Fundamental Modulation Research Articles

Probing the Impact of Surface Functionalization Module on the Performance of Mitoxantrone Prodrug Nanoassemblies: Improving the Effectiveness and Safety.

Prodrug nanoassemblies are emerging as a novel drug delivery system for chemotherapy, comprising four fundamental modules: a drug module, a modification module, a response module, and a surface functionalization module. Among these modules, surface functionalization is an essential process to enhance the biocompatibility and stability of the nanoassemblies. Here, we selected mitoxantrone (MTO) as the drug module and DSPE-PEG2K as surface functionalization module to develop MTO prodrug nanoassemblies. We systematically evaluated the effect of surface functionalization module ratios (10%, 20%, 40%, and 60% of prodrug, WDSPE-mPEG2000/Wprodrug) on the prodrug nanoassemblies. The results indicated that 40% NPs significantly improved the self-assembly stability and cellular uptake of prodrug nanoassemblies. Compared with MTO solution, 40% NPs showed better tumor specificity and pharmacokinetics, resulting in potent antitumor activity with a good safety profile. These findings highlighted the pivotal role of the surface functionalization module in regulating the performance of mitoxantrone prodrug nanoassemblies for cancer treatment.

Rapid Experimental Protocol for PMSM via MBD: Modeling, Simulation, and Experiment

As control algorithms evolve, their enhanced performance is often accompanied by increased complexity, reaching a point where practical experimentation becomes unfeasible. This situation has led to many theoretical studies relying solely on simulations without experimental verification. To address this gap, this study introduces a rapid experimentation protocol (REP) for applying field-oriented control (FOC) strategies to permanent magnet synchronous motors (PMSMs) based on model-based design (MBD) and automated code generation. REP is designed to be user-friendly and straightforward, offering a less complex and more accessible alternative to DSP toolboxes. Its excellent hardware compatibility is conducive to code porting and development. With this protocol, users can quickly conduct FOC strategy experiments with reduced dependency on the complex automated code generation tools often associated with toolboxes. Centered around the PMSM model, this method utilizes only the fundamental modules of MATLAB2023b/Simulink, greatly simplifying the user experience. To demonstrate the feasibility and efficiency of the protocol, models for both sensor-based and sensorless control are developed. The practicality of REP, including sensor-based and sensorless experiments, is successfully validated on an arm-cortex-M4-based GD32 microcontroller.

Performance Analysis of BPSK Modulation: A Theoretical Approach

Abstract: Binary Phase Shift Keying (BPSK) represents a fundamental modulation technique extensively utilized within digital communication systems. The present study presents an analytical examination of BPSK modulation, with a specific focus on its theoretical aspects, MATLAB implementation, and performance analysis. The article commences by providing an introduction to modulation techniques and a concise overview of BPSK, emphasizing its significance and applications. Theoretical foundations encompassing the mathematical representation, key parameters, and characteristics of BPSK are thoroughly deliberated upon. Performance metrics, namely the bit error rate (BER) and signal-to-noise ratio (SNR), are scrutinized, incorporating theoretical derivations and analysis. Following this, the implementation of BPSK modulation and demodulation in MATLAB is expounded, supplemented by coding algorithms and code snippets. Empirical results acquired from the MATLAB implementation are presented and scrutinized, alongside a comparison to the theoretical analysis. The article concludes by summarizing the principal findings and contributions, accentuating the advantages and limitations associated with BPSK modulation, and suggesting potential avenues for future research within this domain. By concentrating on the theoretical aspects and providing comprehensive derivations and mathematical analysis, this research enhances our understanding and exploration of BPSK modulation, thereby circumventing the reliance on simulation-based methodologies

RGMU: A High-flexibility and Low-cost Reconfigurable Galois Field Multiplication Unit Design Approach for CGRCA

Finite field multiplication is a non-linear transformation operator that appears in the majority of symmetric cryptographic algorithms. Numerous specified finite field multiplication units have been proposed as a fundamental module in the coarse-grained reconfigurable cipher logic array to support more cryptographic algorithms; however, it will introduce low flexibility and high overhead, resulting in reduced performance of the coarse-grained reconfigurable cipher logic array. In this article, a high-flexibility and low-cost reconfigurable Galois field multiplication unit (RGMU) is proposed to balance the tradeoffs between the function, delay, and area. All the finite field multiplication operations, including maximum distance separable matrix multiplication, parallel update of Fibonacci linear feedback shift register, parallel update of Galois linear feedback shift register, and composite field multiplication, are analyzed and two basic operation components are abstracted. Further, a reconfigurable finite field multiplication computational model is established to demonstrate the efficacy of reconfigurable units and guide the design of RGMU with high performance. Finally, the overall architecture of RGMU and two multiplication circuits are introduced. Experimental results show that the RGMU can not only reduce the hardware overhead and power consumption but also has the unique advantage of satisfying all the finite field multiplication operations in symmetric cryptography algorithms.

Lightweight high-precision SAR ship detection method based on YOLOv7-LDS.

The current challenges in Synthetic Aperture Radar (SAR) ship detection tasks revolve around handling significant variations in target sizes and managing high computational expenses, which hinder practical deployment on satellite or mobile airborne platforms. In response to these challenges, this research presents YOLOv7-LDS, a lightweight yet highly accurate SAR ship detection model built upon the YOLOv7 framework. In the core of YOLOv7-LDS's architecture, we introduce a streamlined feature extraction network that strikes a delicate balance between detection precision and computational efficiency. This network is founded on Shufflenetv2 and incorporates Squeeze-and-Excitation (SE) attention mechanisms as its key elements. Additionally, in the Neck section, we introduce the Weighted Efficient Aggregation Network (DCW-ELAN), a fundamental feature extraction module that leverages Coordinate Attention (CA) and Depthwise Convolution (DWConv). This module efficiently aggregates features while preserving the ability to identify small-scale variations, ensuring top-quality feature extraction. Furthermore, we introduce a lightweight Spatial Pyramid Dilated Convolution Cross-Stage Partial Channel (LSPHDCCSPC) module. LSPHDCCSPC is a condensed version of the Spatial Pyramid Pooling Cross-Stage Partial Channel (SPPCSPC) module, incorporating Dilated Convolution (DConv) as a central component for extracting multi-scale information. The experimental results show that YOLOv7-LDS achieves a remarkable Mean Average Precision (mAP) of 99.1% and 95.8% on the SAR Ship Detection Dataset (SSDD) and the NWPU VHR-10 dataset with a parameter count (Params) of 3.4 million, a Giga Floating Point Operations Per Second (GFLOPs) of 6.1 and an Inference Time (IT) of 4.8 milliseconds. YOLOv7-LDS effectively strikes a fine balance between computational cost and detection performance, surpassing many of the current state-of-the-art object detection models. As a result, it offers a more resilient solution for maritime ship monitoring.

DOMAS: a data management software framework for advanced light sources.

In recent years, China's advanced light sources have entered a period of rapid construction and development. As modern X-ray detectors and data acquisition technologies advance, these facilities are expected to generate massive volumes of data annually, presenting significant challenges in data management and utilization. These challenges encompass data storage, metadata handling, data transfer and user data access. In response, the Data Organization Management Access Software (DOMAS) has been designed as a framework to address these issues. DOMAS encapsulates four fundamental modules of data management software, including metadata catalogue, metadata acquisition, data transfer and data service. For light source facilities, building a data management system only requires parameter configuration and minimal code development within DOMAS. This paper firstly discusses the development of advanced light sources in China and the associated demands and challenges in data management, prompting a reconsideration of data management software framework design. It then outlines the architecture of the framework, detailing its components and functions. Lastly, it highlights the application progress and effectiveness of DOMAS when deployed for the High Energy Photon Source (HEPS) and Beijing Synchrotron Radiation Facility (BSRF).

A View on Surplus Food Donation App

Abstract: The enormous development in the amount of food waste has prompted a need for charity donations. Food is significantly wasted every day at numerous institutions, including restaurants, parties, social gatherings, university canteens, and many of the other social events in the existing circumstances. Currently, individuals contribute food manually by visiting each agency numerous times to alleviate the concerns with food waste. Although some mechanisms currently in place have made an attempt to aid with food donations, the new web application that is part of the proposed framework offers a platform for recycling extra food to help people who are in need on an individual and group level. This technique has shown to be an effective manner of giving things online to charities, solving the significant problem of food waste. The article includes insights into the aim behind such an application, highlighting the existing process of contributions and how the product operates to serve the community. Under this framework, hotels, restaurants, charities, and individuals would all have access to a single platform for communication. Charities and individuals could then connect with restaurants that have excess food available for immediate donation, and the framework would track the quantity of food donated by each restaurant, awarding food donors with points. The fundamental modules in this design are "Food Donor," which may be any company, organization, or institution eager to provide food and submit new food donation requests, and "Food Receiver," representing meal-seeking charity groups. A new food donation request may be produced on the website, and a message will be issued to the third-party agency responsible for conveying food from the donor to the receiver once the request is allowed.

Space Vector Fundamental Frequency Modulation Compared with the Selective Harmonic Elimination Method

A new modulation strategy for three-phase multilevel-converters, called Space Vector Fundamental Frequency Modulation will be presented. Derived from conventional Space Vector PWM, it was developed to overcome Selective Harmonic Elimination Method in mathematical complexity. Besides the harmonic optimization, the method offers a variable modulation index and is applicable for real-time applications. But in contrast to Selective Harmonic Elimination Method it is an approximation procedure. In this paper, both methods are compared, concerning several criteria such as voltage quality and mathematical complexity. Also, a classification with reference to EN 50160 is presented.

An investigation and analysis on automatic speech recognition systems

<p>A crucial part of a Speech Recognition System (SRS) is working on its most fundamental modules with the latest technology. While the fundamentals provide basic insights into the system, the recent technologies used on it would provide more ways of exploring and exploiting the fundamentals to upgrade the system itself. These upgrades end up in finding more specific ways to enhance the scope of SRS. Algorithms like the Hidden Markov Model (HMM), Artificial Neural Network (ANN), the hybrid versions of HMM and ANN, Recurrent Neural Networks (RNN), and many similar are used in accomplishing high performance in SRS systems. Considering the domain of application of SRS, the algorithm selection criteria play a critical role in enhancing the performance of SRS. The algorithm chosen for SRS should finally work in hand with the language model conformed to the natural language constraints. Each language model follows a variety of methods according to the application domain. Hybrid constraints are considered in the case of geography-specific dialects.</p>

Cation migration of layered oxide cathodes for sodium-ion batteries: fundamental failure mechanisms and practical modulation strategies

Sodium-ion batteries (SIBs) are regarded as competitive candidates for the next generation of electrochemical energy storage (EES) for their low cost and abundant sodium resources. Layered oxide cathode has attracted...

Development of Fundamental Modulation Technique Based Asymmetric Multi-Level Inverter and Multi-Pulse Converter to Prevent Medium Voltage Drive from Harmonic Component

Development of Fundamental Modulation Technique Based Asymmetric Multi-Level Inverter and Multi-Pulse Converter to Prevent Medium Voltage Drive from Harmonic Component

State-Space Based Universal Time-Domain Model for Voltage-Fed Bidirectional IPT Systems

Conventional time-domain model of bidirectional inductive power transfer (BIPT) systems highly rely on specific compensation network and lacks universality. To address this issue, we propose a universal time-domain model for voltage-fed BIPT systems. The proposed state-space model applies to all compensation networks and resonant parameters, The input of the state space consists of the output voltage of the H-bridge. While the voltage combinations of the H-bridge are adaptive to different phase shift conditions. Based on the circuit analysis and by solving the state equations, analytical expressions for the state variables can be obtained. The time-domain analytical model is piecewise and accurately predicts the coil currents and capacitor voltages under all fundamental modulation schemes and compensation topologies. The simulation and experimental results of different compensation topologies and modulation strategies are presented, verifying the effectiveness of the proposed model. Compared with commercial software, the computation burden is obviously alleviated. The simulation is accelerated by 64 times than Simulink and 10 times than Psim.

Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins.

The Internet of Things (IoT) has brought about significant transformations in multiple sectors, including healthcare and navigation systems, by offering essential functionalities crucial for their operations. Nevertheless, there is ongoing debate surrounding the unexplored possibilities of the IoT within the energy industry. The requirement to better the performance of distributed energy systems necessitates transitioning from traditional mission-critical electric smart grid systems to digital twin-based IoT frameworks. Energy storage systems (ESSs) used within nano-grids have the potential to enhance energy utilization, fortify resilience, and promote sustainable practices by effectively storing surplus energy. The present study introduces a conceptual framework consisting of two fundamental modules: (1) Power optimization of energy storage systems (ESSs) in peer-to-peer (P2P) energy trading. (2) Task orchestration in IoT-enabled environments using digital twin technology. The optimization of energy storage systems (ESSs) aims to effectively manage surplus ESS energy by employing particle swarm optimization (PSO) techniques. This approach is designed to fulfill the energy needs of the ESS itself as well as meet the specific requirements of participating nano-grids. The primary objective of the IoT task orchestration system, which is based on the concept of digital twins, is to enhance the process of peer-to-peer nano-grid energy trading. This is achieved by integrating virtual control mechanisms through orchestration technology combining task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. The nano-grid energy trading system's architecture utilizes IoT sensors and Raspberry Pi-based edge technology to enable virtual operation. The evaluation of the proposed study is carried out through the examination of a simulated dataset derived from nano-grid dwellings. This research analyzes the efficacy of optimization approaches in mitigating energy trading costs and optimizing power utilization in energy storage systems (ESSs). The coordination of IoT devices is crucial in improving the system's overall efficiency.

DMMP: A distillation-based multi-task multi-tower learning model for personalized recommendation

Recommendation systems involve the matching and ranking stages. The click-through rate (CTR) and the conversion rate (CVR) predictions are two fundamental modules in recommendation systems. Most candidate generation matching models leverage a two-tower architecture to model the CTR prediction task. However, items with low-quality but attractive titles, i.e., click baits, may be recommended to the user, which worsens the user’s experience. Therefore, both click and conversion tasks should be modeled during the matching stage to improve user engagement. An intuitive way is to model these two tasks with a three-tower matching model. However, its efficiency and effectiveness are limited. By inheriting the merits of knowledge distillation and multi-task learning, we propose a distillation-based multi-task multi-tower model (DMMP) for personalized recommendation. Specifically, the MTL-based teacher network builds the task-shared and task-specific expert networks, and employs a customized multi-gate control network to merge these expert networks adaptively within the task embedding layer. Two auxiliary pCTR and pCTCVR tasks are incorporated to model CVR directly across the whole space. By sharing the feature representation parameters with the CTR modeling, the CVR modeling can be trained with richer samples. Three-tower-based candidate generation student network incorporates a user preference gating network to learn the task scores so that personalized candidates can be produced. Furthermore, we design an adaptive weighting strategy for total loss to eventually adjust the importance and relevance of different networks. Extensive experiments on public and industrial datasets validate the effectiveness of DMMP.

Higher order Kirillov--Reshetikhin modules for 𝐔 q (A n (1)), imaginary modules and monoidal categorification

Abstract We study the family of irreducible modules for quantum affine 𝔰 ⁢ 𝔩 n + 1 {\mathfrak{sl}_{n+1}} whose Drinfeld polynomials are supported on just one node of the Dynkin diagram. We identify all the prime modules in this family and prove a unique factorization theorem. The Drinfeld polynomials of the prime modules encode information coming from the points of reducibility of tensor products of the fundamental modules associated to A m {A_{m}} with m ≤ n {m\leq n} . These prime modules are a special class of the snake modules studied by Mukhin and Young. We relate our modules to the work of Hernandez and Leclerc and define generalizations of the category 𝒞 - {\mathscr{C}^{-}} . This leads naturally to the notion of an inflation of the corresponding Grothendieck ring. In the last section we show that the tensor product of a (higher order) Kirillov–Reshetikhin module with its dual always contains an imaginary module in its Jordan–Hölder series and give an explicit formula for its Drinfeld polynomial. Together with the results of [D. Hernandez and B. Leclerc, A cluster algebra approach to q-characters of Kirillov–Reshetikhin modules, J. Eur. Math. Soc. (JEMS) 18 2016, 5, 1113–1159] this gives examples of a product of cluster variables which are not in the span of cluster monomials. We also discuss the connection of our work with the examples arising from the work of [E. Lapid and A. Mínguez, Geometric conditions for □ \square -irreducibility of certain representations of the general linear group over a non-archimedean local field, Adv. Math. 339 2018, 113–190]. Finally, we use our methods to give a family of imaginary modules in type D 4 {D_{4}} which do not arise from an embedding of A r {A_{r}} with r ≤ 3 {r\leq 3} in D 4 {D_{4}} .

Body size awareness matters when dogs decide whether to detour an obstacle or opt for a shortcut

Body-awareness is one of the fundamental modules of self-representation. We investigated how body-awareness could contribute to dogs' decision making in a novel spatial problem where multiple solutions are possible. Family dogs (N = 68) had to obtain a treat from behind a transparent fence. They had two options: either detour around the fence (7 m), or take a shortcut through a doorway (2 m). We had three conditions: small door open, large door open, and doors closed. Our results indicated that dogs assess the size of the doorway, and if they find it too small, they decide to detour instead, while in the case of the open large door, they rather opted for the shortcut without hesitation. Shorter headed dogs tended to choose open doors more often, while longer headed dogs rather chose detours, probably because of their better peripheral vision. While body size awareness did not manifest differently in dogs with short or long heads, we showed for the first time a connection between head shape and physical cognition in dogs. We showed that dogs rely on their body-awareness in a naturalistic setting where multiple solutions exist simultaneously. Dogs make decisions without lengthy trial-and-error learning and choose between options based on their body-awareness.

Measuring and Energizing Sensor System for Digital Signal Monitoring of an Academic-Experimental CubeSat for Wireless Telemetry Purposes.

Space technology for small satellites has made significant progress in the academic and industrial fields, and an alternative focused on educational institutions is the CubeSat standard, created to promote various scientific projects of space exploration. In this context, a fundamental module of any satellite is the telemetry subsystem, which controls the communication with the Earth through electronic circuits dedicated to remote communication; also, the measurement and power supply modules are integrated into a CubeSat. Its construction costs range from USD 2500 to 55,000, with suppliers from Europe and the United States. This motivates the development of the present project, aimed at an academic-experimental CubeSat-1U prototype, to limit this technological dependence, focusing on the measurement generated by the acceleration sensors, angular velocity, magnetic fields, barometric pressure, temperature and ultraviolet light intensity, and the energization of each of them. For this, the main objective of the research is to identify the four basic subsystems of the CubeSat-1U: (a) energization subsystem, (b) sensing subsystem, (c) transmission and reception subsystem, and (d) control subsystem. To describe in detail the construction of (a) and (b), a set of diagrams is performed, defining their operation and its interaction. To explain the subsystem's construction, the components selection and integration are presented. As a result, the electrical measurements generated by the power system, the output of the sensors in laboratory conditions, and images of the developed circuits are presented, having as a contribution to the methodology of design, integration, and development of the four subsystems, the feasibility of construction and its implementation in an academic satellite.

A Unified Joint Optimization of Training Sequences and Transceivers Based on Matrix-Monotonic Optimization

Channel estimation and data transmission constitute the most fundamental functional modules of multiple-input multiple-output (MIMO) communication systems. The underlying key tasks corresponding to these modules are training sequence optimization and transceiver optimization. Hence, we jointly optimize the linear transmit precoder and the training sequence of MIMO systems using the metrics of their effective mutual information (MI), effective mean squared error (MSE), effective weighted MI, effective weighted MSE, as well as their effective generic Schur-convex and Schur-concave functions. Both statistical channel state information (CSI) and estimated CSI are considered at the transmitter in the joint optimization. A unified framework termed as joint matrix-monotonic optimization is proposed. Based on this, the optimal precoder matrix and training matrix structures can be derived for both CSI scenarios. Then, based on the optimal matrix structures, our linear transceivers and their training sequences can be jointly optimized. Compared to state-of-the-art benchmark algorithms, the proposed algorithms visualize the bold explicit relationships between the attainable system performance of our linear transceivers conceived and their training sequences, leading to implementation ready recipes. Finally, several numerical results are provided, which corroborate our theoretical results and demonstrate the compelling benefits of our proposed pilot-aided MIMO solutions.

Path description for q-characters of fundamental modules in type C

In this paper, we investigate the behavior of monomials in the q-characters of the fundamental modules over a quantum affine algebra of untwisted type C. As a result, we give simple closed formulae for the q-characters of the fundamental modules in terms of sequences of vertices in R 2 , so-called paths, with an admissible condition. This may be viewed as a type C analog of the path description of q-characters in types A and B due to Mukhin–Young.

A White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-Based Cybersecurity Solution for a Smart City Environment.

Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks. DDoS attacks involve overwhelming a system with a huge volume of traffic, causing disruptions and potentially leading to service outages. Mitigating and detecting DDoS attacks in SGs is of great significance to ensuring their stability and reliability. Therefore, this study develops a new White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) technique for a Smart City Environment. The goal of the WSEO-HDLCS technique is to recognize the presence of DDoS attacks, in order to ensure cybersecurity. In the presented WSEO-HDLCS technique, the high-dimensionality data problem can be resolved by the use of WSEO-based feature selection (WSEO-FS) approach. In addition, the WSEO-HDLCS technique employs a stacked deep autoencoder (SDAE) model for DDoS attack detection. Moreover, the gravitational search algorithm (GSA) is utilized for the optimal selection of the hyperparameters related to the SDAE model. The simulation outcome of the WSEO-HDLCS system is validated on the CICIDS-2017 dataset. The widespread simulation values highlighted the promising outcome of the WSEO-HDLCS methodology over existing methods.