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Abstract With the development of wireless communication technology, the number of devices accessing the communication network is increasing. This paper addresses critical issues such as low spectrum resource utilization and the energy constraints of devices. The investigation focuses on the system performance of the shared relay Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) network based on Simultaneous Wireless Information and Power Transfer (SWIPT) network model. Unlike the existing CR-NOMA model in which the secondary network user do not participate in the transmission of information from the primary network, we consider the secondary network near user as shared relay. The shared relay utilizes SWIPT technology to harvest energy using a nonlinear energy harvesting model. Additionally, the shared relay assists in transmitting information from the primary network base station to primary user, as well as from the secondary network base station to far user of the secondary network. Subsequently, we conduct a series of simulations to analyze the effects of Signal-to-Noise Ratio (SNR), power distribution factor, interference threshold, and time-switching (TS) factor on system performance. Furthermore, we compare and analyze the performance of our proposed network model against CR-NOMA network across three dimensions: outage probability, throughput, and energy efficiency. Our results demonstrate that the proposed network model exhibits superior outage performance and enhances user throughput compared to the CR-NOMA network. Additionally, it demonstrates improved energy efficiency compared to the shared relay CR-NOMA network, leading to an overall improvement in network performance.

Abstract In this work, we propose a multiple feedback-based successive interference cancellation (SIC) scheme for an ultra-dense Internet of Things (IoT) device network. Non-orthogonal multiple access (NOMA) enables massive connectivity with improved user fairness and spectral efficiency and is envisaged as a multiple access technique for IoT devices. NOMA simultaneously serves multiple users within a single resource block, leading to unbounded yet regulated multi-user interference. SIC is widely adopted in the NOMA system to detect users’ symbols. Nevertheless, multi-user interference and error propagation in the SIC layer are inherent challenges in NOMA. Recent studies have aimed to minimize interference and error propagation, imposing stringent conditions on the number of users and power allocation. Thus, this paper proposes novel multiple feedback-based SIC algorithms for the uplink multi-user NOMA scenarios that outperform the conventional SIC. Further, the proposed algorithm’s performance is analyzed under the practical case of imperfect channel state information at the receiver node to validate the robustness. The computational complexity of multiple feedback SIC is compared with the conventional SIC.

Abstract The quality score of a biometric sample is intended to predict the sample’s degree of utility for biometric recognition. Different authors proposed different definitions for utility. A harmonized definition of utility would be useful to facilitate the comparison of biometric sample quality assessment algorithms. In this article, we compare different definitions of utility and apply them to both face image and fingerprint image data sets containing multiple samples per biometric instance and covering a wide range of potential quality issues. The results differ only slightly. We show that discarding samples with low utility scores results in rapidly declining false non-match rates. The obtained utility scores can be used as target labels for training biometric sample quality assessment algorithms and as baseline when summarizing utility-prediction performance in a single plot or even in a single figure of merit.

Abstract The aim of the present paper is to optimize the values of different parameters related to the image denoising algorithm involving Caputo Fabrizio fractional integral operator of non-singular type with the Mittag-Leffler function in generalized form. The algorithm aims to find the coefficients of a kernel to remove the noise from images. The optimization of kernel coefficients are done on the basis of different numerical parameters like Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structure Similarity Index measure (SSIM) and Image Enhancement Factor (IEF). The performance of the proposed algorithm is investigated through above-mentioned numeric parameters and visual perception with the other prevailed algorithms. Experimental results demonstrate that the proposed optimized kernel based on generalized fractional operator performs favorably compared to state of the art methods. The uniqueness of the paper is to highlight the optimized values of performance parameters for different values of fractional order. The novelty of the presented work lies in the development of a kernel utilizing coefficients from a fractional integral operator, specifically involving the Mittag-Leffler function in a more generalized form.

Abstract Flexible devices, such as soft bioelectronics and stretchable supercapacitors, have their practical performance limited by electrodes which are desired to have high conductivity and capacitance, outstanding mechanical flexibility and strength, great electrochemical stability, and good biocompatibility. Here, we report a simple and efficient method to synthesize a nanostructured conductive hydrogel to meet such criteria. Specifically, templated by a hyperconnective nanofibrous network from aramid hydrogels, the conducting polymer, polypyrrole, assembles conformally onto nanofibers through in-situ polymerization, generating continuous nanostructured conductive pathways. The resulting conductive hydrogel shows superior conductivity (72 S cm−1) and fracture strength (27.2 MPa). Supercapacitor electrodes utilizing this hydrogel exhibit high specific capacitance (240 F g−1) and cyclic stability. Furthermore, bioelectrodes of patterned hydrogels provide favorable bioelectronic interfaces, allowing high-quality electrophysiological recording and stimulation in physiological environments. These high-performance electrodes are readily scalable to applications of energy and power systems, healthcare and medical technologies, smart textiles, and so forth.

Abstract Stretchable organic light-emitting diodes (SOLEDs) have been the challenging class of OLEDs as they have limited processability to fabricate a design that can withstand external deformation. Herein, we demonstrated the highly efficient top-emitting geometrical stretchable OLED (GSOLED) by incorporating the prestretched elastomer with optical adhesive film. The experimental and theoretical characterizations verified the enhancement of device efficiencies with the light extraction phenomenon brought by nanowavy corrugated structures. Furthermore, GSOLED shows stability in stretchable conditions and displays narrower emission spectrum with improved color purity. The full width at half maximum (FWHM) of 21 nm shows narrowband emission with a high current efficiency and EQE of 221 cd A− 1 and 39.50%. This work marks a significant step forward, providing unprecedented insights into the factors influencing device performance in current and future material systems for stretchable organic light-emitting diodes.

Abstract The clustering algorithm is an effective method for developing energy efficiency routing protocol for wireless sensor networks (WSNs). In clustered WSNs, cluster heads must handle high traffic, thus consuming more energy. Therefore, forming balanced clusters and selecting optimal cluster heads are significant challenges. The paper proposes a sector clustering algorithm based on K-means called KMSC. KMSC improves efficiency and balances the cluster size by employing symmetric dividing sectors in conjunction with K-means. For the selection of cluster heads (CHs), KMSC uses the residual energy and distance to calculate the weight of the node, then selects the node with the highest weight as CH. A hybrid single-hop and multi-hop communication is utilized to reduce long-distance transmissions. Furthermore, the impact of the number of sectors, the threshold for clustering, and the network size on the performance of KMSC has been explored. The simulation results show that KMSC outperforms EECPK-means, K-means, TSC, LSC, and SEECP in terms of FND, HND, and LND.

Abstract Unmanned Aerial Vehicle (UAV) swarms have emerged as a promising technology for various applications, such as delivery, surveillance, and infrastructure inspection. An additional feature of deploying large UAV swarms is their use in mobile offloading networking. At the same time, this implies a key challenge in the efficient management of the computational and networking requirements for these offloading processes. This paper aims to fill this gap through a systematic literature review (SLR) analysing the research on distributed task offloading in UAV swarms. We conducted a systematic search of major scientific databases to identify relevant literature published between 2019 and 2023. A total of 63 papers were selected through a multistage screening process and their key contributions. This SLR aims to provide the current state of research on UAV swarm task offloading, including considerations for computation offloading, the role of UAV swarms, different aspects of UAV swarms, the number of UAVs in swarms impacting performance, and open issues. Our review also highlights UAV swarm offloading in various domains and discusses the challenges and limitations that must be addressed to ensure the widespread adoption of this technology. We outline the future research directions and potential applications of UAV swarm offloading, including its integration with other technologies.

Abstract Energy efficiency and physical-layer security are crucial considerations in the advancement of mobile edge computing systems. This paper addresses the trade-off between secure-reliability and energy consumption in finite blocklength (FBL) communications. Specifically, we examine a three-node scenario involving a user, a legitimate edge computing server, and an eavesdropper, where the user offloads sensitive data to the edge server while facing potential eavesdropping threats. We propose an optimization framework aimed at minimizing energy consumption while ensuring secure-reliability by decomposing the problem into manageable subproblems. By demonstrating the convexity of the objective function concerning the variables, we establish the existence of an optimal parameter selection for the problem. This implies that practical optimization of parameters can significantly enhance system performance. Our numerical results demonstrate that the application of FBL regime and retransmission mechanism can effectively reduce the energy consumption of the system while ensuring secure-reliability. For the quantitative analyses, the retransmission mechanism is 33.1% better than no retransmission, and the FBL regime is 13.1% better than infinite blocklength (IBL) coding.

Abstract While the understanding of altermagnetism is still at a very early stage, it is expected to play a role in various fields of condensed matter research, for example spintronics, caloritronics and superconductivity. In the field of optical magnetism, it is still unclear to which extent altermagnets as a class can exhibit a distinct behavior. Here we choose RuO2, a prototype metallic altermagnet with a giant spin splitting, and CoF2, an experimentally known insulating altermagnet, to study the light-induced magnetism in rutile altermagnets from first-principles. We demonstrate that in the non-relativisic limit the allowed sublattice-resolved orbital response exhibits symmetries, imposed by altermagnetism, which lead to a drastic canting of light-induced moments. On the other hand, we find that inclusion of spin-orbit interaction enhances the overall effect drastically, introduces a significant anisotropy with respect to the light polarization and strongly suppresses the canting of induced moments. Remarkably, we observe that the moments induced by linearly-polarized laser pulses in light altermagnets can even exceed in magnitude those predicted for heavy ferromagnets exposed to circularly polarized light. By resorting to microscopic tools we interpret our results in terms of the altermagnetic spin splittings and of their reciprocal space distribution. Based on our findings, we speculate that optical excitations may provide a unique tool to switch and probe the magnetic state of rutile altermagnets.

Abstract Magnons and phonons are engineered in artificial lattices with tunable modes and band dispersions. Recent advance in magnon-phonon coupling shined a light on combining magnonic and phononic crystals as hybrid magnon-phonon crystals, benefit from the tunable magnon-phonon coupling, the time-reversal symmetry breaking of magnons, and the long lifetime of phonons. This perspective summarizes lattice-based mutual control of magnons and phonons, and proposes the opportunities provided by the hybrid magnon-phonon crystals.

Abstract Contactless hand biometrics has emerged as an alternative to traditional biometric characteristics, e.g., fingerprint or face, as it possesses distinctive properties that are of interest in forensic investigations. As a result, several hand-based recognition techniques have been proposed with the aim of identifying both wanted criminals and missing victims. The great success of deep neural networks and their application in a variety of computer vision and pattern recognition tasks has led to hand-based algorithms achieving high identification performance on controlled images with few variations in, e.g., background context and hand gestures. This article provides a comprehensive review of the scientific literature focused on contactless hand biometrics together with an in-depth analysis of the identification performance of freely available deep learning-based hand recognition systems under various scenarios. Based on the performance benchmark, the relevant technical considerations and trade-offs of state-of-the-art methods are discussed, as well as further topics related to this research field.

Abstract In recent years, the estimation of biometric parameters from facial visuals, including images and videos, has emerged as a prominent area of research. However, the robustness of deep learning-based models is challenged, particularly in the presence of changing illumination conditions. To overcome these limitations and unlock new opportunities, thermal imagery has arisen as a viable alternative. Nevertheless, the limited availability of datasets containing thermal data and the small amount of annotations on them limits the exploration of this spectrum. Motivated by this gap, this paper introduces the Label-EURECOM Visible and Thermal (LVT) Face Dataset for face biometrics. This pioneering dataset includes paired visible and thermal images and videos from 52 subjects along with metadata of 22 soft biometrics and health parameters. Due to the reduced number of existing datasets in this domain, the LVT Face Dataset aims to facilitate further research and advancements in the utilization of thermal imagery for diverse eHealth applications and soft biometric estimation. Moreover, we present the first comparative study between visible and thermal spectra as input images for soft biometric estimation, namely gender age and weight, from face images on our collected dataset.

Abstract A large number of images are available on the Internet with the growth of social networking services, and many of them are face photos or contain faces. It is necessary to protect the privacy of face images to prevent their malicious use by face image de-identification techniques that make face recognition difficult, which prevent the collection of specific face images using face recognition. In this paper, we propose a face image de-identification method that generates a de-identified image from an input face image by embedding facial features extracted from that of another person into the input face image. We develop the novel framework for embedding facial features into a face image and loss functions based on images and features to de-identify a face image preserving its appearance. Through a set of experiments using public face image datasets, we demonstrate that the proposed method exhibits higher de-identification performance against unknown face recognition models than conventional methods while preserving the appearance of the input face images.

Abstract Materials that provide dynamically tunable infrared (IR) response are important for many applications, including active camouflage and thermal management. However, current IR-tunable systems often exhibit limitations in mechanical properties or practicality of their tuning modalities, or require complex and costly fabrication methods. An additional challenge relates to providing compatibility between different spectral channels, such as allowing an object to be reversibly concealed in the IR without making it appear in the visible range. Here, we demonstrate that conducting polymer-cellulose papers, fabricated through a simple and cheap approach, can overcome such challenges. The papers exhibit IR properties that can be electrochemically tuned with large modulation (absolute emissivity modulation of 0.4) while maintaining largely constant response in the visible range. Owing to high ionic and electrical conductivity, the tuning of the top surface can be performed electrochemically from the other side of the paper even at tens of micrometer thicknesses, removing the need for overlaying electrode and electrolyte in the optical beam path. These features enabled a series of electrically tunable IR devices, where we focus on demonstrating dynamic radiative coolers, thermal camouflage, anti-counterfeiting tags, and grayscale IR displays. The conducting polymer-cellulose papers are sustainable, cheap, flexible and mechanically robust, providing a versatile materials platform for active and adaptive IR optoelectronic devices.

Margin tissue detection during intraoperative laparoscopic liver resection (LLR) is required to prevent tumor recurrence and reduce the likelihood of further surgery. This study proposes an electrical impedance spectroscopy (EIS) method for margin tissue detection in LLR to determine the boundary interface of normal and cancerous tissue. The proposed method of this study has three objectives: (1) designing the electrode array configuration to collect multiple EIS impedance measurements, (2) implementing the Feedforward Neural Network (FNN) to classify the orientation of margin tissue relative to the electrode array by using time-difference impedance indexes, and (4) governing the inflection point method based on impedance indexes to detect the margin tissue location. The proposed method is evaluated by a 3D numerical simulation of liver tissue composed of cancerous lumps with Iac = 1 mA alternating injection current at frequencies: lf = 1 kHz and hf = 100 kHz. The electrode array is composed of 16 electrode pairs each for injection current and voltage measurements. The variation of margin tissue orientation relative to the electrode array direction was considered to occur in unidirectional, perpendicular, and diagonal direction with noise variations (Signal-to-Noise-Ratio: 50 to 90 dB). The FNN trained on 2,400 data points achieves True Positive Rate (TPR) value as 90.2%, 99.4%, and 96.6% for diagonal, perpendicular, and unidirectional respectively in margin tissue orientation classification, while the inflection point method detects margin tissue location with 75% location at the unidirectional orientation (y-axis).

Criminal activities frequently involve carryable weapons such as handguns, shotguns, and rifle classes. Frequently, the targets of these weapons that are captured are concealed from plain sight by the people of the crowd. The detection process for these weapons can be assisted by using deep learning. In this case, we intend to identify the model of the firearm that was detected. This research aims to apply one of the deep learning concepts, namely You Only Look Once (YOLO). The authors use versions of YOLOv3-tiny and Yolov4-tiny for the detection and classification of types of weapons, which are one of the fastest and most accurate methods of object detection, outperforming other detection algorithms. However, both require heavy computer architecture. Therefore, YOLOv3-tiny and YOLOv4-tiny, lighter versions of YOLOv3, can be solutions for smaller architectures. YOLOv3-tiny and YOLOv4-tiny have higher FPS, which is supposed to yield faster performance. Since YOLOv3-tiny and YOLOv4-tiny are modified versions of YOLOv3, the accuracy is improved, and YOLOv3 is already outperforming Faster Single Shot Detector (SSD) and Faster Region with Convolutional Neural Network (R-CNN). The authors employ YOLOv3-tiny and YOLOv4-tiny due to the fact that the Frame Per Second (FPS) and Mean Average Precision (mAP) performance of both approaches are superior in object detection. The study found that YOLOv3-tiny had a high FPS and low mAP performance: an average Intersection over Union (IoU) score of 71.54%, an accuracy of 90%, a recall score of 78%, an F1 score of 84%, and an mAP of 86.7%. While YOLOv4-tiny has low FPS and high mAP: an average IoU score of 73.19%, an accuracy of 90%, a recall score of 84%, an F1 score of 87%, and an mAP of 90.7%.

Ground penetrating radar (GPR) is a non-destructive tool for exploring an object buried underground. Currently, GPR is also considered for reinforced concrete inspection. However, the image produced by GPR can not be easily interpreted. Besides, the large observation of building concrete inspection also motivates the researchers to fastening and easing radar image interpretation. Thus, this research proposes a new method to translate GPR scattering data image to its internal structure visualization. The proposed employs a convolutional autoencoder model using amplitude and phase radar data as input of the algorithm. As evaluation, in this stage, we perform numerical analysis by using finite-difference time-domain-based synthetic data that considers three cases: concrete with rebar, concrete with crack, and concrete with rebar and crack. All of those cases are simulated with randomized dimensions and positions that is possible in the real applications. Compared with the baseline method, our method shows superiority, especially in the semantic segmentation perspective. The parameter size of the proposed model is also much smaller, around one-third of the previous method. Therefore, the method is feasible enough to be implemented in real applications addressing an automatic internal structure reinforced concrete visulaization

Humanoid robots closely resemble humans and engage in various human-like activities while responding to queries from their users, facilitating two-way communication between humans and robots. This bidirectional interaction is enabled through the integration of speech-to-text and text-to-speech systems within the robot. However, research on two-way communication systems for humanoid robots utilizing speech-to-text and text-to-speech technologies has predominantly focused on the English language. This study aims to develop a real-time two-way communication system between humans and a robot, with data collected from ten respondents, including eight males and two females. The sentences used adhere to the standard rules of the Indonesian language. The speech-to-text system employs a deep bidirectional long short-term memory algorithm, coupled with feature extraction via the Mel frequency cepstral coefficients, to convert spoken language into text. Conversely, the text-to-speech system utilizes the Python pyttsx3 module to translate text into spoken responses delivered by the robot. The results indicate that the speech-to-text model achieves a high level of accuracy under quiet-room conditions, with noise levels ranging from 57.5 to 60 dB, boasting an average word error rate (WER) of 24.99% and 25.31% for speakers within and outside the dataset, respectively. In settings with engine noise and crowds, where noise levels range from 62.4 to 86 dB, the measured WER is 36.36% and 36.96% for speakers within and outside the dataset, respectively. This study demonstrates the feasibility of implementing a two-way communication system between humans and a robot, enabling the robot to respond to various vocal inputs effectively.

Many studies use radars for Human Activity Recognition (HAR), and numerous techniques for preprocessing FMCW radar data have been explored to improve HAR performances. Our approach employs 1-D radar to classify four human activities, i.e., walking, standing, crouching, and sitting. We use Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT) with Kaiser window to generate range-time and Doppler-time data from inphase and quadrature radar signal. The choice of windowing parameters, i.e., window size and window shape represented by the beta parameter in Kaiser window, is considered to have significant impacts on the performances of deep learning LSTM models, including the F1-score. However, our study in this paper, including statistical analysis using t-tests, shows otherwise. Our results consistently support the null hypothesis, which mean that variations in window size and window shape do not significantly affect the F1-score. In essence, our findings underscore the robustness of our preprocessing methodology, emphasizing the stability and reliability of the selected configurations. This research provides valuable insights into the preprocessing techniques for radar data in the context of human activity recognition, enhancing the consistency and credibility of deep learning models in this domain.

Agriculture plays a very important role in the lives of Indonesian people. With technological advances and the increasing limitation of agricultural land, the patterns of matching crops in societies have changed. Innovations have been implemented, one of which is the use of technology such as hydroponic systems. One of the main factors that influence the success of hydroponic methods is temperature and humidity. The research aims to design and develop an autonomic control system that uses Fuzzy Logic to regulate the temperature and moisture of hydroponic plants, as well as to design the control of the nutrition and pH supply of hydrogen plants. Hydroponics plant control systems are implemented using microcontrollers and DHT22, TDS sensors, and pH sensors. In addition, an Android-based interface has been developed to monitor and control the system remotely via an internet connection. In this study the accuracy of the TDS sensor is 96.5%, the pH sensor was 98.19%, and the precision of the Fuzzy logic system at temperature and humidity is 100%

The implementation of object detection for autonomous vehicles is essential as it is necessary to identify common object on the street so proper response could be designed. While single stage object may be smaller in computations, two-stage object detection is preferred due to the ability to localize the object. In this paper, we propose to use Faster R-CNN with VGG-16 backbone for detections of object on the street. We evaluate the method with open image subset by selecting objects that are common on street. We explore several hyper-parameters setup such as learning rate and the number of ROI regions to find the optimum set-up. We found that the use of learning rate 10-6 with Adam optimizer to be the optimum value for this task. We also found that increasing the number of ROI may benefit the performance. This shows that there is potential for getting a higher mAP with increase the amount of RoI.

Screen-printed carbon electrodes (SPCEs) modified with graphene nanoplatelets (GNPs) and zinc oxide (ZnO) are widely used in electrochemical sensors due to their enhanced electrochemical properties and biocompatibility. Screen-printed carbon electrodes modified with Graphene nanoplatelets (GNPs) /Zinc oxide (ZnO) nanocomposite are described. Thus, in this study, GNPs/ZnO nanocomposite was synthesized, characterized, and applied to an electrochemical sensor. The formation of GNPs/ZnO nanocomposite was characterized by UV-Vis spectroscopy and scanning electron microscopy. Moreover, SPCE-GNPs/ZnO nanocomposite were characterized using cyclic voltammetry to optimize the concentration of nanocomposite. Then, the analytical performance of the sensor was studied by measuring methylparaben as an organic compound using differential pulse voltammetry (DPV) as a preliminary study before using it for biosensing. The result showed a significant improvement in electrocatalytic activity and reproducibility. The ratio of GNPs/ZnO nanocomposite with a concentration of 1 mg/mL produced the highest current response. Moreover, the detection of methylparaben showed high sensitivity with a limit of detection (LOD) around 9.7 μM, indicating high selectivity and good reproducibility of SPCE-GNPs/ZnO. Hence, the proposed sensor of SPCE-GNPs/ZnO displayed good performance, sensitivity, and reproducibility.

Industrial development nowadays affects the increase in types of packaging waste which causes the accumulation of waste that has the potential to damage the environment. This research uses an Internet of Things for Automatic Waste Volume Monitoring System, so that waste management in an area can be improved. The purpose of this research is to make it easier for trashman to monitor the volume of the garbage collector on the notification feature. The research method used is the Rapid Application Development methodology with the Requirement Planning stage to analyze and identify the purpose of the system, then design the tools and system and create tools and system. Testing is used to evaluate the results of the tools and system. The result of the research is a prototype of an Internet of Things-based for Automatic Waste Volume Monitoring System tool equipped with a website-based monitoring system and for each user. Apart from that, the system which is equipped with a sensor that can detect the volume of the garbage collector is also equipped with an automatic opening and closing sensor to maintain the health of its users to provide answers to the problem of waste, especially in urban areas. Keywords: Internet of Things, Monitoring System, Waste, Waste Volume.

This study examines and compares three Autonomous Underwater Vehicles (AUV) steering control techniques utilizing the following three control algorithms: Proportional-Integral-Derivative (PID), Fractional Order PID (FOIPD), and Neural Network-PID (NN-PID). The objective of this investigation is to gain a comprehensive understanding of each controller's response in terms of step input scenarios, trajectory changes, and when encountering disturbances. The response analysis will evaluate the strengths and weaknesses of the controller by examining parameters such as Rise Time, Settling Time, Settling Min, Settling Max, Overshoot, Peak, and Peak Time for each controller response. To determine the accuracy performance of each controller strategy, the root mean square error (RMSE) technique will be applied, allowing users to confidently select the most suitable controller option. FOPID displays the best settling time of 3.2218 seconds, and PID stands out in rise time, achieving 0.4725 seconds. The results indicate that NN-PID is the top performer as it reduces overshoot to 0.3022%. Among the three controllers that were tested, FOPID had the smallest RMSE value, while the NN-PID control's slower response and larger error resulted in a smaller overshoot than PID and FOPID. This factor is due to the online learning process on NN-PID, which requires time. Based on the simulation results, FOPID outperforms PID in settling time and produces the smallest error due to the inclusion of parameters λ and μ, leading to improved control performance.

Abstract Reconfigurable intelligent surface (RIS) is a groundbreaking technology that has a significant potential for sixth generation (6G) networks. Its unique capability to control wireless environments makes it an attractive option. However, the spatial diversity increased by assisting users with all deployed RISs, this investigation has two drawbacks the high complexity design, and the received signals by the far RISs are severely attenuated. Therefore, we propose a RIS selection strategy to select the proper RISs as a pre-stage before the joint beamforming between the base station (BS) and RISs to reduce the high complexity of joint beamforming optimization. Furthermore, the joint active and passive beamforming problem based on the selection is formulated. Hence, achieving spatial diversity by examining cooperation between passive beamforming of multi-hop RIS, leads to a challenging problem. To tackle this issue, we design an algorithm for the RIS selection scheme. Also, to relax the non-convexity of the proposed problem, we decouple the problem into solvable subproblems by utilizing the fractional programming (FP) and quadratic transform (QT) optimization methods. Simulation results have demonstrated through different user locations the effectiveness of the selection strategy in performance enhancement by 30% in the sum rate, besides an obvious reduction in the complexity cost than other techniques.

Abstract The expansion of wireless communication introduces security vulnerabilities, emphasizing the essential need for secure systems that prioritize confidentiality, integrity, and other key aspects of data protection. Since computational security acknowledges the possibility of breaches when adequate computational resources are available, that is why information-theoretic security is being explored, which suggests the existence of unbreakable cryptographic systems even in the presence of limitless processing power. Secret key exchange has traditionally relied on RSA or DH protocols, but researchers are now exploring innovative approaches for sharing secret keys among wireless network devices, leveraging physical or link layer characteristics. This research seeks to revolutionize secure multi-party key acquisition in wireless networks, capitalizing on information-theoretic security and collaborative data extraction. The proposed secret key generation framework comprehensively organizes and explains the information-theoretic aspects of secret key generation within the lower layers of wireless networks, especially the link layer, proposes a novel information-theoretic SKG framework for the dynamic acquisition of symmetric secret keys, and responds to contemporary information security challenges by relying on information-theory principles rather than vulnerable mathematical relationships in the post-quantum period. A new cryptographic key can be generated using a straightforward method, and when it is combined (XORed) with the previous key, it creates a continuously changing secret for encryption and decryption. This approach enhances security because, as attackers attempt to break the encryption, the system generates fresh, dynamic keys, making it progressively more challenging for them to succeed. The research work in question integrates key renewal, or how often keys are updated (dynamic keys), with a security off-period. It introduces a framework for determining the best key refresh rate based on the anticipated rate at which keys might be compromised. Furthermore, the proposed framework is scalable, allowing new nodes to quickly join the existing network. The system was tested with multiple nodes equipped with IEEE 802.11 interfaces, which were set in monitor mode to capture frames at the link layer. Nodes map their on-time frames onto their Bloom filters. Nodes exchange these Bloom filters in a feedback mechanism. Nodes extract those frames from their .pcap files, which are present in all Bloom filters; these are common frames among all nodes. These frames are used to form a shared secret that is passed to HMAC Key Derivation Function by each node to acquire the final encryption key of the required length. The validation of this encryption key is performed using a simple challenge-response protocol; upon successful validation, encrypted communication begins. Otherwise, the key generation process is restarted.

Abstract As image–text matching (a critical task in the field of computer vision) links cross-modal data, it has captured extensive attention. Most of the existing methods intended for matching images and texts explore the local similarity levels between images and sentences to align images with texts. Even though this fine-grained approach has remarkable gains, how to further mine the deep semantics between data pairs and focus on the essential semantics in data remains to be quested. In this work, a new semantic filtering and adaptive approach (FAAR) was proposed to ease the above problem. To be specific, the filtered attention (FA) module selectively focuses on typical alignments with the interference of meaningless comparisons eliminated. Next, the adaptive regulator (AR) further adjusts the attention weights of key segments for filtered regions and words. The superiority of our proposed method was validated by a number of qualitative experiments and analyses on the Flickr30K and MSCOCO data sets.

Abstract In this paper, we undertake a novel two-pronged investigation into the human recognition of deepfake speech, addressing critical gaps in existing research. First, we pioneer an evaluation of the impact of prior information on deepfake recognition, setting our work apart by simulating real-world attack scenarios where individuals are not informed in advance of deepfake exposure. This approach simulates the unpredictability of real-world deepfake attacks, providing unprecedented insights into human vulnerability under realistic conditions. Second, we introduce a novel metric to evaluate the quality of deepfake audio. This metric facilitates a deeper exploration into how the quality of deepfake speech influences human detection accuracy. By examining both the effect of prior knowledge about deepfakes and the role of deepfake speech quality, our research reveals the importance of these factors, contributes to understanding human vulnerability to deepfakes, and suggests measures to enhance human detection skills.

Abstract Twenty-four hours continuous intraocular pressure (IOP) monitoring is beneficial for glaucoma care. Contact lens sensors using LC technology can achieve non-invasive continuous IOP measuring around the clock and are relatively simple in principle and structure, thus dominating the mainstream. Among them, stretchable inductive LC contact lens sensors constructed with liquid metal have advantages in signal quality as well as wearing comfort, but currently its sensitivity is slightly insufficient. Here, we propose an LC sensor that constructed with liquid metal to form the stretchable inductance and capacitance further. The capacitive plate and inductive coil can response to IOP changes simultaneously, thus able to enhance the sensitivity in principle. We modeled the sensing mechanism and conducted design, fabrication, as well as various tests. The device exhibits good characteristics, including reliability, good signal quality, etc. Especially, it has a threefold increase in sensitivity, exceeding the current state-of-the-art contact lens sensors.

Abstract This paper proposes an all-sky star map identification algorithm that can simultaneously achieve high identification probability, low algorithm complexity, and small databases for well photometric and intrinsic parameters-calibrated star sensors. The proposed algorithm includes three main steps. First, a binary radial pattern table is constructed offline. Then, the maximum value matching of the radial pattern is performed between the star spots and the guide stars, and the star pairs (i.e., the minimum star map) after radial pattern matching undergo a coarse matching through angular distance cross-validation. Finally, a reference star map is designed based on the identified star pairs, and the matching of all the star spots in the field of view is realized. Simulation and analysis results show that the database required by the proposed algorithm for 5,000 guide stars is not larger than 200 KB. Also, when false and missing star spots account for 50% of all guide stars and the star spot extraction error is 0.5 pixel (the corresponding pointing error is 26″), the average star map identification time of the proposed algorithm is less than 2 ms, and its identification probability is higher than 98%. The results demonstrate that the proposed algorithm performs better than similar algorithms.

Abstract With the growth of additive manufacturing (AM), there has been increasing demand for fabricating conformal electronics that directly integrate with larger components to enable unique functionality. However, fabrication of conformal electronics is challenging because devices must merge with host substrates regardless of curvilinearity, topography, or substrate material. In this work, we employ aerosol jet (AJ) printing, an AM method for jet printing electronics using ink-based materials, and a custom-made lathe mechanism for mounting flexible substrates and 3D objects on a rotating axis. Using this method of lathe-based AJ printing, conformal electronics are printed around the circumference of rotational bodies with 3D curvilinear surfaces through cylindrical-coordinate motion. We characterize the diverse capabilities of lathe AJ (LAJ) printing and demonstrate flexible conformal electronics including multilayer carbon nanotube transistors. Lastly, a graphene sensor is conformally printed on an inflated catheter balloon for temperature and inflation monitoring, thus highlighting the versatilities of LAJ printing.

Insurance fraud has been a constant presence in the realm of insurance. However, as strategies and methods for committing insurance fraud have evolved, the frequency and volume of such fraudulent activities have also increased. An example of this is vehicle insurance fraud, which involves collaborating to fabricate false or exaggerated claims related to property damage or personal injuries resulting from an accident. Machine learning techniques seems to be more beneficial and great way to address the fraud in the insurance industry. This paper comprehensively examines existing research through a systematic literature review. This review aims to identify previously attempted approaches and evaluate which machine learning algorithm is best suited for this specific problem. This paper proposes a methodology for identifying fraudulent insurance claims. This approach can significantly improve efficiency and cost savings for insurance companies in handling such cases. The most popular traditional machine learning algorithms used to identify insurance fraud in the auto industry were found to be support vector machine, logistic regression, and random forest.

Saat ini pemerintah terus mendorong masyarakat untuk memanfaatkan EBT di Indonesia untuk mencapai target bauran sebanyak 23 % di tahun 2025. Pada penelitian ini dilakukan perancangan PLTS atap on-grid pada salah satu industri yang bergerak di bidang pengolahan bahan pangan hewani. PLTS digunakan untuk melayani beban penerangan dengan standar kelayakan mengikuti panduan EBTKE ESDM (SNI 8395:2017) dan sistem instalasi pemasangan berdasarkan PUIL 2011. Berdasarkan hasil perancangan digunakanlah modul surya jenis monocrystalline 410 Wp sebanyak 108 unit dan satu unit inverter 40 kW yang menghasilkan energi sebesar 63.032,58 kWh/tahun dan pengurangan emisi gas karbon dioksida (CO2) sebanyak 78.790,725 ton/tahun. Hasil kelayakan ekonomi dengan investasi awal Rp. 548.890.000, menghasilkan NPV Rp. 72.019.012, BCR 1,11, dan payback period pada tahun ke-18. Hasil tersebut menunjukkan bahwa PLTS dapat melayani beban penerangan dan melakukan penghematan energi listrik sebesar penggunaan beban penerangan harian di industri.

Robotika telah menjadi kebutuhan primer di bidang industri. Perkembangannya sangat cepat, bahkan saat ini fokus utama di bidang teknologi merupakan bidang robotika. Dalam pengembangannya, robot membutuhkan sebuah kontrol agar mampu dimanfaatkan dengan baik yang disebut sebagai sistem kendali. Penelitian ini bertujuan untuk membuktikan implementasi sistem kendali fuzzy dengan metode citra digital. Aplikasi yang dimanfaatkan pada penelitian ini adalah software Webots dengan robot e-puck, sehingga penelitian ini berfokus dengan simulasi. Sistem kendali fuzzy dengan metode citra digital terbukti mampu digunakan sebagai kontrol robot secara efisien pada dua skema jalur, A dan B. Hasil penelitian menunjukkan robot berhasil melewati kedua jalur dengan modus error 0. Selain itu, robot menghasilkan rata-rata nilai error sebesar -11,54 piksel dan -3,11 piksel pada masing-masing jalur. Hal ini menunjukkan error yang relatif kecil dan performa robot sangat baik.

Perencanaan fire alarm dan pemadam kebakaran pada Gedung Dekanat 7 lantai Universitas Tidar Mertoyudan dilakukan karena terdapat risiko kebakaran yang upaya penanggulangannya sulit dilakukan. Dalam menyelesaikan perencanaan ini memerlukan beberapa tahapan yang mencakup studi literatur, pengumpulan data dan pengolahan data. Berdasarkan hasil perhitungan yang telah dilakukan dapat disimpulkan bahwa sesuai SNI 03-3985-2000 membutuhkan detektor asap 82 unit, sesuai SNI 03-3989-2000 membutuhkan sprinkler 129 unit, volume air yang dibutuhkan 232 m3, Ground Water Tank (GWT) berkapasitas 300 m3, daya pompa yang dibutuhkan yaitu daya pompa elektrik 36 kW, pompa jockey 3,6 kW, dan pompa diesel 140 HP. Perhitungan perencanaan fire alarm dan pemadam kebakaran ini telah sesuai dengan peraturan dan standar diharapkan dapat meningkatkan keselamatan penghuni serta melindungi aset-aset berharga.

Pertumbuhan konsumsi energi listrik mengakibatkan beban pada transformator mengalami peningkatan dan memungkinkan terjadinya kelebihan beban, ketidakseimbangan beban serta potensi rugi daya. Hal ini bisa mengakibatkan kerusakan pada trafo akibat panas yang berlebih, menimbulkan arus netral, mengurangi umur serta keandalan trafo. Agar transformator tetap berfungsi dengan baik, PLN menetapkan standar beban transformator distribusi tidak boleh melebihi 80%. Tujuan penelitian ini untuk mengukur pembebanan transformator distribusi TBL05-34 200 kVA di Ronggowarsito dan menganalisis karakteristik ketidakseimbangan beban yang menimbulkan potensi kerugian daya. Penelitian dimulai dengan mengumpulkan data arus, tegangan, resistansi netral, serta fluktuasi beban subjek trafo di PT. PLN UP3 Semarang. Selanjutnya dilakukan perhitungan pembebanan transformator, ketidakseimbangan beban, dan kerugian daya pada transformator TBL05-34. Hasilnya menunjukkan ketidakseimbangan beban 7,22% saat siang dan 5,59% saat malam. Hal ini masih dikategorikan baik sesuai standar IEEE std 446-1995 berkisar 5% - 20%. Kerugian daya disebabkan arus mengalir melalui netral transformator saat siang 0,877 kW dengan 0,5%, sedangkan malam hari 0,564 kW dengan 0,3%. Pembebanan transformator saat siang masih normal 56,68%, sedangkan malam hari 81,87% merupakan posisi siaga.

Permasalahan pada saluran udara tegangan tinggi (SUTT) 150 kV adalah peningkatan arus yang menyebabkan jatuh tegangan dan rugi-rugi daya pada saluran. Solusinya adalah melakukan evaluasi pada saluran transmisi berdasarkan SPLN No. 72 tahun 1987 dengan batas toleransi tegangan +5% dan -10%. Untuk itu diperlukan perhitungan untuk menentukan seberapa besar jatuh tegangan dan kerugian daya yang terjadi. Penelitian ini membahas tentang jatuh tegangan dan rugi daya pada SUTT 150 kV. Model ditentukan sebagai single line diagram SUTT 150 kV dengan parameter yang ditetapkan adalah: tegangan sistem, arus, spesifikasi konduktor dan panjang saluran. Simulasi pengolahan data dari perhitungan menggunakan ETAP. Obyek penelitian adalah SUTT 150 kV Pekalongan-Comal. Hasil penelitian menunjukkan bahwa ETAP mampu digunakan untuk menyimulasikan rugi-rugi daya dan jatuh tegangan pada SUTT 150 KV. Dari hasil penelitian ini didapat rugi-rugi daya pada perhitungan manual sebesar 744,6 kW, sementara itu dari hasil simulasi ETAP 19.0.1 sebesar 644,34 kW. Jatuh tegangan pada perhitungan manual sebesar 1,04 % dan hasil simulasi sebesar 1,64 %. Dalam kasus ini jatuh tegangan masih dalam ambang batas SPLN yaitu kurang dari 10%.

AC merupakan alat yang relatif boros energi listrik. Pemborosan penggunaan daya listrik karena penggunaan AC salah satunya terjadi di ruang Tata Usaha UPT BLK Kulon Progo. Sering kali ditemui AC menyala saat kondisi ruangan tidak ada orang. Berdasarkan masalah tersebut perlu dibuat alat yang dapat mendeteksi ada atau tidak orang di dalam ruangan kemudian menyalakan dan mematikan AC secara otomatis. Alat juga dibuat dapat menyalakan atau mematikan AC dari jarak jauh menggunakan teknologi berbasis IoT. Metode yang digunakan pada penelitian ini adalah analisis kebutuhan bahan, perancangan hardware dan software, dan tahap pengujian alat. Rancang bangun hardware menggunakan ESP32, Sensor PIR, Sensor DHT11, IR transmitter dan software menggunakan Arduino IDE dan aplikasi Blynk IoT. Hasil rancang bangun alat yang dibuat dapat berfungsi dengan baik untuk menyalakan dan mematikan AC di ruang Tata Usaha UPT BLK. Penghematan penggunaan energi listrik yang diperoleh adalah 18,75% dan penurunan durasi nyala AC adalah 19,42% dibandingkan dengan sebelum penambahan alat.

Permasalahan pada sistem kelistrikan gedung RSIGM adalah nilai faktor daya yang kecil pada Sub Distribution Panel (SDP) Penerangan, disebabkan oleh ballast lampu LED. Dampaknya adalah pemborosan energi dan meningkatnya tagihan listrik. Solusinya adalah perbaikan faktor daya sesuai standar dengan menggunakan capacitor bank. Penelitian ini bertujuan mengetahui perbaikan faktor daya sesuai standar PLN dengan menentukan single line diagram kelistrikan. Parameter yang ditentukan adalah: tegangan, arus, daya aktif, semu, reaktif, dan faktor daya existing. Perhitungan nilai capacitor bank dilakukan untuk mengompensasi nilai faktor daya yang masih di bawah standar. ETAP digunakan untuk simulasi perbaikan faktor daya. Hasil penelitian menunjukkan bahwa ETAP mampu menyimulasikan perbaikan faktor daya, dibuktikan dengan nilai faktor daya sebelum dan sesudah perbaikan dengan target faktor daya = 0,85 / 85%. Faktor daya meningkat pada BUS 1 dari 0,831 / 83,10% menjadi 0,851 / 85,1%, pada BUS 5 dari 0,715 / 71,5% menjadi 0,852 / 85,2%, pada BUS 9 dari 0,804 / 80,4% menjadi 0,851 / 85,1%. Penelitian ini juga menargetkan faktor daya = 1 / 100% pada semua BUS.

Transformator (Trafo) merupakan komponen utama sistem tenaga Listrik. Gangguan fungsi trafo dapat menyebabkan terjadinya blackout pada sistem tenaga listrik. Trafo bekerja secara kontinu selama dua puluh empat jam sehingga perlu pemeliharaan preventif, korektif maupun prediktif. Salah satu aspek penting pada oli trafo yaitu oli yang berfungsi sebagai pendingin dan isolasi belitan. Kegagalan fungsi trafo banyak disebabkan oleh menurunnya kualitas oli. Artikel ini akan membahas analisis kondisi oli trafo berdasar pengujian Dissolve Gas Analysis (DGA) dan Breakdown Voltage Test (BDV). Tahap penelitian yaitu: 1) Analisis kebutuhan yang dilakukan melalui pengamatan, 2) pengambilan sampel oli trafo, 3) pengukuran dan pengujian DGA dan BDV, 4) analisis data dan 5) rekomendasi. Hasil penelitian menunjukkan bahwa kondisi oli trafo masuk pada status DGA 3 yang diindikasikan adanya peningkatan jumlah gas terlarut yang melebihi batas yang diizinkan. Hasil pengujian tegangan tembus oli trafo juga berada di atas batas standar. Berdasarkan kondisi ini maka perlu dilakukan pemurnian atau penggantian oli untuk memastikan trafo dapat bekerja dengan baik.

Studi ini menganalisis penyebab kegagalan inverter DC-AC dalam sistem back-up daya di Gardu Induk Cibatu. Meskipun telah dilengkapi dengan berbagai sistem proteksi seperti overload, overvoltage, short circuit, dan overtemperature, namun adanya insiden kebakaran menunjukkan kegagalan sistem proteksi tersebut. Dengan menggunakan metode Root Cause Analysis (RCA), yang mencakup inspeksi visual, pengumpulan data, analisis sistem proteksi, dan wawancara, ditemukan bahwa kualitas material yang rendah dan usia komponen yang tua adalah penyebab utama kegagalan. Penelitian merekomendasikan penggantian komponen dengan bahan berkualitas tinggi, peningkatan prosedur pemeliharaan, dan monitoring operasional yang lebih ketat.

Abstract This paper proposes a power scaling (PS) technique aimed at mitigating the outage floor problem commonly encountered in multi-user full-duplex (FD) non-orthogonal multiple access (NOMA) systems. Two antenna modes denominated as adaptive antenna mode (AAM) and fixed antenna mode (FAM) are utilized in this work for L FD near users in close proximity. In addition, a combined method of selecting both antenna mode and near user integrated with PS is employed to improve the overall network performance. Moreover, a power allocation between the chosen near user and far user is considered. In the low-to-moderate power regions, by AAM and FAM, we achieve twice of full diversity gain and full diversity gain, respectively. The research presents mathematical expressions for deriving the average capacity and outage probability and supports the theoretical findings with simulation-based evidence. The results of this work show that PS method not only contributes to a greater spatial diversity but also leads to superior performance compared to traditional FD NOMA systems. Moreover, our proposed method overcomes the outage floor and capacity ceiling. Furthermore, the work can be developed to 6G massive MIMO technology in multi-user FD cooperative NOMA systems.

Abstract Software-defined networking (SDN) revolutionizes network administration by centralizing control and decoupling the data plane from the control plane. Despite its advantages, the escalating volume of network traffic induces congestion at nodes, adversely affecting routing quality and overall performance. Addressing congestion has become imperative due to its emergence as a fundamental challenge in network management. Previous strategies often faced drawbacks in handling congestion, with issues arising from the inability to efficiently manage heavy packet surges in specific network regions. In response, this research introduces a novel approach integrating a multiplicative gated recurrent neural network with a congestion-aware hunter prey optimization (HPO) algorithm for effective traffic management in SDN. The framework leverages machine learning and deep learning techniques, acknowledged for their proficiency in processing traffic data. Comparative simulations showcase the congestion-aware HPO algorithm's superiority, achieving a normalized throughput 3.4–7.6% higher than genetic algorithm (GA) and particle swarm optimization (PSO) alternatives. Notably, the proposed framework significantly reduces data transmission delays by 58–65% compared to the GA and PSO algorithms. This research not only contributes a state-of-the-art solution but also addresses drawbacks observed in existing methodologies, thereby advancing the field of traffic engineering and congestion management in SDN. The proposed framework demonstrates notable enhancements in both throughput and latency, providing a more robust foundation for future SDN implementations.

Abstract Aiming at the problem that face images are easily interfered by occlusion factors in uncontrollable environments, and the complex structure of traditional convolutional neural networks leads to low expression recognition rates, slow network convergence speed, and long network training time, an improved lightweight convolutional neural network is proposed for facial expression recognition algorithm. First, the dilation convolution is introduced into the shortcut connection of the inverted residual structure in the MobileNetV3 network to expand the receptive field of the convolution kernel and reduce the loss of expression features. Then, the channel attention mechanism SENet in the network is replaced by the two-dimensional (channel and spatial) attention mechanism SimAM introduced without parameters to reduce the network parameters. Finally, in the normalization operation, the Batch Normalization of the backbone network is replaced with Group Normalization, which is stable at various batch sizes, to reduce errors caused by processing small batches of data. Experimental results on RaFD, FER2013, and FER2013Plus face expression data sets show that the network reduces the training times while maintaining network accuracy, improves network convergence speed, and has good convergence effects.

Abstract Flexible organic light-emitting diodes (OLEDs) are promising light sources for biomedical applications. However, the use of these flexible devices has been restricted by their short shelf lifetimes due to poor ambient stability. Here, the fabrication of a long-lived flexible OLED is reported by replacing air-sensitive metals such as aluminum, and alkali metals used as n dopants, with silver. In addition, to achieve stable and efficient flexible OLEDs we tuned the optical cavity length to the second-order interference maximum. The device design has simple encapsulation and leads to an improvement in the air stability of flexible OLEDs which show a shelf lifetime of greater than 130 days whereas the conventional structure exhibits degradation after only 12 days. The proposed design for making flexible OLEDs demonstrates a great potential for using the devices for wearable bioelectronic applications.

Abstract Fabricating high-conductive composites and constructing highly conductive networks are crucial for high-performance electrothermal film. In this study, an Ag nanowires/graphene (Ag/G) composite synthesized by liquid-phase exfoliation and in-situ photoreduction is mixed with carbon black (CB) to form a composite conductive ink, and a CB/Ag/G composite electrothermal film with a point-line-plane three-dimensional microstructure is obtained via blade coating process. Both the addition of Ag nanowires and a subsequent compression rolling treatment induce the establishment of the effective conductive network in the film, endowing it with an outstanding conductivity of 399.4 S cm−1. The film reaches a T s of 204 °C with an input voltage of 3.0 V, and is successfully applied in water heating and de-icing, demonstrating its extraordinary electrothermal performance and vast potential for practical applications. The film is also used as an electromagnetic shielding film and heat dissipation substrate, showing exceptional electromagnetic shielding (42.5 dB) and heat dissipation properties.

Abstract The unconstrained partitioned-block frequency-domain adaptive filter (PBFDAF) offers superior computational efficiency over its constrained counterpart. However, the correlation matrix governing the natural modes of the unconstrained PBFDAF is not full rank. Consequently, the mean coefficient behavior of the algorithm depends on the initialization of adaptive coefficients and the Wiener solution is non-unique. To address the above problems, a new theoretical model for the deficient-length unconstrained PBFDAF is proposed by constructing a modified filter weight vector within a system identification framework. Specifically, we analyze the transient and steady-state convergence behavior. Our analysis reveals that modified weight vector is independent of its initialization in the steady state. The deficient-length unconstrained PBFDAF converges to a unique Wiener solution, which does not match the true impulse response of the unknown plant. However, the unconstrained PBFDAF can recover more coefficients of the parameter vector of the unknown system than the constrained PBFDAF in certain cases. Also, the modified filter coefficient yields better mean square deviation (MSD) performance than previously assumed. The presented alternative performance analysis provides new insight into convergence properties of the deficient-length unconstrained PBFDAF. Simulations validate the analysis based on the proposed theoretical model.

Abstract Fast Fourier transform (FFT) is a fundamental building block for digital signal processing applications where high processing speed is crucial. Resource utilization in implementing FFT structures can be minimized by optimizing the performance of multipliers and adders used within the design. FFTs are also widely used in various machine learning algorithms. To achieve increased processor efficiency and reduced resource utilization, we propose a hardware design for Radix-2, Radix-4, and Split-Radix FFT architectures that utilizes a novel parallel prefix adder. This design offers lower power consumption, smaller chip area, and faster operation compared to existing architectures. Our performance analysis focuses on metrics such as power consumption, clock speed, and hardware complexity for Radix-2, Radix-4, and Split-Radix FFT algorithms implemented with the proposed adder. We compare these metrics using our proposed arithmetic structure against existing adder designs. The results indicate that the Split-Radix FFT architecture achieves lower power consumption and smaller chip area compared to Radix-4 and Radix-2 methods. Additionally, the Split-Radix FFT exhibits a higher clock speed. Therefore, based on these findings, the Split-Radix algorithm appears to be a compelling choice for implementation on field-programmable gate arrays due to its high speed and lower power consumption.