Minimal Hardware Research Articles

Designing an Effective Job Recommender System based on Embedded Machine Learning Models

The need to automate employment offers to qualify job searchers has gain attentions. For an automated recommendation systems to be used more frequently, a better user-friendly filtering techniques are required. This paper designs an automated process, referred to as “the job recommender”, which focuses on user-centric design and personalization for recommending and matching applicants with appropriate jobs. We use the bottom-up approach that uses dataset based on filtering algorithms to predict and make recommendations for job seekers. The algorithm helps the recruiters to produce the list of the résumé that best meets the job descriptions. In this context, the random forest (RF) and support vector machines (SVM) are adopted to train the data. They are supplied personalized information (qualifications, result of aptitude test, age, and work experience) reported on the résumés of individual candidates from the pool of submissions, and the system train data to learn the evolution of job selection by candidates based on these machine learning tools. The algorithm used would help the recruiters to produce the list of the résumé that best meets the job descriptions. The algorithms are designed to recommend personalized items tailored to each user's interests. Under the minimum hardware and software requirements, the job recommender system was implemented in streamlit - a python template, for designing the frontend.

Hardware Area Efficiently and Real-Time FPGA Implementation of PHMMRGB

For encryption applications on embedded systems, operating in real-time while using minimal system resources is essential. It is expected that efficient and rapid encryption of high-resolution images is to be accomplished with limited hardware resources. Therefore, in order to ensure the desired efficiency of encryption of high-resolution images, the system must possess a digital architecture capable of achieving high speeds with minimal hardware resources. This study considered the limitations of a hardware architecture for an image encryption algorithm based on the Profile Hidden Markov Model called PHMMRGB. The proposed architecture is relatively simple in comparison to its alternatives. The hardware architecture, designed with the objective of using minimal resources, has been implemented on an FPGA. Based on the results of the proposed architecture, it is believed that the implementation of the specified method on an FPGA would yield high efficiency. Experiments conducted using large-sized satellite images confirmed this expectation.

Rapid in situ carbon-13 hyperpolarization and imaging of acetate and pyruvate esters without external polarizer

Hyperpolarized 13C MRI visualizes real-time metabolic processes in vivo. In this study, we achieved high 13C polarization in situ in the bore of an MRI system for precursor molecules of most widely employed hyperpolarized agents: [1-13C]acetate and [1-13C]pyruvate ethyl esters in their perdeuterated forms, enhancing hyperpolarization lifetimes, hyperpolarized to P13C ≈ 28% at 80 mM concentration and P13C ≈ 19% at 10 mM concentration, respectively. Using vinyl esters as unsaturated Parahydrogen-Induced Polarization via Side-Arm Hydrogenation (PHIP-SAH) precursors and our novel polarization setup, we achieved these hyperpolarization levels by fast side-arm hydrogenation in acetone-d6 at elevated temperatures (up to 90°C) and hydrogenation pressures (up to 32 bar). We optimized the hyperpolarization process, reducing it to under 10 s, and employed advanced pulse sequences to enhance the polarization transfer efficiency. The hyperpolarization system has a small footprint, allowing it to be positioned in the same magnet, where 13C MRI is performed. We exemplified the utility of the design with sub-second in situ 13C MRI of ethyl [1-13C]pyruvate-d6. However, challenges remain in side-arm cleavage and purification in the MRI system to extract highly polarized aqueous agent solutions. Our results showcase efficient and rapid 13C hyperpolarization of these metabolite precursors in an MRI system with minimal additional hardware, promising to enhance future throughput and access to hyperpolarized 13C MRI.

Analog reservoir computing via ferroelectric mixed phase boundary transistors

Analog reservoir computing (ARC) systems have attracted attention owing to their efficiency in processing temporal information. However, the distinct functionalities of the system components pose challenges for hardware implementation. Herein, we report a fully integrated ARC system that leverages material versatility of the ferroelectric-to-mixed phase boundary (MPB) hafnium zirconium oxides integrated onto indium–gallium–zinc oxide thin-film transistors (TFTs). MPB-based TFTs (MPBTFTs) with nonlinear short-term memory characteristics are utilized for physical reservoirs and artificial neuron, while nonvolatile ferroelectric TFTs mimic synaptic behavior for readout networks. Furthermore, double-gate configuration of MPBTFTs enhances reservoir state differentiation and state expansion for physical reservoir and processes both excitatory and inhibitory pulses for neuronal functionality with minimal hardware burden. The seamless integration of ARC components on a single wafer executes complex real-world time-series predictions with a low normalized root mean squared error of 0.28. The material-device co-optimization proposed in this study paves the way for the development of area- and energy-efficient ARC systems.

An Echo State Network-Based Light Framework for Online Anomaly Detection: An Approach to Using AI at the Edge

Production efficiency is used to determine the best conditions for manufacturing goods at the lowest possible unit cost. When achieved, production efficiency leads to increased revenues for the manufacturer, enhanced employee safety, and a satisfied customer base. Production efficiency not only measures the amount of resources that are needed for production but also considers the productivity levels and the state of the production lines. In this context, online anomaly detection (AD) is an important tool for maintaining the reliability of the production ecosystem. With advancements in artificial intelligence and the growing significance of identifying and mitigating anomalies across different fields, approaches based on artificial neural networks facilitate the recognition of intricate types of anomalies by taking into account both temporal and contextual attributes. In this paper, a lightweight framework based on the Echo State Network (ESN) model running at the edge is introduced for online AD. Compared to other AD methods, such as Long Short-Term Memory (LSTM), it achieves superior precision, accuracy, and recall metrics while reducing training time, CO2 emissions, and the need for high computational resources. The preliminary evaluation of the proposed solution was conducted using a low-resource computing device at the edge of the real production machine through an Industrial Internet of Things (IIoT) smart meter module. The machine used to test the proposed solution was provided by the Italian company SIFIM Srl, which manufactures filter mats for industrial kitchens. Experimental results demonstrate the feasibility of developing an AD method that achieves high accuracy, with the ESN-based framework reaching 85% compared to 80.88% for the LSTM-based model. Furthermore, this method requires minimal hardware resources, with a training time of 9.5 s compared to 2.100 s for the other model.

Lightweight High‐Throughput TRNG Based on Single‐Node Boolean Chaotic Structure

ABSTRACTWith the rise of the Internet and electronic devices, the security of network information is gaining attention, and the true random number generator (TRNG) is playing an increasingly crucial role in information security. TRNG, based on Boolean chaotic entropy source, has drawn significant interest due to its uncomplicated circuit design and minimal hardware resource usage. However, most existing structures consist of two‐input or three‐input logic devices, forming a complex multinode, geometrically symmetric Boolean chaotic network using multiple logic devices. This network configuration results in increased complexity and reduced throughput. This study introduces an entropy source based on Boolean chaos utilizing single‐node and four‐input XOR gates, which can be easily placed and routed on Xilinx Artix‐7 FPGA. It requires only 29 LUTs and 5 DFFs without any postprocessing, achieving a throughput of up to 700 Mb/s. The output of TRNG has successfully passed various tests including the autocorrelation test, NIST SP800‐22, NIST SP800‐90B, AIS‐31, and TESTU01 tests with favorable results. Furthermore, by applying a three‐stage XOR chain postprocessing on Xilinx Spartan‐6 FPGA and Xilinx Virtex‐6 FPGA, it has passed the NIST SP800‐22 and NIST SP800‐90B tests at 300 Mb/s. The structure was also tested using Xilinx Virtex‐6 FPGA under different temperature and voltage conditions, passing the NIST SP800‐90B IID test.

An enhanced time synchronization method for a network based on Kalman filtering

This paper introduces an enhanced time synchronization method different from IEEE 1588 (PTP). The proposed algorithm employs a unique synchronous message packet structure with a fixed length of 10 bytes and the highest system priority. This design enables preemptive transmission, effectively reducing transmission and queuing delays. Additionally, it applies a Kalman filtering model to mitigate noise interference, including clock drift, network delay jitter, and network asymmetry. The algorithm also features a clock drift compensation mechanism to continuously adjust the clock, ensuring high precision and stability in time synchronization. The algorithm proposed in this paper has the characteristics of being easy to implement, requiring minimal hardware resources, and being applicable to a variety of networks. Simulation results show that, with an 80 MHz crystal oscillator, the time offset between master and slave clocks does not exceed ± 1 clock cycle in symmetric communication links. In asymmetric links, the maximum time offset is within ± 3 clock cycles. Compared to the original PTP algorithm and the Kalman filter-based time synchronization algorithm, this method reduces the time offset from several microseconds to less than 40 ns.

Requirements for upgrading trusted nodes to a repeater chain over 900 km of optical fiber

Abstract We perform a numerical study of the distribution of entanglement on a real-world fiber grid connecting the German cities of Bonn and Berlin. The connection is realized using a chain of processing-node quantum repeaters spanning roughly 900 kilometers. Their placement is constrained by the fiber grid we consider, resulting in asymmetric links. We investigate how minimal hardware requirements depend on the target application, as well as on the number of repeaters in the chain. We find that requirements for blind quantum computing are markedly different than those for quantum key distribution, with the required coherence time being around two and a half times larger for the former. Further, we observe a trade-off regarding how target secret-key rates are achieved when using different numbers of repeaters: comparatively low-quality entangled states generated at a high rate are preferred for higher numbers of repeaters, whereas comparatively high-quality states generated at a lower rate are favored for lower numbers of repeaters. To obtain our results we employ an extensive simulation framework implemented using NetSquid, a discrete-event simulator for quantum networks. These are combined with an optimization methodology based on genetic algorithms to determine minimal hardware requirements.

Embedded ML-based locomotion control for a 12-joint four-legged robot

Reinforcement learning has developed as a promising approach for robot locomotion control, which can save engineering effort compared to conventional approaches. This article presents the implementation of Reinforcement learning on a low-cost, 12 degree of freedom robot known as a quadruped (spider) to optimize locomotion control, enabling the robot to move on different surfaces such as flat surfaces, ramps, speed bumps, and rough terrain. A MATLAB Simulink model is developed as a digital twin of the spider robot. The dynamics of the model were studied and validated using an open-loop algorithm. Then, the model is utilized in a training simulation environment to apply the reinforcement learning algorithm, showing its ability to move along a predefined path as a replacement for conventional motion control systems. Moreover, the work compares the performance and effectiveness of machine learning-based locomotion control with traditional motion control systems regarding navigation accuracy, speed, and adaptability in challenging environments. The minimum hardware requirements are also studied to move the experiment from simulation to reality.

Hardware requirements for trapped-ion-based verifiable blind quantum computing with a measurement-only client

Abstract In blind quantum computing (BQC), a user with a simple client device can perform a quantum computation on a remote quantum server such that the server cannot gain knowledge about the computation. Here, we numerically investigate hardware requirements for verifiable BQC using an ion trap as server and a distant measurement-only client. While the client has no direct access to quantum-computing resources, it can remotely execute quantum programs on the server by measuring photons emitted by the trapped ion. We introduce a numerical model for trapped-ion quantum devices in NetSquid, a discrete-event simulator for quantum networks. Using this, we determine the minimal hardware requirements on a per-parameter basis to perform the verifiable BQC protocol. We benchmark these for a five-qubit linear graph state, with which any single-qubit rotation can be performed, where client and server are separated by 50 km. Current state-of-the-art ion traps satisfy the minimal requirements on a per-parameter basis, but all current imperfections combined make it impossible to perform the blind computation securely over 50 km using existing technology. Using a genetic algorithm, we determine the set of hardware parameters that minimises the total improvements required, finding directions along which to improve hardware to reach our threshold error probability that would enable experimental demonstration. In this way, we lay a path for the near-term experimental progress required to realise the implementation of verifiable BQC over a 50 km distance.

Energy analysis during cold start and warm up period of a methanol engine hybrid power system under several novel energy conversion strategies

The use of methanol in engine hybrid power system has a wide range of potential applications, with the primary challenge being cold start issues. This paper innovatively proposes four strategies for starting and warming up the methanol engine, including motor dragging, motor dragging coupled with exhaust gas recirculation (EGR), motor dragging coupled with exhaust control, and motor dragging coupled with both EGR and exhaust control. These strategies can be implemented in methanol hybrid power system with minimal hardware modification costs, and not generate any emissions compared to traditional gasoline assisted strategy. The warm-up mechanisms of these strategies were analyzed and their effectiveness was verified by experiments. The optimum strategy, motor dragging coupling both EGR and exhaust control, has been demonstrated to enhance power input and improve the energy conversion efficiency substantially. Compared to the traditional gasoline assisted strategy, the energy consumption, energy conversion efficiency and the warm-up time in this strategy are 17.9 % lower, 5.1 % higher, 4.2 s shorter, respectively. These strategies well solve the problem of methanol engines being unable to start at low temperatures, which is expected to eliminate the cold start of the engine and directly enter the hot start.

Best of two worlds: Efficient, usable and auditable biometric ABC on the blockchain

In García-Rodríguez et al. 2024, two generic constructions for biometric-based non-transferable Attribute Based Credentials (biometric ABC) are presented, which offer different trade-offs between efficiency and trust assumptions. In this paper, we focus on the second scheme denoted as BioABC-ZK that tries to remove the strong (and unrealistic) trust assumption on the Reader R, and we show that BioABC-ZK has a security flaw for a colluding R and Verifier V. Besides, BioABC-ZK lacks GDPR-compliance, which requires secure processing of biometrics, for instance in form of Fuzzy Extractors, as opposed to (i) storing the reference biometric template aBio in the user’s mobile phone and (ii) processing of biometrics using an external untrusted R, whose foreign manufacturers are unlikely to adjust their products according to GDPR.The contributions of this paper are threefold. First, we review efficient biometric ABC schemes to identify the privacy-by-design criteria for them. In view of these principles, we propose a new architecture for biometric ABC of Sarier 2021 by adapting the recently introduced core/helper setting. Briefly, a user in our modified setting is composed of a constrained core device (a SIM card) inside a helper device (a smart phone with dual SIM and face recognition feature), which – as opposed to García-Rodríguez et al. 2024 – does not need to store aBio. This way, the new design provides Identity Privacy without the need for an external R and/or a dedicated hardware per user such as a biometric smart card reader or a tamper proof smart card as in current hardware-bound credential systems. Besides, the new system maintains minimal hardware requirements on the SIM card – only responsible for storing ABC and helper data –, which results in easy adoption and usability without loosing efficiency, if deep face fuzzy vault and our modified ABC scheme are employed together. As a result, a total overhead of 500 ms to a showing of a comparable non-biometric ABC is obtained instead of the 2.1 s in García-Rodríguez et al. 2024 apart from the removal of computationally expensive pairings. Finally, as different from García-Rodríguez et al. 2024, auditing is achieved via Blockchain instead of proving in zero-knowledge the actual biometric matching by the user to reveal malicious behavior of R and V.

Automated thermographic detection of blood vessels for DIEP flap reconstructive surgery.

Inadequate perfusion is the most common cause of partial flap loss in tissue transfer for post-mastectomy breast reconstruction. The current state-of-the-art uses computed tomography angiography (CTA) to locate the best perforators. Unfortunately, these techniques are expensive and time-consuming and not performed during surgery. Dynamic infrared thermography (DIRT) can offer a solution for these disadvantages. The research presented couples thermographic examination during DIEP flap breast reconstruction with automatic segmentation approach using a convolutional neural network. Traditional segmentation techniques and annotations by surgeons are used to create automatic labels for the training. The network used for image annotation is able to label in real-time on minimal hardware and the labels created can be used to locate and quantify perforator candidates for selection with a dice score accuracy of 0.8 after 2min and 0.9 after 4min. These results allow for a computational system that can be used in place during surgery to improve surgical success. The ability to track and measure perforators and their perfused area allows for less subjective results and helps the surgeon to select the most suitable perforator for DIEP flap breast reconstruction.

Adapting a group-based, multimodal aphasia treatment for telehealth – co-design of M-MAT Tele

Multi-Modality Aphasia Treatment (M-MAT) is an effective group intervention for post-stroke aphasia. M-MAT employs interactive card games and the modalities of gesture, drawing, reading, and writing to improve spoken language. However, there are challenges to implementation of group interventions such as M-MAT, particularly for those who cannot travel or live in rural areas. To maximise access to this effective treatment, we aimed to adapt M-MAT to telehealth format (M-MAT Tele). The Human-Centred Design Framework was utilized to guide the adaptation approach. We identified the intended context of use (outpatient/community rehabilitation) and the stakeholders (clinicians, people with aphasia, health service funders). People with aphasia and practising speech pathologists were invited to co-design M-MAT Tele in a series of iterative workshops, to ensure the end product was user-friendly and clinically feasible. The use of co-design allowed us to understand the hardware, software and other constraints and preferences of end users. In particular, clinicians (n = 3) required software compatible with a range of telehealth platforms and people with aphasia (n = 3) valued solutions with minimal technical demands and costs for participants. Co-design within the Human-Centred Design Framework led to a telehealth solution compatible with all major telehealth platforms, with minimal hardware or software requirements. Pilot testing is underway to confirm acceptability of M-MAT Tele to clinicians and people with aphasia, aiming to provide an effective, accessible tool for aphasia therapy in telehealth settings.

Fast, high-quality, and unshielded 0.2 T low-field mobile MRI using minimal hardware resources.

To propose a deep learning-based low-field mobile MRI strategy for fast, high-quality, unshielded imaging using minimal hardware resources. Firstly, we analyze the correlation of EMI signals between the sensing coil and the MRI coil to preliminarily verify the feasibility of active EMI shielding using a single sensing coil. Then, a powerful deep learning EMI elimination model is proposed, which can accurately predict the EMI components in the MRI coil signals using EMI signals from at least one sensing coil. Further, deep learning models with different task objectives (super-resolution and denoising) are strategically stacked for multi-level post-processing to enable fast and high-quality low-field MRI. Finally, extensive phantom and brain experiments were conducted on a home-built 0.2 T mobile brain scanner for the evaluation of the proposed strategy. 20 healthy volunteers were recruited to participate in the experiment. The results show that the proposed strategy enables the 0.2 T scanner to generate images with sufficient anatomical information and diagnostic value under unshielded conditions using a single sensing coil. In particular, the EMI elimination outperforms the state-of-the-art deep learning methods and numerical computation methods. In addition, 2 × super-resolution (DDSRNet) and denoising (SwinIR) techniques enable further improvements in imaging speed and quality. The proposed strategy enables low-field mobile MRI scanners to achieve fast, high-quality imaging under unshielded conditions using minimal hardware resources, which has great significance for the widespread deployment of low-field mobile MRI scanners.

Reducing hardware requirements for entanglement distribution via joint hardware-protocol optimization

We conduct a numerical investigation of fiber-based entanglement distribution over distances of up to 1600 km using a chain of processing-node quantum repeaters. We determine minimal hardware requirements while simultaneously optimizing over protocols for entanglement generation and entanglement purification, as well as over strategies for entanglement swapping. Notably, we discover that through an adequate choice of protocols the hardware improvement cost scales linearly with the distance covered. Our results highlight the crucial role of good protocol choices in significantly reducing hardware requirements, such as employing purification to meet high-fidelity targets and adopting a swap as soon as possible policy for faster rates. To carry out this analysis, we employ an extensive simulation framework implemented with NetSquid, a discrete-event-based quantum-network simulator, and a genetic-algorithm-based optimization methodology to determine minimal hardware requirements.

Electromechanical Impedance of Acoustic Emission Sensors used for Self-Diagnosis

Acoustic Emission (AE) is a well established NDT method which is used more and more for continuous monitoring, heading towards SHM. Today, AE sensors in the field are tested with a pencil lead break test (Hsu-Nielsen source), which requires manual interaction. Sensors are replaced if the sensitivity is too low. However, since the test is performed on the monitored structure, the structure itself may influence the test. Therefore, a detailed check of the sensor in the lab, according to SE02 and ISO 24543, is done afterwards. An automated solution, that can test AE sensors in the field, without human interaction, that needs minimal additional hardware would benefit AE based monitoring systems. The electromechanical impedance (EMI) spectrum is mostly used for local damage detection with piezoelectric transducers. In the past it has been shown that the EMI spectrum can also be used to enable automated self-diagnostic capabilities in the low frequency spectrum (Park et al., 2004) and in the higher frequency regime (Mueller, 2017) including the eigenfrequencies of the transducers. The usage of EMI for AE sensors can, thorough research in this matter provided, solve the issues mentioned above. This work presents results of EMI spectrum analysis of two sets of 12 and 17 sensors, respectively, with different levels of damage from pressure vessel burst tests. The results are compared to that of a face-2-face inspection, which is an established method of verification in the lab according to ISO 24543. It is shown that the EMI spectrum is capable of similar self-diagnostic capabilities. Thus, combining the advantages of global monitoring with AE sensors, while providing a remote and automatic method of self-diagnostic of AE sensors at any point with minimal additional hardware and software requirements.

Connecting quantum cities: simulation of a satellite-based quantum network

We present and analyze an architecture for a European-scale quantum network using satellite links to connect Quantum Cities, which are metropolitan quantum networks with minimal hardware requirements for the end users. Using NetSquid, a quantum network simulation tool based on discrete events, we assess and benchmark the performance of such a network linking distant locations in Europe in terms of quantum key distribution rates, considering realistic parameters for currently available or near-term technology. Our results highlight the key parameters and the limits of current satellite quantum communication links and can be used to assist the design of future missions. We also discuss the possibility of using high-altitude balloons as an alternative to satellites.

Thickness Measurements with EMAT Based on Fuzzy Logic.

Metal thickness measurements are essential in various industrial applications, yet current non-contact ultrasonic methods face limitations in range and accuracy, hindering the widespread adoption of electromagnetic ultrasonics. This study introduces a novel combined thickness measurement method employing fuzzy logic, with the aim of broadening the applicational scope of the EMAT. Leveraging minimal hardware, this method utilizes the short pulse time-of-flight (TOF) technique for initial thickness estimation, followed by secondary measurements guided by fuzzy logic principles. The integration of measurements from the resonance, short pulse echo, and linear frequency modulation echo extends the measurement range while enhancing accuracy. Rigorous experimental validation validates the method's effectiveness, demonstrating a measurement range of 0.3-1000.0 mm with a median error within ±0.5 mm. Outperforming traditional methods like short pulse echoes, this approach holds significant industrial potential.

Sensitive Discrete Frequency Mid-Infrared Absorption Spectroscopy Using Digitally Referenced Detection.

Broadly tunable mid-infrared (IR) lasers, including quantum cascade lasers (QCL), are an asset for vibrational spectroscopy wherein high-intensity, coherent illumination can target specific spectral bands for rapid, direct chemical detection with microscopic localization. These emerging spectrometers are capable of high measurement throughputs with large detector signals from the high-intensity lasers and fast detection speeds as short as a single laser pulse, challenging the decades old benchmarks of Fourier transform infrared spectroscopy. However, noise in QCL emissions limits the feasible acquisition time for high signal-to-noise ratio (SNR) data. Here, we present an implementation that is broadly compatible with many laser-based spectrometer and microscope designs to address these limitations by leveraging high-speed digitizers and dual detectors to digitally reference each pulse individually. Digitally referenced detection (DRD) is shown to improve measurement sensitivity, with broad spectral indifference, regardless of imbalance due to dissimilarities among system designs or component manufacturers. We incorporated DRD into existing instruments and demonstrated its generalizability: a spectrometer with a 10-fold reduction in spectral noise, a microscope with reduced pixel dwell times to as low as 1 pulse while maintaining SNR normally achieved when operating 8-fold slower, and finally, a spectrometer to measure vibrational circular dichroism (VCD) with a ∼ 4-fold reduction in scan times. The approach not only proves versatile and effective but can also be tailored for specific applications with minimal hardware changes, positioning it as a simple and promising module for spectrometer designs using lasers.