Visual Collaboration Leader-Follower UAV-Formation for Indoor Exploration

UAVs operating in a leader-follower formation demand the knowledge of the relative pose between the collaborating members. This necessitates the RF-communication of this information which increases the communication latency and can easily result in lost data packets. In this work, rather than relying on this autopilot data exchange, a visual scheme using passive markers is presented. Each formation-member carries passive markers in a RhOct configuration. These markers are visually detected and the relative pose of the members is on-board determined, thus eliminating the need for RF-communication. A reference path is then evaluated for each follower that tracks the leader and maintains a constant distance between the formation-members. Experimental studies show a mean position detection error (5 × 5 × 10cm) or less than 0.0031% of the available workspace [0.5 up to 5m, 50.43° × 38.75° Field of View (FoV)]. The efficiency of the suggested scheme against varying delays are examined in these studies, where it is shown that a delay up to 1.25s can be tolerated for the follower to track the leader as long as the latter one remains within its FoV.

IOT-5G USER TRACKING IN A 5G NETWORK USING 60 GHZ MM-WAVES BASED ON AN ABF-ED ALGORITHMS FOR A CLUTTERED INDOOR ENVIRONMENT

This paper presents a user tracking algorithm in an IoT-5G Network (or IoT-5GN). Hereby, we aim at studying and evaluating the sensing performances of the IoT-5G Access Point (or IoT-5G AP) primary signal by the IoT-5G user in a cluttered indoor environment using an energy detector (or ED) algorithm and an Alpha-&-Beta Filter (ABF or α-β-F) estimator. The 5G primary signal (or 5G-PS) frequency that we would like to detect is: 60 GHz. As a result, the 5G-PS sensing via the proposed ABF-ED algorithm, enabled us to track the IoT-5G user inside of the IoT-5G AP coverage area. The performances of the proposed ABF-ED algorithm in this paper work is evaluated by the probability of total detection error (or PTDE) measure. Through different scenarios simulations, the performances and robustness of the proffered algorithm are demonstrated.

Recording Natural Head Position Using Cone Beam Computerized Tomography

The purpose of this study was to develop a technique to record the natural head position (NHP) of a subject using the scout images of cone beam computerized tomography (CBCT) scans. The first step was to align a hanging mirror with the vertical (XY) plane of the CBCT field-of-view (FOV) volume. Then, two scout CBCT images, at frontal and at sagittal planes, were taken when the subject exhibited a NHP. A normal CBCT scan on the subject was then taken separately. These scout images were used to correct the orientation of the normal CBCT scan. A phantom head was used for validation and performance analysis of the proposed method. It was found that the orientation detection error was within 0.88°. This enables easy and economic NHP recording for CBCT without additional hardware.

Micro pin header defect detection system based on OpenCV

In order to solve the problems of low detection efficiency and large detection error in the process of manual quality inspection, a full-automatic defect detection system is built. The system uses an industrial camera, selects a suitable light source for image acquisition, uses the open source OpenCV visual library for image processing and defect contour recognition, and sets the screening conditions for unqualified products. The system can detect whether the needle arrangement has defects in real time and classify them according to different defect categories, It can greatly improve the detection efficiency of needle arranging production enterprises. Through a large number of experimental tests, the detection success rate can reach 98.67%, which shows that the system is feasible.

High-Occupancy Vehicle Lane Enforcement System

Introduction: An automatic High-Occupancy Vehicle (HOV) lane enforcement system is developed and evaluated. Current manual enforcement practices by the police bring about safety concerns and unnecessary traffic delays. Only vehicles with more than five passengers are permitted to use HOV lanes on freeways in Korea. Hence, detecting the number of passengers in HOVs is a core element for their development. Methods: For a quick detection capability, a YOLO-based passenger detection model was built. The system comprises three infrared cameras: two are for compartment detection and the other is for number plate recognition. Multiple infrared illuminations with the same frequency as the cameras and laser sensors for vehicle detection and speed measurement are also employed. Results: The performance of the developed system is evaluated with real-world data collected on proving ground. As a result, it showed a passenger detection error of nine percent on average. The performances revealed no difference in vehicle speeds and the number of passengers according to ANOVA tests. Conclusion: Using the developed system, more efficient and safer HOV lane enforcement practices can be made.

A novel Acoustic Fluid Level Estimation Method Based on Evidence Fusion Mechanism

In order to reduce the missed detection error and the systematic error caused by acoustic resonance fluid level detection, liquid level estimation method based on evidence fusion mechanism is designed. It establishes a two dimensional dynamic system model of the standingwavelength. The state evidence of wavelength is obtained through the random set description of evidence, and the extension principle of random set is used to get the observation evidence ofwavelength. The evidential reasoning (ER) rule and dependent evidence fusion are used to fuse those evidence, and the estimation value of fluid level can be calculated from fused result based on pignistic expectation. The corresponding liquid level estimation experiment illustrates the validity and feasibilityof the proposed method.

Influence of Configuration Error in Bolted Joints on Detection Error of Clamp Force Detection Method

Clamp force errors in bolted joints often cause accidents in various mechanical structures. Therefore, the clamp force must be controlled accurately and maintained for securing the reliability of mechanical structures such as vehicles. However, the clamp force cannot be controlled easily during tightening. Moreover, it is difficult to detect the clamp force after tightening. We previously proposed a method to easily detect the clamp force of a bolted joint that has been tightened. In that method, the bolt thread protruding from the nut is pulled while the nut’s upper surface is supported. The relationship between tensile force and displacement at the pulling point where the tensile force is applied differs before and after the tensile force reaches the clamp force. The method detects the tensile force at the point, where the relationship changes, as the clamp force. In this study, we investigate the influence of squareness error on the bearing surface of the clamped part in a bolted joint on the detection error of the method using experiments and finite element (FE) analysis. The experimental results show that the squareness error has an influence on the detection accuracy. The average detection error in the experiments increases by approximately 10% with an increase in the squareness error. To understand the cause of this phenomenon, we investigate the effects of backlash between mating thread surfaces of bolts and nuts on the detection error. The results show that the error decreased because of the backlash. Consequently, it is assumed that the error is caused by the non-separation of the mating thread surfaces when the tensile force reached the clamp force. Furthermore, the FE analysis results show that the squareness error on the bearing surface of the clamped part has an influence of the squareness error on the detection accuracy. The results indicate that we should control the tolerance of squareness errors on the bearing surface of the clamped part when the clamp force detection method is applied to bolted joints.

The Use of Template Miners and Encryption in Log Message Compression

Presently, almost every computer software produces many log messages based on events and activities during the usage of the software. These files contain valuable runtime information that can be used in a variety of applications such as anomaly detection, error prediction, template mining, and so on. Usually, the generated log messages are raw, which means they have an unstructured format. This indicates that these messages have to be parsed before data mining models can be applied. After parsing, template miners can be applied on the data to retrieve the events occurring in the log file. These events are made from two parts, the template, which is the fixed part and is the same for all instances of the same event type, and the parameter part, which varies for all the instances. To decrease the size of the log messages, we use the mined templates to build a dictionary for the events, and only store the dictionary, the event ID, and the parameter list. We use six template miners to acquire the templates namely IPLoM, LenMa, LogMine, Spell, Drain, and MoLFI. In this paper, we evaluate the compression capacity of our dictionary method with the use of these algorithms. Since parameters could be sensitive information, we also encrypt the files after compression and measure the changes in file size. We also examine the speed of the log miner algorithms. Based on our experiments, LenMa has the best compression rate with an average of 67.4%; however, because of its high runtime, we would suggest the combination of our dictionary method with IPLoM and FFX, since it is the fastest of all methods, and it has a 57.7% compression rate.

On the α-q-Mutual Information and the α-q-Capacities

The measures of information transfer which correspond to non-additive entropies have intensively been studied in previous decades. The majority of the work includes the ones belonging to the Sharma–Mittal entropy class, such as the Rényi, the Tsallis, the Landsberg–Vedral and the Gaussian entropies. All of the considerations follow the same approach, mimicking some of the various and mutually equivalent definitions of Shannon information measures, and the information transfer is quantified by an appropriately defined measure of mutual information, while the maximal information transfer is considered as a generalized channel capacity. However, all of the previous approaches fail to satisfy at least one of the ineluctable properties which a measure of (maximal) information transfer should satisfy, leading to counterintuitive conclusions and predicting nonphysical behavior even in the case of very simple communication channels. This paper fills the gap by proposing two parameter measures named the α-q-mutual information and the α-q-capacity. In addition to standard Shannon approaches, special cases of these measures include the α-mutual information and the α-capacity, which are well established in the information theory literature as measures of additive Rényi information transfer, while the cases of the Tsallis, the Landsberg–Vedral and the Gaussian entropies can also be accessed by special choices of the parameters α and q. It is shown that, unlike the previous definition, the α-q-mutual information and the α-q-capacity satisfy the set of properties, which are stated as axioms, by which they reduce to zero in the case of totally destructive channels and to the (maximal) input Sharma–Mittal entropy in the case of perfect transmission, which is consistent with the maximum likelihood detection error. In addition, they are non-negative and less than or equal to the input and the output Sharma–Mittal entropies, in general. Thus, unlike the previous approaches, the proposed (maximal) information transfer measures do not manifest nonphysical behaviors such as sub-capacitance or super-capacitance, which could qualify them as appropriate measures of the Sharma–Mittal information transfer.