Error Analysis of PDR System Using Dual Foot-mounted IMU

In this paper, we analyze the position errors of the pedestrian dead reckoning (PDR) system using foot-mounted IMU attached to each foot, and implement PDR system using dual foot-mounted IMU to reduce the analyzed error. The PDR system using foot-mounted IMU is generally based on an inertial navigation system (INS). To reduce bias and white noise errors, INS is combined with zero velocity update (ZUPT), which assumes that the pedestrian shoe velocity is zero at the stance phase. Although ZUPT could compensate the velocity and position, the heading drift still occurs. When analyzing the characteristics of the position error, the error shows a symmetrical characteristic. In order to reduce this error, the previous researches compensate for both positions by applying feet position constraints. The algorithm consists of applying a conventional PDR system to each foot and fusion algorithm combining both. The PDR system using foot-mounted IMU, one on each foot, is based on integration approach separately. The positions of both feet should be in a circle with a radius as step length during walking. The designed filter is constrained so that the position of both feet are in a circular boundary. The heading error that is symmetrically drifted is corrected by the position constraint when the pedestrian moves straight. Experimental results show the performance and usability of each previous algorithm to compensate for symmetric heading errors

Classification methods for the development of genomic signatures from high-dimensional data

Personalized medicine is defined by the use of genomic signatures of patients to assign effective therapies. We present Classification by Ensembles from Random Partitions (CERP) for class prediction and apply CERP to genomic data on leukemia patients and to genomic data with several clinical variables on breast cancer patients. CERP performs consistently well compared to the other classification algorithms. The predictive accuracy can be improved by adding some relevant clinical/histopathological measurements to the genomic data

Integration of Inertial Navigation System with EM-log Using H-infinity Filter

This paper presents the integration of inertial navigation system (INS) with electromagnetic-log (EM-log) as an underwater navigation system using H-infinity filter for robustness from the uncertainty of the sea current model. In underwater environments, the electromagnetic signals are attenuated rapidly, so that the global navigation satellite system is not available in general. Thus, INS is usually chosen for underwater navigation, and other aiding sensors are also used to complement its accumulative errors, one of which is EM-log. Since an EM-log provides the relative velocity to seawater, the integrated navigation cannot be performed accurately unless the sea current speed is compensated properly. Generally, the INS and EM-log can be integrated using extended Kalman filter (EKF). However, EKF guarantees its performance when the stochastic properties of the system’s process and measurement noises are perfectly known. In other words, in the presence of sea current modelling errors, the integration using the EKF is not expected to show good performance. On the other hand, H-infinity filter is a robust filter which can tolerate such uncertainties. In this paper, the integration of INS and EM-log using H-infinity filter is studied. The performance is compared with that of the EKF case by proper computer simulation

Apparent Power Law Scaling of Variable Range Hopping Conduction in Carbonized Polymer Nanofibers

We induce dramatic changes in the structure of conducting polymer nanofibers by carbonization at 800°C and compare charge transport properties between carbonized and pristine nanofibers. Despite the profound structural differences, both types of systems display power law dependence of current with voltage and temperature, and all measurements can be scaled into a single universal curve. We analyze our experimental data in the framework of variable range hopping and argue that this mechanism can explain transport properties of pristine polymer nanofibers as well

Error Analysis of PDR System Using Dual Foot-mounted IMU

In this paper, we analyze the position errors of the pedestrian dead reckoning (PDR) system using foot-mounted IMU attached to each foot, and implement PDR system using dual foot-mounted IMU to reduce the analyzed error. The PDR system using foot-mounted IMU is generally based on an inertial navigation system (INS). To reduce bias and white noise errors, INS is combined with zero velocity update (ZUPT), which assumes that the pedestrian shoe velocity is zero at the stance phase. Although ZUPT could compensate the velocity and position, the heading drift still occurs. When analyzing the characteristics of the position error, the error shows a symmetrical characteristic. In order to reduce this error, the previous researches compensate for both positions by applying feet position constraints. The algorithm consists of applying a conventional PDR system to each foot and fusion algorithm combining both. The PDR system using foot-mounted IMU, one on each foot, is based on integration approach separately. The positions of both feet should be in a circle with a radius as step length during walking. The designed filter is constrained so that the position of both feet are in a circular boundary. The heading error that is symmetrically drifted is corrected by the position constraint when the pedestrian moves straight. Experimental results show the performance and usability of each previous algorithm to compensate for symmetric heading errors

Kinematic Model-Based Pedestrian Dead Reckoning for Heading Correction and Lower Body Motion Tracking

In this paper, we present a method for finding the enhanced heading and position of pedestrians by fusing the Zero velocity UPdaTe (ZUPT)-based pedestrian dead reckoning (PDR) and the kinematic constraints of the lower human body. ZUPT is a well known algorithm for PDR, and provides a sufficiently accurate position solution for short term periods, but it cannot guarantee a stable and reliable heading because it suffers from magnetic disturbance in determining heading angles, which degrades the overall position accuracy as time passes. The basic idea of the proposed algorithm is integrating the left and right foot positions obtained by ZUPTs with the heading and position information from an IMU mounted on the waist. To integrate this information, a kinematic model of the lower human body, which is calculated by using orientation sensors mounted on both thighs and calves, is adopted. We note that the position of the left and right feet cannot be apart because of the kinematic constraints of the body, so the kinematic model generates new measurements for the waist position. The Extended Kalman Filter (EKF) on the waist data that estimates and corrects error states uses these measurements and magnetic heading measurements, which enhances the heading accuracy. The updated position information is fed into the foot mounted sensors, and reupdate processes are performed to correct the position error of each foot. The proposed update-reupdate technique consequently ensures improved observability of error states and position accuracy. Moreover, the proposed method provides all the information about the lower human body, so that it can be applied more effectively to motion tracking. The effectiveness of the proposed algorithm is verified via experimental results, which show that a 1.25% Return Position Error (RPE) with respect to walking distance is achieved

Integration of Inertial Navigation System with EM-log Using H-infinity Filter

This paper presents the integration of inertial navigation system (INS) with electromagnetic-log (EM-log) as an underwater navigation system using H-infinity filter for robustness from the uncertainty of the sea current model. In underwater environments, the electromagnetic signals are attenuated rapidly, so that the global navigation satellite system is not available in general. Thus, INS is usually chosen for underwater navigation, and other aiding sensors are also used to complement its accumulative errors, one of which is EM-log. Since an EM-log provides the relative velocity to seawater, the integrated navigation cannot be performed accurately unless the sea current speed is compensated properly. Generally, the INS and EM-log can be integrated using extended Kalman filter (EKF). However, EKF guarantees its performance when the stochastic properties of the system’s process and measurement noises are perfectly known. In other words, in the presence of sea current modelling errors, the integration using the EKF is not expected to show good performance. On the other hand, H-infinity filter is a robust filter which can tolerate such uncertainties. In this paper, the integration of INS and EM-log using H-infinity filter is studied. The performance is compared with that of the EKF case by proper computer simulation

Fabrication and independent control of patterned polymer gate for a few-layer WSe2 field-effect transistor

We report the fabrication of a patterned polymer electrolyte for a two-dimensional (2D) semiconductor, few-layer tungsten diselenide (WSe2) field-effect transistor (FET). We expose an electron-beam in a desirable region to form the patterned structure. The WSe2 FET acts as a p-type semiconductor in both bare and polymer-covered devices. We observe a highly efficient gating effect in the polymer-patterned device with independent gate control. The patterned polymer gate operates successfully in a molybdenum disulfide (MoS2) FET, indicating the potential for general applications to 2D semiconductors. The results of this study can contribute to large-scale integration and better flexibility in transition metal dichalcogenide (TMD)-based electronics

Cross Domain Solution With Stateful Correlation of Outgoing and Incoming Application- Layer Packets

While Smart Grid offers high efficiency in power delivery, it is susceptible to cyberattacks because of vulnerabilities in the information and communication technologies. Network segregation lowers threats by limiting their consequences within segregated network. Network segregation can be achieved either logically or physically. Logical segregation relies on firewalls to filter and manage network traffic. Whereas physical segregation employs methods such as air gaps or data diodes, which provide heightened security by necessitating physical access for a breach. Although air gaps entirely isolate domains from communication, data diodes allow only unidirectional data flow. Effective communication regulation between domains is emphasized owing to its restricted nature, leading to the development of cross-domain solutions (CDS). Certain types of CDS facilitate bidirectional communication by combining two data diodes. The issue lies in the inability of current CDS solutions to consider application-level protocol intricacies. The Modbus protocol is a representative example. To ensure secure communication, a CDS must match the incoming response packets with outgoing request packets, which requires the extraction and correlation of state variables. However, the current CDS, next-gen firewalls, and intrusion prevention systems lack this capability. Thus, this study proposed a next-gen CDS architecture capable of stateful correlation of outgoing and incoming application-layer packets. The proposed method extracts user-defined state variables from outgoing traffic and evaluates incoming packets based on rulesets. A prototype based on this method exhibits superior filtering accuracy compared to traditional CDS, despite a 51.08% increase in processing delay, thereby demonstrating its potential in enhancing network security