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The Elgar companion to innovation and knowledge creation
This Companion provides a comprehensive, state-of-the-art overview and critical evaluation of existing conceptualizations and new developments in innovation research. Arguing that innovation research requires inter- and trans-disciplinary explanations and methodological pluralism at various levels, it draws on multiple perspectives of innovation, knowledge and creativity from economics, geography, history, management, political science and sociology. The Companion provides the definitive guide to the field and introduces new approaches, perspectives and developments. The Companion systematically analyzes the challenges, problems and gaps in innovation research. Leading scholars reflect upon and critically assess the fundamental topics of the field, including: innovation as a concept; innovation and institutions; innovation and creativity; innovation, networking and communities; innovation in permanent spatial settings; innovation in temporary and virtual settings; innovation, entrepreneurship and market making; innovation governance and management. Innovation researchers and students in economics, economic geography, industrial sociology, innovation studies, international business, management and political science will find the Companion to be an essential resource. It will also appeal to practitioners in innovation and policy makers in economic development, public policy and innovation policy.
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Impact of information transfer on farmers’ uptake of innovative crop technologies: a structural equation model applied to survey data
This study analyses the impact of the transfer of technological information (among other a priori identified factors) on the uptake of innovative crop technologies using structural equation modelling of data from a representative survey of Scottish crop farmers. The model explains 83% of the variance in current technological uptake behaviour and 63% of the variance in intentions to uptake new technologies. Results show economic characteristics (profit orientation, agricultural income, technological investment behaviour and farm labour) to have the strongest effect on both uptake and intentions to uptake novel technologies. Education, access to technological information and perceived usefulness of sources of information transfer are also main influences on behaviour and intentions. Technological uptake behaviour is a strong determinant of intentions to uptake more technologies in the future. The results confirm established evidence from the literature that, besides economic factors, access to technological information and trust in/perceived usefulness of the different information sources will have an impact on technological uptake. The findings are highly policy relevant as they give some indication of the factors influencing the process of targeting specific technological information transfer through the appropriate channels to agricultural producers, which builds a potential driver of behavioural change.
Journal Article
Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding
Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning, our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, where λ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media.
Journal Article
Design Consideration of Bidirectional Wireless Power Transfer and Full-Duplex Communication System via a Shared Inductive Channel
Communication between the primary and secondary sides is pivotal to the wireless power transfer (WPT) system. The system control commands and feedback information need simultaneous wireless information and power transfer (SWIPT). In this paper, a FSK-based SWIPT system with full-duplex communication via a shared channel is provided. Considering the complexity of the coupling relationship in this kind of full-duplex SWIPT system, this paper proposes an analysis method based on the transmission channel, studies the crosstalk between the power channel and the information channel, and between the forward and reverse transfer of information. A design method of full-duplex communication SWIPT system based on shared coupling channels is provided. A 60 W SWIPT prototype with a full-duplex communication rate of 20 kbps is built to verify the proposed method.
Journal Article
Dual-Level Information Transfer for Visible-Thermal Person Re-identification
Visible-thermal person re-identification (VT-ReID) is a challenging pedestrian retrieval problem in the field of security. Due to the intra-modality variations and cross-modality discrepancy caused by different spectrums, it is difficult to extract discriminative features. Existing works are devoted to projecting different-modality features into a shared space, which has weak discriminability and ignores the contextual relationship. In this paper, a novel dual-level information transfer framework is proposed to reduce the modality discrepancy in image level and feature level for VT-ReID. An auxiliary mix-modality is proposed and a mix-visible-thermal (MVT) learning strategy is built to reduce the discrepancy in image level. Firstly, the mix-modality is generated by a mixup scheme which alleviates the direct transfer. Secondly, under the MVT framework, we use ID loss and hetero center triplet loss to guide feature extraction for visible, thermal, and mixed modalities on a one-stream Network. To enhance the robustness of feature extraction, we introduce a graph information transfer module to transfer information across intra-modality and inter-modality in feature level. We build the agent node for modality by using the modality center, where the agent node aggregates the information of all samples in one modality, and then the information from one modality is transmitted to other modalities through the agent nodes. Extensive experimental results on SYSU-MM01 and RegDB datasets show that our method achieves excellent performance.
Journal Article
Spatiotemporal dynamics driven by the maximization of local information transfer
In this paper, a generic type of a spatially extended system, which is driven by the maximization of information transfer in each spatiotemporal point, is proposed. As an expression of the information transfer, transfer entropy is addressed, and a one-dimensional cellular system (whose state transition is governed to maximize the local transfer entropy (LTE) from interacting cells) is introduced. We first show that this system's mechanism of state transition can be considered equivalent to a certain class of cellular automata rules with memory. The spatiotemporal dynamics of the system is then investigated to generate a wide variety of patterns, including spatiotemporal intermittency, according to the length of memory. Furthermore, the spatiotemporal patterns of states and the resulting information dynamics are statistically characterized in detail, expressing the system's diverse nature. In particular, it is found that, within a certain condition of limited memory, even if each cell is driven to maximize the LTE, the entire system cannot reach toward its theoretical maximum value at all due to its intrinsic property, in which the system is dynamically bounding its limit on its own.
Journal Article
Tailored holograms for superimposed vortex states
We present the generation, optimization and full control of superimposed optical vortices (SOVs) using tailored computer generated holograms by utilizing a 2D liquid crystal spatial light modulator. To perform full radial and azimuthal control over the targeted SOVs we apply spatial amplitude modulation via window functions as well as radial and azimuthal phases, encoded in the diffraction mask. In particular we discuss the influence of spatial linear and quadratic radial phases, which is supplemented by an analytical description. The developed formalism further permits the direct shaping in k-space which is highlighted by the radial and azimuthal confinement of SOV states. Our technique enables full real-time control over the spatial structure, the symmetry and azimuthal orientation of the generated SOVs in a common path geometry, which is useful in the context of optical information transfer. We also study the topological properties, i.e. the orbital current S⃗O to determine the topological charge ℓ of the generated SOVs.
Journal Article
Multipopulation-based multi-tasking evolutionary algorithm
Multi-tasking optimization (MTO) has attracted more and more attention from researchers in the area of evolutionary computing. The main factor affecting the success of MTO is knowledge transfer. Nevertheless, knowledge transfer between tasks has positive and negative effects on tasks that are solved simultaneously. In multi-task evolutionary optimization, the negative migration can be suppressed to a certain extent by adjusting random mating probability between tasks, but the negative migration between tasks cannot be completely avoided. This paper proposes a new multi-population-based multi-task evolutionary algorithm (MPEMTO) to weaken the impact of negative knowledge transfer between tasks. The MPEMTO has a novel dual information transfer strategy, an adaptive knowledge screening mechanism, an extended adaptive mating strategy, and a computational resource allocation method. MPEMTO first applies adaptive mating strategy and dual information migration strategy to control the transfer of knowledge between tasks and then applies a transfer information screening mechanism to screen the transfer information to achieve effective use of the transfer information between tasks. The effectiveness of MPEMTO is compared with eight excellent algorithms on single-object MFO test problems. The experimental results demonstrate that the performance of the MPEMTO algorithm is very competitive on most optimization problems.
Journal Article
A comparative study of classification methods for designing a pictorial P300-based authentication system
The response of the P300-based speller is associated with factors like matrix size, inter-stimulus interval, and flashing period. This study proposes the comparison of the novel 2 × 2 image-based speller with the traditional 6 × 6 character-based speller to analyze the effects of the stimulus on the accuracy and information transfer rates. To determine the best classification methodology for the approach suggested, a comparative study was performed using linear and quadratic discrimination analysis, K-nearest neighbor, and support vector machine. In the proposed paradigm, four pictures (objects, special symbols, geometrical shapes, and colors) were randomly placed at four corners of the monitor. Subjects were asked to focus on the target image while ignoring all other images. The proposed method outperformed the traditional method, with an average accuracy of 96.99 ± 1.64% and 86.74 ± 5.19%, respectively, and information transfer rates of 33.82 ± 0.57 bits/min and 23.35 ± 0.79 bits/min, respectively. Results show that a modified speller can play a significant role in optimizing brain-computer interface-driven applications. A repeated measure ANOVA test was performed, which concluded that the improved results are obtained using QDA classifiers in terms of mean accuracy, speed, and error rates.
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Journal Article