With the advent of the Internet-of-Things (IoT) technologies, millions of low-resource devices are constantly used at the network’s edge. As a result, the large amount of private and sensitive data generated by these devices must be securely transported, stored, and processed, posing a challenge because these resource-constrained IoT devices cannot meet the criteria of conventional encryption ciphers. Due to this limitation on IoT-enabled devices, lightweight cryptography has emerged as a new area of study. Lightweight block ciphers, a subfield of lightweight cryptography, include the substitution–permutation network (SPN) and Feistel-based networks. Feistel networks are further divided into two types: classical Feistel networks and generalized Feistel networks (GFN). While classical Feistel ciphers divide a message into two sub-blocks, GFN divides a message into k sub-blocks for some k > 2 called the partition number. One popular form of GFN is the so-called Type-II. Unfortunately, this type of Feistel structure needs a large number of rounds to obtain a full diffusion property. A full diffusion means all output sub-blocks are affected by all input sub-blocks. Therefore, this paper proposed a new lightweight block cipher by modifying the GFN structure, focusing on providing optimal security to the cipher with a small number of rounds. The algorithm was subjected to a series of statistical and cryptographic randomization analyses in order to investigate the avalanche effect on the ciphertext and the algorithm’s random properties, such as confusion, diffusion, and independence. The avalanche criterion and output randomness results show that this algorithm meets the fundamental security requirement for a lightweight block cipher.

Silicon-based platinum (Pt) and silver (Ag) microelectrodes are constructed using photolithography technique and used in detecting arsenic activity in different electrolytes. Pt and Ag have good properties either as a working, a counter, or a reference electrode due to their low electrical resistance, high melting point, and high chemical stability. This chemical sensor has the ability to detect the changes in the level or activity of arsenic in electrolytes. Patterning these metals by wet chemical or dry etching is not a feasible process as these metals cannot be etched properly. The lift-off process can be applied to ease the etching process, but it has a major problem whereby the metal particles or ears may remain at the edges at the end of the process. The process variables, particularly the resist slope, were investigated to reduce possible defects using an image reversal resist. The thickness and angle of the resist side wall were measured by SEM. The effects of many factors that may influence or resist steep angle formation were analyzed and optimized with the Design of Experiment (DOE) technique to achieve the target recipe of resist angle < 60°. The lower angle of the resist side wall resulted in a better percentage yield of good electrode pattern after the lift-off process. The ability of fabricated microelectrode and influence of supporting electrolytes in arsenic determination were discussed.

Variational active contour seeks to segment or extract desired object boundaries for further analysis. The model can be divided into global segmentation and selective segmentation. Selective segmentation, which focuses on segmenting a particular object, is preferable to the global model. Recently, a number of selective segmentation models have been developed to precisely extract an object on grayscale images. Nevertheless, if the input image is vector-valued (colour), these models merely convert it to a grayscale image, resulting in data loss owing to the reduction in image dimension. Furthermore, they may have poor segmentation performance due to the intensity inhomogeneous images. Therefore, a new model on variational selective active contour for segmenting vector-valued images has been proposed that incorporates the concepts of local image fitting and distance-based fitting terms into a variational minimization energy functional. Moreover, a Gaussian function was used as a regularizer to replace the computationally expensive Total Variation term. Then, the proposed model’s Euler Lagrange equation has been provided to solve the model. When segmenting an object in inhomogeneous intensity images, the result of the proposed model was about 30% more accurate based on the Jaccard value and about 3 times faster than other existing methods.

The signed pressure force (SPF) function has recently become a popular function for guiding the curve evolution of the active contour model (ACM) for image segmentation. The aim is to extract the boundaries of digital medical images for shape and image analysis. The recent SPF-based ACM demonstrates effectiveness in image segmentation. However, it may fail if the targeted object is close to a neighbouring object. Additionally, the presence of intensity inhomogeneity and noise in medical images degrades segmentation accuracy and local target areas. Thus, we proposed a new SPF-based ACM, namely the Selective Segmentation with Signed Pressure Force 1 (SSPF1) model, by incorporating the ideas of the SPF function and the distance fitting term based on geometrical constraints. Then, the new SSPF1 model was extended by incorporating an image enhancement technique to develop our second new model, termed the Selective Segmentation with Signed Pressure Force 2 (SSPF2). Numerical results indicated that the SSPF2 model was more recommended than SSPF1 as the SSPF2 model was approximately 4.7% more accurate, as indicated by the Jaccard value and was about 112 times faster in segmenting noisy images compared to the existing selective segmentation model.

This paper presents a new factoring technique on the modulus , where and are large prime numbers. Suppose there exists an integer satisfies the equation , for some unknown integer and is the Euler’s totient function. Our method exploits the term to be the closest integer to the unknown parameter . Hence we show that the unknown parameters and can be recovered from the list of the continued fractions expansion of Furthermore, we present an algorithm to compute the prime factors of in polynomial time after obtaining the correct tuple and.

The modulus of type $N=p^{2}q$ is often used in many variants of factoring-based cryptosystems due to its ability to fasten the decryption process. Faster decryption is suitable for securing small devices in the Internet of Things (IoT) environment or securing fast-forwarding encryption services used in mobile applications. Taking this into account, the security analysis of such modulus is indeed paramount. This paper presents two cryptanalyses that use new enabling conditions to factor the modulus $N=p^{2}q$ of the factoring-based cryptosystem. The first cryptanalysis considers a single user with a public key pair $(e,N)$ related via an arbitrary relation to equation $er-(Ns+t)=\alpha p^2+\beta q^2$, where $r,s,t$ are unknown parameters. The second cryptanalysis considers two distinct cases in the situation of k-users (i.e., multiple users) for $k\ge 2$, given the instances of $(N_i,e_i)$ where $i=1,\dots ,k$. By using the lattice basis reduction algorithm for solving simultaneous Diophantine approximation, the k-instances of $(N_i,e_i)$ can be successfully factored in polynomial time.