Current implementations of attendance checking in the University of the Philippines - Diliman (UPD) has been time consuming and easily cheated. The roll-call and pen-and-paper method cannot monitor student presence for the whole duration of the class session. Attendance checking systems using mobile technology and the Internet of Things attempt to mitigate these problems, but it also introduces new ones such as inclusivity, cost, and complex implementations. This study investigates the use of Bluetooth Low Energy (BLE) beacons, a mobile application, and a web server to create an attendance checking mechanism capable of eliminating queues and attendance cheating, monitoring student presence, and automating records. We created two procedures for sending information to our server, to determine the general advantages and disadvantages of each in terms of features, scalability, and cost-effectiveness. Procedure 1 mainly uses our Android application which was able to automate and record attendance checking in the background. Procedure 2 uses the ESP32 which was capable of scanning for information from these Android smartphones. Both of these Procedures send information to the web server to create reports based on available records. Overall, Procedure 1 served as the more scalable implementation due to its added features such as alarm systems, and ease of monitoring. However, Procedure 2 was simpler to set-up and more energy-efficient for smartphones since it relied on processing capabilities of the server.
In recent years, unmanned aerial vehicle (UAV) communication has not only attracted extensive discussion in academic circles, but also has been applied to practical scenarios. With the rapid development of UAV communication, its secrecy issues have gradually become prominent. In this paper, the physical layer security of mobile UAV relaying network is studied. We give a scheme of confidential communication to ensure the integrity and confidentiality of information. By optimizing the dynamic position of the UAV and transmit power, our goal is to maximize the minimum secrecy rate. Because the problem we put forward can not be solved directly by the solver, we divide the problem into two sub-problems to analyse. The simulation results show that our program improves the fairness of secrecy communication, and the physical layer security of the mobile UAV relaying network has been enhanced.
In this work, the performance of an eigenvalue-based cooperative spectrum sensing for multiuser multiple-input multiple-output (MIMO) cognitive radio networks is investigated under a correlated fading scenario. The secondary user (SU) is modeled as a MIMO system to detect the presence of primary user signal under incomplete channel state information (CSI) and Rayleigh faded channel model. At each SU, an energy detector is used to obtain the local decision statistic. Next, SU’s local decision is sent to the fusion center (FC) via numerous transmit antennas in order to get the transmitting diversity gain to combat the hidden node problem. Further, FC received the local decision statistic with multiple antennas under Racine faded correlated channel with perfect CSI. Finally, a global decision is made at FC based on an eigenvalue-based detection algorithm. The closed-form expression for the detection probabilities is derived at both SUs and FC. A simulation study shows that the target detection probability P_d≥ 0.95 is achieved even at a very low signal to noise ratio value of -5 dB.
Integration of full-duplex (FD) technology with cooperative device-to-device (CD2D) communications system has recently emerged as a potential candidate for the fifth-generation (5G) and beyond technologies to improve the spectral efficiency of a cellular system. This paper investigates the outage performance of a multicarrier FD CD2D communications system wherein, the D2D link source node (S) serves as an FD relay for uplink cellular transmission. The self-interference (SI) occurring at S is suppressed using analog cancellation schemes, and the residual SI (RSI) is further suppressed using digital domain (DD) RSI cancellation techniques. A probabilistic mathematical model is established for the performance evaluation of the proposed system. The closed-form expressions for D2D and cellular outage probability are derived. Simulation results show that the proposed FD CD2D communications system, along with minimum mean square error-based DD RSI cancellation, gives the optimum performance compared to the least square-based RSI cancellation scheme.
Channel Assignment (CA) in wireless mesh networks (WMNs) has not been well studied in scenarios where the network uses Dynamic Spectrum Access (DSA). This work aims to fill some of this gap. We compare metaheuristic algorithms for optimising the CA in a WMN that has both Wi-Fi and DSA radios (where DSA could be Television White Spaces or 6 GHz). We also present a novel algorithm used alongside these metaheuristic algorithms to ensure that the CA solutions are feasible. Feasible solutions meet the interface constraint, i.e. only as many channels are allocated to a node as it has radios. The algorithm also allows the topology to be preserved by maintaining links. Many previous studies tried to ensure feasibility and/or topology preservation by using two separate steps. The first step optimised without checking feasibility and the second step fixed infeasible solutions. This second step often negated the benefits of the previous step and degraded performance. Other CA algorithms tend to use simple on/off interference models, instead of models that more realistically reflect the physical layer environment, such as the Signal to Interference plus Noise Ratio (SINR). We present our more realistic SINR-based model and optimisation objective. Simulated Annealing (SA) and Genetic Algorithm (GA) are applied to the problem. Performance is evaluated and verified through simulation. We find that GA outperforms SA, finding higher quality solutions faster, although both metaheuristics are better than random allocations. GA can be used daily to find good CAs in changing conditions.
With the rapid development of mobile communication technology and the applications of 5G technology, video transmission, compared with file transmission, audio transmission and other media forms, has been more and more widely used. Today, the mobile video broadcasting needs to overcome some difficulties like the transmission noise. A knowledge-enhanced mobile video broadcasting (KMV-Cast) is a scheme utilizing joint source-channel coding and the correlated information in clouds, but in its calculation, there is still an item of noise that cannot be eliminated at the receiver side. In this paper, as same to KMV-Cast, the new scheme also exploits the hierarchical Bayesian model, the correlated information distillation in the clouds and Bayesian estimation algorithm to improve video quality. After the video reconstruction at the receiver, based on the items of the signal and the noise, selectively adds a Wiener filter to reduce the effect of noise. The simulation results show that the proposed KMV-Cast scheme with a proper Wiener filter at the receiver side is superior to that scheme without the Wiener filter and it achieves about 2 dB more of the peak signal-to-noise ratio (PSNR) gain at low-SNR channels (i.e., −10 dB) and about 1.5 dB more of PSNR gain at high-SNR channels (i.e., 10 dB).
In order to protect image copyright and anti-counterfeit authentication, digital watermarking technology arises at the historic moment. The contradiction between robustness and invisibility affects the development of watermarking technology. To solve this problem, a watermarking algorithm based on spread spectrum and DFT is proposed. The main work of this paper is as follows: firstly, image embedding watermarking: the original image is segmented into two-dimensional discrete Fourier transform, then the watermark information through sequence even spread spectrum is superimposed, and finally the image with watermark is obtained by the inverse discrete Fourier transform. Secondly, extracting watermark information: the discrete Fourier transform is applied to the blocks of the original image and the image with watermark, the spread spectrum watermark information is extracted by comparing the amplitude and the original watermark is obtained by the final solution and expansion. Finally, the simulation results show that the proposed algorithm has good performance in robustness and invisibility.
Wireless systems such as cellular networks have begun to see proposals for increased operational flexibility through reuse of the same hardware but with different signal standards. This paper presents an approach to characterise a power amplifier (PA) for multiple signal standards. Following from this, behavioural modeling demonstrates that the same coefficients trained for a single signal standard can be effectively applied to multiple signal standards. This result is used to design and implement a digital predistorter (DPD) capable of linearizing for different signal standards on a Field Programmable Gate Array (FPGA). This implementation is experimentally validated on a state-of-the-art RFSoC FPGA from Xilinx to correct for PA non-linearities in the transmit chain using an efficient hardware design. Additionally the behavioural modelling and DPD solutions have been validated using distinctly different PAs to demonstrate the proposed look up table approach is hardware agnostic and works when the appropriate dimensions are set for the dynamic nonlinear structure in each case.
An intelligent reflecting surface (IRS), is a new era of wireless communication towards intelligent and reconfigurable wireless networks. IRS can enhance communication quality between the network terminals with a small cost, low complexity, and low energy consumption when the direct connection has been blocked. To obtain the IRS features, the acquisition of channel state information (CSI) is substantial but it's challenging in practice, due to the massive number of IRS elements without any capabilities of signal processing. To deal with this challenge in this survey, we first introduce an overview of channel estimation for IRS, then we address the main recent techniques proposed to estimate channels in IRS with various strategies in different applications. Furthermore, we summarize these recent works and list the main points that affect the estimation of the channel in IRS-aided communication system, and finally outline some future researches in IRS channel estimation and the conclusion of this survey.
In this paper, low-profile frequency reconfigurable patch antennas for cognitive radio applications are proposed on the ultra-wideband (UWB) frequency range. The methodology of the proposed antennas is described in this paper, and is achievable by controlling the electrical length of the antenna. additionally, miniaturized patch antennas are achieved by using shorting post technique and partial ground plane. The substrate used in the proposed antennas is the Rogers duroid 6010 with a dielectric constant of 10.2 and thickness of 0.635 mm. The presented antennas are designed and simulated using High-Frequency Structure Simulator (HFSS). The advantage of the proposed antenna designs is the compact size 8 mm × 8 mm (and smaller). The proposed antennas are designed, analyzed, and compared to a conventional microstrip patch antenna, and the total area size reduction is about 89
Wideband spectrum data can provide information on how large portions of the spectrum are being used. Spectrograms are typically used to visualize this data. The interpretation of the spectrogram (e.g., identification of bands and patterns) is left to the user, requires significant domain knowledge and is extremely time consuming. In this paper, we present a methodology for combining quantitative and qualitative information to identify channels and changes in spectrum occupancy. Channel identification and change detection algorithms are applied to real spectrum data collected over several years on two different measurement systems in Chicago. These analyses were then used to formulate queries to a knowledge graph implemented on a neo4j graph database. The results of the queries validated the channel identification and provided validation and explanation of the changes detected. This methodology was tested on measurement data from 470–698 MHz.
In the context of the current normalization of epidemic prevention, the nucleic acid detection process in colleges and universities is limited in human and material resources. Teachers and students who perform nucleic acid detection often cannot maintain a distance of more than one meter from others, and there is a pedestrian group behavior that has a large cross-infection safety hazard. This article uses Depthwise Separable Convolution to improve the YOLOv3 algorithm, and the improved network structure constructs a pedestrian detection, pedestrian tracking, pedestrian counting and pedestrian cluster system based on Deep Learning under the TensorFlow framework. The training parameters and training time of the improved network model are reduced to a certain extent, improved the operation efficiency of the network model. The advantage is that it realizes the function of monitoring centralized nucleic acid detection scenes in colleges and universities and assisting volunteers to maintain a reasonable order, which can effectively prevent cross-infection problems caused by cluster effects.
The digital evolution of Industry 4.0 enabled Operational Technology (OT) infrastructures to operate and remotely maintain cyber-physical systems bridging over IT infrastructures. It has also expanded new attack surfaces and steadily increased the number of malicious cyber incidents for the interconnected smart critical systems. Within Industrial Control System (ICS), Programmable Logic Controller (PLC) plays a crucial function to bridge between cyber and physical environments which made them the victim of sophisticated cyber-attacks that are designed to interrupt and damage their operations. Honey-pots have been used as a key tool for aggregating real threat data e.g., malicious activities and payloads, to observe and determine different attack methods and strategies that can easily affect poorly secured cyber-physical systems. In this research, we deployed T-pot honeypot in Amazon Elastic Compute Cloud (AWS EC2) instance across six different regions to determine the current threat landscape as well as how knowledgeable and ingenious threat actors could be in compromising internet-facing Industrial Control System (ICS).
Internet of Things (IoT) is steadily evolving which allows a new paradigm of smart sensors and lightweight devices interacting with one another without human intervention, also known as machine-to-machine (M2M) communication. This allows solution for various fields such as Smart Home Automation. In this scenario, the satisfaction of security and data privacy requirements play a fundamental role. Blockchain technology with the help of cryptography offers a solution by facilitating transactions and the coordination of devices without the need of a central authority. This study aims to improve the current smart home by developing and implementing a blockchain-based authentication system with the use of the Blockchain data structure, protocols and cryptographic algorithms such as Advanced Encryption Standard (AES), Secure Hash Algorithm (SHA-256), and Keyed-Hashing for Message Authentication (HMAC) on a microcontroller board equipped with hardware acceleration. Our performance analysis showed that hardware acceleration provided significant improvement in processing time with a speedup of 5.53 and 7.94 times for AES-128 and SHA-256, respectively, compared to a software implementation counterpart.
With the rapid development of artificial intelligence, human-computer interaction has drawn more researcher’s attention. As one of the most important ways of human-computer interaction, Gesture recognition has been widely used in many fields. In this paper, an improved YOLOv5s gesture recognition algorithm is proposed, and the results of gesture recognition are used to carry out interactive experiments with the computer. Different gesture selects corresponding style, then the image style transfer network finishes the image style switch according to the image style. At the same time, PyQt5 is used to design an interactive interface to realize gesture recognition and image style conversion. Compared with YOLOv5s, the recall rate of gesture recognition by the improved algorithm is 94.77
The study aims to obtain higher spectrum efficiency of the cognitive radio system, effectively solve the hidden terminal problem caused by single user spectrum sensing, and improve the spectrum sensing performance of cognitive radio. Based on the analysis of the hard decision and soft decision fusion threshold, the linear weighted cooperative sensing algorithm is used. The purpose is to optimize the soft decision fusion cooperative spectrum sensing threshold from the two perspectives of minimizing the error probability and maximizing the average throughput of the cognitive network. The mathematical function model of error probability and throughput sensing threshold is established, the expression of the optimal threshold is derived, and the influence of various spectrum sensing parameters on the optimal decision threshold is analyzed. It is found that: when the appropriate sensing threshold is selected, compared with other algorithm models of radio spectrum sensing, the performance of the optimized soft decision fusion model proposed is better. It can reduce the error probability and improve the detection accuracy. When the throughput capacity of the cognitive network reaches the maximum, the optimal threshold obtained by the soft decision algorithm makes the detection probability higher up to 93.83%, and the overall performance of the cognitive system is better. The results have specific practical significance and practical value for the research of cognitive radio spectrum sensing.
This work addresses position falsification attacks of malicious nodes against spectrum users and devises a strategy to detect such nodes. We conducted over 6 months of measurements to confirm the practicability of using RSSI under varying weather conditions, which confirms that RSSI fluctuates along the mean. Also, the simulation results obtained show that collaborative neighbour monitoring in hybrid (centralized and distributed) networks work well in detecting position falsification attacks in dynamic spectrum access networks, provided that the distance between the actual malicious node position and the falsified position is at least 0.3 km.
In this paper, cooperative spectrum sensing (CSS) of dynamic primary user (PU) is considered in Laplacian noise environment. The dynamic PU is characterized by its transitions from ON (present) state to OFF (absent) state and vice-versa. It means, during the entire sensing duration, the PU appears or disappears intermittently. We assume that each cognitive radio (CR) uses conventional test-statistics such as energy detection (ED), absolute value cumulation detection (AVCD) and improved AVCD (i-AVCD). The hard decision from each CR fuses at the fusion center (FC) according to CSS based on OR rule (CSS-OR), CSS-AND rule and CSS-majority rule to make a final decision on the appearance or disappearance of the PU. We further consider dynamic nature of the PU in terms of its arrival rate $$(\theta _{A})$$ and departure rate $$(\theta _{D})$$ . We present performance of the CSS of dynamic PU using receiver operating characteristic (ROC) and detection probability ( $$P_D$$ ) versus average signal-to-noise ratio (SNR), denoted by $$\gamma $$ , using Monte Carlo simulations. We conclude that the CSS-OR rule based spectrum sensing outperforms CSS-majority rule and CSS-AND rule based spectrum sensing over a wide range of average SNR, i.e., $$-10<\gamma <10$$ dB. We further conclude that CSS-AND rule is unsuitable for enhancing the detection probability of conventional sensing schemes. Furthermore, CSS-majority rule outperforms conventional sensing schemes ED, AVCD and i-AVCD beyond $$\gamma =-1,~-5$$ and $$-6$$ dB, respectively.
With the development of 5G technology, the demand for communication quality has shown exponential growth. Regarding the problem of low communication quality at the edge of the LTE-Advanced cellular network, we can expand the coverage and share base station traffic by introducing relay technology and D2D technology, thereby increasing the throughput of the communication system. The research background of this paper is to combine pass-through (direct) D2D mode with LTE-A relay cellular network in LTE-A cells, and then construct the MINLP optimization problem with maximizing system throughput as the objective function. The convex optimization method is used to realize the optimal allocation of power resources. The final simulation results show the growth of system throughput and user fairness.
Variance-based thresholding is one of the most popular methods for image segmentation. The mechanism of variance-based thresholding methods is to minimize the class variance. A novel minimum class variance thresholding method based on multi-objective optimization has been presented, and the ideal threshold is achieved by minimizing the variance of each class and the sum of them, and this will lead to more satisfactory segmentation result. The presented method possesses the merits of restraining the class probability and the class variance effects, and it is more accurate. Firstly, the proposed method is compared quantitatively with other methods on lots of synthetic images with the convenience of obtaining the ideal thresholds precisely and the ground-truth images exactly. The presented method possess better performance at most magnitudes of the noise. At the same time, experiments over real infrared images and visual images also have illustrated the better performance of the presented method.
