Miracle-dz/UniFAR-FTUs huggingface dataset:
OpenILT: An Open Source Inverse Lithography Technique Framework (Invited Paper)

Semiconductor lithography is a key process for fabricating integrated circuits, but it suffers from various distortions and variations that affect the quality of the printed patterns. Optical proximity correction (OPC) is a technique to improve pattern fidelity and robustness, and inverse lithography technique (ILT) is a promising OPC method that optimizes the mask as an inverse problem of the imaging system. However, ILT is computationally expensive and challenging to implement at a full-chip scale. In this paper, we present OpenILT, an open-source ILT platform that supports the rapid development and evaluation of GPU-accelerated and AI-driven ILT methods. OpenILT provides a modular and flexible framework that integrates various ILT components, such as lithography simulation, objective functions, and evaluation metrics. It also offers a convenient interface to PyTorch, a popular deep learning library, to enable the implementation of GPU-accelerated and AI-driven ILT methods.
Bi-level Optimization of Energy Management for Agro Food Park in Distribution Market

This paper develops a bi-level optimization model for energy management in an Agro Food Park (AFP) microgrid, enabling its participation in the distribution market. The upperlevel highlights minimize the operation costs of the AFP microgrid, and the clearing electricity price is counted as part of the distribution market. The proposed approach maximizes the prominent regulation of the AFP load and optimizes energy operation management. And the lower-level is designed to compute the clearing Distribution Locational Marginal Prices (DLMPs) within the distribution grid. The DLMPs are subsequently sent to the upper-level model. The bi-level model is then reformulated into a Mathematical Programming with Equilibrium Constraints (MPEC) problem, resolving through a branching and delimitation method. Lastly, the effectiveness and verifiability of the proposed model are confirmed via the IEEE33 bus distribution system with an AFP microgrid. The results illustrate that the energy management strategy of the AFP microgrid yields a reduction in overall energy costs.
PINN application for compressible gas flow transient analysis

Simulating transient flow inside pipeline networks has been an important topic in the field of civil engineering safety. The recent development of the Physics-Informed Neural Network (PINN) shed light to solve this problem by the means of machine learning. In this paper, we apply PINN to a transient compressible fluid pipeline flow problem. We show that PINN requires a specific data normalization to preserve all necessary physics information for accurate training. We compare the PINN prediction with the estimation provided by finite difference method (FDM), which previously has been the main tool to solve such problems. As a result, we obtained a mesh-free PINN model with the difference of less than 0.6% for pressure mapping and less than 2.3% for mass flow mapping, compared to the FDM analysis.
Precision Irrigation Decision Model Analysis Using Deep Learning and Embedded Systems

This study innovatively uses deep learning-based image processing methods to make crop fertilization decisions. Herein, we examined the images of tomato leaves captured by an embedded system camera using Raspberry Pi. We also considered the on-site classification of tomato leaf deficiencies and the extraction of information after the leaf segmentation, using the expert knowledge base and the crop fertilizer model. The study provides appropriate fertilization recommendations to improve fertilizer utilization. The convolutional neural network model trained in this study for tomato leaf deficiency recognition achieved 95% verification accuracy. The average pixel accuracy of leaf segmentation and mean intersection-to-union ratio achieved were0.89 and 0.76, respectively.
CSI feedback compression based on deep learning using wavelet transform

Recently, machine learning approaches have been widely applied in the mobile communication field. This paper presents a novel deep learning (DL) model for Channel State Information (CSI) feedback compression using wavelet transform. Specifically, the proposed model incorporates wavelet transform into the DL model. And, the model is trained with a combined loss function to effectively preserve high-frequency components. Based on extensive simulations using New Radio (NR) channel model, the CSI reconstruction performance of the proposed model is improved compared to an existing DL-based method for CSI feedback compression.
Routing Using Genetic Algorithm in Network on Chips with 3D Mesh Topology

The Network-on-Chip (NoC) represents a novel concep to System-on-Chip (SoC) interconnections, streamlining the integration of complex components. Despite its potential, being a nascent technology, it demands dedicated research efforts, particularly in expediting and simplifying the design phases. NoCs offer various topological arrangements, like ring, mesh, and torus. Given the simultaneous transmission of multiple data packets, the adoption of an efficient routing strategy is imperative to mitigate congestion delays. This study delves into the performance of routing using a genetic algorithm within a 3D mesh topology NoC. The routing strategy aims to minimize the latency in packet transmission between tasks. The simulation results underscore the efficacy of Genetic routing, substantiated through a comparative analysis with alternative routing methods.
Enhancing Stochastic Computing using a Novel Hybrid Random Number Generator Integrating LFSR and Halton Sequence

This paper presents a new approach to address the increased hardware complexity and power demands associated with using multiple random number generator (RNG) in stochastic computing (SC). We propose a novel hybrid RNG, which combines a linear feedback shift register (LFSR) and a Halton sequence generator, to facilitate efficient generation of random number sequences in SC. Implemented in a $65- nm$ CMOS technology, our hybrid RNG yields up to 18% and 30% enhancements in area and power, respectively, compared to the traditional LFSR-based equivalent. Further, the proposed RNG’s efficacy is demonstrated through its incorporation in SC-based Robert cross-edge detector algorithm, yielding reductions in area and power by 28% and 35%, respectively, while preserving superior processing quality.
Application and Improvement of LSTM in Power System Prediction

In previous power system predictions, the accuracy of predicting extreme electricity consumption peaks was often insufficient. This study aims to explore the differences in power data prediction among three models: BiLSTM, BiLSTM with self-attention mechanism, and self-attention BiLSTM optimized by WOA. By analyzing and predicting three years of continuous power data, this paper compares the prediction accuracy and stability of these three models and provides a detailed comparison and evaluation of their performance. The BiLSTM model with the self-attention mechanism performs better in terms of prediction accuracy and stability in power data prediction, according to experimental findings. This model can better capture the long-term dependency relationship of time series data and automatically adjust attention, thus predicting future power loads more accurately. In addition, this study found that the self-attention BiLSTM model optimized by WOA can further improve prediction accuracy and stability, and its performance is better than that of traditional BiLSTM models. The experimental findings of this study demonstrate that the WOA optimization and self-attention mechanism play a significant role in enhancing the performance of BiLSTM models in power data prediction, and can increase the accuracy to over 80%. These results can provide reference for future deep learning-based power load prediction and valuable guidance for other time series prediction tasks.
Probability of Heart Disease using various Machine Learning Algorithms

Heart disease is the leading cause of death globally, requiring advanced predictive knowledge and skills. Healthcare systems lack effective analysis tools to uncover hidden patterns. Using Kaggle’s dataset, an automated method aims to predict disease occurrences using data mining techniques, reducing costs and waiting times. Heart disease risk demands a sizable dataset that is excessively large and complex toprocess and analyze using traditional methods. Our goal is to identify an effective and precise method for estimating the probability of heart illness. The experimental results show the highest accuracy, Area under curve is best achieved by the random forest classifier which is about 83.96%, 92.35%, and the highest F1 score is achieved by SVM which is about 80.7%.
Development and verification of usage prediction service for intelligent smart water service

This paper proposes a real-time consumption prediction model that reflects the characteristics of consumers and time-series characteristics so that water supply can be effectively supplied by using a time-series prediction model. In order to reflect the characteristics of consumers, consumers were classified by industry, and each consumer's real-time usage was used to reflect time-series characteristics. There are two major water supply real-time usage prediction models: a short-term forecasting model that predicts daily usage and a long-term forecasting model that predicts weekly usage. Through such research and demonstration, it is expected that water supply and production plans based on predictions will be established to provide high-quality water supply and to enable efficient operation and management of water supply facilities.
A Digital Receive Beamforming IC for High-Frequency Ultrasound Imaging System

An all-digital 16-channel receive beamforming integrated circuit for high-frequency ultrasound imaging systems is presented. The proposed receiver architecture consists of 16 retimers, and a 4-stage delay-and-sum circuit. This architecture can eliminate the time difference received by the receive transducer elements, thereby improving the accuracy of the received data. In addition, compared with the implementation of the receive beamformer using field programmable gate array (FPGA), the proposed beamformer chip has a smaller volume and is easy to integrate with other circuits in the system. The proposed receive beamformer uses a 0.18-μm CMOS technology for a 50-MHz high-frequency linear array with 16 elements. The simulation results indicate that the proposed receive beamforming design can achieve a maximum time delay of 12.33 μs with delay resolution of 20 ns. The supported transducer center frequency ranges from 20 to 50 MHz, and it is highly suitable for high-frequency ultrasound imaging systems.
Bidirectional Guided Policy Search for Aircraft Maneuver

The flight control problem of UAV is a relatively complex problem because of its large state space and the complex dynamics model, and it requires real-time control in the flight process. But the traditional control method can not meet the requirements. A good solution is to use the deep reinforcement learning method based on neural network for training, which can use the powerful nonlinear fitting ability of neural network to learn a better strategy. However, this model-free reinforcement learning method needs to explore a very large state action space, leading to low learning efficiency. In this paper, a method is proposed to train neural network policy by using bidirectional guided policy search under the framework of guided policy search (GPS). Specifically, least squares method is used to estimate the local time-varying linear model, and then the model-based optimal control method is used to improve the local policy. Global neural network is guided to search policy parameters by local policy in a manner of supervised training. The initial nominal trajectory required by the optimal control algorithm can be generated by neural network because of its generalized reasoning ability. This bidirectional guided policy search algorithm can improve the results of both the optimal control algorithm and the neural network policy in an iterative manner. Finally, our neural network strategy is tested in the simulation environment, which can realize the maneuvering control of UAV in any state. On this basis, it will also be conducive to the training of higher-level and more complex flight strategies.
Design and Implementation of a Time Management Self-Help Mobile App for College Students

College students, particularly students in the STEM disciplines, are facing huge challenges in time management, juggling between classes, part-time or full-time jobs, extracurricular activities, and social activities. Evidence has shown that academic success in college students is directly associated with their time management skills. Furthermore, a significant fraction of students suffer from procrastination issues. Unfortunately, such students are not receiving adequate help despite the fact that virtually every college in the US provides a suite of student-centered services such as counseling services. In this paper, we review studies related to academic procrastination, identify primary factors associated with academic procrastination, and major interventions towards reducing academic procrastination. Furthermore, we describe the design and implementation of a time management self-help mobile app to help college students manage their time and overcome issues that lead to procrastination. The self-help app allows a student to set goals on time allocation, to organize tasks, to enter progress made, to review history of performance, and to get support (such as available student services) via a local chatbot. The app incorporates several well-known mechanisms for reducing academic procrastination.
Foldable Forceps with Tactile Sensor Array for Tissue Identification and Suture Needle Status Detection for Surgical Robot

This paper presents a foldable tactile forceps with sensor array for tissue palpation in minimally invasive surgery. The forceps uses a foldable linkage mechanism with a flexible tactile sensor overlay and contains 4 x 8 tactile sensing units. During surgery, the forceps can be folded to enter the body through a tiny incision and then unfolded. The sensor array is based on single-sided electrode piezoresistive effect, which makes it easy to assemble and can detect the hardness of tissue on a large area. The foldable tactile forceps is functionally verified by simulation. The result shows that the tactile forceps can not only be used as a forceps for suture needle status detection during the suturing operation, but also as a probe for tissue palpation after unfolding, which has a good development prospect in minimally invasive surgery.
An Intelligent Security Detection and Response Scheme Based on SBOM for Securing IoT Terminal devices

Recently, with the frequent occurrence of security incidents such as Apache Log4j vulnerabilities and “SolarWinds hack”, the security of open source software (OSS) has gained attention from the academia and industry. In the face of the complex and serious security issues of OSS in various application, like IoT system, software bill of materials (SBOM) has been applied to improve the security of OSS by constructing the relationships among software dependencies. However, SBOM is just appended as a static description without extra exploiting. Therefore, we propose an intelligent security detection and response scheme based on SBOM which is utilized to construct a system component dependency tree (CDT) and a component querier with the Bloom filter. Besides, a security threat assessment algorithm is designed to accurately evaluate the security risk level how the vulnerability discovered in the software system affects software system. Based on the results and analysis, the scheme can realize effective vulnerability detection and evaluation for OSS systems, e.g., IoT systems.
Performance Analysis of MIMO CR Multihop Relaying with Imperfect CSI in Short-Packet URLLCs

This paper evaluates the multiple-input multiple-output underlay cognitive multihop relay networks with short-packet communications, where general and practical scenarios are considered with multiple primary users and imperfect channel state information of the interference channels. For performance evaluation, the closed-form expressions of the end-to-end (E2E) block error rate for the considered systems are derived under consideration of quasi-static Rayleigh fading channels and the finite-blocklength (FBL) regime, from which the E2E throughput, energy efficiency (EE), latency, and reliability are also studied. Based on the analytical results, we adopt a machine learning-aided estimator, i.e., extreme gradient boosting (XGB), to predict the E2E throughput, EE, latency, and reliability for real-time configurations. The XGB-based evaluation achieves equivalent performance while significantly reducing the execution time compared to conventional analytical and simulation methods, which makes XGB an efficient tool to estimate the system performance in real-time applications.
Human Activity Recognition Using Ensemble of CNN-Based Transfer Learning Models

Human activity recognition (HAR) models offer knowledge about human actions that can be used to analyze a person's behavior in a real-world setting. It can be applied in medical assistance, virtual reality, robotics, scene classification, sports analysis, and expert systems. Among various models currently transfer learning based HAR approaches are getting popular among researchers due to the benefits of taking less time and computational resources. In this paper, we propose a neural architecture that exploits the benefits of various convolutional neural network (CNN) based transfer learning models for recognizing human activities. We utilize three pre-trained transfer learning models including ResNet50V2, MobileNetV2, and EfficientNet BO, and fine-tuned them for our human activity recognition task to extract the effective task-specific contextual features. Later, a linear classifier is coupled with each model to get the prediction labels. Finally, we apply a fusion scheme through majority voting to identify the activity label. We conducted our experiments using a human activity detection dataset from Kaggle11https://tinyurl.com/HARDataset. It contains a total of 18000 images with 15 different classes. Experimental results demonstrated the efficacy of our approach over standard baselines. We also provide various insightful findings that might be beneficial for others working in this problem domain.
Regression Based State of Function Estimation of Li-ion batteries for EV Applications

This work presents a Linear Regression based technique to estimate the State of Function of a Li-ion Battery for EV applications. The dynamic model of a vehicle is developed which encorporates the vehicle dynamics on a road surface. An open source tool is deployed to randomly divide the path between two geographical locations into appropriate number of points. The distance and elevation of two different points along the travel path is calculated based on Latitude and Longitude. A Linear Regression model is fitted to the sampled data using gradient-descent algorithm. This Linear Regression model is used to predict the energy requirement of an EV to cover the proposed distance. The decision about the State-of-Function of the Battery is taken based on the predicted energy requirement. The capability of the technique to estimate State-of-Function of EV batteries for different paths using test data is verified using MATLAB/Simulink.
Mimicking GFSK Modulation for WiFi-to-BLE Cross Technology Communication

In the 2.4 GHz ISM band, mutual interference occurs due to the excessive competition for channel resources among various wireless network technologies that adopt different physical (PHY) and MAC standards. To address this issue, cross technology communication (CTC) techniques facilitate direct communication between heterogeneous wireless technologies in the overlapped frequencies. In this paper, we propose a novel method for WiFi-to-BLE CTC that makes a WiFi signal appear like BLE’s Gaussian frequency shift keying (GFSK) signal. This method has not been attempted in previous PHY-CTC studies. The possibility of the newly suggested approach is demonstrated through the GNURadio simulations.
A device driver for the AB21 SoC in Supreme-K system

This paper presents an XPU device driver (XDD) that efficiently supports the OpenCL programming model and development tools for the AB21 SoC, a heterogeneous computational accelerator used in the homegrown prototype of the Supreme-K supercomputer. We propose a dynamic polling-based termination detection mechanism for parallel computing kernel tasks to improve the scheduling performance of OpenCL work groups in a split driver model architecture for heterogeneous compute accelerators connected to the host system via PCIe. Furthermore, we enhance the architecture of XDD to support debugging through the hardware DM of the RISC-V ISA core. This enhancement facilitates the efficient development and optimization of various parallel computing applications.
A Domain-Specific DMA Structure for Per-channel Processing-based CNN Accelerator

Convolutional Neural Networks (CNN) are the most widely used in practical applications. However, with the explosive growth of the number of CNN model parameters, the large number of off-chip memory access becomes a bottleneck to improve the energy-efficiency and performance of dedicated CNN accelerator. In this paper, a per-channel processing-based CNN accelerator and a domain-specific DMA structure are proposed for the edge computing scenario, which allows preloading the parameters required for the next round while the CNN related operation is performing under the current round. The proposed domain-specific DMA structure reduces the CNN processing time and improving the performance of whole system. Synthesized with 55-nm TT Corner CMOS process (25°C, 1.2V), the overall system achieves a maximum clock frequency of 478MHz and an area of 2.84 mm2. With the proposed domain-specific DMA structure, the throughput and energy-efficiency of the LeNet-5 and MobileNet is improved by 25.0%, 42.7% and 11.9%, 36.6%, respectively.
AI-based spatial analysis of crop yield and its relationship with weather variables using satellite agrometeorology

Satellite agrometeorology is the use of satellite remote sensing data to estimate meteorological factors like temperature, precipitation, and radiation, which have a significant impact on crop development and productivity. Remote sensing data, which provides details on the spatial distribution of meteorological parameters, is collected by satellites orbiting the Earth. The researchers intend to investigate the spatial variance in agricultural productivity and its link to weather variables like temperature and precipitation using satellite agrometeorology. The work creates spatially detailed maps of crop productivity and weather parameters using ground-based weather measurements and satellite remote sensing data. The study employed statistical analysis and geographic modelling techniques. Regression analysis is used by researchers to investigate the relationship between agricultural yield and climatic variables. To construct maps of agricultural production and meteorological variables, they also use geospatial modelling. The study’s importance arises from its potential to give farmers, decision-makers, and other stakeholders vital information. The study’s analysis of geographical patterns of crop production and their relationship to climatic variables will assist farmers in making knowledgeable decisions about crop management practices, such as planting dates and irrigation schedules. The research findings can also assist decision-makers in creating efficient agriculture policies that address how crop output is impacted by climate change.