Interactive medical image segmentation plays a crucial role in medical diagnosis, treatment planning, and in-terventional procedures. With the rapid advancement of artificial intelligence and deep learning technologies, this field has not only seen significant improvements in the accuracy and efficiency of segmenting anatomical structures and pathological areas from medical images, but it has also made substantial strides in the development of user-centric interactive tools. The introduction of the Segmentation Anything Model (SAM) represents a significant expansion of prompt-driven approaches within the domain of image segmentation, introducing a plethora of previously untapped functionalities. However, due to the substantial differences between natural and medical images, the generalization capabilities of interactive segmentation models across different imaging techniques remain limited, necessitating the ongoing development of targeted interactive segmentation models. In this work, we provide a comprehensive overview aimed at extending the efficacy of SAM to various medical imaging modalities, encompassing diverse image analyses and domains. Additionally, we explore potential research directions for SAM in different medical imaging fields. Despite these advancements, the accuracy and robustness of segmentation models still largely depend on high-quality annotated data, which is costly and time-consuming to acquire. These research efforts herald significant forthcoming advancements in interactive medical image segmen-tation technology, with the potential to greatly improve patient outcomes and streamline medical procedures, thereby driving further development and innovation in the healthcare industry.

Accurate and reproducible assessment of myocardial conditions is essential for the diagnosis of prior infarctions, cardiomyopathies, and inflammatory diseases. Although cardiac magnetic resonance imaging (CMR) is regarded as the gold standard for evaluating myocardial anatomy and function, manual segmentation remains labor-intensive and susceptible to variability. The demand for high-resolution images, dense predictions, and comprehensive segmentation across all phases of the cardiac cycle introduces significant challenges for Cine-CMR analysis. To address these issues, we present EML-SAM, an innovative interactive segmentation model based on the Segment Anything Model (SAM). EML-SAM incorporates an early-mid-late (EML) multi-scale fusion strategy, effectively mitigating the premature dilution of interaction information, thereby reducing segmentation errors and enhancing feature utilization. Furthermore, the model utilizes a multi-scale linear attention (MLA) transformer to establish an efficient global receptive field. Comprising 12 Transformer layers, the model integrates multi-scale attention with multi-scale perceptrons, striking a balance between computational efficiency and capacity. The proposed methodology offers a robust and efficient solution for high-resolution Cine-CMR segmentation, delivering accurate and real-time segmentation performance throughout the various stages of temporal changes within the cardiac cycle.

Fabric defect detection is a critical research area in the textile industry, with substantial practical implications. However, existing methods often struggle with detecting small and multi-scale defects. To address these challenges, we propose NR-DETR, a lightweight real-time detection model built on an enhanced RT-DETR framework. The model employs multi-scale enhancement and logical distillation to boost detection performance. Specifically, the RB-AIFI and CSCB modules are designed to optimize the stability of multi-scale feature fusion and enhance detection capabilities, while the efficient upsampling module (EUCB) significantly boosts inference efficiency. Furthermore, the model incorporates logical and feature distillation methods, employing hierarchical feature alignment and a shared decoder design to enhance the expressiveness and detection accuracy of the student model. Experimental results on the MVTec Fabric Defect dataset demonstrate that NR-DETR achieves a detection accuracy of 79.2%, a 5.2% improvement over the baseline model, while reducing the parameter count by approximately 10%, showcasing superior performance and efficiency. After applying the proposed joint distillation strategy, detection accuracy further improves by 1.7%, validating the effectiveness of the approach. The proposed model offers robust technical support for real-time and efficient fabric defect detection tasks.

The widespread application of industrial control systems has driven the development of industrial control protocols. However, traditional industrial control protocols suffer from issues such as a lack of security mechanisms, resulting in the existence of many dangerous vulnerabilities in industrial control systems . Fuzzing, as a commonly used technique for vulnerability discovery, has its own set of issues, including low testing efficiency, lack of adaptive capability, and high repetition rate of generated test cases . To solve the existing problems, we propose a low-repetition rate Modbus TCP fuzzing method based on Transformer and Mutation Target Adaptation. Firstly, the syntactic features of the industrial control protocol Modbus TCP are learned by using a simplified Transformer model. The model effectively reduces the training and generation time without decreasing the acceptance rate of test cases; Secondly, in the test case generation phase, in order to improve the mutation efficiency of test cases, the byte mutation probability adaptive strategy is introduced to replace the greedy strategy of Transformer. This strategy can dynamically adjust the mutation probability of each byte in the newly generated test cases , so as to improve the abnormal rate and reduce the repetition rate of test cases; Finally, the mutation results are selected by the mutation byte adaptive selection strategy, which not only improves the mutation adaptivity, but also maintains the diversity of mutations. The experimental results indicate that, compared to traditional methods, our approach has improved acceptance rates and abnormal rates by at least 10%. In comparison to AI-based fuzzing methods, our approach maintains a similar acceptance rate while increasing the abnormal rate by 3% to 25%.

The rise of the Internet of Things (IoT) has increased standby energy consumption due to the growing number of smart devices in homes. Existing approaches use real-time energy data and machine learning to identify and minimize standby energy for residential energy management but rely on cloud-based data aggregation and collaborative training due to limited edge device data. However, such an approach incurs extra cloud service costs, risks personal data leakage, and fails to capture residence diversity, resulting in suboptimal energy management performance.

The trend of Internet of Things is bringing in millions of new smart devices into homes to increase the quality of human life. However, this enormous number of new devices have also brings in an increasing energy consumption in standby for awaiting wire-less communication or status change. To reduce standby energy, existing approaches use real-time consumption data and machine learning techniques to identify standby energy, but aggregate data or intermediate model training updates in the cloud to collaboratively perform load forecasting, which could directly or indirectly cause personal data leakage, alongside with significant communication bandwidth and extra cloud service monetary cost. On the other hand, such a global collaborative model yields unsatisfactory en-ergy management performance as they fail to capture the diversity of each residence. In this paper, we propose a privacy-preserved, communication-efficient, personalized and cloud-service-free resi-dential energy management system (EMS) with personalized feder-ated deep reinforcement learning (PFDRL) framework to tackle the standby energy reduction in residential building.

Energy-harvesting designs typically include highly entangled app-lication-level and energy-management subsystems that span both hardware and software. This tight integration makes developing sophisticated energy-harvesting systems challenging, as developers have to consider both embedded system development and intermit-tent energy management simultaneously. Even when successful, solutions are often monolithic, produce suboptimal performance, and require substantial effort to translate to a new design. Instead, we propose a new energy-harvesting power management architecture, Altair that offloads all energy-management operations to the power supply itself while making the power supply programmable. Altair introduces an energy supervisor and a standard interface to enable an abstraction layer between the power supply hardware and the running application, making both replaceable and recon-figurable. To ensure minimal resource conflict on the application processor, while running resource-hungry optimization techniques in the supervisor, we implement the Altair design in a lower power microcontroller that runs in parallel with the application. We also develop a programmable power supply module and a software library for seamless application development with Altair. We evaluate the versatility of the proposed architecture across a spectrum of IoT devices and demonstrate the generality of the plat-form. We also design and implement an online energy-management technique using reinforcement learning on top of the platform and compare the performance against fixed duty-cycle baselines. Results indicate that sensors running the online energy-manager perform similar to continuously powered sensors, have a l0x higher event generation rate than the intermittently powered ones, 1.8-7x higher event detection accuracy, experience 50% fewer power failures, and are 44% more available than the sensors that maintain a constant duty-cycle.

Commercial Internet of Things (IoT) deployments are mostly closed-source systems that offer little to no flexibility to modify the hardware and software of the end devices. Once deployed, retrofitting such systems to an upgraded functionality requires replacing all the devices, which can be extremely time and cost prohibitive. End users cannot generally leverage deployed infrastructure to add their own sensors or custom data. However, we observe that IoT systems sometimes report battery voltage information to the cloud, and batteries are often user-serviceable. This indicates that perturbing the battery voltage to encode customized information could be a minimally invasive method to retrofit existing IoT devices.

The fast-growing trend of Internet of Things (IoT) has provided its users with opportunities to improve user experience such as voice assistants, smart cameras, and home energy management systems. Such smart home applications often require large numbers of diverse training data to accomplish a robust model. As single user may not have enough data to train such a model, users intent to collaboratively train their collected data in order to achieve better performance in such applications, which raise the concern of data privacy protection. Existing approaches for collaborative training need to aggregate data or intermediate model training updates in the cloud to perform load forecasting, which could directly or indirectly cause personal data leakage, alongside with significant communication bandwidth and extra cloud service monetary cost.

In modern world, the drastic development of driver assistance system has made driving a lot easier than before. In order to increase the safety onboard, a method was proposed to detect STOP sign and estimate distance using a single camera. In STOP sign detection, LBP-cascade classifier was applied to identify the sign in the image, and the principle of pinhole imaging was based for distance estimation. Road test was conducted using a detection system built with a CMOS camera and software developed by Python language with OpenCV library. Results shows that that the proposed system reach a detection accuracy of maximum of 97.6% at 10m, a minimum of 95.00% at 20m, and 5% max error in distance estimation. The results indicate that the system is effective and has the potential to be used in both autonomous driving and advanced driver assistance driving systems.