Biblio

In real-time video streaming, data packets are transported over the network from a transmitter to a receiver. The quality of the received video fluctuates as the network conditions change, and it can degrade substantially when there is considerable packet loss. Forward error correction (FEC) techniques can be used to recover lost packets by incorporating redundant data. Conventional FEC schemes do not work well when scalable video coding (SVC) is adopted. In this paper, we propose a novel FEC scheme that overcomes the drawbacks of these schemes by considering the reference picture structure of SVC and weighting the reference pictures more when FEC redundancy is applied. The experimental results show that the proposed FEC scheme outperforms conventional FEC schemes.

Currently, the pulse position modulation (PPM) schemes are suffering from bandwidth application where the line rate is double that of the initial data rate. Thus, the offset pulse position modulation (OPPM) has been suggested to rectify this concern. Several attempts to improve the OPPM can be found in the open literature. This study focuses on the utilization of Reed Solomon (RS) codes to enhance the forward error correction (FEC) bit error rate, which is not yet explored. The performance of errors of the uncoded OPPM was compared to the one used by RS coded OPPM using the number of photons per pulse, the transmission's efficacy, and bandwidth growth. The results demonstrate that employing FEC coding would increase the system's error performance especially when the RS is operating at its finest settings. Specifically, when operating with a capacity that is equivalent to or even more 0.7, the OPPM with RS code outperforms the uncoded OPPM where the OPPM with MLSD needs only 1.2×103 photons per pulse with an ideal coding rate of about 3/4.

We propose the use of dual coding concatenation for mitigation of post-shaping burst errors in probabilistic amplitude shaping (PAS) architectures. The proposed dual coding concatenation for PAS is a hybrid integration of conventional reverse concatenation and forward concatenation, i.e., post-shaping forward error correction (FEC) layer and pre-shaping FEC layer, respectively. A low-complexity architecture based on parallel Bose–Chaudhuri–Hocquenghem (BCH) codes is introduced for the pre-shaping FEC layer. Proposed dual coding concatenation can relax bit error rate (BER) requirement after post-shaping soft-decision (SD) FEC codes by an order of magnitude, resulting in a gain of up to 0.25 dB depending on the complexity of post-shaping FEC. Also, combined shaping and coding performance was analyzed based on sphere shaping and the impact of shaping length on coding performance was demonstrated.

In this work, we implement a complete probabilistic amplitude shaping (PAS) architecture on a field-programmable gate array (FPGA) platform to study the interplay between probabilistic shaping (PS) and forward error correction (FEC). Due to the fully parallelized input–output interfaces based on look up table (LUT) and low computational complexity without high-precision multiplication, hierarchical distribution matching (HiDM) is chosen as the solution for real time probabilistic shaping. In terms of FEC, we select two kinds of the mainstream soft decision-forward error correction (SD-FEC) algorithms currently used in optical communication system, namely Open FEC (OFEC) and soft-decision quasi-cyclic low-density parity-check (SD-QC-LDPC) codes. Through FPGA experimental investigation, we studied the impact of probabilistic shaping on OFEC and LDPC, respectively, based on PS-16QAM under moderate shaping, and also the impact of probabilistic shaping on LDPC code based on PS-64QAM under weak/strong shaping. The FPGA experimental results show that if pre-FEC bit error rate (BER) is used as the predictor, moderate shaping induces no degradation on the OFEC performance, while strong shaping slightly degrades the error correction performance of LDPC. Nevertheless, there is no error floor when the output BER is around 10-15. However, if normalized generalized mutual information (NGMI) is selected as the predictor, the performance degradation of LDPC will become insignificant, which means pre-FEC BER may not a good predictor for LDPC in probabilistic shaping scenario. We also studied the impact of residual errors after FEC decoding on HiDM. The FPGA experimental results show that the increased BER after HiDM decoding is within 10 times compared to post-FEC BER.

With a record 400Gbps 100-piece-FPGA implementation, we investigate performance of the potential FEC schemes for OIF-800GZR. By comparing the power dissipation and correction threshold at 10−15 BER, we proposed the simplified OFEC for the 800G-ZR FEC.

JPEG-XS offers low complexity image compression for applications with constrained but reasonable bit-rate, and low latency. Our paper explores the deployment of JPEG-XS on lossy packet networks. To preserve low latency, Forward Error Correction (FEC) is envisioned as the protection mechanism of interest. Although the JPEG-XS codestream is not scalable in essence, we observe that the loss of a codestream fraction impacts the decoded image quality differently, depending on whether this codestream fraction corresponds to codestream headers, to coefficient significance information, or to low/high frequency data. Hence, we propose a rate-distortion optimal unequal error protection scheme that adapts the redundancy level of Reed-Solomon codes according to the rate of channel losses and the type of information protected by the code. Our experiments demonstrate that, at 5% loss rates, it reduces the Mean Squared Error by up to 92% and 65%, compared to a transmission without and with optimal but equal protection, respectively.

The discussion about forward error correction (FEC) used for 800G unamplified link (800LR) is ongoing. Aiming at two potential options for FEC bit error ratio (BER) threshold, we propose two FEC schemes, respectively based on channel-polarized (CP) multilevel coding (MLC) and bit interleaved coded modulation (BICM), with the same inner FEC code. The field-programmable gate array (FPGA) verification results indicate that with the same FEC overhead (OH), proposed CP-MLC outperforms BICM scheme with less resource and power consumption.

In order to better cultivate engineering and application-oriented cryptographic talents, it is urgent to establish a joint school enterprise cryptographic laboratory. However, there is a core problem in the existing school enterprise joint laboratory construction scheme: the enterprise is not specialized and has insufficient cooperation ability, which can not effectively realize the effective integration of resources and mutual benefit and win-win results. To solve this problem, we propose a comprehensive evaluation model of cooperative enterprises based on entropy weight method and grey correlation analysis. Firstly, the multi-level evaluation index system of the enterprise is established, and the entropy weight method is used to objectively weight the index. After that, the grey weighted correlation degree between each enterprise and the virtual optimal enterprise is calculated by grey correlation analysis to compare the advantages and disadvantages of enterprises. Through the example analysis, it is proved that our method is effective and reliable, eliminating subjective factors, and providing a certain reference value for the construction of school enterprise joint cryptographic laboratory.

Transmitting data through the internet may have severe security risks due to illegal access done by attackers. Some methods have been introduced to overcome this issue, such as cryptography and steganography. Nevertheless, some problems still arise, such as the quality of the stego data. Specifically, it happens if the stego is shared with some users. In this research, a shared-secret mechanism is combined with steganography. For this purpose, the fuzzy logic edge detection and Prediction Error (PE) methods are utilized to hide private data. The secret sharing process is carried out after data embedding in the cover image. This sharing mechanism is performed on image pixels that have been converted to PE values. Various Peak Signal to Noise Ratio (PSNR) values are obtained from the experiment. It is found that the number of participants and the threshold do not significantly affect the image quality of the shares.

Cloud computing is preferred because of its numerous improvements, such as data security, low maintenance cost, unlimited storage capacity and consistent backups. However, legitimate users take advantage of cloud storage services for storing a considerable amount of sensitive data. After storing data on the cloud, data users pass on control over data to cloud administrators. Although for assuring data security, sensitive information needs to be encrypted before deploying it on the cloud server. In traditional searchable encryption, encrypted data can be searched using keywords on a cloud server without knowing data details, and users can retrieve certain specific files of interest after authentication. However, the results are only related to the exact matching keyword searches. This drawback affects system usability and efficiency, due to which existing encryption methods are unsuitable in cloud computing. To avoid the above problems, this study includes as follows: Firstly, we analyze all fuzzy keyword search techniques that are wildcard based, gram based and trie-traverse. Secondly, we briefly describe certificateless cryptography and suggest a certificateless searchable encryption scheme. Finally, this study gives easy access to developing a fuzzy keyword searchable system for a new researcher to combine the above two points. It provides easy access and efficient search results.

This article is about a criterion based on credibility complex fuzzy set (CCFS) to study the prevailing substitution boxes (S-box) and learn their properties to find out their suitability in image encryption applications. Also these criterion has its own properties which is discussed in detailed and on the basis of these properties we have to find the best optimal results and decide the suitability of an S-box to image encryption applications. S-box is the only components which produces the confusion in the every block cipher in the formation of image encryption. So, for this first we have to convert the matrix having color image using the nonlinear components and also using the proposed algebraic structure of credibility complex fuzzy set to find the best S-box for image encryption based on its criterion. The analyses show that the readings of GRAY S-box is very good for image data.

The WSN nodes are arranged uniformly or randomly on the area of need for gathering the required data. The admin utilizes wireless broadband networks to connect to the Internet and acquire the required data from the base station (BS). However, these sensor nodes play a significant role in a variety of professional and industrial domains, but some of the concerns stop the growth of WSN, such as memory, transmission, battery power and processing power. The most significant issue with these restrictions is to increase the energy efficiency for WSN with rapid and trustworthy data transfer. In this designed model, the sensor nodes are clustered using the FCM (Fuzzy C-Means) clustering algorithm with the Reptile Search Optimization (RSO) for finding the centre of the cluster. The cluster head is determined by using African vulture optimization (AVO). For selecting the path of data transmission from the cluster head to the base station, the adaptive relay nodes are selected using the Fuzzy rule. These data from the base station are given to the server with a DNA cryptography encryption algorithm for secure data transmission. The performance of the designed model is evaluated with specific parameters such as average residual energy, throughput, end-to-end delay, information loss and execution time for a secure and energy-efficient routing protocol. These evaluated values for the proposed model are 0.91 %, 1.17Mbps, 1.76 ms, 0.14 % and 0.225 s respectively. Thus, the resultant values of the proposed model show that the designed clustering-based routing protocol using FCM-RSOA and DNA cryptography for smart building performs better compared to the existing techniques.

With the development of the digital development transformation of the power grid, the classification of power unstructured encrypted data is an important basis for data security protection. However, most studies focus on exact match classification or single-keyword fuzzy match classification. This paper proposes a fuzzy matching classification method for power unstructured encrypted data. The data owner generates an index vector based on the power unstructured file, and the data user generates a query vector by querying the file through the same process. The index and query vector are uploaded to the cloud server in encrypted form, and the cloud server calculates the relevance score and sorts it, and returns the classification result with the highest score to the user. This method realizes the multi-keyword fuzzy matching classification of unstructured encrypted data of electric power, and through the experimental simulation of a large number of data sets, the effect and feasibility of the method are proved.

Identity based digital signature is an important research topic of public key cryptography, which can effectively guarantee the authentication, integrity and unforgeability of data. In this paper, a new fuzzy multi-identity based signature scheme is proposed. It is proved that the scheme is existentially unforgeable against adaptively chosen message attack, and the security of the signature scheme can be reduced to CDH assumption. The storage cost and the communication overhead are small, therefore the new fuzzy multi-identity based signature (FMIBS) scheme can be implemented efficiently.

The security and privacy of the network in Internet of Things (IoT) systems are becoming more critical as we are more dependent on smart systems. Considering that packets are exchanged between the end user and the sensing devices, it is then important to ensure the security, privacy, and integrity of the transmitted data by designing a secure and a lightweight authentication protocol for IoT systems. In this article, in order to improve the authentication and the encryption in IoT systems, we present a novel method of authentication and encryption based on elliptic curve cryptography (ECC) using random numbers generated by fuzzy logic. We evaluate our novel key generation method by using standard randomness tests, such as: frequency test, frequency test with mono block, run test, discrete Fourier transform (DFT) test, and advanced DFT test. Our results show superior performance compared to existing ECC based on shift registers. In addition, we apply some attack algorithms, such as Pollard’s \textbackslashrho and Baby-step Giant-step, to evaluate the vulnerability of the proposed scheme.

In today's power market, it's vital to keep electrical energy affordable to the vast majority of people while maintaining the highest degree of dependability. Due to which, the transmission network must operate beyond transfer limitations, generating congestion on transmission lines. These transmission line difficulties can be alleviated with the use of reactive power adjustment based on FACTS devices. Using a fuzzy tuned Static Synchronous Series Compensator [SSSC], this research proposes a novel method for calculating the effective damping oscillation control signals. The performance of the SSSC is compared to that of fuzzy logic-based controllers using PI controllers. According to the simulation results, the SSSC with fuzzy logic control effectively improves power flow under disrupted conditions

Currently, air pollution is still a problem that requires special attention, especially in big cities. Air pollution can come from motor vehicle fumes, factory smoke or other particles. To overcome these problems, a system is made that can monitor environmental conditions in order to know the good and bad of air quality in an environment and is expected to be a solution to reduce air pollution that occurs. The system created will utilize the Wireless Sensor Network (WSN) combined with Waspmote Smart Environment PRO, so that later data will be obtained in the form of temperature, humidity, CO levels and CO2 levels. From the sensor data that has been processed on Waspmote, it will then be used as input for data processing using a fuzzy algorithm. The classification obtained from sensor data processing using fuzzy to monitor environmental conditions there are 5 classifications, namely Very Good, Good, Average, Bad and Dangerous. Later the data that has been collected will be distributed to Meshlium as a gateway and will be stored in the database. The process of sending information between one party to another needs to pay attention to the confidentiality of data and information. The final result of the implementation of this research is that the system is able to classify values using fuzzy algorithms and is able to secure text data that will be sent to the database via Meshlium, and is able to display data sent to the website in real time.

One of the most challenging issues facing Internet of Medical Things (IoMT) cyber defense is the complexity of their ecosystem coupled with the development of cyber-attacks. Medical equipments lack built-in security and are increasingly becoming connected. Moving beyond traditional security solutions becomes a necessity to protect patients and organizations. In order to effectively deal with the security risks of networked medical devices in such a complex and heterogeneous system, we need to measure security risks and prioritize mitigation actions. In this context, we propose a Fuzzy AHP-based method to assess security attributes of connected medical devices and compare different device models against a selected profile with regards to the user requirements. The proposal aims to empower user security awareness to make well-educated decisions.

Secrete message protection has become a focal point of the network security domain due to the problems of violating the network use policies and unauthorized access of the public network. These problems have led to data protection techniques such as cryptography, and steganography. Cryptography consists of encrypting secrete message to a ciphertext format and steganography consists of concealing the secrete message in codes that make up a digital file, such as an image, audio, and video. Steganography, which is different from cryptography, ensures hiding a secret message for secure transmission over the public network. This paper presents a steganographic approach using digital images for data hiding that aims to providing higher performance by combining fuzzy logic type I to pre-process the cover image and difference expansion techniques. The previous methods have used the original cover image to embed the secrete message. This paper provides a new method that first identifies the edges of a cover image and then proceeds with a difference expansion to embed the secrete message. The experimental results of this work identified an improvement of 10% of the existing method based on increased payload capacity and the visibility of the stego image.

Considering the evidence that conditions accept a considerable place in each of the structures, owing to limited assets available at each sensor center, it is a difficult problem. Vitality safety is the primary concern in many of the implementations in remote sensor hubs. This is critical as the improvement in the lifetime of the device depends primarily on restricting the usage of vitality in sensor hubs. The rationing and modification of the usage of vitality are of the most serious value in this context. In a remote sensor arrangement, the fundamental test is to schedule measurements for the least use of vitality. These classification frameworks are used to frame the classes in the structure and help efficiently use the strength that burdens out the lifespan of the network. Besides, the degree of the center was taken into account in this work considering the measurement of cluster span as an improvement to the existing methods. The crucial piece of leeway of this suggested approach on affair clustering using fuzzy logic is which can increase the lifespan of the system by reducing the problem area problem word.

In order to control users’ access to the information system, it is necessary to develop a security system that can work in real time and easily reconfigure. This problem can be solved using a fuzzy logic. In this paper the authors propose a fuzzy distribution system for access to the student assessment system, which takes into account the level of user access, identifier and the risk of attack during the request. This approach allows process fuzzy or incomplete information about the user and implement a sufficient level of confidential information protection.

In this paper, fuzzy logic was implemented as a proposed approach for modelling of Thickness as an output response of thin film layer in Titanium Nitrite (TiN). The layer was deposited using Physical Vapor Deposition (PVD) process that uses a sputtering technique to coat insert cutting tools with TiN. Central cubic design (CCD) was used for designing the optimal points of the experiment. In order to develop the fuzzy rules, the experimental data that collected by PVD was used. Triangular membership functions (Trimf) were used to develop the fuzzy prediction model. Residual error (e) and prediction accuracy (A) were used for validating the result of the proposed fuzzy model. The result of the developed fuzzy model with triangular membership function revealed that the average residual error of 0.2 is low and acceptable. Furthermore, the model obtained high prediction accuracy with 90.04%. The result revealed that the rule-based model of fuzzy logic could be an efficient approach to predict coatings layer thickness in the TiN.

This paper aims to contribute towards creating ambient abnormal behavior detection for smart security system from real-time human pose estimation using fuzzy-based systems. Human poses from keypoint detected by pose estimation model are transformed to as angle positions of the axis between human bodies joints comparing to reference point in the axis x to deal with problem of the position change occurred when an individual move in the image. Also, the article attempts to resolve the problem of the ambiguity interpreting the poses with triangular fuzzy logic-based system that determines the detected individual behavior and compares to the poses previously learnt, trained, and recorded by the system. The experiment reveals that the accuracy of the system ranges between 90.75% (maximum) and 84% (minimum). This means that if the accuracy of the system at 85%. The system can be applied to guide future research for designing automatic visual human behavior detection systems.

The paper presents the design of fuzzy logic controller based unified power flow controller (UPFC) to improve power system security performance during steady state as well as fault conditions. Fuzzy interference has been design with two inputs Vref and Vm for the shunt voltage source Converter and two inputs for Series Id, Idref, Iq, Iqref at the series voltage source converter location. The coordination of shunt and series VSC has been achieved by using fuzzy logic controller (FLC). The comparative performance of PI based UPFC and fuzzy based UPFC under abnormal condition has been validated in MATLB domain. The combination of fuzzy with a UPFC is tested on multi machine system in MATLAB domain. The results shows that the power system security enhancement as well as oscillations damping.

Human facial emotion recognition pays a variety of applications in society. The basic idea of Facial Emotion Recognition is to map the different facial emotions to a variety of emotional states. Conventional Facial Emotion Recognition consists of two processes: extracting the features and feature selection. Nowadays, in deep learning algorithms, Convolutional Neural Networks are primarily used in Facial Emotion Recognition because of their hidden feature extraction from the images. Usually, the standard Convolutional Neural Network has simple learning algorithms with finite feature extraction layers for extracting information. The drawback of the earlier approach was that they validated only the frontal view of the photos even though the image was obtained from different angles. This research work uses a deep Convolutional Neural Network along with a DenseNet-169 as a backbone network for recognizing facial emotions. The emotion Recognition dataset was used to recognize the emotions with an accuracy of 96%.