User Signal Research Articles

Flexible 2 × 2 multiple access visible light communication system based on an integrated parallel GaN/InGaN micro-photodetector array module

In recent studies, visible light communication (VLC) has been predicted to be a prospective technique in the future 6G communication systems. To suit the trend of exponentially growing connectivity, researchers have intensively studied techniques that enable multiple access (MA) in VLC systems, such as the MIMO system based on LED devices to support potential applications in the Internet of Things (IoT) or edge computing in the next-generation access network. However, their transmission rate is limited due to the intrinsic bandwidth of LED. Unfortunately, the majority of visible light laser communication (VLLC) research with beyond 10 Gb/s data rates concentrates on point-to-point links, or using discrete photodetector (PD) devices instead of an integrated array PD. In this paper, we demonstrated an integrated PD array device fabricated with a Si-substrated GaN/InGaN multiple-quantum-well (MQW) structure, which has a 4×4 array of 50 μm×50 μm micro-PD units with a common cathode and anode. This single-integrated array successfully provides access for two different transmitters simultaneously in the experiment, implementing a 2×2 MIMO-VLLC link at 405 nm. The highest data rate achieved is 13.2 Gb/s, and the corresponding net data rate (NDR) achieved is 12.27 Gb/s after deducing the FEC overhead, using 2.2 GHz bandwidth and superposed PAM signals. Furthermore, we assess the Huffman-coded coding scheme, which brings a fine-grain adjustment in access capacity and enhances the overall data throughput when the user signal power varies drastically due to distance, weather, or other challenges in the channel condition. As far as we know, this is the first demonstration of multiple visible light laser source access based on a single integrated GaN/InGaN receiver module.

Knowledge-aware fine-grained attention networks with refined knowledge graph embedding for personalized recommendation

Recommendation systems aim to provide users with personalized and accurate services by integrating various machine learning technologies. Suffering from the puzzles such as cold-start and data sparsity, recommendation models has received extensive attention by incorporating knowledge graphs as additional supplementary information to effectively solve these problems. More recently, graph neural networks (GNNs) have been adopted to establish knowledge-aware recommendation models and made considerable achievements. Nevertheless, the existing GNN-based approaches are inadequate in the following two aspects: (1) How to achieve sufficient high-order collaborative signals? (2) How to reduce the impact of redundant KG information on representation? To overcome these limitations, we propose a novel framework KFGAN with Knowledge-aware Fine-grained Attention Networks for personalized knowledge-aware recommendation, which captures user’s preferences by encoding the relation paths and associated entities and generates refined knowledge graphs to learn the potential semantic information. Specifically, by integrating the high-order collaborative signals of users and items and the structural information of knowledge graph, KFGAN enriches the feature representation of users and items and realizes the consistency and coherence of CF and KG information. Furthermore, KFGAN draws a lesson from graph contrastive learning approach to accomplish refined knowledge graph embedding, which alleviates the interference of redundant KG signal to the model and mines the latent semantic information in KG. Massive experimental results on three benchmark datasets prove that KFGAN model dramatically outperforms the current state-of-the-art baselines. The code and experimental datasets will be available at https://github.com/weiwang1992/KFGAN to verify and study for further research.

Visual perception and deep learning in smart homes and the Internet of Things

In recent years, IOT (Internet of Things) has been widely used in the field of smart homes. IOT technology is an important stage in the development process of the "digital information age", with a history of more than ten years of development. It is widely used in the communication industry, especially in the field of smart homes. The basic principle of DL is to establish a multi-layer neural learning network, which learns a large amount of data to obtain better features and improve the accuracy of classification and recognition. This article conducts research on the visual perception of smart homes based on DL (Deep Learning) and IOT. Utilizing an intelligent gateway to achieve the mutual conversion of the underlying ZigBee protocol and in ternet protocol, the cloud service platform is interacted with by the Netty middleware and gateway. The entire system is centered around the Internet cloud platform, connecting the underlying IOT devices and the mobile network platform of the application layer to achieve the construction of a smart home system. The device then collects the user's EEG signals in real-time and hands them over to a trained specific network for classification. Finally, based on the classification results, the serial device sends signals to the smart home terminal to achieve the final practical operation actions, such as turning on the lights and fans.

Efficient improvement of energy detection technique in cognitive radio networks using K-nearest neighbour (KNN) algorithm

With the birth of the IoT era, it is evident that the existing number of devices is going to rise exponentially. Any two devices will communicate with each other using the same frequency band with limited availability. Therefore, it is of vital importance that this frequency band used for communication be used efficiently to accommodate the maximum number of devices with the available radio resources. Cognitive radio (CR) technology serves this exact purpose. The stated one is an intelligent radio that is made to automatically identify the optimal wireless channel in the available wireless spectrum at a given instant. An important functionality of CR is spectrum sensing. Energy detection is a very popular algorithm used for spectrum sensing in CR technology for efficient allocation of radio resources to the devices intended to communicate with each other. Energy detection detects the presence of a primary user (PU) signal by continuously monitoring a selected frequency bandwidth. The conventional energy detection technique is known to perform poorly in lower SNR ranges. This paper works towards the improvement of the energy detection algorithm with the help of machine learning (ML). The ML model uses the general properties of the signal as training data and classifies between a PU signal and noise at very low SNR ranges (− 25 to − 10 dB). In this research, a K-nearest neighbours (KNN) model is selected for its versatility and simplicity. Upon testing the model with an out-of-sample dataset, the KNN model produced a detection accuracy of 94.5%.

Hybrid Precoding Design for Wireless Fading Channels

Precoding techniques are widely used to eliminate interference between multiple user signals in a wireless fading channel environment. The linear MMSE precoding technique, one of the most widely used techniques so far, has low computational complexity but has the disadvantage of relatively poor symbol error rate (SER) performance. The symbol-level precoding (SLP) technique, on which much research has been conducted recently, has excellent SER performance but has the disadvantage of being too computationally complex. In this paper, we propose a hybrid precoding technique that simultaneously applies SLP and MMSE precoding to appropriately adjust SER performance and computational complexity performance. If two different types of precoding techniques are applied simultaneously, interference may occur between signals to which different types of precoding are applied, which can significantly deteriorate SER performance. Therefore, in this paper, precoding is designed to prevent interference between the two signals using the null space of the channel matrices. Through computer simulation, the proposed scheme showed that its SER performance was superior to that of the linear MMSE scheme, and the computational complexity was much lower than that of the SLP scheme.

Transmitter design and AE–AR region characterization for NOMA-SLIPT UWOC systems with uniformly distributed message and Imp-SIC

This paper investigates simultaneous lightwave and information transfer (SLIPT) based non-orthogonal multiple access (NOMA) in underwater optical wireless communication (UWOC) systems. At the user signal separation (SS) based SLIPT scheme is employed. Imperfect successive interference cancellation (SIC) at the near user is considered. Firstly, we derive the maximum allowable electrical power for message symbols at the transmitter, ensuring input to the laser diode lies within the linear region and. A novel linearity-constrained transmitter design is proposed for the uniformly distributed message, followed by calculations of instantaneous data rate and instantaneous harvested energy for both users. Then, closed-form expressions for average data rates (AR) and average energy harvested (AE) under weak turbulence-induced fading channels using Holtzman’s approximation are derived. The tightness of the approximation is confirmed by Monte-Carlo simulations. AE–AR boundaries for both users show an initial rise and subsequent decline in data rates with increased harvested energy, owing to higher bias values restricting transmitter power. The study also examines trade-offs between AE and AR across various system parameters including beam divergence angles, imperfection of SIC, power allocation coefficients, and water types, is conducted. We observe shifting and shrinking/expansion of the AE–AR region depending upon the variation of the various system parameters. We also show that the discussed SS scheme is general and encompasses time-switching (TS), and power-splitting (PS) schemes as special cases. We also briefly discuss as to how the developed framework could be extended to more than two users. In summary, the overall effective energy consumption deduces owing to the adoption of SLIPT at the users, making the considered setup more energy-efficient.

Statistical analysis of range of motion and surface electromyography data for a knee rehabilitation device

This work introduces a statistical analysis of knee range of motion (ROM) and surface electromyography (EMG) data gathered from a knee extension rehabilitation device. Real-time ROM and EMG signals of rehabilitation users are measured using a single angle sensor and a two-channel EMG device (for the vastus lateralis and vastus medialis muscles). These signals are collected by the NI-myRIO embedded device in accordance with the designed rehabilitation program. The main contribution and novelty of this study is that real-time signals are automatically processed and transformed into statistical data for use by users and medical experts. A solution for extracting raw signals is proposed, in which several statistical functions such as range, mean, standard deviation, skewness, percentiles, interquartile range, and total knee holding times above the threshold level, are implemented and applied. The proposed solution is tested using data acquired from healthy people, which includes gender, age, body size, knee side, exercise behavior, and surgical experience. Results indicated that real-time signals and related statistical data on the knee’s performance can be efficiently monitored. With this solution, rehabilitation users can practice and learn about their knee performance, while medical experts can evaluate the data and design the best rehabilitation program for users.

Zero-shot test time adaptation via knowledge distillation for personalized speech denoising and dereverberation.

A personalization framework to adapt compact models to test time environments and improve their speech enhancement (SE) performance in noisy and reverberant conditions is proposed. The use-cases are when the end-user device encounters only one or a few speakers and noise types that tend to reoccur in the specific acoustic environment. Hence, a small personalized model that is sufficient to handle this focused subset of the original universal SE problem is postulated. The study addresses a major data shortage issue: although the goal is to learn from a specific user's speech signals and the test time environment, the target clean speech is unavailable for model training due to privacy-related concerns and technical difficulty of recording noise and reverberation-free voice signals. The proposed zero-shot personalization method uses no clean speech target. Instead, it employs the knowledge distillation framework, where the more advanced denoising results from an overly large teacher work as pseudo targets to train a small student model. Evaluation on various test time conditions suggests that the proposed personalization approach can significantly enhance the compact student model's test time performance. Personalized models outperform larger non-personalized baseline models, demonstrating that personalization achieves model compression with no loss in dereverberation and denoising performance.

Systematic analysis of combined oral contraceptive prescription patterns in psychotropic drug users across twelve European countries

ObjectiveTo investigate prescription patterns of combined oral contraceptives (COC) among psychotropic drug users compared to non-psychotropic drug users in routine clinical practice in Europe. Study designA pooled analysis of three large, prospective, multinational cohort studies including women with a new prescription of COC from 12 European countries. We calculated standardized mean differences (SMD) to investigate whether the status of psychotropic drug use (use/no use) or the psychotropic drug class (psycholeptics/psychoanaleptics) is associated with the healthcare professional’s choice of a specific type of COC progestin. ResultsOur analysis comprised 143,069 non-psychotropic drug users and 2174 psychotropic drug users. Progestins with the highest frequency in the cohorts were levonorgestrel (non-psychotropic drug users: 33.8%; psychotropic drug users: 32.4%), nomegestrol/nomegestrol acetate (non-psychotropic drug users: 19.1%; psychotropic drug users: 26.4%), and drospirenone (non-psychotropic drug users: 15.9%; psychotropic drug users: 14.8%). SMD analysis indicated no substantial differences in COC prescription patterns between the two cohorts. However, we observed association signals for users of the herbal antidepressant St. John’s wort in that those individuals more often received a prescription for drospirenone and less frequently for nomegestrol/nomegestrol acetate compared to non-psychotropic drug users. ConclusionsPsychotropic drug user status does not seem to affect healthcare professionals’ decisions when prescribing COC. However, limited evidence suggests that the risk for drug interactions might differ by progestin type, and some COC might be more suitable for psychotropic drug users than others. Specific guidelines should be conveyed to healthcare professionals to assist them in contraceptive counseling. ImplicationsWith exception of St. John’s wort, our analysis showed no differential prescription behavior of combined oral contraceptives in psychotropic drug users and non-users. However, healthcare professionals should carefully consider psychotropic drug use in contraceptive counseling as it is still unclear whether drug interactions exist when co-administered with certain oral contraceptives.

Performance Analysis of Centralized Cooperative Schemes for Compressed Sensing.

This paper presents a performance analysis of centralized spectrum sensing based on compressed measurements. We assume cooperative sensing, where unlicensed users individually perform compressed sensing and send their results to a fusion center, which makes the final decision about the presence or absence of a licensed user signal. Several cooperation schemes are considered, such as and-rule, or-rule, majority voting, soft equal-gain combining (EGC). The proposed analysis provides simplified closed-form expressions that calculate the required number of sensors, the required number of samples, the required compression ratio, and the required signal-to-noise ratio (SNR) as a function of the probability of detection and the probability of the false alarm of the fusion center and of the sensors. The resulting expressions are derived by exploiting some accurate approximations of the test statistics of the fusion center and of the sensors, equipped with energy detectors. The obtained results are useful, especially for a low number of sensors and low sample sizes, where conventional closed-form expressions based on the central limit theorem (CLT) fail to provide accurate approximations. The proposed analysis also allows the self-computation of the performance of each sensor and of the fusion center with reduced complexity.

The ethical significance of user-control in AI-driven speech-BCIs: a narrative review.

AI-driven brain-computed interfaces aimed at restoring speech for individuals living with locked-in-syndrome are paired with ethical implications for user's autonomy, privacy and responsibility. Embedding options for sufficient levels of user-control in speech-BCI design has been proposed to mitigate these ethical challenges. However, how user-control in speech-BCIs is conceptualized and how it relates to these ethical challenges is underdetermined. In this narrative literature review, we aim to clarify and explicate the notion of user-control in speech-BCIs, to better understand in what way user-control could operationalize user's autonomy, privacy and responsibility and explore how such suggestions for increasing user-control can be translated to recommendations for the design or use of speech-BCIs. First, we identified types of user control, including executory control that can protect voluntariness of speech, and guidance control that can contribute to semantic accuracy. Second, we identified potential causes for a loss of user-control, including contributions of predictive language models, a lack of ability for neural control, or signal interference and external control. Such a loss of user control may have implications for semantic accuracy and mental privacy. Third we explored ways to design for user-control. While embedding initiation signals for users may increase executory control, they may conflict with other aims such as speed and continuity of speech. Design mechanisms for guidance control remain largely conceptual, similar trade-offs in design may be expected. We argue that preceding these trade-offs, the overarching aim of speech-BCIs needs to be defined, requiring input from current and potential users. Additionally, conceptual clarification of user-control and other (ethical) concepts in this debate has practical relevance for BCI researchers. For instance, different concepts of inner speech may have distinct ethical implications. Increased clarity of such concepts can improve anticipation of ethical implications of speech-BCIs and may help to steer design decisions.

A Multiantenna Spectrum Sensing Method Based on HFDE‐CNN‐GRU under Non‐Gaussian Noise

In many practical communication environments, traditional feature extraction methods in spectrum sensing fail to fully exploit the information of primary users. Additionally, conventional machine learning methods have weak learning capabilities, making it difficult to maintain efficient and stable spectrum sensing performance in complex noise environments. Furthermore, non‐Gaussian noise can significantly affect the detection performance of spectrum sensing. To address these issues, this paper first proposes a feature extraction method based on Hierarchical Fuzzy Dispersion Entropy (HFDE) to better extract high‐frequency and low‐frequency information from signal samples, providing more comprehensive features for subsequent models to optimize feature extraction effectiveness. Then, a parallel model combining Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) is constructed to enhance learning ability. While CNN extracts local features, GRU processes temporal relationships, and the features output by both are concatenated to achieve effective feature learning and temporal modeling of primary user signal data represented by HFDE. Finally, using the feature vectors output by the CNN‐GRU model, detection statistics and detection thresholds for spectrum sensing are constructed for online detection. Simulation results validate the effectiveness and robustness of this method in spectrum sensing under non‐Gaussian noise. In the presence of significant non‐Gaussian noise intensity and a signal‐to‐noise ratio of −14 dB, the detection probability can reach 97.1%. Additionally, for the detection of unknown signals, the model can still maintain a detection probability of over 90%.

Performance Analysis of New 2D Spatial OCDMA Encoding based on HG Modes in Multicore Fiber

This paper presents a pioneering 2D spatial Optical Code-Division Multiple Access (OCDMA) encoding system that exploits Mode Division Multiplexing (MDM) and Multicore Fiber (MCF) technologies. This innovative approach utilizes two spatial dimensions to enhance the performance and security of OCDMA systems. In the first dimension, we employ Hermite-Gaussian modes (HG00, HG01, HG11) to modulate each user's signal individually. This unique approach offers a robust means of data transmission while ensuring minimal interference among users. The second-dimension leverages MCF encoding, introducing two incoherent OCDMA codes: the Zero Cross Correlation (ZCC) code (λc=0) and the ZFD code (λc=1). These codes are thoughtfully designed and simulated, taking into account their cross-correlation properties to guarantee minimal interference and heightened data security. To assess the efficiency of this novel OCDMA encoding system, we implemented simulations with three active users using the Opti system software. At the transmitter end, each user's signal is modulated individually by their designated HG mode (HG00, HG01, HG11), resulting in separate channels. Subsequently, at the multicore fiber, each user's data is encoded with a unique code-word, and they are directed through specific core groups, ensuring data isolation and integrity. In this paper, the BER and eye pattern are examined with respect to different parameters such as data rate and distance. At a distance of 5 km and data rate of 10 Gbit/s, a BER value around 10-70 is achieved.

Performance Analysis of New 2D Spatial OCDMA Encoding based on HG Modes in Multicore Fiber

This paper presents a pioneering 2D spatial Optical Code-Division Multiple Access (OCDMA) encoding system that exploits Mode Division Multiplexing (MDM) and Multicore Fiber (MCF) technologies. This innovative approach utilizes two spatial dimensions to enhance the performance and security of OCDMA systems. In the first dimension, we employ Hermite-Gaussian modes (HG00, HG01, HG11) to modulate each user's signal individually. This unique approach offers a robust means of data transmission while ensuring minimal interference among users. The second-dimension leverages MCF encoding, introducing two incoherent OCDMA codes: the Zero Cross Correlation (ZCC) code (λc=0) and the ZFD code (λc=1). These codes are thoughtfully designed and simulated, taking into account their cross-correlation properties to guarantee minimal interference and heightened data security. To assess the efficiency of this novel OCDMA encoding system, we implemented simulations with three active users using the Opti system software. At the transmitter end, each user's signal is modulated individually by their designated HG mode (HG00, HG01, HG11), resulting in separate channels. Subsequently, at the multicore fiber, each user's data is encoded with a unique code-word, and they are directed through specific core groups, ensuring data isolation and integrity. In this paper, the BER and eye pattern are examined with respect to different parameters such as data rate and distance. At a distance of 5 km and data rate of 10 Gbit/s, a BER value around 10-70 is achieved.

Smart Room System for Paralysis Patients with Mindwave EEG Sensor Control

Persons with disabilities experience physical, intellectual, mental, or sensory difficulties. One type of disability is paralysis. Paralysis is a condition where there is interference with the nerves that control body movement, causing the limbs to be unable to move. Paralyzed people will find it difficult to move without the help of others. Therefore, research was carried out by creating an intelligent room system to help persons with disabilities manage their own rooms so that they do not always have to be accompanied by a nurse. Paralyzed people can turn lights or fans on and off, and send help messages to their carers via the Telegram bot. This study used the NeuroSky Mindwave EEG headset which detects the user's brain signals with outputs in the form of attention level, relaxation level (meditation), and blink strength level. The resulting signal is processed via a PC and sent via NodeMCU to give commands in the form of turning lights and fans on or off, as well as sending messages to nurses. From this research a system was produced that could turn on the lights based on the value of Attention ≥ 70, turn on the fan based on the Meditation value ≥ 74, then the value of BlinkStrength ≥ 81 which was counted 2 times to turn off the lights, 3 times to turn off the fan, 4 times to turn off the lights and fan, and more than 4 times sending help messages

Smart Room System for Paralysis Patients with Mindwave EEG Sensor Control

Persons with disabilities experience physical, intellectual, mental, or sensory difficulties. One type of disability is paralysis. Paralysis is a condition where there is interference with the nerves that control body movement, causing the limbs to be unable to move. Paralyzed people will find it difficult to move without the help of others. Therefore, research was carried out by creating an intelligent room system to help persons with disabilities manage their own rooms so that they do not always have to be accompanied by a nurse. Paralyzed people can turn lights or fans on and off, and send help messages to their carers via the Telegram bot. This study used the NeuroSky Mindwave EEG headset which detects the user's brain signals with outputs in the form of attention level, relaxation level (meditation), and blink strength level. The resulting signal is processed via a PC and sent via NodeMCU to give commands in the form of turning lights and fans on or off, as well as sending messages to nurses. From this research a system was produced that could turn on the lights based on the value of Attention ≥ 70, turn on the fan based on the Meditation value ≥ 74, then the value of BlinkStrength ≥ 81 which was counted 2 times to turn off the lights, 3 times to turn off the fan, 4 times to turn off the lights and fan, and more than 4 times sending help messages

Smart Room System for Paralysis Patients with Mindwave EEG Sensor Control

Persons with disabilities experience physical, intellectual, mental, or sensory difficulties. One type of disability is paralysis. Paralysis is a condition where there is interference with the nerves that control body movement, causing the limbs to be unable to move. Paralyzed people will find it difficult to move without the help of others. Therefore, research was carried out by creating an intelligent room system to help persons with disabilities manage their own rooms so that they do not always have to be accompanied by a nurse. Paralyzed people can turn lights or fans on and off, and send help messages to their carers via the Telegram bot. This study used the NeuroSky Mindwave EEG headset which detects the user's brain signals with outputs in the form of attention level, relaxation level (meditation), and blink strength level. The resulting signal is processed via a PC and sent via NodeMCU to give commands in the form of turning lights and fans on or off, as well as sending messages to nurses. From this research a system was produced that could turn on the lights based on the value of Attention ≥ 70, turn on the fan based on the Meditation value ≥ 74, then the value of BlinkStrength ≥ 81 which was counted 2 times to turn off the lights, 3 times to turn off the fan, 4 times to turn off the lights and fan, and more than 4 times sending help messages

Explainable empirical risk minimization

AbstractThe successful application of machine learning (ML) methods increasingly depends on their interpretability or explainability. Designing explainable ML (XML) systems is instrumental for ensuring transparency of automated decision-making that targets humans. The explainability of ML methods is also an essential ingredient for trustworthy artificial intelligence. A key challenge in ensuring explainability is its dependence on the specific human end user of an ML system. The users of ML methods might have vastly different background knowledge about ML principles, with some having formal training in the specific field and others having none. We use information-theoretic concepts to develop a novel measure for the subjective explainability of predictions delivered by a ML method. We construct this measure via the conditional entropy of predictions, given the user signal. Our approach allows for a wide range of user signals, ranging from responses to surveys to biophysical measurements. We use this measure of subjective explainability as a regularizer for model training. The resulting explainable empirical risk minimization (EERM) principle strives to balance subjective explainability and risk. The EERM principle is flexible and can be combined with arbitrary ML models. We present several practical implementations of EERM for linear models and decision trees. Numerical experiments demonstrate the application of EERM to weather prediction and detecting inappropriate language in social media.

Characterization of EEG-P300 Signals in Drug Users

The use of drugs for a long time can cause changes in the structure of the brain’s performance. Drug detection is usually done using laboratory testing methods such as urine, blood, and saliva tests. With the development of technology, changes in brain activity can be presented in the form of EEG signals. This study aims to locate the features of the P300 signal and the more effective stimulus to generate the P300 signal. The EEG input data uses secondary data consisting of 40 male participants with an age range of 25–40 years. The data collection process contains a stimulus to generate a P300 signal and record for 50 seconds using 19 channels and a 10-20 system. The results of the study show that the P300 signal features can affect the formation of amplitude and latency. The more effective stimulus in generating the P300 signal for drug users is a similar stimulus shabu with an amplitude value of 18,3 μV and a fast response latency of 252,9 ms.

One-bit spectrum sensing using Gustafson–Kessel fuzzy clustering for cognitive radio network

In this article, we address the problem of one-bit spectrum sensing using a multi-antenna array for cognitive radio networks. Spectrum sensing with one-bit analog-to-digital converter (ADC) is a far-reaching technology as it requires only a simple comparator for conversion and has the advantage of low-cost and high-speed. However, spectrum sensing with one-bit quantized data is challenging because of severe loss of information due to nonlinearities involved. Existing one-bit spectrum sensing techniques utilize arcsine law to approximate original signal covariance matrix from their quantized version and then analytically model the signal features for deriving the spectrum classification rule. However, these techniques are vulnerable to the inaccuracies of the model and it may not be at all possible to derive a classification rule for analytically intractable noise scenarios. In this article, we follow a data-driven approach for one-bit spectrum sensing and propose Gustafson–Kessel fuzzy c-means (GKFCM) clustering based algorithms for detection of primary user signals in white and correlated noise. We identify the features which are conserved in the process of quantization and form decision vectors using them. Decision vectors obtained from the received one-bit quantized data are then clustered using GKFCM to obtain the model parameters and classification is performed to identify the occupancy of the channel. Simulation results reveal that proposed algorithms provide significant improvement in detection performance compared to existing one-bit spectrum sensing techniques in literature.