An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments.To address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed analysis.This analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.

Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

Research Progress on Detection Techniques of Fungicide Residues in Chinese Chives

Chinese chive is a kind of medicinal and edible plant,with many diseases,and chemical fungicides are usually used for control.In order to find out the risk of pesticide residues in Chinese chives,this paper summarized relevant literatures published in recent years,and sorted out and analyzed the types of pesticides used and detection techniques of common diseases in Chinese chives.

Relevance of Advanced Plant Disease Detection Techniques in Disease and Pest Management for Ensuring Food Security and Their Implication: A Review

Plant diseases and pests present significant challenges to global food security, leading to substantial losses in agricultural productivity and threatening environmental sustainability. As the world’s population grows, ensuring food availability becomes increasingly urgent. This review explores the significance of advanced plant disease detection techniques in disease and pest management for enhancing food security. Traditional plant disease detection methods often rely on visual inspection and are time-consuming and subjective. This leads to delayed interventions and ineffective control measures. However, recent advancements in remote sensing, imaging technologies, and molecular diagnostics offer powerful tools for early and precise disease detection. Big data analytics and machine learning play pivotal roles in analyzing vast and complex datasets, thus accurately identifying plant diseases and predicting disease occurrence and severity. We explore how prompt interventions employing advanced techniques enable more efficient disease control and concurrently minimize the environmental impact of conventional disease and pest management practices. Furthermore, we analyze and make future recommendations to improve the precision and sensitivity of current advanced detection techniques. We propose incorporating eco-evolutionary theories into research to enhance the understanding of pathogen spread in future climates and mitigate the risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with scientists, policymakers, and relevant intergovernmental organizations to ensure coordination and collaboration among them, ultimately developing effective disease monitoring and management strategies needed for securing sustainable food production and environmental well-being.

Performance Analysis of Intrusion Detection System in the IoT Environment Using Feature Selection Technique

The increasing number of security holes in the Internet of Things(IoT)networks creates a question about the reliability of existing network intrusion detection systems.This problem has led to the developing of a research area focused on improving network-based intrusion detection system(NIDS)technologies.According to the analysis of different businesses,most researchers focus on improving the classification results of NIDS datasets by combining machine learning and feature reduction techniques.However,these techniques are not suitable for every type of network.In light of this,whether the optimal algorithm and feature reduction techniques can be generalized across various datasets for IoT networks remains.The paper aims to analyze the methods used in this research and whether they can be generalized to other datasets.Six ML models were used in this study,namely,logistic regression(LR),decision trees(DT),Naive Bayes(NB),random forest(RF),K-nearest neighbors(KNN),and linear SVM.The primary detection algorithms used in this study,Principal Component(PCA)and Gini Impurity-Based Weighted Forest(GIWRF)evaluated against three global ToN-IoT datasets,UNSW-NB15,and Bot-IoT datasets.The optimal number of dimensions for each dataset was not studied by applying the PCA algorithm.It is stated in the paper that the selection of datasets affects the performance of the FE techniques and detection algorithms used.Increasing the efficiency of this research area requires a comprehensive standard feature set that can be used to improve quality over time.

Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge.However,due to the computational resources being limited,an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms.Therefore,designing and developing a lightweight detection mechanism is crucial.To address the aforementioned challenges,a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks.The proposed anomaly-based IDS is divided into three steps:pre-processing,feature selection,and decision.In the pre-processing phase,data cleaning and normalization are performed.In the feature selection step,the proposed approach uses two data-driven kernel techniques:kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results.Therefore,in decision step,in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used.To check the efficiency of the developed detection scheme,a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results.The proposed method achieved 99.9%accuracy,99.8%specificity,100%Sensitivity,99.9 F-score.

Major allergens in 8 types of allergenic foods and allergen detection techniques

In recent years,the prevalence of allergens in food warning notices,both domestically and internationally,has become the second leading concern after microbial contamination.Among the various factors that threaten human health reported by the World Health Organization,food allergy ranks fourth,and food allergy has become a global security problem.As of now,no definitive treatment for food allergies exists,making the avoidance of allergen-containing foods the most effective prevention method.Consequently,labeling foods with allergen information serves as a crucial warning for allergic populations.Moreover,to enhance comprehension of food allergies,this article provides a brief overview of their definition and sensitization mechanisms.The main focus lies in highlighting the structure of primary allergens found in eight commonly allergenic foods and the resulting clinical symptoms they cause.Additionally,a summary of commonly employed allergen detection techniques is presented,with an analysis of their principles,advantages,and limitations.Looking ahead,the integration of diverse technological approaches to establish an efficient,accurate,and affordable allergen detection method remains a significant trend.This article has certain reference value for understanding the direction of food allergies.

Research on Full Space Transient Electro-magnetism Technique for Detecting Aqueous Structures in Coal Mines

Based on the transmitting theory of "smoke ring effect", the transient electromagnetism technique was used in coal mines to detect abnormal areas of aquiferous structures in both roofs and floors of coal seams and in front of excavated roadways. Survey devices, working methods and techniques as well as data processing and interpretation are discussed systematically. In addition, the direction of mini-wireframe emission electromagnetic wave of the full space transient electromagnetism technique was verified by an underground borehole for water detection and drainage. The result indicates that this technique can detect both horizontal and vertical development rules of abnormal water bodies to a certain depth below the floor of coal seams and can also detect the abnormal, low resistance water bodies within a certain distance of roofs. Furthermore, it can detect such abnormal bodies in ahead of the excavated roadway front. Limited by the underground environment, the full space transient electromagnetism technique can detect to a depth of only 120 m or so.

Investigating the Use of Remote Sensing and GIS Techniques to Detect Land Use and Land Cover Change: A Review

The accuracy of change detection on the earth’s surface is important for understanding the relationships and interactions between human and natural phenomena. Remote Sensing and Geographic Information Systems (GIS) have the potential to provide accurate information regarding land use and land cover changes. In this paper, we investigate the major techniques that are utilized to detect land use and land cover changes. Eleven change detection techniques are reviewed. An analysis of the related literature shows that the most used techniques are post-classification comparison and principle component analysis. Post-classification comparison can minimize the impacts of atmospheric and sensor differences between two dates. Image differencing and image ratioing are easy to implement, but at times they do not provide accurate results. Hybrid change detection is a useful technique that makes full use of the benefits of many techniques, but it is complex and depends on the characteristics of the other techniques such as supervised and unsupervised classifications. Change vector analysis is complicated to implement, but it is useful for providing the direction and magnitude of change. Recently, artificial neural networks, chi-square, decision tree and image fusion have been frequently used in change detection. Research on integrating remote sensing data and GIS into change detection has also increased.

Bubble counter based on photoelectric technique for leakage detection of cryogenic valves

In order to overcome the inconvenience of manual bubble counting, a bubble counter based on photoelectric technique aiming for automatically detecting and measuring minute gas leakage of cryogenic valves is proposed. Experiments have been conducted on a self-built apparatus, testing the performance with different gas inlet strategies (bottom gas-inlet strategy and side gas-inlet strategy) and the influence of gas pipe length (0, 1, 2, 4, 6, 8, 10 m) and leakage rate (around 10, 20, 30, 40 bubbles/min) on first bubble time and bubble rate. A buffer of 110 cm3 is inserted between leakage source and gas pipe to simulate the down- stream cavum adjacent to the valve clack. Based on analyzing the experimental data, experiential parameters have also been summarized to guide leakage detection and measurement for engineering applications. A practical system has already been suc- cessfully applied in a cryogenic testing apparatus for cryogenic valves.

Ultrasound for Detection of Ascites and for Guidance of the Paracentesis Procedure: Technique and Review of the Literature

Objective: To review the use of ultrasound (US) for the detection of free intraperitoneal fluid (ascites) and for the procedural guidance of the paracentesis procedure. Methods: Two clinical vignettes are presented to review the pertinent diagnostic, management and safety considerations associated with paracentesis. First, US techniques used for the identification of ascites and in the quantification of fluid pockets amenable to aspiration will be discussed. Next, the actual steps required for the performance of US-guided paracentesis will be covered. A review and analysis of the most current literature regarding US and paracentesis then follows. Conclusion: Current literature favors US-guided paracentesis over the traditional blind technique with a significant reduction in both the rate of unsuccessful aspiration of fluid and in the bleeding complications related to this procedure. Use of US for both the diagnostic and therapeutic management of ascites should be advocated as an essential skill for physicians and other health care providers caring for these patients.

New technologies and techniques to improve adenoma detection in colonoscopy

Adenoma detection rate(ADR) is a key component of colonoscopy quality assessment, with a direct link between itself and future mortality from colorectal cancer. There are a number of potential factors, both modifiable and non-modifiable that can impact upon ADR. As methods, understanding and technologies advance, so should our ability to improve ADRs, and thus, reduce colorectal cancer mortality. This article will review new technologies and techniques that improve ADR, both in terms of the endoscopes themselves and adjuncts to current systems. In particular it focuses on effective techniques and behaviours, developments in image enhancement, advancement in endoscope design and developments in accessories that may improve ADR. It also highlights the key role that continued medical education plays in improving the quality of colonoscopy and thus ADR. The review aims to present a balanced summary of the evidence currently available and does not propose to serve as a guideline.

Research Progress on Analysis and Detection Techniques of Veterinary Drug Residues in Animal Foods

As an important guarantee for the prevention and control of animal diseases,veterinary drugs have important functions in improving animal production performance and product quality and maintaining ecological balance.They are an important guarantee for the healthy development of animal husbandry,food safety and public health.However,the irrational use and abuse of veterinary drugs and feed pharmaceutical additives are widespread,causing harmful substances in animal foods and damage to human health,and threatening the sustainable development of the environment and animal husbandry as well.In order to ensure human health,it is urgent to develop a simple,rapid,high-sensitivity,high-throughput and low-cost veterinary drug residue detection technology.In this paper,the sample pretreatment methods and detection techniques for the analysis of veterinary drug residues in animal foods were reviewed.

Using acoustic technique to detect leakage in city gas pipelines

For solving the difficult problem of leakage detection in city gas pipelines, a method using acoustic technique based on instantaneous energy (IE) distribution and correlation analysis was proposed. Firstly, the basic theory of leakage detection and location was introduced. Then the physical relationship between instantaneous energy and structural state variation of a system was analyzed theoretically. With HILBERT-HUANG transformation (HHT), the instantaneous energy distribution feature of an unstable acoustic signal was obtained. According to the relative contribution method of the instantaneous energy, the noise in signal was eliminated effectively. Furthermore, in order to judge the leakage, the typical characteristic of the instantaneous energy of signal in the input and output end was discussed using correlative analysis. A number of experiments were carried out to classify the leakage from normal operations, and the results show that the leakages are successfully detected and the average recognition rate reaches 93.3% among three group samples. It is shown that the method using acoustic technique with IED and correlative analysis is effective and it may be referred in other pipelines.

超声S-Detect技术联合乳腺简化MRI对早期乳腺癌的诊断效能

目的 探讨超声S-Detect技术联合乳腺简化MRI对早期乳腺癌诊断效能的分析。方法 选取2020年1月至2021年12月进行乳腺超声和MRI检查的85个病灶。患者年龄>14岁,且未进行手术及新辅助化疗治疗,肿瘤病灶直径≤2 cm,并进行穿刺或手术获得病理结果。以乳腺穿刺肿瘤病理结果为金标准,评估超声S-Detect技术、乳腺简化MRI及二者联合检查的诊断效能。结果 S-Detect、简化MRI及两者联合诊断的灵敏度分别为:69.5%,81.4%,98.3%,特异度分别为:88.5%,80.8%,76.9%,准确率分别为:75.3%,81.2%,91.8%,与病理一致性检验Kappa值分别为:0.50,0.58,0.80,AUC分别为:0.79,0.81,0.88。差异均有统计学意义(Z=1.979,P<0.05;Z=2.096,P<0.05)。结论 超声S-Detect与乳腺简化MRI技术联合对早期乳腺癌检查的灵敏度、准确率以及与病理一致性比单独使用S-Detect技术或简化MRI高,具有较高的诊断效能,可以对早期乳腺癌的早发现早治疗起到积极作用并为临床科室制定治疗方案提供有效帮助。

Rapid <i>In-Vitro</i>and <i>In-Vitro</i>Detection of <i>Chalara fraxinea</i>by Means of Mass Spectrometric Techniques

For the first time, mass spectrometric (MS) techniques were employed to rapidly detect the pathogen Chalara fraxinea in-vitro and directly in-vivo in tissues of diseased ash trees caused by C. fraxinea, using a range of characteristic novel secondary metabolites of C. fraxinea as chemical markers for the presence of the pathogen. We have found an evident correlation between the presence and amount of these-only for C. fraxinea characteristic and novel-secondary metabolites (named chalarafraxinines) and the degree of disease of respective infected ash seedlings. As demonstrated in this work, the MS based high-throughput-screening approach constitute an alternative to the time consuming and expensive micro biological isolation procedures for detection of the pathogen C. fraxinea and furthermore, can be used to rapidly test ash genotypes for resistance / susceptibility to C. fraxinea infection.

Tamper Detection and Localization for Quranic Text Watermarking Scheme Based on Hybrid Technique

The text of the Quran is principally dependent on the Arabic language.Therefore,improving the security and reliability of the Quran’s text when it is exchanged via internet networks has become one of the most difcult challenges that researchers face today.Consequently,the diacritical marks in the Holy Quran which represent Arabic vowels(i,j.s)known as the kashida(or“extended letters”)must be protected from changes.The cover text of the Quran and its watermarked text are different due to the low values of the Peak Signal to Noise Ratio(PSNR),and Normalized Cross-Correlation(NCC);thus,the location for tamper detection accuracy is low.The gap addressed in this paper to improve the security of Arabic text in the Holy Quran by using vowels with kashida.To enhance the watermarking scheme of the text of the Quran based on hybrid techniques(XOR and queuing techniques)of the purposed scheme.The methodology propose scheme consists of four phases:The rst phase is pre-processing.This is followed by the second phase where an embedding process takes place to hide the data after the vowel letters wherein if the secret bit is“1”,it inserts the kashida but does not insert the kashida if the bit is“0”.The third phase is an extraction process and the last phase is to evaluate the performance of the proposed scheme by using PSNR(for the imperceptibility),and NCC(for the security of the watermarking).Experiments were performed on three datasets of varying lengths under multiple random locations of insertion,reorder and deletion attacks.The experimental results were revealed the improvement of the NCC by 1.76%,PSNR by 9.6%compared to available current schemes.

Detection Technique of Software-Induced Rowhammer Attacks

Side-channel attacks have recently progressed into software-induced attacks.In particular,a rowhammer attack,which exploits the characteristics of dynamic random access memory(DRAM),can quickly and continuously access the cells as the cell density of DRAM increases,thereby generating a disturbance error affecting the neighboring cells,resulting in bit flips.Although a rowhammer attack is a highly sophisticated attack in which disturbance errors are deliberately generated into data bits,it has been reported that it can be exploited on various platforms such as mobile devices,web browsers,and virtual machines.Furthermore,there have been studies on bypassing the defense measures of DRAM manufacturers and the like to respond to rowhammer attacks.A rowhammer attack can control user access and compromise the integrity of sensitive data with attacks such as a privilege escalation and an alteration of the encryption keys.In an attempt to mitigate a rowhammer attack,various hardware-and software-based mitigation techniques are being studied,but there are limitations in that the research methods do not detect the rowhammer attack in advance,causing overhead or degradation of the system performance.Therefore,in this study,a rowhammer attack detection technique is proposed by extracting common features of rowhammer attack files through a static analysis of rowhammer attack codes.

Gamma Approximation Based Multi-Antenna Covert Communication Detection

Covert communication technology makes wireless communication more secure,but it also provides more opportunities for illegal users to transmit harmful information.In order to detect the illegal covert communication of the lawbreakers in real time for subsequent processing,this paper proposes a Gamma approximation-based detection method for multi-antenna covert communication systems.Specifically,the Gamma approximation property is used to calculate the miss detection rate and false alarm rate of the monitor firstly.Then the optimization problem to minimize the sum of the missed detection rate and the false alarm rate is proposed.The optimal detection threshold and the minimum error detection probability are solved according to the properties of the Lambert W function.Finally,simulation results are given to demonstrate the effectiveness of the proposed method.