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Summary A comparative analysis of 3D positioning error for two different configurations using different layouts of visible light communication (VLC) systems is presented in this paper. The Received Signal Strength (RSS) has been implemented for indoor localization systems using Line‐of‐Sight (LoS) and diffused reflection signals. The room size for configuration‐1 is 5 m × 5 m × 3 m, and the distance between adjacent LEDs is 2.5 m, 2.00 m, and 1.5 m for cases‐1, case‐2, and case‐3, respectively, whereas the room size for configuration‐2 is 7 m × 7 m × 5 m, and the separation between the LEDs is 3.5 m, 3 m, and 2.5 m for their respective cases. Through investigation, it has been shown that when only LS signal is considered, the separation between LEDs may not be an issue because positioning error changes by a very small amount as the separation between LEDs changes. The results show that as the distance between adjacent LEDs decreases, the received signal strength for LoS and L‐R1 signals increases. However, positioning error and BER rise, while the bit rate falls. Furthermore, the positioning error Vs receiver plane height for all three cases in configuration‐1 is the same up to a height of 2.89 m, whereas the positioning error in configuration‐2 is the same up to 4.4 m for all cases. The positioning error for case‐1 decreases as the height in configuration‐1 exceeds 2.89 m. Similarly, after reaching a height of 4.4 m for case‐2, the positioning error in configuration‐2 decreases. The LoS positioning error versus semi angle φ1/2$$ {\varphi}_{1/2} $$ of the LED as well as the FOV of the receiver has been simulated for different positions of the receiver in configuration‐1. The investigation shows that the minimum positioning error is achieved at φ1/2$$ {\varphi}_{1/2} $$ and FOV equal to 66.660 for all the positions of the receiver in the room. Thus, before configuring a practical indoor VLC geometrical model, proper VLC configurations such as LED separation, FOV of the receiver, semi angle of LED, and receiver height should be chosen based on the room dimensions. [ABSTRACT FROM AUTHOR]

One of the ways to increase the volume of transmitted information is to increase the bit rate above the Nyquist barrier. However, an increase in bit rate in the case of FTN (Faster-Than-Nyquist) signals leads to an increase in energy costs for receiving information on channels with limited bandwidth, for example, in Digital Video Broadcasting satellite systems like DVB-S2/S2X. It is possible to minimize energy losses by using the processing algorithm "maximum likelihood sequence estimation". However, the computational complexity of this algorithm is extremely high, which limits its use, especially in terrestrial mobile satellite terminals. We propose a new bit-by-bit decision feedback algorithm with maximum likelihood ratio estimation of subsequent symbols in the observation interval. This algorithm provides minimal energy costs comparable to the method "maximum likelihood sequence estimation" at speeds 2–3 times higher than the Nyquist barrier. At the same time, the complexity is two orders of magnitude less. It is shown by simulation for a channel with additive noise that energy losses in relation to the potential bit error rate (BER) are less than 4.5 dB. In the presence of Rayleigh fading, the application of the proposed algorithm makes it possible to provide the processing of FTN signals for double bit rates in urban areas with energy costs equal to 12 dB when using an equalizer. We give numerical estimations of the increase in computational complexity for the proposed processing algorithm. It is shown that an increase in the bit rate by 1.5 times leads to an increase in the computational complexity by more than an order of magnitude. The same conclusion can be reformulated in another form: for the proposed algorithm, each decibel of energy gain is achieved by increasing the number of computational operations by 1.5 × 10 5 . It is experimentally shown that additional energy losses due to non-ideal phase and timing synchronization are no more than 1 dB when the proposed algorithm is applied in a fading channel. The energy costs in fading channels relative to a stationary channel for twice the Nyquist rate are equal to 13.8 dB when using an equalizer. [ABSTRACT FROM AUTHOR]

Video streaming services require adaptive bit rate strategies that optimize video quality based on network conditions and user preferences to provide a cost-effective and scalable solution. In this manuscript, we present a novel architecture that utilizes a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract features from the video stream and predict optimal bit rates for each frame. The CNN feature extractor extracts relevant features from the input video stream, which are then passed on to the RNN predictor, where the optimal bit rate is predicted to ensure a personalized viewing experience tailored to the current network conditions and specific user preferences. Our experimental results demonstrate that the proposed architecture achieves significant improvements in video quality while minimizing bandwidth usage and providing a better user experience. Specifically, our results show a 37.1% improvement in average bit rate, indicating that the proposed architecture optimizes the video quality and reduces bandwidth usage by 37.1% on average. We also observed a 16.6% improvement in Quality of Experience (QoE), meaning that users will perceive the video quality as better. Additionally, rebuffering was reduced by 87.5%, indicating that users enjoy smoother video playback without interruptions. Importantly, our architecture is optimized for Dynamic Adaptive Streaming over HTTP (DASH), addressing the need for efficient streaming over the most widely used protocol in the industry. [ABSTRACT FROM AUTHOR]

In the conventional LTE homogeneous network, sufficient transmit power of user equipment (UE) is determined by open-loop power control (OL-PC) and closed-loop power control (CL-PC) schemes. However, in a Het-Net environment, setting the UE’s transmit power requires delicate responsiveness to handle the severe and complicated uplink interference. In this paper, an interference-aware uplink power control mechanism based on Heuristic game theory approach is proposed for devices coexisting in a heterogeneous wireless network. Various wireless constraints like channel response, path loss, fading, shadowing, interference and metrics like SNR, SINR, throughput and bit rates are considered. Uplink power is controlled by suitably selecting the penalization factor (β) based on a simple Heuristic game theory approach considering the possible wireless constraints of each user depending on its location in the cell under consideration. The algorithm is framed in such a way to reduce inter-cell interference, limit transmit power, enhance bit rates and throughput of users. A significant improvement of 5.2% in the user coverage/ distribution is achieved as a result of interference management compared to conventional power control scheme and power control with convex pricing. [ABSTRACT FROM AUTHOR]

The Adhoc On-Demand Distance Vector (AODV) protocol faces challenges in selecting the best relay nodes, which requires optimization to improve performance in Vehicular ad-hoc networks (VANETs). This study aims to enhance Vehicle-to-Vehicle (V2V) communication in VANETs by implementing the Learning Automata-Driven Ad-hoc On-Demand Distance Vector (LA-AODV) routing protocol. LA-AODV is designed to achieve higher packet delivery ratios and optimize data transfer rates, even under congested network conditions, by dynamically adjusting to changing network scenarios. The performance evaluation includes six key metrics analyzed under varying node densities and time intervals, comparing LA-AODV against the standard AODV protocol. Results indicate that LA-AODV consistently outperforms AODV, demonstrating improved efficiency in flood identifier management, reduced data loss, higher packet delivery ratios, better throughput, and reduced end-to-end delay and jitter. Specifically, under a 20-node scenario, LA-AODV exhibits lower flood ID scores (54 vs. 88), reduced packet loss (11% vs. 12%), higher PDR (88.0% vs. 87.0%), and superior throughput (85.34 Kbps vs. 47.26 Kbps). Additionally, LA-AODV achieves lower end-to-end delay (6.84E+09 ns vs. 3.76E+10 ns) and jitter (2.52E+09 ns vs. 2.15E+10 ns). These findings suggest that LA-AODV significantly enhances Quality of Service (QoS) in vehicular adhoc networks, positioning it as a promising solution for optimizing V2V communication performance. [ABSTRACT FROM AUTHOR]

In this paper, we experimentally illustrate the effectiveness of neural networks (NNs) as non-linear equalisers for multilevel pulse amplitude modulation (PAM-M) transmission over an optical wireless communication (OWC) link. In our study, we compare the bit-error-rate (BER) performances of two decision feedback equalisers (DFEs)—a multilayer-perceptron-based DFE (MLPDFE), which is the NN equaliser, and a transversal DFE (TRDFE)—under two degrees of non-linear distortion using an eye-safe 850 nm single-mode vertical-cavity surface-emitting laser (SM-VCSEL). Our results consistently show that the MLPDFE delivers superior performance in comparison to the TRDFE, particularly in scenarios involving high non-linear distortion and PAM constellations with eight or more levels. At a forward error correction (FEC) threshold BER of 0.0038, we achieve bit rates of ~28 Gbps, ~29 Gbps, ~22.5 Gbps, and ~5 Gbps using PAM schemes with 2, 4, 8, and 16 levels, respectively, with the MLPDFE. Comparably, the TRDFE yields bit rates of ~28 Gbps and ~29 Gbps with PAM-2 and PAM-4, respectively. Higher PAM levels with the TRDFE result in BERs greater than 0.0038 for bit rates above 2 Gbps. These results highlight the effectiveness of the MLPDFE in optimising the performance of SM-VCSEL-based OWC systems across different modulation schemes and non-linear distortion levels. [ABSTRACT FROM AUTHOR]

As we move into the digital era, the pace of technological advancement is accelerating rapidly. Network traffic often becomes congested during the transmission of large data volumes. To mitigate this, data compression plays a crucial role in minimizing transmitted data. Vector quantization (VQ) stands out as a potent compression technique where each image block is encoded independently as an index linked to a codebook, effectively reducing the bit rate. In this paper, we introduce a novel scheme for recompressing VQ indices, enabling lossless restoration of the original indices during decoding without compromising visual quality. Our method not only considers pixel correlations within each image block but also leverages correlations between neighboring blocks, further optimizing the bit rate. The experimental results demonstrated the superior performance of our approach over existing methods. [ABSTRACT FROM AUTHOR]

Summary: With the increase in internet connectivity worldwide, people have resorted to various video streaming applications for entertainment, education, healthcare, and so on. However, this has resulted in huge internet traffic, which is predominantly occurring due to the increased transmission of videos over wireless networks to mobile devices, Transmission of ultra‐high‐definition videos can be effortlessly carried out, although the video quality perceived by the users is normally lesser than the anticipated quality. In video streaming applications, it is a pre‐requisite to provide users with high quality of experience (QoE) but this is generally affected by the dynamic variations in the network conditions. This work presents a novel QoE‐based video delivery system in a collaborative e‐learning platform. Here, a novel deep learning structure recurrent neural network‐long short term memory (RNN‐LSTM) is developed for adaptive bit rate (ABR) selection, thereby providing users with videos of high QoE. Various network, time, and buffer‐based parameters are considered while selecting the bit rate. Additionally, the proposed RNN‐LSTM is assessed for its superiority in providing high QoE videos based on measures, like QoE, buffer size, and bit rate, and is observed to attain values of 0.975, 45.654, and 90.766 b/s, respectively. [ABSTRACT FROM AUTHOR]

The proliferation of remote sensing image data in recent years has posed a pressing need for efficient compression techniques due to constrained transmission bandwidth. While lossless compression preserves image fidelity, it falls short of meeting real-time demands. Conversely, conventional lossy compression methods can attain high compression ratios for real-time applications, but often introduce issues like block artifacts, blurring, and distortions in the decompressed images. Hence, we propose the Global Feature-Assisted Segmentation Compression Network (GFSCompNet) as a solution for high compression ratio lossy compression. Initially, we design a segmentation network utilizing a dual-branch global feature-assisted segmentation approach to precisely detect small targets in remote sensing images. On the compression side of the network, we leverage an attention mechanism and code rate allocation technique to seamlessly merge the segmented small target information with the original image, thereby allocating a higher compression code rate to the small target region. Furthermore, a joint hyper-priority decoding and entropy coding estimation network is proposed to further remove the redundancy in the potential representation and improve the compression ratio. Experimental results conducted under conditions of high compression ratios and comparable bit rates demonstrate that our approach yields higher-quality reconstructed images compared to the JPEG algorithm and outperforms other deep learning-based image compression methods. Additionally, it effectively preserves small target information, thereby enhancing the interpretability of machine learning models. [ABSTRACT FROM AUTHOR]

A novel modulation, that is, multi‐ary polarization shift keying (PolSK) modulation is proposed for wavelength division multiplexing passive optical networks (PONs) to achieve colorless optical network units. The downstream signal applies the proposed multi‐ary PolSK to eliminate the induced crosstalk to the remodulated amplitude shift keying upstream signal. Different from traditional PolSK, there are more than two polarization states in the proposed multi‐ary PolSK, so it provides higher bit rates and spectra efficiency. Four‐ary and 6‐ary PolSK are demonstrated and evaluated by simulation. The results show the proposed multi‐ary PolSK supports high‐speed downstream signal in future PONs with very low crosstalk induced to the remodulated upstream signal. [ABSTRACT FROM AUTHOR]

The capacity of passive optical networks (PONs) is continuously increasing, and it has been standardized up to 50 Gbit/s. The two main standardization organizations, IEEE and ITU-T, are actively working on the next-generation PON, which appears to be a 100G-PON still based on intensity modulation. Even though direct detection would be preferred for its cost and simplicity, the choice of coherent detection seems inevitable when the bit rate reaches 200–400 Gbit/s, specifically to guarantee the optical power budget requirement of an access network. The introduction of coherent systems in the PON scenario, allowing high-power-budget performances, should encouragetelecom operators to merge the metro and access networks into a single domain. This paper analyzes the mentioned metro + PON convergence scenario with experimental results focusing on a 400 Gbit/s fully coherent transmission (50 GBaud PM-16QAM). We characterize three different transceivers, two of which are pre-commercials. We perform experimental demonstrations, with real urban fiber and laboratory set ups, of the metro–access convergence network in terms of the minimum OSNR value of the metro path, producing an acceptable optical power budget within the access network. Our work demonstrates feasibility of merging the metro–access network by using currently coherent optical transceivers for PON applications. [ABSTRACT FROM AUTHOR]

Video streaming involves the continuous delivery of video files from a server to a client, where multimedia streaming is employed for playback through an online or offline media player. Video streaming uses live broadcasts to enhance direct communication with community partners and customers. The existing methods have less video streaming quality and are unable to efficiently adapt to the dynamic conditions of the network. In this research, an adaptive bit rate (ABR) method depending on dynamic adaptive video streaming over hypertext transfer protocol or HTTP (DASH) based deep reinforcement learning (DRL) named DASH-based DRL is proposed to determine the following segment's quality in DASH video streaming with wireless networks. The proposed algorithm significantly improves the quality of experience (QoE) performance by providing a highly stable video quality, reducing the distance factor, and enduring smooth streaming sessions. The performance of the proposed method is analyzed based on performance measures of performance improvement, QoE metrics, mean opinion score, normalized value of QoE, average of normalized value of QoE, switching quality, and rebuffering time. The suggested algorithm obtains a high average normalized QoE of 0.72, average switching quality of 0.15, and an average rebuffering time of 0.16 sec, which is comparatively superior to other algorithms like real-time streaming protocol (RTSP), HTTP live streaming (HLS) and reinforcement learning (RL). [ABSTRACT FROM AUTHOR]

In the progress of research in the field of semantic communication, most efforts have been focused on optimizing the signal-to-noise ratio (SNR), while the design and optimization of the bit rate required for transmission have been relatively neglected. To address this issue, this study introduces an innovative low-bit-rate image semantic communication system model, which aims to reconstruct images through the exchange of semantic information rather than traditional symbol transmission. This model employs an image feature extraction and optimization reconstruction framework, achieving visually satisfactory and semantically consistent reconstruction performance at extremely low bit rates (below 0.03 bits per pixel (bpp)). Unlike previous methods that used pixel accuracy as the standard for distortion measurement, this research introduces multiple perceptual metrics to train and evaluate the proposed image semantic encoding model, aligning more closely with the fundamental purpose of semantic communication. Experimental results demonstrate that, compared to WebP, JPEG, and deep learning-based image codecs, our model not only provides a more visually pleasing reconstruction effect but also significantly reduces the required bit rate while maintaining semantic consistency. [ABSTRACT FROM AUTHOR]

This article presents a developed intensity modulation/direct detection (IM/DD) optical orthogonal frequency division multiplexing (O-OFDM). More precisely, the presented C-O-OFDM is based on the C-transform as a unitary orthogonal transform instead of the state-of-the-art discrete Fourier transform (DFT). Due to the properties of the real C-transform, Hermitian symmetry (HS) is not required to produce real OFDM samples. Therefore, the proposed scheme supports twice the input symbols compared to conventional DFT-based OFDM system. Real data mapping and DC bias technology is considered to evaluate the performance of the presented scheme over optical wireless multipath. The simulation results shows that the proposed C-O-OFDM is more resilience to multipath phenomena than the competitive DFT-O-OFDM and DHT-O-OFDM schemes for similar bit rate. The proposed scheme achieves about 22 dB signal-to-noise ratio (SNR) gain in comparison with the DFT-O-OFDM and about 2.5dB SNR gain in comparison with the DHT-O-OFDM scheme. [ABSTRACT FROM AUTHOR]

This paper studies the input‐to‐state stability of an uncertain switched linear system with external disturbances, whose feedback loop is closed over a digital communication network. In the network, only a finite bit rate can be provided to transmit the feedback signal and unknown network delay always exists. By using reachable set approximation and propagation methods, it can well handle with the effects of model uncertainty, external disturbances and network delay on the stability of the system. To save network resources, event‐triggered sampling is taken here. Under the event‐triggered sampling strategies, a new encoder is proposed, which consumes less feedback bits than the conventional time‐triggered encoders. It is proven that even in the existence of model uncertainty, external disturbances and network delay, the proposed event‐triggered sampling strategies require a lower feedback bit rate to stabilize the switched system than conventional periodic sampling methods. To save network resources, event‐triggered sampling is taken here. Under the event‐triggered sampling strategies, a new encoder is proposed, which consumes less feedback bits than the conventional time‐triggered encoders. It is proven that even in the existence of model uncertainty, external disturbances and network delay, the proposed event‐triggered sampling strategies require a lower feedback bit rate to stabilize the switched system than conventional periodic sampling methods. [ABSTRACT FROM AUTHOR]

In open Radio access network (oRAN) 5G networks, a fronthaul segment between virtual Distribution unit (vDU) edge cloud and other external Remote radio units (RRUs) sites is very important to transmit high data rate to achieve 5G requirements. Performance parameters such as Quality factor (Q-factor) and Bit error rate (BER) of an optical fiber fronthaul segment are the main important parameters to enhance latency and bit rate. An Apodized fiber bragg grating (AFBG) is utilized with different apodization profiles to optimize linewidth of an optical signal leading to enhance performance. The designed AFBG is used with a data rate of 25 Gb/s optical Ethernet module at the vDU edge cloud to optimize the linewidth of the optical signal between the vDU and the RRU in the fronthaul optical fiber segment. The AFBG is investigated at different connections pre, post, and symmetrical connections, where different apodization functions are used in the AFBG design. It is found that the best results for the maximum Q-factor and minimum BER are 9.2 and 2.72e− 21, respectively, obtained from uniform function in the symmetrical connection. Finally, the proposed model linewidth is 0.408 nm, which is better than 1.56 nm that is evaluated by related work. [ABSTRACT FROM AUTHOR]

Live video broadcasting is a popular application properly considered in lately developed standard, Versatile Video Coding (H.266/VVC). In live video broadcasting, both the bandwidth and buffer volume are limited, while a high quality level is demanded. In order to solve these problems, a Quantization Parameter Estimation Algorithm (QEA) is proposed. The core of the proposed algorithm is a neuro-fuzzy system that changes the Quantization Parameter (QP) gradually to produce a bandwidth-compliant bit rate and prohibit buffer saturation and starvation while providing high quality. The estimation is conducted according to the proportional, integral, and derivative components of the bit error. In other words, the proposed QEA is a Proportional-Integral-Derivative (PID) controller. The optimal parameters of the neuro-fuzzy system are obtained through the training process. The required data set for the training process is established by taking advantage of dynamic programming. The experiments affirm that the proposed approach achieves the target rate with an average error equal to 1.41% and fully respects the buffering boundaries. This method has at least a 2.48% bit rate reduction rather than other QEAs. Meanwhile, the proposed QEA is faster than other algorithms. [ABSTRACT FROM AUTHOR]

The foundation of this research is the quantum implementation of two hashing algorithms, namely Secure Hash Algorithm (SHA1) and Message Digest (MD5). Quantum cryptography is a challenging topic in network security for future networks. Quantum cryptography is an outgrowth of two broad topics—cryptology and cryptanalysis. In this paper, SHA1 and MD5 algorithms are designed and implemented for quantum computers. The main aim is to study and investigate the time requirement to build a hash and the bit rate at which a hash value is sent through. In this paper, a comprehensive analysis of these two algorithms is performed. Experiments have been done to compare and contrast the performances of the classical and proposed algorithms. In the experiment, it was found that the total time of execution of quantum SHA1 and quantum MD5 is much higher than the classical SHA1 and MD5. During quantum MD5 execution, it is observed that the time doubles when the number of chunks is increased from 1 to 2. Another experimental observation is that the execution time of the implemented algorithms depends upon the processor's speed. [ABSTRACT FROM AUTHOR]

The key expansion algorithm is an important part of any symmetric block cipher system since its effectiveness directly impacts the security of the entire block cipher; if it is not strong enough, the whole cryptosystem could be broken. Therefore, the round keys must be generated in a very secure way so that they cannot be attacked at all. Despite its great importance, cryptographic algorithm designers were not as interested in creating a secure round keys generation algorithm as they were in encryption itself. In this regard, designing a novel, simple, and flexible key expansion algorithm that generates round keys with secure characteristics is the aim of this research. The proposed Key Expansion algorithm is based on Dynamic Mealy Machine, 3D Logistic Map, and Protein Sequence (KE-DMM3DLMPS). The strength of the proposed KE-DMM3DLMPS algorithm is tested using flexibility, the NIST SP800-22 randomness test suite, histogram analysis, key space analysis, correlation coefficient, hamming distance, number of bit change rate, initial key sensitivity, confusion and diffusion, and differential attack. In comparison to some existing algorithms, experimental results showed that the generated round keys by the proposed KEDMM3DLMPS algorithm passed all the NIST tests with higher randomness, a uniform and ideal distribution of the amino acids present in each round key, and a higher key space of up to 20 x 2471. Furthermore, the KE-DMM3DLMPS avoids the linear relationship between the master secret key and the generated round keys and is capable of effectively blocking differential attacks. The proposed algorithm successfully adheres to key cryptographic principles such as irreversibility, independence, the strict avalanche effect, confusion, and diffusion. Through comprehensive testing and comparisons, the derived conclusion asserts that our algorithm stands as an efficient and secure solution. Its applicability extends to any symmetric block cryptosystem, with the primary goal of enhancing encryption and bolstering security. [ABSTRACT FROM AUTHOR]

Summary: This study focuses on a class of interval type‐2 T‐S fuzzy systems under bit rate constraint(BRC), which can adapt to complex systems more flexibly than the traditional interval type‐1 T‐S fuzzy model. In order to guarantee the security of data transmission, and considering that the channel is affected by the bit rate during transmission, this study introduces a codec scheme in the interval type‐2 T‐S fuzzy system and applies it to the channel from the sensor to the controller. With the support of the codec scheme, this study explores the sufficient conditions to satisfy the data recoverability, and based on the satisfaction of these conditions, the system is proposed to satisfy the sufficient conditions for input‐to‐state stability (ISS) and the controller gain. Through the in‐depth analysis in this paper, this study proposes a control scheme that can fit complex systems more accurately and also confirms the importance of considering bit rate constraints in communication systems. Finally, the paper is validated by simulation with two specific examples, which fully demonstrate the superiority and effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Designing selective encryption (SE) algorithms for H.265/HEVC video has drawn extensive attention. However, most SE algorithms without bit rate increment encrypt only the motion vector difference (MVD) and residual in the bypass mode, which makes them unable to provide a satisfactory level of security. In this paper, we present a chaos-based SE scheme for H.265/HEVC video, which reaches a balance between bit rate and security. To provide a large visual distortion, nine syntax elements of context-based adaptive binary arithmetic coding (CABAC) are selected for encryption. Their values are masked using a keystream sequence generated by the chaotic system to ensure real-time constraints. For the syntax elements containing both regular-coded and bypass-coded bins, we propose new encryption approaches with no bit rate increment, whereas the existing methods all increase the bit rate. In addition, we also suggest an encryption method for the residual sign when the sign bit hiding (SBH) technique is enabled. Experimental results indicate that the proposed algorithm has a bit rate increment of 0%, as well as provides a key space of size 2 212 and average PSNR and SSIM below 11dB and 0.26, respectively. Compared with the state-of-the-art HEVC SE algorithms, our suggested scheme not only offers a higher level of security but also keeps the same bit rate. [ABSTRACT FROM AUTHOR]

A time-differential (TD) Brillouin optical correlation domain analysis (BOCDA) sensor system was applied to measure the Brillouin gain spectrum of a 1 km long sensing optical fiber. The optical delay line used in all BOCDA measurement systems was eliminated in the TD-BOCDA system by using a bit-delayed modulation relationship between the probe and pump lightwaves. These lightwaves were phase modulated using 216-1 pseudo-random binary sequence codes at 5 Gbps. A 2 cm dispersion-shifted fiber placed at the end of the 1 km optical fiber was distinctly identified by the Brillouin frequency extracted from the Brillouin gain spectrum measurement. To investigate the measurement stability of the TD-BOCDA system, experiments were conducted under two different pumping conditions. A semiconductor optical amplifier (SOA) and an intensity modulator (MOD) were compared for the pump chopper used in the TD-BOCDA system to detect the extinction ratio of the pump and the resulting noise in the Brillouin gain measurement. The stability of the Brillouin frequency measurement from the Brillouin gain spectrum in the TD-BOCDA system was investigated by increasing the average value of the measurement using either the SOA or MOD. The repeated-measurement deviation of the system with the SOA was only half of the deviation observed in the system with the MOD. The performance of TD-BOCDA is equivalent to or better than that of conventional BOCDAs in terms of measurement reliability. Moreover, TD-BOCDA is free from the drawbacks of traditional BOCDA, which uses time-delayed fibers and varies the bit rates. [ABSTRACT FROM AUTHOR]

An experimental implementation of a quantum random number generator has been proposed. A new method for the extraction of provably random bit sequences from correlated sequences of photocounts, which are Markov chains, has been implemented experimentally for the first time. The reached generation rate of bits 0 and 1 is 154.5 Mbit/s. Fundamental natural constraints on the achievement of perfect true randomness are also discussed. [ABSTRACT FROM AUTHOR]

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In optical covert communication systems based on gain-switched distributed feedback semiconductor lasers, the trade-off between the modulation frequency and the spectral imperceptibility limits the bit rate of the secure channel. To improve the system performance in terms of the bit rate and covertness, optical time-division multiplexing is introduced to optical covert communication for the first time. The optical time-division multiplexed covert channel can work under both multiple-user and single-user conditions. The optical time-division multiplexed covert communication system is demonstrated via a system simulation. The results show that the covertness is enhanced by the optical time-division multiplexing in the spectral domain. The receiver sensitivity of the multiple-user condition is lower than the single-user one. [ABSTRACT FROM AUTHOR]

Quantum photonic platforms have proven to be essential in realizing fundamentally secure quantum transfer of information, with commercially ready systems already deployed in municipal and terrestrial links. The drive toward higher bit rates and robustness to eavesdropping and noisy channels has focused attention on moving from the present two-dimensional quantum states of polarization, to harnessing all of light's degrees of freedom for multi-dimensional quantum coding with structured photons. In this Perspective, we outline the present state-of-the-art in achieving this control with spatial modes of light, both as single photon and entangled states, highlight the open challenges that remain, and consider the roadmap that might see its full potential realized. [ABSTRACT FROM AUTHOR]

In today's digital landscape, Distributed Denial of Service (DDoS) attacks stand out as a formidable threat to organisations all over the world. As known technology gradually advances and the proliferation of mobile devices, cellular network operators face pressure to fortify their infrastructure against these risks. DDoS incursions into Cellular Long-Term Evolution (LTE) networks can wreak havoc, elevate packet loss, and suboptimal network performance. Managing the surges in traffic that afflict LTE networks is of paramount importance. Queue management algorithms emerge as a viable solution to wrest control over congestion at the Radio Link Control (RLC) layer within LTE networks. These algorithms work proactively, anticipating, and mitigating congestion by curtailing data transfer rates and fortifying defences against potential DDoS onslaughts. In the paper, we delve into a range of queue management methods Drop-Tail, Random Early Detection (RED), Controlled Delay (CoDel), Proportional Integral Controller Enhanced (PIE), and Packet Limited First In, First Out queue (pFIFO). Our rigorous evaluation of these queue management algorithms hinges on a multifaceted assessment that encompasses vital performance parameters. We gauge the LTE network's resilience against DDoS incursions, measuring performance based on end-to-end delay, throughput, packet delivery rate (PDF), and fairness index values. The crucible for this evaluation is none other than the NS3 simulator, a trusted platform for testing and analysis. The outcomes of our simulations provide illuminating insights. CoDel, RED, PIE, pFIFO, and Drop-Tail algorithms emerge as top performers in succession. These findings underscore the critical role of advanced queue management algorithms in fortifying LTE networks against DDoS attacks, offering robust defences and resilient network performance. [ABSTRACT FROM AUTHOR]

This study investigated a novelty system known as optical code division multiple access-free space optics (OCDMA-FSO) based on a new two-dimensional successive weight (2D-SW) code. The proposed approach is developed to be employed in incoherent wireless optical networks to improve channel capacity, support a large number of concurrent users, accommodate a high bits rate, and eliminate the effect of electromagnetic and multiple access interferences. A new mathematical analysis for the proposed system has been detailed, including the expression of signal-to-noise ratio (SNR) and bit error rate (BER). Accordingly, the numerical outcomes of the mathematical analysis at a total data rate of 260 Gbps revealed that the proposed system enhanced the channel capacity to 2.04, 1.6, 3.32, 1.47, 1.68, 1.33, and 2.29 times than those of the systems based on the two-dimensional codes family including perfect difference (2D-PD), both diluted perfect difference (2D-DPD) and dynamic cyclic shift (2D-DCS), hybrid flexible cross-correlation/modified double weight (FCC/MDW), hybrid zero cross-correlation/multi-diagonal (2D-ZCC/MD), modified prime code (2D-MPC), hybrid prime code (2D-HPC), and one-dimensional successive weight (1D-SW), respectively. It can save − 4.9 dBm, − 2.9 dBm, − 4.3 dBm, and − 2.2 dBm of received power in comparison to both 2D-DPD, 2D-DCS, 2D-ZCC/MD, 2D-MPC, and 2D-HPC codes respectively, and it keeps − 6.2 dBm when moving from 1D-SW to 2D-SW. Moreover, the suggested network is also studied using Optisystem software at 30 Gbps of each channel, where it achieves a low BER value reaching 1.72 × 10 - 33 with a high transmission quality reaching 11.99 at a lengthy range attaining 1.3 km. Consequently, the results demonstrate that the proposed system can satisfy optical communication needs and be implemented in the next generations of wireless optical networks. [ABSTRACT FROM AUTHOR]

The High-Efficiency Video Coding (HEVC/H.265) standard was created by a group called the Joint Collaborative Team on Video Coding (JCT-VC) and was released in 2013.In HEVC Rate-Distortion Optimization (RDO) is employed for partitioning the coding tree units into coding units recursively due to this the computational complexity increases. However, changing from macroblocks to coding tree units the compression efficiency increases and increases the encoding of the video sequence. This paper presents a Support Vector Machine (SVM)-based method for finding the fastest coding unit division in intra-prediction HEVC without compromising compression efficiency. All partitions of CTU are assessed using five characteristics: Root Mean Square Error (RMSE), Sub CU Complexity Difference (SCCD), Standard Deviation (SD), Directional Complexity (DC), and Quantization Parameter (QP) to optimize the intra-prediction of HEVC in all intra-configurations. The simulation results reveal combining directional complexity and standard deviation yields a more accurate classification. SVM was used to separate split-unsplit data, and the conventional rate-distortion optimisation technique was applied to separate samples that were difficult to separate. The results show that the encoding process has been completed 67.44% faster, albeit with a marginal increase in bit rate. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a paradigm that has been widely deployed in various areas, connecting commonly used devices, from home to industry and health, among others. IoT devices generally have a small amount of memory, low processing resources, and it is often required that they have low energy consumption. Therefore, one objective of this research is to try to take advantage of a classic cable communications standard, RS485, so as to allow the connection of simple devices to the TCP/IP network through an intermediary node. Thus, the IoT device would only require an asynchronous serial port and an interface to the electrical network. To that end, data transmission tests have been carried out over interfaces based on the THVD8000 integrated circuit. The achieved results encourage to continue working on this issue. [ABSTRACT FROM AUTHOR]

Modern low bit-rate speech coders require efficient coding of the linear predictive coding (LPC) coefficients. Immittance Spectral Frequencies (ISF) and Line Spectral Frequencies (LSF) are currently the most efficient transmission parameters for LPC coefficients in wideband speech coding. In this paper, we propose a new hybrid coding scheme with multi-coder vector quantization for efficient coding of ISF parameters of the wideband speech coder AMR-WB. The coding system was designed based on four structured quantizers under noiseless channel conditions: the split vector quantizer (SVQ), the switched split vector quantizer (SSVQ), the multi-stage vector quantizer (MSVQ), and the multi switched split vector quantizer (MSSVQ). Simulation results show that our proposed AMR-WB ISF coding scheme outperforms conventional wideband ISF quantizers at lower bit rates. [ABSTRACT FROM AUTHOR]

Spin noise based quantum random number generators first appeared in 2008 and have since then garnered little further interest, in part because their bit rate is limited by the transverse relaxation time T 2 which for coated alkali vapour cells is typically in the kbit/s range. Here we present two advances. The first is an improved bit generation protocol that allows generating bits at rates exceeding 1 / T 2 with only a minor increase of serial correlations. The second is a significant reduction of the time T 2 itself by removing the coating, increasing the vapour temperature and introducing a magnetic-field gradient. In this way we managed to increase the bit generation rate to 1.04 Mbit/s. We analyse the quality of the generated random bits using entropy estimation and we discuss the extraction methods to obtain high-entropy bitstreams. We accurately predict the entropy output of the device backed with a stochastic model and numerical simulations. [ABSTRACT FROM AUTHOR]

This article investigates the stabilization of linear systems under event‐triggered sampling, where the feedback signals are transmitted through a band‐limited network suffering from denial‐of‐service (DoS) attacks. We aim to reduce the average bit rate for stabilization while preserving the system's resilience against DoS. First, we consider the simpler case without transmission delay and present an event‐triggered control strategy to stabilize the concerned system by transmitting a finite number of bits. The average bit rate in a long time is zero in that case. Then we consider the joint effects of transmission delay and DoS attacks, and develop a variable bit rate quantization strategy and a periodic event‐triggering strategy. The proposed strategies can reduce the average bit rate compared with time‐triggered sampling methods. A numerical example is taken to verify the effectiveness of the proposed quantization and event‐triggering strategies. [ABSTRACT FROM AUTHOR]

The Visual Multimethod Assessment Fusion (VMAF) algorithm has recently emerged as a state-of-the-art approach to video quality prediction, that now pervades the streaming and social media industry. However, since VMAF requires the evaluation of a heterogeneous set of quality models, it is computationally expensive. Given other advances in hardware-accelerated encoding, quality assessment is emerging as a significant bottleneck in video compression pipelines. Towards alleviating this burden, we propose a novel Fusion of Unified Quality Evaluators (FUNQUE) framework, by enabling computation sharing and by using a transform that is sensitive to visual perception to boost accuracy. Further, we expand the FUNQUE framework to define a collection of improved low-complexity fused-feature models that advance the state-of-the-art of video quality performance with respect to both accuracy, by 4.2% to 5.3%, and computational efficiency, by factors of 3.8 to 11 times! [ABSTRACT FROM AUTHOR]

High-efficiency video coding (HEVC) stands as one of the most extensively employed video coding standards, with the primary encoding challenge revolving around the searching process. To minimize coding complexity of HEVC block partition, a new algorithm is proposed for supporting the partition architecture of coding tree unit (CTU) in intra-coding. In this research, problem of partition architecture decisions of CTU is resolved through two stages. In first phase, the bagged tree method is evaluated for predicting CTU splitting, and in the second phase, the partition issue of 32 × 32 size CU is given as 17 outcome classification. To attain a huge accuracy of prediction, a residual attention-based long short-term memory (LSTM) with parametric rectified linear unit (AT-LSTM-PReLU) is proposed. The proposed method produces a structure of partition quad-tree of CTU that makes multiple decisions at variant depth levels. The performance of proposed method is estimated with a dataset gathered from the joint collaborative team and video coding (JCT-VC). The proposed approach achieved a remarkable bit rate improvement of 3.72%, a significant ΔT enhancement of 97.28%, and minimized video quality degradation by just -0.05dB. These results outperform other deep learning methods like convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM). [ABSTRACT FROM AUTHOR]

In this paper, fixed‐time synchronization of nonlinear stochastic coupling multilayer neural networks is studied. The neural subnets in the multilayer networks are delay Cohen–Grossberg neural networks (DCGNNs). To overcome uncertain factors, we designed an adaptive delay‐dependent controller in synchronization. To describe constraints of communication and other related problems in networks, which are due to limitations for bit rates and bandwidths in communication channels, an adaptive fixed‐time control strategy is purposed by introducing quantization signal input. A theoretical framework about fixed‐time synchronization in multilayer delay Cohen–Grossberg neural networks (MDCGNNs) is established. We find that fixed settling time is related to the scale of MDCGNNs, characteristics of the designed controller parameters, and level of quantization. Finally, the effective of the theoretical framework is validated in an example. [ABSTRACT FROM AUTHOR]