F2ID: a personal identification system using faces and fingerprints

This paper reports the design, implementation, and evaluation of a research work for developing an automatic person identification system using hand signatures biometric. The developed automatic person identification system mainly used toolboxes provided by MATLAB environment‥ In order to train and test the developed automatic person identification system, an in-house hand signatures database is created, which contains hand signatures of 100 persons (50 males and 50 females) each of which is repeated 30 times. Therefore, a total of 3000 hand signatures are collected. The collected hand signatures have gone through pre-processing steps such as producing a digitized version of the signatures using a scanner, converting input images type to a standard binary images type, cropping, normalizing images size, and reshaping in order to produce a ready-to-use hand signatures database for training and testing the automatic person identification system. Global features such as signature height, image area, pure width, and pure height are then selected to be used in the system, which reflect information about the structure of the hand signature image. For features training and classification, the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network (ANN) is used. This paper also investigates the effect of the persons' gender on the overall performance of the system. For performance optimization, the effect of modifying values of basic parameters in ANN such as the number of hidden neurons and the number of epochs are investigated in this work. The handwritten signature data collected from male persons outperformed those collected from the female persons, whereby the system obtained average recognition rates of 76.20% and74.20% for male and female persons, respectively. Overall, the handwritten signatures based system obtained an average recognition rate of 75.20% for all persons.

Electronic Toll Collection (ETC) system is an important part of modern intelligent transport system, which plays an important role in intelligent car parks and congestion charging. Electronic Toll Collection (ETC) system includes automatic vehicle identification system and electronic billing system. The automatic vehicle identification system includes three parts: automatic vehicle identification (AVI), automatic vehicle model identification (AVC) and inspection system (ES).The application of ETC in intelligent car parks and congestion charging also includes automatic vehicle identification system (automatic vehicle identification, automatic vehicle model identification and inspection system) and electronic billing system. In intelligent car parks, ETC technology achieves fast and accurate automatic toll payment by automatically identifying the ETC tags carried by vehicles, which greatly improves the efficiency of car parks and reduces the queuing and waiting time due to manual toll collection. In terms of congestion charging, ETC technology provides an effective means to implement congestion charge collection. By setting up ETC toll lanes in core urban areas or busy road sections, the system can automatically record the information of the vehicles passing through and carry out toll settlement, thus realising dynamic toll collection according to the degree of congestion and the frequency of vehicle use. This congestion charging strategy aims to regulate urban traffic flow through economic means, alleviate traffic congestion and promote the sustainable development of urban transport. Although China's theoretical research on congestion charging, has been relatively mature, but affected by a variety of factors, no city has yet to start the implementation of congestion charging, the lack of experience in practice. Singapore will not stop the car charging system (ETC) technology successfully applied to urban congestion charging and intelligent car parks, ETC system in intelligent car parks and congestion charging application, help to promote the development of urban transport to a more intelligent, green direction. China can learn from the successful experience of foreign countries and apply it to its own intelligent car parks and congestion charging. This paper introduces the basic principle of ETC, discusses the research progress of ETC in terms of technical characteristics, and analyses the application scenarios of ETC in intelligent car parks and congestion charging. By examining the opportunities offered by Electronic Toll Collection (ETC) systems in smart car parks and congestion pricing, this study aims to delve into the transformative impacts of Electronic Toll Collection (ETC) systems on intelligent mobility and contribute to the sustainable development of intelligent mobility.

Face recognition is an important part of today’s emerging biometrics and video surveillance markets. As face recognition algorithms move from research labs to real world product, power consumption and cost become critical issues, and DSP-based implementations become more attractive. Also, “real-time” automatic personal identification system should meet the conflicting dual requirements of accuracy and response time. In addition, it also should be user-friendly. This paper proposes a method of face recognition by the LDA (Linear Discriminant Analysis) Algorithm with the facial feature extracted by chrominance component in color images. We designed a face recognition system based on a DSP (Digital Signal Processor). At first, we apply a lighting compensation algorithm with contrast-limited adaptive histogram equalization to the input image according to the variation of light condition. While we project the face image from the original vector space to a face subspace via PCA (Principal Component Analysis), we use the LDA to obtain the best linear classifier. And then, we estimate the Euclidian distances between the input image’s feature vector and trained image’s feature vector. The experimental results with real-time input video show that the algorithm has a pretty good performance on DSP-based face recognition system.KeywordsFace RecognitionLinear Discriminant AnalysisFace DetectionSkin ToneFace Recognition SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a combination of YOLO (you only look once) target recognition detection algorithm and AIS (Automatic Identification System) automatic ship identification system to assist vessels in inland navigation to carry out vessels and obstacle avoidance. This system proposes a ship-assisted collision avoidance system for ship collision accidents caused by a narrow inland waterway and complex navigation channel. The system can process the image and video information acquired by the high-definition camera mounted on the ship, and frame and mark the ships in the current sea area, and combine the AIS system to realize the visualization of the ship information and improve the global view. The experiment shows that the system can effectively identify and frame the ship and realize the visualization of AIS information, and the transformed image and video information can be used for decision-making by the ship's driver.

Aplicación para el estudio de implantación de un sistema no satelitario de identificación de buques

With the development of computer technology, in industrial production and scientific research, people need to more quickly and accurately access a large amount of data information, and this large amount of information is caused by a variety of uncertainty factors. It is necessary to effectively reduce or even avoid the risk losses brought about by the above-mentioned situations, and to ensure the normal operation of the system must be real-time identification processing to ensure the safety and reliability of the entire system, stable and efficient operation and other objectives, so that we can achieve fault diagnosis and automatic identification through computer vision technology to reduce the cost of production and improve the efficiency of enterprise production. In this paper, based on computer vision, we design and implement a fault automatic identification algorithm based on real-time identification system to carry out image pre-processing, feature extraction and classification and other functional modules, so as to provide some reference for the subsequent research on neural network theory.

The AIS (Automatic Identification System) became a standard under the International Convention for the Safety of Life at Sea (SOLAS) in December 2001. Using the AIS transponder, the vessel will report its position, identity heading and other information to other ship traffic in the same area. The AIS is a significant contribution to the control and safety of shipping. Transport Canada (TC) is a pioneer internationally in using the AIS (R4A airborne AIS transponder) integrated with the remote sensing system (MSS 6000 from Swedish Space Corporation) on board their aircraft, in order to add the information on ship identity and voyage to the information from other sensors. The AIS integration provides a quantum leap in the effectiveness of the surveillance mission. In the Canadian system integration, detailed vessel information obtained through the AIS system is displayed and correlated with information received from other sensors. All information is also presented together with a high-resolution digital map including nautical chart data. The AIS provides the operators with vessel voyage and identity information. It is very useful for vectoring the aircraft to areas where ships have been reported. The advent of AIS simplifies surveillance procedures and makes each hour of surveillance more productive. During last year TC reported that over 9700 vessels were over flown. It is anticipated that over 20,000 AIS targets will be acquired using AIS in 2007–2008. The experience from Canada also indicates an outstanding VHF range. During missions, vessels have been identified as far away as 200 nautical miles (n.m.) from the aircraft. The AIS information together with other sensor images gives a comprehensive image that will dramatically improve the chance to tie a ship to a detected illegal oil spill. Receiving ship identity information will enable aircrews to conduct covert operations and contact the suspect ship from far standoff distances. This will also act as a deterrent to potential polluters. Integrating AIS within the operational requirements of surveillance programs, dedicated to minimizing the adverse effects of shipping on the marine environment, will make a significant contribution towards improving the effectiveness of surveillance missions.

Maritime traffic pattern extraction is a fundamental and crucial factor for maritime surveillance and anomaly detection. Emerging technologies like Automatic Identification System (AIS) provides multi-dimensional data which is used to construct a maritime traffic model. In this paper, we propose a framework of maritime traffic pattern extraction from vessel AIS information, which learns a traffic pattern using an unsupervised technique, and can be applied on historical Automatic Identification System data. AIS data is a kind of spatio-temporal data that contains information of location data, as well as time stamps. In this way, traffic pattern is described by AIS data. Furthermore, we conduct a simulation experiment that extracts traffic pattern from the AIS data through the unsupervised technique. The proposed framework takes advantage of AIS data, which is a type of the spatiotemporal data that consists of vessel motion information, to perform the experiment. The result shows that the unsupervised framework converts useful information from raw AIS data to effective traffic pattern. The proposed method strongly supports the further research on maritime traffic pattern extraction of AIS data. Besides, an overview of the framework and the unsupervised technique for high-level maritime situation awareness is presented.

Chagas disease (CD) is a vector-borne zoonotic disease affecting large parts of the world. It is imposing a tremendous social burden on public health and ranks as one of the most severe threats to human health. CD is often transmitted to humans by the feces of insects called triatomine or kissing bugs. The diagnosis of CD can be performed at any stage of the disease and involves the analysis of clinical, epidemiological, and laboratory data. The CD has two different phases, acute phase and chronic phase. Since controlling and treating CD is easier in the early stages, detecting it in the acute phase plays an essential role in overcoming and controlling it. There are many clinical trials dedicated to this problem, but progress in applicational research (automatic identification) has been slower. Due to this shortcoming and the importance of this problem, this research is dedicated to present two automatic CD vector identification systems that classify several different vectors of kissing bugs with an acceptable and promising identification rate. Our proposed methods are composed of preprocessing, feature extraction, and classification phases. Principal component analysis (PCA) is utilized for feature extraction and Random Forrest (RF) and Support Vector Machine (SVM) are employed in the classification stages. A dataset consisting of more than two thousand kissing bug images is used as input of our methods. The accuracy for the first proposed approach, namely PCA-SVM, is 87.62% for 410 images of 12 Mexican and 75.26% for 1620 images of 39 Brazilian species. The second proposed approach, namely PCA-RF, has an accuracy of 100% for both Brazilian and Mexican species. We achieved perfect results with the PCA-RF method. Our results are promising and outperform the results of other available developed automatic identification systems for CD vectors.

Three-dimensional Feature Recognition-based Automated Patient Treatment Mismatch Verification System for Radiation Therapy

국제해사기구(International Maritime Organization, IMO)에서는 선박의 해상 충돌을 방지하기 위하여 SOLAS의 요구조건에 따라 모든 선박에 대하여 선박자동식별장치(Automatic Identification System, AIS)를 의무적으로 장착하도록 권고하고 있다. AIS를 사용하면 타선에 대한 제원 및 항행정보의 획득이 가능하여 충돌방지뿐만 아니라 광역관제, 조난 선박의 탐색구조 등 안전관리에도 활용이 가능하다. 본 논문에서는 AIS 장비를 사용하여 ADS-B (Automatic Dependent Surveillance - Broadcast) 개념 기반의 항공감시용 송수신기를 구현한 결과에 대하여 살펴보도록 한다. 기본적으로 AIS는 해상에서 사용되기 때문에 고도 정보를 사용하지 않지만, AIS 장비 내부에 사용하는 GPS (Global Positioning System) 칩셋에서 제공하는 고도 정보의 추출이 가능하다. 고도 정보를 포함한 감시 메시지 형식을 정의하고, SOTDMA (Self-Organizing Time Division Multiple Access) 방식을 개선하여 ADS-B 송수신기를 구현하였다. 적용 결과를 확인하기 위하여 지상시험 및 비행시험을 수행하였다. International Maritime Organization (IMO) recommends the installation of an Automatic Identification System (AIS) according to requirements by SOLAS to avoid maritime collision. AIS provides traffic information of other ships that may be used for maritime traffic control, SAR (Search and Rescue) and collision avoidance to apply safety management. In this paper, preliminary results to implement an aeronautical surveillance transceiver using AIS transceiver based on ADS-B concepts are described. Although altitude information is not required for AIS since the AIS is operated at MSL (Mean Sea Level), altitude information can be extracted by a GPS (Global Positioning System) chip-set in the AIS transceiver. ADS-B transceiver is implemented by defining a surveillance message format including the altitude information and modifying SOTDMA (Self-Organizing Time Division Multiple Access) protocol. Ground tests and flight tests are performed to validate the implementation results.

The Automatic Identification System (AIS) is a ship reporting system based on messages broadcast by vessels carrying an AIS transponder. The recent increase of terrestrial networks and satellite constellations of receivers is making AIS one of the main sources of information for Maritime Situational Awareness activities. Nevertheless, AIS is subject to reliability and manipulation issues; indeed, the received reports can be unintentionally incorrect, jammed or deliberately spoofed. Moreover, the system can be switched off to cover illicit operations, causing the interruption of AIS reception. This paper addresses the problem of detecting whether a shortage of AIS messages represents an alerting situation or not, by exploiting the Received Signal Strength Indicator available at the AIS Base Stations (BS). In designing such an anomaly detector, the electromagnetic propagation conditions that characterize the channel between ship AIS transponders and BS have to be taken into consideration. The first part of this work is thus focused on the experimental investigation and characterisation of coverage patterns extracted from the real historical AIS data. In addition, the paper proposes an anomaly detection algorithm to identify intentional AIS on-off switching. The presented methodology is then illustrated and assessed on a real-world dataset.

The synergic utilization of data from different sources, either ground-based or spaceborne, can lead to effective monitoring of maritime activities. To this end, the integration of synthetic aperture radar (SAR) images with data reported by the automatic identification system (AIS) is of high interest. Accurate matching of ships detected in SAR images with AIS data requires compensation of the azimuth offset, which depends on the ship’s velocity. The existing procedures interpolate the route information gathered by AIS to estimate the ship’s velocity at the epoch of the SAR data, to remove the offset. Matching accuracy is limited by interpolation errors and AIS route information unavailability or uncertainties. This paper proposes the use of SAR-based ship velocity estimations to improve the integration of AIS and SAR data. A case study has been analyzed, in which the method has been tested on TerraSAR-X images collected over the Gulf of Naples, Italy. Presented results show that the matching is improved with respect to standard procedures. The proposed method limits the distance between the AIS report and the SAR-based detection to less than 150 m, which is in line with maritime surveillance needs.

In the aftermath of the El Faro sinking, one of the NTSB recommendations was for NOAA to explore increasing the reception of weather data from ships in order to improve the weather forecast products that they distribute. Currently NOAA runs the Voluntary Observing Ship (VOS) program, where ships voluntarily submit weather observations to NOAA. However, this data is submitted infrequently, typically four times per day and is a manual process. One method being explored to automate and increase the frequency of data submittal is by using the Automatic identification System (AIS). Most ships (SOLAS-class vessel for example) have a Class A transponder due to mandatory carriage requirements. Many vessels that are not required, also have AIS transponders (either a Class A or B). All of these AIS transponders can be used to transmit AIS message 8 (broadcast binary message) simply by sending the transponder the appropriate NMEA sentence (BBM). The weather data can be embedded in an AIS message 8 and transmitted by the ship automatically. This transmission will be received by terrestrial receivers (when in range) or by satellite AIS receivers. The AIS weather data can then be converted into the appropriate format and forwarded to the NOAA weather database automatically. By leveraging this installed base of AIS transmitters, the volume of weather data being sent to NOAA could be greatly increased. The USACE and MARAD have been exploring the feasibility of this concept. An initial proof-of-concept was tested on the Cape Wrath in Baltimore in October 2018. After this successful demonstration, a prototype was installed on the T/S Kennedy during her training cruise Jan-Feb 2019. More recently, systems were installed on three commercial vessels operating in the Pacific Ocean. This report will discuss the concept, the demonstrations, and the results to date including the efficacy of the various ASM formats.

Indian Ocean Rim countries (IOR) rely on fisheries for socio-economic well-being and food security. Monitoring fisheries equipped with Automatic Identification Systems (AIS) enables the evaluation of fishing activities. However, only small fractions of large vessels use AIS, and increasingly vessels are avoiding or circumventing AIS use. We used Global Fishing Watch AIS fishing effort data for 2018–2020, combined with independently derived gear- and country-specific catch rates to estimate catch volumes by AIS equipped industrial vessels in the Indian Ocean. We compared these estimates to the catches reported by countries to the Food and Agriculture Organization (FAO), which represent global accountability. Results suggest that AIS-equipped industrial vessels caught an average of 400,000 t∙year−1, of which 57% was by Distant-Water Fishing (DWF) vessels from countries outside the region. Spain, France and South Korea had AIS derived catch estimates substantially lower than catches officially reported by these countries, suggesting these vessels may turn off AIS transponders to potentially elude detection either by authorities or for other operational reasons. In contrast, Taiwan and China had 6 and 4 times more AIS estimated catches than they reported to the FAO. This suggests that these countries may not meet their international reporting obligations. Underreporting fisheries catches undermines management efforts and contributes to the unsustainable use of marine resources. Our study revealed an important role AIS technology can play in estimating fishing activities in the Indian Ocean, despite its currently limited adoption and the increasing evasion tactics by some fleets. Discrepancies between AIS-derived catch estimates and officially reported data underline the challenges in fisheries monitoring and enforcement, and highlight the lack of compliance enforcement by many flag countries.