Adaptive optimizers such as Adam and RMSProp have gained attraction in complex neural networks, including generative adversarial networks (GANs) and Transformers, thanks to their stable performance and fast convergence compared to non-adaptive optimizers. A frequently overlooked limitation of adaptive optimizers is that adjusting the learning rate of each dimension individually would ignore the knowledge of the whole loss landscape, resulting in slow updates of parameters, invalidating the learning rate adjustment strategy and eventually leading to widespread insufficient convergence of parameters. In this paper, we propose HVAdam, a novel optimizer that associates all dimensions of the parameters to find a new parameter update direction, leading to a refined parameter update strategy for an increased convergence rate. We validated HVAdam in extensive experiments, showing its faster convergence, higher accuracy, and more stable performance on image classification, image generation, and natural language processing tasks. Particularly, HVAdam achieves a significant improvement on GANs compared with other state-of-the-art methods, especially in Wasserstein-GAN (WGAN) and its improved version with gradient penalty (WGAN-GP).

This paper proposes a high in-band (IB) linearity gain-boosted N-path filter with bandwidth (BW)-extended. The proposed filter uses a gain-boosted design, where a switched postposition gm-C network achieves the bandpass-bandreject (BPF-BRF) cascade response. This setup selectively enhances the gain-boosted filter's transition band and extends the filter's bandwidth. Fabricated in 130nm SOI CMOS, our proposed filter realizes a 4.0-5.5 dB gain and a 6.89-8.01 dB noise figure (NF) over a 0.1-1.1 GHz tuning range. With postposition gmlinearity and clock boost circuit optimization, our filter at 0.5 GHz exhibits an in-band input-referred −1dB compression point (IB-ICP-1dB) of 0.21 dBm and an in-band input-referred 3rd-order intercept point (IB-IIP3) of 12.59 dBm and an out-of-band (OOB)-IIP3 of 17.28 dBm. The power consumption is 36-74 mW. The chip active area is 0.9 mm2.

This paper presents a dual-mode offset calibration for high-speed dynamic comparators, offering a wide calibration range, low offset, low input-referred noise, and reduced comparison delay. A wide calibration range is achieved in Mode 1 by using a switched-capacitor-biased auxiliary differential current path, while a low-noise, high-precision calibration with reduced comparison delay is provided by Mode 2 using digitally adjustable cross-coupled capacitors which enhance the transconductance of the differential pairs in the pre-amplifier. Designed in a 28-nm CMOS process, simulation results show that at an operating frequency of 4 GHz, the tuning range is from -31.5 to 35.4 mV, with the standard deviation of comparator offset reduced from 8.06 mV to 136 μV. When the two modes are configured to operate synergistically, the input-referred noise is 269 μV with a comparison delay of less than 19.3 ps. Furthermore, the additional power consumption due to the calibration circuits is less than 12%.

Radio frequency fingerprint identification (RFFI) is a promising physical layer authentication technique that uses minute hardware imperfections as device fingerprints. However, various interferences, especially noise, can distort the RF fingerprints embedded in transmission signals, thereby limiting the overall system performance. This paper investigates performance improvements in RFFI systems through multi-receiver training, ensemble learning, and stochastic weight averaging (SWA), with a particular focus on noise immunity. Our experimental results show three main results: First, multi-receiver training strategies significantly improve system robustness, achieving up to 24% accuracy improvement. Second, ensemble learning with multiple models can improve prediction stability. Third, SWA can achieve comparable performance to ensemble learning with reduced complexity. These findings provide practical insights for improving RFFI system performance in challenging noise environments, contributing to more reliable wireless device authentication.

The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance. However, these methods still struggle at large-scale and viewpoint variations, due to the geometric inconsistency resulting from the application of the mutual nearest neighbour criterion (i.e., one-to-one assignment) in patch-level matching. Accordingly, we in-troduce AdaMatcher, which first accomplishes the feature correlation and co-visible area estimation through an elaborate feature interaction module, then performs adaptive assignment on patch-level matching while es-timating the scales between images, and finally refines the co-visible matches through scale alignment and sub-pixel regression module. Extensive experiments show that AdaMatcher outperforms solid baselines and achieves state-of-the-art results on many downstream tasks. Ad-ditionally, the adaptive assignment and sub-pixel refinement module can be used as a refinement network for other matching methods, such as SuperGlue, to boost their performance further. The code will be publicly available at https://github.com/AbyssGaze/AdaMatcher.

Local image feature matching under large appearance, viewpoint, and distance changes is challenging yet important. Conventional methods detect and match tentative local features across the whole images, with heuristic consistency checks to guarantee reliable matches. In this paper, we introduce a novel Overlap Estimation method conditioned on image pairs with TRansformer, named OETR, to constrain local feature matching in the commonly visible region. OETR performs overlap estimation in a two step process of feature correlation and then overlap regression. As a preprocessing module, OETR can be plugged into any existing local feature detection and matching pipeline, to mitigate potential view angle or scale variance. Intensive experiments show that OETR can boost state of the art local feature matching performance substantially, especially for image pairs with small shared regions. The code will be publicly available at https://github.com/AbyssGaze/OETR.

More and more scholars focus on mobile edge computing (MEC) technology, because the strong storage and computing capabilities of MEC servers can reduce the long transmission delay, bandwidth waste, energy consumption, and privacy leaks in the data transmission process. In this paper, we study the cache placement problem to determine how to cache videos and which videos to be cached in a mobile edge computing system. First, we derive the video request probability by taking into account video popularity, user preference and the characteristic of video representations. Second, based on the acquired request probability, we formulate a cache placement problem with the objective to maximize the cache hit ratio subject to the storage capacity constraints. Finally, in order to solve the formulated problem, we transform it into a grouping knapsack problem and develop a dynamic programming algorithm to obtain the optimal caching strategy. Simulation results show that the proposed algorithm can greatly improve the cache hit ratio.