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The purpose of this paper is to design a kernel convolution processor. High-speed image processing is a challenging task for real-time applications such as product quality control of manufacturing lines. Smart image sensors use an array of in-pixel processors to facilitate high-speed real-time image processing. These sensors are usually used to perform the initial low-level bulk image filtering and enhancement.

In this paper, using pulse-width modulated signals and regular nearest neighbor interconnections, a convolution image processor is presented. The presented processor is not only capable of processing arbitrary size kernels but also the kernel coefficients can be any arbitrary positive or negative floating number.

The performance of the proposed architecture is evaluated on a Xilinx Virtex-7 field programmable gate array platform. The peak signal-to-noise ratio metric is used to measure the computation error for different images, filters and illuminations. Finally, the power consumption of the circuit in different operating conditions is presented.

The presented processor array can be used for high-speed kernel convolution image processing tasks including arbitrary size edge detection and sharpening functions, which require negative and fractional kernel values.

Owing to the complex space environment and limited computing resources, traditional and deep learning-based methods cannot complete the task of satellite component contour extraction effectively. To this end, this paper aims to propose a high-quality real-time contour extraction method based on lightweight space mobile platforms.

A contour extraction method that combines two edge clues is proposed. First, Canny algorithm is improved to extract preliminary contours without inner edges from the depth images. Subsequently, a new type of edge pixel feature is designed based on surface normal. Finally, surface normal edges are extracted to supplement the integrity of the preliminary contours for contour extraction.

Extensive experiments show that this method can achieve a performance comparable to that of deep learning-based methods and can achieve a 36.5 FPS running rate on mobile processors. In addition, it exhibits better robustness under complex scenes.

The proposed method is expected to promote the deployment process of satellite component contour extraction tasks on lightweight space mobile platforms.

A pixel feature for edge detection is designed and combined with the improved Canny algorithm to achieve satellite component contour extraction. This study provides a new research idea for contour extraction and instance segmentation research.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

The purpose of this paper is to provide an interview with Eugene Sadler‐Smith, Professor of Management Development and Organizational Behaviour, at the University of Surrey's School of Management and author of The Intuitive Mind.

This briefing is prepared by an independent interviewer

Eugene Sadler‐Smith's research interests are centered upon the role of intuitive judgment in management decision making and management development. His research has been published widely in peer‐reviewed journals such as the Academy of Management Executive, Academy of Management Learning and Education, British Journal of Psychology, Journal of Occupational & Organizational Psychology, Journal of Organizational Behavior, Management Learning, and Organisation Studies. He has researched and published widely in the field of learning and development (L&D) and is author of several books, including Inside Intuition (Routledge, 2008) and The Intuitive Mind (John Wiley and Sons, 2010). His intuition research has featured on BBC Radio 4 and in The Times.

Provides strategic insights and practical thinking that have influenced some of the world's leading organizations.

This interview give some insight into the underlying scientific principles that explain the intuitive mind, offering tools and techniques for developing and deploying informed intuition in leadership and management.

The purpose of this paper is to demonstrate a novel 3D system-in-package (SiP) approach. This new packaging approach is based on stacked silicon submount technology. As demonstrators, a smart lighting module and a sensor systems were successfully developed by using the fabrication and assembly process described in this paper.

The stacked module consists of multiple layers of silicon submounts which can be designed and fabricated in parallel. The 3D stacking design offers higher silicon efficiency and miniaturized package form factor. This platform consists of silicon submount design and fabrication, module packaging, system assembling and testing and analyzing.

In this paper, a smart light emitting diode system and sensor system will be described based on stacked silicon submount and 3D SiP technology. The integrated smart lighting module meets the optical requirements of general lighting applications. The developed SiP design is also implemented into the miniaturization of particular matter sensors and gas sensor detection system.

SiP has great potential of integrating multiple components into a single compact package, which has potential implementation in intelligent applications.

Nanopore-based molecular sensing and measurement, specifically DNA sequencing, is advancing at a fast pace. Some embodiments have matured from coarse particle counters to enabling full human genome assembly. This evolution has been powered not only by improvements in the sensors themselves, but also in the assisting microelectronic CMOS readout circuitry closely interfaced to them. In this light, this paper aims to review established and emerging nanopore-based sensing modalities considered for DNA sequencing and CMOS microelectronic methods currently being used.

Readout and amplifier circuits, which are potentially appropriate for conditioning and conversion of nanopore signals for downstream processing, are studied. Furthermore, arrayed CMOS readout implementations are focused on and the relevant status of the nanopore sensor technology is reviewed as well.

Ion channel nanopore devices have unique properties compared with other electrochemical cells. Currently biological nanopores are the only variants reported which can be used for actual DNA sequencing. The translocation rate of DNA through such pores, the current range at which these cells operate on and the cell capacitance effect, all impose the necessity of using low-noise circuits in the process of signal detection. The requirement of using in-pixel low-noise circuits in turn tends to impose challenges in the implementation of large size arrays.

The study presents an overview on the readout circuits used for signal acquisition in electrochemical cell arrays and investigates the specific requirements necessary for implementation of nanopore-type electrochemical cell amplifiers and their associated readout electronics.

This paper aims to develop a robust person tracking method for human following robots. The tracking system adopts the multimodal fusion results of millimeter wave (MMW) radars and monocular cameras for perception. A prototype of human following robot is developed and evaluated by using the proposed tracking system.

Limited by angular resolution, point clouds from MMW radars are too sparse to form features for human detection. Monocular cameras can provide semantic information for objects in view, but cannot provide spatial locations. Considering the complementarity of the two sensors, a sensor fusion algorithm based on multimodal data combination is proposed to identify and localize the target person under challenging conditions. In addition, a closed-loop controller is designed for the robot to follow the target person with expected distance.

A series of experiments under different circumstances are carried out to validate the fusion-based tracking method. Experimental results show that the average tracking errors are around 0.1 m. It is also found that the robot can handle different situations and overcome short-term interference, continually track and follow the target person.

This paper proposed a robust tracking system with the fusion of MMW radars and cameras. Interference such as occlusion and overlapping are well handled with the help of the velocity information from the radars. Compared to other state-of-the-art plans, the sensor fusion method is cost-effective and requires no additional tags with people. Its stable performance shows good application prospects in human following robots.

Making a workplace entails two essential factors: the physical action of designing, and the design of the work space itself. This paper illustrates how building stock can be used as a vehicle to create new, small and medum‐sized enterprises in which a participatory and learning process gives users control of their costs and stimulates creative channels between tenants. Workplace making can be the source of new understanding and communication between disciplines that do not usually cooperate in innovative situations ‐ demonstrated by the Volvo case study, which resulted in a totally new production system for the final assembly plants. In conclusion, it is paramount that the company itself leads the design process. To outsource workplace making is to outsource the knowledge and ability to use space as a long‐term production resource, which can restrict space productivity. To ensure the best use of future production space it is vital that a company listens not only to the architects but to its own employees.

This study aims to focus on whether and furthermore how aesthetics-based mystery affects consumers’ responses toward relevant products.

Three experimental studies are reported. In Studies 1–2, smartphone ad flyers varying in mystery and non-mystery styles were adopted. A total of 187 undergraduate participants were recruited in Study 1 and 245 undergraduate participants in Study 2. In Study 3, a total of 193 participants who work in a range of businesses were recruited and wristwatch ad flyers were adopted.

Findings demonstrate that consumers are more willing to pay for products promoted via mystery appeal (versus non-mystery). Such positive impacts occur through consumers’ high-end perceptions of the products. Concrete, rather than abstract, verbal description of quality product features facilitate the impact of mystery appeal on consumer purchase decisions.

The findings advance an extant understanding of mystery appeal in advertising. It is among the first few to demonstrate that high-end product perceptions carry over the positive influence of mystery on consumers. This research is enlightening by suggesting an incongruity effect between pictorial stimuli and verbal information in the advertisement. This study’s scope is limited to visual mystery-evoking stimuli and Chinese participants.

When marketers/advertisers promoting products/brands with high prices, aesthetics-based mystery appeal should be considered as an effective option. This appeal is implicated as effective across gender. Moreover, visual mystery-evoking stimuli, combined with a concrete (not abstract) verbal description of product features should be optimal in promoting products.

The findings contribute to the limited empirical research on the influence processes of aesthetics-based mystery appeal. Different from the intuition, it is suggested that incongruity between visual and verbal stimuli in mystery ads that enhances the positive effect of mystery appeal.