Advances In Hardware Research Articles

Design and Research of a Sound-to-RGB Smart Acoustic Device

This paper presents a device that converts sound wave frequencies into colors to assist people with hearing problems in solving accessibility and communication problems in the hearing-impaired community. The device uses a precise mathematical apparatus and carefully selected hardware to achieve accurate conversion of sound to color, supported by specialized automatic processing software suitable for standardization. Experimental evaluation shows excellent performance for frequencies below 1000 Hz, although limitations are encountered at higher frequencies, requiring further investigation into advanced noise filtering and hardware optimization. The device shows promise for various applications, including education, art, and therapy. The study acknowledges its limitations and suggests future research to generalize the models for converting sound frequencies to color and improving usability for a broader range of hearing impairments. Feedback from the hearing-impaired community will play a critical role in further developing the device for practical use. Overall, this innovative device for converting sound to color represents a significant step toward improving accessibility and communication for people with hearing challenges. Continued research offers the potential to overcome challenges and extend the benefits of the device to a variety of areas, ultimately improving the quality of life for people with hearing impairments.

A systematic review of exploratory factor analysis packages in R software

AbstractThe increasing prevalence of exploratory factor analysis (EFA) applications in scholarly literature reflects its popularity and the convenience of computer‐assisted analysis. With advancements in computer hardware and software, the complexity and variations of EFA analysis have also grown. Despite the availability of sophisticated computer programming, the appropriate utilization of EFA necessitates users to make informed judgments. Additionally, users are responsible for searching and identifying suitable statistical software to accommodate their data and analysis requirements. This review aims to enhance understanding of the EFA technique and summarize the analysis options available for EFA in R packages. A total of 50 packages were examined in this study. Specifically, the review focuses on (1) diagnostic functions, (2) factor extraction, (3) factor retention, (4) factor rotation, and (5) complex data and technique features provided by these packages. The review summarizes the available function options in R packages by outlining these five crucial steps in conducting an EFA analysis. This synthesis offers an overview of the similarities and distinctive features of each package, serving as a valuable resource for users in selecting a suitable EFA technique. It is important to note that there is no definitive approach to conducting an exploratory factor analysis. Users need to deliberately select and combine appropriate techniques to achieve optimal results.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Dimension Reduction Software for Computational Statistics > Software/Statistical Software

Diastology with Cardiac MRI: A Practical Guide.

Diastolic filling of the ventricle is a complex interplay of volume and pressure, contingent on active energy-dependent myocardial relaxation and myocardial stiffness. Abnormal diastolic function is the hallmark of the clinical entity of heart failure with preserved ejection fraction (HFpEF), which is now the dominant type of heart failure and is associated with significant morbidity and mortality. Although echocardiography is the current first-line imaging modality used in evaluation of diastolic function, cardiac MRI (CMR) is emerging as an important technique. The principal role of CMR is to categorize the cause of diastolic dysfunction (DD) and distinguish other entities that manifest similarly to HFpEF, particularly infiltrative and pericardial disorders. CMR also provides prognostic information and risk stratification based on late gadolinium enhancement and parametric mapping techniques. Advances in hardware, sequences, and postprocessing software now enable CMR to diagnose and grade DD accurately, a role traditionally assigned to echocardiography. Two-dimensional or four-dimensional velocity-encoded phase-contrast sequences can measure flow and velocities at the mitral inflow, mitral annulus, and pulmonary veins to provide diastolic functional metrics analogous to those at echocardiography. The commonly used cine steady-state free-precession sequence can provide clues to DD including left ventricular mass, left ventricular filling curves, and left atrial size and function. MR strain imaging provides information on myocardial mechanics that further aids in diagnosis and prognosis of diastolic function. Research sequences such as MR elastography and MR spectroscopy can help evaluate myocardial stiffness and metabolism, respectively, providing additional insights on diastolic function. The authors review the physiology of diastolic function, mechanics of diastolic heart failure, and CMR techniques in the evaluation of diastolic function. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

TPCx-AI - An Industry Standard Benchmark for Artificial Intelligence and Machine Learning Systems

Artificial intelligence (AI) and machine learning (ML) techniques have existed for years, but new hardware trends and advances in model training and inference have radically improved their performance. With an ever increasing amount of algorithms, systems, and hardware solutions, it is challenging to identify good deployments even for experts. Researchers and industry experts have observed this challenge and have created several benchmark suites for AI and ML applications and systems. While they are helpful in comparing several aspects of AI applications, none of the existing benchmarks measures end-to-end performance of ML deployments. Many have been rigorously developed in collaboration between academia and industry, but no existing benchmark is standardized. In this paper, we introduce the TPC Express Benchmark for Artificial Intelligence (TPCx-AI), the first industry standard benchmark for end-to-end machine learning deployments. TPCx-AI is the first AI benchmark that represents the pipelines typically found in common ML and AI workloads. TPCx-AI provides a full software kit, which includes data generator, driver, and two full workload implementations, one based on Python libraries and one based on Apache Spark. We describe the complete benchmark and show benchmark results for various scale factors. TPCx-AI's core contributions are a novel unified data set covering structured and unstructured data; a fully scalable data generator that can generate realistic data from GB up to PB scale; and a diverse and representative workload using different data types and algorithms, covering a wide range of aspects of real ML workloads such as data integration, data processing, training, and inference.

Optical Coherence Tomography and Optical Coherence Tomography Angiography: Essential Tools for Detecting Glaucoma and Disease Progression.

Early diagnosis and detection of disease progression are critical to successful therapeutic intervention in glaucoma, the leading cause of irreversible blindness worldwide. Optical coherence tomography (OCT) is a non-invasive imaging technique that allows objective quantification in vivo of key glaucomatous structural changes in the retina and the optic nerve head (ONH). Advances in OCT technology have increased the scan speed and enhanced image quality, contributing to early glaucoma diagnosis and monitoring, as well as the visualization of critically important structures deep within the ONH, such as the lamina cribrosa. OCT angiography (OCTA) is a dye-free technique for noninvasively assessing ocular microvasculature, including capillaries within each plexus serving the macula, peripapillary retina and ONH regions, as well as the deeper vessels of the choroid. This layer-specific assessment of the microvasculature has provided evidence that retinal and choroidal vascular impairments can occur during early stages of glaucoma, suggesting that OCTA-derived measurements could be used as biomarkers for enhancing detection of glaucoma and its progression, as well as to reveal novel insights about pathophysiology. Moreover, these innovations have demonstrated that damage to the macula, a critical region for the vision-related quality of life, can be observed in the early stages of glaucomatous eyes, leading to a paradigm shift in glaucoma monitoring. Other advances in software and hardware, such as artificial intelligence-based algorithms, adaptive optics, and visible-light OCT, may further benefit clinical management of glaucoma in the future. This article reviews the utility of OCT and OCTA for glaucoma diagnosis and disease progression detection, emphasizes the importance of detecting macula damage in glaucoma, and highlights the future perspective of OCT and OCTA. We conclude that the OCT and OCTA are essential glaucoma detection and monitoring tools, leading to clinical and economic benefits for patients and society.

European Respiratory Society/American Thoracic Society technical statement: standardisation of the measurement of lung volumes, 2023 update.

This document updates the 2005 European Respiratory Society (ERS) and American Thoracic Society (ATS) technical standard for the measurement of lung volumes. The 2005 document integrated the recommendations of an ATS/ERS task force with those from an earlier National Heart, Lung, and Blood Institute workshop that led to the publication of background papers between 1995 and 1999 and a consensus workshop report with more in-depth descriptions and discussion. Advancements in hardware and software, new research and emerging approaches have necessitated an update to the 2005 technical standard to guide laboratory directors, physiologists, operators, pulmonologists and manufacturers. Key updates include standardisation of linked spirometry, new equipment quality control and validation recommendations, generalisation of the multiple breath washout concept beyond nitrogen, a new acceptability and grading system with addition of example tracings, and a brief review of imaging and other new techniques to measure lung volumes. Future directions and key research questions are also noted.

Deep transfer learning‐based approach for detection of cracks on eggs

AbstractEggs are the most nutrient‐rich and protein‐dense food that is affordable and consumed by most of the population. But cracks on the eggshells can pave the way for microbial contamination of the eggs posing threat to the health of the consumers. Therefore, it is crucial for the egg industries to check for the quality of eggs before reaching consumers to avoid outbreaks. Automatic visual inspection is one of the techniques that has gained popularity in the field of eggshell crack identification because of the advancements in hardware and deep learning. However, due to the limited training data, classifying eggs using eggshell crack images is challenging. Therefore, deep transfer learning was employed in this study using a pre‐trained DenseNet121 architecture in three ways—fine‐tuning (FT), feature extraction, and training deep features on machine learning classifiers. The results revealed that the FT technique performed best among the three approaches with 98.38% accuracy.Practical ApplicationsDeep transfer learning‐based techniques in food quality assessments are seeing a rise in the food industry. Eggshell crack detection is one of the challenges that can be addressed using the transfer learning approach. The purpose of this study was to investigate the effects of different transfer learning approaches in classifying the image patches extracted from egg images and determine the most effective technique to identify cracks on eggs, despite the difficulty of the classification task using a small data set of egg images. These results can promote the application of transfer learning in non‐destructive egg quality evaluation in the egg processing industries.

Heart Rate Estimation from Facial Image Sequences of a Dual-Modality RGB-NIR Camera.

This paper presents an RGB-NIR (Near Infrared) dual-modality technique to analyze the remote photoplethysmogram (rPPG) signal and hence estimate the heart rate (in beats per minute), from a facial image sequence. Our main innovative contribution is the introduction of several denoising techniques such as Modified Amplitude Selective Filtering (MASF), Wavelet Decomposition (WD), and Robust Principal Component Analysis (RPCA), which take advantage of RGB and NIR band characteristics to uncover the rPPG signals effectively through this Independent Component Analysis (ICA)-based algorithm. Two datasets, of which one is the public PURE dataset and the other is the CCUHR dataset built with a popular Intel RealSense D435 RGB-D camera, are adopted in our experiments. Facial video sequences in the two datasets are diverse in nature with normal brightness, under-illumination (i.e., dark), and facial motion. Experimental results show that the proposed method has reached competitive accuracies among the state-of-the-art methods even at a shorter video length. For example, our method achieves MAE = 4.45 bpm (beats per minute) and RMSE = 6.18 bpm for RGB-NIR videos of 10 and 20 s in the CCUHR dataset and MAE = 3.24 bpm and RMSE = 4.1 bpm for RGB videos of 60-s in the PURE dataset. Our system has the advantages of accessible and affordable hardware, simple and fast computations, and wide realistic applications.

All-Optical Signal Processing in Ultrafast Optical Communication

Optical communication technology plays a crucial role in today's information age, as a result of the continuous advancement of social informatization. An array of nascent technologies, encompassing edge computing, the Internet of Things, big data, and artificial intelligence, have grown rapidly, leading to an increased demand for information transmission, which in turn has resulted in higher requirements for the development of optical communication technology. The emergence of ultrafast optical communication, which uses all-optical signal processing technology, has made data transmission faster and more reliable. This article introduces three basic methods for implementing all-optical signal processing (optical modulation, regeneration, and interconnection) based on all-optical and electronic signal processing. It examines the issues and difficulties that arise within particular application contexts and explores the progression of several correlated technologies in the times ahead. The field of all-optical signal processing is experiencing rapid growth and has the potential to bring revolutionary changes to various industries. Anticipated outcomes entail the attainment of ultra-long distance and ultra-high-capacity transmission, as facilitated by the ongoing advancements in hardware and all-optical signal processing technology. Meanwhile, the relentless pursuit of high-performance goals drives the continuous progress of ultrafast optical communication.

Fuzzy Hardware Tool: An Adaptable Tool to Facilitate the Implementation of Fuzzy Inference Systems in Hardware

A Fuzzy Inference System (FIS) is a system that represents human reasoning based on fuzzy if-then rules to extract valid results from imprecise or uncertain information. These systems have been applied to various engineering fields, such as control and image recognition, and have encouraged the search for development tools that provide facilities for the design, simulation, and implementation of FISs. Most of the available software tools for the design of FISs have been developed by the scientific community, and very few are commercial tools. According to the state of the technology, the fuzzy systems implemented in hardware have shown higher performance than software implementations. For this reason, different strategies for automatic hardware synthesis of FISs have been proposed in the literature. The available tools for FIS implementation, including MATLAB, Simulink, and Xfuzzy environment, require advanced hardware knowledge, and it is necessary to synthesize and implement the program on FPGA each time a change is made to the code. This paper presents a novel approach for the implementation of fuzzy systems on hardware devices that enables the direct implementation of FISs through a graphical user interface. The VHDL code is pre-synthesized and integrated with the interface, allowing users to experiment with different input parameters, such as membership functions and fuzzy rules, without resynthesizing the code on an FPGA. To verify the effectiveness of the proposed approach, experiments were conducted on two similar System-on-Chip (SoC) devices, VEEK-MT and VEEK-MT2. The results showed that the proposed solution represents a significant advancement in the study of fuzzy systems on hardware devices, providing a flexible and user-friendly approach to their design and implementation.

OMPQ: Orthogonal Mixed Precision Quantization

To bridge the ever-increasing gap between deep neural networks' complexity and hardware capability, network quantization has attracted more and more research attention. The latest trend of mixed precision quantization takes advantage of hardware's multiple bit-width arithmetic operations to unleash the full potential of network quantization. However, existing approaches rely heavily on an extremely time-consuming search process and various relaxations when seeking the optimal bit configuration. To address this issue, we propose to optimize a proxy metric of network orthogonality that can be efficiently solved with linear programming, which proves to be highly correlated with quantized model accuracy and bit-width. Our approach significantly reduces the search time and the required data amount by orders of magnitude, but without a compromise on quantization accuracy. Specifically, we achieve 72.08% Top-1 accuracy on ResNet-18 with 6.7Mb parameters, which does not require any searching iterations. Given the high efficiency and low data dependency of our algorithm, we use it for the post-training quantization, which achieves 71.27% Top-1 accuracy on MobileNetV2 with only 1.5Mb parameters.

Unit middleware for implementation of human–machine interconnection intelligent ecology construction

General speech recognition models require large capacity and strong computing power. Based on small capacity and low computing power to realize speech analysis and semantic recognition is a research area with great challenges for constructing intelligent ecology of the Internet of Things. For this purpose, we set up the unit middleware for the implementation of human–machine interconnection, namely human–machine interaction based on phonetics and semantics control for constructing intelligent ecology of the Internet of Things. First, through calculation, theoretical derivation and verification we present a kind of novel deep hybrid intelligent algorithm, which has realized speech analysis and semantic recognition. Second, it is to establish unit middleware using the embedded chip as the core on the motherboard. Third, it is to develop the important auxiliary tools writer-burner and cross-compiler. Fourth, it is to prune procedures and system, download, burn and write the algorithms and codes into the unit middleware and cross-compile. Fifth, it is to expand the functions of the motherboard, provide more components and interfaces, for example including RFID(Radio Frequency Identification, RFID), ZigBee, Wi-Fi, GPRS(General Packet Radio Services, GPRS), RS-232 serial port, USB(Universal Serial Bus, USB) interfaces and so on. Sixth, we take advantage of algorithms, software and hardware to make machines "understand" human speech and "think" and "comprehend" human intentions so as to implement human–machine interconnection, which further structure the intelligent ecology of the Internet of Things. At last, the experimental results denote that the unit middleware have very good effect, fast recognition speed, high accuracy and good stability, consequently realizing the intelligent ecology construction of the Internet of Things.

Assessment of Bitcoin carbon footprint

Bitcoin is a breakthrough financial technology but a volatile asset in financial markets with a complex fundamental consensus algorithm (Proof-of-Work) limiting its large-scale adoption due to environmental-related issues. Hitherto, the role of its technical and infrastructural composition that drives carbon footprint from an ecological perspective is rarely discussed in the literature. Here, we use machine learning and econometric techniques to analyze the past, present, and future changes in Bitcoin's carbon footprint with daily data spanning July 18, 2010 to December 04, 2021. We document technical drivers, decomposition effects, causal nexus, and implications of the Bitcoin blockchain's increasing energy and carbon footprint. We show that Bitcoin's technical drivers could have potential impacts on Bitcoin's carbon footprint, and subsequently, global climate change. For example, the network's hashrate increases mining difficulty––thereby increasing Bitcoin's energy consumption and subsequently, carbon footprint. We observed a direct association between the marginal effect of block size and transaction count––implying that a higher block size improves transaction efficiency and then reduces Bitcoin's energy and carbon footprint. Besides, low mining difficulty increases market capitalization whereas increasing mining difficulty reduces bitcoin mining profit in the long run. This infers the reward for mining Bitcoin has a diminishing return in the long term. Thus, the adoption of advanced hardware for Bitcoin mining will spur energy and carbon intensity, yet will have a low return on investment. We highlight environmental regulations and regulatory changes that could limit Bitcoin's carbon footprint.

Precision orthodontics

Precision medicine (PM) is an emerging approach that advocates tailoring healthcare by personalising medical decisions, treatments, behaviours, or goods to a subset of individuals rather than employing a one-drug-fits-all strategy. Molecular diagnostics, imaging, and analytics are examples of precision medicine tools. Precision medical advances foreshadowed comparable advancements in orthodontics, and they can be used to produce tailored treatment approaches and improve treatment efficiency. This article presents an overview of recent developments in computer technology and biomedicine, as well as their current and expected future uses in precision orthodontics. It is necessary to conduct an evaluation of orthodontically relevant technology and developments in relevant biological research. Within the next few decades, advances in computer software and hardware, as well as 3D imaging technology, will offer for more individualised therapy and biomechanical planning. The prospects for personalised orthodontic bracket’s manufacturing with adequate material qualities and digital technology in the future are both appealing and persuasive.

Omnidirectional water wave-driven triboelectric net-zero power smart ocean network: An advanced hardware solution to long-distance target detection

Collecting ocean energy to build a zero-power smart ocean network that integrates energy harvesting and application is of great significance for achieving the strategy of marine power. This demands a hardware solution with simple structure, high energy efficiency, and high resolving ability. Herein, we proposed a simple fully symmetric triboelectric nanogenerator (FS-TENG) for efficient collection of omnidirectional wave energy without dead spot, based on the 360 degrees of freedom of inertial ball and the simplified double-layer electrode structure responding to arbitrary triggers. Employing the Maxwell’s displacement current generated by FS-TENG, power-free underwater wireless detection was achieved to sense the long-distance (220 m) target in water, which set a new record for triboelectric nanogenerator (TENG) based contactless sensing. The detection strategy is an effective method for marine data acquisition to realize intelligent ocean management demonstrating applications such as noninvasive inspection of pipeline difficult to reach for in-pipe robot, navigation and berthing for ship in need according its dimension, and sensing water level changes without moving part. The work builds a net-zero power smart ocean network based on TENG, providing new ideas for sustainable ocean development.

GoldMiner: Elastic Scaling of Training Data Pre-Processing Pipelines for Deep Learning

Training data pre-processing pipelines are essential to deep learning (DL). As the performance of model training keeps increasing with both hardware advancements (e.g., faster GPUs) and various software optimizations, the data pre-processing on CPUs is becoming more resource-intensive and a severe bottleneck of the pipeline. This problem is even worse in the cloud, where training jobs exhibit diverse CPU-GPU demands that usually result in mismatches with fixed hardware configurations and resource fragmentation, degrading both training performance and cluster utilization. We introduce GoldMiner, an input data processing service for stateless operations used in pre-processing data for DL model training. GoldMiner decouples data pre-processing from model training into a new role called the data worker. Data workers facilitate scaling of data pre-processing to anywhere in a cluster, effectively pooling the resources across the cluster to satisfy the diverse requirements of training jobs. GoldMiner achieves this decoupling in a fully automatic and elastic manner. The key insight is that data pre-processing is inherently stateless, thus can be executed independently and elastically. This insight guides GoldMiner to automatically extract stateless computation out of a monolithic training program, efficiently disaggregate it across data workers, and elastically scale data workers to tune the resource allocations across jobs to optimize cluster efficiency. We have applied GoldMiner to industrial workloads, and our evaluation shows that GoldMiner can transform unmodified training programs to use data workers, accelerating individual training jobs by up to 12.1x. GoldMiner also improves average job completion time and aggregate GPU utilization by up to 2.5x and 2.1x in a 64-GPU cluster, respectively, by scheduling data workers with elasticity.

Interlacing in Atomic Resolution Scanning Transmission Electron Microscopy.

Fast frame rates are desirable in scanning transmission electron microscopy for a number of reasons: controlling electron beam dose, capturing in situ events, or reducing the appearance of scan distortions. While several strategies exist for increasing frame rates, many impact image quality or require investment in advanced scan hardware. Here, we present an interlaced imaging approach to achieve minimal loss of image quality with faster frame rates that can be implemented on many existing scan controllers. We further demonstrate that our interlacing approach provides the best possible strain precision for a given electron dose compared with other contemporary approaches.

Security concerns overIoTrouting using emerging technologies: A review

AbstractThe Internet of Things (IoT) provides a way to reach every hidden corner of the universe and provides valuable information for exploring the universe. With the advancement of hardware and computing technologies, IoT systems are developed based on systematically designed architectures. This paper discussed the routing protocol for low power lossy network (RPL) protocol's security vulnerabilities and proposed solutions. This review provides the main aspects of an IoT stack model with application, perception, and network layers. Then, the details and security concerns of the RPL protocol are given with the comparison of different frameworks. Different kinds of attacks, such as Wormhole, Sinkhole, and Denial of service attacks, are provided with the solution of defense‐based, threshold‐based, and trust‐supported based cryptography. Also, reviewed IoT security literature and focused on IoT security challenges and their solutions using emerging technologies like Machine Learning (ML), Blockchain, and Cloud Storage. A comparison and analysis of emerging technologies in the context of IoT security are also discussed. Due to the vulnerability of IoT devices, the scale of deployment, the attacks and suggested solutions are increased with scope and scale. In addition, related RPL‐based protocols are compared regarding energy efficiency, flexibility, scalability, robustness, and security. In the end, research challenges are identified, and future research direction for IoT security is suggested.

Artificial Intelligence for Computer-Aided Drug Discovery.

The continuous implementation of Artificial Intelligence (AI) in multiple scientific domains and the rapid advancement in computer software and hardware, along with other parameters, have rapidly fuelled this development. The technology can contribute effectively in solving many challenges and constraints in the traditional development of the drug. Traditionally, large-scale chemical libraries are screened to find one promising medicine. In recent years, more reasonable structure-based drug design approaches have avoided the first screening phases while still requiring chemists to design, synthesize, and test a wide range of compounds to produce possible novel medications. The process of turning a promising chemical into a medicinal candidate can be expensive and time-consuming. Additionally, a new medication candidate may still fail in clinical trials even after demonstrating promise in laboratory research. In fact, less than 10% of medication candidates that undergo Phase I trials really reach the market. As a consequence, the unmatched data processing power of AI systems may expedite and enhance the drug development process in four different ways: by opening up links to novel biological systems, superior or distinctive chemistry, greater success rates, and faster and less expensive innovation trials. Since these technologies may be used to address a variety of discovery scenarios and biological targets, it is essential to comprehend and distinguish between use cases. As a result, we have emphasized how AI may be used in a variety of areas of the pharmaceutical sciences, including in-depth opportunities for drug research and development.

Adaptive LDPC coded high data rate based IR-UWB combined with VMIMO system for vehicular communication links

The automotive industry is developing significantly with the evolution of vehicular networks, intelligent transportation systems, and autonomous cars. These technologies are made achievable by recent advances in software, hardware, and communication systems, along with various applications and standardization efforts. On the other side, many impairments have existed due to these evolving, limiting the process of vehicular networks. So, this paper proposes an implementation of adaptive LDPC-coded, high data rate-based Impulse Radio-Ultra Wide Band (IR-UWB) combined with Virtual Multiple Input Multiple Output (VMIMO) systems for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communications. Furthermore, they are merged with orthogonal pulse shaping using a modified Hermite pulse. Besides, the paper compares Zero-Forcing (ZF) and Minimum Mean Square Error (MMSE) detection techniques. To enhance and mitigate the existing impairments facing the vehicular networks. The proposed system’s performance is evaluated using both techniques, utilizing Bit Error Rate (BER), processing time, and resultant throughput analysis for both channel models. The simulation results include Non-Line of Sight (NLoS) and Line of Sight (LoS) scenarios for both channel models. Moreover, the simulation considers the effect of path loss and noise on the transmitted signal. In addition, the recent LDPC decoding algorithm termed “Modified Implementation Efficient Reliability Ratio Weighted Bit Flipping” (MIERRWBF) is evaluated through all latter channel models. At last, a novel adaptive LDPC decoder is proposed to further enhance the performance of all vehicular communication links under study. Consequently, the paper proposed an integrated adaptive LDPC-coded high data rate communication system to enhance vehicular communications using VMIMO mixed with modified Hermite orthogonal pulse shaping and deploying the MMSE recommended detection technique.