A random grid is a two-dimensional binary array in which entries, 0 or 1, are determined in 50% to 50%. A generalized random grid is also a 2-D binary array in which entries are determined but not necessarily 50% to 50%. Using generalized random grids for visual secret sharing, friendly meaningful transparencies, also called shares, can be generated, and the size of each share is the same with the secret image size. In the decoding phase, stacking at least two friendly meaningful shares together, the content of the secret image can be revealed without any computation, and the more shares stacked, the clearer the result. The proposed method can also be implemented for gray and color images.

Li et al. and Yang et al. proposed ($t,s,k,n$)-essential secret image sharing methods to share a secret image into essential shadows and nonessential shadows. However, if the shadows have been distributed to the holders and extra shadows are required, the processes of regenerating and retransmitting shadows are necessary. In the proposed method, when receiving $t$ essential shadows and $k−t$ nonessential shadows, the total size of the adopted shadow is the same as the size of the secret image. Most importantly, the proposed method achieves less size of total required shadows (when using the minimal essential shadows) than what was proposed by Li et al. and achieves less size of essential shadow than the method contended by Yang et al. Moreover, we can have the additional shadows relate to other existing shadows without regenerating.

In health care, medication errors can result in serious health risks to patients, and hospitals require a secure medication administration system to prevent such errors. This paper therefore proposes a secure medication administration method based on threshold sharing technology. When a patient visits a doctor and the doctor prescribes n medications, a photo and the personal information of the patient are encoded into n QR code transparencies that can be decoded by common QR code scanners available for smartphones. The prescription and n QR code transparencies are then stored in a hospitals medication administration system. When the patient receives their medicine, they can scan these n QR code transparencies using a smartphone to ensure that they have all the medicines prescribed by the doctor; this function is accessible even if the patients phone does not have Internet access. The main purpose of the proposed system is to prevent hospitals from giving medicine to the wrong patient, or giving less than the prescribed dosage of medicine to the patient. The focus is not on the internal medicine packaging process in hospitals but on reducing the probability of counter staff giving medicine to the wrong patient. This function is of considerable importance to non-English-speaking people who are not used to reading medicine names in English.

In this study, twelve modified differential evolution algorithms with memory properties and adaptive parameters were proposed to address optimization problems. In the experimental process, these modified differential evolution algorithms were applied to 23 continuous test functions. The results indicate that MBDE2 and IHDE-BPSO3 outperform the original differential evolution algorithm and its extended variants, consistently achieving optimal solutions in most cases. The findings suggest that the proposed improved differential evolution algorithm is highly adaptable across various problems, yielding superior results. Additionally, integrating memory properties significantly enhances the algorithm’s performance and effectiveness.

A multi-purpose image-based QR code is designed in this paper. There are four purposes for the generated image-based QR code. In the first purpose, the basic image-based QR code with the look of a host image is with an ingenious layout to be identified easier. In the second one, a saliency region detection method is adopted for enhancing the quality of the image-based QR code. In the third one, the host image is embedded into the image-based QR code for further access to the host image; Finally, the visual cryptography-based watermarking method is applied to the host image embedded image-based QR code. In the case that the specific users need verification from the image-based QR code, the binary verified image can be retrieved when the public share is available. The experimental results demonstrate that the generated image-based QR code not only looked better than some previous works but also had high quality host image embedded and identification ability.

Partial fingerprint recognition has emerged as a critical method for verifying user authenticity during mobile transactions. As a result, there is a pressing need to develop techniques that effectively and accurately authenticate users, even when the scanner only captures a limited area of the finger. A key challenge in partial fingerprint matching is the inevitable loss of features when a full fingerprint image is reduced to a partial one. To address this, we propose a method that integrates deep learning with feature descriptors for partial fingerprint matching. Specifically, our approach employs a Siamese Network based on a CNN architecture for deep learning, complemented by a SIFT-based feature descriptor to extract minimal yet significant features from the partial fingerprint. The final matching score is determined by combining the outputs from both methods, using a weighted scheme. The experimental results, obtained from varying image sizes, sufficient epochs, and different datasets, indicate that our combined method achieves an Equal Error Rate (EER) of approximately 4% for databases DB1 and DB3 in the FVC2002 dataset. Additionally, validation at FRR@FAR 1/50,000 yields results of about 6.36% and 8.11% for DB1 and DB2, respectively. These findings demonstrate the efficacy of our approach in partial fingerprint recognition. Future work could involve utilizing higher-resolution datasets to capture more detailed fingerprint features, such as pore structures, and exploring alternative deep learning techniques to further streamline the training process.