Programming Scheme Research Articles

A Novel Array Programming Scheme for Large Matrix Processing in Flash-Based Computing-in-Memory (CIM) With Ultrahigh Bit Density

Computing-in-memory (CIM) architecture is a promising approach to breaking the bottleneck in von Neumann’ architecture. To shed light on large matrix operations in flash-based CIM with ultrahigh bit density (4–5 bit/cell), this work presents a novel incremental positive–negative step pulse programming (IPNPP) array programming scheme. The proposed scheme utilizes positive pulses for rough tuning and subsequent negative pulses for fine-tuning to cells’ threshold voltages. By adopting the IPNPP scheme in 55-nm NOR flash CIM arrays, it is shown that the latency and power consumption could be lowered effectively. As for image dehazing of ultrahigh-resolution images, ~180.6-TOPS/W high energy efficiency with great accuracy and variation tolerability has been demonstrated successfully. Our results indicate that the IPNPP is effective for CIMs that require high precision and low power consumption.

Experimental-Correlative Framework to Evaluate Critical State Deformability Parameters and Free Swelling Index of Cohesive Soils Using Gene Expression Programming

Expansive soils stimulate serious problems for infrastructures and the near surface facilities due to volume change patterns it experiences during the wet and dry seasons. This research study is intended to implement Gene Expression Programming (GEP) scheme and the Multiple Linear Regression (MLR) method in order to assess the swelling aspects and deformability parameters of moderately expansive soils based on a comprehensive exploration and testing program for the Northeastern areas of Zarqa Governate in Jordan. Representative soil samples have been collected from twenty different locations in the area. The soil has been tested for free swelling, matric suction, permeability, consolidation, water content, hydrometer, and soil index properties. The free swelling index and critical state deformability parameters, obtained from the consolidation test, are the primary focus of this research. Several models that account for different situations based on opportunities of the data availability using an extensive GEP modeling scheme have been successfully developed. MLP method has been managed to develop one reasonable formula with less efficiency than the ones schemed by GEP procedure. The successful models have been validated using test results and error analysis procedures. The models have been ordered according to their according to a proposed ranking procedure. GEP method has showed higher performance for the given conditions.

Protein remote homology recognition using local and global structural sequence alignment

Protein Remote Homology and fold Recognition (PRHR) is the most crucial task to predict the protein patterns. To achieve this task, Sequence-Order Frequency Matrix-Sampling and Deep learning with Smith-Waterman (SOFM-SDSW) were designed using large-scale Protein Sequences (PSs), which take more time to determine the high-dimensional attributes. Also, it was ineffective since the SW was only applied for local alignment, which cannot find the most matches between the PSs. Hence, in this manuscript, a rapid semi-global alignment algorithm called SOFM-SD-GlobalSW (SOFM-SDGSW) is proposed that facilitates the affine-gap scoring and uses sequence similarity to align the PSs. The major aim of this paper is to enhance the alignment of SW algorithm in both locally and globally for PRHR. In this algorithm, the Maximal Exact Matches (MEMs) are initially obtained by the bit-level parallelism rather than to align the individual characters. After that, a subgroup of MEMs is obtained to determine the global Alignment Score (AS) using the new adaptive programming scheme. Also, the SW local alignment scheme is used to determine the local AS. Then, both local and global ASs are combined to produce a final AS. Further, this resultant AS is considered to train the Support Vector Machine (SVM) classifier to recognize the PRH and folds. Finally, the test results reveal the SOFM-SDGSW algorithm on SCOP 1.53, SCOP 1.67 and Superfamily databases attains an ROC of 0.97, 0.941 and 0.938, respectively, as well as, an ROC50 of 0.819, 0.846 and 0.86, respectively compared to the conventional PRHR algorithms.

Socialization of SAJAG (Magic Powder for Peat Water Purification) for the Establishment of Micro and Small Enterprises (MSE) for Riau University Students

Empowerment for Riau University students and graduates who already own small enterprises (UMK) or who do not yet own them can help a country's economy by boosting its overall prosperity. The location of service activities with this entrepreneurship development scheme in 2022 is on Jl. Merak Sakti Gang Hijrah Panam Pekanbaru City. The method of implementing community service activities with the Entrepreneurship Development Program Scheme is carried out in the form of socialization. The target of community service activities with the entrepreneurship development scheme was the general public, 25 students of the Faculty of Fisheries and Maritime Affairs, University of Riau (UNRI). In this activity, pre-test and post-test were carried out to see the achievement of activities. After the implementation of this activity, it was found that the service activities with this entrepreneurship scheme had been carried out with the target group of 25 alumni and students of the University of Riau with a level of knowledge and understanding reaching 97%. The activity concludes that SAJAG production is feasible to be carried out as Micro Small Business Empowerment (UMK).

Quantitative analysis of irregular channel shape effects on charge-trapping efficiency using massive 3D NAND data

The randomly fluctuating trap efficiency of taper-etched nanowire thin-film transistors was investigated by measuring a very large statistical ensemble of three-dimensional NAND flash memory cells. We made trapping in n-bit multi-level cells using incremental step pulse programming and observed the widening of the threshold voltage distribution caused by abnormal program cells. Further, we classified the components that broaden the threshold voltage distribution in both chip- and wafer-level measurements to analyze behaviors of the randomly fluctuating abnormal program cell. The variations in reading and programming operations are difficult to separate because they act simultaneously in the data reading and writing operations. For each pulse of the programming, the variations occur in reading for verification, in programming itself, and in reading after program completion. To analyze the inseparable variation, we used a method to extract only the intrinsic over-programming by removing variations in the read operation from the entire abnormal program cell. Using this extraction method, we could distinguish between the simultaneous read variation and over-programming. In particular, the unpredictable effect of the three-dimensional structure, such as the irregular shape and different radius due to the tapered channel hole profile from the nonideal etching process, was calculated and verified using experimental data. We quantitatively analyzed the experimental data one by one for each cell within the entire distribution, and each pulse of dozens in the programming scheme. This analysis provides quantitative information on the variations in charge-trapping efficiency and the threshold voltage distribution width by irregular-shaped tunneling layers.

Non‐linear steady‐state data reconciliation: Theoretical perspective and practical scenarios

AbstractData reconciliation (DR) is one of the primary error handling methods to reduce measurement errors in industries that may otherwise cause misleading information about the plant. In this article, the mathematical aspects of measurement errors and their treatment by DR are discussed in detail. The flaws in the existing DR methods have been identified and re‐investigated. More importantly, the feasibility and health check‐up of the DR problem have been discussed. The primary objective of the work is to develop a DR code based on the observations made in the present study, which involves DR solutions by both successive linearization (SL) and sequential quadratic programming (SQP) schemes. Benchmarking of the code with standard cases showed its wider suitability in solving DR problems. The algebraic SL method was found suitable for proper data health check‐ups and reliable solutions, whereas SQP was robust. The developed code was tested successfully for a chemical plant as well.

Improving Literature and Numeration In Elementary School Learning Activities And Increasing Community Income That Comes From Oil Palm In Village Mata PAO

Community service activities aim to significantly improve literacy and numeracy in learning activities and increase community income. This community service is a mentoring activity carried out by students through literacy and numeracy schemes in elementary schools. This activity was carried out for 21 days, starting from August 11, 2022 – August 31, 2022. Community service has a goal, namely to provide experience for students to be directly involved in implementing the learning process at school. The method used in the implementation of this Independent Real Work Lecture is the method of observation, interviews and documentation. Activities carried out by students with assistance include: (1) introduction in the village; (2) introduction to the scope of the school; (3) support the class in the development of literacy and numeracy learning; (4) assisting the community to increase income derived from oil palm fronds. Service activities carried out by students through community service assistance with literacy and numeracy program schemes as well as helping the community have been running well, efficiently, effectively and maximally. The results of this study after doing research that the interest of students in literacy and numeracy factors is good with the implementation of learning outside the classroom while playing and the income function of the community has also increased from the use of palm oil sticks which are used in making various kinds of handicraft products that have functional values.

Task-guided IRL in POMDPs that scales

In inverse reinforcement learning (IRL), a learning agent infers a reward function encoding the underlying task using demonstrations from experts. However, many existing IRL techniques make the often unrealistic assumption that the agent has access to full information about the environment. We remove this assumption by developing an algorithm for IRL in partially observable Markov decision processes (POMDPs). We address two limitations of existing IRL techniques. First, they require an excessive amount of data due to the information asymmetry between the expert and the learner. Second, most of these IRL techniques require solving the computationally intractable forward problem—computing an optimal policy given a reward function—in POMDPs. The developed algorithm reduces the information asymmetry while increasing the data efficiency by incorporating task specifications expressed in temporal logic into IRL. Such specifications may be interpreted as side information available to the learner a priori in addition to the demonstrations. Further, the algorithm avoids a common source of algorithmic complexity by building on causal entropy as the measure of the likelihood of the demonstrations as opposed to entropy. Nevertheless, the resulting problem is nonconvex due to the so-called forward problem. We solve the intrinsic nonconvexity of the forward problem in a scalable manner through a sequential linear programming scheme that guarantees to converge to a locally optimal policy. In a series of examples, including experiments in a high-fidelity Unity simulator, we demonstrate that even with a limited amount of data and POMDPs with tens of thousands of states, our algorithm learns reward functions and policies that satisfy the task while inducing similar behavior to the expert by leveraging the provided side information.

Design of a Computational Heuristic to Solve the Nonlinear Li閚ard Differential Model

In this study, the design of a computational heuristic based on the nonlinear Liénard model is presented using the efficiency of artificial neural networks (ANNs) along with the hybridization procedures of global and local search approaches. The global search genetic algorithm (GA) and local search sequential quadratic programming scheme (SQPS) are implemented to solve the nonlinear Liénard model. An objective function using the differential model and boundary conditions is designed and optimized by the hybrid computing strength of the GA-SQPS. The motivation of the ANN procedures along with GA-SQPS comes to present reliable, feasible and precise frameworks to tackle stiff and highly nonlinear differential models. The designed procedures of ANNs along with GA-SQPS are applied for three highly nonlinear differential models. The achieved numerical outcomes on multiple trials using the designed procedures are compared to authenticate the correctness, viability and efficacy. Moreover, statistical performances based on different measures are also provided to check the reliability of the ANN along with GA-SQPS.

Batteryless, Wireless, and Secure SoC for Implantable Strain Sensing

The past few years have witnessed a growing interest in wireless and batteryless implants, due to their potential in long-term biomedical monitoring of in-body conditions such as internal organ movements, bladder pressure, and gastrointestinal health. Early proposals for batteryless implants relied on inductive near-field coupling and ultrasound harvesting, which require direct contact between the external power source and the human body. To overcome this near-field challenge, recent research has investigated the use of RF backscatter in wireless micro-implants because of its ability to communicate with wireless receivers that are placed at a distance outside the body ( 0.5 m), allowing a more seamless user experience. Unfortunately, existing far-field backscatter designs remain limited in their functionality: they cannot perform biometric sensing or secure data transmission; they also suffer from degraded harvesting efficiency and backscatter range due to the impact of variations in the surrounding tissues. In this paper, we present the design of a batteryless, wireless and secure system-on-chip (SoC) implant for in-body strain sensing. The SoC relies on four features: 1) employing a reconfigurable in-body rectenna which can operate across tissues adapting its backscatter bandwidth and center frequency; 2) designing an energy efficient 1.37 mmHg strain sensing front-end with an efficiency of 5.9 mmHg·nJ/conversion; 3) incorporating an AES-GCM security engine to ensure the authenticity and confidentiality of sensed data while sharing the ADC with the sensor interface for an area efficient random number generation; 4) implementing an over-the-air closed-loop wireless programming scheme to reprogram the RF front-end to adapt for surrounding tissues and the sensor front-end to achieve faster settling times below 2 s.

Designing Meyer wavelet neural networks for the three-species food chain model

<abstract> <p>The current research work is related to present the numerical solutions of three-species food chain model (TS-FCM) by exploiting the strength of Meyer wavelet neural networks (MWNNs) along with the global and local search competencies. The particle swarm optimization technique works as a global operator, while the sequential quadratic programming scheme is applied as a local operator for the TS-FCM. The nonlinear TS-FCM is dependent upon three categories, called consistent of prey populations, specialist predator and top predator. The optimization of an error-based fitness function is presented by using the hybrid computing efficiency of the global and local search schemes, which is designed through the differential form of the designed ordinary differential model and its initial conditions. The proposed results of the TS-FCM are calculated through the stochastic numerical techniques and further comparison is performed by the Adams method to check the exactness of the scheme. The absolute error in good ranges is performed, which shows the competency of the proposed solver. Moreover, different statistical procedures have also been used to check the reliability of the proposed stochastic procedure along with forty numbers of independent trials and 10 numbers of neurons.</p> </abstract>

Mathematical Method to Construct Linear Programming Scheme of Football Training

Abstract Linear programming is a widely applicable mathematical operation method. In this paper, two solutions of linear programming and state parameter extraction can be used to simplify the inequality group and transfer the high-dimensional state utility function in football. This can speed up the generation of joint strategies for football teams in the game. This paper uses the free-kick technical football cooperation as an example to conduct an empirical study on the linear programming method of state parameters. In this paper, combined with the traditional motion information modeling method, a free ball path planning based on the linear programming gradient method is established. At the same time, this paper adopts an asymptotic method based on linear programming to solve the trajectory of any football. Finally, an experimental study on the linear programming gradient method for state feature vectorization is carried out. Experiments verify the effectiveness of this method. The mathematical model accurately predicts the linear programming of football training.

Selecting a Programming Scheme for Memristor Elements

Introduction. An array of memristive elements can be used in prospective neural computing systems as a programmable resistance (analog multiplication factor) when performing operations of analog vector multiplication, discrete in time. To form the required resistance, the memristor should be subjected to a programming procedure. This article discusses conventional programming schemes and proposes a new versatile programming scheme for memristor elements.Aim. To identify or develop an optimal programming scheme for memristors by analyzing the advantages and disadvantages of existing methods.Materials and methods. The programming procedure can be carried out using either SET or RESET, depending on a different direction of movement according to the volt-ampere characteristic of the memory and its transfer to a particular state. The programming process is controlled in the LTspice circuit modeling program.Results. Typical programming schemes of memristors were analyzed; advantages and disadvantages of existing methods were revealed. A new versatile circuit based on a variable resistor was proposed. The circuit was simulated both under a fixed resistance of the variable resistor and when varying the memristor resistance values within their permissible range.Conclusion. In comparison with the RESET mode, the SET programming mode provides for a greater linearity of variations in the memristor resistance. The use of a circuit based on a variable resistor and a bipolar voltage source allows programming of any type and eliminates the need for recommutation of the memristor. The simulation results confirm the feasibility of the proposed method. The proposed circuit can be complemented not only with a comparator, but also with an ADC. This will provide the possibility of selecting various means for measuring the memristor resistance both during programming and for the purpose of monitoring the memristor resistance at the end of the procedure.

Training and Making Guidelines for Organizing Hybrid Exhibitions

Exhibition is a sub-sector of the creative economy and part of the tourism industry that developed well in Indonesia before Covid-19. Exhibition activities, which were actually gathering large numbers of people, became one of the industries hardest hit and even stopped completely, even though alternative technologies that facilitated online meetings began to develop at that time. Currently, after the handling of Covid-19 has improved considerably, the concept of hybrid exhibitions has begun to emerge and provides an alternative for industry players in conducting exhibitions. Unfortunately, many industry players do not understand how the concept of hybrid exhibitions does not try to completely replace conventional exhibitions. In addition, there is a need for training and guidance that will facilitate industry players in carrying out hybrid exhibitions. Community Service with this community partnership program (PKM) scheme helps ASPERAPI (Exhibition Organizer Association) DPD West Java as a partner in making Hybrid Exhibition Organizing Guidelines and training its members. Another objective is to provide additional skills and knowledge for ASPERAPI members in facing the challenges of present and future exhibition activities. The challenges facing partners today are how to restore business while at the same time reaching more audiences by utilizing hybrid technology/applications. The implementation method is mentoring, training and making guides with the outputs of intellectual property rights and scientific articles and service articles as activity reports.

Three-dimensional programming of nanolaser arrays through a single optical microfiber

Efficient programming of integrated small optical sources is a key component of photonic technologies. However, as the density of laser arrays increases, the miniaturization and scalability of laser control using an electric drive are pushed to the limit. This study presents an all-optical drive scheme for complete programming of nanolaser arrays through a single waveguide port. Three photonic crystal nanolasers, printed three-dimensionally on the sides of a 2-µm-diameter fiber with an 18 µm laser spacing, were entirely controlled by modal interference, adjusted by the polarization and pulse width of the pump beam through the same fiber. Our approach is applicable to various small optical sources coupled to a single waveguide, and it will lay the foundation for applications in high-density optical integrated circuits, biosensing, and quantum network technologies.

Inexact Restoration Methods for Semivectorial Bilevel Programming Problem on Riemannian Manifolds

For a better understanding of the bilevel programming on Riemannian manifolds, a semivectorial bilevel programming scheme is proposed in this paper. The semivectorial bilevel programming is firstly transformed into a single-level programming problem by using the Karush–Kuhn–Tucker (KKT) conditions of the lower-level problem, which is convex and satisfies the Slater constraint qualification. Then, the single-level programming is divided into two stages: restoration and minimization, based on which an Inexact Restoration algorithm is developed. Under certain conditions, the stability and convergence of the algorithm are analyzed.

Approximation of optimal control problems for the Navier-Stokes equation via multilinear HJB-POD

We consider the approximation of some optimal control problems for the Navier-Stokes equation via a Dynamic Programming approach. These control problems arise in many industrial applications and are very challenging from the numerical point of view since the semi-discretization of the dynamics corresponds to an evolutive system of ordinary differential equations in very high-dimension. The typical approach is based on the Pontryagin maximum principle and leads to a two point boundary value problem. Here we present a different approach based on the value function and the solution of a Bellman equation, a challenging problem in high-dimension. We mitigate the curse of dimensionality via a recent multilinear approximation of the dynamics coupled with a dynamic programming scheme on a tree structure. We discuss several aspects related to the implementation of this new approach and we present some numerical examples to illustrate the results on classical control problems studied in the literature.

Retention Secured Nonlinear and Self-Rectifying Analog Charge Trap Memristor for Energy-Efficient Neuromorphic Hardware.

A memristive crossbar array (MCA) is an ideal platform for emerging memory and neuromorphic hardware due to its high bitwise density capability. A charge trap memristor (CTM) is an attractive candidate for the memristor cell of the MCA, because the embodied rectifying characteristic frees it from the sneak current issue. Although the potential of the CTM devices has been suggested, their practical viability needs to be further proved. Here, a Pt/Ta2 O5 /Nb2 O5- x /Al2 O3- y /Ti CTM stack exhibiting high retention and array-level uniformity is proposed, allowing a highly reliable selector-less MCA. It shows high self-rectifying and nonlinear current-voltage characteristics below 1µA of programming current with a continuous analog switching behavior. Also, its retention is longer than 105 s at 150°C, suggesting the device is highly stable for non-volatile analog applications. A plausible band diagram model is proposed based on the electronic spectroscopy results and conduction mechanism analysis. The self-rectifying and nonlinear characteristics allow reducing the on-chip training energy consumption by 71% for the MNIST dataset training task with an optimized programming scheme.

Comparison of Garbage Classification Frameworks Using Transfer Learning and CNN

With the never-ending increase in the population, garbage and other waste materials have become one of the major hurdles in forming a healthy environment. The proliferation in the development of such schemes and integration of technology brings up the concept of smart waste management based on its biodegradability. These proposed models can be found useful to the smart waste development program and other likely schemes which require the classification of garbage based on their images. The experiment uncovers the reasons behind the working of VGG19 and A9 architecture CNN-based models which were found to provide the best results in accurately detecting the type of garbage. Experimental evaluation was based on 27 models including out of which A9 and VGG19 models were found to be the most efficient ones with 92.24% and 86.35% accuracy, respectively, which are further compared in detail for understanding these models better.

Optimal Program-Read Schemes Toward Highly Reliable Open Block Operations in 3-D Charge-Trap NAND Flash Memory

3-D NAND flash memory with vertically stacked layers has been widely applied benefiting from its large capacities and high performances. Recently, a novel open block operation scheme was proposed for further improvements of the utilization efficiency in large capacity blocks. In this article, reliability issues of the open block operation in 3-D charge-trap (CT) NAND flash memory are studied by focusing on the high raw bit error rates (RBERs) in the last programmed word-line (WL), which is named as the edge WL (EWL). By systematical characterizations, it is concluded that high RBER in the EWL originates from lateral charge migration (LCM) due to the special structure of 3-D CT NAND flash. To suppress the RBER in EWL, we propose the extra read (ER) and extra program (EP) schemes to compensate for the charge loss from LCM. The experimental results show that the RBER of EWL can be reduced by an average of 59.8% and 86.5% after adopting ER and EP schemes, respectively. Furthermore, for the highly reliable open block, we design a targeted low-density parity-check (LDPC) operation process to enhance the correction capability. By using these two methods, experimental results show that the error correction capabilities of the LDPC hard decoding are increased by 1.92 and 4.76 times, respectively.