Optimizing register transfer level (RTL) code is of vital importance in hardware design. Large language models (LLMs) provide new methods for the automatic generation and optimization of RTL code. However, existing methods for generating RTL code often focus on model fine-tuning and the use of various expansion techniques to enhance the RTL code generation capabilities, lacking attention to the functional correctness. To address this issue, we propose EstCoder, an LLM-powered collaborative agent framework for RTL code generation based on static functional score estimation. EstCoder operates a three-stage paradigm: Generation, Estimation and Correction. During the stages, the functional estimation agent statically evaluates the generated code based on score and assessment results, and decides whether to output the code directly, return it for regeneration, or forward it to the code correction agent. This famework can be applied to various LLMs that designed for RTL code generation, further enhancing the correctness of the generated code. By providing quantitative scores and human-readable requirements comparisons, it improves the transparency of AI-assisted RTL code generation. Experiments show that EstCoder significantly improves the correctness of RTL code generation by generic LLM by 3.2%-9.0%, demonstrating the practical value of our system.

The automatic generation of Verilog code using Large Language Models (LLMs) presents a compelling solution to enhance the efficiency of hardware design flow. However, the state-of-the-art performance of LLMs in Verilog generation remains limited compared to programming languages such as Python. Previous research, Chain of Thought (CoT), has demonstrated that incorporating intermediate reasoning steps can significantly improve the performance of LLMs in code generation. In this paper, we propose the Verilog Tree of Thoughts (VToT) method. This structured prompting technique addresses the abstraction gap between Verilog and CoT by embedding hierarchical design constraints within the prompt. Experimental results on the VerilogEval and RTLLM benchmarks demonstrate that VToT prompting enhances both the syntactic and functional correctness of the generated code. Specifically, according to the RTLLM benchmark, VToT achieved a correctness rate of 75.9% at pass@5, representing an improvement of 10.4%. Furthermore, in the VerilogEval benchmark, VToT achieved state-of-the-art performance with a correctness rate of 52.4% at pass@1 (an increase of 8.9%) and 65.4% at pass@5 (an increase of 9.6%).

Owing to the rapid development of deep learning and the high efficiency of hashing, hashing methods based on deep learning models have been extensively adopted in the area of cross-modal retrieval. In general, in existing deep model-based methods, modality-specific features play an important role during the hash learning. However, most existing methods only use the modality-specific features from the final fully connected layer, ignoring the semantic relevance among modality-specific features with different scales in multiple layers. To address this issue, in this study, we put forward an end-to-end deep hashing method called deep multiscale fusion hashing (DMFH) for cross-modal retrieval. For the proposed DMFH, we first design different network branches for two modalities and then adopt multiscale fusion models for each branch network to fuse the multiscale semantics, which can be used to explore the semantic relevance. Furthermore, the multi-fusion models also embed the multiscale semantics into the final hash codes, making the final hash codes more representative. In addition, the proposed DMFH can learn common hash codes directly without a relaxation, thereby avoiding a loss in accuracy during hash learning. Experimental results on three benchmark datasets prove the relative superiority of the proposed method.

A Siamese network based metric learning method is proposed for SAR target classification with few training samples. The network consists of two identical CNNs sharing the weights. Different from classification networks that predict the category of one sample, the Siamese network implements a metric learning to measure the similarity between two samples. Since the input is the sample pair, the amount of training data dramatically increases which contributes to training a better network. When generating the pairs, a hard negative mining scheme is proposed for improving the performance. To avoid computing the similarity between the test sample and each training sample at the test stage, which is time consuming, a two stages scheme is employed with an additional classification network taking the output of the single branch of Siamese network as the input and predicting the category. Experiments on the MSTAR dataset validate the effectiveness of the proposed method.

WiFi fingerprint-based indoor localization techniques have been proposed and widely used in recent years. Most solutions need a site survey to collect fingerprints from interested locations to construct the fingerprint database. However, the site survey is labor-intensive and time-consuming. To overcome this shortcoming, we record user motions as well as WiFi signals without the active participation of the users to construct the fingerprint database, in place of the previous site survey. In this paper, we develop an indoor localization system called WicLoc, which is based on WiFi fingerprinting and crowdsourcing. We design a fingerprint model to form fingerprints of each location of interest after fingerprint collection. We propose a weighted KNN (K-Nearest Neighbor) algorithm to assign different weights to APs and achieve room-level localization. To obtain the absolute coordinate of users, we design a novel MDS (Multi-Dimensional Scaling) algorithm called MDS-C (Multi-Dimensional Scaling with Calibrations) to calculate coordinates of interested locations in the corridor and rooms, where anchor points are used to calibrate absolute coordinates of users. Experimental results show that our system can achieve a competitive localization accuracy compared with state-of-the-art WiFi fingerprint-based methods while avoiding the labor-intensive site survey.

In existing WiFi-based localization methods, smart mobile devices consume quite a lot of power as WiFi interfaces need to be used for frequent AP scanning during the localization process. In this work, we design an energy-efficient indoor localization system called ZigBee assisted indoor localization (ZIL) based on WiFi fingerprints via ZigBee interference signatures. ZIL uses ZigBee interfaces to collect mixed WiFi signals, which include non-periodic WiFi data and periodic beacon signals. However, WiFi APs cannot be identified from these WiFi signals by ZigBee interfaces directly. To address this issue, we propose a method for detecting WiFi APs to form WiFi fingerprints from the signals collected by ZigBee interfaces. We propose a novel fingerprint matching algorithm to align a pair of fingerprints effectively. To improve the localization accuracy, we design the K-nearest neighbor (KNN) method with three different weighted distances and find that the KNN algorithm with the Manhattan distance performs best. Experiments show that ZIL can achieve the localization accuracy of 87%, which is competitive compared to state-of-the-art WiFi fingerprint-based approaches, and save energy by 68% on average compared to the approach based on WiFi interface.

Background: Clinical studies showed that abdominal acupuncture was an effective and safe treatment for depression. However, the underlying neural mechanism was still largely unknown. The present study investigated the brain spontaneous activity changes following with the abdominal acupuncture treatment in female depressive patients with different levels, to explore the neural basis underpinning abdominal acupuncture treatment efficacy. Methods: Fifteen female depression patients (nine mild and six moderate-serious participants) were recruited for the present study, these patients treated by abdominal acupuncture for 4 weeks. The resting-state functional magnetic resonance imaging (fMRI) data was collected at before and after treatment stages. The changes of resting state regional homogeneity (ReHo) were examined between these two stages, to suggest the acupuncture treatment effect on spontaneous neural activity. Further analysis was performed to explore the correlation between ReHo changes and clinical measurements (Self-rating depression scale SDS and Montgomery-Asberg Depression Rating Scale MADRS scores). Results: The scores of SDS and MADRS had gradually declined after-treatment stage in mild and moderate-serious depression, compared with those of before-treatment stage. There were statistically differences of SDS and MADRS scores after treatment at 1st, 2nd, 3rd and 4th week's (P<;0.05). Compared with the before-treatment state in mild depression, brain regions with increased ReHo were found in Frontal_Sup_L, Frontal_Inf_Tri_L, Frontal_Inf_Tri_R, and Parietal_Inf_R. Meanwhile, the brain regions including Temporal_Pole_Sup_R, Occipital_Inf_R, Occipital_Inf_R, Temporal_Sup_R, Cuneus_R and Temporal_Mid_L were observed with decreased ReHo. In moderate-serious depression, brain regions with increased ReHo were found in Frontal_Med_Orb_Rand, and Cingulum_Mid_R; meanwhile, the brain regions including Lingual_L and Postcentral_R were observed with decreased ReHo. There were a large of quantity regional homogeneity changes which correlated with scores of SDS and MADRS. Conclusion: Abdominal acupuncture treatment could induce spontaneous brain activity change in depression patients. However, there were different regional homogeneity changes in different depression levels. ReHo changes in mild depression were mainly distributed in prefrontal and temporal lobe, but the ReHo change in prefrontal, Parietal, Occipital lobe and limbic system in moderate-serious depression. Prefrontal region took important roles in the acupuncture treatment effect on depression patients. Together, the findings of the present study provided new evidence for that abdominal acupuncture was an effective treatment for depression, and the ReHo was efficacy evaluation objective indicators of acupuncture treatment for depression.