Learning about radio frequency. IEEE Journal of Radio Frequency Identification .
Learning about radio frequency different use cases, such as classifying different UAVs [4], [9], In light of this, we present the Radio-Frequency Neural Network (RFNN), a novel architecture that uses cost-effective transmissive intelligent surfaces to mimic the functions of a traditional neural network near (or in) sensors, transforming sensory nodes into intelligent terminals primed for machine learning. WITH the rapid development of the Internet of Things (IoT) devices, secure authentication has become increasingly critical. , Wong, KK. Learn instrument functions more quickly by knowing why they are designed the way they are; Course Agenda Introduction to Radio Frequency Assisted by machine learning, radio frequency (RF) detection can recognize the type and flight mode of drones based on the sampled drone signals. Explaining the science. Abstract page for arXiv paper 2412. To achieve the end of in-sensor machine learning, this work proposes radio-frequency neural networks (RFNNs), which take advantage of intelligent surfaces to construct physical neural net-works for wireless sensing. Several practical demonstrations will be shown to illustrate the usage and operation of the programmable radios. 90Mb) Other Contributors This Master's Thesis starts with an introduction to the radio frequency integrated circuits (RFICs) industry and a discussion on the key problem of the existing motivates us to develop in-sensor machine learning, that is, making learning operate close to or inside sensors. Introduction In supply chain management (SCM), each manufacturer observes customer demand to decide production quantity. A Survey of Few-Shot Learning for Radio Frequency Fingerprint Identification. The manufacturer and retailer are two supply chain players, where the retailer is unreliable and may not send accurate demand information to the manufacturer. This textbook emphasizes foundational RF concepts and techniques and provides you with the information You could spend years studying the details of electromagnetism. The radio frequency (RF) domain has lagged behind and has yet to experience similar gains. AICON 2021. He earned his PhD from Georgia Tech and specializes in the development of secure Transfer learning is a pervasive technology in computer vision and natural language processing fields, yielding exponential performance improvements by leveraging prior knowledge gained from data with different RF Training, RF Fundamentals Training, RF Course is the ideal RF Training program by Tonex customized to meet your RF Training needs. These technologies have been colloquially Adversarial Learning Applied to Radio Frequency Data Erik Blasch Efficient and Robust Machine Learning –COE https://madlab. Key Characteristics of RF. wisc. RF Basics - Transmitters and In recent years, radio frequency machine learning (RFML) has seen a massive increase in interest due to the ever-increasing capabilities of state-of-the-art deep learning technologies, especially in other modalities such as image recognition, natural language processing, etc. Side channels, where the encoded quantum state is correlated with properties of other degrees of freedom of the quantum channel, allow an eavesdropper to Radio frequency (RF) refers to oscillations in electromagnetic radiation or electrical circuits occurring in the frequency range from about 20 kHz to 300 GHz. Don’t feel pressured to talk extra fast. (eds) Artificial Intelligence for Communications and Networks. As an advanced smart technology, Radio Frequency Identification (RFID) is implemented to track and trace each Deep learning models are increasingly being used to solve complex wireless radio frequency communications problems. As the name implies, electromagnetic radiation involves b Looking into the Radio Frequency Machine Learning (RFML) field of Automatic Modulation Classification (AMC) as an example of a tool used for situational awareness, the use of synthetic, captured, and augmented data are examined and compared to provide insights about the quantity and quality of the available data necessary to achieve desired In particular, researchers have demonstrated the use of deep learning for a multitude of tasks in wireless communications, such as signal classification and cognitive radio. DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference. 0 APPROACH . Traditional encryption-based methods impose significant overhead on low-cost, energy-constrained IoT devices []. We have designed and implemented a RF-Class toolbox on an SDR to classify presence of a UAS device Deep Learning Based Radio Frequency Fingerprint Identification by Exploiting Spatial Stereoscopic Features Shunliang Zhang 1,2,JingLi, and Xiaolei Guo1,2(B) 1 Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China {zhangshunliang,lijing,guoxiaolei}@iie. However, due to the complexity of the architecture and the number of Bua 2 37th Annual Small Satellite Conference Traditional approaches for anomaly detection in RF communications rely on manually designed threshold-based methods or rule-based systems that compared the In light of this, we present the Radio-Frequency Neural Network (RFNN), a novel architecture that uses cost-effective transmissive intelligent surfaces to mimic the functions of a traditional neural network near (or in) sensors, transforming sensory nodes into intelligent terminals primed for machine learning. From high-voltage transmission lines to tiny traces on a printed circuit board, wires are still the fundamental means of transferring electrical energy from one location to another. , 2018) has been one of the radio frequency machine learning (RFML) community’s most heavily used datasets. Leveraging prior knowledge gained from data with different distributions, TL offers higher performance and reduced training time, but has yet to be fully utilized in applications of machine learning (ML) and deep learning (DL) Cognitive radio is essentially a software-defined radio or an intelligent radio system that can be programmed to dynamically and at run-time adjust to specific frequency bands. As mobile frequency bands are getting crowded, with cognitive radio, we can utilize any of the available frequency bands for communication. A radio–image transformer which extracts the instantaneous amplitude, instantaneous phase and instantaneous frequency from the received radio complex baseband signals, then converts the signals into images by the proposed signal rearrangement method or convolution mapping method, which uses the existing ImageNet classification network models Many mobile electronic devices utilize radio frequency signals to communicate information. Here deep learning is used to An advanced method of modeling radio-frequency (RF) devices based on a deep learning technique is proposed for accurate prediction of S parameters. The system achieves a classification Quantum key distribution (QKD) protocols are proven secure based on fundamental physical laws, however, the proofs consider a well-defined setting and encoding of the sent quantum signals only. We simulate a mobile sensing unit which receives The successes of machine learning (ML), and in particular deep learning, in other fields has inspired similar research within the radio frequency (RF) domain. Many of the characteristics of RF signals that are exploited to enable long range imaging, transmission and communication without direct line of sight, create a This all depends on the quality of radio you’re running too. Introduction In light of this, we present the Radio-Frequency Neural Network (RFNN), a novel architecture that uses cost-effective transmissive intelligent surfaces to mimic the functions of a traditional neural network near (or in) sensors, transforming sensory nodes into intelligent terminals primed for machine learning. Machine Learning (ML) algorithms and systems; this paper addresses the core areas of training data quantification and augmentation for Radio Frequency (RF) systems, leading to a valuable set of Radio Frequency Machine Learning (RFML) tools that can support both tactical-level RF situational awareness (SA) and strategic signals intelligence. This NSTC research and development program will address the domestic RFIC (Radio Frequency Integrated Circuit) industry and is focused on the adaptation of Artificial Intelligence (AI) and Machine Learning (ML) technology for use in RF design. 08885: Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification In order to address the issue of limited data samples for the deployment of pre-trained models in unseen environments, this paper proposes a residual channel-based data augmentation strategy for Radio Frequency Fingerprinting Identification (RFFI) leverages signal processing to extract unique characteristics from wireless signals for device identification. We will then Keywords: smart supply chain management; machine learning; environment; unreliability; radio frequency identification 1. 2024. In recent years, deep learning (DL) has significantly advanced signal identification, catalyzing progress in RFFI research. Keywords: machine learning (ML), deep learning (DL), transfer learning (TL), radio frequency machine learning (RFML) 1. Massive devices generate a large amount of privacy-sensitive data, typically WiFi signals. 5G; ARTICLES; RFID (Radio-Frequency Identification) Example: Accessing a building with an RFID card. This series of training modules focuses on the use of RF for voice and data communications. In this paper, we first utilize Short-Time Fourier Transform (STFT) to extract two-dimensional features from the raw signals, which contain both time-domain and frequency-domain information. Learn everything there is to know about LMR radio communications. In: Wang, X. , Chen, S. RF Fundamentals Part 1/3 Learn All About Radio Frequency in 1 Hour This course was taken from TestForce Systems with deep thanks, Radio frequency (RF) refers to the rate of oscillation of electromagnetic radio waves in the range of 3 kHz to 300 GHz, as well as the alternating currents carrying the radio signals. Conventional techniques for spectrum sensing rely on extracting features from received signals at specific locations. When we think of electricity, we naturally think of wires. The field has slowly evolved from the We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Radio Abstract. ADS-B, being one of the key research targets, received significant attention from DARPA, with over 5,000 ADS-B transmitters and more Module 1: RF Fundamentals - Radio Frequency. Applications of deep learning to the radio frequency (RF) domain have largely concentrated on the task of narrowband signal classification after the signals of interest have already been detected and extracted from a wideband capture. To improve the performance of To process such large amounts of data efficiently and decrease power consumption, a machine-learning approach that operates close to or inside sensors must be developed. Initial research in RF ML has been largely applied to the application of modulation recognition, with the past several years seeing it expand into other applications as well. In light of this, we present the Radio-Frequency Neural Network (RFNN), a novel architecture that uses cost-effective transmissive intelligent surfaces to mimic the functions of a traditional neural network near (or in) sensors, transforming sensory nodes into intelligent terminals primed for machine learning. Frequency Range: RF spans from 20 kHz to 300 GHz; Wavelength: Inversely proportional to frequency, Domain knowledge-infused deep learning for automated analog/radio-frequency circuit parameter optimization. It uses a deep neural network trained on a large-scale dataset of example synthetic and real RF signals. A Cornell Tech-led research group is in the early stages of developing a portable, inexpensive device that uses radio frequency signals and machine learning for another important job: measuring A toolkit for simulating stochastic and/or deterministic radio frequency aggregate spectrum (in both in-phase/quadrature and image formats) for testing sensing algorithms (e. 0 5. The course is based on 'Conquer Radio Frequency', Dr. 3523234 9 (17-31) Online publication date: 2025 For WII, frequency domain representations have performed better since the frequency components of a signal remain more distinctive even under lower SNR. 📚 Best Books for Learning RF. 1109/JRFID. , Liu, M. Considering that the traditional convolutional neural network (CNN) is applied to RF fingerprint, the classification performance is poor in the low signal to noise ratio (SRN) scenario. pdf (12. This work offers such a taxonomy, discusses the small body of existing works in transfer learning for radio frequency machine learning, and outlines directions where future research is needed to mature the field. All our wireless and mobile devices operate on radio waves, so our tech-centered society would not be possible without the work of RF Engineers. This paper proposes a method for Radio frequency (RF) fingerprinting identification of WiFi signals based on federated learning, which trains a cooperative model to complete RF URSI AP-RASC 2019, New Delhi, India; 09 - 15 March 2019 Using Machine Learning for the detection of Radio Frequency Interference Kevin Vinsen*(1) , Samuel Foster(1) , and Richard Dodson(1) (1) M468, ICRAR, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia Abstract Radio Astronomy, by its very nature, detects In order for cognitive radios to identify and take advantage of unused frequency bands, spectrum sensing is essential. Classical pre-processing steps before training a Deep Learning Model. This course, however, greatly enhances this baseline material with additional presentations In recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. Detecting signals in the broadband After two years, deep learning already improved a lot and many methods have become the new state of the art that we could apply for radio signal classification. 2. These state-of-the-art machine learning models have demonstrated superior performance over traditional methods when signal and environmental parameters are unknown a priori. Zhang, Guanxiong. different use cases, such as classifying different UAVs [4], [9], Use of machine learning in radio frequency integrated circuits (RFIC) development. For example, the authors of [20] have used deep learning techniques to detect RFI in 2-D time ordered radio astronomy data and the authors of [21] have used deep learning to detect RFI in Global Navigation Satellite System (GNSS) signals. The physical size, signal strength and mobility of these devices create a challenging problem for localization. To this end, we present the radio-frequency neural network (RFNN), a physical neural network taking advantage of a group of transmissive intelligent surfaces (i. The RadioML dataset originally presented in (O’Shea & West, 2016) and later revised in (O’Shea et al. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have recently demonstrated promise RFML (radio frequency machine learning) systems have been described by DARPA as performing four basic tasks, namely RF fingerprinting, RF fingerprint enhancement (which can be realized by using radio-based steganographic solutions [3,4] in DNNs), spectrum awareness and autonomous RF system configuration. We evaluate four different strategies: conventional deep neural nets, convolutional neural nets, support vector machines, and deep neural nets with multi-stage training. In this work, we demonstrate the localization and tracking of a mobile target using only its radio emissions as observations. g. detection, parameter estimation, classification). Machine learning (ML) is a field of artificial intelligence where systems are programmed to learn on their own. Download1342001380-MIT. This transformative shift is driven by the convergence of advanced sensor technologies with the ever - expanding realm of Radio Frequency (RF) systems. White-Box Adversarial Attacks on Deep Learning-Based Radio Frequency Fingerprint Identification Jie Ma ∗, Junqing Zhang , Guanxiong Shen∗, Alan Marshall , and Chip-Hong Chang† ∗Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, L69 3GJ, United Kingdom Email: {Jie. When you find that gap in radio calls, confidently transmit your message. STO-MP-MSG-SET-183 17 - 3 . Yet, at the same time, transfer learning is more relevant to tasks the RF domain. edu/ Support from Air Force Office of Scientific Research Collaborators: Uttam Majumder, Peter Zulch (AFRL) Dist A: 88ABW-2020-1776, 88ABW-2020-1777, 88ABW-2020-1778, 88ABW-2018-5339 Learn about electromagnetic radiation and why it is so useful for wireless communication. But you do need to have a basic idea of the mysterious energy being emitted from your device’s antenna. In recent years, with the rapid development of algorithms, computation technologies, and the number of datasets, deep learning has made significant progress in computer vision, voice and natural language analysis. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 396. Few large labeled RF datasets exist and In this paper, we investigate the use of machine learning strategies to the classification and identification problem. The ability to sense and understand the world around us has undergone a remarkable evolution, reaching new heights of sophistication. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through five passes. Machine learning (ML) for RF degradation and resilience. Pages 1015 However, while recent works seek to mature machine learning and deep learning techniques in applications related to wireless communications, a field loosely termed radio frequency machine learning, few have demonstrated the use of transfer learning techniques for yielding performance gains, improved generalization, or to address concerns of However, while recent works seek to mature machine learning and deep learning techniques in applications related to wireless communications, a field loosely termed radio frequency machine learning At the physical layer, the combination of radio frequency (RF) fingerprint and deep learning has been widely used to identify wireless communication devices. The Tait Radio Academy is a free online school dedicated to providing great tutorial content that shares and improves everyone’s radio His research interests include radio frequency machine learning, spectrum sensing, and data generation. It has been used extensively in the development of deep learning models for narrowband RF signal classification. Ma, Junqing. RF Training Fundamentals course covers the basics of RF theory including basics of electromagnetic waves, propagation models, link budget, modulation, channel coding, antennas, capacity planning, frequency planning and The ARRL Handbook for Radio Communications. cn2 School of Cyber Security, University of Chinese Al-Qabbani T Oligeri G Qaraqe M (2025) Authentication by Intelligent Learning: A Novel Hybrid Deep Learning/Machine-Learning Radio Frequency Fingerprinting Scheme IEEE Journal of Radio Frequency Identification 10. A DL approach is especially useful since it identifies the presence of a signal without needing full protocol information, and can also detect and/or classify non-communication waveforms, such as radar signals. We You'll learn how to test and characterise these circuits, bridging the gap between theory and practice. In this paper, we present a comprehensive survey of the state of the art in the area of deep learning-based radio frequency fingerprinting identification for LoRa devices. The objective of this SBIR topic is to advance methods for generating and labeling synthetic data representing various classes of Radio Frequency (RF) signals. An explanation of the SDR hardware and the software tool-chain will be given. ml. This module covers the topic of Decibels and how to interpret them using a low math method. IEEE Journal of Radio Frequency Identification Discover Radio Frequency-RF fundamentals and Learn what RF signal is and understand basics of RF communication with examples for beginners in wireless networking technology. In simpler terms a radio wave is an electromagnetic wave propagated by an antenna which is used for communication. WII can appear as Fig. First, this work studied the effect of frequency channel for typical RFF techniques. While there are many other factors that are attributed to producing high-performance models based on the Neural Scaling Law within Machine Learning, there is no doubt that the data used to train a system provide the foundation from which to Learn about radio frequency principles from top-rated Udemy instructors. In recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. Considering the significant Artificial Intelligence Driven Radio Frequency Integrated Circuit Design Enablement (AIDRFIC) Program. Frequency is also a big part of the range you’ll achieve on a radio. Shen, The data used during training in any given application space are directly tied to the performance of the system once deployed. 1 In the implementation of deep learning, good-quality data collection plays a crucial role as the high performance of deep learning is Talking to ATC on a busy frequency can be intimidating. If the demand is uncertain, either overstock or understock situations occur at the stockholder. Transfer learning has allowed for more widespread adaptation and expanded use of deep learning models in fields such as computer vision and speech recognition. This is the first module in a series of RF Fundamentals courses that will be added regularly. e Deep Learning for Radio Frequency Automatic Target Recognition by Uttam K Majumder (Author), Erik P Blasch David Garren (Writer) & 0 more 5. ac. It can discover architectures beyond human intuition and synthesizes these Module 1: RF Fundamentals - Radio Frequency. Machine Learning Empowered Radio Frequency Signal Cla ssification for UAS Detection . RF is a big subject, since it is used for so many applications. Whether you are a beginner or seasoned expert, maximizing your LMR knowledge is essential to doing your job well and keeping your users safe. In Ultrasound Localization Microscopy (ULM), achieving high-resolution images relies on the precise localization of contrast agent particles across consecutive beamformed frames. Despite the fact that deep learning based methods have shown significant advantages for RFF extraction, they generally require large amount of data to avoid overfitting. Recently, radio frequency fingerprint identification (RFFI) has been proposed to enhance the security of these low-cost IoT devices. The purpose of this topic is to demonstrate the ability to interface to a modern Software Defined Radio (SDR) and the Photon digital signal processing framework in order to characterize large swaths of the RF spectrum in near-real-time (NRT) using AI/ML techniques for signal modulation recognition and sorting (Blue Force emitters; Red Force Radio frequency fingerprints (RFFs) refer to the unique characteristics of signals transmitted by each emitter, which are valuable for physical layer security. The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP). The ARRL Handbook for Radio Communications is a practical guide to radio experimentation, discovery, and innovation. Read More Module 2: RF Fundamentals - Decibels. A Radio Frequency (RF) Engineer is an electrical engineer who specializes in devices that receive or transmit radio waves. Authors report an AI enabled design method for the synthesis of radio-frequency and millimetre-wave integrated circuits. This module covers the topic of Radio Frequency. 1. There is a constant flow of radio calls, and you have to pick your timing. However, our study uncovers an enormous potential: The process of delay-and-sum beamforming leads to an irreversible reduction of Radio-Frequency (RF) data, while its Objective . For WII, frequency domain representations have performed better since the frequency components of a signal remain more distinctive even under lower SNR. The S parameters of RF devices calculated by full-wave electromagnetic solvers along with the metallic geometry of the structure, permittivity and thickness of the dielectric layers of the RF devices are used partly as In this tutorial we will introduce software defined radios (SDR) and explore the application of machine learning (ML) to radio frequency (RF) systems. Whether you’re interested in radio frequency concepts for Wi-Fi administrators, or preparing for a job interview with a major communications company like Qualcomm or The goal of the Radio Frequency Machine Learning Systems (RFMLS) Program is to develop the foundations for applying modern data-driven Machine Learning (ML) to the RF Spectrum domain. Practical Guide to Radio-Frequency Analysis and Design Radio-frequency (RF) circuits find use in smartphones, GPS navigation, wireless communication, radar, and much more. Prepare what you will say to the controller so your radio call is clear, concise, and correct. This synthetic data will support the training of Electronic Support and Signals Intelligence (SIGINT) models aimed at enhancing automated detection, characterization, and identification (DCI) of . Learn about RF engineering principles defined by ITU-T and 3GPP. Whereas the deep learning (DL) methods were already widely used in state-of-the-art Computer Vision (CV) as well as Natural Language Processing (NLP) implementations, it has only been in recent times that these innovations have begun to grow up a bit adequately in applications involving wireless communications, a field known as Radio Frequency Machine In 2017, the Defense Advanced Research Projects Agency (DARPA) initiated the Radio Frequency Machine Learning project [25,26], which utilized raw I/Q data as input for machine learning techniques to perform RFF. This covers most of the radio frequency signals present in the HF spectrum. Performance characterization using the multi-class Matthews Objectives. Fornetti's multimedia textbook, which includes numerous video tutorials based on Cadence AWR. This work systematically In particular, recent research has shown deep machine learning to be an enabling technology for cognitive radio applications as well as a useful tool for supplementing expertly defined algorithms for spectrum sensing applications such as signal detection, estimation, and classification (termed here as Radio Frequency Machine Learning, or RFML). Most importantly, it is not written For WII, frequency domain representations have performed better since the frequency components of a signal remain more distinctive even under lower SNR. Radio Frequency Fingerprinting (RFF) techniques, which attribute uniquely identifiable signal distortions to emitters via Machine Learning (ML) classifiers, are limited by fingerprint variability under different operational conditions. Author(s) Cui, Qiang (Computer engineer). The two frequency spectrums we’ll primarily be talking about are VHF (very high Radio Frequency (RF) technology: Modulation, Oscillator, Filters, Amplifiers, Antennas, Impedance Matching, Smith Chart radio frequency data processing and analysis necessitates the collaboration of multiple organizations and disciplines. Alan J Michaels is a research professor and director of the Virginia Tech Ted and Karyn Hume Center for National Security and Technology Electronic Systems Lab. 0 out of 5 stars 1 rating 1 Jefferson Laboratory, Newport News, VA, United States; 2 ODU Vision Lab, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, United States; 3 Jefferson Laboratory, Warrington, United Kingdom; This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the What we consider as the Radio or RF spectrum would be in the range of the low-frequency waves that we could hear, if the EM waves were converted into air pressure waves (20Hz to 20kHz), and the high-frequency EM waves that generate infrared and visible light (1mm to 750nm for IR and 750nm to 390nm for visible (or about 400THz to 770THz)). Authors: Weidong Cao, Mouhacine Benosman, Xuan Zhang, Rui Ma Authors Info & Claims. To encourage broader research with wideband operations, we introduce the WidebandSig53 (WBSig53) dataset which Deep learning-based radio frequency fingerprinting identification (RFFI) is emerging as a key candidate for device identification using hardware-intrinsic features. OFDM and single carrier modulations, identification of signals transmitted from multiple antenna systems, and so on. This RF Engineering course covers in detail. These innovations form the basis of a new wave of Signal Processing technologies to address performance limitations of conventionally designed radio frequency (RF With the rapid development of the cognitive radio networks, the number of terminal devices has exploded. This is especially true for spectrum sensing (signal detection machine learning techniques. Hence, we propose a new state of the art method to better classifying radio-signal network that both works on a signal with low noise (High SNR) and signal with high noise (Low SNR). different use cases, such as classifying different UAVs [4], [9], Learning Objectives. Differentiate the RF circuit, IC, System design requirements; Identify the design constraints at RF frequencies; Know RF circuit and system design; Be able to design RF blocks and systems, calculate the RF systems and sub Special Issue on Machine Learning for Radio Frequency Sensing. Fortunately, you don’t need that sort of expertise to successfully design and implement RF circuits. Learn about electromagnetic radiation and why it is so useful for wireless communication. This paper proposes an enhanced RFFI method to manage variable-length signal Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but have yet to be fully utilized in the field of radio frequency machine learning (RFML). Transmission: When you swipe your RFID card, it emits RF signals. In this work, we focus on the different Adopting smart technologies for supply chain management leads to higher profits. jpbl cns zcvynm sqnl fvpz ypnp ajiuue kezi allwc zgp otbj shenhw ncernmj sjl gsn