Automated identification

The research presents an automated vision a system that makes use of the processing of images techniques to identify
plant diseases in agricultural contexts. In order to monitor vast crop fields and automatically identify disease symptoms as soon that they occur on plant leaves, research on automated identification of plant infections is crucial to the agricultural industry. This method uses segmentation, colour modifications, and masking of green pixels to classify data based on learning from some training examples of that category.

Cryptography is a fundamental aspect of secure communication, and classical ciphers have been used for centuries to encrypt messages. However, manually deciphering encrypted texts can be time-consuming and labor-intensive. In recent years, automated approaches leveraging machine learning techniques have shown promise in identifying and decrypting classical ciphers such as Caesar, Substitution, and Vigenère ciphers (1). This research paper presents an in-depth exploration of automated cipher identification and decryption, focusing on the development, training, and evaluation of machine learning models for this purpose. The study aims to provide insights into the effectiveness and limitations of these automated approaches, as well as potential avenues for future research and improvement. In addition to discussing the development, training, and evaluation of machine learning models for automated cipher identification and decryption, this research paper also delves into the historical context of classical ciphers and their significance in cryptography. By examining the evolution of encryption techniques from ancient times to the modern era, readers gain a comprehensive understanding of the challenges posed by classical ciphers and the necessity for automated solutions in today's digital age. Furthermore, the paper explores various machine learning algorithms and techniques employed in automated cipher identification and decryption, including supervised learning, unsupervised learning, and deep learning (2)(4). Through detailed analysis and experimentation, the effectiveness of these algorithms in deciphering encrypted texts is evaluated, highlighting their strengths and weaknesses across different types of classical ciphers.

Objective: This article proposes a method to automatically identify and label event-related potential (ERP) components with high accuracy and precision. Methods: We present a framework, referred to as peak-picking Dynamic Time Warping (ppDTW), where a priori knowledge about the ERPs under investigation is used to define a reference signal. We developed a combination of peak-picking and Dynamic Time Warping (DTW) that makes the temporal intervals for peak-picking adaptive on the basis of the morphology of the data. We tested the procedure on experimental data recorded from a control group and from children diagnosed with developmental dyslexia. Results: We compared our results with the traditional peak-picking. We demonstrated that our method achieves better performance than peak-picking, with an overall precision, recall and F-score of 93%, 86% and 89%, respectively, versus 93%, 80% and 85% achieved by peak-picking. Conclusion: We showed that our hybrid method outperforms peak-picking, when dealing with data involving several peaks of interest. Significance: The proposed method can reliably identify and label ERP components in challenging eventrelated recordings, thus assisting the clinician in an objective assessment of amplitudes and latencies of peaks of clinical interest.

Background: Forensic dentistry identification commonly involves using dental cast models as ante-mortem data. Here, dentists generally send the pictures as well as the dental records. However, in recent times, dentists – especially orthodontists and prosthodontists – are using 3D scanners in view of reducing the space for cast model storage as well as sending the 3D imaging for fabricating clear aligners and other items such as crowns and bridges. This new trend means data transmission and viewing has become more complicated since sophisticated laptops or personal computers are generally required. For more practical use, smartphones would be a better option, meaning various simple ideas for viewing 3D data must be explored. Furthermore, the conclusions must be evaluated in terms of the validity for forensic dentistry use. Purpose: To evaluate a number of smartphone applications that are simple, user friendly, scalable and capable of the measurement and superimposition of 3D imaging ...

Where possible, automation has been a common response of humankind to many activities that have to be repeated numerous times. The routine identification of specimens of previously described species has many of the characteristics of other activities that have been automated, and poses a major constraint on studies in many areas of both pure and applied biology. In this paper, we consider some of the reasons why automated species identification has not become widely employed, and whether it is a realistic option, addressing the notions that it is too difficult, too threatening, too different or too costly. Although recogniz-ing that there are some very real technical obstacles yet to be overcome, we argue that progress in the development of automated species identification is extremely encouraging that such an approach has the potential to make a valuable contribution to reducing the burden of routine identifications. Vision and enterprise are perhaps more limiting at present than p...

Aims:This study aims to verify the applicability of modern dental technologies and their related principles of use to the forensic sciences in the field of personal identification.Background:Personal identification has always had a major role in many legal and administrative actions regarding both living and death beings. The techniques used are much less advanced than the technologies potentially available.Objective:Modern technologies, available to the daily dental clinic practice, as intraoral scanners, combined in particular to the specialist skill in orthodontics, can help redefine the methods of personal identification according to the levels of accuracy, trueness and feasibility greater than those applied in traditional forensic dentistry.Methods:23 corpses (12F;11M) have been selected for intraoral scanning with the Carestream 3500®digital device. The superimposition of initial and late digital models, digital models and radiographs (orthopantomography and full mouth periapi...

One of the safest biometrics of today is finger vein- but this technic  arises with some specific challenges, the most common  one being that the vein pattern is hard to extract because finger vein images are always low in quality, significantly  hampered the feature extraction and classification stages. Professional  algorithms want to be considered with the conventional hardware for capturing finger-vein images is  by using red Surface Mounted Diode (SMD) leds for this aim. For capturing images, Canon 750D camera with micro lens is used. For high quality images the integrated micro lens  is used, and with some adjustments it can also obtain finger print. Features extraction was used by a combination of Hierarchical Centroid and Histogram of Gradients. Results were evaluated with K Nearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results displayed improvement as compared to three latest benchmarks in this field that used 6-fold validation and SDUM...

This paper evaluates a set of enhancement stages for finger vein enhancement that not only has low computational complexity but also high distinguishing power. This proposed set of enhancement stages is centered around fuzzy histogram equalization. Two sets of evaluation are carried out: one with the proposed approach and one with another unique approach that was formulated by rearranging and cropping down the preprocessing steps of the original proposed approach. To extract features, a combination of Hierarchical Centroid and Histogram of Gradients was used. Both enhancement stages were evaluated with K Nearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results showed improvement as compared to three latest benchmarks in this field that used 6-fold validation.

Mastoiditis occurs due to inflammation that can affect the structure of the mastoid bone. The mastoid bone consists of the mastoid air cell system (MACS) which protects the ear structures and regulates air pressure in the ear and has different sizes and characteristics, making it very difficult to identify precisely. This study aims to identify and find the right MACS size by developing an automatic identification model and obtaining the optimal threshold value in the segmentation process using the extended adaptive threshold (eAT) method. The research dataset uses computed tomography (CT)-scan images of 308 slices of 12 patients indicated for mastoiditis. The results of this study provide identification that has the right MACS accuracy and size. Overall, the optimal segmentation process obtained the smallest threshold value of 57 and the largest threshold value of 63, the smallest MACS size is 4.025 cm 2 and the largest is 8.816 cm 2 with an accuracy rate of 93.4%. The smaller MACS size indicates inflammation in the mastoid area and these patients require more intensive treatment.

Simple signs existent in mammograms for diagnosing breast cancer are considered to be microcalcifications or MCs. Therefore, true detection of MCs is needed to minimize schedule diagnosis, efficient care and death rate due to breast cancer. A challenging task is to evaluate and interpret mammograms and, more over to the poor contrast consistency of MCs relative to the remain of the tissue, the precise identification of MCs, such as the minor size and random shape and size of the MC clusters, has several obstacles. These restrictions in the manual analysis of MCs increase the demand for an automated recognition system to help radiologists in mammogram analysis and it is important to design strength algorithm for this purpose. The goal of this paper is to present an efficient procedure that can be used to enhance images for extracting features to give excellent classification. The classifier senses which the region was normal, benign or malignant. The performance of KNN classifier with fuzzy histogram equalization using Otsu's multi threshold segmentation give excellent results in detection and recognition in mammograms for breast cancer distinguished in image mammograms obtained from hospital.

https://www.isecure-journal.com/article_129023.html

Within the process industries there is a significant installed base of regulatory and multivariable model predictive controllers. These controllers in many cases operate very poorly. This paper documents the current state of industrial controller performance, identifies the sources and ramifications of this poor performance, and discusses required attributes of a Process Control Monitoring System (PCMS). Finally, research directions are suggested.

In this simulation study the operation of a naphtha redistillation column (a column with two feeds and three products) was analyzed with the application of Aspen HYSYS® software. The simulator, structure of local controllers and the product-quality estimators were based on the data of an industrial column in the Danube Refinery. The aim of the analysis was to identify the dynamic and steady-state effects of heating and cooling as well as the sidestream of product qualities. The relationship between the tray temperatures and the quality of the products was also identified and inferential calculations were made. Based on the identified relationships, a twolevel hierarchical control structure was developed. On the lowest level of the hierarchy are the local controllers of flowrates, liquid levels, pressure and duty. The inferential calculations are important components of the controller which serve as the controlled variables at the coordination level. The inputs of the estimators are ...

Profit margins from plant operations may be improved by changing the constraints so as to increase the degrees of freedom for control. Due to the presence of disturbances the chances of operating the plant outside the set limits cannot be ruled out. Thus, the expected return should be estimated by taking into account the variability. Bayesian Statistics can be used to estimate these probabilities subject to changes in operating constraint limits. The maximum a posteriori estimate of the process state due to the change in the operating conditions can be inferred using Bayesian methods and the profits or return thus obtained can be estimated. Also the decisions to obtain target value of the return can be made using the Bayesian methods.

Background: Forensic dentistry identification commonly involves using dental cast models as ante-mortem data. Here, dentists generally send the pictures as well as the dental records. However, in recent times, dentists – especially orthodontists and prosthodontists – are using 3D scanners in view of reducing the space for cast model storage as well as sending the 3D imaging for fabricating clear aligners and other items such as crowns and bridges. This new trend means data transmission and viewing has become more complicated since sophisticated laptops or personal computers are generally required. For more practical use, smartphones would be a better option, meaning various simple ideas for viewing 3D data must be explored. Furthermore, the conclusions must be evaluated in terms of the validity for forensic dentistry use. Purpose: To evaluate a number of smartphone applications that are simple, user friendly, scalable and capable of the measurement and superimposition of 3D imaging ...

In this simulation study the operation of a naphtha redistillation column (a column with two feeds and three products) was analyzed with the application of Aspen HYSYS® software. The simulator, structure of local controllers and the product-quality estimators were based on the data of an industrial column in the Danube Refinery. The aim of the analysis was to identify the dynamic and steady-state effects of heating and cooling as well as the sidestream of product qualities. The relationship between the tray temperatures and the quality of the products was also identified and inferential calculations were made. Based on the identified relationships, a twolevel hierarchical control structure was developed. On the lowest level of the hierarchy are the local controllers of flowrates, liquid levels, pressure and duty. The inferential calculations are important components of the controller which serve as the controlled variables at the coordination level. The inputs of the estimators are ...

The Theory of Inventive Problem Solving (TRIZ) can be applied to generate new concepts for process intensification (PI). In order to meet the target performance of the intensified process and to avoid design bottlenecks due to process operation, the suggested concepts need to comprise a feasible control system. Therefore, a design step, where a systematic procedure for variable selection is performed, available measurement devices are mapped, and the control design is initialized, is needed. This chapter presents a systematic approach to tackle these issues in a structured manner in order to enable a smooth transfer from new innovative ideas into feasible process design from operation point of view.

There has been a significant expansion in the use of 3-dimensional (3D) dental images in recent years. In the field of forensic odontology, an automated 3D dental identification system could enhance the identification process. This study presents a novel method for automated human dental identification using 3D digital dental data by utilising a dental identification scenario. The total study sample was divided into two groups: Group A (120 dental models) and Group B (120 Intra-oral scans-IOS). Group A data was composed of 3D scanned dental models of post-orthodontic treated patients (30 maxillary and 30 mandibular). This data was considered as AM digital data. To generate an identical sample, the dental casts (60) of the same patients were retrieved and laser scanned. These models were considered as PM digital data. Group B data (IOS) was obtained from 30 study participants. To reconstruct a dental identification scenario 30 maxillary and 30 mandibular IOS were obtained from 30 participants and were considered as IOS-AM. After one year, another set of IOS (60) were acquired from the same participants and were considered as IOS-PM. The results showed that the AutoIDD (Automated Identification from Dental Data) software was consistent in accuracy; capable of differentiating "correct matches" (high match percentage) from "non-matches" (very low percentage) by 3D image superimposition. The match percentage of the maxillary and mandibular IOS ranged from 64-100% and 81-100%, with a mean percentage of 96.7 and 96.4 respectively. This study demonstrated the feasibility of using 3D scans through a new automated software-AutoIDD in digital forensics to assist the forensic expert in confirming the identity of a deceased individual from the available AM dental records.

The NOVA DAE system is based on orthogonal collocation on finite elements, which is a technology suitable for the solution of a wide range of differential/algebraic equation models. Orthogonal collocation methods provide the mechanism for the equation-based solution of the dynamic models used in the non-linear controller (NLC). The equation-based technology provides flexibility to handle many different specifications of a model, including those for state estimation, state feedback, dynamic data reconciliation and closed loop dynamic optimisation and control.

Presentation and applied case study of a system-wide workflow which supports rapid, systematic and efficient continuous seeded cooling crystallisation process design, with the aim to deliver a robust, consistent process with tight control of particle attributes.

Profit margins from plant operations may be improved by changing the constraints so as to increase the degrees of freedom for control. Due to the presence of disturbances the chances of operating the plant outside the set limits cannot be ruled out. Thus, the expected return should be estimated by taking into account the variability. Bayesian Statistics can be used to estimate these probabilities subject to changes in operating constraint limits. The maximum a posteriori estimate of the process state due to the change in the operating conditions can be inferred using Bayesian methods and the profits or return thus obtained can be estimated. Also the decisions to obtain target value of the return can be made using the Bayesian methods.

Presentation and applied case study of a system-wide workflow which supports rapid, systematic and efficient continuous seeded cooling crystallisation process design, with the aim to deliver a robust, consistent process with tight control of particle attributes.

Linear Model Predictive Control (MPC) continues to be the technology of choice for constrained multivariable control applications in the process industry. Successful deployment of MPC requires "getting right" multiple aspects of the problem. This includes the design of the regulatory controls that receive setpoints from MPC, design of the multivariable controller(s) themselves, test design for model identification, model development, and dealing with nonlinearities. In the following, we highlight approaches and techniques that are successfully applied in practice and provide an overview of recent technological enhancements that are being made to MPC. While significant progress has been made in both the technology and practice, there are challenges with MPC, mostly related to the effort required to develop an application and to ensure adequate performance over time. Suggestions for addressing these issues are included as possible research directions.

Crystallisation is an industrially important unit operation for purifying and separating chemical mixtures. A generic crystallisation modelling framework has been implemented in the general process modelling system (gPROMS) software (of PSE, UK). This framework can be used to model the batch cooling crystallisation of ferrous sulphate heptahydrate (FSH). The parameter estimation and sensitivity of the predicted results with various numerical parameters was studied for batch crystalliser. An Excel "front-end" to the gPROMS model was developed to facilitate the interactive use of the model.

This paper presents a novel approach to test and to pretune advanced controllers to reduce the onsite work of control engineers and to train operators using advanced control solutions. Following the proposed approach a simulation framework has been developed where detailed process model realized in Matlab which is connected via OPC (Object-Linking and Embedding for Process Control) to the Profit Controller of Honeywell. With the application of the resulted simulation system the model predictive control (MPC) of a nonlinear crystallizer has been analysed. The case study demonstrates the efficiency of the proposed approach and the illustrative results show that the linear and robust MPC is an adequate controller of nonlinear crystallizers.

Crystallisers are essentially multivariable systems with high interaction amongst the process variables. Model Predictive Controllers (MPC) can handle such highly interacting multivariable systems efficiently due to their coordinated approach. In the absence of a real continuous crystalliser, a detailed momentum-model was applied using the process simulator in Simulink. This process has been controlled by a model predictive controller widely used in industry. A new framework has been worked out for the incorporation of the Honeywell Profit Suite controller to the simulator of the crystalliser. The engineering model and the controller were connected via OPC (OLE-Object Linking and Embedding for Process Control standard). Models were developed in Profit Suite using the new fully-automated identification method. The feasibility study illustrated that the applied identification tool gave an accurate and robust model, and that the non-linear crystalliser may be controlled and optimised very well with the Honeywell Profit Suite package. The developed system is proven to be useful in research and development.