System Input Research Articles

Development and deployment of a mid-cost CO2 sensor monitoring network to support atmospheric inverse modeling for quantifying urban CO2 emissions in Paris

Abstract. To effectively monitor highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. These dense measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or to separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm for hourly mean measurements. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision cavity ring-down spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12:00–17:00 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects for integrating mid-cost measurements along with high-precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area.

Measurement-device-independent detection of beyond-quantum state

Abstract In quantum theory, a quantum state on a composite system of two parties realizes a non-negative probability with any measurement element with a tensor product form. However, there also exist non-quantum states which satisfy the above condition. Such states are called beyond-quantum states, and cannot be detected by standard Bell tests. To distinguish a beyond-quantum state from quantum states, we propose a measurement-device-independent (MDI) test for beyond-quantum state detection, which is composed of quantum input states on respective parties and quantum measurements across the input system and the target system on respective parties. The performance of our protocol is independent of the forms of the tested states and the measurement operators, which provides an advantage in practical scenarios. We also discuss the importance of tomographic completeness of the input sets to the detection.

Navigating the Future of 3D Laparoscopic Liver Surgeries: Visualization of Internal Anatomy on Laparoscopic Images With Augmented Reality.

Liver tumor resection requires precise localization of tumors and blood vessels. Despite advancements in 3-dimensional (3D) visualization for laparoscopic surgeries, challenges persist. We developed and evaluated an augmented reality (AR) system that overlays preoperative 3D models onto laparoscopic images, offering crucial support for 3D visualization during laparoscopic liver surgeries. Anatomic liver structures from preoperative computed tomography scans were segmented using open-source software including 3D Slicer and Maya 2022 for 3D model editing. A registration system was created with 3D visualization software utilizing a stereo registration input system to overlay the virtual liver onto laparoscopic images during surgical procedures. A controller was customized using a modified keyboard to facilitate manual alignment of the virtual liver with the laparoscopic image. The AR system was evaluated by 3 experienced surgeons who performed manual registration for a total of 27 images from 7 clinical cases. The evaluation criteria included registration time; measured in minutes, and accuracy; measured using the Dice similarity coefficient. The overall mean registration time was 2.4±1.7 minutes (range: 0.3 to 9.5min), and the overall mean registration accuracy was 93.8%±4.9% (range: 80.9% to 99.7%). Our validated AR system has the potential to effectively enable the prediction of internal hepatic anatomic structures during 3D laparoscopic liver resection, and may enhance 3D visualization for select laparoscopic liver surgeries.

Система ввода цифровых предыскажений на основе обобщенной полиномиальной модели с памятью для GaN усилителей мощности

Modern power amplifiers based on GaN transistors face a number of problems related to their physical properties and operating conditions. The high output power of such power amplifiers is accompanied by significant nonlinear distortions, especially when operating in saturation mode. Amplifiers based on GaN transistors have pronounced «short» memory effects, which leads to the dependence of current distortions on previous signal states. At the same time, support for broadband signals requires high processing speeds and large computing resources. Classical digital pre-distortion methods do not always cope with the task of effectively compensating for real-time distortion for power amplifiers based on GaN transistors. These problems complicate the process of linearization of power amplifiers based on GaN transistors and require the development of new methods that can effectively compensate for non-linearities and memory effects without significantly increasing the cost and complexity of the system. The purpose of the work is to create and analyze the effectiveness of a digital pre-imaging system based on a generalized polynomial model with memory to improve the linearity and efficiency of power amplifiers based on GaN transistors in wireless communication systems. The paper presents the architecture of the digital preimage input system based on a generalized polynomial model with memory. The use of this model makes it possible to improve the efficiency of suppressing nonlinear distortions at the output of a power amplifier based on GaN transistors. Practical significance – the proposed architecture can be implemented on FPGAs, VLSI or SOC with a minimum amount of computing resources.

Adaptive control of networked control systems under DoS attacks

This research introduces a new adaptive control approach tailored for networked control systems, aimed at addressing the challenges of Denial of Service (DoS) attacks and round-trip time delays. Initially, a compensation mechanism based on estimated values is introduced to counter DoS attacks; subsequently, for round-trip time delay issues, a predictive delay compensation control strategy is designed. The proposed control strategy does not rely on the specific structure or a particular model of the system, but instead solely updates the controller using the system's input and output values. Moreover, we successfully demonstrate the bounded stability of the system using the direct Lyapunov method. Ultimately, the effectiveness and practicality of this method are confirmed through simulation results.

Identifying Brain Network Structure for an fMRI Effective Connectivity Study Using the Least Absolute Shrinkage and Selection Operator (LASSO) Method.

Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input-output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study.

PEMBERDAYAAN MASYARAKAT MELALUI DIGITALISASI PENGOLAHAN DATA NASABAH di BANK SAMPAH KEMUNING, JATI MELATI, KOTA BEKASI

Household waste in RW 12, Jati Melati, Pondok Melati, Bekasi City if not managed properly will result in piles of garbage that pile up and cause unpleasant odor pollution. This problem can be overcome by the presence of the Kemuning Waste Bank in the local environment. The impact of the waste bank, household waste can be reduced because some of the waste is deposited into the waste bank and provides economic value for the depositor. However, there is a problem faced by the Kemuning Waste Bank, namely in terms of recording customer data which is still done manually because it does not have technological devices that support digital recording. This makes it difficult for officers to recapitulate customer data reports, present information on the amount of waste obtained based on the category and type of waste, and calculate customer savings payments when due. This activity uses the method of designing a data input system, socialization, training and mentoring. The results of Community Service Activities show that officers have the competence to input, process and present data on the same day as the day of deposit and weighing customer waste.

Estimation of probabilities of transitions of markov binary input signal of nonlinear system

The problem of estimating unknown probabilities of transitions of a random Markov binary input signal of a nonlinear one-dimensional discrete system based on estimating the expectation and variance of the output signal is considered. The defined expressions are built on the basis of considering equally probable transitions and the steady-state mode of the algorithm for assessing the state of the system, obtained by approximating the probability density of its output signal by the Pearson type I distribution. An example of comparison of theoretical calculations with the results of imitation mathematical modeling is given.

Exploring User-Defined Gestures as Input for Hearables and Recognizing Ear-Level Gestures with IMUs

Hearables are highly functional earphone-type wearables; however, existing input methods using stand-alone hearables are limited in the number of commands, and there is a need to extend device operation through hand gestures. In previous research on hearables for hand input, user understanding and gesture recognition systems have been developed. However, in the realm of user understanding, investigation concerning hand input with hearables remains incomplete, and existing recognition systems have not demonstrated proficiency in discerning user-defined gestures. In this study, we conducted a gesture elicitation study (GES) assuming hand input using hearables under six conditions (three interaction areas x two device shapes). Then, we extracted ear-level gestures that the device's built-in IMU sensor could recognize from the user-defined gestures and investigated the recognition performance. The results of sitting experiments showed that the gesture recognition rate for in-ear devices was 91.0% and that for ear-hook devices was 74.7%.

Assessment of Solubility Behavior of a Copolymer in Supercritical CO2 and Organic Solvents: Neural Network Prediction and Statistical Analysis.

In the industrial sector, understanding the behavior of block copolymers in supercritical solvents is crucial. While qualitative agreement with polymer solubility curves has been evaluated using complex theoretical models in many cases, quantitative predictions remain challenging. This study aimed to create a rapid and accurate artificial neural network (ANN) model to predict the lower critical solubility and upper critical solubility space of an atypical block copolymer, poly(styrene-co-octafluoropentyl methacrylate) (PSOM), in different supercritical solvent systems over a wide range of temperatures (51.75-182.05 °C) and high pressure (3.28-200.86 MPa). The experimental data set used in this study included one copolymer, five supercritical solvents, one cosolvent, and one initiator. It consisted of seven unique copolymer-solvent combinations (252 cloud point pressures) used to predict the model quantitatively and qualitatively. To predict the PSOM-solvent interactions, the study considered two different input systems: a six-variable system, a five-variable system, and one target output. Initially, we used a three-layer feed-forward neural network to select the best learning algorithm (Levenberg-Marquardt) from 14 different algorithms, considering one sample PSOM-solvent system. Then, the network topology was optimized by varying hidden neuron numbers from 2 to 80 for all seven PSOM-solvent combination systems. The predicted cloud point pressures were in excellent agreement with the experimental cloud point pressures, confirming the model's accuracy. It is clear from the results of a minimum mean square error (≤1.90 × 10-27) and maximum linear regression R 2 (≥0.99) during training, validation, and testing of all the data sets. Further, the ANN model accuracy was tested by statistical analysis, confirming the model's ability to accurately capture the miscibility regions of polymers, enabling efficient processing of various polymer materials. This data-driven approach facilitates the prediction of coexistence curves for other polymers and complex macromolecular systems.

Anti Wind‐Up and Robust Data‐Driven Model‐Free Adaptive Control for MIMO Nonlinear Discrete‐Time Systems

ABSTRACTThis article addresses a solution to one of the main challenges of online data‐driven control (DDC) methods: reducing the sensitivity of the model‐free adaptive control (MFAC) method to initial conditions and control parameters with the new control cost function and added the output error rate and integral along with a new anti‐wind up strategy for multi‐input multi‐output (MIMO) systems. The parameters introduced to the new control law have been validated using the boundary‐input boundary‐output (BIBO) approach to design and converge the controller. The simulation findings on a nonlinear auto‐regressive moving average model with exogenous inputs (NARMAX) system with triangular control input demonstrate that the proposed control rule will outperform to prototype MFAC. Furthermore, to analyze the sensitivity of the controller to the initial conditions and the uncertainties of the control parameters, 30 Monte Carlo simulations were performed with random initial conditions in the presence of disturbance in the control input, and output noise, and the results were compared with the prototype MFAC and conventional PID controller using standard criteria such as integral time absolute error, standard deviation, steady‐state error, and mean maximum error, which shows a noticeable superiority of proposed controller relative to the prototype MFAC.

Wi-Crack: a smartphone keystroke recognition system based on multi-dimensional information

Abstract With the popularity of mobile payment, password protection has become more and more important. Channel state information (CSI) has recently been used to crack passwords in public WiFi environments. The feasibility of password cracking lies in the fact that different keystrokes lead to different finger movement directions and distances, resulting in unique interference to WiFi signal transmission. The unique interference can be recorded by CSI and used for keystroke inference. In this paper, we propose Wi-Crack, a keystroke recognition system for numerical keypad input on smartphones. Two computers equipped with commercial off-the-shelf WiFi NIC comprise the system, with one serving as the transmitter and the other as the receiver. Previous keystroke recognition systems only used the amplitude of CSI for keystroke recognition. Different from them, Wi-Crack combines the amplitude, phase, amplitude difference and phase difference of CSI for keystroke recognition. The use of multi-dimensional information has enabled Wi-Crack to improve keystroke recognition. Experimental results show that Wi-Crack improves the accuracy of keystroke segmentation to consistently above 90%. It also improves the keystroke recognition accuracy on DTW-KNN, SVM, 1D-CNN, and LSTM with over 90% in the best case.

The Role of Population Receptive Field Sizes in Higher-Order Visual Dysfunction.

Population receptive field (pRF) modeling is an fMRI technique used to retinotopically map visual cortex, with pRF size characterizing the degree of spatial integration. In clinical populations, most pRF mapping research has focused on damage to visual system inputs. Herein, we highlight recent work using pRF modeling to study high-level visual dysfunctions. Larger pRF sizes, indicating coarser spatial processing, were observed in homonymous visual field deficits, aging, and autism spectrum disorder. Smaller pRF sizes, indicating finer processing, were observed in Alzheimer's disease and schizophrenia. In posterior cortical atrophy, a unique pattern was found in which pRF size changes depended on eccentricity. Changes to pRF properties were observed in clinical populations, even in high-order impairments, explaining visual behavior. These pRF changes likely stem from altered interactions between brain regions. Furthermore, some studies suggested that pRF sizes change as part of cortical reorganization, and they can point towards future prognosis.

Pierre-Marie Gagey and the Evolution of Posturology: Unraveling the Complexity of the Fine Postural Control System.

The subtle sway of human standing posture began to be studied in the 19th century. Since then, numerous approaches - from the early statokinesiometers to the more advanced force platforms or 3D-posture recording systems - have been developed to understand human postural control physiology and its clinical implications. French physician Pierre-Marie Gagey made significant contributions to the field of posturology, a discipline focused on the intricacies of human postural control. From the 1950s, he advanced the field through observation and experimentation, particularly exploring the relationship between postural muscle tone and the fine postural control system. Gagey's collaboration with researchers of that time led to the refinement of stabilometry, a technique for measuring body sway in standing positions. Throughout his career, Gagey worked across multiple disciplines, including ophthalmology, podiatry, psychology, and the study of the vestibular and stomatognathic systems, to develop a comprehensive approach to postural assessment and treatment. His efforts culminated in the standardization of stabilometry, ensuring consistent and accurate measurements across different platforms. Gagey's work emphasized the complexity of postural control, highlighting the integration of peripheral and central nervous system inputs in maintaining balance. His research has left an enduring impact on the field of posturology, providing a framework for understanding and treating postural disorders. Pierre-Marie Gagey passed away in 2023, but his pioneering contributions continue to influence the study and clinical practice of human postural control. This narrative review aims to pay tribute to Dr. Gagey's career and research activity.

Tooth Loss in Periodontitis Patients-A Risk Factor for Mild Cognitive Impairment: A Systematic Review and Meta-Analysis.

Periodontal disease and tooth loss have been long suggested as risk factors of mild cognitive impairment. The underlying mechanisms could be systemic chronic inflammatory mediators, direct pathologic challenge to the nervous system, malnutrition and/or loss of neurosensory stimulation input causing brain atrophy. This review aimed to examine the existing literature studies linking the effect of periodontal disease and tooth loss on the development of mild cognitive impairment. A systematic review using PEO was conducted. Three electronic databases, namely Embase, Medline and DOSS (UCLan), were searched for relevant articles published up to April 2023. Google Scholar and a hand search were also conducted to ensure no relevant studies had been missed. The Newcastle-Ottawa scale was used to assess the quality of studies. The findings showed that chronic periodontitis and tooth loss, both individually and in combination, led to an increased risk of mild cognitive decline in adults over 50 years. Within the limitations of this review, periodontitis and tooth loss both contribute to an increased risk of mild cognitive impairment and dementia, but the evidence so far is not strong. In future, more robustly designed studies investigating periodontal disease and tooth losslink with cognitive health decline are required with a longer follow-up duration.

THE REGRESSION MODEL CONSTRUCTION OF THE ROAD CARGO TRANSPORTATION SYSTEM IN THE UKRAINE – POLAND DIRECTION

In 2023, exports between Ukraine and other countries underwent significant changes. Thus, Poland took first place in terms of the volume of exports from Ukraine in 2023 (more than 5.6 billion dollars or 15.4%). Instead, China, which was the No. 1 consumer of Ukrainian goods, fell to the 4th place in the ranking of top exporters during the war. Its share in total exports of Ukraine halved - from 11.8% to 5.3%. The use of time slot technology is a modern technology for increasing the efficiency of cargo transportation in international traffic. The essence of the technology of time intervals is that the working time of the terminal is divided into time segments. During these time slots, terminal clients place their service requests. During the reserved time slot, the client's car must enter the territory of the terminal for service. The introduction of time slot technology aims to provide the client with the possibility of guaranteed arrival of cars in a certain time interval, preventing crowding, long waiting and queue formation. On the basis of the developed experimental plan, painted according to the determined maximum and minimum values of the arguments, the linear influence of factors on the time of cargo transportation was considered. The R-squared coefficient is one of the most effective estimates of the adequacy of the regression model. In the case of a linear model without an independent variable, the R2 coefficient = 0.999, in the case of a linear model with an independent variable, R2 = 0.987, which means that the linear model without an independent variable is more accurate. The obtained results indicate a more rational use of a linear model without an independent variable for determining the time of cargo transportation in the direction of Ukraine - Poland. As a result of the conducted experiment, it was established that there is a functional relationship between the inputs of the cargo transportation system in the Ukraine-Poland direction and the outputs. Keywords: experiment, international transportation, transportation time.

Neuroemotions and Behavior in the Purchase Process

Emotions are the fundamental pillar of neuromarketing, the perception of hedonic, utilitarian value, the emotions experienced by customers during the buying process and the effect the behavior of each stage is seen from the brain structures of the mind. The sample selected for the study was 354 real customers of supermarkets in the city of Riobamba and the sample of 8 participants with the use of eye tracking glasses in the store tour during this process. The results, in three stages of the process system (inputs, processes and results) with the emotions involved in each stage. Stage I are: uncertainty, joy, anger/anger/anxiety, happiness, curiosity. The emotions in stages II and III are joy, surprise, satisfaction, security, and confidence and the emotions felt in the Evaluation Stage are satisfaction, exhaustion, demotivation, joy, and anger/anger/anxiety. There are hormones and neurotransmitters that are involved with emotions at different stages of the buying process. The right brain hemispheres are activated in negative emotions, left in positive emotions in all stages of the buying process. Brain structures are also activated at each stage of the buying process. The results of the Eye tracking in the comparative analysis analyzed Meditation, Attention and Blinking in the stages of the purchase process. It is concluded that repurchase and recommendation intentions are determined by positive emotions which, in turn, are determined by the hedonic and utilitarian value of the product and services that reduce negative emotions.

Variable Structure Controller for Energy Savings in an Underwater Sensor Platform.

This paper introduces a new variable structure controller designed for depth control of an autonomous underwater sensor platform equipped with a variable buoyancy module. To that end, the prototype linear model is presented, and a finite element-based method is used to estimate one of its parameters, the hull deformation due to pressure. To manage potential internal disturbances like hull deformation or external disturbances like weight changes, a disturbance observer is developed. An analysis of the observer steady-state estimation error in relation to input disturbances and system parameter uncertainties is developed. The locations of the observer poles according to its parameters are also identified. The variable structure controller is developed, keeping energy savings in mind. The proposed controller engages when system dynamics are unfavorable, causing the vehicle to deviate from the desired reference, and disengages when dynamics are favorable, guiding the vehicle toward the target reference. A detailed analysis determines the necessary switching control actions to ensure the system reaches the desired reference. Finally, simulations are run to compare the proposed controller's performance with that of PID-based controllers recently developed in the literature, assessing dynamic response and energy consumption under various operating conditions. Both the VBM- and propeller-actuated vehicles were evaluated. The results demonstrate that the proposed controller achieves an average energy consumption reduction of 22% compared to the next most efficient PID-based controller for the VBM-actuated vehicle, though with some impact on control performance.

Towards Prosthesis Control: Identification of Locomotion Activities through EEG-Based Measurements

The integration of advanced control systems in prostheses necessitates the accurate identification of human locomotion activities, a task that can significantly benefit from EEG-based measurements combined with machine learning techniques. The main contribution of this study is the development of a novel framework for the recognition and classification of locomotion activities using electroencephalography (EEG) data by comparing the performance of different machine learning algorithms. Data of the lower limb movements during level ground walking as well as going up stairs, down stairs, up ramps, and down ramps were collected from 10 healthy volunteers. Time- and frequency-domain features were extracted by applying independent component analysis (ICA). Successively, they were used to train and test random forest and k-nearest neighbors (kNN) algorithms. For the classification, random forest revealed itself as the best-performing one, achieving an overall accuracy up to 92%. The findings of this study contribute to the field of assistive robotics by confirming that EEG-based measurements, when combined with appropriate machine learning models, can serve as robust inputs for prosthesis control systems.

Enhanced forest fire evacuation planning using real-time sensor and GPS algorithm

Forest fires are the source of countless fatalities and extreme economic repercussions. The safe evacuation of residents of an area affected by forest fires is the highest priority of local authorities, and finding the most optimal course of action has been a primary research focus for years. Previous studies over several decades have attempted to find an optimal solution using the applications of bug navigation systems, road network reconfiguration, graph traversals, swarm optimization, etc. The author, with the motivation to prevent human casualties at the time of such calamity, presents a novel study which solves the problem in nearly linear time computation, surpassing the performance standards of previous research, and accommodates the unpredictability of the spread of forest fires. This includes a proposal of an algorithm which builds upon the application of Spielman and Teng’s Electrical Circuit Approach to solve for maximum flow in a network and implements this with real-time sensor and Global Positioning System input.