Development, Implementation and Testing of Algorithm to Detect Ionopause-Like Structure Using Parallel Processing in the Martian Atmosphere

This study assesses the Martian ionopause using MAVEN datasets between periapsis and 150-600 km. Ionopause is an abrupt reduction of the electron density with increasing altitude. It is also required to verify the simultaneous increase of the electron temperature and variability below 400 km. To address this issue, we have adopted a computational approach in determining the ionopause-like density structure of the ionospheric profile. From computing thermal & magnetic pressures, radial magnetic field components, ionopause-like density gradient are detected and stored. The ionopause (theoretically) is formed where the total ionospheric pressure equals solar wind dynamic pressure. The present algorithm consists of a comprehensive set of conditions to be performed on the dataset sequentially. These include datasets from various instruments simultaneously observed. The primary objective of the present study is to describe the implementation and testing of this algorithm for big datasets of the Martian ionosphere and extract ionopause-like density gradient using automation. Keywords: Ionopause, Mars, Remote sensing, MAVEN dataset, Parallel-processing

An Enhanced Block Pre-processing of PRESENT Algorithm for Fingerprint Template Encryption in the Internet of Things Environment

Many previous studies had proven that The PRESENT algorithm is ultra-lightweight encryption. Therefore, it is suitable for use in an IoT environment. However, the main problem with block encryption algorithms like PRESENT is that it causes attackers to break the encryption key. In the context of a fingerprint template, it contains a header and many zero blocks that lead to a pattern and make it easier for attackers to obtain an encryption key. Thus, this research proposed header and zero blocks bypass method during the block pre-processing to overcome this problem. First, the original PRESENT algorithm was enhanced by incorporating the block pre-processing phase. Then, the algorithm’s performance was tested using three measures: time, memory usage, and CPU usage for encrypting and decrypting fingerprint templates. This study demonstrated that the proposed method encrypted and decrypted the fingerprint templates faster with the same CPU usage of the original algorithm but consumed higher memory. Thus, it has the potential to be used in IoT environments for security.

Realization of combinatorial optimization of small-scale S-box

This method describes a new technology for combinatorial logic optimization in detail, which can combine multiple criteria to reduce the hardware implementation area of the S-box. This technique can be achieved in two steps. The first is to optimize the non-linear part of the S-box. According to the optimization criterion of multiplication complexity, the quality of the S-box nonlinear part optimization can be judged, and the realization of the S-box with the smallest multiplication complexity can be obtained. The second step is to optimize the linear part of the S-box, and optimize on the basis of the results of the first step, focusing on reducing the number of XOR gates, and the optimization is performed through a heuristic-based algorithm. The above combinatorial logic optimization technology can be applied to any small S-box (5 × 5 and below). Finally, the S-box of PRESENT algorithm and CTC2 algorithm are used as examples to illustrate the optimization effect, and the optimal realization under the minimum AND gate condition is obtained.

Non-inverse kinematics of free-floating space robot based on motion planning of sampling

Motion planning is one of the fundamental technologies for robots to achieve autonomy. Free-floating space robots composed manipulators and base satellite that do not actively control its position and attitude has nonholonomic characteristics, and there is a first-order differential relationship between its joint angle and the base attitude. In addition, the planning framework which first converts the goal end-effector pose to its corresponding target configuration, and then plan the trajectory from the initial configuration to the goal configuration still has the following problems: the goal configuration and the initial configuration may not be in the same connected domain. Based on the RRT framework, the motion planning of a free-floating space robot from the initial configuration to the goal end-effector pose is studied. In the algorithm design, in order to deal with the differential constraints of the free-floating space robot, and the requirement that the attitude disturbance of its base cannot exceed its limit, a control-based local planner for random configuration guiding growth of the tree and a control-based local planner for goal end-effector pose guiding growth of the tree that can adjust the attitude of the base when necessary are proposed. The former can ensure the effective exploration of the configuration space, and the latter can avoid the occurrence of singularity while ensuring that the algorithm converges quickly and the base attitude disturbance meets the constraints. The present algorithm does not need to solve the inverse kinematics, can successfully complete the planning task, and ensure that the base attitude disturbance meets the requirements. The simulation verifies the effectiveness of the algorithm.

Algorithms for quadratic forms over global function fields of odd characteristic

This paper presents an adaptation of recently developed algorithms for quadratic forms over number fields in [4] to global function fields of odd characteristics. First, we present algorithm for checking if a given non-degenerate quadratic form is isotropic or hyperbolic. Next we devise a method for computing the dimension of the anisotropic part of a quadratic form. Finally we present algorithms computing two field invariants: the level and the Pythagoras number.

Algorithm design for young children

Algorithms are the essence of computational thinking, which refers to a set of problem-solving processes that help children become logical thinkers in this increasingly digital society. It is important for teachers of young children to carefully plan and implement algorithm design tasks that involve repeated step-by-step procedures to build strong foundational computational thinking skills. In this article, the authors present algorithm tasks, including following a recipe, creating a treasure map, modeling how to perform a task, and sharing a routine, which can be easily integrated in the daily activities in early childhood classrooms. Fostering young children’s aptitude for algorithm-specific thinking-and-doing processes creates a foundation for logical thinking.

Adaptive multi-layer structure with spatial-spectrum combination for hyperspectral image anomaly detection

A new algorithm for hyperspectral image anomaly detection is proposed by designing an adaptive multi-layer structure with spatial-spectral combination information, which is different from the traditional anomaly detection algorithms only considering the spectral difference between the anomaly point and the background pixels, and ignoring the difference between the local spatial structure and spectrum. Firstly, the present algorithm not only calculates the spectral dimension difference between the pixels to be measured and the pixels in the background window, but also measures the spatial structure difference between the internal window and the background window. Mostly, an adaptive multi-layer structure for anomaly detection framework is carried out based on the idea of background suppression, and a multi-layered anomaly detector is constructed. The anomaly detection results of each layer of the detector are taken as the constraints, and the background information of the image input in the detector of the next layer is suppressed, adaptively suppressing the background noises. The experimental results show that the present algorithm makes better use of both the local spatial structure and the spectral dimension information than the traditional two-window models (global RX, local RX and KRX), adaptively suppresses background, reduces the false alarm rate, and improves the detection effect of the abnormal targets with fewer pixels.

Cost Effective Design of RC Building Frame Employing Unified Particle Swarm Optimization

Present paper deals with the cost effective design of reinforced concrete building frame employing unified particle swarm optimization (UPSO). Two building frames with G + 8 stories and G + 10 stories have been adopted to demonstrate the effectiveness of the present algorithm. Effect of seismic loads and wind load have been considered as per Indian Standard (IS) 1893 (Part-I) and IS 875 (Part-III) respectively. Analysis of the frames has been carried out in STAAD Pro software. The design loads for all the beams and columns obtained from STAAD Pro have been given as input of the optimization algorithm. Next, cost optimization of all beams and columns have been carried out in MATLAB environment using UPSO, considering the safety and serviceability criteria mentioned in IS 456. Cost of formwork, concrete and reinforcement have been considered to calculate the total cost. Reinforcement of beams and columns has been calculated with consideration for curtailment and feasibility of laying the reinforcement bars during actual construction. The numerical analysis ensures the accuracy of the developed algorithm in providing the cost optimized design of RC building frames considering safety, serviceability and constructional feasibilities.Further, Monte Carlo simulations performed on the numerical results, proved the consistency and robustness of the developed algorithm. Thus, the present algorithm is capable of giving a cost effective design of RC building frame, which can be adopted directly in construction site without making any changes.