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L. Gǎvruţa (2012) introduced a special kind of frames, named K-frames, where K is an operator, in Hilbert spaces, which is significant in frame theory and has many applications. In this paper, first of all, we have introduced the notion of approximative K-atomic decomposition in Banach spaces. We gave two characterizations regarding the existence of approximative K-atomic decompositions in Banach spaces. Also some results on the existence of approximative K-atomic decompositions are obtained. We discuss several methods to construct approximative K-atomic decomposition for Banach Spaces. Further, approximative _d-frame and approximative _d-Bessel sequence are introduced and studied. Two necessary conditions are given under which an approximative _d-Bessel sequence and approximative _d-frame give rise to a bounded operator with respect to which there is an approximative K-atomic decomposition. Example and counter example are provided to support our concept. Finally, a possible application is given.

To assess and share the experiences of users of a new generation of practical and knowledge‐based job support tools.

To illustrate distinguishing features of job support tools and the use of multi‐media capabilities with examples of sales support tools.

The tools mentioned and other similar tools examined by the underpinning research programme have had a very significant and beneficial impact on performance.

Job support tools can make it easier for important work groups to undertake complex tasks. The essence of the greater success of superstar performers can be captured and shared by means of support tools that can be constructed to encourage adoption, build understanding with each use and change behaviours with benefits for both people and organisations.

Shares the experiences of early adopters and draws practical lessons from them.

The technique for hiding confidential data in specific digital media by enhancing the graphical contents is known as watermarking. The dissemination of information over a secure channel is essential for multimedia applications. The purpose of this study is to develop a secure communication approach for OFDM system.

This paper exploits a secure communication in the orthogonal frequency division multiplexing (OFDM) system using wavelet-based video watermarking technique. In this work, the Chronological-MS algorithm is used for securing the data communication in the OFDM system. Here, the secret message is embedded in video frames using wavelet transform for hiding sensitive information and the hidden information is transmitted over the OFDM system. The Chronological-MS algorithm is used for selecting the optimal regions in the video for embedding secret message. In embedding phase, wavelet coefficients are obtained by applying wavelet transform on the frame for embedding the secret message. Meanwhile, in extraction phase, the inverse wavelet transform is applied to extract the secret message.

Considering number of frames, the maximum Peak signal-to-noise ratio (PSNR) value is attained by proposed Wavelet + Chronological MS method for Video 2 with value 73.643 dB, respectively. Meanwhile, the minimum mean squared error (MSE) attained by the proposed Wavelet + Chronological MS method is when considering number of frames with MSE values as 0.001 for both Videos 1 and 2. The minimum bit error rate (BER) value is attained by the proposed method with value 0.00009 considering random noise with Video 1. Thus, the proposed Wavelet + Chronological MS have shown better results than the existing techniques.

This work proposes a wavelet-based watermarking method using Chronological-MS, for initiating secured communication over an OFDM. One of the main advantages of wavelets is that they offer a simultaneous localization in time and frequency domain. Hence, the proposed method offers the highly secured data transmission over the OFDM.

As an automatic welding process may experience some disturbances caused by, for example, splashes and/or welding fumes, misalignments/poor positioning, thermally induced deformations, strong arc lights and diversified welding joints/grooves, precisely identifying the welding seam has a great influence on the welding quality. This paper aims to propose a robust method for identifying this seam based on cross-modal perception.

First, after a welding image obtained from a structured-light vision sensor (here laser and vision are integrated into a cross-modal perception sensor) is filtered, in a sufficiently small area, the extended Kalman filter is used to prevent possible disturbances to search for its laser stripe. Second, to realize the extraction of the profile of welding seam, the least square method is used to fit a sequence of centroids determined by the scanning result of columns displayed on the tracking window. Third, this profile is then qualitatively described and matched using a proposed character string method.

It is demonstrated that it maintains real time and is clearly superior in terms of accuracy and robustness, though its real-time performance is not the best.

This paper proposes a robust method for automatically identifying and tracking a welding seam.

Providing knowledge is often not enough. Work groups may also require tools that help them apply it. Job support tools capture and spread best practice. Devices such as “traffic‐lighting” can also influence or change behaviour. The right tools increase understanding with each use. Job support tools can use whatever formats, from text and graphics to animations, visual images and video and audio material, best help understanding. Building knowledge into job support tools can make it easier for workgroups to excel at complex tasks. Significant improvements in individual performance and salesforce productivity can be achieved. The greatest benefits are usually achieved with relatively homogeneous groups of people undertaking similar tasks.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

Many concerned people are unaware of the full impacts of their buying decisions on the environment. This paper aims to show how people can be helped to behave more responsibly when they buy.

Sales and purchasing support tools can show buyers the implications of different courses of action, and enable them to select the least harmful option. The paper shows how an approach used to improve salesforce effectiveness can be employed in the purchasing arena. Key lessons from early adopters are summarised, and questions posed for training and development professionals.

The tools examined increase the performance of key workgroups such as front line sales, account management and support staff, and make it easier for people to take difficult buying decisions. The paper gives a weed control example, and mentions applications that have yielded a first‐year return on investment of over 20, 30 and 70 times.

Support tool users experience a range of benefits from extra business and quicker responses to closer relationships with more satisfied customers. Pioneering companies are also reducing cost, risk and stress; and improving quality and compliance.

Many people and boards would like to address challenges such as climate change, but they lack a practical and cost‐effective way of doing so. The approach outlined represents a cost‐effective way of demonstrating corporate social responsibility. In doing what they feel is the right thing people can benefit themselves, their supplier and the planet.

Opportunities abound to make money by packaging and applying information and knowledge, offering information and knowledge based services, and managing intellectual capital more effectively. However, to exploit them a new generation of information, knowledge and learning entrepreneurs need to be developed. Companies that are intent upon creating and exploiting intellectual capital need to encourage and support information entrepreneurs. The current situation and climate for information entrepreneurship need to be assessed, and educators, trainers and developers should define relevant competencies and take appropriate steps to equip people with them.

The space-time variants algorithm will not give good results in practical scenarios; when no tubes increase, these techniques will not give the results. It is challenging to reduce the energy of the output synopsis videos. In this paper, a new optimized technique has been implemented that models and covers every frame in the output video.

In the video synopsis, condensing a video to produce a low frame rate (FR) video using their spatial and temporal coefficients is vital in complex environments. Maintaining a database is also feasible and consumes space. In recent years, many algorithms were proposed.

The main advantage of this proposed technique is that the output frames are selected by the user definitions and stored in low-intensity communication systems and also it gives tremendous support to the user to select desired tubes and thereby stops the criterion in the output video, which can be further suitable for the user's knowledge and creates nonoverlapping tube-oriented synopsis that can provide excellent visual experience.

In this research paper, four test videos are utilized with complex environments (high-density objects) and show that the proposed technique gives better results when compared to other existing techniques.

The proposed method provides a unique technique in video synopsis for compressing the data without loss.