The reliable and accurate indoor pedestrian positioning is one of the biggest challenges for location-based systems and applications. Most pedestrian positioning systems have drift error and large bias due to low-cost inertial sensors and random motions of human being, as well as unpredictable and time-varying radio-frequency (RF) signals used for position determination. To solve this problem, many indoor positioning approaches that integrate the user’s motion estimated by dead reckoning (DR) method and the location data obtained by RSS fingerprinting through Bayesian filter, such as the Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), have recently been proposed to achieve higher positioning accuracy in indoor environments. Among Bayesian filtering methods, PF is the most popular integrating approach and can provide the best localization performance. However, since PF uses a large number of particles for the high performance, it can lead to considerable computational cost. This paper presents an indoor positioning system implemented on a smartphone, which uses simple dead reckoning (DR), RSS fingerprinting using iBeacon and machine learning scheme, and improved KF. The core of the system is the enhanced KF called a sigma-point Kalman particle filter (SKPF), which localize the user leveraging both the unscented transform of UKF and the weighting method of PF. The SKPF algorithm proposed in this study is used to provide the enhanced positioning accuracy by fusing positional data obtained from both DR and fingerprinting with uncertainty. The SKPF algorithm can achieve better positioning accuracy than KF and UKF and comparable performance compared to PF, and it can provide higher computational efficiency compared with PF. iBeacon in our positioning system is used for energy-efficient localization and RSS fingerprinting. We aim to design the localization scheme that can realize the high positioning accuracy, computational efficiency, and energy efficiency through the SKPF and iBeacon indoors. Empirical experiments in real environments show that the use of the SKPF algorithm and iBeacon in our indoor localization scheme can achieve very satisfactory performance in terms of localization accuracy, computational cost, and energy efficiency. The Global Positioning System (GPS) is commonly used for navigation in outdoor environments. However, it is not available for indoor positioning due to the obstruction of signals. As a result, various indoor navigation approaches have been extensively studied to achieve reliable and high accuracy positioning for a person or a device indoors. Researchers have proposed many solutions using radio beacons, inertial sensors, ultrasound, vision, etc. Most indoor positioning systems are classified into two types: non-infrastructure-based  and infrastructure-based .DR-based method, also known as an inertial navigation system (INS), is a self-contained localization system that relies on inertial sensors such as accelerometer, gyroscope, and magnetometer without the assistance of the GPS and infrastructure ,2. PedesRSS fingerprinting is a kind of the localization method using the received signal strength (RSS) from the radio beacons such as WiFi access points (APs) ,4, BluetTo achieve better indoor localization results, many smartphone-based localization approaches that integrate both DR and fingerprinting method through Bayesian filter, such as the Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), have recently been proposed ,8,9. AmoRecently, iBeacon introduced as a new kind of Bluetooth transmitter is Apple’s implementation of Bluetooth low-energy (BLE) wireless technology to provide location-based information and services to smartphones and other mobile devices . Since iIn this paper, we present a localization system that leverages simple DR, RSS fingerprinting using iBeacon and machine learning scheme, and enhanced KF. Using the DR method, the position of the user is predicted by the sensory data (acceleration and heading) of the smartphone. Instead of GPS, the positional measurement of the user can be obtained from the RSS fingerprinting approach using energy-efficient iBeacon and machine learning approaches, such as ANN , KNN , NBC (naive Bayes classifier), and SVM (support vector machine) as in . HoweverThe core of our localization system is the enhanced KF called a sigma-point Kalman particle filter (SKPF), which estimates the position of the user using both the unscented transformation of UKF and the weighting method of PF. The SKPF algorithm proposed in this study is used to provide the enhanced positioning accuracy by integrating noisy positional information estimated by DR method and the location data obtained by RSS fingerprinting with uncertainty. The SKPF algorithm can achieve better positioning accuracy than KF and UKF and comparable performance compared to PF, and it can provide higher computational efficiency compared with PF. We aim to design the localization scheme that can realize the high positioning accuracy, computational efficiency, and energy efficiency through the SKPF and iBeacon indoors. Empirical results in a building show that the use of the SKPF in our indoor localization system can achieve very satisfactory performance in aspect of positioning accuracy and computational cost compared with KF, UKF, and PF. It is also shown in the test results that the positioning system using iBeacon receiver for the RSS fingerprinting can provide more energy-efficient localization than using WiFi module.The rest of this paper is organized as follows. Most non-infrastructure-based localization techniques adopt dead reckoning (DR) as the basic scheme for positioning. DR-based methods depend on an inertial measurement unit (IMU) that contains accelerometer, gyroscope, and magnetometer to calculate the position, orientation, and velocity of the object without the help of the infrastructure . PedestrHowever, low-cost MEMS IMUs may be prone to drift error and large bias. Furthermore, the irrelevant motion of the pedestrian can degrade the performance of the positioning system. Consequently, to rectify the pedestrian location, many sophisticated and complex DR approaches have been studied ,21. CompThe fingerprinting-based techniques require no prior knowledge about infrastructure locations and no propagation model. The main idea is to create a fingerprint map (database) by fingerprinting the surrounding features at every position in the area of interest and then to estimate the associated position by mapping the measured feature against the fingerprint map. RADAR  is an eaIn the positioning and tracking system, the Bayesian filter is employed to fuse sensory data from all different sources to gain better positioning accuracy. Among Bayesian filtering methods, the Kalman filter (KF) and its variants, such as unscented Kalman filter (UKF), have been widely used for the navigation systems, since they are efficient in terms of the computational cost while providing a high accuracy in localization ,28. ReceIn this paper, we propose an enhanced KF called a sigma-point Kalman particle filter (SKPF). By leveraging the unscented transformation of UKF and the weighting method of PF, the SKPF algorithm can achieve better positioning performance than KF and UKF and competitive performance compared to PF, and it can provide higher computational efficiency compared with PF. The SKPF algorithm in this study is used to achieve the enhanced positioning performance by integrating noisy positional information estimated by DR method and the location data obtained by RSS fingerprinting with uncertainty.The sensor part consists of RF receivers and a group of inertial sensors on the smartphone. The RF receivers include WiFi and iBeacon modules, which provide the positioning algorithm with RSS readings measured from WiFi access points and iBeacons, respectively. The inertial sensors include the accelerometer and gyroscope on the smartphone, which measure three-axis acceleration and rotation rate of the phone. The sensory data are used for the machine learning and location estimation in the positioning algorithm.The positioning algorithm can be broken down into two main component parts: offline training and online localization. In the offline phase, the RSS fingerprints obtained from WiFi APs and iBeacons using the smartphone and the heading information of the user gained by the accelerometer and the gyroscope on the phone are collected at selected locations in the area of interest and are sent to the server. Then, a machine learning method, such as ANN, KNN, NBC, and SVM, on the server side converts the collected fingerprints (RSS readings and user’s heading) into vectors (fingerprint database).The online localization contains three phases: step length determination, positional measurement estimation, and sigma-point Kalman particle filtering (SKPF). The SKPF algorithm is composed of a prediction (dead reckoning) phase and an update phase, and it estimates the current position of the user with the smartphone from the two-phase process using the pedestrian model in For every step of the user, the prediction phase predicts the user’s location using both the step length (displacement) calculated according to the magnitude of the acceleration measured by the accelerometer and the heading angle obtained by the accelerometer and the gyroscope. The inference of the adaptive step length and heading is discussed further in Due to the unavailability of GPS in indoor environments, the positional measurement used to correct the predicted position in the update phase of SKPF is obtained by the fingerprint database constructed using the machine learning approach implemented on the server using during the offline phase. When the smartphone client sends a location query with the information about the observed RSS value and user direction to the server, the machine learning method on the server infers the user’s location that best matches the observed information via fingerprint database and sends back the estimated position of the user to the smartphone immediately. The estimate of the positional measurement using the machine learning is addressed in The following sections describe the components of the positioning algorithm used in this paper.In this section, we derive a relationship between the step length and acceleration magnitude for adaptive displacement estimation  and dealThe measurements obtained from the three-axis accelerometer represent periodic patterns for each of its axes in human walking. As can be seen in Afterwards, to identify the user step, the peak detection algorithm is applied to the smoothed acceleration data and then the peak values are found. The peaks represent the user steps. The result of the peak detection is illustrated by l is the estimated step length, p is the maximum peak magnitude of the three-axis accelerations, and l by the smartphone in the test environments described in this paper.By analyzing the peak magnitudes of over 20,000 acceleration values that 50 users record every 10 cm, we could derive the relationship between the actual step length and the maximum peak value of the three-axis acceleration measurements, as shown in Heading information is available for improving the positioning accuracy in indoor environments. The heading data obtained from the accelerometer, gyroscope, and magnetometer can be easily affected by environment dynamics. Particularly, the magnetometer due to the magnetic interference may result in a noisy estimate of the user heading indoors. Therefore, we use the heading value provided by the CMDeviceMotion class of the iOS developer library  to obtaiThe GPS operates poorly due to weak and blocked satellite signals in indoor environments. Therefore, it is difficult to obtain the measurement for the user’s location indoors. To determine the user location in such environments, there are many approaches based on alternative signals, such as acoustic, infrared, and RF. Since RF signals can easily be measured at receivers, such as WiFi and Bluetooth devices, many of the existing localization approaches leverage RSS fingerprints. k-nearest neighbors algorithm), NBC (naive Bayes classifier), and SVM (support vector machine). Hence, the machine learning algorithms used in this study are implemented with the machine learning library of the Open Source Computer Vision (OpenCV) written in optimized C/C++ [In our localization system, the machine learning algorithm is employed to determine the location of the user using the RSS fingerprints. We do not design a new machine learning algorithm but leverage only the conventional algorithms to provide the information about the user location, such as ANN , KNN  and are sent to the server. Then, the machine learning algorithm on the server converts the recorded fingerprints (training data) into vectors (fingerprint database), i.e., the machine learning method builds the fingerprint map. During the site survey, we observed that RSS information is greatly affected by the heading direction of the user. Since the user’s body can be considered to be an obstruction similar to the wall between the RF transmitter and receiver, it may result in the fluctuation in the radio signal. Therefore, in addition to the corresponding RSS fingerprint for each location, the user’s direction is also recorded. The heading information is obtained from the built-in sensors on the smartphone, as described in k can be represented as the vector x-axis and y-axis position in the navigation frame. The navigation frame is the local geodetic frame where the x-axis, y-axis, and z-axis indicate east, true north, and up direction, respectively. Note that z-axis point is not considered for land-pedestrian navigation. Therefore, the state x-axis and y-axis positional information. In this model, the angles k, respectively, and the angle d represents the traveled distance of the pedestrian between time steps k.The pedestrian model used to account for the motion of user in our positioning system is shown in d is calculated by the maximum peak value of the three-axis accelerations between time steps k, and both In the pedestrian model used to represent the pedestrian motion with INS sensors, such as accelerometer and gyroscope, the position of the pedestrian can be denoted byApplying the sine and cosine rule to Equations  and (3) quations  and (3) x-axis and y-axis position in the navigation frame. Since the measurement By assuming that process noise In the positioning system, the Bayesian filters have been used to integrate sensory data from all different sources to obtain better positioning accuracy. Among Bayesian filters, the Kalman filter (KF) and its variants, including the unscented Kalman filter (UKF), have been widely used for the navigation systems, since they are efficient in terms of the computational cost while providing a high accuracy in localization ,28. ReceThe SKPF algorithm exploits the unscented transformation (UT) used in UKF. This enables the SKPF approach to estimate the state of the system using a small number of samples, while the PF relies on many samples to achieve accurate results. Hence, the SKPF can provide higher computational efficiency compared with PF. This is addressed further in Unlike UKF that employs samples with the uniform weight for all system dynamics, the SKPF algorithm uses samples that have the different weight through the weighting method of PF, which evaluates the samples’ weights using the likelihood function proportional to the Gaussian/non-Gaussian posterior density. Consequently, the SKPF approach can offer higher estimation accuracy than KF and UKF and competitive estimation performance compared to PF. This is addressed in more detail in In this paper, we propose the enhanced KF called a sigma-point Kalman particle filter (SKPF), which can achieve better positioning performance compared with KF, UKF, and PF by leveraging both the unscented transformation (UT) of UKF and the weighting method of PF. The SKPF algorithm can provide a balance between the low computational effort of UKF and the high estimation performance of PF when estimating the state (the location of the user) of the positioning and tracking system as follows:n-dimensional state vector n-dimensional state vector and the control vector at time k, respectively. The process equation The SKPF algorithm can be summarized as follows. First, to describe the canonical form of the SKPF algorithm that can be applied to the nonlinear systems without the linearization for the nonlinear functions using the unscented transform (UT), suppose we have a nonlinear discrete-time system with In the SKPF algorithm, the estimates of the state k:The a posteriori state estimate k:On the contrary, the a priori state estimate We employ the term The state estimate and its covariance of SKPF is initialized before any measurements are available. Thus,A set of The principle of the SKPF algorithm is shown in k is predicted through the UT for sigma points (samples) drawn from the a posteriori estimated state k means the a priori estimated state k using the UT. First, ith column (or row) of In the prediction phase, the state The nonlinear system equation k is determined by the weighted sum of the transformed samples:The a priori state estimate The covariance of the estimation error of k is observed, the predicted state can be corrected using the measurement. The following equations are used to update the predicted state estimate k using the UT and measurement In the update phase, when the measurement The nonlinear measurement equation k can be determined by the weighted sum of the transformed samples. Therefore,The estimate of the measurement The covariance of the measurement residual (innovation) Then, the cross covariance between The optimal Kalman gain k is obtained from the reweighted set of the samples given by Equation q and the measurement noise After the measurement Using Equation  and the The resultant weight of each sample can be determined by Equation , and it Using the samples Equation  and theiEquation , the a pk through the pedestrian model introduced in F and the step length and heading of the user obtained from inertial sensors xk−1|k−1i·Propagate the samples 1i using fordo  end for ·Calculate k−1 from  and 18)x^k|k−1 aPrediction (dead reckoning)−·Select the samples 1i using ·Propagate fordo  end for ·Compute rding to  and 22)z^k and S·Determine z^k from ·Calculate the optimal Kalman gain Kk using ·Evaluate rding to ·Normalize weights ki using ·Calculate k|k from  and 25)x^k|k andUpdateEstimate the state and error covariance at every time stepThis section describes the testbed configuration that is used to build the RSS fingerprint database and to evaluate the performance of our positioning algorithm. Our experimental site was located on the first floor of the International Center for Converging Technology in the Korea University. The layout of the floor is shown in In our experiments, the wireless network was comprised of three WiFi APs (ipTime N104T) and three iBeacons (Estimote) that work in 2.4 GHz ISM band. The position of the RF transmitters deployed in the lecture room is represented by pink triangles and blue pentagons marked with a sequence number in 50 users between the ages of 25 and 35 with a variety of walking speeds took part in our experiments. Both green square and orange circle symbols in Since the RSS data from the same RF transmitter can vary significantly at the different locations due to obstructions between the RF transmitter and receiver, to investigate the impact of the obstacles (due to the building structure) on RSS information at a given location, all the WiFi APs and iBeacons in our experiments were intentionally located in the same space. Based on these RF transmitters, our experiments can be classified into two testbeds depending on the test site: Scenarios S1 and S2.In Scenario S1, the pedestrian with mobile device walks along the locations of green square symbols shown in The following sections describe the experimental results for our localization approach in a real environment.train_auto of OpenCV [In this section, we analyze the performance of machine learning algorithms employed to build the fingerprint map (database) using the heading information of user and RSS values obtained from WiFi APs and iBeacons, and accordingly to estimate the positional measurement of the user based on the map. The machine learning algorithms include ANN, KNN, NBC, and SVM introduced in f OpenCV . We usedith physical location ith physical location We assume that the fingerprint map has already been built through the offline phase before the online step of the fingerprinting approach. Given the functionm(zi)=1ziWe used the average value of mapping rate determined by the mobile phone of 50 users in our experiments.In Scenario S1, as the number of location samples measured in the online phase increases, the mapping rate can reach a higher value. However, since much time is spent collecting the samples, the real-time process of the position estimate is not feasible. On the contrary, in Scenario S2, the ratio of the mapping is not affected by the number of the samples during the online step. Therefore, only one location sample was used for the real-time positioning of the user in our experiments.k in our positioning system.The SKPF algorithm proposed in this paper is evaluated through empirical tests to verify the validity of it as an indoor position estimator. During our experiments carried out for the evaluation, the users moved along physical locations marked with a sequence number in Scenarios S1 and S2, and then their position was estimated by the SKPF algorithm.The SKPF can provide higher accuracy of the position estimate than KF and UKF, as shown in We can analyze in more detail these errors by observing m denotes the dimension of the measurement n is the dimension of the state m is equal to the value of n, we can observe that KF has a less complex method and requires less memory than UKF. PF, which uses many particles (samples), requires substantial computational cost and memory usage to estimate the location of the user. Indeed, both these quantities scale as For the computational complexity and memory requirement, KF and UKF scale in general state xk ,40. In oarticles . By contIn contrast, although SKPF has slightly smaller positioning accuracy than PF, it can carry out faster localization. This is because the SKPF algorithm uses the unscented transformation (UT) of UKF. This enables the SKPF approach to estimate the position of the user with a small number of samples, while the PF depends on a large number of samples to achieve accurate results. Therefore, the SKPF algorithm can provide the higher computational efficiency compared with PF.In this section, we aim to validate whether the schemes that use the iBeacon receiver with low leakage power capability can provide more energy-efficient localization compared with the different methods using the WiFi module. To observe the energy consumption of our localization system, the power monitor of Monsoon Solution  was connAs a solution to the problem of indoor pedestrian positioning that suffers from substantial errors and large bias, we have presented an indoor localization system using simple dead reckoning (DR) method, fingerprinting approach using machine learning and energy-efficient iBeacon, and SKPF algorithm, the enhanced KF proposed in this paper. Using the DR method, the position of the user is predicted by the sensory data (acceleration and heading) of the mobile phone. Instead of GPS, the positional measurement of the user can be obtained from the fingerprinting approach in our positioning method. However, there are still errors in positional information obtained from both the DR and fingerprinting method.The core of our localization system is the SKPF algorithm that improves KF by leveraging the unscented transformation of UKF and the weighting method of PF. The SKPF algorithm can achieve better positioning performance than KF and UKF and competitive performance compared to PF, and it can provide higher computational efficiency compared with PF. The SKPF algorithm in our localization system is used to provide enhanced positioning accuracy by integrating noisy positional information estimated by DR method and the location data obtained by the fingerprinting approach with uncertainty. We aim to design the localization scheme that can realize the high positioning accuracy, computational efficiency, and energy efficiency through the SKPF and iBeacon indoors. Empirical results in a building show that the SKPF algorithm in our indoor localization system can provide very satisfactory performance in aspect of positioning accuracy and computational cost compared with KF, UKF, and PF. It is also shown in the test results that the positioning system using iBeacon signal as a location feature for the fingerprinting method can achieve more energy-efficient localization than using WiFi signal. Our future research is to apply our localization system to very different scenarios such as 3D indoor environments, along with more tests for the validation for the system.References


Medium exchange is the process of changing the suspension medium of cells/particles, and has applications in washing, surface modifications, nutrient replenishment, or simply changing the environment of the target entities. Dipping involves diverting the path of target cells in the carrying fluid to immerse them in another fluid for a short duration, and pushing them again into the original medium. In this paper, a simple microfluidic platform is introduced that employs dielectrophoresis to achieve medium exchange and dipping of micro-objects in a continuous manner. The essential feature of the platform is a microchannel that includes two arrays of microelectrodes that partly enter the bottom surface from both sides. In the first step, numerous finite element-based parametric studies are carried out to obtain the optimized geometrical and operational parameters ensuring successful dipping and medium exchange processes. The results of those studies are utilized to fabricate the platform using standard photolithography techniques. The electrodes are patterned on a glass substrate, while the channel, made out of polydimethylsiloxane, is bonded on top of the glass. Trajectories of blood cells from numerical studies and experimentations are reported, and both results exhibited close agreement. Sample preparation is the first and essential step in any kind of analytical system in the fields of biotechnology, food processing, medical diagnostics, and microassay systems. Often, the success of these analyses relies on the accuracy and the quality of the sample being used. The conventional sample preparation procedures are tedious, require highly skilled labor, and are extremely time consuming. Furthermore, the conventional cell analysis techniques are based on extracting the averaged properties of a population of cells that are prone to inaccuracies because of the heterogeneous gene expression and large variations in the behavior and physical properties, even in the same type of cells. The problem gets more complicated when detections/operations of rare cells, for instance, detection and isolation of circulating cancer cells from blood samples, are involved. Microfluidics has emerged as a promising field with the potential to improve traditional sample preparation methods, diagnostics, and prognostic techniques . The abiThe target micro-objects can be manipulated in two common ways: either trapping them in physical or virtual cages, or allowing them to move in a continuous-flow method. The former method is more cumbersome, and requires a special protocol for successful operation. The continuous-flow based method, on the contrary, offers the advantages of being less labor-intensive, has high throughput, and reduces the time of operation significantly. Continuous cell manipulation methods can be further classified into active and passive methods. Active approaches employ an external force for continuous manipulation of cells into a designated position in a microchannel. In passive continuous-flow methods, the cells are influenced by hydrodynamic forces and the geometry of the microchannel.Various innovative techniques have been used efficiently for effective and controlled manipulation of micro-objects. Magnetic techniques have been utilized to divert the paths of mammalian cells laterally in a ferrofluid solution . PiezoelMedium exchange in microchannels is the process of transporting cells suspended in a medium to another parallel-flowing medium inside the same channel. The target cells are deflected once in the microchannel by applying an external force field. Cell dipping is the process of changing the environment of the target cells twice. The cells are diverted from the first carrying medium to the second one for a short duration, and are then brought back to the original medium . TurbuleWe have introduced a dielectrophoresis-based microdevice to perform cell/particle medium exchange and dipping in a continuous-flow manner. The simple design of the microdevice makes it possible to fabricate the microdevice using standard microfabrication only. Hence, the fabrication of the microdevice is easier, as compared to the devices with top-bottom or three-dimensional vertical electrodes arrangement . MoreoveDielectrophoresis describes the migration of neutral (but polarizable) particles in the presence of a non-uniform electric field . It is aWhen a dielectric object is placed in an inhomogeneous electrical field, its surface charges are redistributed: equal and opposite charges are separated at its boundary, leading to the generation of an induced dipole moment. The direction of translation of the micro-objects depends on the relative polarizability of the target objects compared to the suspending medium. If the micro-object is more polarizable than the medium, it is pushed towards the higher electric field region, and the phenomenon is known as “positive dielectrophoresis (pDEP)”. In the opposite direction, called negative dielectrophoresis (nDEP), the object translates towards regions of lower field strength. Mathematically, the time-averaged DEP force acting on a spherical object of radius tated as  (1)FDEP=The relative polarizability of the micro-objects is described in Equation (2) by the real component of Clausius–Mossotti factor Numerous researchers have introduced a variety of microfluidic platforms for accurate cell manipulation and dipping. In some of the devices, an electrical trap or a physical barrier was created inside the microchannel to cage the cells. The trapped cells, such as red blood cells (RBCs), were then exposed to multiple fluids—reagents or washing mediums—that were introduced either serially, or by changing relative flow rates of the fluids in the case of side-by-side flowing mediums in a single channel . ContinuA schematic diagram of the proposed nDEP-based dipping and medium switching device is shown in In addition to the DEP force, the microparticles are subjected to three other significant forces during their motion inside the microchannel. These forces are sedimentation (the combined effect of gravity and buoyancy forces) and drag forces, as shown in Equation (3). Other effects, such as Brownian force, Joule heating effect, virtual mass effect, particle–particle interaction, particle–wall interactions, and wall repulsion, are assumed negligible, and are neglected ,39 . The−2], and −3]. Moreover, the terms −1], respectively. Finally, the term −2]. The second term on the right-hand side of Equation (2) represents the drag force experienced by a single microparticle in the fluid. For the case of multiple particles, the shielding effect reduces the drag forces for microparticles that are in close proximity to each other. The magnitude of the reduced drag is obtained by multiplying the drag force with a drag correction factor corD given in Equation (4) [In Equation (3), tion (4) :(4)Dcor The value of ailed in .TM are used to perform all the parametric studies. Since the two sets of electrodes are identical, the effect of only one set on target objects is studied.In this section, the effect of numerous geometric and operational parameters on particle trajectories is discussed. The parameters studied include channel height, electrode width/gap length, particle diameter, applied voltage, and the flow rate of both mediums. MATLAB and COMSOL MultiphysicsThe particle trajectories for different values of the channel widths and same flow rates of both fluids are displayed in The next parameter studied is the diameter of the microparticles. In ∇(E·E). Hence, higher applied voltages tend to create a higher nDEP force, pushing the microparticles further away from the electrodes.Finally, the effect of applied voltage on the trajectory of a microparticle was studied. The particle trajectories for different applied voltages (peak-to-peak) is shown in The PDMS-based microdevice  to perfoIn order to make the PDMS microchannels, an SU-8 2025 mold is first patterned on silicon (Si) wafer. Separately, the PDMS base and curing agent are mixed at 10:1 by weight. The mixture is then poured on the mold, degassed, and finally cured at 80 °C for two hours. Both the glass wafer and the PDMS microchannel are cleaned in plasma cleaner, and then aligned under the microscope before they are thermally bonded to each other. A few droplets of ethanol are used to delay the bonding process between the PDMS and glass, and perform the alignment. The microdevice is fabricated using the optimized geometrical parameters—e.g., electrode width, electrodes spacing, channel width, etc.—obtained from numerical analyses.p-p at the frequency of 10 kHz was applied to the electrodes using a function generator. The frequency of 10 kHz is selected to ensure nDEP phenomenon for the blood cells. The effect of the nDEP force on the blood cells is observed under a microscope. After the device was completed, modified blood samples to carry out experiments was prepared. Human blood samples from anonymous healthy donors were obtained after addressing appropriate institutional ethics and privacy concerns. The participants gave their informed consent for inclusion before they participated in the study. The study protocol was approved on the 19 January 2015 by the Ethics Committee of the office of research support at Khalifa University. The protocol for sample preparation is as follows: (1) The blood sample is first centrifuged in the centrifuging machine to separate the cells from the high conductivity plasma; (2) After being separated from the plasma, the blood cells are placed in a low conductivity sucrose/dextrose medium (10 mS/m) . In the Separately, the trajectory of a single RBC under the influence of nDEP with one of the electrode sets is also obtained during the experiment. In conclusion, a theoretical model of a microfluidic device for dipping and medium exchange processes is presented. Numerical parametric studies are performed for different geometrical and operational parameters. A microfluidic device is fabricated using the optimized parameters obtained in the parametric studies. The experiments involving actual blood cells are performed, and agreement can be seen in both the experimental and numerical trajectories. Finally, the presented model is capable of performing dipping and medium exchange processes in many polarizable cell lines, as long as the applied frequency is below their crossover frequencies to ensure nDEP. Moreover, dipping and medium exchange are possible, as long as the right parameters are selected from the presented parametric study in this paper.


Neuromorphic Very Large Scale Integration (VLSI) devices emulate the activation dynamics of biological neuronal networks using either mixed-signal analog/digital or purely digital electronic circuits. Using analog circuits in silicon to physically emulate the functionality of biological neurons and synapses enables faithful modeling of neural and synaptic dynamics at ultra low power consumption in real-time, and thus may serve as computational substrate for a new generation of efficient neural controllers for artificial intelligent systems. Although one of the main advantages of neural networks is their ability to perform on-line learning, only a small number of neuromorphic hardware devices implement this feature on-chip. In this work, we use a reconfigurable on-line learning spiking (ROLLS) neuromorphic processor chip to build a neuronal architecture for sequence learning. The proposed neuronal architecture uses the attractor properties of winner-takes-all (WTA) dynamics to cope with mismatch and noise in the ROLLS analog computing elements, and it uses its on-chip plasticity features to store sequences of states. We demonstrate, with a proof-of-concept feasibility study how this architecture can store, replay, and update sequences of states, induced by external inputs. Controlled by the attractor dynamics and an explicit destabilizing signal, the items in a sequence can last for varying amounts of time and thus reliable sequence learning and replay can be robustly implemented in a real sensorimotor system. Mixed-signal analog-digital neuromorphic Very Large Scale Integration (VLSI) systems emulate the biophysics of cortical neurons and synaptic connections between them using the physics of silicon electronic devices  connectivity. The WTA is a computational primitive that leads to continuous-attractor dynamics that were found to be characteristic for many cortical and subcortical neural networks  neuromorphic processor . The silicon neuron circuits implement a model of the adaptive exponential integrate-and-fire (IF) neuron  is the post-synaptic neuron's membrane potential at the time of the pre-synaptic spike arrival. If Vmem is above the threshold θmem, the post-synaptic neuron must be about to spike, leading to the temporal sequence of pre- and post-synaptic spikes that leads to potentiation of the synapse , whereas if Vmem is below the threshold, the post-synaptic neuron is likely to have just spiked, leading to temporal sequence of spikes that correspond to depression of the synapse. The Ca variable represents the neuron's Calcium concentration, which is proportional to the neuron's recent spiking activity. This is the variable representing the “third factor” in this three-factor learning rule that gates plasticity  protocol Boahen,  and can 2 with approximately 12.2 million transistors.The ROLLS was fabricated using a standard 180 nm CMOS 1P6M process. It occupies an area of 51.4 mmImplementing a neural architecture, or model, on the ROLLS chip involves two steps. First, the non-plastic synapses are configured to create the static part of the neural network. For each synapse, one of four available weights can be selected, alongside with their type (inhibitory or excitatory). In the second step, the parameters of the neuron and synapse circuits are selected by setting the on-chip biases. These parameters are globally shared by all neurons and synapses.In this work, we use a simple 1D Dynamic Neural Field (DNF)  on the ROLLS chip, two types of connectivity networks can be configured.In order to realize a soft-WTA behavior , we define the extent of region around each neuron, in which it will be connected to its neighbors with excitatory synapses. Every connection exceeding this excitatory range will form an inhibitory synapse . Each ordinal node, if active, represents a position in a sequence. Ordinal nodes activate each other sequentially. This is achieved through a set of memory nodes (orange region in the figure): each ordinal node activates the respective memory node, which in its turn activates the next ordinal node. Memory nodes feature strong recurrent connections that lead to their self-sustained activity: memory nodes stay active even when the respective ordinal node is not active any more. Thus, the memory nodes “keep track” of the position in the sequence during sequential transitions .The connectivity pattern, shown in Figure content dynamic neural field  represents the perceptual states and/or actions that can be associated with a sequential position  with plastic synaptic connections (green region). The content DNF is connected to the action system of the agent and sets attractors to generate behavior. It also receives perceptual input during sequence learning that creates activity in this field that corresponds to the recognized, e.g., demonstrated, actions.The condition of satisfaction (CoS) system (lilac region in the figure) detects when each initiated action or perceptual state has reached the intended outcome , which leads to decrease of activation in the DNF. If an item was successfully learned and there's no perceptual input (Laser off), the ordinal population “recalls” the learned item in the activity in the content DNF. Since the DNF is connected to the CoS, successful learning  triggers the transition. When the laser is switched on the DVS_on population, driven by the visual input, becomes active and inhibits the CoS, next item can be learned.During sequence replay, on the other hand, the activity peak in the content DNF is supported by the active ordinal node. When the CoS becomes active and inhibits the ordinal nodes, the activity in the content DNF also ceases. In both cases, the decrease of activity in the content DNF leads to deactivation of the CoS node, which releases the inhibition on the ordinal nodes. The next ordinal node can become active now, driven by the asymmetric connection from the previous memory node.Figure To cope with mismatch, we used populations of 10-20 neurons to represent a neuronal node .Ordinal groups: Each ordinal group contains 20 silicon neurons in our experiments, inter-connected via excitatory synapses in an all-to-all fashion. Silicon neurons in different ordinal groups inhibit each other, forming a WTA network. This allows only one ordinal group to be active at a time.Memory groups: each ordinal group excites a corresponding memory group that contains 10 neurons. Memory groups remain active for the whole trial due to their high self-excitation. Each memory group has excitatory synapses to the next ordinal group. At the same time, they signal whether their corresponding ordinal node has already been activated, by slightly inhibiting it. This ensures that the ordinal node that has not yet been activated receives the highest excitatory input in the transition phase.Content group: every content neuron is connected via plastic synapses with ordinal neurons with the possibility to strengthen synaptic weights toward an active region in the content WTA. Initially, all plastic synapses are depressed (have a low weight) and can only become potentiated once the ordinal and content neurons are co-active.CoS group contains 10 neurons that are externally stimulated upon a keystroke in most experiments here in order to trigger a transition.Figure To achieve the desired behavior of the architecture, parameters specifying neuron and synapse dynamics have to be set in order to meet certain requirements posed on neural populations. Since ROLLS chip features only 4 different excitatory weights, we additionally used potentiated plastic synapses to strengethen connections within ordinal and memory groups. Figure Figure Figure The neural activity is initially induced by an external input according to the following stimulation protocol. First, the sequence gets “launched" by the stimulation of the first ordinal group of neurons for 3,000 ms with 200 Hz spiking input applied via virtual synapses . The transition signal between sequential items, which suppresses the activity of ordinal neurons, is triggered by stimulating the CoS group for 500 ms with 800 Hz. An input to the content neurons, which will be replaced with sensory input in section 3.1, consists of Poisson spike trains with firing rate distributed along the content population according to a Gaussian-function, centered at a selected “current item” neuron  and leads to strengthening of the connections between the active region in the content population and active neurons in one of the ordinal populations, according to the on-chip learning rule.reset group which suppresses activity of the memory groups, resetting the ordinal system of the architecture. After the complete suppression of the neural activity in the ordinal system, the recall is triggered by an external stimulation of the first ordinal group . Transitions between states are initiated by an external stimulation of the CoS group and can take place at arbitrary moments in time . Despite this noise, the system is capable to reproduce the A-B-C sequence, thanks to the WTA dynamics of the content population  CoS population. In the recall session, the content WTA/DNF does not receive any external stimulation. Activity of neurons in the content DNF is solely triggered by the formed (learned) associations (potentiated plastic synapses) connecting subsets of ordinal and content populations.Figure Figure As can be seen in Figure Additional handling for repeated items is often required when sequences are learned in recurrent neural networks, e.g., Hochreiter and Schmidhuber . In our Another property of the presented serial order architecture is its ability to forget and update previously learned sequences. When a sequence has been learned, activation of an ordinal node induces activation of a certain region on the content DNF, through the plastic synapses. A strong external input to the content DNF, however, can overcome this activation: the winner-take-all connectivity of the content population induces competition between the old item and a new item. If the external input is strong enough to overcome the global inhibition of the content DNF, new activity peak will be created. This new activity peak, in its turn, suppresses the activity induced through the learned plastic synapses. Thus, the synapses that conduct external input to the content DNF need to be stronger than the potentiated plastic synapses for such reset to work .When a new activity peak is formed in the content DNF, the learning rule leads to potentiation of the plastic synapses leading from the active ordinal node to the newly activated region on the content DNF. At the same time, the synapses to the old item will be slowly “forgotten,” because of the synaptic depression working on synapses with post-synaptic neurons having low  firing rate. Thus, the new sequence can be learned, while the old sequence will slowly be forgotten using the synaptic depression of the on-chip learning rule  state.An experiment designed to test the forgetting mechanism is shown in Figure In this figure we can see that the plastic synapses that formed between the first ordinal group and the item C get depressed after stimulating the content neurons with the new sequence B - A - C a single time . The content DNF population here receives input from a neuromorphic camera Dynamic Vision Sensor (DVS)  protocol, typically used for spike-based communication.The DVS sends events directly as input spikes to the virtual synapses of the content neurons. Each event is a tuple of pixel coordinates  and polarity (pol), which signals whether the detected brightness change is positive or negative (we don't distinguish between events with different polarities here). Each DVS event triggers stimulation of a neuron in the content DNF according to the x coordinate of the active pixelObserving the highlighted region at different positions in the DVS's field of view creates activity at different locations of the content WTA on ROLLS. Simultaneous high activity in the content DNF population and an active ordinal population lead to potentiation of plastic synapses between these populations during sequence learning. During sequence recall, activity of the ordinal populations is transmitted to the content DNF population over the potentiated synapses for each sequential item. The active regions of the content WTA are read-out to set the turning angles for the robot , DVS_on population in Figure The learning phase is followed by a global reset that suppresses the activity of the memory nodes, which otherwise keep track of the unfolding sequence. A sequence recall is triggered by a “go” signal—external activation of the first ordinal neural population and a new population, which sustains its activity through the whole experiments and excites the DVS_on population in order to suppress the CoS signal. The target positions, stored in plastic synapses during learning, are reproduced and drive the robot to turn toward the orientation that puts the cued positions in the center of field of view of the DVS. Once the movement execution toward the recalled orientation is finished, the DVS_on population is inhibited externally leading to activation of the CoS signal and thus a transition in the sequence.Figure In a proof-of-concept demonstration we have shown how sequences can be stored in a mixed signal analog/digital neuromorphic device with on-chip plasticity . Of course, on the current prototype stage, neuromorphic hardware requires a regular computer  to connect the device to the robot and monitor its activity, and an FPGA to be configured efficiently, but in a final embedded application, all computation can happen in the device. The main remaining challenge to achieve this are AER interfaces to different sensors and motors, which are currently being developed  and attractor dynamics of neural fields allow to nevertheless produce reliable behavior with these noisy elements, thus unleaching this ultra-low power of analog neuromorphic hardware for practical use.The proposed neuromorphic architecture presents a crucial building block for complex “cognitive” neuromorphic robotic systems, since sequence learning and sequence generation are key to the most basic robotic tasks, such as map formation  or production of motor sequences. Showing that sequences can be learned, updated, and replayed with flexible timing on a neuromorphic device is thus a crucial stepping stone for neuromorphic cognitive robots and for autonomous learning using plastic on-board synapses that realize a local learning rule.The particular model for sequence representation, used here (Sandamirskaya and Schöner, RK and DA were the main driving force in neuromorphic realization and conducting experiments with the robots, NQ and GI provided support with neuromorphic hardware and bias tuning. YS has initiated the project, provided the main idea, and guided its implementation in neuromorphic hardware. All contributed to writing.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.


Since the honeybee possesses eusociality, advanced learning, memory ability, and information sharing through the use of various pheromones and sophisticated symbol communication , this remarkable social animal has been one of the model symbolic animals for biological studies, animal ecology, ethology, and neuroethology. Karl von Frisch discovered the meanings of the waggle dance and called the communication a “dance language.” Subsequent to this discovery, it has been extensively studied how effectively recruits translate the code in the dance to reach the advertised destination and how the waggle dance information conflicts with the information based on their own foraging experience. The dance followers, mostly foragers, detect and interact with the waggle dancer, and are finally recruited to the food source. In this review, we summarize the current state of knowledge on the neural processing underlying this fascinating behavior. Apis mellifera) is well known to possess the ability to communicate to its nestmates to convey the locational information of a profitable food source she has visited [The honeybee  of error from a mean direction of all waggle runs that a dancer performed ,10,11. T–15° of eRecent advances in the understanding of the dance shine a light on the importance of odors during dancing. Successful foragers may encounter flower odors during nectar collection. If a dancing bee has an odor that the follower experienced previously during her successful foraging, this follower will fly to her previous feeder even if the dance indicates another feeder . DancingAlthough the outcomes of bee activities  vary widely among colonies, sometimes negative , it is wApis florea, considered the most primitive in Apis, has a habit to build an open-nest, not like A. mellifera, which suggests this species could use visual signals for their communication including the waggle dance. Other Apis species, A. mellifera and A. cerana live in lightless hives, which suggests these species must use other than visual sensory signals. Honeybees possess olfactory communication for sharing floral scents. Before and after the waggle dance, the dancers often contact their mouthparts with their hive mates via trophallaxes [2+ imaging [hallaxes . This trhallaxes , suggesthallaxes . These ohallaxes . In the  imaging  and asso imaging . UnfortuAs mentioned above, the honeybees are attracted toward the learned odor, as it is associated with a reward. In the hive it is difficult to analyze the odor-induced behavior because it is often disturbed by congestion. Therefore, in our study, tethered insects with a fixed dorsal tergum on a floating ball were used to analyze the behavior of individuals free of such disturbance. We analyzed the locomotion patterns induced by reward-associated odor on proboscis extension response (PER) conditioning. In response to the learned odor, bees walked locally with alternate left and right turns during odor stimulation to search for the reward-associated odor source. Just after the learned odor stimulation, bees walked long paths with large turn angles to explore the odor plume . It suggThe waggle dance produces two types of vibration: First, at low frequency (15–25 Hz) through abdominal movements from one side to the other side and second, at high frequency (250 Hz) through wingbeats. These vibrations can be transmitted on the surface of the “dance floor” . The forSince von Frisch’s discovery, it has not yet been clarified which sensory signals in the waggle dance are critical parameters for encoding the distance to the profitable flower. Frisch and Lindauer  analyzedNear-field sound is detected by the vibration-sensitive sensory organs in the antennae, called Johnston’s organs (JO) . It has Drosophila, the antennal mechanosensory center including the arborization of the vibration-sensitive interneurons has been identified and is referred to as the antennal mechanosensory and motor center (AMMC). The AMMC has five zones, A–E, which have somatotopy [The central projection of JO has been identified ,43. The matotopy ; neverthHoneybees, like other insects, accumulate electric charge in flight, and when their body parts are moved or rubbed together. Greggers et al.  suggesteThe dancer indicates the azimuth to a target by their body orientation in the waggle run. How does the follower detect the dancer’s body orientation? Most followers face the dancer laterally and extend their antennae towards her body to get in direct contact . In the The duration of the waggle run increases proportionally with the distance to the flower source . During In addition to the particle velocity vibrations caused by wingbeats, waggling movements both cause tactile contact of a follower’s antenna with the dancer’s body and modulate the electrostatic field. They may also function as a signal related to the waggle dance and pick up the low frequency component of abdomen wagging ,31,46. TAll the studies focusing on the potential neural substrates of distance coding have been limited to the pulses of high frequency airborne signals as received by the JO. Although the duration of the waggle run correlates well with the distance flown by the dancer, the other components of the waggle run correlate also with the distance, the number of waggles, the length of the waggle run and the duration of the whole round including waggle run and return run. Furthermore, the high frequency components of airborne signals from the vibration of the wings are just only one of several stimuli produced by the waggling dancer. It will be important for future work to include all these signals and study the whole range of frequency components. The picture emerging from the data collected so far allows to propose a first glance into the potential neural circuitry of distance coding.A fundamental approach to the analysis of local neural circuits with a specific function is the measurements of neural response to a stimulus related to the behavior. For analysis of a local circuit for waggle dance information processing, neural responses in the DL, were recorded to a waggle dance-mimicking vibration stimulus on the antenna . The recMorphological modeling and morphometric analysis of neurons could represent a useful approach for analyzing the neural circuit. Neurons have a complex structure similar to tree branches, and they have own characteristics in their arborization patterns. Software for automatic extractions of morphology from confocal and electron microscopy images are being developed and applied to various neural segmentations, but trial and error approach is still needed to extract complex neural structure. For example, we are currently extracting morphologies and constructing the model of neurons, which are arborizing in the DL region, by combination of automatic and manual segmentations . CurrentThe standard brain of the honeybee, HSB, is used as the platform for integrating morphological information of neurons . The regAt present, the estimation of synaptic strength is generally analyzed based on the positional relationship between axons and dendrites of neurons based on light microscope images. However, further experimental studies would be necessary for determining synaptic strength more accurately. A detailed analysis based on the electron microscope image would be suitable for analyzing details of the synaptic connection morphologically. However, questions still remain, for example, about what the functional or actual strength is. Simultaneous measurement of multiple neuron responses by multiple electrodes or optical recording method might provide us with the dynamical properties of neural connections . By applWhile waggle dance communication of honeybees has been studied at the behavioral level for decades, investigation of the underlying neural processes has only recently begun ,42,43, ahttp://modeldb.yale.edu/239413). When stimulation with vibration stimuli of different pulse parameters was simulated, the model neurons showed responses that qualitatively resembled the corresponding experimental data . Properties and timing of the responses of these neurons suggest a disinhibitory circuit involved in the processing of waggle dance vibration pulses D. Disinhtal data A. The inEvidence for the relevance of the interneuron circuits in the honeybee antennal mechanosensory center for the waggle dance communication comes from investigations of changes in these neurons during maturation . Adult hKumaraswamy et al.  analyzedConsistent with the specific changes observed in morphology, the responses of DL-Int-1 neurons showed changes indicating specific adaptations in signal processing. While the main features and overall pattern of response  were preserved across age groups, there were quantitative changes between young and forager bees, notably an enhanced difference between baseline and inhibitory response, and increased response amplitudes in on-phasic response as well as post-inhibitory rebound . These cWhile the question remains open whether the observed differences between young and older honeybees are a result of a predetermined maturation process or of sensory experience, these findings are in line with patterns of development in other structures ,68 and iThe honeybee recruits that decipher the spatial information encoded in the waggle dance start their flights at the hive entrance. During the foraging flight the foragers orientate themselves to the indicated direction. As a result of sunlight scattering in the atmosphere, the skylight is partially plane-polarized, and the celestial e-vectors (electric-field vectors of the light waves) are arranged in a concentric pattern around the sun ,70. It iPolarization vision in insects is mediated by a specialized region in the compound eye called the dorsal rim area (DRA), in which the ommatidia have extremely high polarization sensitivity because of their structural and physiological properties. In honeybees, the rhabdom is non-twisted and contains orthogonally arranged microvilli ,77, whicSkylight polarization is a line symmetrical pattern in which the solar meridian is the axis. Therefore, e-vector information from the sky indicates the orientation of the animal’s body axis relative to the solar meridian. However, the e-vector alone is not sufficient to deduce the heading azimuth . How does the bee uniquely identify its heading azimuth? It has been reported that the bee discriminates between the solar and the antisolar half of the sky based on the spectral cue of the skylight . In addiBehaviorally, it has been proven that honeybees estimated their travel distances by optic flow, an image flow caused by self-motion. Foragers flying through short, narrow tunnels with visually textured walls perform waggle dances that indicate an overestimated flight distance ,94. BehaIn this review we summarized the current research related to waggle dance communication and its potential neural mechanisms. We are still far from understanding the complete neural mechanisms for encoding and processing the waggle dance information, but our neuroethological approach combined with computational analyses using the HSB has been fruitful in the past and offers a promising path for future progress.


Force sensing system (FSS) is widely used to simulate the control force of aircrafts for pilots. Conventional FSS employs multiple single-axis motors and complex transmission mechanisms to achieve multiple degree-of-freedom (DOF) force output of joystick, which may cause mismatched inertia and affect the output performance of FSS significantly. Therefore, one novel FSS with multiple DOF direct-drive spherical actuator is proposed in this paper to reduce the simulator’s extra inertia. To analyze its output performance systematically, a hybrid modeling method is proposed to formulate both Ampere torque and cogging torque mathematically. Equivalent current method along with Ampere force law is used to obtain the Ampere torque due to irregular structure of magnet and coil poles. The cogging torque is then obtained from airgap flux density via Maxwell stress method. From the derived analytical model, an adaptive particle swarm optimization (PSO) algorithm based on expectation  is proposed for multiple-parameter structure optimization. It can avoid local optimization effectively. The study shows that the optimized value greatly helps to improve the torque generation. Then, one research prototype and one testbed is developed. The comparison between experimental result and analytical model shows that the two sets of data fit with each other well. Therefore, the analytical model could be employed for motion control of the system at the next stage. Force sensing system (FFS) is an aircraft utility device to provide pilots with the sense of control force feedback from the rudder load ,2,3. It The inertia-redundant force greatly affects the performance of FSS ,16. TradTherefore, this paper proposes a novel FSS driven by spherical actuator with two-dimensional (2D) camber Halbach array to reduce the inertia-redundant force ,20,21. SThe rest of this paper is organized as follows. As shown in As shown in A are then defined as follows: the radial distance r is the distance from the origin O to A; the inclination (or polar angle) z axis and the line segment x axis to the orthogonal projection of the line segment As shown in As shown in We assume that the ferromagnetic material works in the linear portion without hysteresis, and the permeability of ferromagnetic material is much greater than that of air gap . ConsideThe cross-sectional drawing of the magnetic circuit is shown in To obtain the output torque generated by one coil, we assume the coil consists of numerous differential segments. It can be verified that only the radial component earchers  In additN is the number of winding turns. i is magnitude of current input, i-th PM pole in R is rotor radius. Since the radial force does not produce torque, we have where torque component To facilitate the calculation of Ampere force, the stator windings are equivalent to magnets and PMs are equivalent to coils. The coil assumes a conical-shaped object embedded in the stator shell to facilitate the formulation of the actuator torque . The mag PM pole ,37. ThusThus, the total electromagnetic torque by a single coil isz-axis. Thus,i-th iron core, i-th iron core, and R is rotor radius. Since the radial magnetic field force does not produce torque, we obtain radial torque asAccording to Maxwell’s stress equation, we can obtain two formulas of magnetic force between magnets asThus, the total cogging torque by a single coil i-th PM. Thus, the x-axis component of the electromagnetic torque of the entire actuator isBy summing whole coils’ torque x-axis component of total cogging torque, r direction, and Therefore, the output torque of the actuator isTx+Txf=2JConsidering the cogging torque of the actuator, the actual output torque of the 2-DOF actuator ism particles in a dimensional search space; the position of particle i is defined as D is dimension; the individual optimal position is i-th particle in generation w is inertial weight, Traditional PSO can be expressed as follows: assume a particle swarm composed of In traditional PSO algorithm, the particle quantity is difficult to be determined. The calculation result may not meet the accuracy requirement due to improper selection of particle quantity. Therefore, a novel adaptive PSO algorithm with anti-local optimization is proposed. An external nested loop algorithm is embedded to increase or decrease particles quantity in PSO iteration. Then, the PSO algorithm conducts iteration in double learning loops, gradually optimizing the number of particles, at the same time modifying the final error to meet the optimization requirement. The structure of the proposed adaptive algorithm is shown in P is the total number of particles, L is the particle layer of PSO algorithm, Y is the actual output. The expectation is average value of minimum errors and is used to evaluate whether the PSO algorithm achieves the optimization goal. When the PSO iteration number meets the requirement, the attenuation rate D is defined asThe modification rule of particle quantity in the adaptive PSO algorithm is as follows. In the first place, the initial particle quantity in the second layer of PSO algorithm is set by considering the actual situation, in this case 5. Then, the PSO algorithm iterates to modify the number of particles (increase or decrease), until it reaches iteration requirement. The PSO algorithm expectation definition is given asT-th iteration, T, calculate Initialize the parameters of PSO algorithm randomly. When the number of iterations reaches If If If If In this study, the cost function isThe iteration accuracy curves of PSO from 5 to 35 particles are shown in The calculation results are shown in The electromagnetic parameters of the initial, PSO and Adaptive PSO designs are calculated, and the torque outputs are compared. The comparisons of output torque The torque output of 2D longitudinal camber Halbach PM array with ferromagnetic structure is compared with that of traditional alternative radial magnetization PM array with air-core coil. The major numerical model parameters is shown in Generally, nonlinearity is not preferred for the design of electric actuators. However, the spherical actuator proposed in this paper is applied to the FSS of the flight simulator. The variation of torque with respect to the rotor orientation helps the pilot to feel the external loading from rudders. Therefore, the nonlinearity of output torque is necessary.The inertia moment comparison between new FSS and traditional FSS is shown in One research prototype is developed as shown in The experimental testbed is shown in To further observe the variation of torque output and validate the analytical torque model, the experimental results are compared with the analytical model. The same structural parameters are employed for experimental and analytical computations. The comparisons of output torque Differences of between experimental result and analytical model may be caused by the geometrical approximation of magnet poles, specifically spherical crown entity in analytical model whereas block PM poles for the prototype development due to the convenience of fabrication. The stator shell and coil iron cores are geometrically approximated in analytical model for the convenience of computation.One novel FSS with spherical actuator is proposed in this paper to reduce the simulator’s redundant inertia force. A hybrid approach is employed to formulate the torque output analytically. Specifically, the Ampere torque is modeled from the equivalent current method, and the cogging torque is obtained from Maxwell stress method. Subsequently, an adaptive PSO method with expectation is proposed for design optimization. It can avoid local minimum value, and obtain global optimization conveniently. The simulation result shows that the optimization can improve the torque generation greatly. The torque comparison between the proposed and the traditional designs shows that the former can greatly improve the output torque and provide torque ripple to perceive the variation of rudder load. Compared with the traditional FSS, the proposed one can reduce the inertia-redundant force and improve the torque servo performance. Based on the optimum values, one research prototype as well as one testbed has been developed. Experiments are conducted on the output torque of the research prototype. The experimental result is compared with the analytical model. It shows that the analytical model fits well with the experimental result. Therefore, the validated analytical torque solution could be used for future studies on motion-control implementations.


Wire ropes used in various applications such as elevators and cranes to safely carry heavy weights are vulnerable to breakage or cross-sectional loss caused by the external environment. Such damage can pose a serious risk to the safety of the entire structure because damage under tensile force rapidly expands due to concentration of stress. In this study, the magnetic flux leakage (MFL) method was applied to diagnose cuts, corrosion, and compression damage in wire ropes. Magnetic flux signals were measured by scanning damaged wire rope specimens using a multi-channel sensor head and a compact data acquisition system. A series of signal-processing procedures, including the Hilbert transform-based enveloping process, was applied to reduce noise and improve the resolution of signals. The possibility of diagnosing several types of damage was verified using enveloped magnetic flux signals. The characteristics of the MFL signals according to each damage type were then analyzed by comparing the extracted damage indices for each damage type. For automated damage type classification, a support vector machine (SVM)-based classifier was trained using the extracted damage indices. Finally, damage types were automatically classified as cutting and other damages using the trained SVM classifier. Wire ropes are used in various applications such as elevators and cranes to safely carry heavy weight. However, local damage such as breakage or cross-sectional loss can be caused by the external environment .Damaged parts of the wire rope under tensile force can pose a serious risk to the safety of an entire structure as the damage can rapidly propagate due to concentration of stress. To minimize such risks and accurately diagnose local damage at early stages, a suitable nondestructive evaluation (NDE) technique is required. However, small size damages are not easily detected due to the long length of wire ropes and complex structure of their cross sections. As a result, some wire ropes are used under dangerous conditions .To overcome this situation, a magnetic-sensing-based NDE offers an efficient method of detecting defects in wire ropes because wire rope is made of ferromagnetic materials, which are easily magnetized. In addition, this method is a non-contact NDE, which enables rapid diagnosis. In this method, a wire rope is magnetized using a strong magnetic field, and a magnetic sensor detects variations in the magnetic flux density or leakages of the magnetic field caused by defects in the wire rope ,4,5.Among various magnetic NDE options, the magnetic flux leakage (MFL) method is a particularly suitable diagnostic technique for wire ropes due to their continuous ferromagnetic structures. Currently, the MFL technique is widely applied to the diagnosis of various ferromagnetic continuum structures such as pipes, railways, and wire ropes ,7,8,9. MIn this study, the possibility of MFL-based damage detection of various damage types including not only cutting but corrosion and compression were investigated through an experimental study. Furthermore, characteristics of the MFL signals were analyzed to classify damage type.Wire ropes are generally made using steel materials, which is a ferromagnetic material that can be easily magnetized. When the ferromagnetic wire rope is sufficiently magnetized, the wire rope has the same characteristics as the magnet, and the wire rope and the magnetizing yoke form a closed magnetic path.When local damage such as cutting or cross-sectional loss of the wire rope occurs, the cross-sectional area through which the magnetic field is passing is reduced at the damaged point. Since the air in the gap of damage cannot have sufficient magnetic field density as the magnet, the magnetic flux in the closed magnetic path leaks at the damaged point to the surrounding space. To establish magnetic flux in a sample to be diagnosed, a magnetization yoke that can create a strong magnetic field magnetizes the wire rope specimen. If no damage has occurred, the magnetic flux flow in the specimen remains uniform. MFL is generated if local defects are present, as shown in An MFL signal can be measured by scanning a specimen which is by measuring the strength of the magnetic field. Magnetic sensors are normally placed between the poles of the magnetization yoke to measure magnetic flux stability. In this study, In-Sb Hall sensor, HW300A model , based oHI) is allowed to flow between the input terminals a–b of the hall sensor and a magnetic field is applied to the Hall sensor, electromotive force is generated between the output terminals c–d by the Lorentz force. Therefore, Hall output voltage (HV), which is potential difference is generated in proportional to the magnetic flux density B [θ, Hall output voltage can be obtained by multiplying the magnetic flux by the sinθ. The Hall voltage signal can be measured and filtered and amplified through a data acquisition (DAQ) device. The measured Hall voltage signal indicating the magnetic flux can be used to diagnose the condition of the specimen by analyzing the variations in signal patterns.When a constant current  was performed to improve the resolution of the signal. First, high-frequency electrical noise was removed by low-pass filtering. The drift and offset were then removed by extracting the low-order mode waveform and subtracting it from the raw magnetic flux signal.After de-noising, a Hilbert transform-based enveloping process was carried out to clarify the flux leakage and improve the sensitivity of damage detection . This enThree-dimensional (3D) visualization was applied to create an effective representation of the diagnostic results. Magnetic flux values, measured simultaneously on eight channels, were plotted in a 3D graph. The x-axis is the distance along the wire rope specimen and the y-axis is the measurement channel number that represents the position of the sensor in the circumferential direction. The amplitude of the magnetic flux is color-coded and displayed on the z-axis. Magnetic flux signals were visualized in a cylindrical shape, similar to a wire rope. As shown in Equation (1), y- and z-dimensional damage indicators were calculated from the enveloped magnetic flux signals for the visualization process.DIY and DIZ are the y- and z-dimensional damage indicators, respectively, cMF is the magnetic flux from the wire rope, r is a virtual radius used for wire rope visualization, and θ is the circumferential angle of the location of each sensing channel [ channel . These cThis 3D magnetic flux graph plots the damage location information in the longitudinal and circumferential directions and shows the level of damage from any angle. Damaged areas are highlighted by the threshold covering process. The circumferential direction and damage size can be represented more efficiently by using cross sections of the 3D magnetic flux graph.In this study, the learning-based support-vector machine (SVM) pattern recognition technique is applied for automatic classification of wire rope damage types. SVM is an automated learning system that classifies the characteristics of training samples by optimal separating hyperplane in high-dimensional feature space . HyperplHowever, when the hyperplane is linear, it generally has a high misclassification rate when applied to nonlinear samples. Therefore, a nonlinear SVM technique, that determines an optimized nonlinear hyperplane by mapping a two-dimensional feature space into a high-dimensional feature space is required for high accuracy classification.An optimized nonlinear hyperplane can be obtained by using kernel function that is defined as )·Ф(xi)) . In thisA series of experimental studies were carried out to examine the diagnostic possibilities for the various types of damage. A linear moving machine capable of moving the sensor head at constant speed was prepared and a damaged wire rope was fixed to its top. To obtain magnetic flux signals, the experimental set-up was composed of a multi-channel MFL sensor head to scan the magnetic flux of each wire rope specimen and a DAQ equipment with a terminal board to operate the Hall sensors, as shown in A multi-channel MFL sensor head was fabricated by assembling eight independent sensing modules, each of which was composed of a Hall sensor and a neodymium magnet yoke, that can independently measure magnetic flux signals. These eight modules were circumferentially arranged in a circular configuration to measure eight magnetic flux signals simultaneously from the entire cross section of the wire rope ,21, as sThe eight sensing modules were connected using elastic rubber O-rings to maintain a constant lift-off, which is important for constant sensitivity . This meIn order to obtain magnetic flux signals from various types of damage, three steel wire rope specimens (10 mm in diameter and 800 mm in length) were machined to contain cutting, corrosion, and compression damages.The linear moving machine was used to scan the fixed wire rope while moving at a constant speed. The MFL sensor head was connected to a bracket of the linear moving machine using a linear bushing and moved along the fixed wire rope specimen at a constant velocity of 2 m/s to measure magnetic flux signals. A set of compact DAQ devices was used to measure the eight magnetic flux signals simultaneously via eight sensing modules as the MFL sensor head moved. The sampling rate was 10 kHz, and signals were measured 40 times along each specimen. Signal processing for noise reduction and the enveloping process based on the Hilbert transform were performed to facilitate effective damage detection.For comparison with the results measured from the corrosion and compression damage shown in In , magnetiIn addition, the most sensitive sensing channel for damage was the one nearest to the damaged point in the circumferential direction. In addition, lower level of MFL signals were detected in the ambient sensing channels. This shows that the sensitivity for cutting damage detection can be improved when the distance between the damage and the sensing channel is made smaller. Therefore, it is shown that the circumferential direction of damage can be estimated using this NDE method in cutting damage case.For the MFL measurement experiment used for corrosion diagnosis, the same set-up that was used to detect cutting damage was employed. Two identical wire rope specimens with three corroded areas were prepared, as shown in A 100 mm long segment of corrosion damage was formed at the mid-point (400 mm) of a wire specimen after being wetted by a chemical reagent for 10 days. Additionally, 30 mm and 60 mm long segments of corrosion were formed on either side of the specimen, 250 mm from the center, by being wetted by a chemical reagent for five days, as shown in In the specimen with corrosion damage, unlike the specimen with cutting damage, it was difficult to specify the sensing channel due to the difficulty of quantitative precision processing. No quantitative index could be used to measure the progress of corrosion, and only the ability to detect damage could be judged. The sampling rate was 10 kHz, the sensor head moved at a speed of 2 m/s, and the signal was measured 40 times.MFL measurement results from the two corroded specimens are shown for each channel in The shapes of the signals are different, but the following common results can be seen in both In addition, because the reduction of the cross section was slight, the amplitude of the MFL signals was not large, relatively wide and gentle peaks were generated at the corroded point.The leaking magnetic flux signal of the first corroded area (at approximately the 140 mm point) exceeded the threshold by slightly different values in some sections and some channels. However, for the third corroded area (at approximately the 680 mm point), MFL signal of just over the threshold in magnitude was detected in some channels only in the case of corrosion specimen #2, but none of the channels could detect the damage in the case of corrosion specimen #1; the measured flux was lower in magnitude than the threshold in all channels. In the case of MFL signals detected from corrosion damage, even if the magnitude of the peak was not large, the MFL signals were detected across all channels (as opposed to a specific measurement channel) to show the shape of the corrosion damage. This can be seen in the above results and As it was confirmed that leaking magnetic flux signals can be measured together in all channels, it appears that corrosion damage can be detected effectively using the sum of the total magnetic flux of all channels. Magnetic flux signals from all each sensing channel were summed to calculate the total flux value. When using the total flux signal as shown in In the results for corrosion specimen #1 , the firIn the diagnosis results of corrosion specimen #2 , the thiThrough the above procedure, it was confirmed that cutting damage and corrosion damage can be diagnosed with the MFL method. An MFL signal was measured at the starting point and end point of a damaged area. It is assumed that the surface was oxidized by the progression of corrosion and that the magnetic component of the corroded section changed and divided into separate magnetic sections. Alternatively, an MFL signal may have been caused by a change in the roughness at the surface, where minute damages are emerge.The MFL signal generated by corrosion had a small peak and a relatively gentle slope. In addition, MFL signals were uniformly measured across all channels, rather than being sensitively measured by a particular channel, confirming that the total flux method is effective for diagnosing corrosion damage. Shape collapse damage occurs when the cross section of a wire rope deforms without breaking. This can be caused by excessive loads when driving the wire rope, mechanical abnormalities, and external impacts. However, a wire rope is usually under a constant load during use and collapse damage may not be visible, making it impossible to visually detect this type of damage during use. In addition, because there is no change in the cross-sectional area of the wire rope, there should be no theoretical change in magnetic flux. It is therefore generally expected that this type of damage cannot be diagnosed using MFL-based NDE equipment and little research has been conducted to confirm the diagnostic possibility of collapse damage.This study examined the possibility of damage diagnosis of compression damage for various types of collapse damages. To prepare a specimen damaged by compression, three kinds of crushing damage were formed on a wire rope , as shown in Compression damage was formed at 250, 400, and 550 mm along the wire rope; these are referred to as compression damages no. 1 to no. 3, from the left to the right, respectively. To form compression damage, the wire rope was crimped in the up and down directions so that deformation would occur only in the shape of the cross section without any loss of the wire. Compression damage no. 1 had a flatness of 82%, with a minimum diameter of 9 mm and a maximum diameter 11 mm. Compression damage no. 2 had a flatness of 67%, with a minimum diameter of 8 mm and a maximum diameter of 12 mm, and compression damage no. 3 had a flatness of 75%, with a minimum diameter of 9 mm and a maximum diameter of 12 mm. The sensor head had moved at a constant speed of 2 m/s and the magnetic flux signal was measured 40 times at a sampling rate of 10 kHz.The test results for compression damage detection are displayed in MFL signals were observed at 250, 400, and 550 mm along the rope, corresponding to compression damage areas 1, 2, and 3 respectively. Enveloped signals at all three damaged points exceeded the threshold value, and therefore compression damages could be detected. In addition, the largest leakage flux was shown at area 2, which corresponds to the greatest pressed damage. This demonstrates the possibility of quantifying the amount of compression damage. Looking at the shape of the leakage signal in the magnetic flux signal, a W-shaped MFL signal appeared around the center of the pressed damage. This is in contrast to the corrosion damage case, in which the MFL signal appeared at both ends of the damage. In addition, as shown in The total flux was obtained by the same method as the corrosion damage case, as shown in In general, detection of magnetic flux leakage should be difficult in the case of compression damage according to the basic principles of the MFL method. Because, in order for the MFL to occur, the cross-sectional area that the magnetic flux passes through must be changed. However, in the case of compression damage in this study, only the shape of the cross-section changed and there was no change in the cross-sectional area.However, an MFL signal was detected in the above experiment, likely because magnetic flux leaking from the surface was measured apart from the MFL signal. The magnetization intensity of the system was oversaturated above the knee point . An instantaneous change occurred in the measured value of the magnetic flux signal due to the change in magnetization intensity caused by the change of lift-off. This was due to the momentary upward and downward movement of the sensor module when passing through an area that has experienced crushing damage.The possibility of damage detection using the MFL technique was investigated for damage types commonly experienced by wire ropes: cutting, corrosion, and compression damages. According to the experimental results, the MFL method can detect all the three types of damage. However, the MFL signal of each type of damage was slightly different. The characteristics of the MFL signal according to the damage type are summarized below.For the purpose mentioned, the measured MFL signals from corrosion, compression, and cutting (with a cut depth of 0.5 mm) are compared. First, as shown in For cutting damage, as shown in This response could change if the cutting damage occurred in the entire circumferential direction, but the probability of such damage in real wire ropes is low, and this study did not consider that type of multiple and complex damage.For corrosion and compression damage, similar, relatively low leakage fluxes were detected in all sensing channels for both types of damage. To utilize these characteristics, the ratio between the MFL magnitude of a single channel where the maximum peak appeared and the total MFL magnitude of all the channels was applied as an index. The average magnetic flux signal was obtained by dividing the total flux by the number of sensing channels in the sensor head. To distinguish differences by type of damage, the magnetic flux signal where the maximum peak appeared and the average magnetic flux signal were compared, as shown in First, a comparison of the maximum magnetic flux signal detected by sensing channel #3 and the average magnetic flux signal in the case of the cutting damage shown in To quantify this characteristic, two kinds of indices were extracted. The peak of the single channel was divided by the sum of the flux values of the remaining seven channels to calculate the peak/total-7 ratio (PT7).Moreover, when the size of the damage was large, MFL signals could be detected even in the multiple sensing channels near the damage due to the blooming phenomenon. To maximize the ratio of the total flux to a single peak, the total flux 5 through the sum of the magnetic flux from five channels excluding three channels near the maximum peak detected was calculated. The maximum peak was then divided by the total flux 5 value to calculate the peak/total-5 ratio (PT5).To analyze the characteristics of individual MFL peaks, examples of single MFL peak from each type of damage are displayed in By comparing the peaks of the cutting, corrosion, and compression damages, it becomes clear that the peak from cutting damage was narrower and larger in magnitude than the peaks associated with other damage types. This becomes even more apparent as the depth of the cutting damage gets increased. Corrosion and crushing produced shorter and wider peaks.Due to the above characteristics, the frequency characteristics of the MFL peak were also affected according to the damage type. To analyze this, the power spectrum of the peak of each damage type was obtained through fast Fourier transform (FFT). These results are plotted repeatedly in As shown in The position of the maximum peak is indexed to levels and shown in As shown in By comparing the characteristics of the MFL signals for each type of damage, several features were found to distinguish cutting from the other types of damage. The usable features were the number of simultaneous detection channels at the time of damage detection and the ratio of the height and width of the peak. These were quantified by determining the magnetic flux of a single channel divided by the total flux and the frequency range of the maximum amplitudes in the power spectra, respectively. By using these indices, it is expected that the damage type can be separated into two types: cutting and other types of damage. Based on the relevance of the accident occurrence, the scope of this study was limited to classifying the damage type into cutting and others (including corrosion and compression).To train a classification to automatically classify the damage type as cutting or others, a support vector machine (SVM)-based pattern recognition technique was applied. When the magnetic flux signal measured through the sensor head exceeded the set threshold value, the part was regarded as damage. A search for the channel on which the largest MFL signal is measured was then made. Damage indices were extracted based on the magnetic flux signal of the channel.Using the three kinds of extracted damage indices as parameters, the SVM-based pattern recognition algorithm classified the type of damage. In this study, PT7, PT5, and peak of power spectrum were used as parameters to train the SVM. Three kinds of indices for training data set extracted from 160 peaks for each type of damage are shown as a 3D graph in The SVM classifier was trained based on the above set of damage indices, and the trained SVM classifier was used for the automatic damage classification for wire rope NDE. To verify the performance of the trained SVM classifier, a new data set collected in the same measurement conditions and times was classified through the trained SVM classifier. The label for cutting damage was set to 1, the label for other damages was set to 2, and the classification results are shown in A total of 480 newly measured MFL peak signals from damage point were classified using the trained SVM classifier, which classified the damage types of 160 as cutting damage and 320 as other damages with 100% accuracy. We therefore concluded that the damage type of wire rope can be effectively estimated by classifying the damage type automatically using the trained classifier based on SVM pattern recognition.In this study, three types of damage commonly experienced by wire ropes  were diagnosed using the MFL technique. This was done through a series of experiments, and the characteristics of the MFL signals in accord with to each damage type were analyzed. The possibility of objective damage diagnosis using threshold values was confirmed, and various characteristics of the MFL signal corresponding to damaged areas were analyzed.When detecting cutting damage, MFL signals caused by cutting damage were detected clearly only in sensing channels close to the damaged points. Peripheral sensing channels also detected damage when the size of the damage expanded. On the other hand, in the damage case of corrosion and compression, low-level amplitude MFL was detected evenly in all the sensing channels. It is therefore advantageous to detect corrosion and compression damage using total flux, which is the sum of flux leakages of all channels.Using these characteristics of MFL signals, a damage index was proposed to classify damage type; this index is defined as the ratio between the peaks of a single sensing channel and total flux.In addition, cutting and other damage types also differed by the slope of the peak in the MFL signal; the position of the peak of the power spectrum in the frequency domain can be extracted through FFT analysis to quantify a peak’s slope.The support vector machine method using the three indices extracted was applied to automatically classify the damage type. When classifying the newly measured MFL signals using the trained classifier, the classifier estimated the type of damage as cutting and other damage types with 100% accuracy.Overall, these results demonstrated that the proposed MFL- and SVM-based damage detection and classification method is able to diagnose damages and to estimate the damage type in the wire ropes used in lifting structures.However, further studies are needed on more various types of damages for the in situ application of the proposed method. In addition, a pattern recognition algorithm that can quantify the damage size will need to be studied for an automated damage analysis.It is expected that the MFL-based automated NDE technique can be utilized as an advanced inspection tool for a reliable remote wire rope monitoring system through the convergence with various smart technologies.