Data Conversion Research Articles

Controller Area Network (CAN) Bus Transceiver with Authentication Support and Enhanced Rail Converters

This paper presents an advanced Controller Area Network (CAN) bus transceiver designed to enhance security using frame-level authentication with the concept of a nonphysical virtual auxiliary data channel. We describe the newly conceived transceiver security features and provide results concerning the design, implementation, fabrication and test of the transceiver to validate its functionality and robust operation in the presence of systemic error sources including Process, Voltage, and Temperature (PVT) variations. The virtual auxiliary channel integrates CAN frame authentication signatures into the primary data payload via phase modulation while also providing compatibility with existing CAN protocols, interoperability with non-enhanced systems and requiring no network or software modifications. Enhanced rail converters are designed to facilitate single-rail to dual-rail data conversion and vice versa, preserving phase information and minimizing phase errors across various nonideal effects such as frequency drift, Process, Voltage, and Temperature (PVT) variations, and cable phase mismatch. This ensures reliable data transmission and robust authentication in the presence of adversarial cyberattacks such as packet injection. The receiver recovers both the CAN frame data and the security signature, comparing the latter with an authorized signature to provide a real-time “GO/NO_GO” signal for verifying packet authenticity and without exceeding the CAN clock jitter specifications.

Fluid Grey 2: How Well Does Generative Adversarial Networks Learn Deeper Topology Structure in Architecture That Matches Images?

Taking into account the regional characteristics of ‘intrinsic’ and ‘extrinsic’ properties of space is an essential issue in architectural design and urban renewal, which is often achieved step by step using image and graph-based GANs. However, each model nesting and data conversion may cause information loss, and it is necessary to streamline the tools to facilitate architects and users to participate in the design. Therefore, this study hopes to prove that I2I GAN also has the potential to recognize topological relationships autonomously. Therefore, this research proposes a method for quickly detecting the ability of pix2pix to learn topological relationships, which is achieved by adding two Grasshopper-based detection modules before and after GAN. At the same time, quantitative data is provided and its learning process is visualized, and changes in different input modes such as greyscale and RGB affect its learning efficiency. There are two innovations in this paper: 1) It proves that pix2pix can automatically learn spatial topological relationships and apply them to architectural design. 2) It fills the gap in detecting the performance of Image-based Generation GAN from a topological perspective. Moreover, the detection method proposed in this study takes a short time and is simple to operate. The two detection modules can be widely used for customizing image datasets with the same topological structure and for batch detection of topological relationships of images. In the future, this paper may provide a theoretical foundation and data support for the application of architectural design and urban renewal that use GAN to preserve spatial topological characteristics.

Digital detection of Alzheimer’s disease using smiles and conversations with a chatbot

In super-aged societies, dementia has become a critical issue, underscoring the urgent need for tools to assess cognitive status effectively in various sectors, including financial and business settings. Facial and speech features have been tried as cost-effective biomarkers of dementia including Alzheimer’s disease (AD). We aimed to establish an easy, automatic, and extensive screening tool for AD using a chatbot and artificial intelligence. Smile images and visual and auditory data of natural conversations with a chatbot from 99 healthy controls (HCs) and 93 individuals with AD or mild cognitive impairment due to AD (PwA) were analyzed using machine learning. A subset of 8 facial and 21 sound features successfully distinguished PwA from HCs, with a high area under the receiver operating characteristic curve of 0.94 ± 0.05. Another subset of 8 facial and 20 sound features predicted the cognitive test scores, with a mean absolute error as low as 5.78 ± 0.08. These results were superior to those obtained from face or auditory data alone or from conventional image depiction tasks. Thus, by combining spontaneous sound and facial data obtained through conversations with a chatbot, the proposed model can be put to practical use in real-life scenarios.

Not in the file: How competency committees work with undocumented contributions.

Competence committees (CCs) centre their work around documentation of trainees' performance; undocumented contributions (i.e. informal, unrecorded material like personal judgements, experiential anecdotes and contextual information) evoke suspicion even though they may play a role in decision making. This qualitative multiple case study incorporates insights from a social practice perspective on writing to examine the use of undocumented contributions by the CCs of two large post-graduate training programmes, one in a more procedural (MP) speciality and the other in a less procedural (LP) one. Data were collected via observations of meetings and semi-structured interviews with CC members. In the analysis, conversations were organised into triptychs of lead-up, undocumented contribution(s), and follow-up. We then created thick descriptions around the undocumented contributions, drawing on conversational context and interview data to assign possible motivations and significance. We found no instances in which undocumented contributions superseded the contents of a trainee's file or stood in for missing documentation. The number of undocumented contributions varied between the MP CC (six instances over two meetings) and the LP CC (22 instances over three meetings). MP CC discussions emphasised Entrustable Professional Activity (EPA) observations, whereas LP CC members paid more attention to narrative data. The divergent orientations of the CCs-adding an 'advis[ing]/guid[ing]' role versus focusing simply on evaluation-offers the most compelling explanation. In lead-ups, undocumented contributions were prompted by missing and flawed documentation, conflicting evidence and documentation at odds with members' perceptions. Recognising other 'red flags' in documentation often required professional experience. In follow-ups, purposes served by undocumented contributions varied with context and were difficult to generalise; we, therefore, provide deeper analysis of two vignettes to illustrate. Our data suggest undocumented contributions often serve best efforts to ground decisions in documentation. We would encourage CC practices and policies be rooted in more nuanced approaches to documentation.

Transform Financial Data into Strategic Insights Using the Sap Business Technology Platform

This article examines the groundbreaking fusion of SAP Business Technology Platform (BTP) and Artificial Intelligence (AI) in the realm of financial reporting, emphasizing their transformative role in translating financial data into strategic insights for executive leadership. By introducing automated commentary generation, this integration enables BTP to convert intricate financial datasets into coherent narratives that furnish decision-makers with actionable intelligence. Positioned at the nexus of AI progress and financial analysis, BTP facilitates the seamless conversion of complex data into narrative or commentary reports, fostering coherence, insightfulness, and accessibility for decision-makers. The discourse delves into the underlying mechanisms of BTP functionality in financial reporting, spanning data interpretation, language processing, commentary generation, and customization, thereby enhancing report efficiency, precision, and accessibility. Through meticulous exploration, this article elucidates BTP's contributions to financial reporting commentary, including enhanced efficiency, accuracy, scalability, and personalized delivery of financial insights. Furthermore, it confronts the challenges inherent in BTP application, encompassing technical intricacies, ethical considerations, and the constraints of current technologies in capturing financial intricacies. Looking ahead, the article envisions a future where ongoing advancements in AI and machine learning refine BTP capabilities, furnishing even more sophisticated and nuanced financial insights to underpin strategic decision-making at the highest echelons of business leadership.

Real-Time and Long-Term Monitoring of Coastal Water Turbidity Using an Ocean Buoy Equipped with an ADCP

In this study, an acoustic Doppler current profiler (ADCP) operating at 600 kHz was installed on an ocean data buoy in the Qigu waters, Taiwan, to gather real-time sound echo intensity data. These data were then correlated with turbidity measurements obtained by a turbidimeter mounted on the buoy’s mooring line at a water depth of 13 m. The data buoy operated from 6 June to 16 August 2017. During this period, turbidity measurements were recorded from 6 to 21 June 2017. This study established a calibration between the sound echo intensity measured by the ADCP and the turbidity measured using the turbidimeter; a strong linear correlation was discovered between these two variables. This correlation enabled the conversion of echo intensity data into a continuous time series of turbidity measurements, facilitating real-time and long-term monitoring of coastal water turbidity through the deployment of a buoy equipped with an ADCP. The relationships between turbidity and environmental factors such as rainfall, tides, current speeds, and wave activity over an extended period were then investigated. The results revealed that stronger tides and currents in the Qigu waters often lead to higher turbidity, suggesting that these two factors are the primary driving forces for sediment transport in the Qigu waters. Additionally, sampling of water in the Qigu area revealed sediment particles of size ranging from 2 to 120 μm.

Two-Step Clustering for Mineral Prospectivity Mapping: A Case Study from the Northeastern Edge of the Jiaolai Basin, China

The advancement of geological big data has rendered data-driven methodologies increasingly vital in Mineral Prospectivity Mapping. The effective integration of quantitative and qualitative data, including experiential and knowledge-based insights, is crucial in geological data fusion. Specifically, the conversion of raw data into samples and the selection of predictive methods are two core issues that constitute the focus of this study. Traditional clustering methods require the user to specify the number of clusters in advance. The two-step clustering can automatically determine the clustering result ‘k’ while analyzing both continuous and categorical variables, by building a Cluster Feature (CF) and using information criteria to merge nodes. In this study, we conducted an analysis utilizing stream sediment element data, residual gravity anomalies, and fault distribution through the two-step clustering method. Factor analysis (FA) was employed to reduce 16 elemental variables from stream sediments into five uncorrelated continuous variables; additionally, residual gravity anomalies were transformed from continuous to categorical variables via an interval-based method before being combined with fault distribution, resulting in seven variables for clustering. The research findings indicate that categorical variables significantly influence clustering results; concurrently, as the importance of continuous variables within the cluster increases, so does k. When only one categorical variable is present, residual gravity anomalies show significantly better clustering than fault distribution; however, when two categorical variables are involved, it is essential to consider the quantity of categories: more categories lead to poorer quality. The results from the Jiaolai Basin’s northeastern margin indicate a significant correlation with known gold deposits; two-step clustering is a promising and effective method for improving mineral prospecting efforts.

Capturing multiple emotions from conversational data using fine tuned transformers

Emotion detection is one of the crucial topics of Natural Language Processing (NLP) in recent years, and now one of the biggest motivating factors in correct identification and interpretation of a wide range of emotional expressions in textual data. This research examines the use of Bidirectional Encoder Representations from Transformers (BERT), a trained transformer model for emotion detection in textual data. This analysis evaluates how good BERT is at identifying emotions such as surprise, anger, fear, happiness and sadness compared with ordinary machine learning as well as deep learning techniques. The addition of weighted emotions approach enhances the model performance and gives a deeper emotional context awareness making it more effective to deal with complicated emotional utterances. The purpose of the research is to develop a fine-tuned BERT model with a weighted emotion framework to enhance the accuracy of emotion classification in conversational text. The context is improving emotion recognition in scenarios where multiple emotions co-exist, addressing limitations in traditional models by capturing subtle and overlapping emotional expressions. In terms of training the methods multiple datasets are considered and also the research examines various models performance. The article further discusses possible application areas of BERT modified in light of NLP.

Utilizing 3D seismic velocities within the Triassic Bacton Group to estimate the exhumation of the Rotliegend Group, Blocks 48/8 and 48/9, UK Southern North Sea

In the UK Southern North Sea, the exploration of pre-Zechstein structures depends upon accurate depth conversion of reflection seismic data. In Quadrant 48, depth conversion by conventional methods, such as linear velocity v. depth methods ( V = kZ + V 0 , or ‘ V 0 K ’), is challenging due to the influence of the Sole Pit Inversion. This leads to velocities being higher over the inversion axis than present-day burial would suggest and strong lateral velocity gradients within consistent intervals. To overcome this, velocity modelling was undertaken utilizing advanced seismic velocity extraction techniques to improve spatial resolution over that of well-based point data alone. The seismic velocities have been used to calculate the post-burial uplift of the Bacton Group, using seismic velocities of the unit. The results, which have been calibrated for local halokinesis, approximate Rotliegend Group uplift and aim to further de-risk the depth conversion uncertainty at target level, offering new insight into the inversion of the pre-salt section, with implications for future hydrocarbon exploration.

BIM and IFC Data Readiness for AI Integration in the Construction Industry: A Review Approach

Building Information Modelling (BIM) has been increasingly integrated with Artificial Intelligence (AI) solutions to automate building construction processes. However, the methods for effectively transforming data from BIM formats, such as Industry Foundation Classes (IFC), into formats suitable for AI applications still need to be explored. This paper conducts a Systematic Literature Review (SLR) following the PRISMA guidelines to analyse current data preparation approaches in BIM applications. The goal is to identify the most suitable methods for AI integration by reviewing current data preparation practices in BIM applications. The review included a total of 93 articles from SCOPUS and WoS. The results include eight common data types, two data management frameworks, and four primary data conversion methods. Further analysis identified three barriers: first, the IFC format’s lack of support for time-series data; second, limitations in extracting geometric information from BIM models; and third, the absence of established toolchains to convert IFC files into usable formats. Based on the evidence, the data readiness is at an intermediate level. This research may serve as a guideline for future studies to address the limitations in data preparation within BIM for AI integration.

A Generic Transformation Approach for Complex Laboratory Data Using the Fast Healthcare Interoperability Resources Mapping Language: Method Development and Implementation.

Reaching meaningful interoperability between proprietary health care systems is a ubiquitous task in medical informatics, where communication servers are traditionally used for referring and transforming data from the source to target systems. The Mirth Connect Server, an open-source communication server, offers, in addition to the exchange functionality, functions for simultaneous manipulation of data. The standard Fast Healthcare Interoperability Resources (FHIR) has recently become increasingly prevalent in national health care systems. FHIR specifies its own standardized mechanisms for transforming data structures using StructureMaps and the FHIR mapping language (FML). In this study, a generic approach is developed, which allows for the application of declarative mapping rules defined using FML in an exchangeable manner. A transformation engine is required to execute the mapping rules. FHIR natively defines resources to support the conversion of instance data, such as an FHIR StructureMap. This resource encodes all information required to transform data from a source system to a target system. In our approach, this information is defined in an implementation-independent manner using FML. Once the mapping has been defined, executable Mirth channels are automatically generated from the resources containing the mapping in JavaScript format. These channels can then be deployed to the Mirth Connect Server. The resulting tool is called FML2Mirth, a Java-based transformer that derives Mirth channels from detailed declarative mapping rules based on the underlying StructureMaps. Implementation of the translate functionality is provided by the integration of a terminology server, and to achieve conformity with existing profiles, validation via the FHIR validator is built in. The system was evaluated for its practical use by transforming Labordatenträger version 2 (LDTv.2) laboratory results into Medical Information Object (Medizinisches Informationsobjekt) laboratory reports in accordance with the National Association of Statutory Health Insurance Physicians' specifications and into the HL7 (Health Level Seven) Europe Laboratory Report. The system could generate complex structures, but LDTv.2 lacks some information to fully comply with the specification. The tool for the auto-generation of Mirth channels was successfully presented. Our tests reveal the feasibility of using the complex structures of the mapping language in combination with a terminology server to transform instance data. Although the Mirth Server and the FHIR are well established in medical informatics, the combination offers space for more research, especially with regard to FML. Simultaneously, it can be stated that the mapping language still has implementation-related shortcomings that can be compensated by Mirth Connect as a base technology.

Meta-analysis accelerator: a comprehensive tool for statistical data conversion in systematic reviews with meta-analysis

BackgroundSystematic review with meta-analysis integrates findings from multiple studies, offering robust conclusions on treatment effects and guiding evidence-based medicine. However, the process is often hampered by challenges such as inconsistent data reporting, complex calculations, and time constraints. Researchers must convert various statistical measures into a common format, which can be error-prone and labor-intensive without the right tools.ImplementationMeta-Analysis Accelerator was developed to address these challenges. The tool offers 21 different statistical conversions, including median & interquartile range (IQR) to mean & standard deviation (SD), standard error of the mean (SEM) to SD, and confidence interval (CI) to SD for one and two groups, among others. It is designed with an intuitive interface, ensuring that users can navigate the tool easily and perform conversions accurately and efficiently. The website structure includes a home page, conversion page, request a conversion feature, about page, articles page, and privacy policy page. This comprehensive design supports the tool’s primary goal of simplifying the meta-analysis process.ResultsSince its initial release in October 2023 as Meta Converter and subsequent renaming to Meta-Analysis Accelerator, the tool has gained widespread use globally. From March 2024 to May 2024, it received 12,236 visits from countries such as Egypt, France, Indonesia, and the USA, indicating its international appeal and utility. Approximately 46% of the visits were direct, reflecting its popularity and trust among users.ConclusionsMeta-Analysis Accelerator significantly enhances the efficiency and accuracy of meta-analysis of systematic reviews by providing a reliable platform for statistical data conversion. Its comprehensive variety of conversions, user-friendly interface, and continuous improvements make it an indispensable resource for researchers. The tool’s ability to streamline data transformation ensures that researchers can focus more on data interpretation and less on manual calculations, thus advancing the quality and ease of conducting systematic reviews and meta-analyses.

The Role of Social Media Engagement in Customer Service: A Study on the Brazilian Financial Sector

This article investigates the importance of social media in customer service and relationships, causing engagement with the brand. For this, this research's corpus delimited the actions on social media of the five largest banks in Brazil in terms of customer volume. The data were collected and analyzed in two moments: first, through a structured questionnaire to test the use and importance of social media in the service and relationship of banks; second, through the collection of interaction and conversation data on the social media of each bank studied. The quantitative and descriptive research uses correlation and engagement analysis based on social media metrics. The results indicate the banks' different strategies to provoke engagement and relationship with their consumer audiences in each media, but the data reveal low engagement. However, consumers pointed out social media's relevance in seeking services and relationships with their banks, citing the convenience, speed, and accessibility it provides. The study concludes that consumers value banks' social media, especially looking for customer service and relationships. In contrast, interaction and conversation data showed that banks have a routine of working in this category, combining others with their products' promotion and sponsorships and events. However, it became clear that the engagement that the banks' social media actions generate is minimal, in proportion to the volume of their followers.

Using Generative AI for Bibliographic Description: A Study with ChatGPT 4

This study explores the use of Generative Artificial Intelligence (GAI) in bibliographic description in university library catalogs. It focuses on GAI's potential in enhancing efficiency and maintaining consistency of bibliographic description while complying with cataloging standards such as MARC21, AACR2 and RDA. The study followed a qualitative methodology, examining 10 use cases in metadata extraction, RDA compliance of GAI generated catalog records, and error checking of existing catalog records. The study used OpenAI’s ChatGPT 4 to execute the practical tests. The study concludes that GAI can significantly assist in improving cataloging efficiency. It was highly satisfactory in error correction of existing MARC21 records. GAI was also a prospective tool for educating novice librarians on the use of MARC21 and RDA standards. CATMELK, a custom GPT model created with ChatGPT 4 could effectively convert images from books’ title, copyright and cover pages into RDA compliant MARC21 records. However, when the tests were conducted in January 2024, ChatGPT 4 faced difficulties in direct conversion of Sinhala and Tamil data from images to MARC2 1 records. Considering the rapid development of GAI technology and applications, it is recommended to repeat the tests on a continuing basis.

Adaptive group sample with central momentum contrast loss for unsupervised individual identification of cows in changeable conditions

Individual identification is the basis for accurate breeding and management of dairy cows. Most of the studies on individual identification of dairy cows focused on closed data sets. Meanwhile, the training process requires lots of manual labeling data as support. Aiming at solving the above shortcomings, an unsupervised cow individual identification model named AC-Cowid (Adaptive Group Sample-Central Momentum Contrastive loss-Cowid) was proposed. The innovation of the model lied in the novel sampler and loss function. Adaptive Group Sampler made up for the sampling imbalance problem in the current samplers. The sampler would self-update with model optimization. The central constraint term in Central Momentum Contrastive loss made the updated features more inclined to the cluster center, effectively solved the discomfort of Contrastive loss to cow image data. Feature similarity matching was adopted to meet the challenge of data conversion from a closed set to an open and changeable set. The unsupervised training reduced the workload of manual data labeling. The CMC-1 and mAP index of the final model were 99.50 % and 77.90 %, respectively. Ablation and hyper-parameter experiments proved the effectiveness of the proposed method. The comparison experiment tested eight unsupervised methods and five supervised algorithms in the state-of-the-art. The mAP and CMC-1 of the proposed method was the best among the comparison algorithms. Above all, the proposed method was 4.60 % and 5.38 % higher on mAP and CMC-1 compared with the best algorithm in supervised learning. This study could provide technical support for individual identification of cows in large-scale cattle farms.

Semiotic packaging of teacher talk for affective recyclings: A usage-based approach to children's construction development

This study examines how iconically designed teacher talk facilitates preschool novice L2 learners' construction development through playful interactions. It combines two approaches: ethnomethodological conversation analysis and usage-based linguistics. The conversational data were retrieved from teacher-children interactions filmed at an all-English school in Japan. The microanalysis of 945-min classroom interactions illustrated that when a part of teacher talk was packaged with iconic semiotic resources, such as gestures, situated meanings, tones of voice, and affective stances, it became accessible L2 resources for the children to learn and recycle in their subsequent utterances. Simultaneously, the analysis also revealed the pragmatic mechanism of teachers' assignment of interactional responsibility, inviting children to perform language learning of target constructions through recyclings. The recycled part of the teacher talk indicated the children's microgenetic usage-based language learning as they modified and developed L2 constructions to accomplish certain social actions—sanction of a deviated action, attention-getting, and storytelling in situ. This study indicates that language learning opportunities can be integrated into children's processes of achieving specific social actions, and language learning performance can be invited as an appropriate practice in situ by certain teacher talk designs.

A New Framework for Integrating DNN-Based Geographic Simulation Models within GISystems

As a crucial spatial decision support tool, Geographic Information Systems (GISystems) are widely used in fields such as digital watersheds, resource management, environmental assessment, and regional governance, with their core strength lying in the integration of geographic simulation models from various disciplines, enabling the analysis of complex geographical phenomena and the resolution of comprehensive spatial problems. With the rapid advancement of artificial intelligence, deep neural network-based geographic simulation models (DNN-GSMs) have increasingly replaced traditional models, offering significant advantages in simulation accuracy and inference speed, and have become indispensable components in GISystems. However, existing integration methods do not adequately account for the specific characteristics of DNN-GSMs, such as their formats and input/output data types. To address this gap, we propose a novel tight integration framework for DNN-GSMs, comprising four key interfaces: the data representation interface, the model representation interface, the data conversion interface, and the model application interface. These interfaces are designed to describe spatial data, the simulation model, the adaptation between spatial data and the model, and the model’s application process within the GISystem, respectively. To validate the proposed method, we construct a spatial morphology simulation model based on CNN-LSTM, integrate it into a GISystem using the proposed interfaces, and conduct a series of predictive experiments on island morphology evolution. The results demonstrate the effectiveness of the proposed integration framework for DNN-GSMs.

Research on University Data Governance Scheme Based on Data Platform

A data governance solution for universities based on a data center is proposed to address various data management issues encountered in the construction of smart campuses. The solution consists of three parts: a data base, a data resource system, and a data center. With data assetization as the core and data serviceization as the goal, a complete set of mechanisms including data collection, standardization, cleaning and conversion, supporting management, and interface openness are adopted to cover the full lifecycle management of data, explore the value behind data, achieve synchronous improvement of data quality and data service quality, and promote the development of information technology and smart campus construction in universities.

They Will Surveil You to Death: Gangstalking as a Cultural Concept of Distress.

Understanding local worldviews is a challenge during clinical encounters, especially when they involve cultural references without acceptance from the medical community. Gangstalking is a Western cultural notion which refers to systematic harassment, surveillance, and torture from unseen or covert assailants or networks. It is not a 'real phenomenon' compared with genuine stalking, but experients report worse depression, post-traumatic symptoms, suicidal ideation, and longer lasting encounters. They report physical pain and impossible feats of espionage technologically orchestrated by unknown malevolent actors. Using conversational data from targeted individual podcasts, I explore gangstalking as a cultural concept of distress (CCD) by highlighting associated explanations, idioms, and symptoms. Clinically, gangstalking is likely diagnosed as paranoid schizophrenia. However, its association with frightening events parallels Susto and Nervios. Physical symptoms parallel Open Mole and Brain Fag Syndrome. Like many CCDs, gangstalking is a multi-dimensional phenomenon not neatly mapped onto psychiatric categories. Misinterpreting gangstalking cases as unique or isolated is a likely outcome even when they fit within a well-known Western subculture and techno-science belief system. Moving past prior, outdated notions of folk illnesses and culture-bound syndromes, gangstalking as a CCD helps end the assumption that only the other has exotic or non-psychiatric categories of distress.

A novel Real-time Control System for next generation gravitational-wave detectors

This contribution presents the INFN Pisa group research and development efforts aimed at defining and implementing a novel Real-time Control System (RCS) for next-generation interferometers, focusing on the initial phase of the Einstein Telescope (ET) gravitational wave detector. The RCS handles complex closed-loop systems, managing data collection from sensing elements, processing and actuator control within well defined time constraints. It integrates multiple spatially distributed control units, facilitating communications among them, with external systems as well as with users. Two distinct hardware approaches are explored: “Katane” and “Zancle”. These systems primarily differ in their core processing elements. The “Katane” system is built on powerful DSP processor boards, based on the Super-Attenuator control system, developed at INFN Pisa for Advanced Virgo detector. It uses the MicroTCA.4 architecture standard, ensuring flexibility, easy maintenance, and fast data exchange among the various custom modules, including several types of Front-End data converter boards, FPGA-based pre-processing boards, DSPs and standard processors. On the other hand, ”Zancle” explores GPU-based processing, aiming to use cost-effective consumer market systems and to leverage substantial computational power of these devices, also incorporating machine learning algorithms.