Autonomous service robots and drones are increasingly deployed in urban spaces, yet evaluating their designs in situ is constrained by permits, logistics, and the cost of repeated user studies. We contribute SHOWTIME, a generative video-fusion framework that synthesizes realistic, controllable evaluation clips by combining structured prompts, encoding navigation behavior, proxemic policies, and task context with real urban scenes. Across two studies, participants rated SHOWTIME’s generative videos on par with physical recordings for realism, behavioral plausibility, ecological appropriateness, and comfort, and could not reliably distinguish real from generated footage; perceptual differences were driven by environmental structure and crowd density rather than modality. A follow-up experiment showed that generative prototyping elicits robust perceptual contrasts across design variants: communicative signaling (lights, functional cues) increases acceptability and comfort, whereas erratic motion reduces them. The pipeline achieves over a 10× reduction in per-clip time compared to physical trials, enabling rapid, human-in-the-loop iteration on behaviors and UI cues. Together, these results demonstrate human-level perceptual fidelity and practical viability, positioning SHOWTIME as a cost-effective, scalable method for refining autonomous systems and their interfaces in real-world urban contexts.

3D printing has revolutionized DIY (Do-It-Yourself) IoT prototyping, enabling cost-effective, creative custom device creation. However, this freedom also presents challenges due to the interplay between components within an IoT design, which can influence the overall utility and performance of the prototype. Optimizing these designs is difficult due to limited means of estimating their efficacy. To address this, we introduce TOAD, a novel tool for profiling IoT prototypes and gauging their performance impact. TOAD uses thermal imaging and video analysis to extract and compare design performance characteristics. Unlike existing solutions that only profile overall performance, our tool assesses component interactions and overall design effects. It offers an affordable, non-intrusive method without needing device access or code instrumentation. Extensive benchmarks show TOAD accurately extracts performance data, aiding in selecting the best design for IoT applications. Additionally, it provides insights into how casing factors like thickness and material influence thermal behavior and performance. We demonstrate practical applications by optimizing offloading decisions based on thermal behavior, highlighting casing impacts on design performance. TOAD paves the way for efficient IoT prototype designs, offering a better understanding of component interactions and significantly enhancing the utility of custom IoT designs and their effectiveness.

We present SNAKE, an innovative method that harnesses heat transferred from human touch interactions to estimate product quality. SNAKE offers an accessible and cost-effective solution that seamlessly integrates with existing retail practices; for example, it can be integrated with scales and cashiers already present in shops. Rigorous and systematic experiments demonstrate that SNAKE achieves a high level of accuracy (83%) and outperforms optical sensing and WiFi sensing baselines. We also provide evidence that SNAKE can capture touch interactions of different durations and maintain consistency across diverse user profiles and operating environments. To assess the potential for practical impact, we also carry out an additional user study (N = 100) which suggests that SNAKE has potential to improve consumer purchasing decisions by at least 25% and reduce food waste (or increase promotional opportunities) by 10%–15%. In summary, our contribution offers a novel solution for leveraging smart IoT solutions to support retailing and foster sustainable retail practices.

In response to the inefficiency of traditional testing and limitations of existing platforms for chip verification, this article introduces a method for automatically generating verification instruction sets for DSP chips. It aims to address scalability, portability, and ensure comprehensive coverage of chip functionalities. The article defines instruction templates based on DSP chip instructions' content and format address, serving as a foundation for diverse verification instruction sets. Constraints and coverage evaluation standards are formulated to guarantee full chip function coverage and high code coverage. An algorithm facilitates automatic generation. A simulation verification platform is designed to validate the method, seamlessly integrating with existing infrastructure. It enables automatic generation of module-specific verification instruction sets and comprehensive verification of the ALU floating-point arithmetic unit. This methodology successfully handles verification tasks with millions of instructions. Experimental results demonstrate full functional coverage and 100%operand and instruction option coverage, surpassing traditional approaches. Automatic generation enhances the verification of DSP chip components, improving overall quality and reliability.

Analog circuit is an important component of modern electronic systems. However, the soft fault diagnosis of analog circuits is challenging due to their large parameter variability and complex internal structure. So, this paper proposes an automatic fault diagnosis method based on Multi-Scale 1D Convolutional Neural Network (MS-1D-CNN) for analog circuits. Considering that faults may disappear or weaken during the propagation process and cannot be manifested in the output signals, the fault diagnosis model is trained and constructed based on an optimum set of test points with the maximum degree of fault isolation and least test points. Furthermore, because the data at different test points and time periods have different influences on fault diagnosis, a mixed attention mechanism combining both channel and spatial attention is adopted to extract more critical information in the fault diagnosis model, achieving a more accurate soft fault diagnosis for analog circuits. Experiments are conducted on four widely used benchmark circuits. The results show that our method has higher accuracy of fault diagnosis than the existing methods, and the fault diagnosis model based on multi-test points data has higher accuracy than that solely based on the output signals for analog circuits.

Hand-grip strength is a widely recognized indicator of muscle strength and overall health of individuals, particularly among older adults. Hand-grip strength measurements are typically obtained using dynamometers or specifically tailored devices, limiting the context in which measurements can be taken to health checks and clinical settings. In this demo, we showcase a new smart ring, namely HIPPO. The smart ring implements an innovative approach that offers a non-intrusive and opportunistic way to extract handgrip strength measurements from individuals. HIPPO re-purposes off-the-shelf light sensors available in existing wearable devices, e.g., smartwatches, and exploits the principle of light reflectivity, such that as an individual interacts with everyday objects, changes in their surfaces can be used to derive the hand-grip measurements.

Underwater environments are emerging as a new frontier for data science thanks to an increase in deployments of underwater sensor technology. Challenges in operating computing underwater combined with a lack of high-speed communication technology covering most aquatic areas means that there is a significant delay between the collection and analysis of data. This in turn limits the scale and complexity of the applications that can operate based on these data. In this article, we develop underwater fog computing support using low-cost micro-clouds and demonstrate how they can be used to deliver cost-effective support for data-heavy underwater applications. We develop a proof-of-concept micro-cloud prototype and use it to perform extensive benchmarks that evaluate the suitability of underwater micro-clouds for diverse underwater data science scenarios. We conduct rigorous tests in both controlled and field deployments, using river and sea waters. We also address technical challenges in enabling underwater fogs, evaluating the performance of different communication interfaces and demonstrating how accelerometers can be used to detect the likelihood of communication failures and determine which communication interface to use. Our work offers a cost-effective way to increase the scale and complexity of underwater data science applications, and demonstrates how off-the-shelf devices can be adopted for this purpose.

The paper presents an approach to fault extraction and test generation at RTL (register transfer level). The proposed ATPG (automatic test pattern generation) is targeting to cover all the extracted faults rather than a specific fault, and based on simulation with unspecified inputs. It uses a request-echo strategy called X-Pulling to greatly reduce the unnecessary backtrack and implication, which makes the algorithm very efficient. Experimental results demonstrate that our approach is better than ARTIST in three aspects: on average, the CPU time is shorter by three orders of magnitude, the test length is shorter by 52% and the fault coverage is higher by 0.89%.

Read and spontaneous discourses are two different but very significant speech styles to be investigated. So phonetic labeling on read and spontaneous discourse corpora are made one is ASCCD, a 10 hours read discourse corpus and the other is CASS, a 4 hours spontaneous discourse corpus. First the principles and conventions of transcription are presented. Then, these two speech styles are compared from phonetic and syntactic point of view, including the statistic results of different phonetic units got from the annotated corpora.

Speech corpus of Chinese discourse (ASCCD) was setup and annotated on segmental and prosodic and syntactic tiers. SAMPA -C and C-ToBI conventions are used for segmental and prosodic labeling. Sound variation such as assimilation, insertion and deletion are investigated on the labeled database. The prosodic research focuses on the sentence stress that involves the specification of relative prominence in prosodic structure,