Degree Of Freedom Robotic Arm Research Articles

Robotic-based terahertz spectroscopy imaging for non-destructive monitoring of water loss-induced damage in timber materials under low temperature stress

Timber materials are widely used in various industries, from construction to furniture manufacturing. One of the critical factors affecting the structural integrity and longevity of timber is water loss-induced damage, which often occurs under low-temperature stress conditions. Monitoring and assessing this damage traditionally involve invasive and time-consuming methods. This study explores the application of robotic-based terahertz spectroscopy 3D imaging as a non-destructive and efficient system for assessing the impact of water loss on timber materials under low-temperature stress. The detection and localization of damage in a various species of timber-based structures were experimentally investigated by performing pulsed terahertz time-domain spectroscopy and using a six- DOF (degree-of-freedom) robotic arm. The thickness and damage length were monitored by designing the reflection geometry of the terahertz scanner. Additionally, the terahertz source was controlled with a six-DOF robot arm for path planning. The corresponding measured dielectric property reference value for the damage identification was computed using a captured terahertz wave at 12% of moisture content. The reflected signals were analyzed to determine the thickness, defectiveness, and locations of the defects through signal processing in the time and frequency domains. Finally, the validity of the structural health monitoring approach for 3D detection and visualization of water loss-induced defects in timber structures using terahertz waves, was discussed.

Simulation of Kinematic and Dynamic Model of Two Degrees of Freedom Robot Arm Using Simulink

The aim of this study is to realize the physical model of a robot arm with two degrees of freedom in the MATLAB Simulink environment, to show that the physical model of the robot arm matches the mathematical model and that the results of these models match. For this purpose, kinematic and dynamic modeling of a two-degree-of-freedom robotic arm is performed in the MATLAB Simulink environment. To ensure the accuracy of the physical model created in the MATLAB Simulink environment, mathematical equations are defined to a function within the physical model, in other words, a mathematical model is created. The kinematic equations of the robotic arm are examined in two steps as forward kinematics and inverse kinematics equations, and the geometric approach is used when obtaining the kinematic equations. The Lagrange-Euler method is used to obtain dynamic equations. The results obtained show that the mathematical kinematic and dynamic equations match the physical model in the MATLAB Simulink environment and reveal that the system is reliable.

Dynamic Optimization Fabrics for Motion Generation

Optimization fabrics are a geometric approach to real-time local motion generation, where motions are designed by the composition of several differential equations that exhibit a desired motion behavior. We generalize this framework to dynamic scenarios and nonholonomic robots and prove that fundamental properties can be conserved. We show that convergence to desired trajectories and avoidance of moving obstacles can be guaranteed using simple construction rules of the components. In addition, we present the first quantitative comparisons between optimization fabrics and model predictive control and show that optimization fabrics can generate similar trajectories with better scalability, and thus, much higher replanning frequency (up to 500 Hz with a 7 degrees of freedom robotic arm). Finally, we present empirical results on several robots, including a nonholonomic mobile manipulator with 10 degrees of freedom and avoidance of a moving human, supporting the theoretical findings.

The Biomechanical Role of the Deltoid Ligament on Ankle Stability: Injury, Repair, and Augmentation

Background: Deltoid ligament injuries occur in isolation as well as with ankle fractures and other ligament injuries. Both operative treatment and nonoperative treatment are used, but debate on optimal treatment continues. Likewise, the best method of surgical repair of the deltoid ligament remains unclear. Purpose: To determine the biomechanical role of native anterior and posterior components of the deltoid ligament in ankle stability and to determine the efficacy of simple suture versus augmented repair. Study Design: Controlled laboratory study. Methods: Ten cadaveric ankles (mean age, 51 years; age range, 34-64 years; all male specimens) were mounted on a 6 degrees of freedom robotic arm. Each specimen underwent biomechanical testing in 8 states: (1) intact, (2) anterior deltoid cut, (3) anterior repair, (4) tibiocalcaneal augmentation, (5) deep anterior tibiotalar augmentation, (6) posterior deltoid cut, (7) posterior repair, and (8) complete deltoid cut. Testing consisted of anterior drawer, eversion, and external rotation (ER), each performed at neutral and 25° of plantarflexion. A 1-factor, random-intercepts, linear mixed-effect model was created, and all pairwise comparisons were made between testing states. Results: Cutting the anterior deltoid introduced ER (+2.1°; P = .009) and eversion laxity (+6.2° of eversion; P < .001) at 25 degrees of plantarflexion. Anterior deltoid repair restored native ER but not eversion. Tibiocalcaneal augmentation reduced eversion laxity, but tibiotalar augmentation provided no additional benefit. The posterior deltoid tear showed no increase in laxity. Complete tear introduced significant anterior translation, ER, and eversion laxity (+7.6 mm of anterior translation, +13.8° ER and +33.6° of eversion; P < .001). Conclusion: A complete deltoid tear caused severe instability of the ankle joint. Augmented anterior repair was sufficient to stabilize the complete tear, and no additional benefit was provided by posterior repair. For isolated anterior tear, repair with tibiocalcaneal augmentation was the optimal treatment. Clinical Relevance: Deltoid repair with augmentation may reduce or avoid the need for prolonged postoperative immobilization and encourage accelerated rehabilitation, preventing stiffness and promoting earlier return to preinjury activity.

Biomechanical Evaluation of Posterior Shoulder Instability With a Clinically Relevant Posterior Glenoid Bone Loss Model

Background: Existing biomechanical studies of posterior glenoid bone loss and labral pathology are limited by their use of anterior instability models, which differ in both orientation and morphology and have been performed in only a single, neutral arm position. Purpose: To evaluate the biomechanical effectiveness of a posterior labral repair in the setting of a clinically relevant posterior bone loss model in various at-risk arm positions. Study Design: Controlled laboratory study. Methods: Ten fresh-frozen cadaveric shoulders were tested in 7 consecutive states using a 6 degrees of freedom robotic arm: (1) native, (2) posterior labral tear (6-9 o’clock), (3) posterior labral repair, (4) mean posterior glenoid bone loss (7%) with labral tear, (5) mean posterior glenoid bone loss with labral repair, (6) large posterior glenoid bone loss (28%) with labral tear, and (7) large posterior glenoid bone loss with labral repair. Bone loss was created using 3-dimensional printed computed tomography model templates. Biomechanical testing consisted of 75 N of posterior-inferior force and 75 N of compression at 60° and 90° of flexion and scaption. Posterior-inferior translation, lateral translation, and peak dislocation force were measured for each condition. Results: Labral repair significantly increased dislocation force independent of bone loss state between 10.1 and 14.8 N depending on arm position. Dislocation force significantly decreased between no bone loss and small bone loss (11.9-13.5 N), small bone loss and large bone loss (9.4-14.3 N), and no bone loss and large bone loss (21.2-26.5 N). Labral repair significantly decreased posterior-inferior translation compared with labral tear states by a range of 1.0 to 2.3 mm. In the native state, the shoulder was most unstable in 60° of scaption, with 29.9 ± 6.1-mm posterior-inferior translation. Conclusion: Posterior labral repair improved stability of the glenohumeral joint, and even in smaller to medium amounts of posterior glenoid bone loss the glenohumeral stability was maintained with labral repair in this cadaveric model. However, a labral repair with large bone loss could not improve stability to the native state. Clinical Relevance: This study shows that larger amounts of posterior glenoid bone loss (>25%) may require bony augmentation for adequate stability.

A Hybrid Reinforcement Learning Approach With a Spiking Actor Network for Efficient Robotic Arm Target Reaching

The increasing demand for applications in competitive fields, such as assisted living and aerial robots, drives contemporary research into the development, implementation and integration of power-constrained solutions. Although, deep neural networks (DNNs) have achieved remarkable performances in many robotics applications, energy consumption remains a major limitation. The paper at hand proposes a hybrid variation of the well-established deep deterministic policy gradient (DDPG) reinforcement learning approach to train a 6 degree of freedom robotic arm in the target-reach task <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.youtube.com/watch?v=238693OpvD0.</uri> In particular, we introduce a spiking neural network (SNN) for the actor model and a DNN for the critic one, aiming to find an optimal set of actions for the robot. The deep critic network is employed only during training and discarded afterwards, allowing the deployment of the SNN in neuromorphic hardware for inference. The agent is supported by a combination of RGB and laser scan data exploited for collision avoidance and object detection. We compare the hybrid-DDPG model against a classic DDPG one, demonstrating the superiority of our approach.

Analytical and deep learning approaches for solving the inverse kinematic problem of a high degrees of freedom robotic arm

Inverse kinematics is the basis for controlling the motion of robotic manipulators. It defines the required joint variables for the robotic end-effector accurately reach the desired location. Due to the derivation difficulty, computation complexity, singularity problem, and redundancy, analytical Inverse kinematics solutions pose numerous challenges to the operation of many robotic arms, especially for a manipulator with a high degree of freedom. This paper develops different Deep Learning networks for solving the Inverse kinematics problem of six- Degrees of Freedom robotic manipulators. The implemented neural architectures are Artificial Neural Network, Convolutional Neural Network, Long–Short Term Memory, Gated Recurrent Unit, and Bidirectional Long–Short Term Memory. In this context, we associate the proposed results with a specific tuning of Deep Learning network hyper-parameters (number of hidden layers, learning rate, Loss function, optimization algorithm, number of epochs, etc.). The Bidirectional Long–Short Term Memory network outperformed all proposed architectures. To be close as possible to the experimental results, we have included two types of noise in the training data set to validate which of the five proposed neural networks is more efficient. Furthermore, in this study, we compare the performance of analytical and soft computing solutions in generating robots’ trajectories. We include this scenario, focusing on the advantage of implementing neural networks in avoiding the singularity problem that can occur using the analytical approach. In addition, we used the RoboDK simulator to show simulation results with real-world meaning. The performance of Deep Learning models depends on the complexity of the posed problem. Moreover, the complexity of the Inverse Kinematics problem is related to the number of Degrees of Freedom. At the end of this work, we evaluate the influence of the complexity of robotic manipulators on the proposed Deep Learning networks’ performance. The results show that the implemented Deep Learning mechanisms performed well in reaching the desired pose of the end-effector. The proposed inverse kinematics strategies apply to other manipulators with different numbers of Degrees of Freedom.

Development of a prototype 6 degree of freedom robot arm

The study presents a design alternative, simulates the operation, and analyzes the new structure of the 6 DOF robot arm. The proposed robot model is based on reference shapes from AR3 robot arm, with modifications to details and structures to accommodate the actual and economic circumstances in Vietnam. Solidwork software was first utilized for the arm structure's design, strength testing, and optimization. The material is chosen with the goal of reducing production cost and ensuring the robot's operation properly. The study concludes by applying the topology optimization algorithm to a machine component to determine the optimal structure of the robot arm. To validating the design results, the prototype is manufacturers using CNC machine. The experimental results show the robot arm basic functions effectively. This research could be used as references for designing and manufacturing a 6 DOF robot arm with relative robot's structure.

Projection operator‐based robust adaptive control of an aerial robot with a manipulator

AbstractThis paper describes a quadcopter manipulator system, an aerial robot with an extended workspace, its controller design, and experimental validation. The aerial robot is based on a quadcopter with a three degree of freedom robotic arm connected to the base of the vehicle. The work aims to create a stable airborne robot with a robotic arm that can work above and below the airframe, regardless of where the arm is attached. Integrating a robotic arm into an underactuated, unstable system like a quadcopter can enhance the vehicle's functionality while increasing instability. To execute a mission with accuracy and reliability during a real‐time task, the system must overcome the inter‐coupling effects and external disturbances. This work presents a novel design for a robust adaptive feedback linearization controller with a model reference adaptive controller and hardware implementation of the quadcopter manipulator system with plant uncertainties. The closed‐loop stability of the aerial robot and the tracking error convergence with the robust controller is analyzed using Lyapunov stability analysis. The quadcopter manipulator system is custom developed in the lab with an off‐the‐shelf quadcopter and a 3D‐printed robotic arm. The robotic system architecture is implemented using a Jetson Nano companion computer for autonomous onboard flight. Experiments were conducted on quadcopter manipulator system to evaluate the autonomous aerial robot's stability and trajectory tracking with the proposed controller.

Myoelectric human computer interaction using CNN-LSTM neural network for dynamic hand gesture recognition

Human-computer interaction(HCI) has broad range of applications. One particular application domain is rehabilitation devices. Several bioelectric signals can potentially be used in HCI systems in general and rehabilitation devices in particular. Surface ElectroMyoGraphic(sEMG) signal is one of the more important bioelectric signals in this context. The sEMG signal is formed by muscle activation although the details are rather complex. Applications of sEMG are referred is commonly referred to as myoelectric control since the dominant use of this signal is to activate a device even if (as the term control may imply) feedback is not always used in the process. With the development of deep neural networks, various deep learning architectures are used for sEMG-based gesture recognition with many researchers having reported good performance. Nevertheless, challenges remain in accurately recognizing sEMG patterns generated by gestures produced by hand or the upper arm. For instance one of the difficulties in hand gesture recognition is the influence of limb positions. Several papers have shown that the accuracy of gesture classification decreases when the limb position changes even if the gesture remains the same. Prior work by our team has shown that dynamic gesture recognition is in principle more reliable in detecting human intent, which is often the underlying idea of gesture recognition. In this paper, a Convolutional Neural Network (CNN) with Long Short-Term Memory or LSTM (CNN-LSTM) is proposed to classify five common dynamic gestures. Each dynamic gesture would be performed in five different limb positions as well. The trained neural network model is then used to enable a human subject to control a 6 DoF (Degree of Freedom) robotic arm with 1 DoF gripper. The results show a high level of accurate performance achieved with the proposed approach. In particular, the overall accuracy of the dynamic gesture recognition is 84.2%. The accuracies vary across subjects but remain at approximately 90%for some subjects.

Boomerang Algorithm based on Swarm Optimization for Inverse Kinematics of 6 DOF Open Chain Manipulators

In this study, a feasible swarm intelligence algorithm is proposed that computes the inverse kinematics solution of 6 degree of freedom (DOF) industrial robot arms, which are frequently used in industrial and medical applications. The proposed algorithm is named as Boomerang algorithm due to its recursive structure. The proposed algorithm aims to reduce the computation time to feasible levels without increasing the position and orientation errors. In order to reduce the computational time in swarm optimization algorithms and increase feasibility, an alternative definition method was used instead of the DH method in defining the robot arm kinematic configuration. The effect of the proposed alternative definition method in reducing the computational time is presented through example inverse kinematic analysis. The proposed algorithm was compared with 3 different particle swarm optimization (PSO) variants that include orientation in the inverse kinematic solution of 6 DOF robot arms. Comparative simulation studies were carried out with 20 randomly selected position and orientation data from the workspaces of PUMA 560 and ABB IRB120 manipulators to measure performance of the algorithms. Using the error and computation time values obtained from the simulation results, the algorithms are compared using the Wilcoxon nonparametric statistical test. When the simulation results are analysed by considering the calculation time, positioning accuracy and solution finding rates, it is seen that the Boomerang algorithm is more feasible than the other PSO variants. Verification of the simulation results, and the physical applications were carried out with the ABB IRB120 6 DOF robot arm. Simulation studies and experimental studies showed that the proposed algorithm may be an efficient method for inverse kinematics of time-critical applications.

Effect of Posterior Malleolar Fixation on Syndesmotic Stability

Background: Transsyndesmotic fixation with suture buttons (SBs), posterior malleolar fixation with screws, and anterior inferior tibiofibular ligament (AITFL) augmentation using suture tape (ST) have all been suggested as potential treatments in the setting of a posterior malleolar fracture (PMF). However, there is no consensus on the optimal treatment for PMFs. Purpose: To determine which combination of (1) transsyndesmotic SBs, (2) posterior malleolar screws, and (3) AITFL augmentation using ST best restored native tibiofibular and ankle joint kinematics after 25% and 50% PMF. Study Design: Controlled laboratory study. Methods: Twenty cadaveric lower-leg specimens were divided into 2 groups (25% or 50% PMF) and underwent biomechanical testing using a 6 degrees of freedom robotic arm in 7 states: intact, syndesmosis injury with PMF, transsyndesmotic SBs, transsyndesmotic SBs + AITFL augmentation, transsyndesmotic SBs + AITFL augmentation + posterior malleolar screws, posterior malleolar screws + AITFL augmentation, and posterior malleolar screws. Four biomechanical tests were performed at neutral and 30° of plantarflexion: external rotation, internal rotation, posterior drawer, and lateral drawer. The position of the tibia, fibula, and talus were recorded using a 5-camera motion capture system. Results: With external rotation, posterior malleolar screws with AITFL augmentation resulted in best stability of the fibula and ankle joint. With internal rotation, all repairs that included posterior malleolar screws stabilized the fibula and ankle joint. Posterior and lateral drawer resulted in only small differences between the intact and injured states. No differences were found in the efficacy of treatments between 25% and 50% PMFs. Conclusion: Posterior malleolar screws resulted in higher syndesmotic stability when compared with transsyndesmotic SBs. AITFL augmentation provided additional external rotational stability when combined with posterior malleolar screws. Transsyndesmotic SBs did not provide any additional stability and tended to translate the fibula medially. Clinical Relevance: Posterior malleolar fixation with AITFL augmentation using ST may be the preferred surgical method when treating patients with acute ankle injury involving an unstable syndesmosis and a PMF ≥25%.

Robot and ultrasound assisted needle insertion to the transverse carpal ligament

BackgroundA potential alternative treatment to surgery for carpal tunnel syndrome is to inject enzymes into the transverse carpal ligament to decrease its stiffness and alleviate pressure off the median nerve. An accurate injection is needed for delivery to achieve the effects of tissue degradation. The purposes of this study were to 1) determine injection sites using 3D reconstructed anatomy, and 2) insert the needle to the middle of the transverse carpal ligament thickness in situ. MethodsSix fresh-frozen cadaveric hands were used in this study. Five injection sites were determined in the sagittal plane along the center of the transverse carpal ligament thickness ulnar to the thenar muscle attachment using 3D ultrasonographic reconstruction. Each injection was delivered by rigidly fixing a 27-gauge needle to a six degrees of freedom robot arm programmed to insert the needle tip to the intended target. Ultrasound images were taken of the needle after insertion to measure accuracy and precision of the needle placement. FindingsThe needle tip was successfully delivered to the middle region of the transverse carpal ligament thickness and visualized using ultrasound imaging. The accuracy and precision of the needle insertion were 0.83 and 0.31 mm, respectively. InterpretationMethodology was established for robot-assisted needle insertion to the transverse carpal ligament using 3D ultrasonographic reconstructed anatomy. This methodology can be used in the future to deliver enzymatic injections to the transverse carpal ligament as a potential treatment for carpal tunnel syndrome.

Acoustic Signature: A focused ultrasound guidance technique with sub-millimeter accuracy

Accurate targeting is paramount for focused ultrasound (FUS) procedures. Here we describe a method for targeting FUS dubbed acoustic signature, which uses acoustic feedback from a target’s unique reflection patterns to guide the transducer to a previously defined orientation. We demonstrate convergence to the desired target in a 3 degree of freedom (DOF) water tank scenario and a 5 DOF robotic arm scenario. We also tested the acoustic signature technique for targeting with a phantom skull on a 3 DOF motor stage and a 5 DOF manual stereotactic frame designed for transcranial FUS procedures in an MRI environment. In both the water tank and the robotic arm convergence tests, the method converges to the target. The convergence was sufficiently smooth for automated gradient descent. In the 3-DOF test, the net targeting error was 0.30 ± 0.27 mm (n = 10). In the stereotactic frame 5-DOF test, the targeting error was 2.11 ± 0.99 mm and 3.2 ± 2.2° (n = 10) summed rotational error. The accuracy of this technique is limited by the positioning apparatus. This method may enable precise and repeatable FUS targeting for therapies which require multiple FUS sessions at the same target.

X-ray computed tomography with seven degree of freedom robotic sample holder

We present an x-ray Computed Tomography setup that integrates a seven degrees of freedom robotic arm as a sample holder within an existing laboratory x-ray computed tomography setup. We aim to provide a flexible sample holder that is able to execute non-standard and task-specific trajectories for complex samples. The robotic arm is integrated with a unified software package that allows for path planning, collision detection, geometric calibration and reconstruction of the sample. The calibration is necessary to identify the accurate pose of the sample which deviates from the expected pose due to inaccurate placement of the robotic arm. With our software the user is able to command the robotic arm to execute arbitrary trajectories for a given sample in a safe manner and output its reconstruction to the user. We present experimental results with a circular trajectory where the robotic sample holder achieves identical visual quality compared to a conventional sample holder.

A positioning error compensation method for multiple degrees of freedom robot arm based on the measured and target position error

A novel positioning error compensation method (PECM) based on the measured and target position error for multiple degrees of freedom (DOF) robot arm control is proposed, which is used to improve positioning accuracy of multiple-DOF robot arm end-effector in accurate positioning applications such as in the medical and mechanical fields. Based on the idea of PID to increase adaptiveness and robustness of the method, a positioning compensation model between the measured and target position error tracked by an optical tracking system and compensated joint angle is derived with inverse kinematic model. Based on error analysis of the compensation model regarding geometry errors, the proportional joint angle compensated coefficient deduced from Jaccobian matrix of the error is proposed and identified with position error data. Calibration experiments for position conversion matrix and positioning accuracy verification experiments are conducted consisting of an optical tracking system and a robot arm. The results of positioning accuracy verification experiments show that the average resultant positioning error in three directions reduces from 1.89 mm (before compensation, model based on Denavit-Hartenberg (DH)), 0.39 mm (model based on modified Denavit-Hartenberg (MDH) with Levenberg-Marquardt (LM)) to 0.34 mm (decreasing 82%, 15%), which demonstrates the efficiency and robustness of the method.

Reverse Engineer 5 Degrees of Freedom Robot Arm using Programmable Logic Controller

Many types of robot arms have been developed in the market. Whenever a robot arm broke down, it is a challenge to do reverse engineer and repair the robot arm. This project has successfully reused the existing 5 degrees of freedom robot arm structure and electronic components while replacing the controller with a new Programmable Logic Controller (PLC) and LLD model. The project uses the existing DC servo motor, encoder, sensors, power supply, and interface board. Since the structure and most electronic components are already available, they are important to be analyzed and understood before they can be reused. Hence the reverse engineering activities are needed. In addition, a low-cost PLC is used to save cost. Thus, it is not possible to move all axes at the same time. Instead, the robot arm can only move one axis at a time. Additionally, the robot arm has been improved to be able to be programmed manually or automatically and repeat previously predetermined position sequences. In total, 90 rungs, 96 built-in commands, and custom functions have been used. The refurbished system is very stable and reliable.

Using Full Pose Measurement for Serial Robot Calibration.

To ensure smooth robot operations, parameters of its kinematic model and a registration transformation between robot base and world coordinate frame must be determined. Both tasks require data acquired by external sensors that can measure either 3D locations or full 6D poses. We show that use of full pose measurements leads to much smaller robot orientation errors when compared with the outcome of calibration and registration procedures based on 3D data only. Robot position errors are comparable for both types of data. The conclusion is based on extensive simulations of 7 degrees of freedom robot arm and different levels of pseudo-noise perturbing both positional and rotational components of pose.

Design and implementation of Arduino based robotic arm

&lt;p&gt;&lt;span&gt;This study presents the model, design, and construction of the Arduino based robotic arm, which functions across a distance as it is controlled through a mobile application. A six degree of freedom robotic arm has been designed and implemented for the purpose of this research. The design controlled by the Arduino platform receives orders from the user’s mobile application through wireless controlling signals, that is Bluetooth. The arm is made up of five rotary joints and an end effector, where rotary motion is provided by the servomotor. Each link has been first designed using solid works and then printed by 3D printer. The assembly of the parts of the robot and the motor’s mechanical shapes produce the final prototype of the arm. The Arduino has been programmed to provide rotation to each corresponding servo motor to the sliders in the designed mobile application for usage from distance.&lt;/span&gt;&lt;/p&gt;

User intent estimation during robot learning using physical human robot interaction primitives

As robotic systems transition from traditional setups to collaborative work spaces, the prevalence of physical Human Robot Interaction has risen in both industrial and domestic environments. A popular representation for robot behavior is movement primitives which learn, imitate, and generalize from expert demonstrations. While there are existing works in context-aware movement primitives, they are usually limited to contact-free human robot interactions. This paper presents physical Human Robot Interaction Primitives (pHRIP), which utilize only the interaction forces between the human user and robot to estimate user intent and generate the appropriate robot response during physical human robot interactions. The efficacy of pHRIP is evaluated through multiple experiments based on target-directed reaching and obstacle avoidance tasks using a real seven degree of freedom robot arm. The results are validated against Interaction Primitives which use observations of robotic trajectories, with discussions of future pHRI applications utilizing pHRIP.