Incoming Signal Research Articles

Gelatin-Modified Bioactive Glass for Treatment of Dentin Hypersensitivity

When dentin is directly exposed to the oral cavity for various reasons, such as a lack of enamel on the tooth surface, external stimuli to the dentin often cause transient discomfort known as dentin hypersensitivity. In order to block the incoming stimulus signal, an ideal treatment is to induce the production of minerals to block the dentinal tubules. In this work, a dentin-desensitizing plugging material was prepared by modifying mesoporous bioactive glass with gelatin, the mineralization and desensitization effects of which were compared with Gluma in in vitro experiments. These experiments confirmed that gelatin-modified bioactive glass (MBG@PDA@Gel) is more effective than traditional desensitizing agents at blocking dentin tubules. Following the successful synthesis of MBG@PDA@Gel, as confirmed by scanning electron microscopy, transmission electron microscopy, and other tests, the treatment of demineralized dentin with MBG@PDA@Gel demonstrated that the dentinal tubules were tightly blocked under scanning electron microscopy. MBG@PDA@Gel induces minerals in deeper layers of dentinal tubules, promoting remineralization and forming a unified structure with the tubule blockage. Animal studies showed that MBG@PDA@Gel can remineralize demineralized dentin, and it is stable in the oral cavity and does not fall out. MBG@PDA@Gel not only enhances the biocompatibility of the nanoparticle but also results in an overall uniform and rapid remineralization of the demineralized dentin.

Signal growth in a pure time-modulated transmission line and the loss effect.

We present the first comprehensive study for signal growth in transmission lines (TL) with purely time-modulated characteristic impedance (infinite superluminality). This study pioneers the investigation into the effects of varying the cell's electrical length and the impact of loss on momentum bandgaps and amplification levels. It also thoroughly examines how time-modulated transmission line truncation by a static load influences the sensitivity of amplification gain to the relative phase between the incoming signal and modulation, comparing these findings with the case of parametric amplification. Varying is accomplished by loading TLs with a sinusoidally time-modulated capacitor (TMC). The study starts with a simple lumped model cell to facilitate understanding of the phenomena. Following this, transmission lines are introduced, and the effects of incorporating loss are examined. To accomplish this, three models are investigated: a lossless L-C TL lumped model loaded with a shunt lossless TMC and a TL loaded with a shunt lossless and lossy TMC. Dispersion diagrams are plotted and momentum bandgaps are identified at a modulation frequency double the signal frequency. Within the momentum bandgap, only imaginary frequencies are found and correlated to momentum bandgap width and signal growth level. Signal growth is confirmed using harmonic balance and transient simulations, and the results are consistent with the dispersion diagram outcomes.

Interpretable logical-probabilistic approximation of neural networks

The paper proposes the approximation of DNNs by replacing each neuron by the corresponding logical-probabilistic neuron. Logical-probabilistic neurons learn their behavior based on the responses of initial neurons on incoming signals and discover all logical-probabilistic causal relationships between the input and output. These logical-probabilistic causal relationships are, in a certain sense, most precise – it was proved in the previous works that they are theoretically (when probability is known) can predict without contradictions. The resulting logical-probabilistic neurons are interconnected by the same connections as the initial neurons after replacing their signals on true/false. The resulting logical-probabilistic neural network produces its own predictions that approximate the predictions of the original DNN. Thus, we obtain an interpretable approximation of DNN, which also allows tracing of DNN by tracing its excitations through the causal relationships. This approximation of DNN is a Distillation method such as Model Translation, which train alternative smaller interpretable models that mimics the total input/output behavior of DNN. It is also locally interpretable and explains every particular prediction. It explains the sequences of logical probabilistic causal relationships that infer that prediction and also show all features that took part in this prediction with the statistical estimation of their significance. Experimental results on approximation accuracy of all intermedia neurons, output neurons and softmax output of DNN are presented, as well as the accuracy of obtained logical-probabilistic neural network. From the practical point of view, interpretable transformation of neural networks is very important for the hybrid artificial intelligent systems, where neural networks are integrated with the symbolic methods of AI. As a practical application we consider smart city.

Introduction of oblique plane waves through a forcing source term in Euler equations

The propagation of shock waves generated by a transonic flow at the tip of a propeller blade is numerically calculated in order to determine the pressure footprint on an aircraft fuselage. An academic case is first proposed to validate the methodology. An incoming signal is built up as oblique harmonic plane waves. The signal is introduced in the computational domain using a Gaussian volume forcing term in the conservation of mass and energy equations. The Euler equations are solved in two dimensions using finite-difference schemes with low dispersion and dissipation. Selective filtering has also been integrated in the algorithm to remove grid-to-grid oscillations. The numerical solution is compared to an analytical solution based on the tailored Green function. An illustration for a realistic open rotor is then considered. The pressure signal near the blade tip, determined from a preliminary RANS simulation, is introduced using a volume source in a solver of the Euler equations.

The critical involvement of monocytes/macrophages in the pathogenesis of primary Sjögren's syndrome: New evidence from Mendelian randomization and single-cell sequencing

BackgroundPrimary Sjögren's syndrome (pSS) stands as a chronic autoimmune disease characterized by an elusive pathogenesis. The synergy of single-cell RNA sequencing and Mendelian randomization (MR) analysis provides an opportunity to comprehensively unravel the contributory role of monocytes/macrophages in the intricate pathogenesis of pSS. MethodsDifferentially expressed genes (DEGs) of various types of immune cells were analyzed after annotating single-cell RNA sequencing (scRNA-seq) data. MR analysis of expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) was conducted to search for key pathogenic genes and proteins. Cellular localization of pathogenic genes was performed based on scRNA-seq data. Variations in signaling pathways between immune cells were further analyzed. ResultsA total of 1434 significant DEGs were identified. Among these, 60 genes exhibited strong relevance to the occurrence of pSS, of which 32 genes differentially expressed in monocytes/macrophages. CTSS was found to be a significant risk protein with a p-value of 0.001 and an odds ratio of 1.384 (1.147–1.669), showing pronounced expression in monocytes/macrophages. Furthermore, monocytes/macrophages displayed heightened expression levels of MXD1, AMPD2, TNFSF10, FTL, UBXN11, CSF3R, and LILRA5. The analysis of intercellular signaling revealed increased signal intensity in both incoming and outgoing signals in monocytes/macrophages. The signaling interactions between monocytes/macrophages, B cells, and T cells exhibited varying degrees of deviation. ConclusionsThis study highlights the significant involvement of monocytes/macrophages in the pathogenesis of pSS, as evidenced by MR analysis and scRNA-seq analysis. This suggests monocytes/macrophages as a focal point for pathogenesis research and potential therapeutic targeting in pSS.

ARCHITECTURAL AND CIRCUIT MODEL OF NAVIGATION DEVICE FOR ULTRA-LIGHT LAUNCH VEHICLE

The paper considered the architectural and circuitry model of a navigation device for an ultra-lightweight launch vehicle designed to improve the accuracy and reliability of positioning and orientation in flight conditions. The study utilized modern approaches to the integration of global navigation satellite system (GNSS) and inertial navigation system (INS) to achieve high accuracy and stability of the system. The key components of the device including UM980 module, LSM6DSV32X inertial measurement module, STM32 F7 series microcontroller, E220-400T30S radio module, LMR50410XDBVR converter based power supply and RS422 interface were described in detail. The UM980 module, including SAW and LNA filter blocks, demonstrated the ability to efficiently process incoming RF signals. The NebulasIV main function block combined GNSS BB, GNSS RF, interfaces and power management unit (PMU) modules, supporting multiple communication interfaces such as UART, SPI and I²C, as well as RTK and PPS signals. The study analyzed the integration and accuracy aspects of the module in navigation systems. The signal processing scheme of the inertial navigation system module was also presented, including digital filters, free-fall and activity/inactivity detection functions, and interfaces for data communication. User bias functions and filter modes are considered to ensure the accuracy and stability of the navigation device. The work lays the groundwork for further improvements in navigation technology, especially in the context of increasing availability and accuracy for small scale space launches.

A Deep Learning-based Approach for Channel Estimation in Multi-access Multi-antenna Systems

This paper studies estimating the channel state information at the end of receiver (CSIR) for multiple transmitters communicating with only one receiver so that the latter can decode the incoming signal more efficiently. The transmitters and the receiver are all equipped with multi-antennas and using orthogonal space-time block codes (OSTBC). An algorithm is developed based on deep learning for estimating multi-user multiple-input multiple-output (MU-MIMO) channels. The algorithm could estimate the CSIR using a single pilot block. The proposed convolutional neural network (CNN) architecture designed for this task begins with an input layer that accepts grayscale images, followed by six convolutional blocks for feature extraction and processing. The network concludes with a fully connected layer to output the estimated channel information. It is trained using a regression loss function to map input images to accurate channel information accurately. The performance of the proposed method is compared with classical methods like least square and subspace-based methods, including Capon and rank revealing QR (RRQR) methods. CNN achieved better performance in comparison with the reference. Computer simulations are included to validate the proposed method.

Small-Size Eight-Element MIMO Metamaterial Antenna with High Isolation Using Modal Significance Method.

This article presents a symmetrical reduced-size eight-element MIMO antenna array with high electromagnetic isolation among radiators. The array utilizes easy-to-build techniques to cover the n77 and n78 new radio (NR) bands. It is based on an octagonal double-negative metamaterial split-ring resonator (SRR), which enables a size reduction of over 50% for the radiators compared to a conventional disc monopole antenna by increasing the slow-wave factor. Additionally, due to the extreme proximity between the radiating elements in the array, the modal significance (MS) method was employed to identify which propagation modes had the most impact on the electromagnetic coupling among elements. This approach aimed to mitigate their effect by using an electromagnetic barrier, thereby enhancing electromagnetic isolation. The electromagnetic barriers, implemented with strip lines, achieved isolation values exceeding 20 dB for adjacent elements (<0.023 λ) and approaching 40 dB for opposite ones (<0.23 λ) after analyzing the surface current distribution by the MS method. The elements are arranged in axial symmetry, forming an octagon with each antenna port located on a side. The array occupies an area of 0.32 λ2 at 3.5 GHz, significantly smaller than previously published works. It exhibits excellent performance for MIMO applications, demonstrating an envelope correlation coefficient (ECC) below 0.0001, a total active reflection coefficient (TARC) lower than -10 dB for various incoming signals with random phases, and a diversity gain (DG) close to 20 dB.

Possible roles of heteroreceptor complexes in excitotoxic processes

Excitotoxicity represents a neuropathological process, describing the toxic actions of excitatory neurotransmitters, where the excessive or prolonged activation of glutamate receptors triggers a cascade of events leading to neuronal injury or death. Under conditions of reduced energy availability and increased oxidative stress neurons become particularly vulnerable to excitotoxicity and a large body of available evidence indicates that excitotoxicity represents a central mechanism in the pathogenesis of acute and degenerative diseases of the central nervous system. Astrocytes represent key elements in the regulation of glutamate homeostasis by their opposing functions of glutamate uptake and release, and microglial cells play an important role in the response to damage. Depending on the phenotype they assume when activated, microglial cells can trigger immune defense or neuroprotective processes. To perform their functions both glial cell populations monitor the extracellular space through a panel of receptors. Furthermore, a variety of signaling pathways also contribute to the modulation of the glutamatergic transmission, acting on specific cell receptors expressed by neurons, astrocytes, and microglia. In the last decades, evidence has been provided that receptors of almost all families can establish structural receptor-receptor interactions, leading to the formation of heteroreceptor complexes at the cell membrane of neurons and glial cells. The cooperativity that emerges in the actions of ligands of the monomers forming these assemblies provides the cell decoding apparatus with flexible dynamics in terms of recognition and signal transduction and allows an integration of the incoming signals already at the membrane level. Available data on possible modulatory roles played by heteroreceptor complexes in excitotoxic processes will be here reviewed and discussed. From the pharmacological standpoint, these findings may offer possibilities to explore novel therapeutic strategies targeting receptor complexes to address disorders of the central nervous system associated with dysregulation of glutamatergic signaling.

Muscle spindles provide flexible sensory feedback for movement sequences.

Sensory feedback is essential for motor performance and must adapt to task demands. Muscle spindle afferents (MSAs) are a major primary source of feedback about movement, and their responses are readily modulated online by gain-controller fusimotor neurons and other mechanisms. They are therefore a powerful site for implementing flexible sensorimotor control. We recorded from MSAs innervating the jaw musculature during performance of a directed lick sequence task. Jaw MSAs encoded complex jaw-tongue kinematics. However, kinematic encoding alone accounted for less than half of MSA spiking variability. MSA representations of kinematics changed based on sequence progression (beginning, middle, or end of the sequence, or reward consumption), suggesting that MSAs are flexibly tuned across the task. Dynamic control of incoming feedback signals from MSAs may be a strategy for adaptable sensorimotor control during performance of complex behaviors.

Enhanced‐Resolution Learning‐Based Direction of Arrival Estimation by Programmable Metasurface

AbstractDue to its growing importance and wide range of applications, direction‐of‐arrival (DOA) estimation has become a major research topic, particularly in the field of communication systems. While traditional DOA estimation methods rely on antenna arrays and complex algorithms, recent progress achieved in the design and implementation of metasurfaces has proved their effectiveness as promising alternatives. This study presents a distinct approach for DOA estimation that combines the use of a programmable metasurface with deep learning. The programmable metasurface together with a radio‐frequency power detector placed at the focal point, acts as a parabolic reflector antenna with an adjustable pointing direction, which scans the azimuth plane in 5° increments to receive the power level of incoming signals. The collected data is then fed into a pre‐trained multilayer neural network to enable DOA estimation with a resolution of lower than 1° without requiring fine‐tuning of the scanning procedure. This approach ensures accurate and fast estimations, paving the way for advanced solutions in detection and localization for various applications.

An Anatomical and Physiological Basis for Flexible Coincidence Detection in the Auditory System.

Animals navigate the auditory world by recognizing complex sounds, from the rustle of a predator to the call of a potential mate. This ability depends in part on the octopus cells of the auditory brainstem, which respond to multiple frequencies that change over time, as occurs in natural stimuli. Unlike the average neuron, which integrates inputs over time on the order of tens of milliseconds, octopus cells must detect momentary coincidence of excitatory inputs from the cochlea during an ongoing sound on both the millisecond and submillisecond time scale. Here, we show that octopus cells receive inhibitory inputs on their dendrites that enhance opportunities for coincidence detection in the cell body, thereby allowing for responses both to rapid onsets at the beginning of a sound and to frequency modulations during the sound. This mechanism is crucial for the fundamental process of integrating the synchronized frequencies of natural auditory signals over time.

Molecular Classification of HNSCC Based on Inflammatory Response-Related Genes - Integrated Single-Cell and Bulk RNA-Seq Analysis.

Tumor cells, inflammatory cells, and chemical factors collaboratively orchestrate a sophisticated signaling network, culminating in the formation of the inflammatory tumor microenvironment (TME). The present study sought to explore the nature of the inflammatory response in HNSCC and to decipher its influence on immunotherapeutic. A thorough analysis was performed utilizing the TCGA cohort along with two GEO cohorts. Unsupervised clustering of 200 inflammatory response-related genes (IRGs) was applied using the k-means algorithm to explore the heterogeneity of HNSCC. Additionally, a prognostic signature based on IRGs genes was constructed using Lasso regression. Meanwhiles, the expression of IRGs were identified in tumors and paracancerous tissues at the single-cell level. The crosstalk between IRGs was explored using CellChat and the patterns of incoming and outgoing signals were identified. Finally, qPCR was used to verify the expression of hub genes. There were significant differences in immune-cell function and immune-cell infiltration among three inflammatory response clusters. Additionally, we also constructed a prognostic model which could predicted the responses of common chemotherapeutic drugs and immunotherapy. Furthermore, qPCR and sc-RNA seq corroborated that the expression profiles of the prognostic genes were largely in alignment with the findings from the bioinformatics analysis. Ultimately, the molecular docking demonstrated favorable binding affinities between the pivotal gene-SCC7 and four chemotherapeutic drugs. This research has uniquely shed light on the intricate connection between the inflammatory response profiles and the immune infiltration patterns in HNSCC.

Analysis of Comparability of PCV in Surveying-Grade GNSS Antenna – Topcon HIPER-VR Case Study

ABSTRACT It is well known that the phase center of a Global Navigation Satellite System (GNSS) antenna is not a stable point. For any given GNSS antenna, the phase center will change with the direction of the incoming signal from a satellite, as well as the frequency. Ignoring these phase center variations (PCVs) in GNSS data processing can lead to notable errors, especially in vertical position component determination. To avoid the problem, antenna PCV together with the phase center offset (PCO) information are recommended to be used in GNSS observation processing. We currently distinguish between individual and type-mean phase center correction (PCC) models. These models describe the variations in the phase center of the antenna as a function of the elevation angle and azimuth. In general, the primary difference between individual and type-mean models lies in their specificity. Individual models are highly precise but are valid only for a particular antenna model, while the type-mean models are more general and can be applied to a broad range of antennas of the same type, but may suffer from a lower level of precision. This paper aims to analyze the comparability of PCV in surveying-grade GNSS antennas. For the analyses, we propose to use an originally designed bench with precisely defined relative positions of the seven antenna mounting points. Preliminary studies have been performed using GPS observations on L1 and L2 frequencies recorded by seven Topcon HIPER-VR antennas. The results proved that the comparability of PCV for this antenna is high. The position error did not exceed 3 mm. It could be assumed that the type-mean PCC model could describe PCV all antennas of this type with good accuracy.

Microwave signal processing using an analog quantum reservoir computer

Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training takes place in the classical domain, avoiding the issue of barren plateaus in parameterized-circuit quantum neural networks. It is natural to consider using a quantum processor based on microwave superconducting circuits to classify microwave signals that are analog—continuous in time. However, while there have been theoretical proposals of analog QRC, to date QRC has been implemented using the circuit model—imposing a discretization of the incoming signal in time. In this paper we show how a quantum superconducting circuit comprising an oscillator coupled to a qubit can be used as an analog quantum reservoir for a variety of classification tasks, achieving high accuracy on all of them. Our work demonstrates processing of ultra-low-power microwave signals within our superconducting circuit, a step towards achieving a quantum sensing-computational advantage on impinging microwave signals.

Hybrid quantum key distribution: A novel approach for secure end-to-end communication in network infrastructure

The Quantum Key Distribution (QKD) network approach has emerged as a promising solution and has garnered significant interest across various domains. QKD networks can be broadly categorized into two main types: those based on measurement-device-dependent (MDD) protocols and those based on measurement-device-independent (MDI) protocols. A novel network scheme is proposed combining these two protocols to guarantee end-to-end secure communication. It comprises two interconnected networks — the first uses a measurement-device-dependent protocol to connect routers with nearby nodes, while the second employs a measurement-device-independent protocol to interconnect the routers. In the first network, the process begins with Alice (Router) generating two types of weak quantum signals — the first has intensity, frequency and polarization or phase randomly varied, while the second with different delay times. Alice randomly transmits either signal states carrying information or decoy states with varying properties to the receivers (Bobs) over the quantum channels. She also sends a synchronization signal along with the quantum signals. At the receiver end, the synchronization pulse timestamps the incoming quantum signals. Each Bob then randomly shifts the frequencies of the received signals and measures their phase/polarization of the first signal and measures the delay times of the second signal. The secret key is derived by combining the frequency, the delay time and the phase/polarization information of the quantum signals, resulting in longer key lengths. Encoding information across multiple degrees of freedom enables extended key generation for secure communication. However, between the routers, MDI-QKD protocols are used to securely share the keys. This hybrid approach aims to ensure secure end-to-end communication across the network.

Schizophrenia diagnosis using the GRU-layer's alpha-EEG rhythm's dependability

Verifying schizophrenia (SZ) can be assisted by deep learning techniques and patterns in brain activity observed in alpha-EEG recordings. The suggested research provides evidence of the reliability of alpha-EEG rhythm in a Gated-Recurrent-Unit-based deep-learning model for investigating SZ. This study suggests Rudiment Densely-Coupled Convolutional Gated Recurrent Unit (RDCGRU) for the various EEG-rhythm-based (gamma, beta, alpha, theta, and delta) diagnoses of SZ. The model includes multiple 1-D-Convolution (Con-1-D) folds with steps greater than 1, which enables the model to programmatically and effectively learn how to reduce the incoming signal. The Con-1-D layers and numerous Gated Recurrent Unit (GRU) layers comprise the Exponential-Linear-Unit activation function. This powerful activation function facilitates in-deep-network training and improves classification performance. The Densely-Coupled Convolutional Gated Recurrent Unit (DCGRU) layers enable RDCGRU to address the training accuracy loss brought on by vanishing or exploding gradients, and this might make it possible to develop intense, deep versions of RDCGRU for more complex problems. The sigmoid activation function is implemented in the digital (binary) classifier's output nodes. The RDCGRU deep learning model attained the most excellent accuracy, 88.88 %, with alpha-EEG rhythm. The research achievements: The RDCGRU deep learning model's GRU cells responded superiorly to the alpha-EEG rhythm in EEG-based verification of SZ.

Automatic tuning of radio stations based on listener’s preference using Software Defined Radio and MATLAB

This work introduces a real-time system to automate the selection of radio stations based on the listener’s preference (either speech/music) by analyzing the incoming audio signals using a speech/music classifier (SMC) using machine learning approaches. Radio Frequency data from different Frequency Modulated (FM) stations are directly read in MATLAB using the Communications Toolbox Support Package for Register Transfer Level-Software Defined Radio (RTL-SDR). Further, the work is divided into two phases. Initially, the efficiency of different state-of-the-art features for designing a speech/music classifier is studied on new speech/music corpora developed for Indian radio stations. Box plots and Region of Convergence (ROC) plots were used to study feature importance. The learning from the experiments was used to select optimum sets of features for efficient working of the real-time model. The models were tested for both offline and online test data. The best-performing features (Mel-Frequency Cepstral Coefficients (MFCC) + Variance Spectral Roll-off) vectors were then concatenated to obtain the best classification accuracy of 93.06% and 83.91% for offline and real-time data, respectively, using Gaussian Mixture Model (GMM) classifier. We also studied the efficiency of recently proposed Empirical Mode decomposition (EMD)-based statistical and Hilbert Spectrum-based features on both standard (Slaney, GTZAN, Musan dataset) and newly created datasets. We achieved an overall accuracy of 94.91%, 93.20%, 91.72%, and 95.40% for SS, GTZAN, MUSAN, and BITM datasets respectively. However, concatenating several features increased the latency of the algorithm. This work also proposes a new speech/music corpus based on recordings from Indian radio stations in the Hindi language.

Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations.

The auditory system comprises multiple subcortical brain structures that process and refine incoming acoustic signals along the primary auditory pathway. Due to technical limitations of imaging small structures deep inside the brain, most of our knowledge of the subcortical auditory system is based on research in animal models using invasive methodologies. Advances in ultrahigh-field functional magnetic resonance imaging (fMRI) acquisition have enabled novel noninvasive investigations of the human auditory subcortex, including fundamental features of auditory representation such as tonotopy and periodotopy. However, functional connectivity across subcortical networks is still underexplored in humans, with ongoing development of related methods. Traditionally, functional connectivity is estimated from fMRI data with full correlation matrices. However, partial correlations reveal the relationship between two regions after removing the effects of all other regions, reflecting more direct connectivity. Partial correlation analysis is particularly promising in the ascending auditory system, where sensory information is passed in an obligatory manner, from nucleus to nucleus up the primary auditory pathway, providing redundant but also increasingly abstract representations of auditory stimuli. While most existing methods for learning conditional dependency structures based on partial correlations assume independently and identically Gaussian distributed data, fMRI data exhibit significant deviations from Gaussianity as well as high-temporal autocorrelation. In this paper, we developed an autoregressive matrix-Gaussian copula graphical model (ARMGCGM) approach to estimate the partial correlations and thereby infer the functional connectivity patterns within the auditory system while appropriately accounting for autocorrelations between successive fMRI scans. Our results show strong positive partial correlations between successive structures in the primary auditory pathway on each side (left and right), including between auditory midbrain and thalamus, and between primary and associative auditory cortex. These results are highly stable when splitting the data in halves according to the acquisition schemes and computing partial correlations separately for each half of the data, as well as across cross-validation folds. In contrast, full correlation-based analysis identified a rich network of interconnectivity that was not specific to adjacent nodes along the pathway. Overall, our results demonstrate that unique functional connectivity patterns along the auditory pathway are recoverable using novel connectivity approaches and that our connectivity methods are reliable across multiple acquisitions.

Phase shifter intelligent reflective surface design for 2.4 GHz

In this study, an intelligent reflective surface (IRS) design consisting of a 3x4 patch surface that intentionally changes the phase angles of incoming signals for the 2.4 GHz operating frequency is presented. In the design of this IRS, FR-4 is chosen as the dielectric material, and copper is used to create both the reflecting elements and the ground plane. Phase shifting characteristic is achieved by using varactor diodes on the designed two-dimensional surface. In order to test the behaviour of the IRS, a horn antenna is also designed to be used as a source with the ANSYS HFSS (v21) program. The obtained results are presented in terms of return loss and gain values of the IRS. The results of the simulations reveal that the phase angles of the incoming signals can be adjusted as expected by changing the capacity values of the varactor diodes modelled by their equivalent circuits on the IRS.