Description Language Research Articles

Design and implementation of UART receiving module based on FPGA

In modern electronic systems, data transmission is an essential requirement within and between boards, or between lower and upper computers. To ensure data transmission accuracy, communication protocols are established that must be followed by all parties involved. These protocols include UART (universal asynchronous transmitter and receiver), IIC (Inter-Integrated Circuit), SPI (Serial Peripheral Interface), USB2.0/3.0(Universal Serial Bus), and Ethernet. Among these protocols, UART is the most basic one and is widely used in embedded devices due to its simple circuit structure and low cost. With the exponential growth of information technology, UART-based embedded devices can easily achieve wired and wireless communication through various communication interfaces and wireless modules. In this paper, the author presents an example of a receiving module for UART communication that converts parallel data into string data. The entire module is developed using the hardware description language Verilog HDL. Simulations are performed using ModelSim, and the results demonstrate that the simulation waveform is consistent with the expected receiving data. This approach facilitates the transformation of serial data to parallel data, improving the efficiency and accuracy of data transmission.

OSPAR: A Corpus for Extraction of Organic Synthesis Procedures with Argument Roles.

There is a pressing need for the automated extraction of chemical reaction information because of the rapid growth of scientific documents. The previously reported works in the literature for the procedure extraction either (a) did not consider the semantic relations between the action and argument or (b) defined a detailed schema for the extraction. The former method was insufficient for reproducing the reaction, while the latter methods were too specific to their own schema and did not consider the general semantic relation between the verb and argument. In addition, they did not provide an annotated text that aligned with the structured procedure. Along these lines, in this work, we propose a corpus named organic synthesis procedures with argument roles (OSPAR) that is annotated with rolesets to consider the semantic relation between the verb and argument. We also provide rolesets for chemical reactions, especially for organic synthesis, which represent the argument roles of actions in the corpus. More specifically, we annotated 112 organic synthesis procedures in journal articles from Organic Syntheses and defined 19 new rolesets in addition to 29 rolesets from an existing language resource (Proposition Bank). After that, we constructed a simple deep learning system trained on OSPAR and discussed the usefulness of the corpus by comparing it with chemical description language (XDL) generated by a natural language processing tool, namely, SynthReader. While our system's output required more detailed parsing, it covered comparable information against XDL. Moreover, we confirmed that the validation of the output action sequence was easy as it was aligned with the original text.

Treatment of Alec: "History does not have to repeat itself".

Treatment of patients with pathological narcissism presents several challenges and there is paucity of published case reports that document meaningful and durable change in patients suffering from this condition. Using descriptive and atheoretical language, this paper presents a treatment of a young manin his transition from young adulthood to middle adulthood while he was negotiating complex residues of his experiences of growing up along with developmental challenges related to work and love. Against the backdrop of these transitions, the patient was working through various aspects of functioning related to pathological narcissism. Initially, given academic pressures and past romantic disappointments, he was confronting issues related to perfectionism, self-criticism, and avoidance. While he was able to move past some of these dynamics and function academically, later challenges related to becoming an independent adult led to a retreat into an avoidant state of futility and pessimism. Working through painful family dynamics related to not being seen and controlled, along with a deepening attachment in therapy as well as confrontation with realities of his life, led him to take steps towardgreater independence. Thereafter, his treatment focused on learning from life experiences such as a newly developed career and romantic life, accepting the complexity of self and others, and tolerating disillusionments.

Experimental Observation of Chaotic Attractors from the FPGA implementation of 3D Chaotic Systems

In recent years, researchers have designed and implemented chaotic oscillators using digital hardware elements to develop applications like secure communications and random number generation. Due to its programmability and low-cost characteristics, one of the most used digital devices to synthesize chaotic systems is the field-programmable gate array (FPGA), which is recommended for fast prototyping. In this manner, this paper shows the experimental observation of seven chaotic attractors that are simulated using the Verilog hardware description language, and implemented in an FPGA. The seven designs are Lorenz-type systems, which are designed from the discretization of their equations using the well-known Forward Euler method. The block description of the seven chaotic systems employs the fixed-point numerical format using 32 bit. The simulations of the chaotic systems using Matlab and Active-HDL software, along with the block diagrams of the designs, are presented. The final part of the work illustrates the experimental attractors on a Teledyne Lecroy oscilloscope, obtained by using an FPGA Cyclone II EP2C35F672C6 and a 16-bit digital-to-analog converter.

Enhancing code summarization with action word prediction

Code summarization refers to automatically generating concise description in natural language from a code snippet. Good code summaries could effectively facilitate program comprehension and software maintenance. In recent years, various learning-based code summarization techniques have achieved impressive performance. Most of these models treat code summarization as an end-to-end model and directly generate the summaries, which ignores the fact that action words are crucial to code summaries. An essential characteristic of code summaries is the concentration of action word distribution. For instance, in the Funcom dataset, the top forty most-common action words account for 72% of all samples. To incorporate this valuable prior domain knowledge into code summarization models, we develop a method for assisting code summarization through an additional action word prediction module, where an action predictor is employed to predict the primary action in the code summary, which is then used as a prompt to enhance the performance of the summary generation model. Our approach can be conveniently integrated into the existing models. We evaluate our approach on two Java datasets and a C/C++ dataset. The results show that our approach can efficiently improve the performance of the code summarization models. Furthermore, our action word prediction module can enhance the performance of a large pre-trained language model by prompting it with the predicted action words. This work suggests that a precise action word prediction model can significantly improve the performance of code summarization through the proposed action word guidance mechanism.

Two Birds with One Stone: Boosting Code Generation and Code Search via a Generative Adversarial Network

Automatically transforming developers' natural language descriptions into source code has been a longstanding goal in software engineering research. Two types of approaches have been proposed in the literature to achieve this: code generation, which involves generating a new code snippet, and code search, which involves reusing existing code. However, despite existing efforts, the effectiveness of the state-of-the-art techniques remains limited. To seek for further advancement, our insight is that code generation and code search can help overcome the limitation of each other: the code generator can benefit from feedback on the quality of its generated code, which can be provided by the code searcher, while the code searcher can benefit from the additional training data augmented by the code generator to better understand code semantics. Drawing on this insight, we propose a novel approach that combines code generation and code search techniques using a generative adversarial network (GAN), enabling mutual improvement through the adversarial training. Specifically, we treat code generation and code search as the generator and discriminator in the GAN framework, respectively, and incorporate several customized designs for our tasks. We evaluate our approach in eight different settings, and consistently observe significant performance improvements for both code generation and code search. For instance, when using NatGen, a state-of-the-art code generator, as the generator and GraphCodeBERT, a state-of-the-art code searcher, as the discriminator, we achieve a 32% increase in CodeBLEU score for code generation, and a 12% increase in mean reciprocal rank for code search on a large-scale Python dataset, compared to their original performances.

RISC processor implementation 32-bit MIPS-based: an approach to teaching and learning

This article describes the development of the design of a processor based on the RISC architecture, taking the 32-bit MIPS microprocessor as a basis. The RISC architecture, which stands for Reduced Instruction Set Computer, is characterized by having a reduced instruction set, aiming to optimize the processor's overall performance. The designed MIPS processor follows a 5-stage pipeline, which comprises the instruction fetch, instruction decode, execution, preparation and memory access phases. The main objective of this article is to carry out the structural development of the processor, using the hardware description language. This implies the creation of a Verilog representation that will later be used to generate the extraction of the processor's logic circuit. Furthermore, the project involves generating a timing diagram that illustrates the temporal behavior of processor operations and, ultimately, the physical implementation of the processor core. This work seeks to contribute knowledge in the field of computer architecture, providing a practical implementation of a RISC processor based on the 32-bit MIPS architecture, which can be relevant both for educational purposes and for practical applications in embedded systems.

Проєктування апаратного декомпресора

The common lossless compression algorithms were analyzed, and the LZW algorithm was selected for the hardware implementation. To express parallelism, this algorithm is represented as a cyclo-dynamic dataflow (CDDF). A hardware synthesis method for designing pipelined datapath is proposed, which optimizes CDDF considering the features of the FPGA primitives and maps it to hardware using VHDL language description. Using this method, an LZW de¬compressor is developed, which exhibits a high performance-to-hardware cost ratio. The de¬com¬¬¬pressor can be utilized in communication channels and other application-specific systems for data loading from memory, generating graphical stencils, and more.

A synthetic cardiac episode generator for explainable, pathology based action potential using heuristic polynomial signatures

Synthetic generation of biophysical signals caters to diverse applications of test vectors generation, exploration, data augmentation and machine learning training. Arrhythmiator, our contribution is a Python framework to generate synthetically, action potential for explain-ability and explore-ability of pathology (Arrhythmia) influenced cardiac episodes. In this work, we address the problem of large cardiac data-sets need, for deep learning models which are hindered by privacy laws by synthetic generation. We have addressed the problem of explainable computations, multi-scale, by virtue of novel stack sliced polynomial signatures, piece-wise linear and application context driven fitting/filtering in algorithmic framework. To account the problem of lack of standardised pathology specifications we developed PDL (Pathology descriptive language) portal which can be customised. We also addressed the challenge of high turnaround development cycle, inter-operate-ability computation issue by developing a single unified Python framework. To address computational costs, we leverage domain-specific heuristics through sliced state space control algorithms and a constant temporal resolution. We demonstrate the utility of our framework by generating two cases of Tachycardia, one influenced by sodium pump inhibition of spontaneous diastolic depolarisation (SDD) slope and other by ischaemic episode causing maximum diastolic depolarisation (MDP) shift. The concept of the pacemaker hierarchy is encompassed by developing pathology explainable computation as a structure (PECS). We showcase the flexibility and explore-ability of our framework through ease of filter connect profile plug-ins for action potential states upstroke and plateau. We validated the generated waveforms by developing trace and debug algorithms and a reference deviation score which marks the pathology influence. The validator module is a vectored attribute of action potential based on state space control algorithms.

An accurate circuit model of Ge/Si single photon avalanche diode

Ge/Si single-photon avalanche diodes (SPADs) have benefits of low after-pulsing probability, low-cost and compatibility with CMOS electronics compared to InGaAs/InP counterpart, thus presenting great potentials for imaging and optical quantum applications in short-wave infrared (SWIR). In this work, an equivalent circuit model mimicking the behavior of pseudo-planar Ge/Si SPAD is constructed to provide accurate electronic information for the co-design of front-end circuit. The DC properties are modelled by nonlinear resistors, voltage-controlled switches and voltage sources, and the AC characteristics are emulated by junction and stray capacitances. To accurately evaluate the statistical behavior, thermal excitation and trap-assisted tunneling are incorporated in the built SPAD circuit models, where three-level traps are adopted to effectively fit the after-pulsing probability. The key model parameters such as electric field profile, breakdown voltage and avalanche triggering probability, are extracted from 2D device simulation. The developed Ge/Si SPAD circuit model implemented in Verilog-A hardware description language (HDL) with a gated passive quenching/reset architecture is validated by circuit simulation platform. The simulation results are in good agreement with the previously reported measurement data, demonstrating the effectiveness and accuracy of our Ge/Si SPAD circuit model.

A Review of Transformer-Based Approaches for Image Captioning

Visual understanding is a research area that bridges the gap between computer vision and natural language processing. Image captioning is a visual understanding task in which natural language descriptions of images are automatically generated using vision-language models. The transformer architecture was initially developed in the context of natural language processing and quickly found application in the domain of computer vision. Its recent application to the task of image captioning has resulted in markedly improved performance. In this paper, we briefly look at the transformer architecture and its genesis in attention mechanisms. We more extensively review a number of transformer-based image captioning models, including those employing vision-language pre-training, which has resulted in several state-of-the-art models. We give a brief presentation of the commonly used datasets for image captioning and also carry out an analysis and comparison of the transformer-based captioning models. We conclude by giving some insights into challenges as well as future directions for research in this area.

A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive illness with a slow start that lasts many years; the disease's consequences are devastating to the patient and the patient's family. If detected early, the disease's impact and prognosis can be altered significantly. Blood biosamples are often employed in simple medical testing since they are cost-effective and easy to collect and analyze. This research provides a diagnostic model for Alzheimer's disease based on federated learning (FL) and hardware acceleration using blood biosamples. We used blood biosample datasets provided by the ADNI website to compare and evaluate the performance of our models. FL has been used to train a shared model without sharing local devices' raw data with a central server to preserve privacy. We developed a hardware acceleration approach for building our FL model so that we could speed up the training and testing procedures. The VHDL hardware description language and an Altera 10 GX FPGA are utilized to construct the hardware-accelerator approach. The results of the simulations reveal that the proposed methods achieve accuracy and sensitivity for early detection of 89% and 87%, respectively, while simultaneously requiring less time to train than other algorithms considered to be state-of-the-art. The proposed algorithms have a power consumption ranging from 35 to 39 mW, which qualifies them for use in limited devices. Furthermore, the result shows that the proposed method has a lower inference latency (61 ms) than the existing methods with fewer resources.

FPGA-Based Hardware-in-the-Loop (HIL) Emulation of Power Electronics Circuit Using Device-Level Behavioral Modeling

Accurate models of power electronic converters can greatly enhance the accuracy of hardware-in-the-loop (HIL) simulators. This can result in faster and more cost-effective design cycles in industrial applications. This paper presents a detailed hardware model of the IGBT and power diode at the device level suggested for emulating power electronic converters on a field programmable gate array (FPGA). The static visualization of the IGBT component involves an arrangement of equivalent models for both the MOSFET and bipolar transistor in a cascading configuration. The dynamic aspect is represented by inter-electrode nonlinear capacitances. In an effort to expedite the development process while still producing reliable results, the algorithm for the simulation system was built utilizing FPGA-based rapid prototyping via the HDL Coder in MATLAB software (R2019b). Essentially, the HDL Coder transforms the Simulink blocks of these devices within MATLAB into a hardware description language (HDL) suitable for implementation on an FPGA. To evaluate the suggested IGBT hardware model and the nonlinear circuit simulation technique, a chopper circuit is replicated, and an FPGA-in-the-loop simulation is carried out to compare the efficacy and accuracy of the model with both offline simulation results and real-time simulation results using MATLAB Simulink software and the Altera FPGA Cyclone IV GX development board.

Drug provision model of patients based on an architectural approach in the context of digital transformation of military healthcare

According to architectural modeling (the architectural structure of an open group), typed blocks of elements forming the basic components or layers of the architecture of a military medical organization are considered, combined into six domains with a schematic representation as typical architecture. The rational allocation of layers, their contents, interrelations, and directions of development provide an opportunity to effectively manage the complex multidisciplinary military medical organization during digital transformation. For the introduction and implementation of digital solutions, the analysis of the creation of an integrated module of a single digital contour for managing medical property resources indicates the feasibility of substantiating and developing reference functional models according to a single standard for describing business processes and the need to choose the most optimal methodology and notation for modeling reference functional architectures of a military medical organization. In a comparative analysis of the selected notations according to reasonable criteria, the business process model and designations were determined as the optimal and universal description language that most fully meets the needs of functional modeling of the architecture of information systems for the subsequent automation and digitalization of the management of medical property resources in a military medical organization. The reference functional model of the information system for the management of medical property resources is presented by the description of the processes of drug provision of admitted patients, which is one of the main target areas of the military medical organization considering its state task. Moreover, a well-founded and developed standard functional architecture in the notation of the business process model and notation 2.0 is presented as interacting pools displaying the subjects (participants) of the process with the tasks assigned to them. The methods and methodological approaches presented in the article, as well as models of functional architectures for providing medicines to patients admitted to a military medical organization, can be used as a reference in the digital transformation of the management of medical property resources at various levels of military healthcare.

Integration of Home Automation and Security System Controller with FPGA Implementation

A home automation system is essential for promoting a safe and comfortable living environment and notable energy conservation for the user. However, the system’s favour had been obstructed by cost, power usage, inadequate security, complexity, and no emergency backup power. Current home automation systems with controllers were limited by their number of ports, fixed architecture, non-durable and non-parallel executions. Keeping this in view, integration of home comfort system, security system, and the automatic load transfer switch features are proposed using the base of Cyclone IV E: EP4CE115F29C7 FPGA Board (DE2-115). The top-level module is developed via Verilog Hardware Descriptive Language (HDL) with the bottom-up technique and used test bench for functional verification via ModelSim-Altera. The PWM method was applied to the lighting system to control the dimming of light through its digital signals via a maximum 500000 counter to improve energy efficiency for the proposed design. In this project, 200Hz pulses are successfully simulated to prevent visible flickering of lights in duty cycle generation. The light intensity of 40% and 100% are verified and successfully generated according to the inputs provided by the status of the LDR sensor and IR sensor. The proposed controller gives correct corresponding outputs to the 13 actuators based on the detected input stimuli. The proposed design utilized a total of 162 (<1%) logic elements, 32 registers, and total pins of 74 (14%). The proposed design successfully integrated the three-sub module and provided control on comfort and security system operations to prevent service failure during power blackout conditions at the top-level and utilized a low ratio of the FPGA.

Deep Learning Approaches on Image Captioning: A Review

Image captioning is a research area of immense importance, aiming to generate natural language descriptions for visual content in the form of still images. The advent of deep learning and more recently vision-language pre-training techniques has revolutionized the field, leading to more sophisticated methods and improved performance. In this survey article, we provide a structured review of deep learning methods in image captioning by presenting a comprehensive taxonomy and discussing each method category in detail. Additionally, we examine the datasets commonly employed in image captioning research, as well as the evaluation metrics used to assess the performance of different captioning models. We address the challenges faced in this field by emphasizing issues such as object hallucination, missing context, illumination conditions, contextual understanding, and referring expressions. We rank different deep learning methods’ performance according to widely used evaluation metrics, giving insight into the current state-of-the-art. Furthermore, we identify several potential future directions for research in this area, which include tackling the information misalignment problem between image and text modalities, mitigating dataset bias, incorporating vision-language pre-training methods to enhance caption generation, and developing improved evaluation tools to accurately measure the quality of image captions.

A framework for analyzing authentication risks in account networks

Our everyday life depends more and more on online services and, therefore, access to related user accounts. The security of user accounts, again, is tied to the security of the corresponding primary and fallback authentication methods. Accounts can be linked to each other – by fallback authentication, through SSO, or by using the same authentication devices – creating an account network. These account networks enhance login comfort and are needed in case of account recovery, but they also increase each account's attack surface. In addition, misconfigurations might result in account inaccessibility. However, these problems can only be detected by analyzing single accounts first and then the resulting account networks. Despite the importance to understand account security and accessibility, almost no analysis methods exist.To address this need, this article presents the Authentication Analysis Framework (AAF). AAF evaluates account types and primary and fallback authentication methods for each account, before analyzing the overall account network. By detecting transitive risks, weak links can be discovered and subsequently strengthened. We further propose maturity models to rank the primary and fallback authentication methods based on risks and a description language to exchange the required information. AAF is implemented as a plugin for the password manager KeePass to assist end users and as a standalone tool for researchers.

Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding.

Temporal grounding is the task of locating a specific segment from an untrimmed video according to a query sentence. This task has achieved significant momentum in the computer vision community as it enables activity grounding beyond pre-defined activity classes by utilizing the semantic diversity of natural language descriptions. The semantic diversity is rooted in the principle of compositionality in linguistics, where novel semantics can be systematically described by combining known words in novel ways(compositional generalization). However, existing temporal grounding datasets are not carefully designed to evaluate the compositional generalizability. To systematically benchmark the compositional generalizability of temporal grounding models, we introduce a new Compositional Temporal Grounding task and construct two new dataset splits, i.e., Charades-CG and ActivityNet-CG. We empirically find that they fail to generalize to queries with novel combinations of seen words. We argue that the inherent compositional structure(i.e., composition constituents and their relationships) inside the videos and language is the crucial factor to achieve compositional generalization. Based on this insight, we propose a variational cross-graph reasoning framework that explicitly decomposes video and language into hierarchical semantic graphs, respectively, and learns fine-grained semantic correspondence between the two graphs. Meanwhile, we introduce a novel adaptive structured semantics learning approach to derive the structure-informed and domain-generalizable graph representations, which facilitate the fine-grained semantic correspondence reasoning between the two graphs. To further evaluate the understanding of the compositional structure, we also introduce a more challenging setting, where one of the components in the novel composition is unseen. This requires more sophisticated understanding of the compositional structure to infer the potential semantics of the unseen word based on the other learned composition constituents appearing in both the video and language context, and their relationships. Extensive experiments validate the superior compositional generalizability of our approach, demonstrating its ability to handle queries with novel combinations of seen words as well as novel words in the testing composition.

Generating Parametric BRDFs from Natural Language Descriptions

AbstractArtistic authoring of 3D environments is a laborious enterprise that also requires skilled content creators. There have been impressive improvements in using machine learning to address different aspects of generating 3D content, such as generating meshes, arranging geometry, synthesizing textures, etc. In this paper we develop a model to generate Bidirectional Reflectance Distribution Functions (BRDFs) from descriptive textual prompts. BRDFs are four dimensional probability distributions that characterize the interaction of light with surface materials. They are either represented parametrically, or by tabulating the probability density associated with every pair of incident and outgoing angles. The former lends itself to artistic editing while the latter is used when measuring the appearance of real materials. Numerous works have focused on hypothesizing BRDF models from images of materials. We learn a mapping from textual descriptions of materials to parametric BRDFs. Our model is first trained using a semi‐supervised approach before being tuned via an unsupervised scheme. Although our model is general, in this paper we specifically generate parameters for MDL materials, conditioned on natural language descriptions, within NVIDIA's Omniverse platform. This enables use cases such as real‐time text prompts to change materials of objects in 3D environments such as “dull plastic” or “shiny iron”. Since the output of our model is a parametric BRDF, rather than an image of the material, it may be used to render materials using any shape under arbitrarily specified viewing and lighting conditions.

Which Information Behavior Concepts Bridge the Gap from Research to Reference Practice?

ABSTRACTAlthough there is much discussion of the theory/practice gap in library and information science, there is little research about how librarians use formal theory in their practice. Using a card sort and interviews, we explore the extent to which public librarians encounter or use theoretical concepts and models from information behavior in their reference practice. Results from our pilot study show that our procedures, including plain language descriptions of theoretical concepts and models printed on cards, are effective for eliciting examples of theory in practice. Librarians are clear about which concepts are relevant to their practice and can easily provide examples of real‐world application. Results of the study will suggest which theoretical concepts might be most important for instructors to teach to students in reference courses and which translate best from the research to the practice environment. In addition, the examples provided by librarians of how they use the concepts in their practice will be useful for instructors as they try to engage students in learning.