Object Detection-Based Security Monitoring System in The Framework of Architectural and Structural Design Optimization

Optimal and acceptable reliabilities for structural design

Now a day's Security has been a prime concern in the home or office management. Digital door lock security system provides security and safety to house or office owners, belongings, assets from being damaged by external agent or undesired strangers. We have used a new technology, incoming number verification system which gives more protection for controlling & security system. As Conventional security system does not use any password, there is a chance to hack or break the system. In this regard we used a desire mobile number without verification which doesn't allow the door to be opend.This system is composed of the microcontroller based by using matrix keypad & GSM/CDMA network. The microcontroller based digital door lock security system is an access control system that allows only authorized persons to access restricted area. The password is stored in PROM so that we can change it any time. The system has a matrix keypad .When anyone enter the code in the matrix keypad, microcontroller verify the codes. If that code is correct the device will operate and the door will be open. But if someone enter wrong code a red signal will be shown which means that the entered code is wrong.GSM/CDMA module can be used to operate the device, when anyone make a call from his mobile the receiving device which is set in main circuitry will receive the call. If the call is from desired number then operate the device and the door will be unlocked. An IPS circuit can be used for giving backup in case of emergency when there is a power failure. Keywords: DTMF encoder and decoder, Global System for Mobile Communication, Micro-Controller, Mobile phone, and Matrix Keypad.

Due to the technological advances in computer hardware and software tools, structural optimization has been gaining continuously increasing interest over the last two decades. The purpose of the present work is to quantitatively compare three metaheuristic optimization algorithms, namely the Differential Evolution, Harmony Search and Particle Swarm Optimization methods, in the framework of structural optimization. The comparison of the optimizers is performed with reference to their efficiency (overall computing demands) and robustness (capability to detect near-optimal solutions). The optimum design of a real-world overhead traveling crane is used as the test bed application for conducting optimization test runs.

Conventional security systems are often plagued by inherent flaws, leading to frequent security breaches. To address these vulnerabilities, automated biometric systems have emerged, leveraging individuals' physiological and behavioural traits for precise identification. Among these biometric modalities, iris-based authentication is a highly reliable, distinctive, and contactless method for user recognition. This research endeavours to enhance the accuracy of iris liveness detection by combining features extracted from the TSBTC n-Ary (Thepade’s Sorted Block Truncation Coding) method with those derived from the Triangle Thresholding method. Two distinct datasets, namely IIIT Delhi and Clarkson 2015, have been employed to evaluate the efficacy of these combined features. The study involves extracting features from three sources: TSBTC, TSBTC+Triangle, and Triangle methods. These features are subsequently input into the WEKA tool, which employs various classifiers to assess accuracy. The findings of this investigation reveal a notable increase in the accuracy of Iris Liveness Detection (ILD) by incorporating handcrafted techniques like TSBTC in conjunction with the Thresholding method. In essence, this research underscores the potential for improving the robustness of security systems by harnessing the synergy of distinct biometric methods, thereby mitigating the shortcomings of conventional security systems and fortifying the foundations of secure user authentication.

Currently Security for every organization is a bigger challenge. Conventional security systems have lot of drawbacks that need to be addressed. The use of Biometrics in security systems is current trend. We propose a framework of fusion and encryption of multimodal biometrics authentication system. In this two Unimodal traits Iris and Fingerprint is used collectively for generation of secure cryptographic template. The process is categorized into three modules 1) Pre-processing of acquired iris and fingerprint, 2) Extraction of discriminable Features, 3) cryptographic Multimodal biometric template generation. Initially, the preprocessing are perform separately for iris and fingerprint. Followed by the minutiae point's extraction from Fingerprint, which includes termination, bifurcation, and angle of orientation of each point respectively. Subsequently, the iris features are extracted using wavelet transform. Then feature level fusion is performed. Finally, a 120bit secure cryptographic template is generated from the multi-biometric template. We test our results on standard iris CASIA database and the real Fingerprint captured in our own college. The several experimental results demonstrate the effectiveness of the proposed approach. Also the security of biometric template is improved with the help of encryption.

In the context of global digitalization trends, the problem of the impact of cyberattacks on the company is significantly relevant. The rapid evolution and growth of the internet through the last decades led to more concern about cyber-attacks that are continuously increasing and changing. As a result, an effective intrusion detection system was required to protect data, and the discovery of machine learning is one of the most successful ways to address this problem. This article is devoted to the impact of cyberattacks on the US firms’ market value since it is an indicator of firm performance and how it can be solved by using machine learning technology. The paper’s central hypothesis is the assumption that a cyberattack announcement is supposed to change market reaction, which is predicted to be harmful since cybercrime incidents can lead to high implicit and explicit costs. The paper explores the effect of firm-specific and attack-specific characteristics of cyberattacks on the CAR (Cumulative Abnormal Returns) with the data of cyberattacks for US firms from 2011 to 2020. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. It discusses recent machine learning work with various network implementations, applications, algorithms, learning approaches, and datasets to develop an operational intrusion detection system in cybersecurity. This work should serve as a guide for new researchers and those who want to immerse themselves in the field of machine learning techniques within cybersecurity in US firms.

In the context of global digitalization trends, the problem of the impact of cyberattacks on the company is significantly relevant. The rapid evolution and growth of the internet through the last decades led to more concern about cyber-attacks that are continuously increasing and changing. As a result, an effective intrusion detection system was required to protect data, and the discovery of machine learning is one of the most successful ways to address this problem. This article is devoted to the impact of cyberattacks on the US firms’ market value since it is an indicator of firm performance and how it can be solved by using machine learning technology. The paper’s central hypothesis is the assumption that a cyberattack announcement is supposed to change market reaction, which is predicted to be harmful since cybercrime incidents can lead to high implicit and explicit costs. The paper explores the effect of firm-specific and attack-specific characteristics of cyberattacks on the CAR (Cumulative Abnormal Returns) with the data of cyberattacks for US firms from 2011 to 2020. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. It discusses recent machine learning work with various network implementations, applications, algorithms, learning approaches, and datasets to develop an operational intrusion detection system in cybersecurity. This work should serve as a guide for new researchers and those who want to immerse themselves in the field of machine learning techniques within cybersecurity in US firms.

The paper presents a new component for conventional video monitoring and security systems in air transportation, aiming at improving the overall system performance, eliminate nuisance alarms, optimize network bandwidth and systems storage occupation. Innovative analysis technologies were employed for the development of the new system. The proposed solution relies on a server application referred to as "Third Party Core" installed on a hardware platform residing in the same network with the video monitoring systems and other security systems. This server application uses data captured by video cameras, other security systems (e.g. access control, intrusion detection, radar surveillance) field equipment, data gathered and used in other server application (e.g. database servers, storage servers, other system servers) with to purpose of integrating different functionalities, unify the operator - machines interfaces, predict events, assist operators in video surveillance and improve the security of perimeter. The new server application can easily run on regular low cost hardware platforms (e.g. server or station available on the IT market) and was developed using SQL, C++ and languages connectors.

The reactive double dividing-wall distillation column (R-DDWDC) can simultaneously integrate and effectively coordinate reaction–separation coupling (RSC) and separation–separation coupling (SSC), thereby offering greater technical advantages and development potential than the conventional reactive distillation column and the reactive single dividing-wall distillation column. However, the application of two dividing walls and the introduction of external recycle flows inevitably lead to multiple candidate configurations of the R-DDWDC, and this significantly adds complexity and computational burden to its synthesis and design. To address the issue, we propose an effective methodology for developing the R-DDWDC, which involves a two-step strategy: the first step is to determine the configuration of external recycle flows by searching for the RSC from non-sharp separation to sharp separation of reaction mixtures, and the second step is to adjust the arrangement of the dividing walls to intensify the SSC. The former serves to provide the greatest flexibility for the inclusion of the SSC, and the latter helps to achieve full coordination with the RSC, thereby allowing the determination of the optimal design with low complexity and computational intensity. The methodology is fundamentally a conceptual design and structural optimization framework that can be implemented using either generic process simulation platforms or custom computational programs. Four representative examples, including the metathesis of 2-pentene; the acetalization of ethanol with butanal; the transesterification of propylene glycol monomethyl ether with methyl acetate; and the esterification of lactic acid with methanol, are selected to assess the derived procedure, and the obtained results confirm its simplicity and efficiency. Because the procedure proposed in this work is independent of the number of components contained in reaction mixtures and their relative volatility rankings, it can be regarded as a general methodology for developing other complicated reactive dividing-wall distillation columns.

In recent years, computer vision technology has developed rapidly, as one of its important research directions, object detection has received widespread attention due to its high accuracy. Meanwhile, object detection has many application fields, such as intelligent transportation, medical and health, security systems, etc. Traditional object detection methods have limitations when applied to complex real-world scenarios. To improve the shortcomings of conventional methods, deep learning based object detection algorithms have significantly improved the efficiency of object detection and become a research hotspot in object detection. This article summarizes the algorithms into two-stage and one-stage object detection algorithms based on the technical processes and structural differences in handling object detection tasks. Firstly, several common two-stage and one-stage object detection algorithms and their applications in real-world scenarios are introduced. Then their data sets and algorithm performance are analyzed and compared. Finally, according to the results of the comparison, the existing problems of the two-stage object detection algorithm and the one-stage object detection method are discussed, and their future development directions are pointed out.

In some special engineering cases, the lower and upper bounds of uncertain parameters are appropriately quantified as fuzzy variables instead of deterministic values. To address such cases, a possibility-based robust design optimization (PBRDO) framework is suggested for the hybrid uncertain structures with fuzzy-boundary interval (FuBI) variables. Firstly, an optimization model with FuBI variables is established where FuBI uncertainties exist in both the objective and constraint functions. The so-called dual robust design is presented and it is adopted to create the optimization objective. The first robust design aims to handle fuzziness while the second one attends to tackle interval property. The failure possibility is employed to construct the optimization constraints in possibilitic context. Then, the fuzzy-boundary interval Taylor series-central difference method (FITS-CDM) is derived to manage FuBI uncertainties and calculate the optimization objective efficiently. Next, the target performance approach (TPA) is employed to process the possibilistic constraints and the simplified constraints can be easily solved by FITS-CDM. The nested-loop PBRDO with FuBI variables can be simplified to a single-loop one based on FITS-CDM and TPA. Finally, the effectiveness of the proposed optimization approach on dealing with FuBI uncertainties is demonstrated by three examples.

Object detection in real time applications are more challenging and thus faces more difficulties in designing object detection applications. Object detection is a method of identifying predefined objects from images, videos or from other input sources. It consist of different steps for identifying the object from its surroundings. Earlier detection system uses different techniques for objection detection with computationally demanding networks that requires high GPU and needs more power. Considering the current circumstances in the society, it is better to design real time object detection system for security applications. The newly evolved technique such as Tensorflow(TF) along with OpenCV can be used used for object detection. The CCTV footages can be used as input and the monitoring of it can be done inorder to find any security violations. The proposed real time object detection system for bank security mainly aims in providing security to bank vaults. This system can also be used for other security applications where human entry is restricted. The proposed model can be used for different security related applications effectively.

Sensor devices are regularly used on unmanned aerial vehicles (UAVs) as reconnaissance and intelligence gathering systems and as support for front line troops on operations. This platform provides a wealth of sensor data and has limited computational power available for processing. The objective of this work is to detect and identify objects in real-time, with a low power footprint so that it can operate on a UAV. An appraisal of current computer vision methods is presented, with reference to their performance and applicability to the objectives. Experimentation with real-time methods of background subtraction and motion estimation was carried out and limitations of each method described. A new, assumption free, data driven method for object detection and identification was developed. The core ideas of the development were based on models that propose that the human vision system analyses edges of objects to detect and separate them and perceives motion separately, a function which has been modelled here by optical flow. The initial development in the temporal domain combined object and motion detection in the analysis process. This approach was found to have limitations. The second iteration used a detection component in the spatial domain that extracts texture patches based on edge contours, their profile, and internal texture structure. Motion perception was performed separately on the texture patches using optical flow. The motion and spatial location of texture patches was used to define physical objects. A clustering method is used on the rich feature set extracted by the detection method to characterise the objects. The results show that the method carries out detection and identification of both moving and static objects, in real-time, irrespective of camera motion.

The aim of this work is the development of a novel framework for structural optimization using bio-inspired remodelling algorithm adapted to additive manufacturing. The fact that polylactic acid (PLA, E = 3145 MPa (Young’s modulus) according to the supplier for parts obtained by injection) shows a similar parameterized behavior with ductile metals, in the sense that both materials are characterized by a bi-linear elastic-plastic law, allows to simulate and prototype parts to be further constructed in ductile metals at a lower cost and then be produced with more expensive fabrication processes. Moreover, cellular materials allow for a significant weight reduction and therefore reduction of production costs. Structural optimization algorithms based on biological phenomena were used to determine the density distribution of the infill density of the specimens. Several simple structures were submitted to distinct complex load cases and analyzed using the mentioned optimization algorithms combined with the finite element method and a meshless method. The surface was divided according to similar density and then converted to stereolitography files and infilled with the gyroid structure at the desired density determined before, using open-source slicing software. Smoothing functions were used to smooth the density field obtained with the remodeling algorithms. The samples were printed with fused filament fabrication technology and submitted to mechanical flexural tests similar to the ones analyzed analytically, namely three- and four-point bending tests. Thus, the factors of analysis were the smoothing parameter and the remodeling method, and the responses evaluated were stiffness, specific stiffness, maximum force, and mass. The experimental results correlated (obtaining accuracy of 35% for the three-point bending load case and 5% for the four-point bending load case) to the numerical results in terms of flexural stiffness and it was found that the complexity of the load case is relevant for the efficiency of the functional gradient. The fused filament fabrication process is still not accurate enough to be able to experimentally compare the results based of finite element method and meshless method analyses.

Four alternative analytical procedures are recommended by the design codes for the structural analysis of buildings under earthquake loading. The objective of this chapter is to assess these procedures by integrating them in the framework of structural optimization. The evaluation is based on the European seismic design code, where procedures based on both linear and nonlinear response history analysis are adopted. In order to realistically simulate seismic actions, suites of both natural and artificial ground-motion records are used. For the solution of the optimization problem an evolutionary algorithm is adopted. The results obtained demonstrate the advantages of using more elaborate seismic design procedures, based on a detailed simulation of the structural behaviour and the applied seismic loading, as opposed to the commonly used simplified design methodologies. Designs with less material cost combined with better seismic performance are obtained when nonlinear response history analysis is performed.