Thresholding Computing with Heterogeneous Integration of Memristive Kernel with Metal-Oxide-Semiconductor Capacitor for Temporal Data Analysis.

Precise event detection within time-series data is increasingly critical, particularly in noisy environments. Reservoir computing, a robust computing method widely utilized with memristive devices, is efficient in processing temporal signals. However, it typically lacks intrinsic thresholding mechanisms essential for precise event detection. This study introduces a new approach by integrating two Pt/HfO2/TiN (PHT) memristors and one Ni/HfO2/n-Si (NHS) metal-oxide-semiconductor capacitor (2M1MOS) to implement a tunable thresholding function. The current-voltage nonlinearity of memristors combined with the capacitance-voltage nonlinearity of the capacitor forms the basis of the 2M1MOS kernel system. The proposed kernel hardware effectively records feature-specified information of the input signal onto the memristors through capacitive thresholding. In electrocardiogram analysis, the memristive response exhibited a more than ten-fold difference between arrhythmia and normal beats. In isolated spoken digit classification, the kernel achieved an error rate of only 0.7% by tuning thresholds for various time-specific conditions. The kernel is also applied to biometric authentication by extracting personal features using various threshold times, presenting more complex and multifaceted uses of heartbeats and voice data as bio-indicators. These demonstrations highlight the potential of thresholding computing in a memristive framework with heterogeneous integration.

Evaluation of a low-cost portable NIRS device for monitoring muscle ischemia.

The main objective of this study is to evaluate the low-cost, open-source HEGduino device as a tissue oximetry monitor to advance the research of somatic NIRS monitoring. Specifically, this study analyzes the use of this portable functional NIRS system for detecting the cessation of blood flow due to vascular occlusion in an upper limb. 19 healthy patients aged between 25 and 50 were recruited and monitored using HEGduino device. Participants underwent a vascular occlusion test on one forearm. Raw values collected by HEGduino as well as the processed variables derived from the measurements were registered. Additional variables to characterize the signal noise during the tests were also recorded. The results of the data distribution curves for all the subjects in the study accurately detected the physiological events associated with transient tissue ischemia. The statistical analysis of the recorded data showed that the difference between the baseline values recorded by the red led (RED) and its normalized minimum variable was always different from zero (p < 0.014). Furthermore, the difference between the normalized baseline values recorded by the infrared led (IR) and the corresponding normalized minimum value was also different from zero (p < 0.001). The R-squared coefficient of determination for the noise variables considered in this study on the normalized RED and IR values was 0.08 and 0.105, respectively. The study confirms the potential of HEGduino system to detect an interruption of the blood flow by means of variations in regional tissue oxygen saturation. This study demonstrates the potential of the HEGduino device as a monitoring alternative to advance the study of the applicability of NIRS in muscle tissue oximetry.

Optimizing Fixation and Biologic Augmentation in Aseptic Femoral Nonunion Management: A Retrospective Case Series.

Nonunion poses significant difficulties for both patients and orthopedic surgeons, often requiring intricate reconstructive surgeries to achieve bone healing and eliminate infections. Surgeons must navigate numerous contributing factors to nonunion, and they also face challenging hardware issues during revision procedures. These issues can include infections, loose or failing hardware, misaligned components, or inappropriate hardware configurations. This case series includes five cases of nonunion femur fractures and the goal is to carefully analyze the best treatment option for treating nonunion. All the cases underwent the removal of whole or part of the hardware followed by bone grafting and attainment of the stable construct with load-sharing devices and augmentation with neutralizing plates. All the cases had a radiological bone union at an average of four to seven months with improvement of Harris Hip Score.

The clinical outcome comparison between trans-syndesmotic fixation and anatomic deltoid ligament repair in unstable ankle fractures with medial clear space widening: A systematic review and meta-analysis.

BACKGROUND: Due to the variability in evidence supporting either trans-syndesmosis fixation or deltoid ligament repair in unstable ankle fractures with medical clear space (MCS) widening makes it unclear which surgical technique leads to the best patient outcomes. The goal of our systematic review and meta-analysis was to compare clinical outcomes of trans-syndesmotic fixation versus anatomic deltoid ligament repair in the management of unstable ankle fractures with MCS widening. METHODS: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were utilized in this study. A comprehensive and systematic search was conducted using the PubMed, Embase, Web of Science and Cochrane Library databases. Outcomes investigated in this review included the rates of syndesmotic malreduction, removal of hardware, postoperative complications including wound issues, and functional/pain scores. RESULTS: A total of five level-3 studies were selected in this review, with 280 unstable ankle fractures with MCS widening: 165 for the trans-syndesmotic fixation group and 115 for the anatomic deltoid ligament repair group. Three out of five studies evaluated syndesmotic malreduction using CT. Compared to the trans-syndesmosis fixation group, the deltoid repair group showed significant lower rates of syndesmotic malreduction rates and removal of hardware: 6.5 % (4/61) Vs. 27 % (16/59) (RR=0.26, 95 % CI=[0.10, 0.68]), and 2.6 % (3/115) Vs.54.5 % (90/165) (RR=0.06, CI=[0.02, 0.14]), respectively. No significant differences were found between the two groups in postoperative wound complications, reoperations, and functional scores including AOFAS and VAS pain score. CONCLUSIONS: Based on our findings, anatomic deltoid ligament repair was associated with a lower rate of syndesmotic malreduction and the need for hardware removal while there was no significant difference in terms of postoperative wound complications, reoperation, AOFAS score, or VAS pain score. These results should be interpreted with caution due to limitations related to heterogeneity among the studies. Further high-level RCTs with larger sample sizes are necessary to establish a robust consensus.

A robust deep learning attack immune MRAM-based physical unclonable function.

The ubiquitous presence of electronic devices demands robust hardware security mechanisms to safeguard sensitive information from threats. This paper presents a physical unclonable function (PUF) circuit based on magnetoresistive random access memory (MRAM). The circuit utilizes inherent characteristics arising from fabrication variations, specifically magnetic tunnel junction (MTJ) cell resistance, to produce corresponding outputs for applied challenges. In contrast to Arbiter PUF, the proposed effectively satisfies the strict avalanche criterion (SAC). Additionally, the grid-like structure of the proposed circuit preserves its resistance against machine learning-based modeling attacks. Various machine learning (ML) attacks employing multilayer perceptron (MLP), linear regression (LR), and support vector machine (SVM) networks are simulated for two-array and four-array architectures. The MLP-attack prediction accuracy was 53.61% for a two-array circuit and 49.87% for a four-array circuit, showcasing robust performance even under the worst-case process variations. In addition, deep learning-based modeling attacks in considerable high dimensions utilizing multiple networks such as convolutional neural network (CNN), recurrent neural network (RNN), MLP, and Larq are used with the accuracy of 50.31%, 50.25%, 50.31%, and 50.31%, respectively. The efficiency of the proposed circuit at the layout level is also investigated for simplified two-array architecture. The simulation results indicate that the proposed circuit offers intra and inter-hamming distance (HD) with a mean of 0.98% and 49.96%, respectively, and a mean diffuseness of 49.09%.

Distal Junctional Failure: A Feared Complication of Multilevel Posterior Spinal Fusions.

Objectives: Distal junctional failure (DJF) is less commonly described than proximal junctional failure following posterior spinal fusion, and particularly adult spinal deformity (ASD) surgery. We describe a case series of patients with DJF, taking into account sagittal spinopelvic alignment, and suggest potential risk factors in light of the current literature. Methods: We performed a single-center, retrospective review of posterior spinal fusion patients with DJF who underwent subsequent revision surgery between June 2009 and January 2019. Demographics and surgical details were collected. Radiographical measurements included the following: preoperative and postoperative sagittal and coronal alignment for each index or revision surgery. The upper-instrumented vertebra (UIV), lower instrumented vertebra (LIV), and fusion length were recorded. Results: Nineteen cases (64.7 ± 13.5 years, 12 women, seven men) were included. The mean follow-up was 4.7 ± 2.4 years. The number of instrumented levels was 6.79 ± 2.97. Among the patients, 84.2% (n = 16) presented at least one known DJF risk factor. LIV was frequently L5 (n = 10) or S1 (n = 2). Six patients had an initial circumferential fusion at the distal end. Initial DJFs were vertebral fracture distal to the fusion (n = 5), screw pull-out (n = 9), spinal stenosis (n = 4), instability (n = 4), and one early DJK. The distal mechanical complications after a first revision included screw pull-out (n = 4), screw fracture (n = 3), non-union (n = 2), and an iatrogenic spondylolisthesis. Conclusions: In this case series, insufficient sagittal balance restoration, female gender, osteoporosis, L5 or S1 LIV in long constructs were associated with DJF. Restoring spinal balance and circumferentially fusing the base of constructs represent key steps to maintain correction and prevent revisions.

Hardware-Efficient Configurable Ring-Oscillator-Based Physical Unclonable Function/True Random Number Generator Module for Secure Key Management.

The use of physical unclonable functions (PUFs) linked to the manufacturing process of the electronic devices supporting applications that exchange critical data over the Internet has made these elements essential to guarantee the authenticity of said devices, as well as the confidentiality and integrity of the information they process or transmit. This paper describes the development of a configurable PUF/TRNG module based on ring oscillators (ROs) that takes full advantage of the structure of modern programmable devices offered by Xilinx 7 Series families. The proposed architecture improves the hardware efficiency with two main objectives. On the one hand, we perform an exhaustive statistical characterization of the results derived from the exploitation of RO configurability. On the other hand, we undertake the development of a new version of the module that requires a smaller amount of resources while considerably increasing the number of output bits compared to other proposals previously reported in the literature. The design as a highly parameterized intellectual property (IP) module connectable through a standard interface to a soft- or hard-core general-purpose processor greatly facilitates its integration into embedded solutions while accelerating the validation and characterization of this element on the same electronic device that implements it. The studies carried out reveal adequate values of reliability, uniqueness, and unpredictability when the module acts as a PUF, as well as acceptable levels of randomness and entropy when it acts as a true random number generator (TRNG). They also illustrate the ability to obfuscate and recover identifiers or cryptographic keys of up to 4096 bits using an implementation of the PUF/TRNG module that requires only an array of 4×4 configurable logic blocks (CLBs) to accommodate the RO bank.

A Comprehensive Review of Hardware Acceleration Techniques and Convolutional Neural Networks for EEG Signals.

This paper comprehensively reviews hardware acceleration techniques and the deployment of convolutional neural networks (CNNs) for analyzing electroencephalogram (EEG) signals across various application areas, including emotion classification, motor imagery, epilepsy detection, and sleep monitoring. Previous reviews on EEG have mainly focused on software solutions. However, these reviews often overlook key challenges associated with hardware implementation, such as scenarios that require a small size, low power, high security, and high accuracy. This paper discusses the challenges and opportunities of hardware acceleration for wearable EEG devices by focusing on these aspects. Specifically, this review classifies EEG signal features into five groups and discusses hardware implementation solutions for each category in detail, providing insights into the most suitable hardware acceleration strategies for various application scenarios. In addition, it explores the complexity of efficient CNN architectures for EEG signals, including techniques such as pruning, quantization, tensor decomposition, knowledge distillation, and neural architecture search. To the best of our knowledge, this is the first systematic review that combines CNN hardware solutions with EEG signal processing. By providing a comprehensive analysis of current challenges and a roadmap for future research, this paper provides a new perspective on the ongoing development of hardware-accelerated EEG systems.

Deep Learning for Automated Classification of Hip Hardware on Radiographs.

PURPOSE: To develop a deep learning model for automated classification of orthopedic hardware on pelvic and hip radiographs, which can be clinically implemented to decrease radiologist workload and improve consistency among radiology reports. MATERIALS AND METHODS: Pelvic and hip radiographs from 4279 studies in 1073 patients were retrospectively obtained and reviewed by musculoskeletal radiologists. Two convolutional neural networks, EfficientNet-B4 and NFNet-F3, were trained to perform the image classification task into the following most represented categories: no hardware, total hip arthroplasty (THA), hemiarthroplasty, intramedullary nail, femoral neck cannulated screws, dynamic hip screw, lateral blade/plate, THA with additional femoral fixation, and post-infectious hip. Model performance was assessed on an independent test set of 851 studies from 262 patients and compared to individual performance of five subspecialty-trained radiologists using leave-one-out analysis against an aggregate gold standard label. RESULTS: For multiclass classification, the area under the receiver operating characteristic curve (AUC) for NFNet-F3 was 0.99 or greater for all classes, and EfficientNet-B4 0.99 or greater for all classes except post-infectious hip, with an AUC of 0.97. When compared with human observers, models achieved an accuracy of 97%, which is non-inferior to four out of five radiologists and outperformed one radiologist. Cohen's kappa coefficient for both models ranged from 0.96 to 0.97, indicating excellent inter-reader agreement. CONCLUSION: A deep learning model can be used to classify a range of orthopedic hip hardware with high accuracy and comparable performance to subspecialty-trained radiologists.

Evaluating the duration of antimicrobial therapy for the treatment of orthopedic hardware infections.

The optimal duration of antimicrobial therapy for orthopedic hardware infections is unclear. We identified 216 patients with orthopedic hardware infections, of whom 42 (19%) later had relapsed infection. Chronic suppressive antimicrobial therapy beyond 12 weeks was not significantly associated with lower odds of relapse.IMPORTANCEThere is debate about how long to continue antibiotics after initial treatment of bone and joint infections when hardware remains in place. This study found no benefit from continuing antibiotics longer than 12 weeks when trying to prevent recurrent infection.

Staged complex reconstruction of infected thoracic aortic endograft and adjacent spinal hardware using latissimus wrapped lateral aortic graft.

Thoracic endovascular aortic repair (TEVAR) enables rapid and effective treatment of life-threatening aortic injuries. The occurrence of long-term complications from TEVAR and their management is ill-defined in young patients. This report describes a complex case of a 38-year-old male patient who underwent staged interventions for different acute pathologies instigated by blunt thoracic spinal trauma. The patient was initially treated with a TEVAR for aortic pseudoaneurysm in the setting of infected spinal hardware, which later resulted in an aortobronchial fistula and eroded spinal hardware. This report illustrates a successful multidisciplinary approach for definitive treatment with graft explant and aortic reconstruction.

Bio-mimicking DNA fingerprint profiling for HLS watermarking to counter hardware IP piracy.

The multifaceted, multivendor-based global design supply chain induces hardware threats of intellectual property (IP) piracy for modern computing and electronic systems. Current hardware watermarking techniques fall short either in terms of watermark strength (size of covert constraints generated) or number of security layers/variables involved in the security constraints generation process. This paper presents a novel approach for high level synthesis (HLS) watermarking by bio-mimicking DNA fingerprint profiling to counter hardware IP piracy. The proposed approach effectively captures the vital DNA fingerprint profiling phases such as DNA sequencing, DNA fragmentation, fragment replication, DNA ligase, etc. and bio-mimics them to generate a digital watermarking framework. The presented approach has been demonstrated on convolutional layer and JPEG compression-decompression (CODEC) algorithms that are widely used in several medical and machine learning applications. The proposed approach has been thoroughly compared with several state-of-the-art approaches. The proposed approach depicts superior security in the probability of coincidence of up to ~ 104 and tamper tolerance of up to ~ 10368 at 0% overhead as compared to the prior approaches.

Design and implementation of Type-3 intuitionistic fuzzy logic MPPT controller for PV solar system: Comparative study.

The performance of a photovoltaic (PV) solar system is affected by partial shading conditions (PSC) and environmental conditions, such as solar irradiance and ambient temperature, which vary throughout the day. This results in variations in the maximum power point (MPP) on the solar PV output characteristic curve. Therefore, various classical MPP tracking (MPPT) techniques have been used to track the MPP and extract maximum power from PV systems. However, these techniques have drawbacks such as lower stability, increased oscillation around the steady state, and slower convergence to the MPP. To overcome this problem, the newly proposed interval Type-3 intuitionistic fuzzy logic (T3IFL) controller has been proposed. The T3IFL MPPT controller combines the uncertainty of Type-3 fuzzy logic (T3FL) controller with intuitionistic concepts. The T3IFL controller is more accurate and offers faster convergence to the MPP under changing climatic and steady-state conditions than classical techniques and T3FL controller. The T3IFL algorithm provides better performance with excellent MPP tracking by controlling the duty cycle of the DC-DC buck converter. Four cases studied were investigated: uniform radiation conditions, a step change in solar radiation with constant temperature, replacing the battery load with the ohmic load with constant radiation and temperature, and partial shading conditions. Experimental validation of the T3IFL was performed on a DC-DC buck converter using real-time hardware-in-the-loop (HIL). Finally, the simulation and experimental results with comparative studies verified the accuracy of the proposed method in tracking the desired value and disturbance/uncertainty attenuation with better response.

Complex trauma sequelae: Mycobacterium goodii and Priestia endophytica Hardware infection in a patient with Ehlers-Danlos syndrome.

A 26-year-old man with Ehlers-Danlos syndrome, recurrent otitis externa, and chronic otitis media sustained a left lower extremity amputation and open femur fracture with internal hardware fixation after a motor vehicle collision in Arizona. He presented to the emergency department at our institution with severe left leg pain and purulent discharge despite receiving two unidentified antibiotics upon discharge. Evaluations revealed an abscess and malunion of the femur. Initial cultures yielded scant Priestia endophytica, leading to daptomycin treatment. His condition worsened until Gram-positive bacilli identified as Mycobacterium goodii, a rare nosocomial mycobacterial species, were found. Significant improvement occurred with appropriate antibiotics. This case highlights the challenges in diagnosing and managing M. goodii infections in immunocompromised patients with orthopedic complications and notes P. endophytica as a previously unreported, possibly opportunistic human pathogen.

Verification and Validation of Advanced Control Systems for a Spinal Joint Wear Simulator.

Wear simulation aims to assess wear rates and their dependence on factors like load, kinematics, temperature, and implant orientation. Despite its significance, there is a notable gap in research concerning advancements in simulator control systems and the testing of clinically relevant waveforms. This study addresses this gap by focusing on enhancing the conventional proportional-integral-derivative (PID) controller used in joint simulators through the development of a fuzzy logic-based controller. Leveraging a single-input multiple-output (SIMO) fuzzy logic control system, this study aimed to improve displacement control, augmenting the traditional proportional-integral (PI) tuning approach. The implementation and evaluation of a novel Fuzzy-PI control algorithm were conducted on the Leeds spine wear simulator. This study also included the testing of dailyliving (DL) profiles, particularly from the hip joint, to broaden the scope of simulation scenarios. While both the conventional PI controller and the Fuzzy-PI controller met ISO tolerance criteria for the spine flexion-extension (FE) profile at 1 Hz, the Fuzzy-PI controller demonstrated superior performance at higher frequencies and with DL profiles due to its real-time adaptive tuning capability. The Fuzzy-PI controller represents a significant advancement in joint wear simulation, offering improved control functionalities and more accurate emulation of real-world physiological dynamics.

Human-heart-model for hardware-in-the-loop testing of pacemakers.

To guarantee the safety of medical devices, including embedded systems, it is essential to consider both electronic components and the natural environment during validation and verification. In contrast to prior research, we present a hardware-in-the-loop environment that connects a real medical system to a biological model in real time for validation, including the modeling of the mechanical component of the heart valves in addition to the modeling of the electrical conduction and electrical stimulation of the heart chambers. Our model accounts for the dynamic adaptation of the temporal processes in the heart chambers to the pacing frequency of the individual chambers as a function of the action potential. This study investigates two additional risk factors affecting the heart under different conditions: pacemaker syndrome and electrical stimulation during the vulnerable phase. Both can be life-threatening to the patient if left untreated. In implementing our concept on a physical pacemaker connected to our software-based model of the heart, we discovered that the test pacemaker was unable to generate the required heart rate in three of the scenarios we tested. Additionally, our tests revealed occurrences of pacemaker syndrome and stimulation in the vulnerable phase.

Sensorizing a Beehive: A Study on Potential Embedded Solutions for Internal Contactless Monitoring of Bees Activity.

Winter is the season of main concern for beekeepers since the temperature, humidity, and potential infection from mites and other diseases may lead the colony to death. As a consequence, beekeepers perform invasive checks on the colonies, exposing them to further harm. This paper proposes a novel design of an instrumented beehive involving color cameras placed inside the beehive and at the bottom of it, paving the way for new frontiers in beehive monitoring. The overall acquisition system is described focusing on design choices towards an effective solution for internal, contactless, and stress-free beehive monitoring. To validate our approach, we conducted an experimental campaign in 2023 and analyzed the collected images with YOLOv8 to understand if the proposed solution can be useful for beekeepers and what kind of information can be derived from this kind of monitoring, including the presence of Varroa destructor mites inside the beehive. We experimentally found that the observation point inside the beehive is the most challenging due to the frequent movements of the bees and the difficulties related to obtaining in-focus images. However, from these images, it is possible to find Varroa destructor mites. On the other hand, the observation point at the bottom of the beehive showed great potential for understanding the overall activity of the colony.

Real-time hardware emulation of neural cultures: A comparative study of in vitro, in silico and in duris silico models.

Biological neural networks are well known for their capacity to process information with extremely low power consumption. Fields such as Artificial Intelligence, with high computational costs, are seeking for alternatives inspired in biological systems. An inspiring alternative is to implement hardware architectures that replicate the behavior of biological neurons but with the flexibility in programming capabilities of an electronic device, all combined with a relatively low operational cost. To advance in this quest, here we analyze the capacity of the HEENS hardware architecture to operate in a similar manner as an in vitro neuronal network grown in the laboratory. For that, we considered data of spontaneous activity in living neuronal cultures of about 400 neurons and compared their collective dynamics and functional behavior with those obtained from direct numerical simulations (in silico) and hardware implementations (in duris silico). The results show that HEENS is capable to mimic both the in vitro and in silico systems with high efficient-cost ratio, and on different network topological designs. Our work shows that compact low-cost hardware implementations are feasible, opening new avenues for future, highly efficient neuromorphic devices and advanced human-machine interfacing.

Computer-controlled electrical stimulation of facial muscles by facial neuromuscular electrical stimulation (fNMES): Hardware and software solutions.

BACKGROUND: Computer controlled electrical stimulation of facial muscles is a promising method to study facial feedback effects, though little guidance is available for new adopters. NEW METHOD: Facial Neuromuscular Electrical Stimulation (fNMES) offers a spatially and temporally precise means of manipulating facial muscles during experiments, and can be combined with EEG to study the neurological basis of facial feedback effects. Precise delivery of stimulation requires hardware and software solutions to integrate stimulators and a stimulus-presenting computer. We provide open-source hardware schematics and relevant computer code in order to achieve this integration, so as to facilitate the use of fNMES in the laboratory. RESULTS: Hardware schematics are provided for the building of a bespoke control module, which allows researchers to finely control stimulator output whilst participants complete computer tasks. In addition, we published code that new adopters of NMES can use within their experiments to control the module and send event triggers to another computer. These hard- and software solutions were successfully used to investigate the effects of facial muscle activation on felt and perceived emotion. We summarise these findings and discuss the integration of fNMES with EEG and peripheral physiological measures. COMPARISON WITH EXISTING METHODS: Our inexpensive hardware solution allows fNMES parameters to be computer controlled, and thus allows to stimulate facial muscles with high precision. This opens up new possibilities to investigate, for example, facial feedback effects. CONCLUSIONS: We provide tools and guidance to build a control module in order to precisely deliver electrical stimulation to facial muscles using a stimulus computer (while recording EEG or other peripheral physiology).