Back-end Architecture Research Articles

Delivering Systematic and Repeatable Area-Based Conservation Assessments: From Global to Local Scales

Protected and conserved areas are a key area-based strategy to address the biodiversity and climate change crises. Indicators are fundamental to understanding performance over time. The Digital Observatory for Protected Areas (DOPA) was born in 2013 as a set of open-access web services and applications to be used to assess, monitor, and report on protected and conserved areas. For over a decade, it has delivered over 50 indicators to support policy processes, national and regional governments, and practitioners. DOPA has also developed a versatile and efficient back-end approach that is widely applicable in other area-based conservation contexts. Here, we describe the methods and workflows behind DOPA’s back end and provide examples of policy relevant questions it can answer. We discuss the key advantages and limitations of this systematic and replicable approach and explore the use of this back-end architecture to inform progress in area-based conservation targets for the following decades. This approach, embedded in multiple services provided by the Knowledge Centre for Biodiversity of the European Commission (KCBD), can also support the implementation and monitoring of area-based targets of the Kunming Montreal Global Biodiversity Framework at international, regional, and national levels.

Overcoming Communication Barriers in Clinical Care: A Digital Translation Platform.

Language barriers in healthcare can lead to misdiagnosis, inappropriate treatment, and increased medical errors. Efforts to mitigate these include using interpreters and translation tools, but these measures often fall short, particularly when cultural nuances are overlooked. Consequently, medical professionals may have to rely on their staff or patients' relatives for interpretation, compromising the quality of care. This formative pilot study aims to assess the feasibility of Translatly, a digital translation platform, in clinical practice. Specifically, the study focuses on evaluating: 1. how healthcare professionals overcome language barriers and their acceptance of an on-demand video telephony platform, 2. the feasibility of the platform during medical consultations, and 3. identifying potential challenges for future development. The study included ethnographic interviews with healthcare professionals and an observational pilot to assess the use of the Tranlatly platform in clinical practice. Translatly was developed to make real-time translation easy and accessible on both Android and iOS devices. The system's backend architecture uses Java-based services hosted on DigitalOcean. The app securely exchanges data between mobile devices and servers, with user information and call records stored in a MySQL database. An admin panel helps manage the system, and Firebase integration enables fast push notifications to ensure that healthcare professionals can connect with translators whenever they need to. The platform was piloted in a German university hospital with 170 volunteer non-professional translators, mainly medical students, supporting translation in over 20 languages, including Farsi, Dari and Arabic. Ethnographic research conducted by interviewing healthcare professionals in Frankfurt am Main and other German cities revealed that current practices for overcoming language barriers often rely on family members or digital tools like Google Translate, raising concerns about accuracy and emotional distress. Respondents preferred an on-demand translation service staffed by medically experienced translators, such as medical students, who understand medical terminology and can empathize with patients. The observational pilot study recorded 39 requests for translation services, 16 (41%) of which were successfully completed. The translations covered 6 different languages and were carried out by a team of 10 translators. Most requests came from departments such as infectious diseases (5, 31.25%) and emergency (4, 25%). Challenges were identified around translator availability, with 23 (59%) of requests going unanswered, which was further evidenced by user feedback. The pilot study demonstrates the feasibility of the Translatly platform in real-world healthcare settings. It shows potential to improve communication and patient outcomes by addressing language barriers. Despite its potential, challenges such as translator availability highlight the need for further development.

Revolutionizing Cloud Storage Security: Searchable Public-Key Encryption

The Secure Cloud Storage App, developed using React.js for the frontend and MongoDB for thebackend, addresses the growing need for a reliable and privacy-focused solution for storing and managing digital assetsin the cloud. In response to the escalating demand for a dependable and privacy-centric solution to handle digital assetsin the cloud, the Secure Cloud Storage App has emerged, meticulously crafted with React.js powering its frontend andMongoDB driving its backend architecture. This innovative application stands as a beacon for users seeking a securehaven for their data. At its core, security takes precedence, with the implementation of cutting-edge encryptionprotocols meticulously woven into every aspect of data transmission and storage. This fortification ensures the utmostconfidentiality and integrity of user information throughout its journey within the cloud infrastructure. The userinterface, expertly fashioned using React.js, stands as a testament to seamless design and intuitive interaction. Users areempowered with effortless capabilities to upload, organize, and access their files, enhancing productivity andconvenience in managing digital assets. MongoDB, a stalwart in backend database solutions, offers a robust foundationcharacterized by scalability and flexibility, adeptly catering to diverse storage requirements. Moreover, the SecureCloud Storage App encompasses comprehensive user authentication and authorization mechanisms, bolstering accesscontrol and privacy measures. Through the amalgamation of secure authentication protocols and end-to-end encryption,paramount importance is placed on shielding user data from prying eyes, fostering an environment of trust andconfidence in cloud-based file management. The synergy between React.js and MongoDB transcends meretechnological integration; it epitomizes a harmonious marriage of functionality and security. Not only does this pairingensure a responsive and dynamic user interface, but it also lays down a resilient and secure infrastructure for the storageand retrieval of sensitive information within the cloud ecosystem. In essence, the Secure Cloud Storage App stands as abeacon of innovation, ushering in a new era of secure and efficient cloud-based file management solutions.

Artificial Intelligence Based Store Management

The project proposes innovative ideas such as personalized customer interactions through a mobile application and optimizing queues through the sliding checkout model. It also leverages existing kiosks for digital customer connections. The project's methodology is based on comprehensive needs analysis, consultations with industry experts, and the identification of processes suitable for automation. It also prioritizes Research and Development (R&D) in retail merchandising by securing R&D licenses from industry giants. The project's technological infrastructure is designed for the Azure cloud environment, ensuring operational efficiency and seamless integration with various systems. A robust logging infrastructure is in place to maintain an uninterrupted connection between artificial intelligence support and the backend architecture. The project also develops a mobile application with user-friendly interfaces and cross-platform functionality using Flutter. The anticipated benefits of the project include time savings for store managers, data-driven decision-making, and experimental positioning for testing and implementing novel methods in store operation. Overall, the "Artificial Intelligence Based Store Management" project aims to set new industry standards by integrating artificial intelligence and machine learning into retail merchandising.

LABS: Laboratory Automation and Batch Scheduling - A Modular Open Source Python Program for the Control of Automated Electrochemical Synthesis with a Web Interface.

With LABS, an open source Python-based lab software is established that enables users to orchestrate automated synthesis setups. The software consists of a user-friendly frontend for data input and system monitoring. A flexible backend architecture enables the integration of multiple lab devices. The software allows users to easily modify experimental parameters or routines and switch between different lab devices. Compared to previously published projects, we aim to provide a more widely applicable and easily customizable automation software for any experimental setup. The usefulness of this tool was demonstrated in the oxidative coupling of 2,4-dimethylphenol to the corresponding 2,2'-biphenol. In this context, the suitable electrolysis parameters for flow electrolysis were optimized by way of design of experiments.

Deep Learning Methods for Bug Bite Classification: An End-to-End System

A bite from a bug may expose the affected person to serious, life-threatening conditions, which may require immediate medical attention. The identification of the bug bite may be challenging even for experienced medical personnel due to the different manifestations of the bites and similarity to other skin conditions. This motivated our work on a computer-aided system that offers information on the bug bite based on the classification of bug bite images. Recently, there have been significant advances of methods for image classification for the detection of various skin conditions. However, there are very few sources that discuss the classification of bug bites. The goal of our research is to fill in this gap in the literature and offer a comprehensive approach for the analysis of this topic. This includes (1) the creation of a dataset that is larger than those considered in the related sources; (2) the exploration and analysis of the application of pre-trained state-of-the-art deep learning architectures with transfer learning, used in this study to overcome the challenges of low-size datasets and computational burden; (3) the further improvement of the classification performance of the individual CNNs by proposing an ensemble of models, and finally, (4) the implementation and description of an end-to-end system for bug bite classification from images taken with mobile phones, which should be beneficial to the medical personnel in the diagnostic process. In this paper, we give a detailed discussion of the models’ architecture, back-end architecture, and performance. According to the general evaluation metrics, DenseNet169 with an accuracy of 78% outperformed the other individual CNN models. However, the overall best performance (accuracy of 86%) was achieved by the proposed stacking ensemble model. These results are better than the results in the limited related work. Additionally, they show that deep CNNs and transfer learning can be successfully applied to the problem of the classification of bug bites.

SBMYv3: Improved MobYOLOv3 a BAM attention‐based approach for obscene image and video detection

AbstractCountless cybercrime instances have shown the need for detecting and blocking obscene material from social media sites. Deep learning methods (DLMs) outperformed in recognizing obscene content flooded on many online platforms. However, these contemporary DLMs primarily treat the recognition of obscene content as a simple task of binary classification, rather than focusing on the labelling of obscene areas. Hence, many of these methods could not pay attention to the fact that misclassification samples are so diverse. Therefore, this paper focuses on two aspects (i) developing a deep learning model that could classify and label the obscene portion, and (ii) generating a labelled obscene image dataset with a wide variety of obscene samples to minimize the risks of inaccurate recognition. We have proposed a method named S3Pooling based bottleneck attention module (BAM) embedded MobileNetV2‐YOLOv3 (SBMYv3) for automatic detection of obscene content using an attention mechanism and a suitable pooling strategy. The key contributions of our article are: (i) generation of a well‐labelled obscene image dataset with a variety of augmentation strategies using Pix‐2‐Pix GAN (ii) modifications to the backend architecture of YOLOv3 using MobileNetV2 and BAM to ensure focused and accurate feature extraction, and (iii) selection of an optimal pooling strategy, that is, S3Pooling strategy, while taking the design of the feature extractor into account. The proposed SBMYv3 model outperformed other state‐of‐the‐art models with 99.26% testing accuracy, 99.39% recall, 99.13% precision, and 99.13% IoU values respectively.

Toward an Online Continual Learning Architecture for Intrusion Detection of Video Surveillance

With recent advances in deep learning technologies, many commercialized video surveillance systems have adopted Artificial Intelligence (AI)-powered video analytics technologies as a way to make our life smarter and safer. Nevertheless, there is no robust architecture with an appropriate network model for commercial services considering both high accuracy and low computational cost. Existing deep learning technologies would not be enough to model and represent the dynamics of the real-world scene, so it is difficult to satisfy all environments using a generic model. Appropriate training data from false-alarm and/or missed cases can address this limitation but is rarely available due to legal issues relating to the privacy of personal data and the unpredictability of new incoming data. In this paper, we propose a novel end-to-end hybrid video surveillance architecture for reliable object detection, consisting of front-end and back-end intelligence. For the intelligent front-end, we propose a new object detector with a Multi-scale ResBlock scheme to consider the scalability and flexibility of the system. We are also developing a new domain adaptation method to replace the generic model with each camera&#x2019;s individual personal model by understanding real-time space and context information for intelligent back-end architecture. It is an iterative and continuous process in which new upcoming data and previous models are consistently engaged in a continuous improvement process. We conducted a series of experiments, including an interesting proof-of-concept tests called the <i>Chameleon</i> project, which demonstrated the high accuracy and versatility of the new architecture, while producing robust results that can be implemented in practice.

Quantum Voting in Reach for Ethereum and Algorand

This White Paper introduces and contributes the first implementation of the Decentralized Voting Algorithm. First, Part I provides an overview for the software structures relevant to this work. Second, Part II introduces a decentralized voting algorithm for transferring value on blockchain networks. Third, Part III explains the voting algorithm’s implementation in reach, including the backend architecture, web deployment, and quantum integration. Perhaps most significantly, this paper solves the Decentralized Voting Problem with a new quantum consensus system.

METATRYP v 2.0: Metaproteomic Least Common Ancestor Analysis for Taxonomic Inference Using Specialized Sequence Assemblies-Standalone Software and Web Servers for Marine Microorganisms and Coronaviruses.

We present METATRYP version 2 software that identifies shared peptides across the predicted proteomes of organisms within environmental metaproteomics studies to enable accurate taxonomic attribution of peptides during protein inference. Improvements include ingestion of complex sequence assembly data categories (metagenomic and metatranscriptomic assemblies, single cell amplified genomes, and metagenome assembled genomes), prediction of the least common ancestor (LCA) for a peptide shared across multiple organisms, increased performance through updates to the backend architecture, and development of a web portal (https://metatryp.whoi.edu). Major expansion of the marine METATRYP database with predicted proteomes from environmental sequencing confirms a low occurrence of shared tryptic peptides among disparate marine microorganisms, implying tractability for targeted metaproteomics. METATRYP was designed to facilitate ocean metaproteomics and has been integrated into the Ocean Protein Portal (https://oceanproteinportal.org); however, it can be readily applied to other domains. We describe the rapid deployment of a coronavirus-specific web portal (https://metatryp-coronavirus.whoi.edu/) to aid in use of proteomics on coronavirus research during the ongoing pandemic. A coronavirus-focused METATRYP database identified potential SARS-CoV-2 peptide biomarkers and indicated very few shared tryptic peptides between SARS-CoV-2 and other disparate taxa analyzed, sharing <1% peptides with taxa outside of the betacoronavirus group, establishing that taxonomic specificity is achievable using tryptic peptide-based proteomic diagnostic approaches.

Improving the efficiency of using cloud services by implementing IEEE 802.11ах

This article discusses one method for improving the efficiency of an information system that is capable of providing cloud services. The topic is relevant, because over time, more and more users are switching or planning to switch to cloud services. To consider methods for increasing the efficiency of an information system, an analysis is made of saving user funds and resources, ways of creating the possibility of remote access to services, methods of obtaining the possibility of using individual services at a certain level of abstraction in order to match these services to various user needs. The task is to study methods of increasing the efficiency of the use of cloud services by introducing IEEE 802.11ax technology and determining the directions and prospects of its use. To achieve the goal, empirical methods were used in the analysis of services, signs for their classification are determined, namely: a data storage or backup service; On-line office; On-line accounting; specialized Internet applications; closed private clouds for team management and customer relationship management; combinational services; applied cloud services. The advantages of cloud services are determined: scalability, flexibility, relevance, ease of access. Defines the cloud computing front-end and back-end architecture. This makes it possible to formulate the requirements for telecommunication access facilities to provide cloud services, as far as possible, namely: good quality of connection in places with a high density of users; creation of a Wi-Fi network with high bandwidth and low latency; support for previous versions of Wi-Fi devices; expansion of application in IoT networks; application of Wi-Fi infrastructure in target markets; creation of applications for IoT using technology and management of the parameter of the target wakeup time (Target-Wakeup Time); application for creating virtual reality (Virtual reality, VR); application to create augmented reality (AR); application for providing video content in Ultra HD mode; application in real-time applications, significant energy savings in the connected device. An analysis is performed of Wi-Fi technologies that are capable of meeting the specified requirements. It is concluded that this is possible when using the IEEE 802.11 standard.

Implementation of Micro Services Architecture on Comrades Backend

This research aims to increase the scalability and maintainability of Comrades backend. The experimental method that was used in this research is Domain-Driven Design. Applying Domain-Driven Design, Comrades backend will have more scalable and high-level scalability architecture, namely, microservices architecture. The result of this research is a new model of backend architecture and a prototype to measure the performances of this new model. To achieve this result, the transformation of architecture from monolithic into several context and implementation of the context to be microservices architecture are the main steps of this research. Moreover, complete software architecture and infrastructure will be developed as the output of this research. To make it possible, the research uses several technologies including Docker, Kubernetes, and Gateway API. By using this architecture, Comrades developer are able to create Comrades backend more reliable because each functional of Comrades will have their architecture. They can use the services to give a better experience for Comrades users in accessing the features in comrades. For the conclusion, a new model of Comrades backend architecture has more scalable level without any error.

Imagining Future Cities: Aesthetic Design Guidelines for Wireless Small Cells in Urban Landscapes

Wireless towers with big antennas and large equipment shelters are decreasing as the primary type of wireless infrastructure in favor of more diversified designs, varying in size, network architecture, and capacity. The definitional boundary between typical macro sites of the 1990s and increasingly popular small cell networks are slowly blending as the backend architecture and the equipment on a site are designed to meet a greater variety of capacity and coverage needs through heterogeneous networks. As the design of wireless network architecture is changing, so are their appearance in our surrounding environment. As a result, municipal authorities are increasingly under pressure by the Federal Communication Commission (FCC) and wireless carriers to adapt and evolve their siting and permitting practices to accommodate a variety of different types of facilities. This paper will evaluate how innovation in LTE and 5G equipment is changing the design of physical wireless networks, establish an understanding of ‘contextual appropriateness’ in the blending of new small cells with the previously built environment, and prescribe aesthetic design guidelines for improving people’s experience with new urban wireless facilities. The methodology will include identifying technological trajectories and possibilities in wireless small cell designs, categorization of existing available assets for their deployment, an analysis of contextual appropriateness, and the development of objective design principles that will facilitate structured guidelines for network deployment. First, the paper will consider how wireless network equipment is evolving in different types of network topologies. Second, as small cells are typically placed on poles, these deployments will be categorized based on commonly available vertical assets in the Rights of Way, types of street furniture available for small cell integration opportunities, and other types of design ideas that leverage existing structures. Third, the definition of contextual appropriateness will be examined through city planning and architectural integration principles and typical constraints within the urban landscape. This paper will heavily utilize visual examples of previously deployed designs to analyze their technological components, design elements, and methods of construction and composition. Many of these visual examples are available in FCC docket filings relating to the recent relevant proceedings (WT Docket 17-79 and related), as well as in many articles and online government resources. Fourth, this research will contribute to a systematic method of evaluating the quality of small cell designs on a methodical basis, replacing the existing common practice of relying on subjective, improvised, and often contradictory aesthetic preferences. The resulting design recommendations and architectural best practices will facilitate many local efforts to develop preferential guidelines that achieve long-term compromise between preserving the character of their community and advancing to the smart cities of the future. The guidelines will also promote a more positive public perception and a more welcoming attitude toward the widespread integration of wireless facilities. The way that wireless technology will become deployed in the future of a given urban neighborhood will become its history, its character, and its legacy for advanced technological systems yet to come.

Challenges and Opportunities of Cloud Computing in Social Network; Survey

Nowadays, there has been fast growth in cloud computing and social networking technologies. Cloud computing translates the computing resources to a third party, eliminating the need to purchase, configure and maintain those resources. With the increasingly ubiquitous nature of Social networks and Cloud computing, users are starting to explore new ways to interact with, and exploit these developing paradigms. Social networks are used to reflect real world relationships that allow users to share information and form connections between one another, essentially creating dynamic Virtual Organizations. Cloud Computing and Social Network Sites (SNS) are among the most controversially discussed developments in recent years. The opportunities of using powerful computing resources on demand via the web are considered as a possible driver for the growth of the world economy. Social networks provide a virtual place where people can interact and share. With the increasing user base and the competition social networks needed a scalable, cost-effective backend architecture and efficient business. Online social media network has become part of human life by transforming the way users create new social relations or relate with family and friends. A social network, as the name suggests is a network of individuals created for social communication. It is a massive platform where people interact with others anywhere in the world online based on some relationship. The relationship can be friendship, family membership or others with some shared interests. Despite these considerable benefits, there are serious concerns and challenges about this new technology. The most important issue is related to security and privacy subjects in cloud-based environments. Accordingly, challenges and concerns related to cloud based environments have been identified and most appropriate current solutions for each challenge have been described. In overall, it is anticipated that cloud computing will be the most important and challenging issue in IT industry. As regarding the importance of this technology, we hope this paper will contribute to a better understanding of the vision of cloud computing and the challenges ahead for further researches. Likewise, social networks have seen enormous growth, with millions of Internet users actively participating across various social networking websites.

FlyBase 2.0: the next generation.

FlyBase (flybase.org) is a knowledge base that supports the community of researchers that use the fruit fly, Drosophila melanogaster, as a model organism. The FlyBase team curates and organizes a diverse array of genetic, molecular, genomic, and developmental information about Drosophila. At the beginning of 2018, ‘FlyBase 2.0’ was released with a significantly improved user interface and new tools. Among these important changes are a new organization of search results into interactive lists or tables (hitlists), enhanced reference lists, and new protein domain graphics. An important new data class called ‘experimental tools’ consolidates information on useful fly strains and other resources related to a specific gene, which significantly enhances the ability of the Drosophila researcher to design and carry out experiments. With the release of FlyBase 2.0, there has also been a restructuring of backend architecture and a continued development of application programming interfaces (APIs) for programmatic access to FlyBase data. In this review, we describe these major new features and functionalities of the FlyBase 2.0 site and how they support the use of Drosophila as a model organism for biological discovery and translational research.

DiTeX: Disease-related topic extraction system through internet-based sources.

This paper describes the web-based automated disease-related topic extraction system, called to DiTeX, which monitors important disease-related topics and provides associated information. National disease surveillance systems require a considerable amount of time to inform people of recent outbreaks of diseases. To solve this problem, many studies have used Internet-based sources such as news and Social Network Service (SNS). However, these sources contain many intentional elements that disturb extracting important topics. To address this challenge, we employ Natural Language Processing and an effective ranking algorithm, and develop DiTeX that provides important disease-related topics. This report describes the web front-end and back-end architecture, implementation, performance of the ranking algorithm, and captured topics of DiTeX. We describe processes for collecting Internet-based data and extracting disease-related topics based on search keywords. Our system then applies a ranking algorithm to evaluate the importance of disease-related topics extracted from these data. Finally, we conduct analysis based on real-world incidents to evaluate the performance and the effectiveness of DiTeX. To evaluate DiTeX, we analyze the ranking of well-known disease-related incidents for various ranking algorithms. The topic extraction rate of our ranking algorithm is superior to those of others. We demonstrate the validity of DiTeX by summarizing the disease-related topics of each day extracted by our system. To our knowledge, DiTeX is the world’s first automated web-based real-time service system that extracts and presents disease-related topics, trends and related data through web-based sources. DiTeX is now available on the web through http://epidemic.co.kr/media/topics.

A 250-[formula omitted]m pitch 36-channel silicon photo multiplier array prototype using silicon on insulator technology

A 6×6 silicon photomultiplier (SiPM) array aimed at one-to-one coupling to a finely separated scintillator array was fabricated and characterized. All SiPMs were formed in the bulk substrate layer of a silicon on insulator (SOI) wafer for enabling future SiPMs which consists of 3-D integrated electronics without mechanical bonding. Each channel had a size of 250×250μm2 and was arranged for satisfying the counting-rate requirements of over 2 Mcps/mm2 in energy-resolvable X-ray photon counting computed tomography. To minimize the cost and complexity of the system’s back-end architecture and data acquisition, a novel two-dimensional coordinate readout was designed in anode terminals which contributes to the number of readout wires reduction without the position resolution degradation. As a feasibility study, the basic performance of a prototype analog SiPM array using the commercial SOI process was fabricated and characterized. Several features, such as a fast recovery time under 20 ns and a gain of 1×105 were within requirements to realize photon counting CT.

The Living Atlases community in action: the GBIF Benin data portal

Atlas of Living Australia (ALA) (https://www.ala.org.au/) is the Global Biodiversity Information Facility (GBIF) node of Australia. They developed an open and free platform for sharing and exploring biodiversity data. All the modules are publicly available for reuse and customization on their GitHub account (https://github.com/AtlasOfLivingAustralia). GBIF Benin, hosted at the University of Abomey-Calavi, has published more than 338 000 occurrence records from 87 datasets and 2 checklists. Through the GBIF Capacity Enhancement Support Programme (https://www.gbif.org/programme/82219/capacity-enhancement-support-programme), GBIF Benin, with the help of GBIF France, is in the process of deploying the Beninese data portal using the GBIF France back-end architecture. GBIF Benin is the first African country to implement this module of the ALA infrastructure. In this presentation, we will show you an overview of the registry and the occurrence search engine using the Beninese data portal. We will begin with the administration interface and how to manage metadata, then we will continue with the user interface of the registry and how you can find Beninese occurrences through the hub.

Integration of a Zero-footprint Cloud-based Picture Archiving and Communication System with Customizable Forms for Radiology Research and Education

The purpose of this study was to integrate web-based forms with a zero-footprint cloud-based Picture Archiving and Communication Systems (PACS) to create a tool of potential benefit to radiology research and education. Web-based forms were created with a front-end and back-end architecture utilizing common programming languages including Vue.js, Node.js and MongoDB, and integrated into an existing zero-footprint cloud-based PACS. The web-based forms application can be accessed in any modern internet browser on desktop or mobile devices and allows the creation of customizable forms consisting of a variety of questions types. Each form can be linked to an individual DICOM examination or a collection of DICOM examinations. Several uses are demonstrated through a series of case studies, including implementation of a research platform for multi-reader multi-case (MRMC) studies and other imaging research, and creation of an online Objective Structure Clinical Examination (OSCE) and an educational case file.

Cloud Computing-Based Non-Invasive Glucose Monitoring for Diabetic Care

Near infrared photoacoustic spectroscopy is utilized for the development of a continuous non-invasive glucose monitoring system for diabetics. A portable embedded system for taking photoacoustic measurements on tissues to estimate glucose concentration is implemented using field programmable gate array (FPGA). The back-end architecture for high-speed data acquisition and de-noising of photoacoustic measurements operates at 274.823 MHz on a Xilinx Virtex-II Pro FPGA. The glucose measurement technique is verified in vitro on glucose solutions and in vivo on tissues, with photoacoustic signal amplitude varying linearly with sample glucose concentration. A kernel-based regression algorithm using multiple features of the photoacoustic signal is used to estimate glucose concentration from photoacoustic measurements. The calibration algorithm provides a superior performance over previous efforts with a mean absolute relative difference of 8.84% and Clarke Error Grid distribution of 92.86% and 7.14% over Zones A and B of the grid. A cloud computing platform for automated monitoring of blood glucose levels is proposed to enable individuals with diabetes to connect with doctors and caretakers. The developed system is connected to the cloud service using a mobile device, which facilitates implementation of computationally intensive calibration tasks and the storage and analysis of measurement data for treatment and monitoring.