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As a high-market-value sector, the consumer electronics industry is particularly vulnerable to reputational damage from surface defects in shipped products. However, the high level of automation and the short product life cycles in this industry make defect sample collection both difficult and inefficient. This challenge has led to a severe shortage of publicly available, comprehensive datasets dedicated to surface defect detection, limiting the development of targeted methodologies in the academic community. Most existing datasets focus on general-purpose object categories, such as those in the COCO and PASCAL VOC datasets, or on industrial surfaces, such as those in the MvTec AD and ZJU-Leaper datasets. However, these datasets differ significantly in structure, defect types, and imaging conditions from those specific to consumer electronics. As a result, models trained on them often perform poorly when applied to surface defect detection tasks in this domain. To address this issue, the present study introduces a specialized optical sampling system with six distinct lighting configurations, each designed to highlight different surface defect types. These lighting conditions were calibrated by experienced optical engineers to maximize defect visibility and detectability. Using this system, 14,478 high-resolution defect images were collected from actual production environments. These images cover more than six defect types, such as scratches, plain particles, edge particles, dirt, collisions, and unknown defects. After data acquisition, senior quality control inspectors and manufacturing engineers established standardized annotation criteria based on real-world industrial acceptance standards. Annotations were then applied using bounding boxes for object detection and pixelwise masks for semantic segmentation. In addition to the dataset construction scheme, commonly used semantic segmentation methods were benchmarked using the provided mask annotations. The resulting dataset has been made publicly available to support the research community in developing, testing, and refining advanced surface defect detection algorithms under realistic conditions. To the best of our knowledge, this is the first comprehensive, multiclass, multi-defect dataset for surface defect detection in the consumer electronics domain that provides pixel-level ground-truth annotations and is explicitly designed for real-world applications.

The proposed work contains three major contribution, such as smart data collection, optimized training algorithm and integrating Bayesian approach with split learning to make privacy of the patent data. By integrating consumer electronics device such as wearable devices, and the Internet of Things (IoT) taking THz image, perform EM algorithm as training, used newly proposed slit learning method the technology promises enhanced imaging depth and improved tissue contrast, thereby enabling early and accurate disease detection the breast cancer disease. In our hybrid algorithm, the breast cancer model achieves an accuracy of 97.5 percent over 100 epochs, surpassing the less accurate old models which required a higher number of epochs, such as 165.

Nowadays, artificial intelligence is used everywhere in the world and is becoming a key factor for innovation and progress in many areas of human life. From medicine to industry to consumer electronics, its influence is ever-expanding and permeates all aspects of our modern society. This article presents the use of artificial intelligence (prediction) for the control of three motors used for effector control in a spherical parallel kinematic structure of a designed device. The kinematic model used was the “Agile eye” which can achieve high dynamics and has three degrees of freedom. A prototype of this device was designed and built, on which experiments were carried out in the framework of motor control. As the prototype was created through the means of the available equipment (3D printing and lathe), the clearances of the kinematic mechanism were made and then calibrated through prediction. The paper also presents a method for motor control calibration. On the one hand, using AI is an efficient way to achieve higher precision in positioning the optical axis of the effector. On the other hand, such calibration would be rendered unnecessary if the clearances and inaccuracies in the mechanism could be eliminated mechanically. The device was designed with imperfections such as clearances in mind so the effectiveness of the calibration could be tested and evaluated. The resulting control of the achieved movements of the axis of the device (effector) took place when obtaining the exact location of the tracked point. There are several methods for controlling the motors of mechatronic devices (e.g., Matlab-Simscape). This paper presents an experiment performed to verify the possibility of controlling the kinematic mechanism through neural networks and eliminating inaccuracies caused by imprecisely produced mechanical parts.

Continued growth and development in the consumer electronic market have greatly increased in the realm of home automation. With this swelling in smart, Internet-connected consumer electronics, there is a need to ensure the safe and secure use of these products. So how does one authenticate each product in a large connected environment? How can the authors minimise counterfeiting, cloning, and the presence of Trojans in customer electronics? In this study, they explore their method of using various physically unclonable functions (PUFs) as a potential seed for a pseudorandom number generators (PRNGs) element. These can then be used to authenticate consumer electronic devices or protect communication over a large interconnected network. The advantage of this work is that their method increases the difficulty of attackers to learn patterns of the seed of each PRNG while optimising PUF-based constraints in different consumer electronic domains. Through this work they enhance the function of PRNGs, increasing the difficulty of attackers’ ability to model security systems, as well as present a lightweight and efficient solution to the growing security concerns. By making the PRNG more difficult to model, malicious actors are less able to overcome their proposed security enhancement leading to a safe and secure environment.

Flexible and stretchable electronics have emerged as highly promising technologies for the next generation of electronic devices. These advancements offer numerous advantages, such as flexibility, biocompatibility, bio-integrated circuits, and light weight, enabling new possibilities in diverse applications, including e-textiles, smart lenses, healthcare technologies, smart manufacturing, consumer electronics, and smart wearable devices. In recent years, significant attention has been devoted to flexible and stretchable pressure sensors due to their potential integration with medical and healthcare devices for monitoring human activity and biological signals, such as heartbeat, respiratory rate, blood pressure, blood oxygen saturation, and muscle activity. This review comprehensively covers all aspects of recent developments in flexible and stretchable pressure sensors. It encompasses fundamental principles, force/pressure-sensitive materials, fabrication techniques for low-cost and high-performance pressure sensors, investigations of sensing mechanisms (piezoresistivity, capacitance, piezoelectricity), and state-of-the-art applications.

Frequent new product releases pose significant challenges for firms as they manage successive generations of product diffusion. We develop an analytical model to study the effect of different purchase options by strategic consumers on a firm’s profit and the firm’s strategies for the timing and pricing of its successive generations of product diffusion. We show that consumers’ strategic behavior, although adversely affecting the sales of the first-generation product, positively influences the sales of the second-generation product through an initial “seeding” effect. The influence of strategic consumers on profit and sales depends largely on the discount-to-price ratio of the first generation relative to the performance improvement in the second generation. When the relative discount is small, the seeding effect on the second-generation product dominates. When the relative discount is large, the “cannibalization” effect on the first-generation product dominates. We further demonstrate that the optimal entry timings recommended in the literature (i.e., “now,” “maturity,” or “never”) can occur under different market conditions. In general, higher performance improvement and lower salvage value would support a higher optimal price, a larger discount, and a later introduction time. In addition, the firm can benefit from patient consumers when the performance improvement is relatively small, and it can induce the complete substitution of the later generation for the earlier generation when the performance improvement is relatively large. Overall, our model provides a theoretical foundation for understanding the effect of consumer strategic behavior on product diffusion, and our results offer important insights about firms’ multigeneration product diffusion strategies. The online appendix is available at https://doi.org/10.1287/isre.2017.0720 .

Recently, a range of organisations, including car and consumer electronics manufacturers, have applied so-called patent pledges. A patent pledge is a publicly announced intervention by patent-owning entities ('pledgers') to out-license active patents to the restricted or unrestricted public free from or bound to certain conditions for a reasonable or no monetary compensation. Despite growing research to better understand this phenomenon, the underlying terminology remains contradictory. We apply an inductive research approach using qualitative coding to analyse 60 patent pledges made by 80 organisations. Based on this analysis, we propose a three-dimensional taxonomy that distinguishes eight types of patent pledges. Extending this taxonomy using case examples, we then propose a generalised patent licensing taxonomy. This second taxonomy can be used to distinguish patent licensing strategies, including other frequently used approaches, such as patent pools and cross-licenses. Finally, we use the patent pledge taxonomy to illustrate how patent owners change their licensing strategies over time and how it can support strategic decision processes within an organisation. We contribute to the field of patent management by building an ontology of patent pledges through proposing a definition and eight types. The patent licensing taxonomy enables organisations to devise and choose licensing strategies, and to illustrate licensing approaches of competitors, for instance.

A central question for firms releasing successive generations of a product is whether they should pursue a market-driven approach and align own product releases to existing industry-level patterns. While an alignment with industry patterns enables firms to capitalize on general market receptivity, it may also entail dilution and competitive interference effects. Using data on the consumer electronics and automotive industries, we show that the effectiveness of such alignment depends on two additional timing-related decisions: the firm’s release regularity for successive product generations and its preannouncement timing. Firms benefit from alignment to the industry only if they release successive generations in a regular manner (to create anticipation) and refrain from early preannouncements (to avoid competitive counteraction). For all other combinations of release regularity and preannouncement timing, not aligning to the industry rhythm leads to higher levels of firm performance. Taken together, our findings enable a nuanced view of the interplay of timing-related launch decisions that provides actionable guidance for managers.

Manufacturing micro-components has become a key area of interest for research, owing to the growing demand for miniaturized components and assemblies, and a series of applications across multiple industrial sectors [...]