Software-based Approach Research Articles

Green IN Artificial Intelligence from a Software Perspective: State-of-the-Art and Green Decalogue

This work presents a structured view of the state-of-the-art research on Artificial Intelligence (AI), from the point of view of efficiency and reduction of the energy consumption of AI software. We analysed the current research on energy consumption of AI algorithms and its improvements, which gave us a starting literature corpus of 2,688 papers that we identified as Green AI with a software perspective. We structure this corpus into Green IN AI and Green BY AI, which led us to discover that only 36 of them could be considered Green IN AI. After some quick insights about Green BY AI, we then introduce our main contribution: a systematic mapping of Green IN AI. We provide an in-depth analysis of the AI models that we observed during the mapping, and what solutions have been proposed for improving their energy efficiency. We also analyse the energy evaluation methodologies employed in Green IN AI, discovering that most papers opt for a software-based energy estimation approach and 27% of all papers do not document their methodology. We finish by synthetising our insights from the mapping into a Decalogue of Good Practices for Green AI.

Optimizing long-short term memory neural networks for electroencephalogram anomaly detection using variable neighborhood search with dynamic strategy change

Electroencephalography (EEG) serves as a crucial neurodiagnostic tool by recording the electrical brain activity via attached electrodes on the patient’s head. While artificial intelligence (AI) exhibited considerable promise in medical diagnostics, its potential in the realm of neurodiagnostics remains underexplored. This research addresses this gap by proposing an innovative approach employing time-series classification of EEG data, leveraging long-short-term memory (LSTM) neural networks for the identification of abnormal brain activity, particularly seizures. To enhance the performance of the proposed model, metaheuristic algorithms were employed for optimizing hyperparameter collection. Additionally, a tailored modification of the variable neighborhood search (VNS) is introduced, specifically tailored for this neurodiagnostic application. The effectiveness of this methodology is evaluated using a carefully curated dataset comprising real-world EEG recordings from both healthy individuals and those affected by epilepsy. This software-based approach demonstrates noteworthy results, showcasing its efficacy in anomaly and seizure detection, even when working with relatively modest sample sizes. This research contributes to the field by illuminating the potential of AI in neurodiagnostics, presenting a methodology that enhances accuracy in identifying abnormal brain activities, with implications for improved patient care and diagnostic precision.

A software-based observational coding approach for evaluating paediatric dental pain, anxiety, and fear.

Dental practitioners desire non-pharmacological methods to alleviate anxiety, fear, and pain in children receiving dental care; high-quality evidence, however, is required to evaluate methods' efficacy. This study aimed to develop and validate an observation-based coding approach (paediatric dental pain, anxiety, and fear coding approach [PAFCA]) to evaluate non-pharmacological behavior management techniques for anxiety, fear, and pain. Objective (video-based) and subjective (self-reported) anxiety, fear, and pain data were collected from a pilot clinical trial evaluating animal-assisted therapy (AAT) in paediatric dentistry, in which 37 children aged 7-14 were assigned to AAT or control before dental treatment (restorations or extractions). A coding approach utilizing a codebook, a gold standard calibration video, and a user training guide was developed. Trained examiners coded the gold standard video for inter-rater agreement, and masked, calibrated examiners analyzed videos using the Noldus Observer XT software. A novel, software-based coding approach was developed, with moderately high inter-rater agreement. Using PAFCA, we found children reporting higher levels of pain, fear, and anxiety exhibited treatment-interfering behaviors, including crying/moaning, attempts to dislodge instruments, and more upper and lower body movements. PAFCA shows promise as a reliable tool for assessing anxiety, pain, and fear in behavioral research for paediatric dentistry.

Exploring the dynamics of a four-dimensional Chua-like system: Numerical simulations and hardware investigations

In this work, we employ simulations to investigate deterministic and stochastic dynamics of a system governed by four coupled nonlinear ordinary differential equations (ODEs) derived from the canonical three-dimensional Chua model, replacing the piecewise nonlinearity with a cubic function. We employed two simulation approaches: (i) using software — where we performed numerical simulations (digital computation) in the ODEs model, and (ii) using hardware — where we performed experimental studies (analog computation) in the ODEs model. In the initial software-based approach for simulations, we performed stability and bifurcation analyses of the trivial equilibrium point, which were analytically conducted, along with the numerical continuation method. We solved the model using digital computation and explored its intricate dynamical behavior through nonlinear dynamics techniques applied to the parameter planes. In the second approach, employing hardware for simulations through analog computation, we designed an electronic circuit using analog electronics to solve the model in a continuous-time integration. The experimental setup mirrored the numerical simulations, yielding comparable results in terms of dynamical behaviors. This study underscores significant agreement between numerical and experimental findings, highlighting the robustness of the model across digital and analog contexts and offering practical insights into the various nonlinear dynamics presented in this system.

SPC-Indexed Indirect Branch Hardware Cache Redirecting Technique in Binary Translation

In the domain of process virtual machine (PVM) binary translation, the difference in address space layout between the guest program and the translated program requires the recalculation of jump instruction targets, resulting in suboptimal execution efficiency. This paper presents a novel method called SPC-Indexed Indirect Branch Hardware Cache Redirecting (SPCIC) technique. SPCIC utilizes specialized branch instruction to represent indirect branches from guest programs while frequently-used target addresses are cached in a customized hardware mapping table. When translating an indirect branch, SPCIC queries the jump target cache first to achieve a fast redirection unless the destination address is not cached. Besides, SPCIC merely falls back to the software-based remapping approach when the query fails, improving the translation efficiency to the greatest extent. SPCIC is implemented on the QEMU platform to accelerate the translation of ARM payloads into RISC-V. Experiments are carried on SPEC2006 to demonstrate the effectiveness of SPCIC for reducing the runtime overhead of indirect branch translation. The experimental results indicate up to 11% average improvement and 35% maximum improvement are obtained on the selected benchmark.

Hide Text within a Video Using Data Encryption Standard (DES) Technology

It is possible to encode a message using one of the encryption algorithms and embed the text within a selected frame from the video, as this is considered a more secure method for handling confidential information. The concealment is deemed unbreakable because the message is encrypted in a manner that prevents decryption, making it challenging to retrieve the hidden text. In this study, Data Encryption Standard (DES) technology was employed to embed the last bits of the chosen video frame using a software-based approach. The frames, decoders, PSNR, and encrypted messages are illustrated in various videos. The results demonstrated minimal impact on the video's accuracy, as concealment occurs in the least significant bits (LSB), preserving the overall precision of the video.

Using simulation modelling to transform hospital planning and management to address health inequalities

Health inequalities are a perennial concern for policymakers and in service delivery to ensure fair and equitable access and outcomes. As health inequalities are socially influenced by employment, income, and education, this impacts healthcare services among socio-economically disadvantaged groups, making it a pertinent area for investigation in seeking to promote equitable access. Researchers widely acknowledge that health equity is a multi-faceted problem requiring approaches to understand the complexity and interconnections in hospital planning as a precursor to healthcare delivery. Operations research offers the potential to develop analytical models and frameworks to aid in complex decision-making that has both a strategic and operational function in problem-solving. This paper develops a simulation-based modelling framework (SimulEQUITY) to model the complexities in addressing health inequalities at a hospital level. The model encompasses an entire hospital operation (including inpatient, outpatient, and emergency department services) using the discrete-event simulation method to simulate the behaviour and performance of real-world systems, processes, or organisations. The paper makes a sustained contribution to knowledge by challenging the existing population-level planning approaches in healthcare that often overlook individual patient needs, especially within disadvantaged groups. By holistically modelling an entire hospital, socio-economic variations in patients' pathways are developed by incorporating individual patient attributes and variables. This innovative framework facilitates the exploration of diverse scenarios, from processes to resources and environmental factors, enabling key decision-makers to evaluate what intervention strategies to adopt as well as the likely scenarios for future patterns of healthcare inequality. The paper outlines the decision-support toolkit developed and the practical application of the SimulEQUITY model through to implementation within a hospital in the UK. This moves hospital management and strategic planning to a more dynamic position where a software-based approach, incorporating complexity, is implicit in the modelling rather than simplification and generalisation arising from the use of population-based models.

Implementing Internet of Things Service Platforms with Network Function Virtualization Serverless Technologies

The need for adaptivity and scalability in telecommunication systems has led to the introduction of a software-based approach to networking, in which network functions are virtualized and implemented in software modules, based on network function virtualization (NFV) technologies. The growing demand for low latency, efficiency, flexibility and security has placed some limitations on the adoption of these technologies, due to some problems of traditional virtualization solutions. However, the introduction of lightweight virtualization approaches is paving the way for new and better infrastructures for implementing network functions. This article discusses these new virtualization solutions and shows a proposal, based on serverless computing, that uses them to implement container-based virtualized network functions for the delivery of advanced Internet of Things (IoT) services. It includes open source software components to implement both the virtualization layer, implemented through Firecracker, and the runtime environment, based on Kata containers. A set of experiments shows that the proposed approach is fast, in order to boost new network functions, and more efficient than some baseline solutions, with minimal resource footprint. Therefore, it is an excellent candidate to implement NFV functions in the edge deployment of serverless services for the IoT.

An optimized chaotic S-box for real-time image encryption scheme based on 4-dimensional memristive hyperchaotic map

As digital products communicate over public networks, digital image security becomes essential. Despite advancements in encryption technology, protecting data from secure images remains a complex computational issue that necessitates using an encrypted environment to protect data transmitted from devices over networks. Encryption is critical for protecting sensitive information, especially images, from unauthorized access or failure. Image encryption is distinct from text encryption. Images contain extensive data with high redundancy and a high correlation between nearby pixels, making them difficult to process with traditional technology. In recent decades, chaos maps have become popular in the crypto community. This work proposes a new encryption technology based on chaotic map substitution boxes (S-box) and cellular automata (CA) to address the frequent challenges in chaotic encryption methods. To address the inadequate randomness provided by the 1D chaotic map, this work presented a 4D memristive hyperchaos with a more excellent chaotic range, increased uncertainty, and ergodicity as an alternative to the software-based approach, which is vulnerable and offers low throughput. The suggested encryption technique was implemented on an Intel Cyclone IV EP4CE115F29C7 FPGA. It is less prone to tampering and has a better throughput. This suggested encryption scheme employs 1384 LEs. In comparison to traditional algorithms, the suggested Number of Pixels Change Rate (NPCR) (99.9706%), unified averaged changed intensity (UACI) (33.3956%), and information entropy (IE) (7.984) accomplish more effective. Statistical studies such as IE, histogram, correlation, peak signal-to-noise ratio (PSNR), differential analysis such as NPCR and UACI, and noise attack analyses are employed to validate the suggested encryption design. Regarding security, the suggested hybrid method outperforms conventional algorithms while protecting image quality.

Energy-efficient Techniques in SDN: Software, Hardware, and Hybrid Approaches

In recent times, software-defined networking (SDN) has garnered significant attention due to its approach of separating the control plane from the data plane. This abstraction allows managers to have a centralized perspective on the network, which in turn enables them to control traffic flow and routing policies dynamically without configuring individual devices. The increased adoption of SDN has resulted in the need to optimize energy consumption without compromising network performance. Creating an energy-efficient solution presents a challenge due to the intricate process of finding the right equilibrium between network performance and energy conservation. This article examines various techniques for achieving energy efficiency in SDN and classifies them into three categories – software-based, hardware-based, and hybrid approach. These categories are further divided into their subcategory. Furthermore, a comprehensive exploration will be conducted to delve into the benefits and drawbacks of each energy efficiency approach, followed by an in-depth comparative analysis. The article also discusses the research issues and challenges associated with each technique. Finally, the paper provides future prospects for investigating energy efficiency approaches in SDN, which are expected to yield substantial benefits for upcoming researchers in this domain.

Methodology for automated and computer-aided work plan generation using voxel-based accessibility analysis through reinforcement learning in one-off manufacturing

One-off manufacturing is characterized by high workpiece-related planning costs. Current process chains are designed manually and based on implicit knowledge, leading to non-optimal process chains on the one hand, while also disregarding alternative manufacturing options on the other. This paper presents a software-based approach for automated work plan generation that follows a feature-independent process chain design based on identified deficits. For this purpose, the workpiece is divided into discrete voxels to be able to represent the individual intermediate states of manufacturing. The methodology is divided into five phases: The modelling and training of agents, the analysis and discretization of workpiece input information, the initial workpiece definition and technology configuration, the simulation and verification as well as the combination of alternative technology configurations. In the concept reinforcement learning is used for optimization which requires previous training of the agents applied. The agents trained for this approach represent the intelligent clamping system and machining tool. A prototype implementation provides promising results, particularly in the automated identification of clamping positions, regardless of geometry and process-related accessibility, using a prismatic workpiece model. While 97.6 % of the machined elements have been successfully subtracted, there is still a need for optimization to improve the accuracy of the model. In conclusion, the approach offers a promising path to efficient and automated process chain planning of complex workpieces in one-off manufacturing.

Exploring dengue genome to design effective multi epitope-based peptide vaccine by immunoinformatics approach against all serotypes of dengue virus

Dengue fever is an acute tropical disease that is spread by Aedes species mosquitoes. It is a virus-mediated illness that causes headaches, fever, severe joint and muscle pain, swollen glands (lymphadenopathy), and rashes. Every year, 390 million people around the world suffer from it and ∼20 % die from cases that are left untreated. The virus is transmitted to humans by mosquito bites of infected Aedes species. When it gets into the body, its envelope protein attaches to the host cell surface receptor and initiates the viral infection cycle by the formation of a smooth coat with icosahedral symmetry. Due to its high mutation-prone nature and differences in protein sequences among serotypes, it is difficult to develop a stable vaccine suitable against all serotypes. In this study, a software-based vaccinology approach was used to design a vaccine construct against the envelope protein of all four subtypes of the dengue virus. This construct was designed by joining two epitopes of the protein. One epitope was taken from the envelope protein of DEN-2 which was conserved for DEN-1, DEN-2, and DEN-3, and another epitope from the same protein of DEN-4 serotype as the sequence composition of the protein is slightly different in these two groups of serotypes. Here, firstly 18 epitopes for dengue virus-1, 2, 3, and 12 epitopes for dengue virus-4 were retrieved based on the highest combined score of NetCTL server analysis of human leukocyte antigen (HLA) supertypes. Among them, the epitope MAILGDTAW has shown 100 % conservancy in strains of dengue virus 1, 2, 3, and KLRIKGMSY, TTAKEVALL, TPRSPSVEV, FRKGSSIGK, KTWLVHKQW have shown 100 % conservancy in dengue virus 4. In population coverage analysis, MAILGDTAW has shown 88.63 % and FRKGSSIGK has shown 90.72 % (highest among other DEN-4 epitopes) population coverage of the entire world population. These two epitopes along with their 15-mer extensions (MHCII binding peptides) were found as non-allergen and non-toxic. Finally, the two different 15-mer extensions (to get 100 % sequence conservancy) of the epitope MAILGDTAW and the 15-mer extension of the epitope FRKGSSIGK were put together using an EAAAK linker to construct the multi-epitope vaccine. In-vivo analysis of the proposed vaccine construct would aid in the development of a functional immune response that would eradicate dengue complications worldwide.

Integrating Aerial and 3D Data into a Data-Driven Decision-Making Workflow for Nature-Based Stormwater Solutions

Urbanization and climate change have increased the need for stormwater management and nature-based solutions. Decisions made at the project level impact the emergence of the systemic traits of the stormwater network and the functionality of the catchment areas in urban planning. To that end, it is vital to introduce the decision-making tools for analysing both the utilities and amenities of nature-based solutions (NBS) to increase their adoption to reduce the peak loads in the stormwater system and, to that end, mitigate the impacts of climate change. There is a deficiency in employing a software-based approach to analyse the qualitative and quantitative aspects of NBSs to back up design decisions. This paper demonstrates a workflow using drone-based photogrammetry, 3D modelling, and simulation software to generate visual and functional models assisting in informed decision-making in the design of stormwater systems as functional landscape architecture. Using aerial data from drones and modelled design solutions, the proposed workflow simulates rain events, infiltration, evaporation, water flow, and the accumulation of stormwater in a way that allows the visual and quantified analysis of detailed landscape architecture designs. The paper provides an example of a rooftop site simulation demonstrating the infiltration and flow of water to the drainage. The visual decision-making method provided can aid in investment decisions for functional landscape design in support of stormwater management.

Software-Based Security Approach for Networked Embedded Devices

As the Internet of Things (IoT) continues to expand, data security has become increasingly important for ensuring privacy and safety, especially given the sensitive and, sometimes, critical nature of the data handled by IoT devices. There exist hardware-based trusted execution environments used to protect data, but they are not compatible with low-cost devices that lack hardware-assisted security features. The research in this paper presents software-based protection and encryption mechanisms explicitly designed for embedded devices. The proposed architecture consists of two parts: the Agent, which is designed to work with low-cost, low-end devices without requiring modifications to the underlying hardware, and the Computing Module, which is designed for slightly more computationally powerful devices. The Computing Module enables devices to write data in protected memory and continuously verifies its integrity to provide protection. Additionally, it utilizes the Agents located on the device to safeguard device applications against attacks by requesting the Agent to generate an application code signature and validating it. The proposed solution is an alternative data security approach for low-cost IoT devices without compromising performance or functionality. Our work underscores the importance of developing secure and cost-effective solutions for protecting data in the context of IoT.

An efficient permutation approach for SbPN-based symmetric block ciphers

It is challenging to devise lightweight cryptographic primitives efficient in both hardware and software that can provide an optimum level of security to diverse Internet of Things applications running on low-end constrained devices. Therefore, an efficient hardware design approach that requires some specific hardware resource may not be efficient if implemented in software. Substitution bit Permutation Network based ciphers such as PRESENT and GIFT are efficient, lightweight cryptographic hardware design approaches. These ciphers introduce confusion and diffusion by employing a 4 × 4 static substitution box and bit permutations. The bit-wise permutation is realised by simple rerouting, which is most cost-effective to implement in hardware, resulting in negligible power consumption. However, this method is highly resource-consuming in software, particularly for large block-sized ciphers, with each single-bit permutation requiring multiple sub-operations. This paper proposes a novel software-based design approach for permutation operation in Substitution bit Permutation Network based ciphers using a bit-banding feature. The conventional permutation using bit rotation and the proposed approach have been implemented, analysed and compared for GIFT and PRESENT ciphers on ARM Cortex-M3-based LPC1768 development platform with KEIL MDK used as an Integrated Development Environment. The real-time performance comparison between conventional and the proposed approaches in terms of memory (RAM/ROM) footprint, power, energy and execution time has been carried out using ULINKpro and ULINKplus debug adapters for various code and speed optimisation scenarios. The proposed approach substantially reduces execution time, energy and power consumption for both PRESENT and GIFT ciphers, thus demonstrating the efficiency of the proposed method for Substitution bit Permutation Network based symmetric block ciphers.

Calibration Considering the Direction of Rotation for Contact Type Three-Dimensional Position-Measuring Instruments

In Japan, the aging of sewage facilities due to long-term use has become a significant social issue. As a result, there has been a growing interest in portable and easy-to-operate standalone pipe inspection robots in recent years. However, the pipes being inspected not only have level differences due to joint connections but also pose a high risk of toppling over due to deterioration, collapses, and sludge caused by external disturbances. Many standalone robots address this by suppressing tipping through tire shape and axle adjustments. However, this method is for suppression and not complete prevention. Therefore, we are exploring a software-based approach to prevent tipping through control of robot movement and aiming to achieve advanced self-position estimation. Currently, we are in the stage of verifying this self-position estimation. However, due to the curved nature of the pipes, it is difficult to accurately measure the robot's position and orientation using conventional measurement instruments. Thus, we have developed a specialized three-dimensional position measurement instrument for pipe inspection robots. Furthermore, although we performed calibration in the translational direction of the instrument, while improvements in accuracy were observed in the translational direction, the accuracy in the rotational direction deteriorated. In this paper, we propose a method to simultaneously calibrate the translational and rotational accuracy of the instrument to address this issue. We discuss the mechanism model, calibration parameters, elimination of redundant parameters by introducing redundancy, and parameter estimation using the Newton method. Additionally, through comparative validation using the instrument, we confirm that the position accuracy after calibration is within the range of approximately ±1.0 mm, and the orientation accuracy is within the range of approximately ±0.3 degrees. This demonstrates the effectiveness of the proposed method.

Improved analysis of derivatized steroid hormone isomers using ion mobility-mass spectrometry (IM-MS).

Over the last decade, applications of ion mobility-mass spectrometry (IM-MS) have exploded due primarily to the widespread commercialization of robust instrumentation from several vendors. Unfortunately, the modest resolving power of many of these platforms (~40-60) has precluded routine separation of constitutional and stereochemical isomers. While instrumentation advances have pushed resolving power to >150 in some cases, chemical approaches offer an alternative for increasing resolution with existing IM-MS instrumentation. Herein we explore the utility of two reactions, derivatization by Girard's reagents and 1,1-carbonyldiimidazole (CDI), for improving IM separation of steroid hormone isomers. These reactions are fast (≤30min), simple (requiring only basic lab equipment/expertise), and low-cost. Notably, these reactions are structurally selective in that they target carbonyl and hydroxyl groups, respectively, which are found in all naturally occurring steroids. Many steroid hormone isomers differ only in the number, location, and/or stereochemistry of these functional groups, allowing these reactions to "amplify" subtle structural differences and improve IM resolution. Our results show that resolution was significantly improved amongst CDI-derivatized isomer groups of hydroxyprogesterone (two-peak resolution of Rpp = 1.10 between 21-OHP and 11B-OHP), deoxycortisone (Rpp = 1.47 between 11-DHC and 21-DOC), and desoximetasone (Rpp = 1.98 between desoximetasone and fluocortolone). Moreover, characteristic collision cross section (DTCCSN2) measurements can be used to increase confidence in the identification of these compounds in complex biological mixtures. To demonstrate the feasibility of analyzing the derivatized steroids in complex biological matrixes, the reactions were performed following steroid extraction from urine and yielded similar results. Additionally, we applied a software-based approach (high-resolution demultiplexing) that further improved theresolving power (>150). Overall, our results suggest that targeted derivatization reactions coupled with IM-MS can significantly improve the resolution of challenging isomer groups, allowing for more accurate and efficient analysis of complex mixtures.

The ARTEMIS project: Mixed-Signal IC for Edge-AI-based Classification of ECG Signals

Abstract Atrial fibrillation (AF) is a common heart arrhythmia and is closely associated with causing strokes. Diagnosis is usually performed with Holter monitors over longer periods of time, causing discomfort to the patient. The proposed mixed-signal integrated circuit (IC) is designed for small patch electrocardiogram (ECG) devices and combines, an analog front-end (AFE) with tailored recording channel characteristics and 12-bit successive-approximation-register analog digital converter (SAR ADC) as well as an RISC-V based microcontroller (μC) for edge artificial intelligence (AI)-based AF-detection. The digital signal processing is supported with hardware accelerators. Including 160 kB of SRAM, the system on chip (SoC) requires 25.56 mm² in silicon area in a 180 nm technology. The recording channel shows promising simulation results with an input impedance of 230 MΩ, an input referred noise of below 1.6 μVrms and a CMMR of 95 dB. The digital part enables the integration of AI-based classification on the IC. Due to the flexibility of the software-based classification approach, this IC can also be used to detect other arrhythmias.

Design, Formulation, and Evaluation of Risperidone Mucoadhesive Buccal Patches

Objectives: To construct and optimize Risperidone (RIS) mucoadhesive buccal films for systemic distribution as an alternate route. To make buccal patches of Risperidone utilizing natural polymers such sodium alginate (SA), Hydroxypropyl methylcellulose (HPMC), Na CMC, and Carbopol 934 (CP 934). Method: Solvent casting created Risperidone buccal patches. The optimization study used a software-based response surface methodology approach with 23 factorial designs to evaluate 8 formulations of Risperidone mucoadhesive buccal patches to determine the significant effect of selected independent variables on the dependent variable. To assess patch appearance, thickness, weight homogeneity, folding durability, medication content, surface pH, swelling index, and FTIR. Invitro dissolution, ex-vivo permeation, residence time, and stability tests. Results: FTIR and DSC showed that Risperidone was entirely entrapped in polymer carrier bonds with no chemical interaction. Drug distribution was uniform in buccal patches at 90.14± 0.07 and 98.75± 0.80. Conclusion: Buccal patch medicine release and penetration depended on polymer type. Hydrophilic polymers boosted buccal patch drug release. F6 was the best of F1–F8. Formulation F6 had 82.03 ±0.82% in-vitro drug release and 75.21 ± 0.42% ex-vivo permeability after 7 hours. Stability tests did not modify appearance, surface pH, content homogeneity, in-vitro residence length, medication release, or ex-vivo penetration.

Calibration of the Deposited Energy in CMOS Imagers for Particle Detection on Nanosatellites

Commercial off-the-shelf (COTS) CMOS sensors are increasingly used in scientific applications on nanosatellites. Applying a software-based approach and in addition to their image acquisitions tasks, these CMOS sensors can be used to detect ionizing particles to improve the fault tolerance of imaging instruments on nanosatellites without the need for additional hardware. A challenge in using COTS components for this approach is that essential radiation test data and important parameters such as the thickness of the sensitive epitaxial layer are typically not available. With a simplified calibration approach, we determine the epitaxial layer thickness and calibrate the deposited energy sensitivity with minimal measurement time and steps and minor requirements on the test facility. A forward model for particle track length determination with an increased angle scattering of incident protons is used to handle stronger parameter uncertainties of the test setup. It is shown that the currently used CMOS sensor (HWK1910A) is a suitable candidate for a radiation monitor, based on the determined epitaxial layer thickness and the deposited energy calibration factor, in combination with the in-orbit mission data. This enables capabilities for more individual protection measures in case of unexpected radiation environments.