Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105
The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM) systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and control systems are operated by different units at NYCDOT as an independent database or operation system. New York City experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City. This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS, involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and wireless communication features for data collection and reduction; 2) interface development among existing database and control systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day. GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Elhadi M. Shakshu Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data
Efficient biometric-based identity management on the Blockchain for smart industrial applications
مدیریت هویت مبتنی بر بیومتریک کارآمد در Blockchain برای کاربردهای صنعتی هوشمند-2021
In this work, we propose a new Blockchain-based Identity Management system for smart industry. First, we describe an efficient biometric-based anonymous credential scheme, which supports selective disclosure, suspension/thaw and revocation of credentials/entities. Our system provides non-transferability through a freshly computed hidden biometric attribute, which is generated using a secure fuzzy extractor during each authentication. This mechanism combined with offchain storage guarantees GDPR compliance, which is required for protecting user’s data. We define blinded (Brands) DLRep scheme to provide multi-show unlinkability, which is a lacking feature in Brands’ credential based systems. For larger organizations, we re-design the system by replacing the Merkle Tree with an accumulator to improve scalability. The new system enables auditing by adapting the standard Industrial IoT (IIoT) Identity Management Lifecycle to Blockchain. Finally, we show that the new proposal outperforms BASS, i.e. the most recent blockchain-based anonymous credential scheme designed for smart industry. The computational cost at the user-side (can be a weak IoT device) of our scheme is 8-times less than that of BASS. Thus, our system is more suitable for IIoT.© 2020 Elsevier B.V. All rights reserved.
Keywords: Identity management | Smart industry | Blockchain | Non-transferability | Biometrics | DLRep | Multi-show unlinkability | Selective disclosure | Accumulators
Towards resilient and sustainable supply of critical elements from the copper supply chain: A review
به سمت تأمین انعطاف پذیر و پایدار عناصر حیاتی از زنجیره تامین مس: یک مرور-2021
The highly specialized materials needed for the de-carbonization of energy, smart devices and the internet of things have created supply concerns of critical elements used in these applications. Several critical elements are produced as by-products from base metal mining and processing. Increasing the capture of critical elements from existing operations should lead to a more resilient and sustainable supply of these elements. Towards this goal, this paper presents a review of the distribution behavior of five critical elements (selenium, tellurium, arsenic, antimony and bismuth) through the primary copper pyrometallurgical supply chain. This review identifies gaps in the distribution/concentration data of these elements in deposits and during mineral processing. Smelter dusts, refinery slimes and electrolyte are points of enrichment that can be targeted for additional recovery of these elements. Using published data, copper smelter dusts appear to contain enough arsenic and bismuth to meet the world’s supply needs. Industrial data collected from 29 refineries and represents ~46% of the worlds electrorefining production was extrapolated to examine the contained annual content of these five elements. Copper anodes contain 7900 tones/yr of selenium, 2300 tonnes/yr of tellurium, 24,000 tones/yr arsenic, 7100tonnes/yr of antimony and 5100 tones/yr of bismuth. The selenium and tellurium contents are 2–3 times and 4–5 times more than the current world’s annual production of these elements, respectively. While technology development in the processing of smelter dusts and refinery slimes could provide important breakthroughs, government and corporate collaboration are likely needed to encourage increased recovery of selenium, tellurium, arsenic, antimony and bismuth from the primary copper pyrometallurgical supply chain.
Keywords: Critical elements | Copper | Ore | Flotation | Smelting | Refining
Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG
Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG-2021
Increasingly smart techniques for counterfeiting face and fingerprint traits have increased the potential threats to information security systems, creating a substantial demand for improved security and better privacy and identity protection. The internet of Things (IoT)-driven fingertip electrocardiogram (ECG) acquisition provides broad application prospects for ECG-based identity systems. This study focused on three major impediments to fingertip ECG: the impact of variations in acquisition status, the high computational complexity of traditional convolutional neural network (CNN) models and the feasibility of model migration, and a lack of sufficient fingertip samples. Our main contribution is a novel fingertip ECG identification system that integrates transfer learning and a deep CNN. The proposed system does not require manual feature extraction or suffer from complex model calculations, which improves its speed, and it is effective even when only a small set of training data exists. Using 1200 ECG recordings from 600 individuals, we consider 5 simulated yet potentially practical scenarios. When analyzing the overall training accuracy of the model, its mean accuracy for the 540 chest- collected ECG from PhysioNet exceeded 97.60 %, and for 60 subjects from the CYBHi fingertip-collected ECG, its mean accuracy reached 98.77 %. When simulating a real-world human recognition system on 5 public datasets, the validation accuracy of the proposed model can nearly reach 100 % recognition, outperforming the original GoogLeNet network by a maximum of 3.33 %. To some degree, the developed architecture provides a reference for practical applications of fingertip-collected ECG-based biometric systems and for information network security.
Keywords: Off-the-person | Fingertip ECG biometric | Human identification | Convolutional neural network (CNN) | Transfer learning
Person-identification using familiar-name auditory evoked potentials from frontal EEG electrodes
شناسایی فرد با استفاده از پتانسیل نام-آشنا شنوایی الکترودهای EEG جلو برانگیخته-2021
Electroencephalograph (EEG) based biometric identification has recently gained increased attention of re- searchers. However, state-of-the-art EEG-based biometric identification techniques use large number of EEG electrodes, which poses user inconvenience and consumes longer preparation time for practical applications. This work proposes a novel EEG-based biometric identification technique using auditory evoked potentials (AEPs) acquired from two EEG electrodes. The proposed method employs single-trial familiar-name AEPs extracted from the frontal electrodes Fp1 and F7, which facilitates faster and user-convenient data acquisition. The EEG signals recorded from twenty healthy individuals during four experiment trials are used in this study. Different com- binations of well-known neural network architectures are used for feature extraction and classification. The cascaded combinations of 1D-convolutional neural networks (1D-CNN) with long short-term memory (LSTM) and with gated recurrent unit (GRU) networks gave the person identification accuracies above 99 %. 1D-convolutional, LSTM network achieves the highest person identification accuracy of 99.53 % and a half total error rate (HTER) of 0.24 % using AEP signals from the two frontal electrodes. With the AEP signals from the single electrode Fp1, the same network achieves a person identification accuracy of 96.93 %. The use of familiar-name AEPs from frontal EEG electrodes that facilitates user convenient data acquisition with shorter preparation time is the novelty of this work.
Keywords: Auditory evoked potential | Biometrics | Deep learning | Electroencephalogram | Familiar-name | Person identification
Deep belief network-based hybrid model for multimodal biometric system for futuristic security applications
مدل ترکیبی مبتنی بر باور عمیق برای سیستم بیومتریک چند حالته برای برنامه های امنیتی آینده-2021
Biometrics is the technology to identify humans uniquely based on face, iris, and fingerprints, etc. Biometric authentication allows the person recognition automatically on the basis of behavioral or physiological charac- teristics. Biometrics are broadly employed in several commercial as well as the official identification systems for automatic access control. This paper introduces the model for multimodal biometric recognition based on score level fusion method. The overall procedure of the proposed method involves five steps, such as pre-processing, feature extraction, recognition score using Multi- support vector neural network (Multi-SVNN) for all traits, score level fusion, and recognition using deep belief neural network (DBN). The first step is to input the training images into pre-processing steps. Thus, the pre-processing of three traits, like iris, ear, and finger vein is done. Then, the feature extraction is done for each modality to extract the features. After that, the texture features are extracted from pre-processed images of the ear, iris, and finger vein, and the BiComp features are acquired from individual images using a BiComp mask. Then, the recognition score is computed based on the Multi-SVNN classifier to provide the score individually for all three traits, and the three scores are provided to the DBN. The DBN is trained using the chicken earthworm optimization algorithm (CEWA). The CEWA is the integration of the chicken swarm optimization (CSO), and earthworm optimization algorithm (EWA) for the optimal authentication of the person. The analysis proves that the developed method acquired a maximal accuracy of 95.36%, maximal sensitivity of 95.85%, and specificity of 98.79%, respectively.
Keywords: Multi-modal Bio-metric system | Chicken Swarm Optimization | Earthworm Optimization algorithm | Deep Belief Network | Multi-SVNN
Secure mobile internet voting system using biometric authentication and wavelet based AES
سیستم رای گیری اینترنتی تلفن همراه با استفاده از احراز هویت بیومتریک و AES مبتنی بر موجک-2021
The number of mobile phone users increases daily, and mobile devices are used for various applications like banking, e-commerce, social media, internet voting, e-mails, etc. This paper presents a secure mobile internet voting system in which a biometric method authenticates the voter. The biometric image can either be encrypted at the mobile device and send to the server or process the biometric image at the mobile device to generate the biometric template and send it to the server. The implementation of biometrics on mobile devices usually requires simplifying the algorithm to adapt to the relatively small CPU processing power and battery charge. This paper proposes a wavelet-based AES algorithm to speed up the encryption process and reduce the mobile device’s CPU utilization. The experimental analysis of three methods(AES encryption, wavelet-based AES encryption, and biometric template generation) exhibits that wavelet-based AES encryption is much better than AES encryption and template generation. The security analysis of three methods shows that AES and wavelet-based AES encryption provides better security than the biometric template’s protection. The study of the proposed internet voting system shows that biometric authentication defeats almost all the mobile-based threats.
Keywords: Internet voting | Fingerprint template | Iris code | AES encryption | Wavelet based AES encryption
Analysis and enhancement of secure three-factor user authentication using Chebyshev Chaotic Map
تجزیه و تحلیل و افزایش احراز هویت کاربر سه عامل امن با استفاده از نقشه آشفته Chebyshev-2021
The most popular solution for a variety of business applications such as e-banking and e-healthcare is the multi-server environment. The user registration of individual servers is not the primary concern in such an environment. Here, the user can get various services from different servers by registering him/her under one server. To get secure services through this environment, the authenticity of users and servers are crucial. In this observation, the smart card based biometric authentication system is well popular and easy to use. This paper delineates that the security flaws can be found in a trusted authentication scheme proposed by Chatterjee et al. (2018). Besides, this work proposes an enhanced authentication scheme namely, asymmetric encryption based secure user authentication (ASESUA) to eliminate the drawbacks of Chatterjee et al.’s scheme. The formal security analysis of ASESUA has been done with the help of random oracle model and verified by the well popular AVISPA tool. The analysis shows that ASESUA performs better concerning security, communication cost, and computation cost than other related existing schemes.
Keywords: Attack | Authentication | Biometric | Smart card | Security
An efficient biometric based authenticated geographic opportunistic routing for IoT applications using secure wireless sensor network
یک مسیریابی فرصت طلبانه جغرافیایی معتبر مبتنی بر بیومتریک برای برنامه های IoT با استفاده از شبکه حسگر بی سیم امن-2021
The applications of Wireless Sensor Networks (WSNs) are been broadly utilized in the field of Internet of Things (IoT) under communication framework. Notwithstanding services gave by the WSNs, numerous IoT-related applications necessitate reliable and secure delivery of data over unsteady remote connec tions. In-order to ensure secure and reliable delivery of data, many existing paper works accomplish authentication based routing algorithms with numerous forwarders within the Wireless Sensor Networks. Be that as it may; these types of approaches are vulnerable to genuine attacks like Denial of Service (DoS), where countless duplicate data packets are intentionally dispatched to destination node which disturbs the typical activities of wireless sensor networks. So, here we propose a new scheme of security algorithm for the wireless sensor networks. Our method, Biometric based-Authenticated Geographic Opportunistic Routing (BAGOR) algorithm depends on the user biometrics to shield the violation of DoS attacks, in order to meet out the validness requirements and reliability in the network. By examining biometric and statistic state information (SSI) of remote connections, BAGOR uses a trust model as statistic state information to get better proficiency of packet delivery. Dissimilar to past pioneering routing algorithm, BAGOR guarantees data honesty by building up an entropy-deployed selective validation algorithm and can detach DoS aggressors and reduce the computational expense. Thus, the eveloped procedure is assessed and compared with already existing security techniques. The simulations show that BAGOR decreasing system traffic, shielding against Denial of Service attacks, and expanding the lifetime of a sensor node in the network. Thus, the usefulness and execution of the whole system is enhanced.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Emerging Trends in Materials Science, Technology and Engineering.
Keywords: Biometric authentication | BAGOR algorithm | Denial-of-Service attacks | Geographic opportunistic routing | Statistic state information
oReview on fingerprint-based identification system
مرور سیستم شناسایی مبتنی بر اثر انگشت-2021
The Biometric fingerprints are the widely utilized personal recognition tool because of their uniqueness, reliability and individuality. The fingerprint images consist of a design of the canyon & corrugation on human’s fingertips. Fingerprint validation is perhaps the most experienced methods for every biometric technique that has been rigorously substantiate through several applications. Every human being recognition methods using fingerprints are depending on one of the 3 methods: hybrid, correlation-based and Minutiae-based. This paper gives the review of different fingerprint recognition methods & then discusses the general minutiae-depend fingerprint identification systems. In present time the best form of recognizing the person or investigation of any case is figure print. Identifying speculate depend on fingerprint is a proceeding that is exceedingly important to the forensics & law for enforcement agencies. A small numbers of minutiae & the noise attribute make it exceedingly difficult to instinctive match the fingerprints to their acquaintance full prints that are accumulated in databases.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Virtual Conference on Sustainable Materials (IVCSM-2k20).
Keywords: Correlation | Finger prints | Histogram | Ridges | Segmentation