Simple Implementation Research Articles

Implementation of Plea Bargaining in the Indonesian Criminal Justice System

There is a need for a new system in the Indonesian Criminal Justice System that is thought to make case handling more efficient, specifically by adopting plea bargaining as a problem-solving method for the Indonesian criminal justice system. In this study, the issue formulation is how to implement plea bargaining in the present criminal justice system and the urgency of plea bargaining in the renewal of the Indonesian criminal justice system. The Normative Juridical Research technique was employed to generate this research. Normative Juridical Research is a type of legal research that involves literature study or just secondary information. The regulation on the application of Plea Bargaining in the current criminal justice system is not based on the value of justice, as Article 4 paragraph (2) of Law No. 48 of 2009 concerning Judicial Power mandates that the judicial process must be carried out quickly. At a low cost, but based on the problems that the author described in the previous sub-chapter, the criminal justice process has not been able to reach a simple judicature to this day. The complexity of the criminal justice process in Indonesia now prevents simple, quick, and low-cost judicial implementation from being accomplished in the criminal justice process in Indonesia. In this case, there is a need for a renovation of Indonesia's criminal justice system. This is the legal basis for the urgent need to establish Plea Bargaining in Indonesia.

A Hardware-Based Orientation Detection System Using Dendritic Computation

Studying how objects are positioned is vital for improving technologies like robots, cameras, and virtual reality. In our earlier papers, we introduced a bio-inspired artificial visual system for orientation detection, demonstrating its superiority over traditional systems with higher recognition rates, greater biological resemblance, and increased resistance to noise. In this paper, we propose a hardware-based orientation detection system (ODS). The ODS is implemented by a multiple dendritic neuron model (DNM), and a neuronal pruning scheme for the DNM is proposed. After performing the neuronal pruning, only the synapses in the direct and inverse connections states are retained. The former can be realized by a comparator, and the latter can be replaced by a combination of a comparator and a logic NOT gate. For the dendritic function, the connection of synapses on dendrites can be realized with logic AND gates. Then, the output of the neuron is equivalent to a logic OR gate. Compared with other machine learning methods, this logic circuit circumvents floating-point arithmetic and therefore requires very little computing resources to perform complex classification. Furthermore, the ODS can be designed based on experience, so no learning process is required. The superiority of ODS is verified by experiments on binary, grayscale, and color image datasets. The ability to process data rapidly owing to advantages such as parallel computation and simple hardware implementation allows the ODS to be desirable in the era of big data. It is worth mentioning that the experimental results are corroborated with anatomical, physiological, and neuroscientific studies, which may provide us with a new insight for understanding the complex functions in the human brain.

Current Sensorless Pole-Zero Cancellation Output Voltage Control for Uninterruptible Power Supply Systems with a Three-Phase Inverter

This article presents a proportional–derivative (PD) type output voltage regulator without the current feedback, taking into account system parameter and load variations. The main advantages are given as follows: First, the first-order output voltage derivative observer is developed without the requirement of system parameter information, which makes it possible to stabilize the system without current sensing. Second, a simple self-tuner implements the feedback-loop adaptation by updating the desired dynamics accordingly. Third, the observer-based active damping injection for the PD-type controller results in the closed-loop system order reduction to 1 by the pole-zero cancellation, including the disturbance observer as a feed-forward term. The prototype uninterruptible power supply system comprised of a 3 kW three-phase inverter, inductors, and capacitors verifies the practical merits of the proposed technique for linear and nonlinear loads.

DESIGN MODELS OF BIT-STREAM ONLINE-COMPUTERS FOR SENSOR COMPONENTS

Context. Currently, distributed real-time control systems need the creation of devices that perform online computing operations close to the sensor. The proposed online-computers of elementary mathematical functions can be used as components for the functional conversion of signals in the form of pulse streams received from measuring sensors with frequency output.
 Objective. The objective of the study is the development of mathematical, architectural and automata models for the design of bit-stream online-computers of elementary mathematical functions in order to create a unified approach to their design, due to which the accuracy of calculating functions can be increased, functional capabilities expanded, hardware costs reduced, and design efficiency increased.
 Method. Mathematical models of devices were developed using the method of forming increments of ascending step functions based on inverse functions with minimization of calculation error. Automata models of online-computers based on Moore’s Finite State Machine have been developed, the graph diagrams of which made it possible to ensure the clarity of function implementation algorithms, to increase visibility and invariance of implementation in formal languages of programming and hardware description.
 Results. The paper presents the results of research, development and practical approbation of design models of bit-stream onlinecomputers of power functions and root extraction function. A generalized architecture of an online-computer was proposed.
 Conclusions. The considered functional online-computers are effective from the point of view of calculation accuracy, simplicity of technical implementation, and universality of the architecture

Experimental Study of Magnetron’s Power-Pulled Characteristic to Realize a Quasi-Dual-Frequency Microwave Output

This article presents experimental evidence demonstrating the feasibility of implementing a quasi-dual-frequency microwave signal generator using a 1 kW commercial magnetron. The injection-pulled magnetron may generate quasi-dual-frequency output with nearly identical amplitudes by injecting signal at specific powers and frequencies out of the Adler locking band. This generator offers the advantage of developing a large-power and tunable quasi-dual-frequency signal at low cost and with simple implementation. In the present study, a tunable quasi-dual-frequency signal with a frequency interval ranging from 0.48 MHz to 13.74 MHz is generated experimentally. Furthermore, the effects of anode voltage ripples on the dual-frequency outputs are experimentally investigated. An empirical relationship between the frequency interval and injection ratio is obtained and presented. In addition, neither the output power nor the efficiency of the magnetron in the dual-frequency mode decreases. This research is promising for improving microwave heating uniformity in large-scale industrial applications.

Microwave wireless power transfer efficiency analysis framework for a thin film space solar power satellite

Space-based solar power systems (SSPS) envision the usage of focused microwave beams to transfer power from space to ground. To supply a large amount of power and to be able to focus a microwave beam efficiently, a large infrastructure must be assembled in orbit, thus, making the implementation of SSPS a arduous task. In the history of SSPS designs, aside from economical obstacles, technical obstacles such as large ohmic losses due to long DC lines in the structure, large rotary joints to decouple beam pointing and solar power collection, heating problems from power lines converging into a single point, and complex control mechanisms, manufacture, and assembly are present. A simpler implementation using multiple foldable thin film membrane satellite modules is proposed as a feasible design, born from the advancement of thin film electronics. Its economical feasibility is achieved through the reduced weight, large surface area, and membrane’s ability to be stowed. The thin film SSPS combines advantageous properties of past designs such as a simple structure, simple control mechanism, and a distributed power collection and transfer architecture avoiding the ohmic loss and heating problems. However, due to the thin film membrane hosting both the antenna and the solar cells, competition occurs between maximum solar power collection and high efficiency power transfer capabilities. In this paper, we propose a framework to analyze the performance of the thin film SSPS solar cell-antenna integration design in terms of the wireless power transfer efficiency and fuel consumption, from which we identified the trade-offs on designing and operating the station. We used this framework to analyze three different antenna element designs in combination with three different attitude control goals, and found out that the key aspects of the antenna design are beam width, radiation efficiency, and efficient solar cell integration.

A security-enhanced scheme for MQTT protocol based on domestic cryptographic algorithm

Recently, the rapid advancement of Industrial Internet of Things (IIoT) technology has led to the utilization of various communication protocols. Among these, the Message Queue Telemetry Transport (MQTT) protocol has become prevalent. Operating on a publish/subscribe model, MQTT’s popularity in IIoT because of simplicity, lightweight nature, and ease of implementation. However, traditional MQTT protocol standards have neglected critical security concerns during the communication process, such as ineffective identity authentication, weak data confidentiality, and a lack of access control functions. To address these challenges, this paper introduces a security-enhanced scheme for the MQTT protocol based on domestic cryptographic algorithms. The proposed method enforces mutual identity authentication between the MQTT Client and the MQTT Broker using digital certificates. It also implements access control through an Access Control List (ACL) and ensures end-to-end data security via the symmetric encryption algorithm SM4. Through comprehensive security analysis and experimental validation, this paper demonstrates that the enhanced scheme effectively rectifies the MQTT protocol’s security deficiencies, achieving these improvements with small overhead.

Theoretical and Empirical Analysis of a Fast Algorithm for Extracting Polygons from Signed Distance Bounds

Recently, there has been renewed interest in signed distance bound representations due to their unique properties for 3D shape modelling. This is especially the case for deep learning-based bounds. However, it is beneficial to work with polygons in most computer graphics applications. Thus, in this paper, we introduce and investigate an asymptotically fast method for transforming signed distance bounds into polygon meshes. This is achieved by combining the principles of sphere tracing (or ray marching) with traditional polygonization techniques, such as marching cubes. We provide theoretical and experimental evidence that this approach is of the O(N2logN) computational complexity for a polygonization grid with N3 cells. The algorithm is tested on both a set of primitive shapes and signed distance bounds generated from point clouds by machine learning (and represented as neural networks). Given its speed, implementation simplicity, and portability, we argue that it could prove useful during the modelling stage as well as in shape compression for storage.

Using clustering as pre-processing in the framework of signal unmixing for exhaustive exploration of archaeological artefacts in Raman imaging

Analytical chemistry on archaeological material is an essential part of modern archaeological investigations and from year to year, instrumental improvement has made it possible to generate data at a high spatial and temporal frequency. In particular, Raman spectral imaging can be successfully applied in archaeological research by its simplicity of implementation to study past human societies through the analysis of their material remains. This technique makes it possible to simultaneously obtain spatial and spectral information by preserving sample integrity. However, because of the inherent complexity of the samples in Archaeology (e.g. seniority, fragility, lack or full absence of any information about its composition), chemical interpretation can be difficult at first glance. Indeed, specific problems of spectral selectivity related to unexpected chemical compounds could appear due to their state of conservation. Furthermore, detecting minor compounds becomes challenging as major components impose their contributions in the acquired spectra. Therefore, a relevant chemometric approach has been introduced in this context to characterize distinct spectral sources in a Raman imaging dataset of an archaeological specimen – a mosaic fragment. The fragment was unearthed during the Ruscino archaeological dig on the outskirts of Perpignan, France. It dates back to the oppidum period. The aim is to extract selective spectral information from pixel clustering analysis in order to enhance the initial optimisation step within the Multivariate Curve Resolution and Alternating Least-Squares (MCR-ALS) algorithm, a well-known signal unmixing technique. The underlying principle of the MCR-ALS is that the acquired spectra can be expressed as linear combinations of pure spectra of all individual components present in the chemical system under study. Sometimes it can be difficult to obtain the desired results through the algorithm, particularly if initial estimates of spectral or concentration profiles are inaccurate due to complex signals, noise or lack of selectivity, resulting in rank deficiency (i.e. a poor estimation of the total number of pure signals). For this reason, an innovative threshold-based clustering algorithm, combined with multiple Orthogonal Projection Approaches (OPA), has been developed to improve matrix rank investigation and thus the initialisation step of the MCR-ALS approach before optimisation. The effective analysis of Raman imaging data for an archaeological mosaic played a crucial role in uncovering significant chemical information about a particular biogenic material. This insight sheds light on the origins of mortar manufacture during the oppidum period.

A Low-Power Fully Differential Level-Crossing ADC Based on Single-Reference Comparator for Wireless Medical Implantable Devices

This paper introduces a low-power fully differential fixed window level-crossing analog-to-digital converter (LC-ADC) for wireless medical implantable devices. The LC-ADC could be an excellent candidate for low-power systems due to the reduction of sampling points for bio-potential signals. Different from existing fixed window LC-ADCs, which use a 1-bit DAC or scaler and two reference levels to move the input signal to the comparison window, a simplified scheme is proposed in which the DAC or scaler is removed and a single-reference level is used to create the comparison window. The proposed LC-ADC utilizes a single-reference comparator, which leads to a simplified implementation and significant reduction in power consumption and circuit area. In addition, using a single reference level and removing DAC, leads to a decrease in the complexity of the controlling logic. The proposed LC-ADC is simulated in 0.18[Formula: see text][Formula: see text]m CMOS technology. The simulation results achieve an effective number of bits (ENOB) of up to 6.5 bits with about 59–141 nW power consumption under 0.8[Formula: see text]V supply and input signal bandwidth from 5[Formula: see text]Hz to 4[Formula: see text]kHz.

Torque and flux control of induction motors fed by synchronized and symmetrical voltage pulses

SummaryThe low switching frequency in high‐power motor drives is mandatory for reducing thermal stress on power devices but often results in significant current harmonics. Synchronized space vector modulation (SynSVM), a widely used low‐carrier‐ratio PWM strategy, offers a simple implementation and reduced harmonics. However, implementing SynSVM in traditional AC motor current regulation poses challenges due to fixed voltage samples. To address this, a high‐performance control scheme based on SynSVM is proposed in this manuscript. It ensures the stator flux vector follows a predefined trajectory by integrating voltage pulses from the original SynSVM. The basic voltage vector switching is facilitated through high‐frequency monitoring and compensation of torque and flux errors, resulting in a fast torque response. Furthermore, synchronized and symmetrical voltage pulses, inherent in the original SynSVM, are equivalently generated, to effectively mitigate current harmonics. This proposed method liberates designers from the complexities of current regulation based on SynSVM. Both simulations and experiments validate the efficacy of the proposed method.

Feature Fusion from Head to Tail for Long-Tailed Visual Recognition

The imbalanced distribution of long-tailed data presents a considerable challenge for deep learning models, as it causes them to prioritize the accurate classification of head classes but largely disregard tail classes. The biased decision boundary caused by inadequate semantic information in tail classes is one of the key factors contributing to their low recognition accuracy. To rectify this issue, we propose to augment tail classes by grafting the diverse semantic information from head classes, referred to as head-to-tail fusion (H2T). We replace a portion of feature maps from tail classes with those belonging to head classes. These fused features substantially enhance the diversity of tail classes. Both theoretical analysis and practical experimentation demonstrate that H2T can contribute to a more optimized solution for the decision boundary. We seamlessly integrate H2T in the classifier adjustment stage, making it a plug-and-play module. Its simplicity and ease of implementation allow for smooth integration with existing long-tailed recognition methods, facilitating a further performance boost. Extensive experiments on various long-tailed benchmarks demonstrate the effectiveness of the proposed H2T. The source code is available at https://github.com/Keke921/H2T.

A constant-per-iteration likelihood ratio test for online changepoint detection for exponential family models

Online changepoint detection algorithms that are based on (generalised) likelihood-ratio tests have been shown to have excellent statistical properties. However, a simple online implementation is computationally infeasible as, at time T, it involves considering O(T) possible locations for the change. Recently, the FOCuS algorithm has been introduced for detecting changes in mean in Gaussian data that decreases the per-iteration cost to O(logT)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(\\log T)$$\\end{document}. This is possible by using pruning ideas, which reduce the set of changepoint locations that need to be considered at time T to approximately logT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\log T$$\\end{document}. We show that if one wishes to perform the likelihood ratio test for a different one-parameter exponential family model, then exactly the same pruning rule can be used, and again one need only consider approximately logT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\log T$$\\end{document} locations at iteration T. Furthermore, we show how we can adaptively perform the maximisation step of the algorithm so that we need only maximise the test statistic over a small subset of these possible locations. Empirical results show that the resulting online algorithm, which can detect changes under a wide range of models, has a constant-per-iteration cost on average.

Designing and modeling an IoT-based software system for land suitability assessment use case

Assessing the quality of land is a very important step that precedes the planning of land use and taking management decisions; for example, in the agricultural field, it can be used to evaluate the suitability of the land for planting crops, determine the suitable irrigation system type, or adjust the agricultural inputs such as fertilizers and pesticides according to the requirements of each zone in the land. The spatial–temporal dynamic nature of land characteristics entails also updated evaluation process and updated management plan. The present paper tries to exploit the advances in information and communication technologies to develop a conceptual design of a dynamic system that accommodates the spatial–temporal dynamics of the agricultural soil characteristics to realize a land suitability assessment (LSA) based on a factor analysis method. The proposed design combines IoT technologies, web development, database, and digital mapping and tries to consolidate the system with other functionalities useful for decision support and suitable for different cases. The paper conducted a survey and made comparisons to select the best technologies that fit the current use case implementation and presents its reproducible conceptual modeling by developing the static and dynamic views through schemas, diagrams, message sequence charts, IoT messaging topic tree, pseudocode, etc. The functionality of the design was validated with a simple implementation of the system model. To our knowledge, there is no previous significant contribution that has addressed a LSA IoT use case. The proposed design automates the LSA process for more accurate decision-making, saving cost, time, and effort consumed in repeated field trips. It is characterized by flexibility and centralization in its offered services of spatial analysis, detection, visualizations, and status monitoring. The design also allows for remote control of field machinery.

A Hermite-type collocation mesh-free approach for simulating incompressible viscous fluid flows

In this work, a novel Hermite-type mesh-free model for incompressible viscous fluid flows is proposed, as a way, to overcome the numerical difficulties experienced by solving under weak or strong formulation the pure stream-function Navier–Stokes equation. In this model a dimensionless virtual domain is utilized, in order to make an accurate dimensionless shape-functions of the recently developed Hermite-type collocation Weighted Least Square approximation, by using dimensionless scale parameters. Then, a Jacobian transformation matrix is built to express the shape-functions spatial derivatives from the virtual to the considered domain. The resolution of the obtained nonlinear algebraic system is performed by the High Order Continuation Method. Numerical tests were investigated on incompressible viscous isothermal fluid flows in lid-driven cavity, backward-facing step, sudden expansions and around various obstacle shapes. They show the advantages of the proposed model compared to the reference Hermite-type collocation models used for pure stream-function equation and the weak Galerkin procedures as Finite Element Method and Element Free Galerkin method, in term of computation accuracy, number of degree of freedom, implementation simplicity and computation time. The numerical and experimental results of literature confirm the validity and capability of the proposed dimensionless model to simulate fluid flows in a expended-lengths inflow–outflow geometry and around obstacles of circular, inclined elliptical and Joukowski airfoil shapes.

CRISPR-powered quantitative keyword search engine in DNA data storage

Despite the growing interest of archiving information in synthetic DNA to confront data explosion, quantitatively querying the data stored in DNA is still a challenge. Herein, we present Search Enabled by Enzymatic Keyword Recognition (SEEKER), which utilizes CRISPR-Cas12a to rapidly generate visible fluorescence when a DNA target corresponding to the keyword of interest is present. SEEKER achieves quantitative text searching since the growth rate of fluorescence intensity is proportional to keyword frequency. Compatible with SEEKER, we develop non-collision grouping coding, which reduces the size of dictionary and enables lossless compression without disrupting the original order of texts. Using four queries, we correctly identify keywords in 40 files with a background of ~8000 irrelevant terms. Parallel searching with SEEKER can be performed on a 3D-printed microfluidic chip. Overall, SEEKER provides a quantitative approach to conducting parallel searching over the complete content stored in DNA with simple implementation and rapid result generation.

A direct transcription method for solving distributed-order fractional optimal control problems

This paper considers the numerical solution of distributed-order Fractional Optimal Control Problems (FOCPs). The common approaches for solving these problems involve approximating the problem with a multi-term FOCP and subsequently discretizing the resulting multi-term problem using either a direct or indirect method. However, this paper bypasses the need for approximation with a multi-term FOCP by introducing innovative approximation formulas for distributed-order fractional derivatives. These approximation formulas are directly derived from the conventional Grünwald–Letnikov, L1, and trapezoidal formulas. Building upon these formulas, we develop the relevant fractional-order distributed-order operational matrices. Utilizing the operational matrix, we easily discretize the distributed-order FOCP to a Non-Linear Programming (NLP), which can be solved by a suitable NLP-solver. Additionally, to enhance the efficiency of solving the resulting NLP, we determine the closed-forms of both the Jacobian of constraints and the gradient of the objective function. The presented method is characterized by its speed, simplicity, and ease of implementation. Moreover, it can be used to solve a wide range of distributed-order FOCPs, such as those involving nonlinear dynamics, free final times, free terminal conditions, and path constraints. By means of ample numerical tests, the accuracy and efficiency of the proposed method are assessed.

An adaptative differential evolution with enhanced diversity and restart mechanism

Differential Evolution (DE) stands out as an exceptional intelligent evolutionary algorithm, acclaimed for its simplicity in implementation and the ability to optimize without necessitating differentiable conditions. However, a significant pitfall of DE lies in its susceptibility to getting trapped in local minima, leading to algorithmic stagnation and substantial performance impairments. To counteract the shortcoming, the Adaptative Differential Evolution with Diversity Maintenance and Restart Mechanism (ADE-DMRM) is proposed. This approach integrates three primary innovations. Firstly, the algorithm controls the number of generations that individuals stay in the external archive based on the successful evolution rate, thereby optimizing the mutation strategy to effectively maintain population diversity. Secondly, ADE-DMRM proposes a novel restart mechanism in which the current stagnant individuals are identified by combining a stagnation tracker and a diversity assessment indicator and then regenerated using a dimension-learning-based approach. Thirdly, wavelet basis functions and Cauchy distributions are employed for scaling factor implementation across different stages, while the dimension change information of successfully evolved individuals is harnessed to refine the adaptive parameter control scheme. Finally, comprehensive experiments were undertaken on the CEC2013, CEC2014, and CEC2017 test sets, assessing accuracy, convergence speed, the efficacy of each module, and time complexity. The results confirm that ADE-DMRM is an efficient single-objective optimization algorithm that outperforms current advanced variants.

Progressive Multiple Alignment of Graphs

The comparison of multiple (labeled) graphs with unrelated vertex sets is an important task in diverse areas of applications. Conceptually, it is often closely related to multiple sequence alignments since one aims to determine a correspondence, or more precisely, a multipartite matching between the vertex sets. There, the goal is to match vertices that are similar in terms of labels and local neighborhoods. Alignments of sequences and ordered forests, however, have a second aspect that does not seem to be considered for graph comparison, namely the idea that an alignment is a superobject from which the constituent input objects can be recovered faithfully as well-defined projections. Progressive alignment algorithms are based on the idea of computing multiple alignments as a pairwise alignment of the alignments of two disjoint subsets of the input objects. Our formal framework guarantees that alignments have compositional properties that make alignments of alignments well-defined. The various similarity-based graph matching constructions do not share this property and solve substantially different optimization problems. We demonstrate that optimal multiple graph alignments can be approximated well by means of progressive alignment schemes. The solution of the pairwise alignment problem is reduced formally to computing maximal common induced subgraphs. Similar to the ambiguities arising from consecutive indels, pairwise alignments of graph alignments require the consideration of ambiguous edges that may appear between alignment columns with complementary gap patterns. We report a simple reference implementation in Python/NetworkX intended to serve as starting point for further developments. The computational feasibility of our approach is demonstrated on test sets of small graphs that mimimc in particular applications to molecular graphs.

TinyGS vs. SatNOGS: A Comparative Analysis of Open-Source Satellite Ground Station Networks

In recent years, two of the largest open-source ground station (GS) networks capable of enabling Earth–satellite communication have emerged: TinyGS and SatNOGS. These open-source projects enable anyone to build their own GS inexpensively and easily, integrate into a GS network, and receive data from satellites listed in the database. Additionally, it enables satellite developers to add satellites to the databases of these projects and take advantage of this GS network to receive data from the satellites. This article introduces the TinyGS and SatNOGS projects and conducts a comparative analysis between them. Generally, the TinyGS project seems to have simpler implementation as well as lower associated costs. In a deeper analysis, it was observed that on the 29 July 2023, the TinyGS project had a higher number of online GSs and a more favorable geographic distribution. On the other hand, the SatNOGS project managed to communicate and decode a larger number of satellites up to 29 July 2023. Additionally, in both projects, it was noted that frequencies between 436 and 437 had the highest number of satellites with decoded data. Ultimately, the choice between these projects depends on critical parameters defined by the reader.