and movement characteristics. The modified Wolf Motor Test and the Fugl-Meyer upper motor assessment test were utilized. Eight healthy subjects and two stroke patients participated in this experiment. Four healthy subjects improved in both the simple and complex tasks, and two patients improved the limb function –30.Perry from the University of Washington developed a 7-DOF arm rehabilitation robot named CADEN-77 driven by rope besides forearm rotation , 32. It The University of California and the University of Irvine developed 5-DOF T-WREX (training Wilmington robotic exoskeleton) , 35 and Rice University developed a composite group organization 5-DOF forearm rehabilitation robot named MAHI Exo II . The firNorthwestern University in collaboration with the Rehabilitation Institute of Chicago studied a 10-DOF (8 + 2) of the upper limb rehabilitation robot named IntelliArm . It can Queen's University designed the 6-DOF upper limb rehabilitation robot MEDARM driven by a rope . SternocCanada's ETS cooperation with McGill University has developed a 7-DOF arm rehabilitation robot system named MARSE ETS –42. It cThe Swiss Royal Institute of Technology cooperation with the affiliated hospital of Balgrist University developed the famous 6-DOF arm rehabilitation robot ARMin . It can In summary, the exoskeleton robot can realize more accurately the assist motion, and the end-effector rehabilitation robot has better performance on feedback and evaluation. The exoskeleton has parallel motion range and space with human joints, so it can reduce inertia more effectively and have a more compact structure. However, the end-effector is a series mechanism, which has larger movement space and more freedom. The end-effector is more suitable for multifreedom training tasks. Besides, the end-effector can effectively solve the joint coincidence problem, but underactuated exoskeleton can also realize it. Exoskeletons are increasingly becoming a major option in clinical treatment compared to the end-effector.The overview of end-effector upper limb rehabilitation robots and exoskeleton upper limb rehabilitation robots is shown in Tables Human-machine interaction is an important part of the robot system, which can influence the recovery effect and treatment process largely. This paper argues that human-machine interaction refers to communication methods, which are distinguished by the type of control signal. From this point of view, human-machine interaction can divide into bioelectric signal interaction method and physical signal interaction method.BCI technology is one of research hotspots in recent years that formed in the 1970s . The priWhen the brain is doing conceptual work, creating motion mind, or receiving outside stimulation, the nerve cells will produce dozens of millivolts of microelectrical activity. The electrical activity of a large number of nerve cells transmitted to the surface of the scalp to form brainwaves. This EEG  will reflect some characteristics of rhythm and spatial distribution and can be detected by a certain method. By signal processing, the intention signal of humans is discriminated and converted into a control command to realize controlling of external equipment and communication with the outside .  FigureThe two kinds of signal acquisition methods commonly used in the BCI system are intrusive and noninvasive. The signal collected by the equipment is amplified by amplifiers and pretreated by the processor, which is converted into digital signals and is stored in the computer. In BCI, the following signals are often collected: visual evoked potential (VEP), event-related potential (ERP) P300, slow cortical potential (SCP), spontaneous EEG Alpha wave, and Mu and beta rhythm signal.With the development of neuroscience, scientists found that in the whole process of life, the function of the damaged central nervous system can restore through the action of reasonable physiological potential –50. We cThe surface electromyogram signal produced by the contraction of the muscle is obtained by the surface electrode from the muscle surface of the human body. The electrical control system obtains multichannel sEMG from the human skin surface by the electrode and then performs eigenvalue extraction and action discrimination. The processor determines the motion state of the corresponding joints and muscles according to the identification results. And then it can control peripheral devices.Researches show that in the process of joint movement, sEMG can not only reflect the muscle information such as fatigue state and contraction intensity but also reflect movement intentions and other information in the process of different body joints movement .The identification of motor intention relies heavily on the selection of the signal acquisition site. After extracting and processing the EMG signal, the upper limb rehabilitation robot estimates joint torque and designs moving targets, then the processor controls the robot to complete the corresponding movement according to the instruction. Niigata University developed the upper limb rehabilitation robot based on the electromyogram signal control. The robot consists of seven DC motors, encoder, point meter, and the force and torque sensors, which can assist patients to complete seven degrees of freedom movement and training .Compared with bioelectric signals, the motion parameters' interactive signals have advantages of accuracy and reliability. The ways of motion parameters are force control, position control, and force/position hybrid control. Force sensors in real time, which are processed, judged, and translated into instructions to control the machine, often collect signals. Similarly, the variation of the position signal is used as the control signal. These different kinds of way of interaction often use the impedance control algorithm, PID control algorithm, and trajectory tracking control algorithm.In recent years, the interactive mode of motion parameters are often combined with virtual reality technology and applied in the field of motion rehabilitation. It shows that using virtual reality (VR) and computer game techniques in post-stroke upper limb rehabilitation may enhance neuronal plasticity –57. ReceIn summary, the interaction mode based on bioelectric signal has better effects on nerve rehabilitation treatment and has greater clinical application potential. The physical signal interaction method is more suitable for patients with some motion capability and has more potential in the application of family rehabilitation. Bioelectric signal interaction can express the patient's needs and physical condition at the neural level, especially brain signal interaction which has crucial significance in clinical neurological rehabilitation. Though physical signals are more stable and accurate, they cannot better represent people's intentions. In the future, the rehabilitation robot will be controlled based on the brain signals combined with physical feedback and virtual reality environment so that the user's neural activity can be better activated.In recent reviews, many researchers summarized human-machine interaction, training modes, and control strategies into one type to categorize and compare . This paDifferent control strategies of robot can be realized by the processing of human-machine interaction signals in different methods and algorithms. The control strategy can be divided into following parts.The position control is also known as trajectory tracking control. The angular displacement of each joint is determined by the kinematic inversion of the planned trajectory that controls the torque output of the motor in the process of computer operation to drive the motion of each joint and realize the movement along the planned trajectory .Each joint moment sensor captures the motion of the upper limbs of hemiplegic stroke patients in real time. The control system can convert joint torque to the equivalent force of the end effector according to the characteristics of institutions. Then, the controller can drive the motor to achieve the multijoint motion of the upper limb according to this equivalent force. Some researchers have also validated the clinical efficacy of force feedback. Johnson et al. designed the experiment to research the effect of using force-feedback control in robot-assisted stroke therapy .Researchers put forward that independently the control force and position at the same time are the optimal schemes of the robot control method. In theory, the free force space and position free space of the robot is two complementary orthogonal subspaces; the parameters are independently controlled in each space. The constraint environment is considered as a geometric problem with no deformation at this time –74.Impedance control is proposed by Hogan, who points out that the relationship between the position and the force can be described by a generalized nonlinear impedance model with a property of inertia, damping, or stiffness , 76..The relationship between speed and force is called mechanical impedance. The objective of impedance control is to mediate the mechanical impedance of the robot to maintain the ideal dynamic relationship of the contact force and position between the end-effector and the environment . TherefoIn a word, position control can effectively control joint angles and provide real-time feedback. For different patients, position control can make the training more personalized. Force control requires accurate acquisition of muscle and machine interactions so it relies more on the layout of the sensor. Impedance focuses on realizing the flexibility of the rehabilitation robot which avoids excess between mechanical structure and limbs. This method could provide a natural, comfortable, and safe touch interface and avoid secondary damage effectively. In practical applications, force/position hybrid control is a better control method in passive training mode and impedance is more suitable in active training processes.There are three common driving modes for upper limb rehabilitation robots: motor drive, hydraulic drive, and pneumatic muscle drive. The advantages and disadvantages of the three driving modes are shown in Robotic intervention therapy for stroke patients shows clear improvement of scores and strength, but these progresses are not reflected daily. It is necessary to focus on the targeted stage of recovery . TherefoPassive training is almost performed by a physical therapist or by a device. It requires the movement of joint in the regular range and prevents contractures to maintain the static length of the muscles . The pasThere is a limitation of passive training mode because patients cannot make effort to engage training . Two typIn the active rehabilitation training, the main motion form is autonomous movement of the patient, and the robot can judge the patient's motor ability according to the movement information of the patients to provide appropriate assistance. Active rehabilitation training can be divided into active power mode and active impedance. The robot can judge the sport ability and recovery degree by gathering motion information of the interaction between the robot and the patient and decide to provide assistance or resistance.In the past few decades, the upper limb robots applied to clinical rehabilitation have brought positive significance to stroke patients. In this paper, the development of upper limb rehabilitation robot, human machine interaction, training mode, driving mode, and control strategy is reviewed. The upper limb rehabilitation robots have shown encouraging clinical outcomes and rehabilitation efficiency. Previous researchers have accomplished a lot of work to promote the development of this technology. However, this study still faces many challenges.In terms of mechanical structure design, the complexity of the upper limb joints and redundancy DOFs bring many difficulties. The simplified model of arm exoskeleton movement usually may lead to several possible problems. During the training process of physical therapy, the unanticipated force generated during the man-machine collaboration may reduce the comfort level of humans and robot system. It may be better to optimize and improve institutional material and guarantee the flexible contact between patients and robots. Wearable robots often require as little weight as possible, but the simplified model may reduce the working space of the upper limb exoskeleton system thereby limiting its application scope. Most equipment is stationary; the patients cannot flexibly use rehabilitation equipment anytime and anywhere because of the style of installation of equipment. It cannot satisfy the portable, mobile, and other requirements and greatly affect patient rehabilitation experience, so it is necessary to make machine more lightweight and portable in the future study.Among the upper limb joints, the shoulder joint is a complex joint because the center of rotation is moving with nonlinear trace in the process of movement. Therefore, it is necessary to solve the problem that the rotation center of machine misalignment with the rotation center of the human body in future studies. Compared with end-effector rehabilitation robots, the exoskeleton has better bionic characteristics, because its motion is similar to human joint motion. In the future, the exoskeleton should be more anthropomorphic and the design of motion should be similar to biomechanical joint motion.From the perspective of the human biomechanics aspect, one of the important features is the physical properties of the musculotendinous and their resulting impedance. Therefore, exoskeletons should accordingly adapt to these variations of impedance to guarantee the smoothness of contacting and preventing spasm. In the process of active control, the exoskeleton mechanism singularity exists in its working space, increasing the difficulty of the control algorithm. It is helpful to reduce the complexity of the control algorithm to avoid the singularity in the actual workspace by optimizing a machine design.In the process of working conditions, it is essential for a rehabilitation robot to guarantee the patient's safety. Therefore, the robot should guarantee the safety of the subject from the following aspects: using the joint sensor to monitor the force information of the subjects during the movement, when the reaction force caused by the muscle tension is too high. When the patient appears with muscle spasm, the robot should automatically stop the current movement so as not to cause the muscle strain. The working space should be limited in the reasonable range by limiting the switch in order to avoid the repeated injury for the patient. The movement speed and displacement of the robot should also be limited by software. The operator should reasonably set the parameters of the driving device and monitor the robot motion state in real time. A press-and-stop button is extremely important to avoid accident situations. Also, self-locking of robot should be taken into account to avoid the damage by the rotation of the joints caused by gravity.It has been shown that using virtual reality (VR) and computer game techniques in stroke rehabilitation may enhance the function of neuronal plasticity. Further, enhancing patient sensorial state can be accomplished by merging together with VR, haptic and vibrotactile feedback. However, real-time synchronization of signals that is dedicated to reconstruct VR may be delayed due to the large number of required devices. This may lead to a bad real-time environmental reconstruction, thus increasing the task difficulty. It is also necessary to improve the response speed of the signal to ensure the timeliness of the interaction between the virtual environment and humans.BCI technology provides a new way of communication and control without language or body movement. It can directly express ideas or manipulate machine by the brain that has profound research significance in neurological rehabilitation. However, there is a difference in physiological characteristics of the brain between patients and normal individuals. Heterogeneity in post-damage expression inevitably complicates the decoding of brain signals responsible for neuronal plasticity recovery; this may lead to complicate extraction of suitable control inputs. The best way is integrating BCIs with actual rehabilitation methods to establish a powerful and accurate rehabilitative scenario. So far, there are BCI systems that hardly exist that can provide a high-accuracy control .It is often thought that the rehabilitation of the proximal arm may benefit the distal arm. As revealed by many studies, the proximal improvements in the arm do not necessarily transfer to the distal arm or vice versa and that arm improvements do not manifest as improved ADL performance. Therefore, how to design a more reasonable device to ensure the best recovery of the proximal and distal end is also a problem that needs to study.


Finding out interested targets from synthetic aperture radar (SAR) imagery is an attractive but challenging problem in SAR application. Traditional target detection is independent on SAR imaging process, which is purposeless and unnecessary. Hence, a new SAR processing approach for simultaneous target detection and image formation is proposed in this paper. This approach is based on SAR imagery formation in time domain and human visual saliency detection. First, a series of sub-aperture SAR images with resolutions from low to high are generated by the time domain SAR imaging method. Then, those multiresolution SAR images are detected by the visual saliency processing, and the corresponding intermediate saliency maps are obtained. The saliency maps are accumulated until the result with a sufficient confidence level. After some screening operations, the target regions on the imaging scene are located, and only these regions are focused with full aperture integration. Finally, we can get the SAR imagery with high-resolution detected target regions but low-resolution clutter background. Experimental results have shown the superiority of the proposed approach for simultaneous target detection and image formation. Synthetic aperture radar (SAR) can obtain high-resolution microwave images, with day or night operation capability ,2,3. AndNowadays, people are interested in not only imaging processing but also interpretation or recognition of the real-world targets from radar imagery ,7,8,9,10A large number of SAR target detection algorithms have been proposed in recent years, and those algorithms can generally be classified into two distinct categories : single Generally, almost all the existing target detection methods are carried out on the obtained high-resolution SAR images. In other words, the target detection stage is independent on the SAR imagery formation. In reality, there is a flaw in such a framework with sequential operations. The target detection stage must be proceeded after the imaging processing. However, in practice, only the ROIs on the imaging scenes, such as the regions containing vehicles, ships, buildings, etc., could be concerned, while other clutter regions are often unwanted and negligible. Therefore, high resolution imaging for the whole reconnaissance scene before target detection is purposeless and unnecessary. It is desirable to obtain a framework that can detect the ROIs during SAR imaging processing such that those regions are focused with high-resolution processing. Meanwhile, the remaining clutter regions are ignored or focused with low-resolution processing.In this paper, we propose a new SAR processing approach which can simultaneously carry out target detection and image formation. First, a series of multiresolution SAR images are generated by time domain SAR imaging algorithm. Then, those multiresolution SAR images are detected by the visual saliency method, and the corresponding intermediate saliency maps with different confidence levels are obtained. The saliency maps are accumulated until the result with a sufficient confidence level. After screening, the ROIs on the imaging scene are located, and those regions will be focused with full-aperture integration. Finally, the output of the proposed SAR processing approach is the imagery with high-resolution target detection results but low-resolution clutter background.The remainder of this paper consists of the following sections. The capability of human visual system to find out the targets of interest is effective and reliable ,23. It hInspired by this rationale, a novel SAR processing approach for simultaneous target detection and image formation is proposed based on the time domain SAR imaging  and visuSince the basic scheme of the proposed approach has been modeled, next we will discuss the implementation of the proposed approach.While some SAR imaging methods in time domain exist, the most widely used method for implementation is the back-projection (BP) algorithm . BP algoSuppose the SAR sensor travels along a flight path and transmits the signal The visual saliency method is employed to detect the multiresolution SAR images generated by BP, and to obtain their corresponding intermediate saliency maps in the proposed approach. There are many detection methods based on visual saliency principle ,31,32. IFrom the perspective of information theory, the image information can be decomposed into the innovation and the prior knowledge. The innovation means the novelty part, and the prior knowledge denotes the redundant information should be suppressed during target detection. The saliency detection method based on spectral residual analyzes the log spectrum of the SAR image and calculate the spectral residual. Then the spectral residual is transformed into spatial domain, thus the saliency map is obtained.k, its spectrum can be calculated byGiven an input SAR image Thus, the spectral residual can be calculated byAfter two dimensional inverse Fourier transform and Gaussian filtering, the saliency map can be obtained in spatial domain:With the BP generating a series of multiresolution SAR images, the visual saliency detection method obtains their corresponding saliency maps. Because the intermediate SAR images are with resolution levels from low to high, the detection results on the saliency maps also have different confidence levels. The intermediate SAR image integrated from a short sub-aperture has low resolution in cross range. Hence, the visual quality of this image is poor, so the detection result is also with a low confidence level, and vice versa.In order to get an accurate detection result during SAR imaging, a reliable way is accumulating those intermediate saliency maps until the results with a sufficient confidence level. Weighted summation of the intermediate saliency maps is a simple and effective method to make accumulation. Given a series of the intermediate saliency maps With the saliency accumulating, the target regions decision from the accumulated saliency map is also carried out by a threshold segmentation. The target regions decision is obtained bym successive decision results with the same target regions, they are of a sufficient confidence level.As the decision processes continue, we can get a series of a is the sum of the region pixels under decision, b is the length of the major axis of the ellipse that has the same normalized second central moments as the region under decision, Although the decision result has been obtained by the above processing, there may be some false alarm regions on the decision result. Thus, some discriminating operations should be carried out on the decision result. The geometrical features of the target regions are utilized to remove the false alarms. For simplicity, we use two geometrical features here for discriminating. The first one is the area of the target region: if The ROIs on the imaging scene are located after discriminating. Hence, those regions can be focused with full aperture integration. Finally, the SAR imagery with high-resolution target detection regions is obtained.So far, the implementation of the proposed SAR processing approach for simultaneous target detection and image formation has been described. The whole implementation process and its saliency map generation module are summarized in M, and there are K echoes along the cross range for sub-aperture integration. Besides, there are l iterations for target detection, the number of the ROIs on the imaging scene is p, and the size of each ROI is Now, we analyze the computational complexity of the proposed SAR processing approach. Suppose the size of the SAR imagery is l saliency maps is The computational complexity of the sub-aperture integration is In most cases, In this section, the proposed SAR processing approach will be evaluated based on two SAR imaging scenes, namely a heterogeneous sea scene and a complex ground scene, which are shown in  (MSTAR)  clutter N, and the threshold parameter In order to simulate the whole process of the proposed approach, the SAR echoes are generated with those two imaging scenes under spotlight pattern. Then the proposed SAR processing approach for target detection and imaging will be conducted based on those echoes. The parameters of SAR imagery formation and visual saliency detection in our method are set as follows. The velocity of the platform is 100 In the experiments, the detection and ROIs imaging results of the sea and ground scenes will be illustrated by the proposed SAR processing approach. Besides, the detection performance of the proposed approach will be compared with two other methods, CFAR method based on the ribution  and the ribution , which aFrom Now we will test the detection performances of CFAR, VWIE and the proposed approach. From From In this subsection, we utilize figure-of-merit (FoM)  to quantThe number of correct detections, false alarms, real targets on the two scenes, and the corresponding FoMs of the detection results for the three methods are listed in All the experiments carried out have shown that the proposed approach has a good capability in simultaneous target detection and image formation.In this paper, a novel SAR processing approach is proposed for simultaneous target detection and image formation. Inspired by the human visual system, this approach is conducted based on the time domain SAR imaging and visual saliency detection. The multiresolution SAR images are generated by time domain SAR imaging algorithm, and the intermediate saliency maps are detected by the visual saliency process based on those images. The accumulated maps are iteratively generated until the result with a sufficient confidence level. Thus, the target regions are located after some screening operations, and the SAR imagery with high-resolution detected target regions and low-resolution background are obtained. We have carried out extensive experiments, and the results have shown that the proposed approach can accurately detect the target regions from both the sea and ground scenes, and simultaneously obtain the high resolution imaging results of those detected target regions.


Wireless Sensor Networks (WSNs) are a particular type of distributed self-managed network with limited energy supply and communication ability. The most significant challenge of a routing protocol is the energy consumption and the extension of the network lifetime. Many energy-efficient routing algorithms were inspired by the development of Ant Colony Optimisation (ACO). However, due to the inborn defects, ACO-based routing algorithms have a slow convergence behaviour and are prone to premature, stagnation phenomenon, which hinders further route discovery, especially in a large-scale network. This paper proposes a hybrid routing algorithm by combining the Artificial Fish Swarm Algorithm (AFSA) and ACO to address these issues. We utilise AFSA to perform the initial route discovery in order to find feasible routes quickly. In the route discovery algorithm, we present a hybrid algorithm by combining the crowd factor in AFSA and the pseudo-random route select strategy in ACO. Furthermore, this paper presents an improved pheromone update method by considering energy levels and path length. Simulation results demonstrate that the proposed algorithm avoids the routing algorithm falling into local optimisation and stagnation, whilst speeding up the routing convergence, which is more prominent in a large-scale network. Furthermore, simulation evaluation reports that the proposed algorithm exhibits a significant improvement in terms of network lifetime. Wireless Sensor Networks (WSNs) comprise a self-managed network system, which consist of numerous distributed sensor nodes. Generally, the purpose of a WSN is to monitor the conditions of an environment, such as temperature and humidity, by means of data collection . In suchHierarchical (clustering) technology is particularly promising and has received much attention in the research community. Multiple algorithms have been proposed to arrange the nodes in meaningful clusters for various applications, such as an adaptive clustering algorithm in  and Low-Recently, many new routing protocols based on Ant Colony Optimisation (ACO)  have beepreying, swarming and following with the local search of an individual fish capable of reaching the global optimum ..39]. HerIn the initial phase of the algorithm, the crowd factor At the end of route discovery, the impact of the crowd factor becomes insignificant according to Equations  and 7)..7). The In general, in the initial stage, this hybrid routing algorithm avoids overcrowding of the sub-optimal route, while in the later stage, it can ensure that the routing algorithm can quickly converge to the optimal route.j, from which a BANT has come. The shorter path (less hops) will get a larger pheromone increment.When the minimum energy value in this path is larger, the pheromone increment is also larger, which avoids more data traffic routed through this path.The average energy of a relatively high path will get more attention, and therefore will attract more data flow.The closer the node is, the more pheromones are obtained.The global pheromone update procedure will be applied when the FANT arrives at the sink node. The global pheromone includes information about a route obtained by long-term learning from a FANT. After arriving at the sink node, the FANT will be converted into a BANT. The BANT inherits all route statistic information from the FANT, including path length, energy levels and the visited node list along that path. Meanwhile, the sink node calculates the amount of pheromone value attached to this path according to the route statistics. We calculate Equation .(9)τij=To assess the performance of the proposed FSACO protocol, we developed a study under a simulated environment on NS-2 (Version 2.35). We obtained the results in terms of different performances from simulating FSACO in comparison with EEABR , SensorAThe parameters in our simulation are shown in ice from . FurtherEquation  as 0.1 aEquation  as 0.9 fg to the .For the series of simulations carried out, we use the network model as described in From several results obtained from our simulation experiments, we made use of the following performance metrics for evaluation purposes.Energy consumption: The energy consumption is the sum of used energy of all the sensor nodes in the network during the period of the experiment in Joules (J). We estimate the node energy consumption considering the energy model proposed in .Energy efficiency: This denotes the ratio of the total packets delivered at the sink node to the total energy consumed by the sensor nodes (Kbits/J).Energy standard deviation. This gives the average variance between energy consumed on all nodes. It describes the energy consumption distribution of the networks. A low standard deviation indicates that the data points tend to be close to the expected value of the set, while a high standard deviation indicates that the data point is spread out over a wider range of values. The motivation behind this definition is that the algorithms should result in a low energy consumption standard deviation, which implies an even consumption distribution and good load-balance.E is the total initial energy, N is the total number of nodes in network and i. The basic motivation behind this definition is that an algorithm should try to maximise average remaining energy levels of nodes, but with a minimal standard deviation.Network lifetime prediction. This is defined as the difference of the total initial energy of the network and the summation of the average used energy of nodes and the standard deviation of their energy levels.Route setup time: This represents the time spent by a protocol to discover routes from the source node to the sink node. It is used to measure the time that the first effective route is discovered. We design a series of dynamic networks of different sizes to analyse this metric and evaluate the performance of the route discovery scheme and local heuristic update strategy in our proposed routing algorithm.Convergence time: We define the convergence time as the optimised route setup time. It is complex and non-trivial to detect if the optimal route is established. Nonetheless, for an ACO algorithm used in simulated routing algorithms, convergence has been proven , which mThroughput: The number of messages per second received at thedestination. The throughput of the network is the sum of the throughput from all destinations.All results are summarised in It can be seen from As shown in Intuitively, as the network scale increased, routing protocols took more time to find the optimal route, which means that the convergence time would increase as a consequence. This has been proven in other applications of ant colony optimisation algorithms ,41. In FIn order to assess the performance impact of the AFSA optimisation on the FSACO routing algorithm, we divided FSACO into two variants and analysed the different performance in terms of the first route setup time and routing convergence time. The results are reported in We used the energy standard deviation to evaluate the balanced consumption of energy. More specifically, an energy-efficient routing algorithm should maximise the average remaining energy levels of nodes, but with a small standard deviation to prolong the network lifetime.In In In our proposed hybrid FSACO routing algorithm, the data transmission preferred the route with the shorter path as described in ACO-based routing algorithms described in This paper proposes a hybrid routing algorithm combining AFSA and ACO to solve these issues. We present an AFSA-based initial route discovery to obtain effective feasible routes towards the sink node quickly. The simulation results demonstrate that the algorithm can speed up the time of first route establishment, as well as route discovery convergence time. This performance improvement is more prominent in large-scale networks. Furthermore, to avoid the stagnation phenomenon in ACO-based routing algorithms, FSACO combines the crowd factor in AFSA and the pseudo-random proportional route select model in ACO to propose a hybrid route discovery algorithm. According to the simulations, the dynamic crowd factor can effectively prevent the routing algorithm from falling into a local optimum and find the stable optimal path finally. Furthermore, FSACO presented an improved pheromone update strategy by considering both the energy levels and path length as key metrics when updating the local heuristic value and pheromone trail. The simulation results reported that FSACO showed a significant improvement over EEABR, IACO and SensorAnt in terms of energy efficiency and network lifetime.All of our results were obtained from simulation evaluation. However, the modelling of network behaviours in a simulation does not always reflect real-world scenarios. More accurate evaluation results can be gained through the implementation in a physical environment. We plan to implement the proposed algorithm on a physical test bed to study the impact of varying conditions on the performance of these algorithms.


However, due to the lack of clinical samples and records, the molecular cause for rare diseases is always hard to identify, significantly limiting the number of rare Mendelian diseases diagnosed through sequencing technologies. Clinical phenotype information therefore becomes a major resource to diagnose rare diseases. In this article, we adopted both a phenotypic similarity method and a machine learning method to build four diagnostic models to support rare disease diagnosis. All the diagnostic models were validated using the real medical records from RAMEDIS. Each model provides a list of the top 10 candidate diseases as the prediction outcome and the results showed that all models had a high diagnostic precision (≥98%) with the highest recall reaching up to 95% while the models with machine learning methods showed the best performance. To promote effective diagnosis for rare disease in clinical application, we developed the phenotype-based Rare Disease Auxiliary Diagnosis system (RDAD) to assist clinicians in diagnosing rare diseases with the above four diagnostic models. The system is freely accessible through  Rare diseases are rare conditions that occur only in a precious few people. Currently, there is no unified, widely accepted definition for rare diseases  and molecular mechanisms of rare diseases systematically, revealing many novel associations between rare diseases  provides an accurate curated resource of human variations with corresponding phenotypes for rare metabolic diseases , Q = , the cosine similarity is represented using a dot product and magnitude as follows:The input data of the PICS diagnostic model were the curated rare disease-phenotype associations, and we selected the curated phenotypes as the features. Cosine similarity is defined as the evaluation of the similarity between two vectors by calculating the value of the angle cosine. The similarity between the two vectors of the same vector cosine is 1, and the similarity of the two vectors at 90 degrees is 0. If the two vectors are the opposite, the similarity is −1. Cosine similarity is used in the positive space and the value is to be neatly bound in  , Q = , the Tanimoto coefficient is calculated as follows:The Tanimoto coefficient is extended by the Jaccard coefficient. Given two feature phenotype spatial vectors, To provide an antidiastole and to rank the candidate rare diseases in descending order of probability, the score is calculated as follows , Q = , the cosine similarity, is represented using a dot product as follows:Given two feature gene spatial vectors, G = , Q = , the Tanimoto coefficient, is represented as follows:Given two feature gene spatial vectors, P1 = , P2 = , the similarities between two phenotype sets are defined as follows:Given two phenotype sets, Based on the rare disease associated phenotype-gene spatial vector obtained from Data Set II, we used the above methods to construct the PGAS model.The input data of the CPML diagnostic model were the curated rare disease-phenotype associations, and we selected the curated phenotypes as the features. Similarly, the input data of the APML diagnostic model were the curated rare disease-phenotype associations and the text mined rare disease-phenotype associations, and we selected the curated phenotypes and text mined phenotypes as the features.Based on the TF-IDF-Hierarchy information content matrix of rare disease associated phenotype spatial vector obtained from Data Set III and Data Set IV, the CPML model, and the APML model take classifier performance into consideration. We first adopted Logistic Regression, KNN, Random Forest, Extra Trees, Naive Bayes, and Deep Neural Network machine learning classification algorithms as classifiers, respectively, and then used the Bayesian averaging algorithm in both models to leverage the prediction results of the six classifiers, ranking candidate rare diseases by their scores.The training sets for the four diagnostic models contained in the RDAD system are listed in Table To facilitate rare disease diagnosis, we applied the phenotypic TF-IDF-Hierarchy information content on the phenotype semantic hierarchy of Human Phenotype Ontology (HPO), and then built the phenotypic TF-IDF-Hierarchy information content based on the rare disease similarity model (PICS), the phenotype-gene association based rare disease similarity model (PGAS), and the curated feature phenotype spatial vector based rare disease machine learning prediction model (CPML), as well as the curated and text mined feature phenotype spatial vector based rare disease machine learning prediction model (APML). The four diagnostic models contained in RDAD with their corresponding classifiers are listed in Table Precision measures the fraction of correct predictions made by the four diagnostic models contained in the RDAD system. Recall (or specificity) measures the fraction calculated by dividing the number of correct choices by the total number of choices available to each model. True positives (TP) are the number of correctly predicted rare diseases, false positives (FP) are the number of incorrectly predicted rare diseases and false negatives (FN) are the number of rare diseases that are not predicted. The F1-score is an aggregate measure for the accuracy of a classifier that calculates a weighted average of Precision and Recall defined as follows  performed better than traditional disease similarity models (PICS and PGAS). Compared to other models, the CPML model showed the best performance Figure .Given the input phenotypes, we examined the candidate rare diseases detected, ranked as top 1, top 10 and others with the four diagnostic models in RDAD. We found that 62.1% of the designated rare diseases were ranked as top 1 with the PICS model, the good performance of this model is most likely due to the accuracy of the associated phenotype of rare diseases and the direct calculation between the spatial vectors while the other three models undergo a series of transformations during data processing, resulting in information loss and error amplification. In contrast, 95.5% of the correct rare diseases were ranked as top 10 with the CPML model. Thus, our results clearly demonstrate that the four diagnostic models contained in the RDAD system are suitable for finding rare diseases that are known to be associated with phenotypes. In general, the model built by the machine learning method, showed better performance. The four diagnostic models successfully ranked the most likely candidate rare diseases in the top 10  and diverse patients in this study also restrict the performance of the models. At present, although users are strongly recommended to choose the CPML model in the RDAD system to assist rare disease diagnosis, the RDAD still provides all 4 diagnostic models as alternative to rare disease diagnosis. On the one hand, although the current result show that machine learning models perform better than disease similarity models, PICS performs the best in ranking the top condition . On the other hand, the CPML model performs better than the APML model, but the diagnosis can only be reliable when candidate diseases have corresponding phenotypic annotation in the HPO. For diseases that only have text mined phenotypes, APML will be a better choice; therefore, the four different models can complement each other under different circumstance. It is anticipated that with the accumulation of clinical phenotypes of rare diseases, the performance of our models will improve gradually.TS supervised the study. JJ and ZA designed and developed the diagnostic models. RW and ZA built up the website. YG and XN verified the validation results. JJ wrote the manuscript. All authors have read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of  Agricultural machinery  automatiA local autonomous navigation method by applying the boundary line of tillage soil is presented in this paper, with the tractor works in tillage mode inspired by the existing plant line navigation method. Moreover, a fast image-processing algorithm using optimized templates combined with guided filtering ,10,11,12Here, a traditional tractor of type AXION 850 (CLAAS) was modified into an intelligent tractor with the aid of a tractor-driving robot that drives the tractor as shown in The tillage procedure of the intelligent tractor included two steps. Firstly, the tractor tilled a round line under manual control or teleoperation control to create a boundary between the tillage soil. Then, the tractor worked an autonomous navigation model based on the boundary calculated using guided filtering, improved anti-noise morphology, and Hough transformation in sequence, in consideration of the severe randomness and non-uniformity of the agricultural filed environment.The local linear model is mostly used for non-analytic functions, defined as the adjacent points on a function which have a linear relationship with others. The definition shows that a complex function can be represented by multiple adjacent local simple linear functions, where each point value can be obtained by averaging the weights of all the linear functions excluding that point.The input image is denoted as After a gradient operation on both sides of Equation (1), we getThe process of calculating linear function coefficients is called linear regression. The true value of the fitting function In view of the fact that a pixel can be described by multiple linear functions, all linear function values containing the point are weighted and averaged when calculating the output value of the point, as shown in Equation (6).The process of weighted averaging is a linear translational variation filtering process, and the calculation of the guided filtering is based on this process.The processing process of the guided filter is shown in Introducing the concept of mathematical morphology ,17,18,19In the mathematical morphology algorithm, structuring elements should be selected according to actual needs. Small-scale structuring elements can make the extracted image edges more detailed and coherent, and obtain more accurate edge localization, whereas large-scale structuring elements can reflect the large edge contours in the image and have a good noise suppression effect. Therefore, small-scale structuring elements were selected for obtaining complete edges in this paper.The edge of the image is calculated as follows:In Equations (7) and (8), Via some ordinary operations, the edge information can be easily obtained from the images of the edges detected by Equations (7) and (8). For detecting more detailed edges, as well as improving the anti-noise ability of The tillage soil boundary line extracted by using the algorithm mentioned above is shown in The tractor completed the first returning tillage manually via human driving or tele-operational driving before implementing the autonomous image-aided navigation operation. The navigation line of the tractor was calculated by using a Hough transformation from the processed tillage soil boundary in For simplifying the adjustment algorithm of the navigation line, the actual direction angle During tractor navigation using the tillage soil boundary line, The tractor needs to turn left if the navigation angle The detailed algorithm of improved anti-noise morphology is shown in In this paper, the computer employed for the tillage soil boundary navigation line extraction was configured as follows: 64-bit Windows 10 operating system, 8 GB memory, and a two-core processor Intel (R) Core (TM) i5-4200H central processing unit (CPU) @ 2.80 GHz. The first experiment was carried out to verify the effectiveness of the proposed algorithm mentioned above using several tillage images of different farms. Secondly, the navigation algorithm was used for smart tractor tillage in an experimental farm of Nanjing Agricultural University. Moreover, the optimal template was studied to improve the efficiency of the algorithm.The time consumption and each optimal effect component of the YCbCr, HSV, HIS, and 2R-G-B ,24,25,26Furthermore, the histogram of the best gradation component in each image, shown in As the illumination strength in the field does not remain constant, to make the algorithm robust, images used in filter selection were captured in both weak and strong light environments, as shown in The homomorphic filtering ,28,29,30The homomorphic filtering (HF) method and other algorithms such as Tarel , multi-sThe contrastive calculation time of the filtering algorithms mentioned above is shown in Therefore, the guided filtering algorithm was selected for tillage soil boundary identification, considering the image-processing speed and the contrast of the tillage soil.The edge extraction results and the Hough transform results of the image obtained using the basic morphology and improved anti-noise morphology algorithms are shown in Different edge extraction results obtained by applying the improved anti-noise morphology algorithm and other popular existing edge extraction algorithms such as Sobel ,38,39,40The time consumption of the above algorithms is shown in In order to further improve the real-time vision navigation during linear tillage operation of the tractor, the appropriate image should be cropped from the whole original image, because a larger picture needs more computer processing time. The tractor tillage operation includes two work modes, i.e., a linear mode and a turning mode. During the linear mode, the longest line of the tillage soil boundary is firstly created using the above improved anti-noise morphology algorithm. Then, a rectangle centered at the middle of the longest line is used to crop a part of the original picture for navigation line calculation, because the position and the navigation angle of boundary line of the soil change a little. During the turning mode, the original image should be adopted for navigation because the position of the boundary in the image varies a lot, which leads to boundary information loss in the rectangle.(1)The original image is transformed to grayscale and uniformly scaled to (2)The middle of the longest line is used as a reference point.(3)Different size rectangles centered at the reference point are used for navigation precision comparation, as shown in The optimization template selection scheme is as follows:The experimental results, shown in The autonomous navigation experiment using the proposed tillage soil boundary was carried out in the farmland of Nanjing Agricultural University using a tractor (CLAAS AXION 850 model) equipped with the tractor-driving robot developed by our team. For comparing the navigation precision, an RTK GPS  was applied, and the navigation line was used as the reference, as shown in Before the autonomous navigation tillage operation, the tractor was teleoperationally controlled for the first round trip of tillage, and then the tractor longitudinal direction was adjusted to be parallel to the tillage soil boundary line. A navigation line identification diagram in the experiment is shown in Firstly, the applied condition on the optimal template containing  results  indicateTherefore, the ideal maximum working speed of the tractor was Based on the above study, the vision navigation method is shown in a flowchart . The optAutomatic navigation in the field is a pivotal technology for intelligent agricultural machinery . SatelliInspired by the existing visual navigation methods in the field, this paper put forward a visual navigation method based on the color difference between the tillage soil during tillage operation, and the proposed improved anti-noise morphology vision navigation algorithm combined with guided filtering algorithm worked well in a complex agricultural field environment with a non-uniform nature, uneven illumination, and straw disturbance. The accuracy of the navigation angle of the proposed algorithm was not less than  and IMU .The optimal size of the image was studied for improving the algorithm speed. The optimal template of The experiment results show that the navigation line extraction algorithm in this paper took less time and had a good effect in a complex farmland environment. Therefore, the fast navigation line extraction method based on improved anti-noise morphology has the advantages of short time consumption and high precision, and it can meet the requirements of real-time vision navigation in field tillage using an intelligent tractor, which has important practical application value.https://www.sensefly.com/education/datasets/); the results showed that, overall, the time consumption and navigation precision of our proposed algorithm have significant advantages compared to other previous operators, as shown in Furthermore, a comparison between the existing operators and the proposed improved anti-noise morphology algorithm was carried out using standard datasets (downloaded from Future work will introduce the thin-plate spline interpolation algorithm to calibrate colors in sRGB space , as well