Execution Time Research Articles

MATLAB Application for Determination of 12 Combustion Products, Adiabatic Temperature and Laminar Burning Velocity: Development, Coding and Explanation

The determination of the characteristics and main combustion properties of fuels is necessary for post-implementation in different applications. Among the most important combustion properties of a fuel are the combustion products, flame temperature and laminar burning velocity. Therefore, this paper describes the step-by-step development and coding of a MATLAB application that can determine 12 combustion products, flame temperature and laminar burning velocity in order to understand the logic of calculus procedure, so any user would be able to make improvements of new functionalities (add more fuels, add more combustion products, etc.). The numerical procedure and methods (Gaussian elimination, Taylor Series and Newton–Raphson) parallel with their implementation as code lines for the development of the application are carried out using flow charts. In addition, simulations in Ansys Chemkin were performed and included in the application as part of the results comparison. It was found that: (1) The MATLAB Application codification and development were successfully explained in detail, (2) the functions and execution sequence are described by using flow charts and code extract, (3) the application is available to everyone for modifications, (4) the application can only be used for hydrocarbons fuels, (5) the application execution time registered was less than 8 s.

ENHANCED MODEL-FREE PREDICTIVE CONTROL FOR VOLTAGE SOURCE INVERTERS USING AN ADAPTIVE OBSERVER

In this paper, a free model predictive control based on the active vector execution time (AVET-MFPC) using an adaptive observer is proposed for two-level voltage source inverters. The traditional model-free predictive control (MFPC) uses the sampling period to select one voltage vector for all candidate vectors according to the minimizing cost function principle. With the proposed control, two vectors are selected at one sampling period. The first vector is an active vector that uses the execution time of the active vector to select it, while the second one is a zero vector as it is applied after the active vector. The execution time is calculated using the ultra-local model (ULM) equation. In the traditional MFPC, the factor in the ULM is chosen with approximate values ranging between ±50 % of the nominal value. This paper proposes an adaptive sliding mode observer (ASMO) with an improved design to observe the variation of this factor, especially in case of mismatch parameters and during a step change in the reference signal. Combining the proposed ASMO observer and the AVET-MFPC controller gives faster system response, good tracking results, and less computational burden. Finally, the effectiveness of the proposed control model is approved and confirmed under various conditions, as well as the simulations carried out and the results obtained.

3D Lightweight Cryptosystem Design For IoT Applications Based On Composite S-Box

The security of the Internet of Things (IoT) depends on strong cryptographic functions that require extensive computation and resources. The security of Internet of Things (IoT) relies on the use of strong cryptographic functions that demand extensive computation and resources. Therefore, the selection of a cryptographic system is influenced by the computational and communicational capabilities of IoT devices, including their energy requirements, memory constraints, and execution time. This paper aims to develop a lightweight and secure encryption algorithm for IoT applications, focusing on advancing cryptanalysis techniques. We propose a new lightweight algorithm named enhanced 3D RECTANGLE, designed to deliver robust security for IoT applications and optimized for cell phones with minimal memory usage, low power consumption, and efficient performance. The RECTANGLE block cipher was chosen for this research due to its high efficiency and speed relative to other lightweight algorithms, despite some security issues. The proposed enhanced 3D RECTANGLE algorithm improves confusion and diffusion properties through a new 3D array block rotation method for 4x4 plaintext, based on a 128-bit key and 16 rounds. Additionally, we designed a 4x4 composite S-Box with a Galois field pipelining structure, offering a more advanced solution compared to the Look-Up Table method. The cryptanalysis, avalanche effect, and bit error rate tests were conducted to verify the security strength of the proposed algorithm. The proposed algorithm was evaluated against two existing algorithms, RECTANGLE and Extended 3D RECTANGLE. The enhanced 3D RECTANGLE algorithm provides better cryptanalysis, demonstrating a 40% avalanche effect and achieving a bit error rate (BER) of 31%, indicating its greater effectiveness compared to the other two lightweight algorithms.

Parallel Computing Techniques for Solving Large-Scale Partial Differential Equation

Two-layered partial differential equations (PDEs) will be structured in this investigation using a variety of parallel mathematical techniques, such as Red Dull Gauss Seidel and Gauss Seidel. For the solution of this study, the two-layered PDEs of the elliptic and illustrated sorts were selected. Parallel Virtual Machine (PVM) is used on the correspondence among all chips of the Parallel Handling System. PVM is well-known for its item system, which enables several diverse computers to be used as flexible and easy-to-use concurrent handling resources. An evaluation of the parallel exhibition assessment including the Gauss Seidel and Red Gauss Seidel techniques in terms of execution time, speed, capability, ampleness, and transient execution will follow the graphical presentation of the mathematical results. Since the goal of this work was to create a successful Two-Dimensional PDE Solver (TDPDES), performance evaluations are simple. Numerous design and mathematical sectors will see an improvement in their examination and investigation systems thanks to this new, effective TDPDES technology..

Optimized Machine Learning Classifiers for Symptom-Based Disease Screening

This work presents a disease detection classifier based on symptoms encoded by their severity. This model is presented as part of the solution to the saturation of the healthcare system, aiding in the initial screening stage. An open-source dataset is used, which undergoes pre-processing and serves as the data source to train and test various machine learning models, including SVM, RFs, KNN, and ANNs. A three-phase optimization process is developed to obtain the best classifier: first, the dataset is pre-processed; secondly, a grid search is performed with several hyperparameter variations to each classifier; and, finally, the best models obtained are subjected to additional filtering processes. The best-results model, selected based on the performance and the execution time, is a KNN with 2 neighbors, which achieves an accuracy and F1 score of over 98%. These results demonstrate the effectiveness and improvement of the evaluated models compared to previous studies, particularly in terms of accuracy. Although the ANN model has a longer execution time compared to KNN, it is retained in this work due to its potential to handle more complex datasets in a real clinical context.

A CNN-based approach for anomaly detection in smart grid systems

Power grids are always a major center of attraction for attackers due to their large coverage area, complex technological infrastructure, remote access facilities, connectivity, and many more. The power grids that have bidirectional power flow, information interchange, and advanced communication technologies to improve the quality of power transmission and distribution are called smart grids. DoS attacks and false data injection attacks are the two major attacks on smart grids. This paper considers a false data injection attack considering noise reduction, state estimation, system parameters, and control system generation. Firstly, we measure the noise and mitigate it using the data matrix of the devices (sensors, generators, substations, and breaker systems). Secondly, state estimation uses the CNN to track the injected attack types. Finally, a control system is designed to provide anomaly patterns to the devices as input to prevent attacks in the future. The experimental results show that the proposed method achieves a higher anomaly detection rate with a lower error rate (approximately . 1.43%). The network performance parameters are providing significant results; throughput (12 Mbps), PDR (0.005%), execution time (1.43 s), and energy consumption are reduced by 12%–15% as compared to the existing research findings.

Towards High Performance QNNs via Distribution-Based CNOT Gate Reduction

Quantum Neural Networks (QNNs) are one of the most promising applications that can be implemented on NISQ-era quantum computers. In this study, we observe that QNNs often suffer from gate redundancy, which hugely declines the performance and accuracy of the network. Even state-of-the-art architecture search techniques like QuantumNAS do not completely alleviate this problem. Especially, We find that CNOT gates are major contributors to the execution delay and noise in quantum circuits, and there are many redundant CNOT gates in the QNN post-training. This motivates us to propose a novel distribution-based greedy-search circuit optimization technique, that can be employed after the completion of the training process. Our technique significantly reduces the number of CNOT gates in QNNs without affecting the accuracy of the network. With this technique, we have achieved an average of 3 × improvement in execution time while reaching a maximum of 12.4 × improvement.

Parallel finite-element codes for the Bogoliubov-de Gennes stability analysis of Bose-Einstein condensates

We present and distribute a parallel finite-element toolbox written in the free software FreeFEM for computing the Bogoliubov-de Gennes (BdG) spectrum of stationary solutions to one- and two-component Gross-Pitaevskii (GP) equations, in two or three spatial dimensions. The parallelization of the toolbox relies exclusively upon the recent interfacing of FreeFEM with the PETSc library. The latter contains itself a wide palette of state-of-the-art linear algebra libraries, graph partitioners, mesh generation and domain decomposition tools, as well as a suite of eigenvalue solvers that are embodied in the SLEPc library. Within the present toolbox, stationary states of the GP equations are computed by a Newton method. Branches of solutions are constructed using an adaptive step-size continuation algorithm. The combination of mesh adaptivity tools from FreeFEM with the parallelization features from PETSc makes the toolbox efficient and reliable for the computation of stationary states. Their BdG spectrum is computed using the SLEPc eigenvalue solver. We perform extensive tests and validate our programs by comparing the toolbox's results with known theoretical and numerical findings that have been reported in the literature. Program summaryProgram Title: FFEM_BdG_ddm_toolbox.zipCPC Library link to program files:https://doi.org/10.17632/w9hg964wpb.1Licensing provisions: GPLv3Programming language:FreeFEM (v 4.12) free software (www.freefem.org)Nature of problem: Among the plethora of configurations that may exist in Gross-Pitaevskii (GP) equations modeling one or two-component Bose-Einstein condensates, only the ones that are deemed spectrally stable (or even, in some cases, weakly unstable) have high probability to be observed in realistic ultracold atoms experiments. To investigate the spectral stability of solutions requires the numerical study of the linearization of GP equations, the latter commonly known as the Bogoliubov-de Gennes (BdG) spectral problem. The present software offers an efficient and reliable tool for the computation of eigenvalues (or modes) of the BdG problem for a given two- or three-dimensional GP configuration. Then, the spectral stability (or instability) can be inferred from its spectrum, thus predicting (or not) its observability in experiments.Solution method: The present toolbox in FreeFEM consists of the following steps. At first, the GP equations in two (2D) and three (3D) spatial dimensions are discretized by using P2 (piece-wise quadratic) Galerkin triangular (in 2D) or tetrahedral (in 3D) finite elements. For a given configuration of interest, mesh adaptivity in FreeFEM is deployed in order to reduce the size of the problem, thus reducing the toolbox's execution time. Then, stationary states of the GP equations are obtained by a Newton method whose backbone involves the use of a reliable and efficient linear solver judiciously selected from the PETSc1 library. Upon identifying stationary configurations, to trace branches of such solutions a parameter continuation method over the chemical potential in the GP equations (effectively controlling the number of atoms in a BEC) is employed with step-size adaptivity of the continuation parameter. Finally, the computation of the stability of branches of solutions (i.e. the BdG spectrum), is carried out by accurately solving, at each point in the parameter space, the underlying eigenvalue problem by using the SLEPc2 library. Three-dimensional computations are made affordable in the present toolbox by using the domain decomposition method (DDM). In the course of the computation, the toolbox stores not only the solutions but also the eigenvalues and respective eigenvectors emanating from the solution to the BdG problem. We offer examples for computing stationary configurations and their BdG spectrum in one- and two-component GP equations.Running time: From minutes to hours depending on the mesh resolution and space dimension.

Kibernetski napadi preko stranskih kanalov

When implementing cryptographic primitives in software, we often resort to optimizing the software code in order to reduce unnecessary computation time. Such decisions can have negative consequences on data leakage outside the implementation. Attackers can exploit the additional information generated by the execution of the software solution in a way that gives them additional insights into the operation of the software, the values calculated and access to the data itself. Insights can be gained in the form of execution time, memory access patterns, power consumption, and other seemingly unrelated system parameters. Such attacks are referred to as “side-channel attacks”. This paper presents several examples of such attacks and the magnitude of information leakage, as well as how to protect against such attacks. Time-based cryptanalysis, power-based cryptanalysis, including a special variant – video-based cryptanalysis – and memory-based cryptanalysis are described. A simple timing cryptanalysis attack is used as an example to present the analysis process and the results before and after the implementation of the proposed mitigation. Članek predstavlja več primerov takšnih napadov in razsežnosti uhajanja informacij ter načinov zaščite pred napadi. Opisane so časovna kriptoanaliza, kriptoanaliza na podlagi porabe energije in njena posebna različica -- kriptoanaliza na podlagi videoposnetka -- ter pomnilniška kriptoanaliza. Na primeru preprostega napada s časovno kriptoanalizo je predstavljen postopek analize in rezultati pred in po implementaciji predlagane zaščite.

Point containment algorithms for constructive solid geometry with unbounded primitives

We present several algorithms for evaluating point containment in constructive solid geometry (CSG) trees with unbounded primitives. Three algorithms are presented based on postfix, prefix, and infix notations of the CSG binary expression tree. We show that prefix and infix notations enable short-circuiting logic, which reduces the number of primitives that must be checked during point containment. To evaluate the performance of the algorithms, each algorithm was implemented in the OpenMC Monte Carlo particle transport code, which relies on CSG to represent solid bodies through which subatomic particles travel. Two sets of tests were carried out. First, the execution time to generate a rasterized image of a 2D slice of three CSG models of varying complexity was measured. Use of both prefix and infix notations offered significant speedup over the postfix notation that has traditionally been used in particle transport codes, with infix resulting in a 6× reduction in execution time relative to postfix for a model of a tokamak fusion device. We then measured the execution time of neutron transport simulations of the same three models using each of the algorithms. The results and performance improvements reveal the same trends as for the rasterization test, with a 5.52× overall speedup using the infix notation relative to the original postfix notation in OpenMC for the tokamak model.

AMP: Total Variation Reduction for Lossless Compression via Approximate Median-based Preconditioning

With the increasing scale of cloud computing applications of next-generation embedded systems, a major challenge that domain scientists are facing is how to efficiently store and analyze the vast volume of output data. Compression can reduce the amount of data that needs to be transferred and stored. However, most of the large datasets are in floating-point format, which exhibits high entropy. As a result, existing lossless compressors cannot provide enough performance for such applications. To address this problem, we propose a total variation reduction method for improving the compression ratio of lossless compressors (namely, FPC + and FPZIP + ), which employs a median-based hyperplane to precondition the data. In particular, we first try to exploit the space-filling curve (SFC), a well-known technique to preserve data locality for a multi-dimensional dataset. We show and explain why a raw SFC, such as Hilbert and Z-order curves, cannot improve the compression ratio. Then, we explore the opportunity and theoretical feasibility of the proposed total variation reduction-based algorithm. The experiment results show the effectiveness of the proposed method. The compression ratios are improved up to 48.2% (20.6% on average) for FPZIP and 42.4% (18.4% on average) for FPC. Moreover, through observing the time composition of the proposed method, it is found that the median finding holds a high percentage of the execution time. Hence, we further introduce an approximate median finding algorithm, providing a linear-time overhead reduction scheme. The experiment results clearly demonstrate that this algorithm reduces execution time by an average of 56.7% and 40.7% compared to FPC + and FPZIP + , respectively.

Towards online training for RL-based query optimizer

AbstractJoin query optimization aims to find the best join order for tables in a query, which is critical for query processing performance. Recently, reinforcement learning models have been proposed to solve the challenges existing with query processing and join query optimization. However, changes in the data distribution can turn the trained reinforcement learning models into obsolete models, resulting in longer execution times. In this paper, we propose a new training strategy in order to extend the existing reinforcement learning models and improve their adaptation when the data distribution changes. The experiments show that the proposed strategy has a significant benefit in decreasing training time for the models given the changes in the data distribution.

Towards Analysing Cache-Related Preemption Delay in Non-Inclusive Cache Hierarchies

The impact of preemptions has to be considered when determining the schedulability of a task set in a preemptively scheduled system. In particular, the contents of caches can be disturbed by a preemption, thus creating context-switching costs. These context-switching costs occur when a preempted task needs to reload data from memory after a preemption. The additional delay created by this effect is termed cache-related preemption delay (CRPD). The analysis of CRPD has been extensively studied for single-level caches in the past. However, for two-level caches, the analysis of CRPD is still an emerging area of research. In contrast to a single-level cache, which is only affected by direct preemption effects, the second-level cache in a two-level hierarchy can be subject to indirect interference after a preemption. Accesses that could be served from the L1 cache in the absence of preemptions, may be forwarded to the L2 cache, as the relevant data was evicted by a preemption. These accesses create the indirect interference in the L2 cache and can cause further evictions. Recently, a CRPD analysis for two-level non-inclusive cache hierarchies was proposed. In this paper, we show that this state-of-the-art analysis is unsafe as it potentially underestimates the CRPD. Furthermore, we show that the analysis is pessimistic and can overestimate the indirect preemption effects. To address these issues, we propose a novel analysis approach for the CRPD in a two-level non-inclusive cache hierarchy. We prove the correctness of the presented approach based on the set of feasible program execution traces. We implemented the presented approach in a worst-case execution time (WCET) analysis tool and compared the performance to existing analysis methods. Our evaluation shows that the presented analysis increases task set schedulability by up to 14 percentage points compared to the state-of-the-art analysis.

Intelligent Fuzzy Traffic Signal Control System for Complex Intersections Using Fuzzy Rule Base Reduction

In this study, the concept of symmetry is employed to implement an intelligent fuzzy traffic signal control system for complex intersections. This approach suggests that the implementation of reduced fuzzy rules through the reduction method, without compromising the performance of the original fuzzy rule base, constitutes a symmetrical approach. In recent decades, urban and city traffic congestion has become a significant issue because of the time lost as a result of heavy traffic, which negatively affects economic productivity and efficiency and leads to energy loss, and also because of the heavy environmental pollution effect. In addition, traffic congestion prevents an immediate response by the ambulance, police, and fire brigades to urgent events. To mitigate these problems, a three-stage intelligent and flexible fuzzy traffic control system for complex intersections, using a novel hybrid reduction approach was proposed. The three-stage fuzzy traffic control system performs four primary functions. The first stage prioritizes emergency car(s) and identifies the degree of urgency of the traffic conditions in the red-light phase. The second stage guarantees a fair distribution of green-light durations even for periods of extremely unbalanced traffic with long vehicle queues in certain directions and, especially, when heavy traffic is loaded for an extended period in one direction and the short vehicle queues in the conflicting directions require passing in a reasonable time. The third stage adjusts the green-light time to the traffic conditions, to the appearance of one or more emergency car(s), and to the overall waiting times of the other vehicles by using a fuzzy inference engine. The original complete fuzzy rule base set up by listing all possible input combinations was reduced using a novel hybrid reduction algorithm for fuzzy rule bases, which resulted in a significant reduction of the original base, namely, by 72.1%. The proposed novel approach, including the model and the hybrid reduction algorithm, were implemented and simulated using Python 3.9 and SUMO (version 1.14.1). Subsequently, the obtained fuzzy rule system was compared in terms of running time and efficiency with a traffic control system using the original fuzzy rules. The results showed that the reduced fuzzy rule base had better results in terms of the average waiting time, calculated fuel consumption, and CO2 emission. Furthermore, the fuzzy traffic control system with reduced fuzzy rules performed better as it required less execution time and thus lower computational costs. Summarizing the above results, it may be stated that this new approach to intersection traffic light control is a practical solution for managing complex traffic conditions at lower computational costs.

Disaster tweet classification using enhanced salp swarm algorithm

Twitter and Facebook are widely recognized as crucial tools for situational information during disasters. Given that the classification of disaster related tweets is computationally challenging due to the high dimension of textual data caused by the redundant and irrelevant features. Hence for optimal feature selection (FS) and classification of disaster tweets, this work utilizes binary salp swarm algorithm (BSSA) and proposed two enhancements over it (PBcSSA). The commensalism phase from symbiotic organisms search (SOS) is integrated with BSSA to enhance its feature space searchability and then its parallel implementation is done using Apache Spark framework to reduce the execution time. The experiments were performed in a cross-disaster setting on nine groups of datasets including biological, earthquake, flood, hurricane, industrial, societal, transportation, wildfire, and environmental. The proposed PBcSSA combined with the Naive Bayes (NB) classifier in wrapper mode and its performance is compared with standard BSSA, binary sine cosine algorithm (BSCA), binary particle swarm optimization (BPSO), binary grey wolf optimization (BGWO), and binary whale optimization algorithm (BWOA). The experimental results reveal that the proposed PBcSSA outperforms other algorithms in disaster tweet classification and achieved highest average F1-score with lowest feature set in a reduced execution time.

PyJedAI: A Library with Resolution-Related Structures and Procedures for Products

This work presents an open-source Python library, named pyJedAI, which provides functionalities supporting the creation of algorithms related to product entity resolution. Building over existing state-of-the-art resolution algorithms, the tool offers a plethora of important tasks required for processing product data collections. It can be easily used by researchers and practitioners for creating algorithms analyzing products, such as real-time ad bidding, sponsored search, or pricing determination. In essence, it allows users to easily import product data from the possible sources, compare products in order to detect either similar or identical products, generate a graph representation using the products and desired relationships, and either visualize or export the outcome in various forms. Our experimental evaluation on data from well-known online retailers illustrates high accuracy and low execution time for the supported tasks. To the best of our knowledge, this is the first Python package to focus on product entities and provide this range of product entity resolution functionalities. History: Accepted by Ted Ralphs, Area Editor for Software Tools. Funding: This was partially funded by the EU project STELAR (Horizon Europe) [Grant 101070122]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0410 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0410 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Code and Data Repository for pyJedAI: A Library with Resolution-Related Structures and Procedures for Products

This work presents an open-source Python library, named pyJedAI, which provides functionalities supporting the creation of algorithms related to Product Entity Resolution. Building over existing state-of-the-art resolution algorithms (Papadakis et al. 2021a), the tool offers a plethora of important tasks required for processing product data collections. It be can easily used by researchers and practitioners for creating algorithms analyzing products, such as real-time ads bidding, sponsored search, or pricing determination. In essence, it allows to easily import product data from the possible sources, compare products in order to detect either similar or identical products, generate a graph representation using the products and desired relationships, and either visualize or export the outcome in various forms. Our experimental evaluation on data from well-known online retailers illustrates high accuracy and low execution time for the supported tasks. To the best of our knowledge this is the first Python package to focus on product entities and provide this range of Product Entity Resolution functionalities.

Empirical evaluation of feature selection methods for machine learning based intrusion detection in IoT scenarios

This paper delves into the critical need for enhanced security measures within the Internet of Things (IoT) landscape due to inherent vulnerabilities in IoT devices, rendering them susceptible to various forms of cyber-attacks. The study emphasizes the importance of Intrusion Detection Systems (IDS) for continuous threat monitoring. The objective of this study was to conduct a comprehensive evaluation of feature selection (FS) methods using various machine learning (ML) techniques for classifying traffic flows within datasets containing intrusions in IoT environments. An extensive benchmark analysis of ML techniques and FS methods was performed, assessing feature selection under different approaches including Filter Feature Ranking (FFR), Filter-Feature Subset Selection (FSS), and Wrapper-based Feature Selection (WFS). FS becomes pivotal in handling vast IoT data by reducing irrelevant attributes, addressing the curse of dimensionality, enhancing model interpretability, and optimizing resources in devices with limited capacity. Key findings indicate the outperformance for traffic flows classification of certain tree-based algorithms, such as J48 or PART, against other machine learning techniques (naive Bayes, multi-layer perceptron, logistic, adaptive boosting or k-Nearest Neighbors), showcasing a good balance between performance and execution time. FS methods' advantages and drawbacks are discussed, highlighting the main differences in results obtained among different FS approaches. Filter-feature Subset Selection (FSS) approaches such as CFS could be more suitable than Filter Feature Ranking (FFR), which may select correlated attributes, or than Wrapper-based Feature Selection (WFS) methods, which may tailor attribute subsets for specific ML techniques and have lengthy execution times. In any case, reducing attributes via FS has allowed optimization of classification without compromising accuracy. In this study, F1 score classification results above 0.99, along with a reduction of over 60% in the number of attributes, have been achieved in most experiments conducted across four datasets, both in binary and multiclass modes. This work emphasizes the importance of a balanced attribute selection process, taking into account threat detection capabilities and computational complexity.

Efficient Implementation of Multilayer Perceptrons: Reducing Execution Time and Memory Consumption

A technique is presented that reduces the required memory of neural networks through improving weight storage. In contrast to traditional methods, which have an exponential memory overhead with the increase in network size, the proposed method stores only the number of connections between neurons. The proposed method is evaluated on feedforward networks and demonstrates memory saving capabilities of up to almost 80% while also being more efficient, especially with larger architectures.

Comparative analysis of upper body kinematics in stroke, Parkinson's disease, and healthy subjects: An observational study using IMU-based targeted box and block test

BackgroundThe Box and Block Test (BBT) is an essential and widely used test in rehabilitation for the assessment of gross unilateral manual dexterity. Although it is a valid, simple, and ecological instrument, it does not provide a quantitative measure of the upper limb trajectories during the test. Research questionThe study introduces a new motion-capture-based method (using ecological Inertial Measurement Units - IMUs) to evaluate upper body kinematics while performing a targeted version of BBT (tBBT). MethodsThis observational study compares data from 35 healthy subjects, 35 subjects with Parkinson’s disease, and 35 post-stroke individuals to evaluate upper limb kinematics during tBBT quantitatively. Seven IMUs were placed on the trunk, head, and upper limb of each subject. The joint angles and kinematic scores were calculated and analyzed. Motor task execution time and kinematic scores were statistically correlated with clinical assessment measures. Kruskal-Wallis between groups test and Dunn-Bonferroni post-hoc were used. ResultsThe statistics revealed significant differences (p<0.05) among the three groups. The analyzed joint angles highlight various compensatory strategies in neurological subjects, such as using the trunk to complete a motor task instead of the shoulder and using the wrist instead of the elbow, along with differences in movement fluidity (DimensionLess-Jerk, p<0.05). A positive correlation was found between kinematics and the Fugl-Meyer Assessment-Upper Limb (r=0.7344; p<0.01), and a negative correlation between kinematics and the Unified Parkinson's Disease Rating Scale (r=-0.5286; p<0.01). SignificanceThe quantitative assessments of joint kinematics correlated to clinical assessments could guarantee a new method of assessment of the upper limb in subjects with motor deficits. This would allow to capture new insight into the characteristics of the subject’s disability, with implications for the choice of a personalized rehabilitation treatment focused on the motor recovery of the upper limb.