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Breast asymmetry is an important radiological sign of cancer. This paper describes the first approach aiming to detect all types of asymmetry; previous asymmetry-based research has been focussed on the detection of mass lesions. The conventional approach is to search for brightness or texture differences between corresponding locations on left and right breast images. Due to the difficulty in accurately identifying corresponding locations, asymmetry cues generated in this way are insufficiently specific to be used as prompts for small and subtle abnormalities in a computer-aided diagnosis system. We have undertaken studies to discover more about the visual cues utilized by radiologists. As a result, we propose a new automatic method for detecting asymmetry based on the comparison of corresponding anatomical structures, identified by an automatic segmentation of breast tissue types. We describe methods for comparing the shape and brightness distribution of these regions, and we present promising results obtained by combining evidence for asymmetry.

This paper discusses detection and matching of arbitrary image features or patterns. The common characteristics of feature extraction and matching are summarized which show that they can be considered as special cases of a more general problem—signal detection. However, the existing signal detection theories do not solve feature extraction and matching problems readily. Therefore, a general formulation of feature extraction and matching as a problem of signal detection is presented. This formulation unifies feature extraction and matching into a more general framework so that the two can be better integrated to form an automatic system for image matching or object recognition. Following this formulation, guidelines for designing algorithms for detection or matching of arbitrary image features or patterns which can be easily implemented or reconfigurated for many practical applications are derived. Sample algorithms resulting from this formulation and the associated experimental results with real image data are provided which demonstrate the performance and robustness of the methods.

This paper presents a modified implementation of QR decomposition for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) detection based on the Givens rotation method. The QR decomposition hardware is constructed using the coordinate rotation digital computer (CORDIC) algorithm operating with fewer gate counts and lower power consumption than do triangular systolic array (TSA) structures. Accurate signal transmission is essential to wireless communication systems. Thus, a more effective data detection algorithm and precise channel estimation method play vital roles in MIMO systems. Implementing data detection with QR decomposition helps reduce the complexity of MIMO-OFDM detection. Implementation results reveal that the proposed recursive QR decomposition (RQRD) architecture has lower clock latency than do TSA structures, and has a smaller hardware area than do Gram–Schmidt structures.

Detecting intrusions in real-time within cloud networks presents a multifaceted challenge involving intricate processes such as feature representation, intrusion type classification and post-processing procedures. Distributed Intrusion Detection Systems (DIDSs) constitute a complex landscape characterized by diverse contextual nuances, distinct functional advantages and limitations specific to deployment scenarios. Despite these challenges, DIDS offers immense potential for addressing evolving intrusion detection challenges through tailored contextual adaptations and unique functional advantages. Moreover, exploring the limitations associated with different deployment contexts facilitates targeted improvements and refinements, unlocking new avenues for innovation in intrusion detection technologies. Notably, deep learning (DL) integrated with blockchain technology emerges as a superior approach in terms of security, while bioinspired models excel in Quality of Service (QoS). These models demonstrate higher accuracy across various network scenarios, underscoring their efficacy in intrusion detection. Additionally, edge-based models exhibit high accuracy and scalability with reduced delay, complexity and cost in real-time network environments. The fusion of these models holds promise for enhancing classification performance across diverse attack types, offering avenues for future research exploration. This text conducts a comprehensive comparison of performance metrics, including accuracy, response delay, computational complexity, scalability and deployment costs. The proposed Novel DIDS Rank (NDR) streamlines model selection by considering these metrics, enabling users to make well-informed decisions based on multiple performance aspects simultaneously. This unified ranking approach facilitates the identification of DIDS that achieves high accuracy and scalability while minimizing response delay, cost and complexity across varied deployment scenarios.

In this growing world of the internet, most of our daily routine tasks are somehow connected to the internet, from smartphones to internet of things (IoT) devices to cloud networks. Internet users are growing rapidly, and the internet is accessible to everyone from anywhere. Data phishing is a cyber security attack that uses deception to trick internet users to get their content and information. In this attack, malicious users try to steal personal data such as login credentials, credit card details, health care information, etc., of the users on the internet. They exploit users’ sensitive information using vulnerabilities. Information stealers are known as phishers. Phishers use different techniques for phishing. One of the most common methods is to direct the users to a false website to enter their login credentials and their details on these phishing sites. Phishing websites look like the original websites. Phishers use these details to get access to the user’s accounts and hijack them for monetary purposes. Many internet users fall for this trap of phishing sites and share their personal and sensitive details. In this paper, we will analyze and implement machine learning (ML) techniques to detect phishing attacks. There are different methods to identify phishing attacks, one of them is by checking the uniform resource locator (URL) address using ML. ML is used to teach a machine to differentiate between phishing and original site URLs. There are many different techniques to overcome this attack. This research paper aims to provide accurate and true phishing detection with less time complexity.

We consider the nonlinear bistable dynamic system that is the archetypal system giving way to the phenomenon of stochastic resonance for noise-improved signal processing. Independently of a strict stochastic resonance effect, we use this bistable system as a nonlinear filter for a detection task on a binary signal. We expose a methodology to tune the nonlinear filter at its best performance that minimizes its probability of detection error. The optimally tuned nonlinear filter is then compared to the ideal matched filter, which is the optimal filter for the detection with Gaussian noise. We show that the performance of the nonlinear filter, although (expectedly) not as good, comes close to that of the ideal matched filter operating in its strict nominal conditions. We next examine several possible departures, quite plausible in practical operation, from the nominal conditions of the ideal matched filter. We demonstrate that in such degraded conditions, the nonlinear filter can catch up and surpass the performance of the matched filter. This reveals a robustness superiority of the nonlinear filter, compared to the matched filter operating outside its strict nominal conditions.

Pedestrian detection is one of the most challenging research areas in computer vision. Compared to traditional hand-crafted methods, convolutional neural networks (CNNs) have superior detection results. The single-stage detection networks, particularly the state-of-the-art You Only Look Once (YOLO) network, have attained a satisfactory performance without compromising the computation speed in object detection. YOLO framework can be leveraged in pedestrian detection as well. In this work, we propose an improved YOLOv2, called DSM-IDM-YOLO. The proposed model uses a modified DarkNet19 integrated with three new modules, two depth-wise separable convolution modules and one inception depth-wise convolution module, leading to a comprehensive feature of an object in the image. The modules are integrated on top of features from multiple levels of the network. The proposed framework is computationally less expensive owing to its convolution design and a moderate number of layers. It aims to improve performance with minimal computational overhead. The proposed method is compared with state-of-the-art detection methods, i.e., Faster R-CNN, YOLOv2, YOLOv3, YOLOv4-tiny and Single Shot Multibox Detector (SSD). The performance results attest that the proposed method has effectively improved the detection. Three benchmark pedestrian datasets are used for experimental analysis: INRIA, PASCAL VOC 2012 and Caltech.

Gravitational wave is predicted by Einstein’s general relativity, which conveys the information of source objects in the universe. The detection of the gravitational wave is the direct test of the theory and will be used as new tool to investigate dynamical nature of the universe. However, the effect of the gravitational wave is too tiny to be easily detected. From the first attempt utilizing resonant antenna in the 1960s, efforts of improving antenna sensitivity were continued by applying cryogenic techniques until approaching the quantum limit of sensitivity. However, by the year 2000, resonant antenna had given the way to interferometers. Large projects involving interferometers started in the 1990s, and achieved successful operations by 2010 with an accumulated extensive number of technical inventions and improvements. In this memorial year 2015, we enter the new phase of gravitational-wave detection by the forthcoming operation of the second-generation interferometers. The main focus in this paper is on how advanced techniques have been developed step by step according to scaling the arm length of the interferometer up and the history of fighting against technical noise, thermal noise, and quantum noise is presented along with the current projects, LIGO, Virgo, GEO-HF and KAGRA.

Most complex systems today contain software, and systems failures activated by software faults can provide lessons for software development practices and software quality assurance. This paper presents an analysis of software-related failures of medical devices that caused no death or injury but led to recalls by the manufacturers. The analysis categorizes the failures by their symptoms and faults, and discusses methods of preventing and detecting faults in each category. The nature of the faults provides lessons about the value of generally accepted quality practices for prevention and detection methods applied prior to system release. It also provides some insight into the need for formal requirements specification and for improved testing of complex hardware-software systems.

Ionizing particles detection based on phonons counting are considered as a growing research point of great interest. Phononic crystal (PnC) detectors have a higher resolution than other detectors. In the present work, we shall prepare a setup of a radiation detector based on a 1D PnC. The PnC detector can be used in detection and discrimination between protons and alpha particles with incident energy 1MeV. We have proposed a model capable of filtering the energies of two different ionizing particles (proton and alpha particle) of specific lattice frequencies in steps. Firstly, the high probability of phonons production was found at transmitted energy 5KeV from the whole path of protons and alpha particles through a vertical thin sheet made from Mylar and Polymethyl methacrylate (PMMA), respectively. The outgoing elastic waves are subjected to propagate through the proposed PnCs structure (Teflon-Polyethylene)² that shows the different transmission percentage to each particle. Therefore, the detection and discrimination between ionizing ions were achieved.

Like any other large and complex software systems, Service-Based Systems (SBSs) must evolve to fit new user requirements and execution contexts. The changes resulting from the evolution of SBSs may degrade their design and quality of service (QoS) and may often cause the appearance of common poor solutions in their architecture, called antipatterns, in opposition to design patterns, which are good solutions to recurring problems. Antipatterns resulting from these changes may hinder the future maintenance and evolution of SBSs. The detection of antipatterns is thus crucial to assess the design and QoS of SBSs and facilitate their maintenance and evolution. However, methods and techniques for the detection of antipatterns in SBSs are still in their infancy despite their importance. In this paper, we introduce a novel and innovative approach supported by a framework for specifying and detecting antipatterns in SBSs. Using our approach, we specify 10 well-known and common antipatterns, including Multi Service and Tiny Service, and automatically generate their detection algorithms. We apply and validate the detection algorithms in terms of precision and recall two systems developed independently, (1) Home-Automation, an SBS with 13 services, and (2) FraSCAti, an open-source implementation of the Service Component Architecture (SCA) standard with more than 100 services. This validation demonstrates that our approach enables the specification and detection of Service Oriented Architecture (SOA) antipatterns with an average precision of 90% and recall of 97.5%.

Identifier lexicon may have a direct impact on software understandability and reusability and, thus, on the quality of the final software product. Understandability and reusability are two important characteristics of software quality. REpresentational State Transfer (REST) style is becoming a de facto standard adopted by software organizations to build their Web applications. Understandable and reusable Uniform Resource Identifers (URIs) are important to attract client developers of RESTful APIs because good URIs support the client developers to understand and reuse the APIs. Consequently, the use of proper lexicon in RESTful APIs has also a direct impact on the quality of Web applications that integrate these APIs. Linguistic antipatterns represent poor practices in the naming, documentation, and choice of identifiers in the APIs as opposed to linguistic patterns that represent the corresponding best practices. In this paper, we present the Semantic Analysis of RESTful APIs (SARA) approach that employs both syntactic and semantic analyses for the detection of linguistic patterns and antipatterns in RESTful APIs. We provide detailed definitions of 12 linguistic patterns and antipatterns and define and apply their detection algorithms on 18 widely-used RESTful APIs, including Facebook, Twitter, and Dropbox. Our detection results show that linguistic patterns and antipatterns do occur in major RESTful APIs in particular in the form of poor documentation practices. Those results also show that SARA can detect linguistic patterns and antipatterns with higher accuracy compared to its state-of-the-art approach — DOLAR.

Treating An Aging Population – Innovative Dutch Solutions: Netherlands Foreign Investment Agency.

Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and there exist several essential survey papers. However, the research on camouflage object detection and tracking is limited due to the complexity of the problem. Existing work on this problem has been done based on either biological characteristics of the camouflaged objects or computer vision techniques. In this paper, we review the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical point of view. This paper also addresses several issues of interest as well as future research direction in this area. We hope this paper will help the reader to learn the recent advances in camouflaged object detection and tracking.

Motivated by biological neural networks and distributed sensing networks, we study how pooling networks – or quantizers – with random thresholds can be used in detection tasks. We provide a brief overview of the use of deterministic quantizers in detection by presenting how quantizers can be optimally designed for detection purposes. We study the behavior of these networks when they are used in a problem for which they are not optimal (mismatching). We show that adding random fluctuations to the thresholds of the networks can then enhance the performance of the quantizers, thus helping in the recovery of "a kind of" optimality. We also show that (for a small number of thresholds) it suffices to use random uniform quantizers, for which we provide a study of the behavior as a function of several parameters (size, fluctuation nature, observation noise nature). The conclusion to these studies are the robustness of the uniform quantizer used as a detector with respect to fluctuations added on its thresholds.

Unmanned aerial vehicles (UAVs) equipped with high definition (HD) cameras can obtain a large number of detailed inspection images. The insulator is an indispensable component in the transmission lines. Detecting insulator in image video quickly and accurately can provide a reliable basis for the ranging and the obstacle avoidance flight of UAV close to the tower and transmission line. At the same time, the insulator is a serious threat to the safety of the power grid due to the multiple faults of the insulator, and the computer technology should be fully utilized to diagnose the fault. Detection of the insulator images with the complex aerial background is implemented by constructing a convolutional neural network (CNN), which has the classic architecture of five modules of convolution and pooling, two modules of fully connected layers. In this paper, we propose a recognition algorithm for explosion fault based on saliency detection, which uses the trained network model to extract the features. Then, we put the saliency maps into a self-organizing feature map (SOM) network and build the mathematical module via super pixel segmentation, contour detection and other image processing methods. The test shows that the algorithm can reduce the error that may be caused by manual analysis. It also demonstrates that the detection of the insulator and the recognition of explosion fault can effectively improve the efficiency and intelligence level.

In this review, we report on the newly developed area of research devoted to the formation of self-assembled monolayers of metallophthalocyanines by focusing on some significant examples dedicated to electroanalytical applications. We also summarize recent examples on the use of electropolymerized metallophthalocyanine films in electroanalysis. In both cases, activation and detection of thiols are the main targeted applications.

The design of novel substituted phthalocyanines closely follows the requirement of their planned applications. In this study, synthesize of novel pyridone derivatives metal-free and symmetrical cobalt(II) phthalocyanines was carried out to improve brightness. For this purpose; starting with 4-nitrophthalonitrile and 4-hydroxy-6-methyl-3-nitro-2-pyridone, 4-(6-methyl-3-nitro-2-oxo-1,2-dihydropyridin-4-yloxy)phthalonitrile was prepared. Then 2(3),9(10),16(17),23(24)-tetrakis[6-methyl-3-nitro-2-oxo-1,2-dihydropyridin-4-yloxy] metal-free phthalocyaninato and 2(3),9(10), 16(17),23(24)-tetrakis[6-methyl-3-nitro-2-oxo-1,2-dihydropyridin-4-yloxy] phthalocyaninato Co(II) were synthesized by using 4-(6-methyl-3-nitro-2-oxo-1,2-dihydropyridin-4-yloxy)phthalonitrile, lithium metal and cobalt(II) acetate tetrahydrate in amyl alcohol, respectively. The sensing behavior of the film of derivatives metal-free and symmetrical cobalt(II) phthalocyanines for the online detection of heavy metal ions in water samples was investigated by utilizing an AT-cut quartz crystal resonator. It was observed that the adsorption of the analytes on the coating surface cause a reversible negative frequency shift of the resonator. Quartz crystal microbalance (QCM) functionalized with phthalocyanine, derivatives metal-free and symmetrical cobalt(II) phthalocyanines, are demonstrated to be sensors for the detection of heavy metal ions like Cd${}^{2+}$, Zn${}^{2+}$, Cu${}^{2+}$, Cr${}^{2+}$ and Co${}^{2+}$. Thus, QCM based sensor arrays are considered a promising platform for the direct analysis of aqueous samples.

The synthesis of the porphyrin-calix[4]arene conjugates was carried out using the Pd(0)-catalyzed amination of Zn(II) meso-(3-bromophenyl)porphyrinate with bis(3-aminopropoxy)substituted calix[4]arenes (in cone and 1,3-alternate conformations). One of the conjugates was demetalated to give free porphyrin base derivative. The investigation of the fluorescence of the conjugates was studied in the presence of 18 metal perchlorates. The zinc porphyrinate derivatives were found to quench fluorescence in the presence of Cu(II), Al(III) and Cr(III) cations as well as on protonation. Metal-free conjugate was shown to act as a molecular probe for Zn(II), Cu(II) and Cd(II) cations due to strong and different changes of the emission caused by these metals.

In biomedical signal processing, artificial intelligence techniques are used for identifying and extracting relevant information. However, it lacks effective solutions based on machine learning for the prediction of cardiac arrhythmias. The heart diseases diagnosis rests essentially on the analysis of various properties of ECG signal. The arrhythmia is one of the most common heart diseases. A cardiac arrhythmia is a disturbance of the heart rhythm. It occurs when the heart beats too slowly, too fast or anarchically, with no apparent cause. The diagnosis of cardiac arrhythmias is based on the analysis of the ECG properties, especially, the durations (P, QRS, T), the amplitudes (P, Q, R, S, T), the intervals (PQ, QT, RR), the cardiac frequency and the rhythm. In this paper we propose a system of arrhythmias diagnosis assistance based on the analysis of the temporal and frequential properties of the ECG signal. After the features extraction step, the ECG properties are then used as input for a convolutional neural network to detect and classify the arrhythmias. Finally, the classification results are used to perform a prediction of arrhythmias with nonlinear regression model. The method is illustrated using the MIT-BIH database.