In the realm of Single Image Super-Resolution (SISR), the meticulously crafted Nonlocal Sparse Attention-based block demonstrates its efficacy in noise reduction and computational cost reduction for nonlocal (global) features. However, it neglect the traditional Convolutional-based block, which proficient in handling local features. Thus, merging both the Nonlocal Sparse Attention-based block and the Convolutional-based block to concurrently manage local and nonlocal features poses a significant challenge. To tackle the aforementioned issues, this paper introduces the Channel Contrastive Attention-based Local-Nonlocal Mutual block (CCLN) for Super-Resolution (SR). (1) We introduce the CCLN block, encompassing the Local Sparse Convolutional-based block for local features and the Nonlocal Sparse Attention-based network block for nonlocal features. (2) We introduce Channel Contrastive Attention (CCA) blocks, incorporating Sparse Aggregation into Convolutional-based blocks. Additionally, we introduce a robust framework to fuse these two blocks, ensuring that each branch operates according to its respective strengths. (3) The CCLN block can seamlessly integrate into established network backbones like the Enhanced Deep Super-Resolution network (EDSR), achieving in the Channel Attention based Local-Nonlocal Mutual Network (CCLNN). Experimental results show that our CCLNN effectively leverages both local and nonlocal features, outperforming other state-of-the-art algorithms.

This letter proposes a scheme for the backward transmission of the propagation-delay based three-user X channel, which is reciprocal to the forward transmission. The given scheme successfully delivers 10 expected messages in 6 time-slots by cyclic interference alignment without loss of degrees of freedom, which supports efficient bidirectional transmission between the two ends of the three-user X channel.

With the rapid advancement of graphics processing units (GPUs), Virtual Reality (VR) experiences have significantly improved, enhancing immersion and realism. However, these advancements also raise security concerns in VR. In this paper, I introduce a new attack leveraging known WebVR vulnerabilities to track the activities of VR users. The proposed attack leverages the user’s hand motion information exposed to web attackers, demonstrating the capability to identify consumed content, such as 3D images and videos, and pilfer private drawings created in a 3D drawing app. To achieve this, I employed a machine learning approach to process controller sensor data and devised techniques to extract sensitive activities during the use of target apps. The experimental results demonstrate that the viewed content in the targeted content viewer can be identified with 90% accuracy. Furthermore, I successfully obtained drawing outlines that precisely match the user’s original drawings without performance degradation, validating the effectiveness of the attack.

Terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) is envisioned as a key enabling technology of 6G wireless communication. In UM-MIMO systems, downlink channel state information (CSI) has to be fed to the base station for beamforming. However, the feedback overhead becomes unacceptable because of the large antenna array. In this letter, the characteristic of CSI is explored from the perspective of data distribution. Based on this characteristic, a novel network named Attention-GRU Net (AGNet) is proposed for CSI feedback. Simulation results show that the proposed AGNet outperforms other advanced methods in the quality of CSI feedback in UM-MIMO systems.

In recent years, the increasing complexity of deep network structures has hindered their application in small resource constrained hardware. Therefore, we urgently need to compress and accelerate deep network models. Channel pruning is an effective method to compress deep neural networks. However, most existing channel pruning methods are prone to falling into local optima. In this paper, we propose a channel pruning method via Improved Grey Wolf Optimizer Pruner which called IGWO-Pruner to prune redundant channels of convolutional neural networks. It identifies pruning ratio of each layer by using Improved Grey Wolf algorithm, and then fine-tuning the new pruned network model. In experimental section, we evaluate the proposed method in CIFAR datasets and ILSVRC-2012 with several classical networks, including VGGNet, GoogLeNet and ResNet-18/34/56/152, and experimental results demonstrate the proposed method is able to prune a large number of redundant channels and parameters with rare performance loss.

Emerging data-intensive services in distribution grid impose requirements of high-concurrency access for massive internet of things (IoT) devices. However, the lack of effective high-concurrency access management results in severe performance degradation. To address this challenge, we propose a cloud-edge-device collaborative high-concurrency access management algorithm based on multi-timescale joint optimization of channel pre-allocation and load balancing degree. We formulate an optimization problem to minimize the weighted sum of edge-cloud load balancing degree and queuing delay under the constraint of access success rate. The problem is decomposed into a large-timescale channel pre-allocation subproblem solved by the device-edge collaborative access priority scoring mechanism, and a small-timescale data access control subproblem solved by the discounted empirical matching mechanism (DEM) with the perception of high-concurrency number and queue backlog. Particularly, information uncertainty caused by externalities is tackled by exploiting discounted empirical performance which accurately captures the performance influence of historical time points on present preference value. Simulation results demonstrate the effectiveness of the proposed algorithm in reducing edge-cloud load balancing degree and queuing delay.

The very high path loss caused by molecular absorption becomes the biggest problem in Terahertz (THz) wireless communications. Recently, the multi-band ultra-massive multi-input multi-output (UM-MIMO) system has been proposed to overcome the distance problem. In UM-MIMO systems, the impact of mutual coupling among antennas on the system performance is unable to be ignored because of the dense array. In this letter, a channel model of UM-MIMO communication system is developed which considers coupling effect. The effect of mutual coupling in the subarray on the functionality of the system has been investigated through simulation studies, and reliable results have been derived.

Low capacity and reliability are the challenges in the development of ionosphere communication channel systems. To overcome this problem, one promising and state-of-the-art method is applying a multi-carrier modulation technique. Currently, the use of multi-carrier modulation technique is using a single transmission frequency with a bandwidth is no more than 24 kHz in real-world implementation. However, based on the range of the minimum and maximum ionospheric plasma frequency values, which could be in the MHz range, the use of these values as the main bandwidth in multi-carrier modulation techniques can optimize the use of available channel capacity. In this paper, we propose a multi-carrier modulation technique in combination with a model variation of Lowest Usable Frequency (LUF) and Maximum Usable Frequency (MUF) values as the main bandwidth to optimize the use of available channel capacity while also maintaining its reliability by following the variation of the ionosphere plasma frequency. To analyze its capacity and reliability, we performed a numeric simulation using a LUF-MUF model based on Long Short Term-Memory (LSTM) and Advanced Stand Alone Prediction System (ASAPS) in Near Vertical Incidence Skywave (NVIS) propagation mode with the assumption of perfect synchronization between transmitter and receiver with no Doppler and no time offsets. The results show the achievement of the ergodic channel capacity varies for every hour of the day, with values in the range of 10 Mbps and 100 Mbps with 0 to 20 dB SNR. Meanwhile, the reliability of the system is in the range of 8% to 100% for every hour of one day based on two different Mode Reliability calculation scenarios. The results also show that channel capacity and system reliability optimization are determined by the accuracy of the LUF-MUF model.

In this work, template attacks that aimed to leak the nonce were performed on 256-bit ECDSA hardware to evaluate the resistance against side-channel attacks. The target hardware was an ASIC and was revealed to be vulnerable to the combination of template attacks and lattice attacks. Furthermore, the attack result indicated it was not enough to fix the MSB of the nonce to 1 which is a common countermeasure. Also, the success rate of template attacks was estimated by simulation. This estimation does not require actual hardware and enables us to test the security of the implementation in the design phase. To clarify the acceptable amount of the nonce leakage, the computational cost of lattice attacks was compared to that of ρ method which is a cryptanalysis method. As a result, the success rate of 2-bit leakage of the nonce must be under 62% in the case of 256-bit ECDSA. In other words, SNR must be under 2-4 in our simulation model.

A channel coding problem with cost constraint for general channels is considered. Verdú and Han derived ϵ-capacity for general channels. Following the same lines of its proof, we can also derive ϵ-capacity with cost constraint. In this paper, we derive a formula for ϵ-capacity with cost constraint allowing overrun. In order to prove this theorem, a new variation of Feinstein's lemma is applied to select codewords satisfying cost constraint and codewords not satisfying cost constraint.

In the field of machine learning security, as one of the attack surfaces especially for edge devices, the application of side-channel analysis such as correlation power/electromagnetic analysis (CPA/CEMA) is expanding. Aiming to evaluate the leakage resistance of neural network (NN) model parameters, i.e. weights and biases, we conducted a feasibility study of CPA/CEMA on floating-point (FP) operations, which are the basic operations of NNs. This paper proposes approaches to recover weights and biases using CPA/CEMA on multiplication and addition operations, respectively. It is essential to take into account the characteristics of the IEEE 754 representation in order to realize the recovery with high precision and efficiency. We show that CPA/CEMA on FP operations requires different approaches than traditional CPA/CEMA on cryptographic implementations such as the AES.

In software defined radio systems, a channelizer plays an important role in extracting the desired signals from a wideband signal. Compared to the conventional methods, the proposed scheme provides a solution to design a digital channelizer extracting the multiple subband signals at different center frequencies with low complexity. To do this, this paper formulates the problem as an optimization problem, which minimizes the required multiplications number subject to the constraints of the ripple in the passbands and the stopbands for single channel and combined multiple channels. In addition, a solution to solve the optimization problem is also presented and the corresponding structure is demonstrated. Simulation results show that the proposed scheme requires smaller number of the multiplications than other conventional methods. Moreover, unlike other methods, this structure can process signals with different bandwidths at different center frequencies simultaneously only by changing the status of the corresponding multiplexers without hardware reimplementation.

Orthogonal frequency division multiplexing (OFDM) is very sensitive to the carrier frequency offset (CFO). The CFO estimation precision heavily makes impacts on the OFDM performance. In this paper, a new Bayesian learning-assisted joint CFO tracking and channel impulse response estimation is proposed. The proposed algorithm is modified from a Bayesian learning-assisted estimation (BLAE) algorithm in the literature. The BLAE is expectation-maximization (EM)-based and displays the estimator mean square error (MSE) lower than the Cramer-Rao bound (CRB) when the CFO value is near zero. However, its MSE value may increase quickly as the CFO value goes away from zero. Hence, the CFO estimator of the BLAE is replaced to solve the problem. Originally, the design criterion of the single-time-sample (STS) CFO estimator in the literature is maximum likelihood (ML)-based. Its MSE performance can reach the CRB. Also, its CFO estimation range can reach the widest range required for a CFO tracking estimator. For a CFO normalized by the sub-carrier spacing, the widest tracking range required is from -0.5 to +0.5. Here, we apply the STS CFO estimator design method to the EM-based Bayesian learning framework. The resultant Bayesian learning-assisted STS algorithm displays the MSE performance lower than the CRB, and its CFO estimation range is between ±0.5. With such a Bayesian learning design criterion, the additional channel noise power and power delay profile must be estimated, as compared with the ML-based design criterion. With the additional channel statistical information, the derived algorithm presents the MSE performance better than the CRB. Two frequency-selective channels are adopted for computer simulations. One has fixed tap weights, and the other is Rayleigh fading. Comparisons with the most related algorithms are also been provided.

A power-scalable sub-sampling phase-locked loop (SSPLL) is proposed for realizing dual-mode operation; high-performance mode with good phase noise and power-saving mode with moderate phase noise. It is the most efficient way to reduce power consumption by lowering the supply voltage. However, there are several issues with the low-supply millimeter-wave (mmW) SSPLL. This work discusses some techniques, such as a back-gate forward body bias (FBB) technique, in addition to employing a CMOS deeply depleted channel process (DDC).

This brief presents a side-channel attack (SCA) technique on a high-speed asynchronous successive approximation register (SAR) analog-to-digital converter (ADC). The proposed dual neural network based on multiple noise waveforms separately discloses sign and absolute value information of input signals which are hidden by the differential structure and high-speed asynchronous operation. The target SAR ADC and on-chip noise monitors are designed on a single prototype chip for SCA demonstration. Fabricated in 40 nm, the experimental results show the proposed attack on the asynchronous SAR ADC successfully restores the input data with a competitive accuracy within 300 mV rms error.

The existing target-dependent scalable image compression network can control the target of the compressed images between the human visual system and the deep learning based classification task. However, in its RNN based structure controls the bit-rate through the number of iterations, where each iteration generates a fixed size of the bit stream. Therefore, a large number of iterations are required at the high BPP, and fine-grained image quality control is not supported at the low BPP. In this paper, we propose a novel RNN-based image compression model that can schedule the channel size per iteration, to reduce the number of iterations at the high BPP and fine-grained bit-rate control at the low BPP. To further enhance the efficiency, multiple network models for various channel sizes are combined into a single model using the slimmable network architecture. The experimental results show that the proposed method achieves comparable performance to the existing method with finer BPP adjustment, increases parameters by only 0.15% and reduces the average amount of computation by 40.4%.

This paper proposes a multiple code block transmission scheme using hierarchical modulation (HM) for a broadcast channel in the orthogonal frequency division multiplexing (OFDM) downlink. We investigate the average bit error rate (BER) performance of two-layer HM using 16 quadrature amplitude modulation (QAM) and three-layer HM using 64QAM in multipath Rayleigh fading channels. In multiple code block transmission using HM, the basic information bits are demodulated and decoded to all users within a cell that satisfy the bit error rate (BER) requirement. Hence, we investigate non-uniform QAM constellations to find one that suppresses the loss in the average BER of the basic information bits for HM to a low level compared to that using the original constellation in which only the basic information bits are transmitted while simultaneously minimizing the loss in the average BER of the secondary and tertiary information bits from the original constellations in which the information bits of the respective layers are transmitted alone. Based on the path loss equations in the Urban Macro and Rural Macro scenarios, we also investigate the maximum distance from a base station (BS) for the information bits of each layer to attain the required average received signal-to-noise power ratio (SNR) that achieves the average BER of 10-3.

Theoretically secure cryptosystems, digital signatures may not be secure after being implemented on Internet of Things (IoT) devices and PCs because of side-channel attacks (SCA). Because RSA key generation and ECDSA require GCD computations or modular inversions, which are often computed using the binary Euclidean algorithm (BEA) or binary extended Euclidean algorithm (BEEA), the SCA weaknesses of BEA and BEEA become a serious concern. Constant-time GCD (CT-GCD) and constant-time modular inversion (CTMI) algorithms are effective countermeasures in such situations. Modular inversion based on Fermat's little theorem (FLT) can work in constant time, but it is not efficient for general inputs. Two CTMI algorithms, named BOS and BY in this paper, were proposed by Bos, Bernstein and Yang, respectively. Their algorithms are all based on the concept of BEA. However, one iteration of BOS has complicated computations, and BY requires more iterations. A small number of iterations and simple computations during one iteration are good characteristics of a constant-time algorithm. Based on this view, this study proposes new short-iteration CT-GCD and CTMI algorithms over Fp borrowing a simple concept from BEA. Our algorithms are evaluated from a theoretical perspective. Compared with BOS, BY, and the improved version of BY, our short-iteration algorithms are experimentally demonstrated to be faster.

Conventional Nyquist folding receiver (NYFR) uses zero crossing rising (ZCR) voltage times to control the RF sample clock, which is easily affected by noise. Moreover, the analog and digital parts are not synchronized so that the initial phase of the input signal is lost. Furthermore, it is assumed in most literature that the input signal is in a single Nyquist zone (NZ), which is inconsistent with the actual situation. In this paper, we propose an improved architecture denominated as a dual-channel NYFR with adjustable local oscillator (LOS) and an information recovery algorithm. The simulation results demonstrate the validity and viability of the proposed architecture and the corresponding algorithm.

Hardware oriented security and trust of semiconductor integrated circuit (IC) chips have been highly demanded. This paper outlines the requirements and recent developments in circuits and packaging systems of IC chips for security applications, with the particular emphasis on protections against physical implementation attacks. Power side channels are of undesired presence to crypto circuits once a crypto algorithm is implemented in Silicon, over power delivery networks (PDNs) on the frontside of a chip or even through the backside of a Si substrate, in the form of power voltage variation and electromagnetic wave emanation. Preventive measures have been exploited with circuit design and packaging technologies, and partly demonstrated with Si test vehicles.