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Protections are suggested on the basis of a current relay with two capacitors and a reed switch, which is mounted at a safe distance from a busbar inside the busduct enclosure. The relay does not need in current transformers thus saving copper, steel, and insulating materials. It differs from reed switch based analogs in the noise immunity due to signaling after two actuations of the reed switch; non-operation in the case of sealing (due to the control of duration of breaker contact); the presence of a reed switch fastening structure, and built-in test diagnostics by mean of current supply into the reed switch winding. A technique for selecting protection parameters and assessing their sensitivity is presented. The inductions of magnetic fields which affect the reed switch are calculated with the use of the simplest formulas for the shielding coefficient and of the Bio–Savart–Laplace law, where experimentally derived correcting coefficients are introduced. Examples of the calculation of the induction of protection operation and of reed switch selection are presented along with an oscillogram which confirms the cutoff operability and speed.

A product code with single parity-check component codes can be described via the tools of a multi-kernel polar code, where the rows of the generator matrix are chosen according to the constraints imposed by the product code construction. Following this observation, successive cancellation decoding of such codes is introduced. In particular, the error probability of single parity-check product codes over binary memoryless symmetric channels under successive cancellation decoding is characterized. A bridge with the analysis of product codes introduced by Elias is also established for the binary erasure channel. Successive cancellation list decoding of single parity-check product codes is then described. For the provided example, simulations over the binary input additive white Gaussian channel show that successive cancellation list decoding outperforms belief propagation decoding applied to the code graph. Finally, the performance of the concatenation of a product code with a high-rate outer code is investigated via distance spectrum analysis. Examples of concatenations performing within $0.7$ dB from the random coding union bound are provided., Comment: Submitted to the IEEE Transactions on Information Theory. The revised version of the first submission. Major changes: 1) No dedicated section for numerical results. Instead, simulations are provided right after the relevant section. 2) More simulation results are added, including those for the BEC. Comparisons to the state-of-art polar codes and 5G-NR LDPC codes provided

Guessing random additive noise decoding (GRAND) is a noise-centric decoding method, which is suitable for ultra-reliable low-latency communications, as it supports high-rate error correction codes that generate short-length codewords. GRAND estimates transmitted codewords by guessing the error patterns that altered them during transmission. The guessing process requires the generation and testing of error patterns that are arranged in increasing order of Hamming weight. This approach is fitting for binary transmission over additive white Gaussian noise channels. This letter considers transmission of coded and modulated data over flat fading channels and proposes a variant of GRAND, which leverages information on the modulation scheme and the fading channel. In the core of the proposed variant, referred to as symbol-level GRAND, is an analytical expression that computes the probability of occurrence of an error pattern and determines the order with which error patterns are tested. Simulation results demonstrate that symbol-level GRAND produces estimates of the transmitted codewords notably faster than the original GRAND at the cost of a small increase in memory requirements., 5 pages, 5 figures, 1 table

A huge advantage of the wavelet transform in image and video compression is its scalability. Wavelet-based coding of medical computed tomography (CT) data becomes more and more popular. While much effort has been spent on encoding of the wavelet coefficients, the extension of the transform by a compensation method as in video coding has not gained much attention so far. We will analyze two compensation methods for medical CT data and compare the characteristics of the displacement compensated wavelet transform with video data. We will show that for thorax CT data the transform coding gain can be improved by a factor of 2 and the quality of the lowpass band can be improved by 8 dB in terms of PSNR compared to the original transform without compensation.

The palindrome pattern matching (pal-matching) is a kind of generalized pattern matching, in which two strings $x$ and $y$ of same length are considered to match (pal-match) if they have the same palindromic structures, i.e., for any possible $1 \le i < j \le |x| = |y|$, $x[i..j]$ is a palindrome if and only if $y[i..j]$ is a palindrome. The pal-matching problem is the problem of searching for, in a text, the occurrences of the substrings that pal-match with a pattern. Given a text $T$ of length $n$ over an alphabet of size $\sigma$, an index for pal-matching is to support, given a pattern $P$ of length $m$, the counting queries that compute the number $\mathsf{occ}$ of occurrences of $P$ and the locating queries that compute the occurrences of $P$. The authors in~[I et al., Theor. Comput. Sci., 2013] proposed an $O(n \lg n)$-bit data structure to support the counting queries in $O(m \lg \sigma)$ time and the locating queries in $O(m \lg \sigma + \mathsf{occ})$ time. In this paper, we propose an FM-index type index for the pal-matching problem, which we call the PalFM-index, that occupies $2n \lg \min(\sigma, \lg n) + 2n + o(n)$ bits of space and supports the counting queries in $O(m)$ time. The PalFM-indexes can support the locating queries in $O(m + \Delta \mathsf{occ})$ time by adding $\frac{n}{\Delta} \lg n + n + o(n)$ bits of space, where $\Delta$ is a parameter chosen from $\{1, 2, \dots, n\}$ in the preprocessing phase., Comment: Accepted to 34th Annual Symposium on Combinatorial Pattern Matching (CPM) 2023

Digital phase reconstruction of directly detected MP optical signals using the KK algorithm has recently drawn attention because of its low hardware complexity. Only a single photodiode is required to detect the intensity of the optical signal and KK relations are used to retrieve the phase information. To make sure the signal detected by the photodiode is MP, a carrier tone needs to be inserted. In this work, we digitally add the carrier tone at the transmitter, omitting the need for additional hardware to generate the carrier tone. However, digitally adding the carrier tone increases quantization noise resulting from the digital-to-analog conversion at the transmitter. A DRE can reduce effective transmitter quantization noise by moving the quantization noise outside the signal band. We investigate the applicability of DRE to compensate for the increased transmitter quantization noise caused by the digital carrier tone insertion. We experimentally validate this concept by demonstrating 64-QAM transmission at 25 GBd, showing that DRE increases transmission performance for a 6 bits DAC system. Furthermore, employing DRE allows the reduction of the resolution of the DAC system to 4 or 5 bits with a limited performance penalty.

We consider the coding problem for lossy source coding with side information at the decoder, which is known as the Wyner-Ziv source coding problem. The goal of the coding problem is to find the minimum rate such that the probability of exceeding a given distortion threshold is less than the desired level. We give an equivalent expression of the minimum rate by using the chromatic number and a notion of covering of a set. This allows us to analyze the coding problem in terms of graph coloring and covering.

Background Multiscale entropy (MSE) has become increasingly common as a quantitative tool for analysis of physiological signals. The MSE computation involves first decomposing a signal into multiple sub-signal ‘scales’ using a coarse-graining algorithm. Methods The coarse-graining algorithm averages adjacent values in a time series to produce a coarser scale time series. The Haar wavelet transform convolutes a time series with a scaled square wave function to produce an approximation which is equivalent to averaging points. Results Coarse-graining is mathematically identical to the Haar wavelet transform approximations. Thus, multiscale entropy is entropy computed on sub-signals derived from approximations of the Haar wavelet transform. By describing coarse-graining algorithms properly as Haar wavelet transforms, the meaning of ‘scales’ as wavelet approximations becomes transparent. The computed value of entropy is different with different wavelet basis functions, suggesting further research is needed to determine optimal methods for computing multiscale entropy. Conclusion Coarse-graining is mathematically identical to Haar wavelet approximations at power-of-two scales. Referring to coarse-graining as a Haar wavelet transform motivates research into the optimal approach to signal decomposition for entropy analysis.

A joint message and state communication problem over a fading Gaussian channel with additive state is considered. The state process is assumed to be independent and identically distributed (i.i.d.) Gaussian, and known non-causally at the transmitter, while the instantaneous fading gain is assumed to be known at both the encoder and the decoder. The receiver not only has to decode the message from the transmitter, but also needs to estimate the state process to meet a prescribed distortion limit. A complete characterization of the optimal rate versus distortion performance is provided. Moreover, the converse proof presented here might be of independent interest, since it gives a simpler and more compact way of establishing the converse for settings with non-fading links studied in the literature – by virtue of relying only upon the differential entropy maximizing property of Gaussian random variables for a given variance.

This research proposed the dilated hybrid edge detection on the three most significant bits (MSB) pixels of cover images with the aim of expanding the edge area so as to increase the data embedding capacity in image steganography. This technique can perform extraction without the need to save edge detection on the original cover image. This is because edge detection is performed on 3-bits MSB images, whereas messages are embedded at x LSB and y LSB values, where x is the number of LSB bits replaced in the edge area and y is the number of bits replaced in the non-edge area, where x and y cannot reach 3-bits MSB, so the edge detection of the cover image and the stego image will be the same. Based on the results of tests carried out, the proposed steganography technique succeeded in improving the quality of imperceptibility, where the PSNR value increased by about 1 to 2 dB when compared to the previous method. Similarly, the message embedding capacity can be increased due to a wider edge area, with the same image dataset obtained an average increase of 4,350–12,364 pixels of the edge area, however, the quality of the stego image imperceptibility can be maintained.

We investigate the secrecy performance and energy-efficiency trade-offs associated to the secure communication between a full-duplex (FD) power beacon (PB) and an energy harvesting (EH) device, in the presence of an eavesdropper. Specifically, we analyze the feasibility of a jamming strategy implemented at the FD-PB under several practical constraints, such as imperfect self-interference (SI) cancellation, EH non-linearity, channel aging and energy-information correlation. The design of the optimal time-splitting factor for the simultaneous wireless information and power transfer (SWIPT) strategy, the adequacy of different beamforming strategies for proper system operation, and the impact of channel correlation between the energy and information transmission phases in SWIPT are thoroughly discussed. Results indicate that under practical constraints such as EH non-linearity and imperfect SI cancellation, the transmit powers at the FD-PB for the generation of energy and jamming signals are the key parameters to be optimized from both points of view: secrecy and energy efficiency. We also verify the positive impact of correlation between the energy and information links in wireless power transfer systems, from a physical layer security perspective.

In this work, a non-binary low density parity check (LDPC) coded high dimensional multiple input multiple output (MIMO) scheme with partial mapping for high order modulation is proposed. For the proposed scheme, when $M$ -ary quadrature amplitude modulation (QAM) is employed, then non-binary LDPC code constructed over Galois field with order $\sqrt {M}$ is used for partial mapping, where $\sqrt {M}$ is an integer. At the receiver side, a real-valued expectation propagation (REP) based detection algorithm is used to couple with the non-binary decoder seamlessly. Meanwhile, the symbol-wise a-priori information returned by the non-binary decoder is used to aid the REP detection. Furthermore, a symbol-wise extrinsic information transfer (SEXIT) chart based iterative optimization algorithm is used to optimize the concatenated non-binary LDPC code. This work proposed to model the a-priori information of non-zero codes/symbols of detector/decoder as the output of a cyclic-symmetric additive white Gaussian noise (AWGN) channel and a simplified method based on interpolation is proposed to calculate the component-EXIT chart of the REP-detector for massive MIMO. The proposed method can facilitate the optimization and avoid a large amount of simulations. Both SEXIT chart based analysis and numerical simulation results demonstrate the validity of the above idea.

The exploiting modification direction scheme is a well-known irreversible steganographic method because of its high embedding efficiency and imperceptibility. The shortcomings of the exploiting modification direction scheme are the use of a ( $2n +1$ )-ary notational system secret digit and low payload. Some studies transform secret data into a sequence of secret digits in the 8-ary notational system to overcome the disadvantage of using a non-binary secret data problem, which also increases the payload. Additionally, some studies have proposed a two-layer embedding method to enhance data security, which also increases the payload. Besides, most of the EMD-based scheme has the fall of boundary problems when the pixel group is located in the boundary area, which increase the distortion of the stego-image. In this study, we proposed a multilayer steganographic method using an improved exploiting modification direction scheme, which includes the abovementioned advantages, using binary secret data, increasing the payload, enhance data security and preventing the fall of boundary problem. In addition, we demostrate a three-layer EMD-based data hiding example. The experimental results show that the proposed method achieves a high payload (approximately 4.5 bpp) and the quality of the stego-image satisfying the human vision system sensitivity (PSNR value is greater than 30 dB).

In the past few years, deep learning has been widely used in various fields due to its outstanding progress. One of the latest applications of deep learning is to use a neural network (NN) with trainable multiplicative weights to design decoders for error-correcting codes. High quality data are essential for deep learning to train robust NN models. In this study, two novel semi-/auto-adaptive SNR algorithms are proposed to efficiently train the neural decoders based on the Sum-Product Algorithm (SPA). For illustration, several neural SPA decoders for the Bose-Chaudhuri-Hocquenghem (BCH) code and low-density parity-check (LDPC) code have been constructed as examples. Simulation results show that, compared with the original neural decoders, the performance of these neural decoders trained by the proposed algorithms can be improved in the range of 0.2 to 0.6 dB. Moreover, the training time required for these decoders to achieve convergence can be reduced by up to 28.8% for the BCH code, and up to 35.6% for the LDPC code, without increasing decoding complexity.

In harmonic radar (HR), the radio frequency transmitter illuminates a nonlinear target (the tag), causing the return signal to consist of harmonics at multiples of the transmitted carrier frequency. Of them, the second harmonic is usually the strongest and the one to which the receiver is tuned. This frequency difference distinguishes the tag reflection from environmental clutter, which remains at the uplink (transmitter to tag) frequency. However, the passive nature of HR tags severely limits the reflected power, and therefore the range of the downlink (tag to receiver) path. We propose to increase the range and/or signal to noise ratio (SNR) by novel restructuring at the physical and signal levels. For this, we accompany the original transmitter with auxiliary transmitters able to send simple tones that are synchronized to arrive at the tag in phase, and we design the receiver to detect an intermodulation component. The resulting range and SNR are much greater than those of the original, conventional HR system, and greater even than if the original system were to transmit with power equal to the aggregate power of our new system. Achieving mutually coherent, i.e., in phase, arrival of the tones at the tag is the focus of the present paper. We provide a system framework that models the tag and the uplink and downlink, then present the adaptive phase coherence algorithm and analyze the probabilistic growth of the output signal power. We also account for the effects of frequency shifts due to transmitter mobility and the frequency offset errors in the transmitter local oscillators., 13 pages

In this paper, massive multiple‐input‐multiple‐output (MIMO) wireless communication systems are considered to investigate joint transceiver beamforming. A base station (BS) equipped with a uniform planar array (UPA) serves several multi‐antennas users in a single cell. Based on the channel state information (CSI), the low complexity design of transceiver beamforming to minimize the transmit power subject to some quality of service (QoS) constraints is investigated. As the upper bound of the transmit power performance, the existing iteration‐based algorithms are leveraged as a reference. A general deep learning (DL)‐based framework and deep neural network (DNN) structure are proposed to reduce the complexity of the existing algorithms, where the properly trained DNN structure can learn directly from CSI. Consider the complexity of the DNN structure itself, a heuristic algorithm is proposed to replace the DNN structure, which takes the max‐eigenvalue‐eigenvector of the CSI as the direction of receive beamforming directly. The DNN structure is trained in the offline stage, therefore, only the complexity in the online stage is taken into consideration. Based on the numerical simulation, the complexity of the proposed DL‐based framework and the transceiver beamforming algorithms is reduced significantly while maintaining nearly the optimal performance compared with the existing iterative algorithms.

Efficient quantizer design is vital in high‐speed optical receivers. In this paper, the authors propose a three‐level (1.5‐bit) reliability‐based quantizer for low‐complexity soft decoding of low‐density parity‐check (LDPC) codes, and show that it outperforms a conventional quantizer employing eight levels (3 bits). Different approaches for obtaining the quantization thresholds are discussed, and it is shown that the achievable channel capacity with the proposed quantizer is within 0.4 dB of the Shannon limit of the unquantized channel. Rate‐compatible LDPC codes are also designed tailored to the asymmetric optical channel terminated with the proposed reliability‐based quantizer, and it is shown that the resulting quantizer‐aware LDPC codes significantly outperform other existing codes of similar rates.

In this paper, we develop low complexity Golden code sphere-decoding (SD) algorithms for high-density M-ary quadrature amplitude modulation (M-QAM). We define the high-density M-QAM as having modulation orders ( $M$ ) of at least 64, i.e. $M\ge 64. $ High-density M-QAM symbols deliver high data rates under good wireless channels. Future wireless systems must deliver high data rates and simultaneously low end-to-end latency. However, higher M-QAM modulation orders increase the Golden code SD search breadth, thus increasing decoding latency. We, therefore, propose two forms of low complexity Golden code SD to achieve low decoding latency while maintaining the near-optimal SD bit-error rate (BER). The proposed low complexity SD algorithms are based on the SD with sorted detection subsets (SD-SDS). The literature shows the SD-SDS to achieve lower detection complexity relative to the Schnorr-Euchner SD (SE-SD). The first form of the proposed Golden code SD is the SD-SDS-Descend algorithm with instantaneously varying subset lengths and a search tree search order sorted based on the worst-first search strategy. The second form of the proposed Golden code SD is an SD-SDS algorithm called SD-SDS-ES-DNN with a deep learning-based early stopping search criterion. Our proposed algorithms achieve at most 57% and 70% reduction in Golden code decoding latency relative to SD-SDS, at low SNR, for 64-QAM and 256-QAM, respectively. At high SNR, the proposed algorithms achieve 40% and 37% in Golden code decoding latency reduction relative to the SD-SDS for 64-QAM and 256-QAM, respectively. The decoding latency reduction is achieved while maintaining near-optimal BER performances.

In highly compressed images, i.e. quality factor $q \leq 10$ , JPEG compression causes severe compression artifacts including blocking, banding, ringing and color distortion. The compression artifacts seriously degrade image quality, which is not conducive to subsequent tasks, such as object detection and semantic segmentation. In this paper, we propose a weakly connected dense generative adversarial network for artifacts removal of highly compressed images, named WCDGAN. WCDGAN has three main ingredients of mixed convolution, weakly connected dense block (WCDB), and mixed attention. In the loss function, we add a perceptual loss to generate photo-realistic images with compression artifact removal. Experimental results show that WCDGAN successfully removes compression artifacts and produces sharp edges, clear textures and vivid colors even in highly compressed images. Moreover, WCDGAN outperforms state-of-the-art methods for compression artifact removal in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).

In this paper, we propose a secure image encryption system based on compressed sensing (CS) with a scrambling mechanism. For efficient encryption, we use a sparse measurement matrix, where the nonzero elements are generated by a linear feedback shift register (LFSR) based keystream generator. Then, several pairs of data scramblers, also based on LFSR, are attached behind the CS-encryption for diffusion. While guaranteeing theoretically reliable CS-decryption, numerical results indicate that the proposed cryptosystem has more reliable CS-decryption performance than other CS-based cryptosystems. Examining the histogram, entropy and correlation of the ciphertexts, experimental results demonstrate that the proposed cryptosystem has strong statistical security. Moreover, it turns out that the proposed cryptosystem has higher plaintext sensitivity than other CS-based cryptosystems, thanks to the bit-level diffusion from the scramblers.

We investigate propagation characteristics for wireless channels, applicable to Integrated Access and Backhaul (IAB) and relay-based networks with lamppost-height nodes at 5.5 GHz. We compare our empirical results with a variety of models that have been proposed for system simulation. Our work is based on an extensive measurement campaign in an urban environment, where we simultaneously measured base-relay, relay-mobile and base-mobile links. This simultaneity allows us to conclude that low-height relay nodes offer a minor path-loss advantage over the base-user link. Moreover, within the range of relay heights that we measured of 2.8 and 4.7 m, we observed no signi?cant gain associated with choosing the higher relay placement. Our results however also show that the base-relay link is quite stable over time and thus will lend itself to multi-antenna techniques requiring a small overhead in channel state information feedback. Our results add to the empirical data that the standards models are based on, providing path-loss results obtained simultaneously for all links of an urban relay-based system.

3D objects have come to play an essential role in industry. They can also be very large, sometimes containing millions of vertices, and therefore it is vital that they are compressed, particularly when it comes to uploading them to the web and during real time rendering. The Draco 3D object compression system, proposed by Google, is becoming an industry standard for the compression of 3D objects structured as geometric meshes or point clouds. These 3D objects are important assets which also need to be secured. In this paper, we propose the first 3D object crypto-compression method, which integrates an encryption step into Google’s Draco compression scheme, by adding an AES encryption step during Draco’s entropy encoding step. Our proposed Draco 3D object crypto-compression scheme is format compliant, has no size expansion and does not require additional information such as an auxiliary file. After the entire decoding process (joint decryption and decompression), the reconstructed 3D object obtained is identical to the one after a standard compression by Draco. Experimental results on real 3D objects and a security analysis show our proposed method is efficient.

In this era of 5G technology, the ever‐increasing demands for high data rates lead researchers to develop hybrid satellite‐terrestrial (HST) networks as a substitution to the conventional cellular terrestrial systems. Since an HST network suffers from a masking effect which can be mitigated by adopting the terrestrial relaying strategy, in this work, wireless multicasting is focused on through an HST relaying network (HSTRN) in which a satellite sends messages to multiple terrestrial nodes via multiple relays under the wiretapping efforts of multiple eavesdroppers. The concern is to protect the multicast messages from being eavesdropped taking advantage of the well‐known opportunistic relaying technique. It is considered that the satellite links follow Shadowed Rician fading whereas the terrestrial links undergo α−μ fading. The secrecy performance of the proposed HSTRN model is accomplished by deriving expressions for the probability of nonzero secrecy multicast capacity, ergodic secrecy multicast capacity, and secure outage probability for multicasting in closed‐form. Capitalizing on the derived expressions, the authors analyze how a perfect secrecy level can be preserved in spite of harsh channel conditions and also present a secrecy trade‐off in terms of the number of relays, multicast users, and multiple eavesdroppers. Finally, the numerical analyses are corroborated via Monte‐Carlo simulations.

Background Advances in sequencing technology have drastically reduced sequencing costs. As a result, the amount of sequencing data increases explosively. Since FASTQ files (standard sequencing data formats) are huge, there is a need for efficient compression of FASTQ files, especially quality scores. Several quality scores compression algorithms are recently proposed, mainly focused on lossy compression to boost the compression rate further. However, for clinical applications and archiving purposes, lossy compression cannot replace lossless compression. One of the main challenges for lossless compression is time complexity, where it takes thousands of seconds to compress a 1 GB file. Also, there are desired features for compression algorithms, such as random access. Therefore, there is a need for a fast lossless compressor with a reasonable compression rate and random access functionality. Results This paper proposes a Fast and Concurrent Lossless Quality scores Compressor (FCLQC) that supports random access and achieves a lower running time based on concurrent programming. Experimental results reveal that FCLQC is significantly faster than the baseline compressors on compression and decompression at the expense of compression ratio. Compared to LCQS (baseline quality score compression algorithm), FCLQC shows at least 31x compression speed improvement in all settings, where a performance degradation in compression ratio is up to 13.58% (8.26% on average). Compared to general-purpose compressors (such as 7-zip), FCLQC shows 3x faster compression speed while having better compression ratios, at least 2.08% (4.69% on average). Moreover, the speed of random access decompression also outperforms the others. The concurrency of FCLQC is implemented using Rust; the performance gain increases near-linearly with the number of threads. Conclusion The superiority of compression and decompression speed makes FCLQC a practical lossless quality score compressor candidate for speed-sensitive applications of DNA sequencing data. FCLQC is available at https://github.com/Minhyeok01/FCLQC and is freely available for non-commercial usage.

There is a tremendous demand from various industries for next-generation 5G networks, which has dramatically increased the need for high data rates and energy efficiency. It is an indisputable fact that next-generation networks should be not only energy-efficient but also resource-efficient. Given the fact that 10% of the current energy consumption in the world is caused by Information and Communication Technology, it is evident that energy-efficiency has become the most crucial performance criteria in the next-generation communication techniques. Based on these needs, we previously suggested the hexagonal quadrature amplitude modulation (HQAM) aided spatial modulation (SM) technique (HQAM-SM) to the literature. In this study, we found it appropriate to do this research to further increase the performance of the HQAM-SM scheme through the antenna selection technique and to investigate the effects of antenna selection technique on HQAM-SM. Moving from this point, in this article, rational capacity-optimized antenna selection (COAS), SM, and energy-efficient HQAM techniques are combined, and a new system called COAS-HSM is presented. Hexagonal constellations are to optimize the constellation points to form a hexagonal constellation to minimize the Hamming distance between the constellation points. This layout not only offers better energy efficiency than traditional QAM constellations but also performs almost the same BER performance as QAM at high signal-to-noise ratio (SNR) values. On the other hand, antenna selection algorithms are one of the transmission schemes that have been frequently encountered in the literature in recent years, and that considerably increases the performance of various multiple-input multiple-output (MIMO) communication structures. In particular, the COAS transmission scheme is an intelligent method of selecting transmission antennas over the highest channel amplitudes. Performance analysis of the proposed COAS-HSM technique is carried out in Rayleigh fading channels.

In this article, we propose a new pilot mapping method to jointly estimate the channel state information of the user while reducing the interference effects of multiuser multicell mobile scenarios. The problem of achieving the wireless performance of the minimum mean square error (MMSE) receiver with the maximum ratio (MR) combiner for massive multiple‐input multiple‐output (MIMO) systems in the frequency bands of 2.5 GHz and 28 GHz is addressed. By transmitting fewer pilots in the uplink communications and arranging those pilots to reduce interuser interference, we can achieve higher spectral efficiency (SE) with the MR and MMSE receivers. Numerical results show significantly fewer floating‐point operations for MIMO processing when the proposed pilot mapping strategy is combined with Kalman filtering estimation and MR processing, achieving an SE comparable to the MMSE receiver.

Optical camera communication (OCC) is considered an important transmission scheme in implementing indoor navigation, beacons, and other Internet of Things applications. One of the disadvantages of OCC, however, is that transmission is successful only if the line‐of‐sight requirement is satisfied between transmitting LED lamps and receiving image sensors. Any obstacle between them or movement of the receiver direction causes a reduction in the light signal and leads to a burst error, and its compensation is not easy. A low‐density parity check (LDPC) code is employed in the OCC to overcome burst error. To improve the performance further, a region of interest area detection is proposed that utilizes only the centre part of an image frame for LDPC decoding rather than using the whole area measurement. The LDPC codes are tested using an appropriate OCC packet structure, and its performance is compared with that of polar codes. The experimental results indicate that LDPC coding is more suitable in OCC systems. It is found that the proposed LDPC coding scheme not only recovers the data from burst errors but also enhances the transmission distance. In particular, soft‐decision LDPC decoding shows more resistance to burst errors and bit loss.

Since its invention, generative adversarial networks (GANs) have shown outstanding results in many applications. GANs are powerful, yet resource-hungry deep learning models. The main difference between GANs and ordinary deep learning models is the nature of their output and training instability. For example, GANs output can be a whole image versus other models detecting objects or classifying images. Thus, the architecture and numeric precision of the network affect the quality and speed of the solution. Hence, accelerating GANs is pivotal. Data transfer is considered the main source of energy consumption, that is why memory compression is a very efficient technique to accelerate and optimize GANs. Two main types of memory compression exist: lossless and lossy ones. Lossless compression techniques are general among all models; thus, we will focus in this paper on lossy techniques. Lossy compression techniques are further classified into (a) pruning, (b) knowledge distillation, (c) low-rank factorization, (d) lowering numeric precision, and (e) encoding. In this paper, we survey lossy compression techniques for CNN-based GANs. Our findings showed the superiority of knowledge distillation over pruning alone and the gaps in the research field that needs to be explored like encoding and different combination of compression techniques.

Wireless devices’ energy efficiency and spectrum shortage problem has become a key concern worldwide as the number of wireless devices increases at an unparalleled speed. Wireless energy harvesting technique from traditional radio frequency signals is suitable for extending mobile devices’ battery life. This paper investigates a cognitive radio network model where primary users have their specific licensed band, and secondary users equipped with necessary hardware required for energy harvesting can use the licensed band of the primary user by smart sensing capability. Analytical expressions for considered network metrics, namely data rate, outage probability, and energy efficiency, are derived for uplink and downlink scenarios. In addition, optimal transmission power and energy harvesting power are derived for maximum energy efficiency in downlink and uplink scenarios. Numerical results show that outage probability improves high transmission power in the downlink scenario and high harvested power in the uplink scenario. Finally, the result shows that energy efficiency improves using optimum transmission power and energy harvesting power for downlink and uplink scenarios.

Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our theory obeys fundamental mathematical limitations on information content inherited from the sensory periphery, describing redundant codes when there are many more cortical neurons than primary sensory neurons. The theory predicts that if the brain uses its nonlinear population codes optimally, then more informative patterns should be more correlated with choices. More specifically, the theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices that reveals the decoding efficiency. This relationship holds for optimal feedforward networks of modest complexity, when experiments are performed under natural nuisance variation. We analyze recordings from primary visual cortex of monkeys discriminating the distribution from which oriented stimuli were drawn, and find these data are consistent with the hypothesis of near-optimal nonlinear decoding., Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. Here, the authors present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information which indicates near-optimal nonlinear decoding.

Readily available processing hardware and "off-the-shelf" (OTS) simulation software has made "high fidelity" first principles models of both steady and transient states, for both axial and centrifugal industrial compressors, relatively easy to construct. These high-fidelity models are finding their way into "real-time. digital twin" performance monitors, front-end engineering design, and post-design – pre-construction compressor performance evaluation. The compressor models are useful for reliably demonstrating the compressor and – to some degree, based on the complexity of the model – process response to various operating conditions. Once the model is constructed, it is trivial to run a "what-if" analysis of compressor performance to answer questions related to (a) recommendations or validation of the recycle/vent valve size and actuation speed, (b) general piping layout and sizing around the compressor, (c) and hot gas bypass requirements, to name a few. This paper takes a practical approach in discussing the compressor and process parameters necessary for building these dynamic "high-fidelity" industrial-compressor models. We identify compressor inputs and compressor responses that are faithfully modeled by first-principle equations available in the simulation software and those that typically require a compromise between an "ab initio" and data-fitting approximation. We discuss the simulation's tendency to overstate pressure excursions during surge events and understate the compressor operation in the "stonewall" region. We also discuss using the simulator software's compressor-stage enthalpy calculations to predict and quantify the compressor train reverse rotation. We use our broad experience and understanding of the compressor operation and simulation and our experience with the AVEVA™ Dynamic-Simulation "OTS" simulation software as the basis for this discussion.

Background Reed has high lignin content, wide distribution and low cost. It is an ideal raw material for replacing wood in the paper industry. Reeds are rich in resources, but the density of reeds is low, leading to high transportation and storage costs. This paper aims to study the compression process of reeds and the creep behaviour of compressed reeds, and provide theoretical guidance for the reed compressor management, bundling equipment and the stability of compressed reed bales. Results We have established a multi-layer perceptron network prediction model for the creep characteristics of reeds, and the prediction rate R2 of this model is greater than 0.997. The constitutive equation, constitutive coefficient and creep quaternary model of the reed creep process were established by using the prediction model. The creep behaviour of the reed bale is positively correlated with the initial maximum compressive stress (σ0). During the creep of the reed, the elastic power and the viscous resistance restrict each other. The results show that the proportion of elastic strain in the initial stage is the largest, and gradually decreases to 99.19% over time. The viscoelastic strain increases rapidly with time, then slowly increases, and finally stabilizes to 0.69%, while the plastic strain accounts for the proportion of the total strain. The specific gravity of the reed increases linearly with the increase of creep time, and finally accounts for 0.39%, indicating that as time increases, the damage of the reed's own structure gradually increases. Conclusions We studied the relationship between the strain and time of the reed and the strain and creep behaviour of the reed bag under different holding forces under constant force. It is proved that the multi-layer perceptron network is better than the support vector machine regression in predicting the characteristics of reed materials. The three stages of elasticity, viscoelasticity and plasticity in the process of reed creep are analysed in detail. This article opens up a new way for using machine learning methods to predict the mechanical properties of materials. The proposed prediction model provides new ideas for the characterization of material characteristics.

Layered decoding (LD) facilitates a partially parallel architecture for performing belief propagation (BP) algorithm for decoding low-density parity-check (LDPC) codes. Such a schedule for LDPC codes has, in general, reduced implementation complexity compared to a fully parallel architecture and higher convergence rate compared to both serial and parallel architectures, regardless of the codeword length or code-rate. In this paper, we introduce a modified shuffling method which shuffles the rows of the parity-check matrix (PCM) of a quasi-cyclic LDPC (QC-LDPC) code, yielding a PCM in which each layer can be produced by the circulation of its above layer one symbol to the right. The proposed shuffling scheme additionally guarantees the columns of a layer of the shuffled PCM to be either zero weight or single weight. This condition has a key role in further decreasing LD complexity. We show that due to these two properties, the number of occupied look-up tables (LUTs) on a field programmable gate array (FPGA) reduces by about 93% and consumed on-chip power by nearly 80%, while the bit error rate (BER) performance is maintained. The only drawback of the shuffling is the degradation of decoding throughput, which is negligible for low values of$$E_b/N_0$$Eb/N0until the BER of 1e−6.

We introduce the 2-colour parity. It is a theory of parity for a large class of virtual links, defined using the interaction between orientations of the link components and a certain type of colouring. The 2-colour parity is an extension of the Gaussian parity, to which it reduces on virtual knots. We show that the 2-colour parity descends to a parity on free links. We compare the 2-colour parity to other parity theories of virtual links, focusing on a theory due to Im and Park. The 2-colour parity yields a strictly stronger invariant than the Im-Park parity. We introduce an invariant, the 2-colour writhe, that takes the form of a string of integers. The 2-colour writhe is a concordance invariant, and so obstructs sliceness. It is also an obstruction to (±)-amphichirality and chequerboard colourability within a concordance class.

Recently, deep learning (DL) has been successfully applied in computer vision and natural language processing. The communication physical layer based on deep learning has received widespread attention. Introducing domain‐knowledge into neural networks (NNs), autoencoder based end‐to‐end communication system, incorporating radio transformer networks (RTNs) (RTNs‐AE) has achieved desirable performance with a channel model in the middle layer. The advent of RTNs underscores the power of expertise at DL. However, a tap‐length in the design of RTNs network must be assumed, which requires some channel information. To address this issue, a new deep learning wireless transceiver named pilot‐aided autoencoder (PA‐AE) is proposed. It can decode on a multipath fading channel without knowing the channel information and the equalization module design. The proposed scheme introduces a well‐designed auxiliary pilot, which carries the learned channel information into decoding with the transmitted signal. The decoding part recovers the sent information from the collected signal without specially designed modules for parameter estimation and equalization.

In this work, the authors consider the security performance for the mixed radio frequency link and visible‐light link cooperative relaying system, where the relay has the energy collection ability and can extract the direct current part existing in the visible‐light signal. Then, the relay uses the collected energy to demodulate the received visible‐light signal and retransmit it through the radio frequency link. With this equipment, some analytical expressions of the average secrecy capacity and the secrecy outage probability are derived. Furthermore, the asymptotic expression is also derived to examine the influence of the various configuration parameters on the secrecy performance of the mixed relaying system. At last, some numerical and simulation results are given to verify the theoretical analysis.

Neural compression algorithms are typically based on autoencoders that require specialized encoder and decoder architectures for different data modalities. In this paper, we propose COIN++, a neural compression framework that seamlessly handles a wide range of data modalities. Our approach is based on converting data to implicit neural representations, i.e. neural functions that map coordinates (such as pixel locations) to features (such as RGB values). Then, instead of storing the weights of the implicit neural representation directly, we store modulations applied to a meta-learned base network as a compressed code for the data. We further quantize and entropy code these modulations, leading to large compression gains while reducing encoding time by two orders of magnitude compared to baselines. We empirically demonstrate the feasibility of our method by compressing various data modalities, from images and audio to medical and climate data., TMLR camera ready

The massive multiple-input multiple-output systems (M-MIMO) and orthogonal frequency-division multiplexing (OFDM) are considered to be some of the most promising key techniques in the emerging 5G and advanced wireless communication systems nowadays. Not only are the benefits of applying M-MIMO and OFDM for broadband communication well known, but using them for the application of the Internet of Things (IoT) requires a large amount of wireless transmission, which is a developing topic. However, its high complexity becomes a problem when there are numerous antennas. In this paper, we provide an effective two-stage multiuser detector (MUD) with the assistance of the accelerated over-relaxation (AOR) iterative algorithm and Chebyshev acceleration for the uplink of M-MIMO OFDM systems to achieve a better balance between bit error rate (BER) performance and computational complexity. The first stage of the receiver consists of an accelerated over-relaxation (AOR)-based estimator and is intended to yield a rough initial estimate of the relaxation factor ω, the acceleration parameter γ, and transmitted symbols. In the second stage, the Chebyshev acceleration method is used for detection, and a more precise signal is produced through efficient iterative estimation. Additionally, we call this proposed scheme Chebyshev-accelerated over-relaxation (CAOR) detection. Conducted simulations show that the developed receiver, with a modest computational load, can provide superior performance compared with previous works, especially in the MU M-MIMO uplink environments.

Full-duplex (FD) communication in many-antenna base stations (BSs) is hampered by self-interference (SI). This is because a FD node’s transmitting signal generates significant interference to its own receiver. Recent works have shown that it is possible to reduce/eliminate this SI in fully digital many-antenna systems, e.g., through transmit beamforming by using some spatial degrees of freedom to reduce SI instead of increasing the beamforming gain. On a parallel front, hybrid beamforming has recently emerged as a radio architecture that uses multiple antennas per FR chain. This can significantly reduce the cost of the end device (e.g., BS) but may also reduce the capacity or SI reduction gains of a fully digital radio system. This is because a fully digital radio architecture can change both the amplitude and phase of the wireless signal and send different data streams from each antenna element. Our goal in this paper is to quantify the performance gap between these two radio architectures in terms of SI cancellation and system capacity, particularly in multi-user MIMO setups. To do so, we experimentally compare the performance of a state-of-the-art fully digital many antenna FD solution to a hybrid beamforming architecture and compare the corresponding performance metrics leveraging a fully programmable many-antenna testbed and collecting over-the-air wireless channel data. We show that SI cancellation through beam design on a hybrid beamforming radio architecture can achieve capacity within 16% of that of a fully digital architecture. The performance gap further shrinks with a higher number of quantization bits in the hybrid beamforming system.

International audience; This paper characterizes the trade-offs between the information and energy transmission rates, the decoding error probability, and the energy outage probability in simultaneous information and energy transmission over an additive white Gaussian noise channel. The results in this paper take into account the impact of energy harvester (EH) non-linearities on the harvested energy. The analysis is carried out in the finite block-length regime with finite constellations. Improved converse and achievability bounds that account for the EH non-linearities are presented.

We consider the attributes of a point cloud as samples of a vector-valued volumetric function at discrete positions. To compress the attributes given the positions, we compress the parameters of the volumetric function. We model the volumetric function by tiling space into blocks, and representing the function over each block by shifts of a coordinate-based, or implicit, neural network. Inputs to the network include both spatial coordinates and a latent vector per block. We represent the latent vectors using coefficients of the region-adaptive hierarchical transform (RAHT) used in the MPEG geometry-based point cloud codec G-PCC. The coefficients, which are highly compressible, are rate-distortion optimized by back-propagation through a rate-distortion Lagrangian loss in an auto-decoder configuration. The result outperforms RAHT by 2--4 dB. This is the first work to compress volumetric functions represented by local coordinate-based neural networks. As such, we expect it to be applicable beyond point clouds, for example to compression of high-resolution neural radiance fields., 30 pages, 29 figures

This paper reports on the state-of-the-art in application of multidimensional scaling (MDS) techniques to create semantic maps in linguistic research. MDS refers to a statistical technique that represents objects (lexical items, linguistic contexts, languages, etc.) as points in a space so that close similarity between the objects corresponds to close distances between the corresponding points in the representation. We focus on the use of MDS in combination with parallel corpus data as used in research on cross-linguistic variation. We first introduce the mathematical foundations of MDS and then give an exhaustive overview of past research that employs MDS techniques in combination with parallel corpus data. We propose a set of terminology to succinctly describe the key parameters of a particular MDS application. We then show that this computational methodology is theory-neutral, i.e. it can be employed to answer research questions in a variety of linguistic theoretical frameworks. Finally, we show how this leads to two lines of future developments for MDS research in linguistics., 40 pages; pre-print; accepted for publication in Corpus Linguistics & Linguistic Theory (Nov. 2021)

Being spectrally efficient, easily implemented, and highly immune to selective channel imperfections and multipath fading, the Orthogonal Frequency Division Multiplexing (OFDM) can provide a sufficiently robust and high data rate modulation technique for emerging wired and wireless telecommunication applications. However, a major drawback of OFDM that is represented by its high Peak-to-Average Power Ratio (PAPR) of the transmitted signal, which leads to degrade the system’s transmission accuracy. In this paper, a Compressed Sampling (CS) based approach is considered for reducing the PAPR without lowering its transmission capacity or affecting its Bit Error Rate (BER) performance. The proposed scheme adds a sampling stage after the IFFT block in the transmitter side, that is compressively represents the transmitted signal by fewer symbols that is transmitted instead of the original signal. At the receiver side, the received compressively sampled signal is then recovered before the FFT block by following the Basis Pursuit (𝓵1–norm) algorithm. The proposed scheme shows an enhanced PAPR and BER performances while preserving the rest of the system performance aspects.

There are various types of distributed generators (DGs) with different grid integration strategies. The transient characteristics of the fault currents provided by the DGs are different to those of conventional synchronous generators. In this paper, a distribution network with multi-type DGs is investigated, including consideration of DG low-voltage ride through (LVRT). The fault current characteristics of two typical DGs, i.e. an inverter-interfaced distributed generator (IIDG) and a doubly-fed induction generator (DFIG), are analyzed, considering the specific operation modes. Based on analysis of the fault characteristics, an equivalent model of the multi-type DGs under symmetrical/asymmetrical fault conditions is established. A fast-iterative fault calculation method for enhancing the calculation efficiency while avoiding local convergence is then proposed using an improved particle swarm optimization (PSO) algorithm. A simulation system of the distribution network with multi-type DGs is established in PSCAD/EMTDC. The simulation results validate the high accuracy and calculation efficiency of the proposed calculation method of the fault components. This can assist in the settings of the protection threshold.

Exposed to strong cochannel/adjacent-channel interference, digital multiple-input–multiple-output (MIMO) receivers require high-dynamic-range analog-to-digital converters (ADCs). Hybrid beamforming featuring spatial filtering before the ADCs can adaptively mitigate interference in both the analog and digital domains; hence relaxing the required dynamic ranges of the ADCs. This article demonstrates the effectiveness of hybrid beamforming in this mitigation utilizing adaptive minimum mean square error (MMSE) and error vector magnitude (EVM) as an optimization criterion. Extensive EVM measurements are carried out with a conductive setup using a four-element 22-nm FD-SOI CMOS prototype MIMO receiver chip to verify the performance of the adaptive MMSE algorithm. Over-the-air (OTA) measurements with a linear four-element dipole antenna array with half-wavelength spacing in the 2.4-GHz industrial scientific and medical (ISM) band quantify the improvement for real-world scenarios, e.g., having a multipath propagation channel and mutual coupling between the antenna array elements. OTA results show that a rejection of 22.5 and 24.5 dB can be achieved on average in an in-door laboratory environment utilizing vector modulator (VM) constellations with 16 and 64 points, respectively.

This article analyzes the robustness and timeliness for cooperative lossy communications in unmanned aerial vehicle (UAV) networks. The analytical framework consists of two steps: 1) calculating the outage probability, and 2) characterizing the Age-of-Information (AoI) for a given outage probability. Initially, we determine the outage probability based on the Shannon's lossy source-channel separation theorem. Numerical results indicate that joint decoding reduces the outage probability, and the system can achieve higher diversity order for less stringent distortion requirement. Then, we derive a closed-form expression of the lower bound on the average AoI for the communication system where outage events are constrained to an acceptable level. Moreover, we conduct a series of simulations for verifying the lower bound on the average AoI and evaluating the impact of UAV locations on the average AoI. It is demonstrated that the lower bound is tight when the server utilization ratio is either relatively busy or idle. For the case with a relatively large outage probability, we propose an intermittent transmission scheme to refine the AoI performance without extra energy consumption. The effectiveness of the intermittent transmission scheme for reducing the average AoI is verified by both the simulations and the lower bound analysis.

The distance-decay effect of molecular signals makes communication range a major challenge for diffusion-based Molecular Communication (MC). To solve this problem, the intermediate nano-machine is deployed as a relay between the transmitter and its intended receiver nano-machines. In this work, we employ the Depleted Molecule Shift Keying (D-MoSK) to model a Decode-and-Forward (DF) relay communication scheme. The closed-form expression of Bit Error Rate (BER) for the concerned DF relay with D-MoSK is derived. Meanwhile, the maximum a posteriori probability, minimum error probability, and maximum likelihood schemes are formulated for data detection. The relationships between BER and other key parameters, including the number of released molecules, receiving radius, and relay position, are investigated in detail. Simulation results show that the proposed scheme can improve communication reliability significantly. Moreover, the performance gain can be maximized by optimizing the position of the relay and the receiving radius.

This paper introduces a new methodology that mitigates the impact of interference produced by full‐duplexing transmission in the device‐to‐device (D2D) environment. One of the most significant issues is the approach with which the D2Ds' receivers recognise and decode the outcomes detected from the multi‐codewords sent by the peer D2Ds' transmitters. To address this issue, a novel proposed framework exchanged by the D2D users is applied and guided by two policies. Initially, all the D2Ds' transmitters expressly characterise and interchange the transmission policy regarding the modulation index, number of streams, and the order of the transmitted codewords. Specifically, this work introduces a novel 3GPP‐compliant binary integer optimisation model subject to several practical constraints for defining the transmission policy at the transmitters. Second, a new classification method is proposed, where the Bayesian decision theory is utilised at the D2Ds' receivers for acquiring the decision parameters from the exchanged transmission policy. Hence, this classification offers a determination for each codeword through enabling symbols' estimation and recognition in terms of posterior probability statements. A series of numerical and comprehensive simulations shows that using the proposed approach leads to several improvements on the conventional full‐duplex D2D schemes.

The OFDM system with multiple transceiver antennas is attractive to increase the spectrum or energy efficiency. Depending on the channel state information (CSI), precoding techniques offer array and multiplexing gain in such systems. However, if terminal equipment is fast moving, the CSI feedback would be outdated when it is applied to data transmission. This paper proposes a novel channel prediction method applicable for high‐speed environment. Benefiting from the proposed structure, this method greatly improves the prediction accuracy, and meanwhile retains the low feedback overhead. Numerical results show that the performance of the proposed method outperforms conventional precoding methods in high‐speed scenarios.

In smart cities, the Internet-of-Thing (IoT) provides an enabling communication infrastructure to support tremendous amount of data exchange. Most IoT applications, e.g., wireless local area network, Bluetooth and so forth, utilize the channel-hopping scheme to suppress the transmission security threats. In this paper, to reduce the mutual interference and external jamming attacks, multiple novel channel-hopping patterns, i.e., traditional no-hit-zone (NHZ) hopping pattern and generalized NHZ hopping pattern, are introduced to suit to IoT networks. Particularly the design of probabilistic hopping pattern is first proposed, which has the various usage probabilities with regard to various channels. The properties of these hopping patterns are investigated by the step-to-step examples. Then, the error-rate performance of the multi-node IoT systems adopting these hopping patterns in the presence of jamming attacks is comprehensively analyzed. The extensive simulations show that the traditional/generalized NHZ hopping patterns are in favor of combating the mutual interference but with the limited capability of reducing jamming attacks, while the probabilistic hopping pattern possesses the opposite feature, that is, it has the predominant merit in suppressing jamming attacks. Thus, the novel channel-hopping pattern-based IoT could provide the secure transmission for communication applications in smart cities. Note that, as the physical-layer security technique, the channel-hopping patterns investigated in this paper are convenient to integrate with the security policies implemented in upper layers (e.g., encryption, authentication and so forth).