Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques

A correct food tray sealing is required to preserve food properties and safety for consumers. Traditional food packaging inspections are made by human operators to detect seal defects. Recent advances in the field of food inspection have been related to the use of hyperspectral imaging technology and automated vision-based inspection systems. A deep learning-based approach for food tray sealing fault detection using hyperspectral images is described. Several pixel-based image fusion methods are proposed to obtain 2D images from the 3D hyperspectral image datacube, which feeds the deep learning (DL) algorithms. Instead of considering all spectral bands in region of interest around a contaminated or faulty seal area, only relevant bands are selected using data fusion. These techniques greatly improve the computation time while maintaining a high classification ratio, showing that the fused image contains enough information for checking a food tray sealing state (faulty or normal), avoiding feeding a large image datacube to the DL algorithms. Additionally, the proposed DL algorithms do not require any prior handcraft approach, i.e., no manual tuning of the parameters in the algorithms are required since the training process adjusts the algorithm. The experimental results, validated using an industrial dataset for food trays, along with different deep learning methods, demonstrate the effectiveness of the proposed approach. In the studied dataset, an accuracy of 88.7%, 88.3%, 89.3%, and 90.1% was achieved for Deep Belief Network (DBN), Extreme Learning Machine (ELM), Stacked Auto Encoder (SAE), and Convolutional Neural Network (CNN), respectively

Food tray sealing fault detection using hyperspectral imaging and PCANet

Food trays are very common in shops and supermarkets. Fresh food packaged in trays must be correctly sealed to protect the internal atmosphere and avoid contamination or deterioration. Due to the speed of production, it is not possible to have human quality inspection. Thus, automatic fault detection is a must to reach high production volume. This work describes a deep neural network based on Principal Component Analysis Network (PCANet) for food tray sealing fault detection. The input data come from hyperspectral cameras, showing more characteristics than regular industrial cameras or the human eye as they capture the spectral properties for each pixel. The proposed classification algorithm is divided into three main parts. In the first part, a single image is extracted from the hypercube by using pixel-level fusion method: the cube hyperspectral images are transformed into two-dimensional images to use as the input to the PCANet. Second, a PCANet structure is applied to the fused image. The PCANet has two filter bank layers and one binarization layer (three stages), obtaining a feature vector. Finally, a classification algorithm is used, having the feature vector as input data. The SVM and KNN classifiers were used. The database used in this work is provided by food industry professionals, containing eleven types of contamination in the seal area of the food tray and using metallic opaque cover film. Obtained results show that the design of our framework proposed achieves accuracy of 90% (87% F-measure) and 89% (89% F-measure) for SVM and KNN, respectively. Computation time for classification shows that a food tray speed of 65 trays per second could be reached. As a final result, the influence of the dataset size is analyzed, having PCANet a similar behavior for an extended and a reduced dataset

Experimental Analysis of IoT Networks Based on LoRa/LoRaWAN under Indoor and Outdoor EnvirMedusonments: Performance and Limitations

Nowadays, Internet of Things (IoT) has multiple applications in different fields. This concept allows physical devices to connect to the internet in order to establish a strong infrastructure that facilitates many device control and monitoring tasks. Low Power Wide Area (LPWA) communication protocols become widely used for IoT networks because of their low power consumption and the broad range communication. LPWA enables devices to transmit small amounts of data in a long distance. Among LPWA protocols, LoRa technology gained a lot of interest recently from the research community and many companies. LoRa is a long range and low power wireless communication technology regulated by the LoRaWAN standard. It can be o good candidate to deploy node network where long distance and extended battery life is required. A LoRaWAN architecture is deployed in a star-of-stars topology and based on a systematic evaluation of a long-term operation of the network monitoring. This works describes experimental results of testing LoRa in indoor and outdoor environments to understand how it works, evaluate its performance, and limitations. As expected, results show that LoRa performs better outdoor. It is also interesting to note that elevating the gateway in order to have a free line of sight with the IoT node, or close to it, increases the signal quality received by the end-node devices, and consequently, longer distances can be achieved

A Novel Approach of a Low-Cost UWB Microwave Imaging System with High Resolution Based on SAR and a New Fast Reconstruction Algorithm for Early-Stage Breast Cancer Detection

In this article, a new efficient and robust approach¿the high-resolution microwave imaging system¿for early breast cancer diagnosis is presented. The core concept of the proposed approach is to employ a combination of a newly proposed delay-and-sum (DAS) algorithm and the specific absorption rate (SAR) parameter to provide high image quality of breast tumors, along with fast image processing. The new algorithm enhances the tumor response by altering the parameter referring to the distance between the antenna and the tumor in the conventional DAS matrices. This adjustment entails a much clearer reconstructed image with short processing time. To achieve these aims, a high directional Vivaldi antenna is applied around a simulated hemispherical breast model with an embedded tumor. The detection of the tumor is carried out by calculating the maximum value of SAR inside the breast model. Consequently, the antenna position is relocated near the tumor region and is moved to nine positions in a trajectory path, leading to a shorter propagation distance in the image-creation process. At each position, the breast model is illuminated with short pulses of low power waves, and the back-scattered signals are recorded to produce a two-dimensional image of the scanned breast. Several simulations of testing scenarios for reconstruction imaging are investigated. These simulations involve different tumor sizes and materials. The influence of the number of antennas on the reconstructed images is also examined. Compared with the results from the conventional DAS, the proposed technique significantly improves the quality of the reconstructed images, and it detects and localizes the cancer inside the breast with high quality in a fast computing time, employing fewer antennas

Sensor Node Network for Remote Moisture Measurement in Timber Based on Bluetooth Low Energy and Web-Based Monitoring System

This paper proposes an IoT system based on wireless BLE connectivity to monitor the moisture content of wood, using a compact and low-cost moisture device that relies on a resistance measurement method valid for an ultra-wide range of resistance values. This device is digitally controlled with a BLE-incorporated micro-controller characterized by its small size and low power consumption, providing long-life battery. The proposed system consists of two main parts: first, the BLE moisture device including the moisture content measurement and wireless capability (BLE); second, the cloud-based monitoring platform, providing remote visualization and control for all the sensor nodes of the network. The complete infrastructure shows how multiple nodes can read and transmit moisture content of timber in buildings using small and unattended devices, with data saved in a central database and monitored by multiple commercial devices such as PC, smartphone, tablet, etc. The proposed system is innovative, scalable and low cost, and it can be deployed in wooden buildings and the wood industry, providing a practical solution that will help to avoid rot and other damaging effects caused by the moisture content

Study on the performance of the Sarawak Industrial and Entrepreneurial Information Centre (SIEIC) / Abdul Rahman Deen ... [et al.]

The twenty-first century will see an intense demand for information and knowledge. Trade liberalisation will offer expanding markets opportunities only to be reaped with market intelligence in the knowledge economy. Even businesses confined within national boundaries will need information to respond adequately to foreign competition. Besides serving as inputs to the business, industrial and entrepreneurial community, information and knowledge will also be the end-products for the increasingly sophisticated consuming public. Recognising the challenges of the new millennium, the Ministry of Industrial Development, Sarawak (MID) established the Sarawak Industrial and Entrepreneurial Information Centre (SIEIC) way back in 1995 (see Appendix 1 on Objectives, Functions, Activities and Facilities of SIEIC). The socio-economic and political scenario has changed drastically since 1995. The accelerated development of information technology (IT) has brought forth new opportunities as well as threats. Inter-governmental negotiations in the World Trade Organisation and various economic groupings are already in place. They will impinge on the Malaysian economy in general and the Sarawak economy in particular. The Asian financial crisis in 1997 and the ensuing economic recession have impacted on the local community. Malaysia's foreign exchange control and attempts at financial and banking restructuring thereafter further changes the business and investment climate. Above all is the resource constraint in achieving aspired objectives and targets

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Desarrollo y análisis de sensores inalámbricos y sistemas asociados de IoT

En los últimos años, la tecnología del Internet de las cosas (IoT) se ha vuelto cada vez más importante. Al ofrecer un medio para conectar dispositivos y establecer conexiones en tiempo real, el IoT ha facilitado el monitoreo y la gestión de entidades tangibles, como electrodomésticos, vehículos y edificios. Típicamente, un sistema IoT incorpora numerosos dispositivos que se comunican entre sí a través de gateways y servidores en la nube que alojan aplicaciones web y móviles. Los usuarios finales pueden interactuar sin problemas con sus dispositivos IoT, lo que les otorga una mayor accesibilidad y conveniencia. Dado su capacidad para proporcionar control y automatización en tiempo real, el IoT presenta numerosas oportunidades para ahorrar costos e incrementar la eficiencia en varios sectores, incluyendo salud, manufactura y transporte. Como tal, el IoT tiene el potencial de mejorar nuestras vidas y crear nuevas oportunidades para los negocios, convirtiéndolo en una tecnología esencial para el futuro. Sin embargo, a medida que las aplicaciones IoT continúan volviéndose cada vez más complejas y diversas, hay una creciente demanda de sensores capaces de recopilar datos altamente específicos de manera precisa y confiable, y transmitirlos a otros dispositivos dentro de la red. Además, IoT está en constante evolución, con nuevas tecnologías emergiendo a un ritmo acelerado. Por lo tanto, es necesario estudiar y analizar las tecnologías de comunicación empleadas, y mantenerse al día con los últimos desarrollos para abordar de manera efectiva la necesidad de soluciones IoT altamente especializadas y personalizadas y para garantizar un diseño, implementación y gestión efectivos de estas aplicaciones. Además, el desarrollo de nuevos sensores puede ofrecer una solución a este problema al optimizar el diseño para casos de uso específicos, logrando así un rendimiento y funcionalidad óptimos en los sistemas IoT. Al desarrollar un sensor novedoso, se puede ejercer un mayor control sobre el hardware y software, lo que permite una mayor flexibilidad y personalización. En esta tesis se presenta un análisis detallado de los sistemas IoT en cuanto a su comunicación, consumo de energía y seguridad. El estudio analizó las tecnologías de comunicación utilizadas en los sistemas IoT, incluidos los sistemas de corto alcance basados en protocolos como Wi-Fi y Bluetooth, así como los sistemas de largo alcance basados en tecnologías como LoRa. Además, se desarrollaron nuevos sensores específicos para aplicaciones particulares. Asimismo, se abordaron los riesgos de seguridad asociados con los sistemas IoT centrados en la nube mediante la incorporación de la tecnología blockchain, que proporciona una infraestructura descentralizada de base de datos y añade una capa extra de seguridad. Los resultados de estos estudios pueden ayudar a mejorar la comprensión y la optimización de los sistemas IoT, mejorando su rendimiento y funcionalidad, y proporcionando información valiosa sobre el diseño, implementación y gestión de sistemas IoT para investigadores, profesionales de la industria y otros interesados en esta tecnología.The advent of the Internet of Things (IoT) technology has become increasingly important in recent years. By offering a means of linking devices and establishing real-time connections, IoT has facilitated the monitoring and management of tangible entities, such as appliances, vehicles, and buildings. Typically, an IoT system consists of numerous devices that communicate with one another via gateways, and cloud servers that host web and mobile applications. The end-users can seamlessly interact with their IoT devices, thereby granting greater accessibility and convenience. Given its capacity for providing real-time control and automation, IoT presents numerous opportunities for cost savings and increased efficiency across various sectors, including healthcare, manufacturing, and transportation. As such, IoT has the potential to improve our lives and create new prospects for businesses, making it an essential technology for the future. However, as IoT applications continue to become increasingly complex and diverse, there is an growing demand for sensors capable of accurately and reliably collecting highly specific data and transmitting it to other devices within the network. Additionally, the IoT landscape is constantly evolving, with new technologies emerging at a rapid pace. Therefore, to effectively address the need for highly specialized and customized IoT solutions, it is necessary to study and analyze the communication technologies employed, and remain up-to-date with the latest developments in order to ensure effective design, implementation, and management of these applications. Furthermore, developing new sensors from scratch can offer a solution to this problem by optimizing the design for specific use cases, thereby achieving optimal performance and functionality in IoT systems. By developing a sensor from scratch, greater control can be exercised over the hardware and software, thereby allowing for greater flexibility. This thesis presents a systematic analysis of IoT systems in terms of communication, power consumption, and security. The study analyzed the communication technologies used in IoT systems, including short-range systems based on protocols such as Wi-Fi and Bluetooth, as well as long-range systems based on technologies like LoRa. The thesis includes the development of new sensors from scratch for specific applications, enabling greater control over the hardware and software. Additionally, the thesis addresses the security risks associated with cloud-centric IoT systems by incorporating blockchain technology, which provides a decentralized database infrastructure, thereby adding an extra layer of security. These studies help to better understand IoT systems and optimize their deployment, resulting in improved performance and functionality, and offers valuable insights into the design, implementation, and management of IoT systems for researchers, industry practitioners, and stakeholders

Efficient Extraction of Deep Image Features Using a Convolutional Neural Network (CNN) for Detecting Ventricular Fibrillation and Tachycardia

To safely select the proper therapy for ventricular fibrillation (VF), it is essential to distinguish it correctly from ventricular tachycardia (VT) and other rhythms. Provided that the required therapy is not the same, an erroneous detection might lead to serious injuries to the patient or even cause ventricular fibrillation (VF). The primary innovation of this study lies in employing a CNN to create new features. These features exhibit the capacity and precision to detect and classify cardiac arrhythmias, including VF and VT. The electrocardiographic (ECG) signals utilized for this assessment were sourced from the established MIT-BIH and AHA databases. The input data to be classified are time–frequency (tf) representation images, specifically, Pseudo Wigner–Ville (PWV). Previous to Pseudo Wigner–Ville (PWV) calculation, preprocessing for denoising, signal alignment, and segmentation is necessary. In order to check the validity of the method independently of the classifier, four different CNNs are used: InceptionV3, MobilNet, VGGNet and AlexNet. The classification results reveal the following values: for VF detection, there is a sensitivity (Sens) of 98.16%, a specificity (Spe) of 99.07%, and an accuracy (Acc) of 98.91%; for ventricular tachycardia (VT), the sensitivity is 90.45%, the specificity is 99.73%, and the accuracy is 99.09%; for normal sinus rhythms, sensitivity stands at 99.34%, specificity is 98.35%, and accuracy is 98.89%; finally, for other rhythms, the sensitivity is 96.98%, the specificity is 99.68%, and the accuracy is 99.11%. Furthermore, distinguishing between shockable (VF/VT) and non-shockable rhythms yielded a sensitivity of 99.23%, a specificity of 99.74%, and an accuracy of 99.61%. The results show that using tf representations as a form of image, combined in this case with a CNN classifier, raises the classification performance above the results in previous works. Considering that these results were achieved without the preselection of ECG episodes, it can be concluded that these features may be successfully introduced in Automated External Defibrillation (AED) and Implantable Cardioverter Defibrillation (ICD) therapies, also opening the door to their use in other ECG rhythm detection applications