RESUMEN

In recent years, optical camera communication (OCC) has garnered attention as a research focus. OCC uses optical light to transmit data by scattering the light in various directions. Although this can be advantageous with multiple transmitter scenarios, there are situations in which only a single transmitter is permitted to communicate. Therefore, this method is proposed to fulfill the latter requirement using 2D object size to calculate the proximity of the objects through an AI object detection model. This approach enables prioritization among transmitters based on the transmitter proximity to the receiver for communication, facilitating alternating communication with multiple transmitters. The image processing employed when receiving the signals from transmitters enables communication to be performed without the need to modify the camera parameters. During the implementation, the distance between the transmitter and receiver varied between 1.0 and 5.0 m, and the system demonstrated a maximum data rate of 3.945 kbps with a minimum BER of 4.2×10-3. Additionally, the system achieved high accuracy from the refined YOLOv8 detection algorithm, reaching 0.98 mAP at a 0.50 IoU.

RESUMEN

Optical wireless communication is a promising emerging technology that addresses the limitations of radio-frequency-based wireless technologies. This study presents a new hybrid modulation method for optical camera communication (OCC), which integrates two waveforms transmitted from a single transmitter light-emitting diode (LED) and receives data through two rolling shutter camera devices on the receiver side. Then, a smart camera with a high-resolution image sensor captures the high-frequency signal, and a low-resolution image sensor from a smartphone camera captures the low-frequency signal. Based on this hybrid scheme, two data streams are transmitted from a single LED, which reduces the cost of the indoor OCC device compared with transmitting two signals from two different LEDs. In the proposed scheme, rolling-shutter orthogonal frequency-division multiplexing is used for the high-frequency signals, and M-ary frequency-shift keying is used for the low-frequency signals in the time domain. This proposed scheme is compatible with smartphone and USB cameras. By controlling the OCC parameters, the hybrid scheme can be implemented with high performance for a communication distance of 10 m.

RESUMEN

Optical camera communication (OCC) is one of the most promising optical wireless technology communication systems. This technology has a number of benefits compared to radio frequency, including unlimited spectrum, no congestion due to high usage, and low operating costs. OCC operates in order to transmit an optical signal from a light-emitting diode (LED) and receive the signal with a camera. However, identifying, detecting, and extracting data in a complex area with very high mobility is the main challenge in operating the OCC. In this paper, we design and implement a real-time OCC system that can communicate in high mobility conditions, based on You Only Look Once version 8 (YOLOv8). We utilized an LED array that can be identified accurately and has an enhanced data transmission rate due to a greater number of source lights. Our system is validated in a highly mobile environment with camera movement speeds of up to 10 m/s at 2 m, achieving a bit error rate of 10-2. In addition, this system achieves high accuracy of the LED detection algorithm with mAP0.5 and mAP0.5:0.95 values of 0.995 and 0.8604, respectively. The proposed method has been tested in real time and achieves processing speeds up to 1.25 ms.

RESUMEN

Due to the accelerated growth of the PV plant industry, multiple PV plants are being constructed in various locations. It is difficult to operate and maintain multiple PV plants in diverse locations. Consequently, a method for monitoring multiple PV plants on a single platform is required to satisfy the current industrial demand for monitoring multiple PV plants on a single platform. This work proposes a method to perform multiple PV plant monitoring using an IoT platform. Next-day power generation prediction and real-time anomaly detection are also proposed to enhance the developed IoT platform. From the results, an IoT platform is realized to monitor multiple PV plants, where the next day's power generation prediction is made using five types of AI models, and an adaptive threshold isolation forest is utilized to perform sensor anomaly detection in each PV plant. Among five developed AI models for power generation prediction, BiLSTM became the best model with the best MSE, MAPE, MAE, and R2 values of 0.0072, 0.1982, 0.0542, and 0.9664, respectively. Meanwhile, the proposed adaptive threshold isolation forest achieves the best performance when detecting anomalies in the sensor of the PV plant, with the highest precision of 0.9517.

RESUMEN

Photovoltaic (PV) systems have immense potential to generate clean energy, and their adoption has grown significantly in recent years. A PV fault is a condition of a PV module that is unable to produce optimal power due to environmental factors, such as shading, hot spots, cracks, and other defects. The occurrence of faults in PV systems can present safety risks, shorten system lifespans, and result in waste. Therefore, this paper discusses the importance of accurately classifying faults in PV systems to maintain optimal operating efficiency, thereby increasing the financial return. Previous studies in this area have largely relied on deep learning models, such as transfer learning, with high computational requirements, which are limited by their inability to handle complex image features and unbalanced datasets. The proposed lightweight coupled UdenseNet model shows significant improvements for PV fault classification compared to previous studies, achieving an accuracy of 99.39%, 96.65%, and 95.72% for 2-class, 11-class, and 12-class output, respectively, while also demonstrating greater efficiency in terms of parameter counts, which is particularly important for real-time analysis of large-scale solar farms. Furthermore, geometric transformation and generative adversarial networks (GAN) image augmentation techniques improved the model's performance on unbalanced datasets.

RESUMEN

The security and privacy risks posed by unmanned aerial vehicles (UAVs) have become a significant cause of concern in today's society. Due to technological advancement, these devices are becoming progressively inexpensive, which makes them convenient for many different applications. The massive number of UAVs is making it difficult to manage and monitor them in restricted areas. In addition, other signals using the same frequency range make it more challenging to identify UAV signals. In these circumstances, an intelligent system to detect and identify UAVs is a necessity. Most of the previous studies on UAV identification relied on various feature-extraction techniques, which are computationally expensive. Therefore, this article proposes an end-to-end deep-learning-based model to detect and identify UAVs based on their radio frequency (RF) signature. Unlike existing studies, multiscale feature-extraction techniques without manual intervention are utilized to extract enriched features that assist the model in achieving good generalization capability of the signal and making decisions with lower computational time. Additionally, residual blocks are utilized to learn complex representations, as well as to overcome vanishing gradient problems during training. The detection and identification tasks are performed in the presence of Bluetooth and WIFI signals, which are two signals from the same frequency band. For the identification task, the model is evaluated for specific devices, as well as for the signature of the particular manufacturers. The performance of the model is evaluated across various different signal-to-noise ratios (SNR). Furthermore, the findings are compared to the results of previous work. The proposed model yields an overall accuracy, precision, sensitivity, and F1-score of 97.53%, 98.06%, 98.00%, and 98.00%, respectively, for RF signal detection from 0 dB to 30 dB SNR in the CardRF dataset. The proposed model demonstrates an inference time of 0.37 ms (milliseconds) for RF signal detection, which is a substantial improvement over existing work. Therefore, the proposed end-to-end deep-learning-based method outperforms the existing work in terms of performance and time complexity. Based on the outcomes illustrated in the paper, the proposed model can be used in surveillance systems for real-time UAV detection and identification.

RESUMEN

Complete autonomous systems such as self-driving cars to ensure the high reliability and safety of humans need the most efficient combination of four-dimensional (4D) detection, exact localization, and artificial intelligent (AI) networking to establish a fully automated smart transportation system. At present, multiple integrated sensors such as light detection and ranging (LiDAR), radio detection and ranging (RADAR), and car cameras are frequently used for object detection and localization in the conventional autonomous transportation system. Moreover, the global positioning system (GPS) is used for the positioning of autonomous vehicles (AV). These individual systems' detection, localization, and positioning efficiency are insufficient for AV systems. In addition, they do not have any reliable networking system for self-driving cars carrying us and goods on the road. Although the sensor fusion technology of car sensors came up with good efficiency for detection and location, the proposed convolutional neural networking approach will assist to achieve a higher accuracy of 4D detection, precise localization, and real-time positioning. Moreover, this work will establish a strong AI network for AV far monitoring and data transmission systems. The proposed networking system efficiency remains the same on under-sky highways as well in various tunnel roads where GPS does not work properly. For the first time, modified traffic surveillance cameras have been exploited in this conceptual paper as an external image source for AV and anchor sensing nodes to complete AI networking transportation systems. This work approaches a model that solves AVs' fundamental detection, localization, positioning, and networking challenges with advanced image processing, sensor fusion, feathers matching, and AI networking technology. This paper also provides an experienced AI driver concept for a smart transportation system with deep learning technology.

RESUMEN

Unsafe electrical appliances can be hazardous to humans and can cause electrical fires if not monitored, analyzed, and controlled. The purpose of this study is to monitor the system's condition, including the electrical properties of the appliances, and to diagnose fault conditions without deploying sensors on individual appliances and analyzing individual sensor data. Using historical data and an acceptable range of normal and leakage currents, we proposed a hybrid model based on multiclass support vector machines (MSVM) integrated with a rule-based classifier (RBC) to determine the changes in leakage currents caused by installed devices at a certain moment. For this, we developed a sensor-based monitoring device with long-range communication to store real-time data in a cloud database. In the modeling process, RBC algorithm is used to diagnose the constructed device fault and overcurrent fault where MSVM is applied for detecting leakage current fault. To conduct an operational field test, the developed device was integrated into some houses. The results demonstrate the effectiveness of the proposed system in terms of electrical safety monitoring and detection. All the collected data were stored in a structured database that could be remotely accessed through the Internet.

Asunto(s)

RESUMEN

The industrial internet of things (IIoT), a leading technology to digitize industrial sectors and applications, requires the integration of edge and cloud computing, cyber security, and artificial intelligence to enhance its efficiency, reliability, and sustainability. However, the collection of heterogeneous data from individual sensors as well as monitoring and managing large databases with sufficient security has become a concerning issue for the IIoT framework. The development of a smart and integrated IIoT infrastructure can be a possible solution that can efficiently handle the aforementioned issues. This paper proposes an AI-integrated, secured IIoT infrastructure incorporating heterogeneous data collection and storing capability, global inter-communication, and a real-time anomaly detection model. To this end, smart data acquisition devices are designed and developed through which energy data are transferred to the edge IIoT servers. Hash encoding credentials and transport layer security protocol are applied to the servers. Furthermore, these servers can exchange data through a secured message queuing telemetry transport protocol. Edge and cloud databases are exploited to handle big data. For detecting the anomalies of individual electrical appliances in real-time, an algorithm based on a group of isolation forest models is developed and implemented on edge and cloud servers as well. In addition, remote-accessible online dashboards are implemented, enabling users to monitor the system. Overall, this study covers hardware design; the development of open-source IIoT servers and databases; the implementation of an interconnected global networking system; the deployment of edge and cloud artificial intelligence; and the development of real-time monitoring dashboards. Necessary performance results are measured, and they demonstrate elaborately investigating the feasibility of the proposed IIoT framework at the end.

Asunto(s)

RESUMEN

The development of the advanced metering infrastructure (AMI) and the application of artificial intelligence (AI) enable electrical systems to actively engage in smart grid systems. Smart homes with energy storage systems (ESS) and renewable energy sources (RES)-known as home microgrids-have become a critical enabling technology for the smart grid. This article proposes a new model for the energy management system of a home microgrid integrated with a battery ESS (BESS). The proposed dynamic model integrates a deep learning (DL)-based predictive model, bidirectional long short-term memory (Bi-LSTM), with an optimization algorithm for optimal energy distribution and scheduling of a BESS-by determining the characteristics of distributed resources, BESS properties, and the user's lifestyle. The aim is to minimize the per-day electricity cost charged by time-of-use (TOU) pricing while considering the day-basis peak demand penalty. The proposed system also considers the operational constraints of renewable resources, the BESS, and electrical appliances. The simulation results from realistic case studies demonstrate the validation and responsibility of the proposed system in reducing a household's daily electricity cost.

Asunto(s)

RESUMEN

Embedding optical camera communication (OCC) commercially as a favorable complement of radio-frequency technology has led to the desire for an intelligent receiver system that is eligible to communicate with an accurate light-emitting diode (LED) transmitter. To shed light on this issue, a novel scheme for detecting and recognizing data transmitting LEDs has been elucidated in this paper. Since the optically modulated signal is captured wirelessly by a camera that plays the role of the receiver for the OCC technology, the process to detect LED region and retrieval of exact information from the image sensor is required to be intelligent enough to achieve a low bit error rate (BER) and high data rate to ensure reliable optical communication within limited computational abilities of the most used commercial cameras such as those in smartphones, vehicles, and mobile robots. In the proposed scheme, we have designed an intelligent camera receiver system that is capable of separating accurate data transmitting LED regions removing other unwanted LED regions employing a support vector machine (SVM) classifier along with a convolutional neural network (CNN) in the camera receiver. CNN is used to detect every LED region from the image frame and then essential features are extracted to feed into an SVM classifier for further accurate classification. The receiver operating characteristic curve and other key performance parameters of the classifier have been analyzed broadly to evaluate the performance, justify the assistance of the SVM classifier in recognizing the accurate LED region, and decode data with low BER. To investigate communication performances, BER analysis, data rate, and inter-symbol interference have been elaborately demonstrated for the proposed intelligent receiver. In addition, BER against distance and BER against data rate have also been exhibited to validate the effectiveness of our proposed scheme comparing with only CNN and only SVM classifier based receivers individually. Experimental results have ensured the robustness and applicability of the proposed scheme both in the static and mobile scenarios.

Asunto(s)

RESUMEN

With the swift evolution of wireless technologies, the demand for the Internet of Things (IoT) security is rising immensely. Elliptic curve cryptography (ECC) provides an attractive solution to fulfill this demand. In recent years, Edwards curves have gained widespread acceptance in digital signatures and ECC due to their faster group operations and higher resistance against side-channel attacks (SCAs) than that of the Weierstrass form of elliptic curves. In this paper, we propose a high-speed, low-area, simple power analysis (SPA)-resistant field-programmable gate array (FPGA) implementation of ECC processor with unified point addition on a twisted Edwards curve, namely Edwards25519. Efficient hardware architectures for modular multiplication, modular inversion, unified point addition, and elliptic curve point multiplication (ECPM) are proposed. To reduce the computational complexity of ECPM, the ECPM scheme is designed in projective coordinates instead of affine coordinates. The proposed ECC processor performs 256-bit point multiplication over a prime field in 198,715 clock cycles and takes 1.9 ms with a throughput of 134.5 kbps, occupying only 6543 slices on Xilinx Virtex-7 FPGA platform. It supports high-speed public-key generation using fewer hardware resources without compromising the security level, which is a challenging requirement for IoT security.

RESUMEN

Radio-frequency technologies are widely applied in many fields such as mobile systems, healthcare systems, television and radio broadcasting, and satellite communications. However, one major problem in wireless communication based on radio frequencies is its impact on human health. High frequencies adversely impact human health more than low frequencies if the signal power transgresses the permissible threshold. Therefore, researchers are investigating the use of visible light waves (instead of the radio-frequency band) for data transmission in three major areas: visible light communication, light fidelity, and optical camera communication. In this paper, we propose a scheme that upgrades the camera on-off keying (COOK) scheme by using it with the multiple-input multiple-output (MIMO) scheme; COOK has been recommended by the IEEE 802.15.7-2018 standard. By applying technologies, such as matched filter, region of interest, and MIMO, our proposed scheme promises to improve the performance of the conventional scheme by improving the data rate, communication distance, and bit error rate. By controlling the exposure time, the focal length in a single camera and using channel coding, our proposed scheme can achieve the communication distance of up to 20 m, with a low error rate.

RESUMEN

Localization has become an important aspect in a wide range of mobile services with the integration of the Internet of things and service on demand. Numerous mechanisms have been proposed for localization, most of which are based on the estimation of distances. Depending on the channel modeling, each mechanism has its advantages and limitations on deployment, exhibiting different performances in terms of error rates and implementation. With the development of technology, these limitations are rapidly overcome with hybrid systems and enhancement schemes. The successful approach depends on the achievement of a low error rate and its controllability by the integration of deployed products. In this study, we propose and analyze a new distance estimation technique employing photography and image sensor communications, also named optical camera communications (OCC). It represents one of the most important steps in the implemented trilateration localization scheme with real architectures and conditions of deployment which is the second our contribution for this article. With the advantages of the image sensor hardware integration in smart mobile devices, this technology has great potential in localization-based optical wireless communication.

RESUMEN

Research on electronic healthcare (eHealth) systems has increased dramatically in recent years. eHealth represents a significant example of the application of the Internet of Things (IoT), characterized by its cost effectiveness, increased reliability, and minimal human eff ort in nursing assistance. The remote monitoring of patients through a wearable sensing network has outstanding potential in current healthcare systems. Such a network can continuously monitor the vital health conditions (such as heart rate variability, blood pressure, glucose level, and oxygen saturation) of patients with chronic diseases. Low-power radio-frequency (RF) technologies, especially Bluetooth low energy (BLE), play significant roles in modern healthcare. However, most of the RF spectrum is licensed and regulated, and the effect of RF on human health is of major concern. Moreover, the signal-to-noise-plus-interference ratio in high distance can be decreased to a considerable extent, possibly leading to the increase in bit-error rate. Optical camera communication (OCC), which uses a camera to receive data from a light-emitting diode (LED), can be utilized in eHealth to mitigate the limitations of RF. However, OCC also has several limitations, such as high signal-blockage probability. Therefore, in this study, a hybrid OCC/BLE system is proposed to ensure efficient, remote, and real-time transmission of a patient's electrocardiogram (ECG) signal to a monitor. First, a patch circuit integrating an LED array and BLE transmitter chip is proposed. The patch collects the ECG data according to the health condition of the patient to minimize power consumption. Second, a network selection algorithm is developed for a new network access request generated in the patch circuit. Third, fuzzy logic is employed to select an appropriate camera for data reception. Fourth, a handover mechanism is suggested to ensure efficient network allocation considering the patient's mobility. Finally, simulations are conducted to demonstrate the performance and reliability of the proposed system.

Asunto(s)

RESUMEN

This paper describes in brief features of various experimental devices constructed for half-ton synthesis of gadolinium(Gd)-loaded liquid scintillator (GdLS) and also includes the performances and detailed chemical and physical results of a 0.5% high-concentration GdLS. Various feasibility studies on useful apparatus used for loading Gd into solvents have been carried out. The transmittance, Gd concentration, density, light yield, and moisture content were measured for quality control. We show that with the help of adequate automated experimental devices and tools, it is possible to perform ton scale synthesis of GdLS at moderate laboratory scale without difficulty. The synthesized GdLS was satisfactory to meet chemical, optical, and physical properties and various safety requirements. These synthesizing devices can be expanded into massive scale next-generation neutrino experiments of several hundred tons.

RESUMEN

This paper presents a modulation scheme in the time domain based on On-Off-Keying and proposes various compatible supports for different types of image sensors. The content of this article is a sub-proposal to the IEEE 802.15.7r1 Task Group (TG7r1) aimed at Optical Wireless Communication (OWC) using an image sensor as the receiver. The compatibility support is indispensable for Image Sensor Communications (ISC) because the rolling shutter image sensors currently available have different frame rates, shutter speeds, sampling rates, and resolutions. However, focusing on unidirectional communications (i.e., data broadcasting, beacons), an asynchronous communication prototype is also discussed in the paper. Due to the physical limitations associated with typical image sensors (including low and varying frame rates, long exposures, and low shutter speeds), the link speed performance is critically considered. Based on the practical measurement of camera response to modulated light, an operating frequency range is suggested along with the similar system architecture, decoding procedure, and algorithms. A significant feature of our novel data frame structure is that it can support both typical frame rate cameras (in the oversampling mode) as well as very low frame rate cameras (in the error detection mode for a camera whose frame rate is lower than the transmission packet rate). A high frame rate camera, i.e., no less than 20 fps, is supported in an oversampling mode in which a majority voting scheme for decoding data is applied. A low frame rate camera, i.e., when the frame rate drops to less than 20 fps at some certain time, is supported by an error detection mode in which any missing data sub-packet is detected in decoding and later corrected by external code. Numerical results and valuable analysis are also included to indicate the capability of the proposed schemes.

RESUMEN

"Green" and energy-efficient wireless communication schemes have recently experienced rapid development and garnered much interest. One such scheme is visible light communication (VLC) which is being touted as one of the next generation wireless communication systems. VLC allows communication using multi-color channels that provide high data rates and illumination simultaneously. Even though VLC has many advantageous features compared with RF technologies, including visibility, ubiquitousness, high speed, high security, harmlessness for the human body and freedom of RF interference, it suffers from some problems on the receiver side, one of them being photo sensitivity dissimilarity of the receiver. The photo sensitivity characteristics of a VLC receiver such as Si photo-detector depend on the wavelength variation. The performance of the VLC receiver is not uniform towards all channel colors, but it is desirable for receivers to have the same performance on each color channel. In this paper, we propose a mitigation technique for reducing the performance variation of the receiver on multi-color channels. We show received power, SNR, BER, output current, and outage probability in our simulation for different color channels. Simulation results show that, the proposed scheme can reduce the performance variation of the VLC receiver on multi-color channels.

Asunto(s)