Error Rate Reduction Research Articles

PULMO: Precise utterance-level modeling for speech anti-spoofing

In recent years, most state-of-the-art approaches for spoofed speech detection have been based on convolutional neural networks (CNNs). Most neural networks, including CNNs, are trained in minibatch units, where all input data in each minibatch must have the same shape. Therefore, for minibatch training, each utterance is first either padded or truncated because utterances are variable-length sequences and thus cannot be directly fed into networks in minibatch units. However, modeling either a padded or truncated utterance, rather than the original one, makes it unfeasible to capture the entire context as is: padding could propagate even unwanted information, like artifacts, in the original utterance, and truncation inevitably loses some information. With these information distortions, model could get stuck in a suboptimal solution. To fill this gap, we proposeÚ a method for precise utterance-level modeling that enables minibatch-wise utterance-level modeling of variable-length utterances while minimizing the information distortions. The proposed method comprises sequence segmentation followed by segment aggregation. Sequence segmentation feeds variable-length utterances in the minibatch unit by decomposing each of them into fixed-length segments, which enables parallel processing of variable-length utterances without the uncertainty in input length. Segment aggregation plays a role in aggregating the segment embeddings by utterance to encode the entire information of each utterance. The experimental results of the evaluation trials of ASVspoof 2019 and 2021 indicate that the proposed method shows up to 84.9 % and 97.6 % relative equal error rate reductions on logical and physical access scenarios, respectively. Furthermore, the proposed method reduced the FLOPs for an epoch by 6 %.

Tensile damage evolution of unidirectional ceramic matrix composites under thermal stress

Ceramic matrix composites (CMCs) have been widely used in aerospace thermal-structures due to their excellent high-temperature performance. It is essential to understand the damage evolution of CMCs. However, in previous research work, the effect of thermal stress induced damage during CMCs fabrication on tensile response was often ignored. A damage evolution model that considers axial and radial thermal stresses to predict tensile response of unidirectional ceramic matrix composites was proposed in this study. The average relative errors between the current prediction and experimental data in the literature were calculated as 1.64 % and 1.91 %, validating this approach. Meantime, the damage evolution laws of interface debonding and the critical stress of matrix cracking in the Budiansky–Hutchinson–Evans (BHE) model were corrected to satisfy the discontinuous interfacial shear stress. According to the experimental data, the current model predicted critical stresses of matrix cracking better than the BHE model, with error rate reductions of 6.52 % and 15.38 %.

Analysis and Design of Customer Relationship Management System on the SMEs Using Iconix Process

Background: Integrating Customer Relationship Management (CRM) systems is crucial for small and medium enterprises (SMEs) to enhance customer relations and profitability. Many SMEs in Indonesia, including Go-Sumber Plastik, still need to fully utilize CRM systems, which are essential for managing customer data, improving satisfaction, and retaining customers. Objective: The purpose of this research is to analyze and design a web-based CRM system for Go-Sumber Plastik using the Iconix Process methodology to enhance user interaction and overall system effectiveness. Methods: The study employed the Iconix Process methodology, which includes a use case, robustness, sequence diagrams, a GUI prototype, and a test plan. The design was tested using Maze to measure user interaction efficiency and satisfaction. Results: The research revealed significant challenges in user understanding of the CRM system, particularly in managing activities and adding customer information. Tasks such as reporting and logging in had good user performance. The overall user interaction score was 81.1, indicating moderate effectiveness of the initial design. Conclusion: The results underscore the necessity for a more intuitive and streamlined CRM system interface for Go-Sumber Plastik. Implementing an effective CRM system can improve SMEs' competitiveness and profitability by systematically enhancing communication, managing customer data, and boosting business performance. Future research should focus on refining the user interface to reduce error rates and improve task completion efficiency. Enhanced visibility and user guidance are recommended to optimize system usability and customer satisfaction.

Optimized multi-head self-attention and gated-dilated convolutional neural network for quantum key distribution and error rate reduction

ABSTRACT Quantum key distribution (QKD) is a secure communication method that enables two parties to securely exchange a secret key. The secure key rate is a crucial metric for assessing the efficiency and practical viability of a QKD system. There are several approaches that are utilized in practice to calculate the secure key rate. In this manuscript, QKD and error rate optimization based on optimized multi-head self-attention and gated-dilated convolutional neural network (QKD-ERO-MSGCNN) is proposed. Initially, the input signals are gathered from 6G wireless networks which face obstacles to channel. For extending maximum transmission distances and improving secret key rates, the signals are fed to the variable velocity strategy particle swarm optimization algorithm, then the signals are fed to MSGCNN for analysing the quantum bit error rate reduction. The MSGCNN is optimized by intensified sand cat swarm optimization. The performance of the QKD-ERO-MSGCNN approach attains 15.57%, 23.89%, and 31.75% higher accuracy when analysed with existing techniques, like device-independent QKD utilizing random quantum states, practical continuous-variable QKD and feasible optimization parameters, entanglement and teleportation in QKD for secure wireless systems, and QKD for large scale networks methods, respectively.

SSG2: A new modeling paradigm for semantic segmentation

State-of-the-art models in semantic segmentation primarily operate on single, static images, generating corresponding segmentation masks. This one-shot approach leaves little room for error correction, as the models lack the capability to integrate multiple observations for enhanced accuracy. Inspired by work on semantic change detection, we address this limitation by introducing a methodology that leverages a sequence of observables generated for each static input image. By adding this “temporal” dimension, we exploit strong signal correlations between successive observations in the sequence to reduce error rates. Our framework, dubbed SSG2 (Semantic Segmentation Generation 2), employs a dual-encoder, single-decoder base network augmented with a sequence model. The base model learns to predict the set intersection, union, and difference of labels from dual-input images. Given a fixed target input image and a set of support images, the sequence model builds the predicted mask of the target by synthesizing the partial views from each sequence step and filtering out noise. We evaluate SSG2 across four diverse datasets: UrbanMonitor, featuring orthoimage tiles from Darwin, Australia with four spectral bands at 0.2 m spatial resolution and a surface model; ISPRS Potsdam, which includes true orthophoto images with multiple spectral bands and a 5 cm ground sampling distance; ISPRS Vahingen, which also includes true orthophoto images and a 9 cm ground sampling distance; and ISIC2018, a medical dataset focused on skin lesion segmentation, particularly melanoma. The SSG2 model demonstrates rapid convergence within the first few tens of epochs and significantly outperforms UNet-like baseline models with the same number of gradient updates. However, the addition of the temporal dimension results in an increased memory footprint. While this could be a limitation, it is offset by the advent of higher-memory GPUs and coding optimizations. Our code is available at https://github.com/feevos/ssg2.

Industrial Metaverse: A Comprehensive Review, Environmental Impact, and Challenges

The Industrial Metaverse paradigm can be broadly described as a virtual environment that integrates various technologies such as augmented reality and mixed reality to enhance business operations and processes. It aims to streamline workflows, reduce error rates, improve efficiency, and provide a more engaging experience for employees. The promise of the Industrial Metaverse to drive sustainability and resource efficiency is compelling. Using advanced technologies such as the Industrial Metaverse is vital in an endeavor to have a competitive edge in a rapidly evolving business environment. However, the environmental impact of the technologies underpinning the Industrial Metaverse, like data centers and network infrastructure, should not be overlooked. The ecological footprint of these technologies must be considered in the sustainability equation. Researchers have warned that, by 2025, without sustainable artificial intelligence (AI) practices, AI will consume more energy than the human workforce, significantly offsetting zero carbon gains. As the Metaverse persists in evolving and gaining momentum, it will be necessary for companies to prioritize sustainability and explore new ways to balance technological advancements with environmental stewardship. However, recent studies have conjectured that the Metaverse holds the potential to reduce carbon emissions, as digital replacements for physical goods become more prevalent and physical activities like mobility and construction are reduced. Moreover, the specific extent to which this substitution can alleviate environmental concerns remains an open issue, presenting a knowledge gap in understanding the real-world impact of digital replacements. Thus, the objective of this paper is to provide a comprehensive review of the Industrial Metaverse, as well as explore the environmental impact of the Industrial Metaverse. The integrative literature review design and methodological approach involved multiple sources from the Web of Science and databases such as the ACM library, IEEE Library, and Google Scholar, which were analyzed to provide a comprehensive understanding of the developments in the Industrial Metaverse. Firstly, by considering the Industrial Metaverse’s architecture, we elucidate the Industrial Metaverse concept and the associated enabling technologies. Secondly, we performed an exploration through a discussion of the prevalent use cases and the deployment of the emerging Industrial Metaverse. Thirdly, we explored the impact of the Industrial Metaverse on the environment. Lastly, we address novel security and privacy risks, as well as upcoming research challenges, keeping in mind that the Industrial Metaverse is based on a strong data fabric. The results point to the Industrial Metaverse as having both positive and negative environmental effects in terms of energy consumption, e-waste, and pollution. Research, however, indicates that most Industrial Metaverse applications have a positive environmental impact and subsequently trend toward sustainability. Finally, for sustainability in the Industrial Metaverse, enterprises may consider utilizing renewable energy sources and cloud services. Furthermore, we examined the effects of products on the environment, as well as in the creation of a circular economy.

Decoupled structure for improved adaptability of end-to-end models

Although end-to-end (E2E) trainable automatic speech recognition (ASR) has shown great success by jointly learning acoustic and linguistic information, it still suffers from the effect of domain shifts, thus limiting potential applications. The E2E ASR model implicitly learns an internal language model (LM) which characterises the training distribution of the source domain, and the E2E trainable nature makes the internal LM difficult to adapt to the target domain with text-only data. To solve this problem, this paper proposes decoupled structures for attention-based encoder–decoder (Decoupled-AED) and neural transducer (Decoupled-Transducer) models, which can achieve flexible domain adaptation in both offline and online scenarios while maintaining robust intra-domain performance. To this end, the acoustic and linguistic parts of the E2E model decoder (or prediction network) are decoupled, making the linguistic component (i.e. internal LM) replaceable. When encountering a domain shift, the internal LM can be directly replaced during inference by a target-domain LM, without re-training or using domain-specific paired speech-text data. Experiments for E2E ASR models trained on the LibriSpeech-100h corpus showed that the proposed decoupled structure gave 15.1% and 17.2% relative word error rate reductions on the TED-LIUM 2 and AESRC2020 corpora while still maintaining performance on intra-domain data. It is also shown that the decoupled structure can be used to boost cross-domain speech translation quality while retaining the intra-domain performance.

Interpolation-based reference image estimation for video display field communication

Display field communication (DFC) is a frequency-based display-to-camera (D2C) communication technology. The digital display and the camera act as transmitter and receiver, respectively, and data are transmitted in a video via the D2C link. The essence of DFC is to minimize image distortion for the user while concealing data within the image for transmission. Unlike traditional Video-DFC methods that may suffer from high error rates due to reliance on a single reference image, our proposed Interpolated Video-DFC technique employs a novel interpolated reference image estimation strategy. This method significantly enhances data detection by leveraging interpolation between two reference image frames, enabling accurate estimation of each reference image frame corresponding to data-embedded frames. Comparative experiments highlight the advantages of our method, showing a data rate improvement of approximately 2.3kbps for frame packet length N = 10. Additionally, we achieved a substantial reduction in error rates to approximately 69% lower than those of conventional methods. These findings indicate a promising direction for improving the performance and reliability of D2C communication systems.

Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.

DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery

Surface water area estimation is essential for understanding global environmental dynamics, yet it presents significant challenges, particularly when dealing with small water bodies like ponds and narrow width rivers. Surface water areas for these small bodies are often inaccurately represented by existing methods due to the spatial resolution limitations in commonly used remote sensing images. This study introduces DeepWaterFraction (DWF), a deep learning approach, to estimate percent surface water area from Landsat mission imagery. DWF is trained with a self-training method, which creates training data by upscaling remote sensing images and water map labels to a lower resolution, enabling the creation of a large-scale, global coverage training dataset. DWF demonstrates superior accuracy in estimating areas for small water bodies compared to several existing methods for surface water area estimation, with a pixel-wise root mean squared error of 14.3 %. Specifically, it reduces error rates by 54.3 % for water bodies with a minimum area of 0.001 km2 and by 22.6 % for those with a minimum area of 0.01 km2. DWF’s application in global river discharge inversion is also explored, showcasing its capability to capture width variations in narrow rivers (<90 m) better than existing methods, and its robustness across environments including wetland, tree covers, and urban areas. Even for wider rivers (>150 m), DWF’s performance remains superior, as its ability to accurately quantify mixed water pixel areas effectively reflects discharge variations when the variation area is small. We find that self-training is an effective strategy for generating extensive global training datasets for water mapping, with a high upscaling factor being critical for ensuring label accuracy. This study presents a step forward in the accurate global mapping of water resources.

Opto-electronic machine learning network for Kramers-Kronig receiver linearization

We numerically demonstrate the use of an opto-electronic network comprising a photonic reservoir and an electronic feedforward equalizer (FFE) to linearize a Kramers-Kronig (KK) receiver. The KK receiver is operated under stringent conditions, with restricted sampling rates and low carrier powers, resulting in a nonlinear behavior. We propose two different network configurations, varying in the placement of the FFE component, and evaluate their ability to linearize the KK receiver. By training these networks on back-to-back systems, we arrive at a generic solution that significantly enhances the receiver performance, independent of specific link characteristics. The trained networks are tested in a plug-and-play manner across diverse short-reach links that employ standard digital signal processing blocks for the qualization. Our results show significant improvement in receiver linearity, resulting in a reduction in bit error rate of up to a factor of four.

A Bio-Inspired Retinal Model as a Prefiltering Step Applied to Letter and Number Recognition on Chilean Vehicle License Plates

This paper presents a novel use of a bio-inspired retina model as a scene preprocessing stage for the recognition of letters and numbers on Chilean vehicle license plates. The goal is to improve the effectiveness and ease of pattern recognition. Inspired by the responses of mammalian retinas, this retinal model reproduces both the natural adjustment of contrast and the enhancement of object contours by parvocellular cells. Among other contributions, this paper provides an in-depth exploration of the architecture, advantages, and limitations of the model; investigates the tuning parameters of the model; and evaluates its performance when integrating a convolutional neural network and a spiking neural network into an optical character recognition (OCR) algorithm, using 40 different genuine license plate images as a case study and for testing. The results obtained demonstrate the reduction of error rates in character recognition based on convolutional neural networks (CNNs), spiking neural networks (SNNs), and OCR. It is concluded that this bio-inspired retina model offers a wide spectrum of potential applications to further explore, including motion detection, pattern recognition, and improvement of dynamic range in images, among others.

Age-related changes in the effects of induced positive affect on executive control in younger and older adults—evidence from a task-switching paradigm

ABSTRACT Positive affect has been shown to promote task-switching performance in healthy young adults. Given the well-documented age-related decline in executive functioning, we asked whether induced positive affect also helps to improve task-switching performance in older adults. Sixty-eight younger and older adults performed a switching task before and after they had watched cartoon clips (positive affect group) or documentaries (neutral affect group). Positive affect was associated with reduced error rates across all trial types in both age groups. In older adults, the increase in accuracy came at the expense of slower response times for task-switch trials, resulting in greater switch costs. This pattern of findings is inconsistent with the popular notion that positive affect supports greater cognitive flexibility. Instead, positive affect may trigger adjustments in response control settings – such as a shift in the speed-accuracy trade-off toward more cautious responding – depending on the experienced level of task difficulty.

CAM: A cross-lingual adaptation framework for low-resource language speech recognition

In this paper, a novel cross-lingual adaptation framework called CAM is presented for low-resource language speech recognition (LLSR). It is based on the recent popular adapter method. CAM is achieved by adapting self-supervised speech models (SSMs) from source languages to target low-resource languages in a two-stage process. CAM fills two research gaps existing in current methods: (i) language similarity is not effectively considered; (ii) the performance-efficiency trade-off is not well balanced. Specifically, two key components, a similarity-aware fusion module (SAFM) and an adapter weight-sharing strategy (AWSS), are designed. A well-trained adapter is introduced by SAFM to compute precise language similarities via dot product. AWSS takes both performance and efficiency into consideration by sharing adapter weights. Experimental results on two corpora, FLEURS and Common Voice, demonstrate that CAM equipped with these two designs, denoted as performance-oriented CAM (P-CAM), obtains state-of-the-art (SOTA) performance with satisfactory efficiency compared to current leading methods. Besides, an efficiency-oriented CAM (E-CAM) by introducing a weight-space fusion module (WSFM) is presented. The core of WSFM is to average the weights of multiple adapters into a new adapter. Compared to full fine-tuning, only 5.0% of trainable parameters are required for E-CAM while a 2.3% relative average word error rate (WER) reduction is obtained. CAM brings a performance- or efficiency-oriented trade-off to meet the different needs of speech recognition systems.

Stable and wavelength-tunable multiwavelength laser for high-rate measurement device independent quantum key distribution

Measurement device independent quantum key distribution (MDI QKD) has attracted growing attention for its immunity to attacks at the measurement unit, but its unique structure limits the secret key rate. Utilizing the wavelength division multiplexing (WDM) technique and reducing error rates are effective strategies for enhancing the secret key rate. Reducing error rates often requires active feedback control of wavelengths using precise external references. However, for a multiwavelength laser, employing multiple references to stabilize each wavelength output places stringent demands on these references and significantly increases system complexity. Here, we demonstrate a stable, wavelength-tunable multiwavelength laser with an output wavelength ranging from 1270 to 1610 nm. Through precise temperature control and stable drive current, we passively lock the laser wavelength, achieving remarkable wavelength stability. This significantly reduce the error rate, leading to an almost doubling of the secret key rate compared to previous experiments. Furthermore, the exceptional wavelength stability offered by our multiwavelength laser, combined with the WDM technique, has further boosted the secret key rate of MDI QKD. With a wide wavelength tuning range of 5.1 nm, our multiwavelength laser facilitates flexible operation across multiple dense wavelength division multiplexing channels. Coupled with high wavelength stability and multiple wavelength outputs simultaneously, this laser offers a promising solution for a high-rate MDI QKD system.

Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases

In the empire of image processing and computer vision, the demand for advanced segmentation techniques has intensified with the growing complexity of visual data. This study focuses on the innovative paradigm of fuzzy mountain-based image segmentation, a method that harnesses the power of fuzzy logic and topographical inspiration to achieve nuanced and adaptable delineation of image regions. This research primarily concentrates on determining the age of tigers, a critical and challenging task in the current scenario. The primary objectives include the development of a comprehensive framework for FMBIS and an in-depth investigation into its adaptability to different image characteristics. This research work incorporates those domains of image processing and data mining to predict the age of the tiger using different kinds of color images. Fuzzy mountain-based pixel segmentation arises from the need to capture the subtle gradients and uncertainties present in images, offering a novel approach to achieving high-fidelity segmentations in diverse and complex scenarios. The proposed methods enable image enhancement and filtering and are then assessed during process time, retrieval time, to give a more accurate and reduced error rate for producing higher results for real-time tiger image database.

Performance and energy efficiency enhancement of existing optical communication systems by incorporating resource allocation on demand technique in FiWi networks

Abstract Fiber-wireless (FiWi) networks have begun to be fully integrated into contemporary optical access network systems as a result of utilizing the combined advantages of wired and wireless techniques. Passive optical networks (PON) are heavy energy consumers and one of the main contributors to greenhouse gas emissions (GHG) that are a result of climate change. These systems also present economic challenges. In this work, three different FiWi systems are presented such as (i) even/odd active transmitters with only free space optical (FSO) based optical network units (ONUs)/with dual channel option such as distribution fiber (DF)/FSO supported ONUs, (ii) a novel resource allocation on demand (RAoD) incorporated bidirectional FiWi system supporting DF/FSO based ONUs and four wave mixing (FWM) generated upstream wavelengths and (iii) transmitter diversity enabled 5G supported FiWi system supporting bidirectional communication. It is observed that the energy efficiency of 26.04 % is obtained in even/odd active transmitter and only FSO supported ONUs and under the harsh weather conditions, by adopting DF channel instead of FSO, 30 % energy efficiency further added. However, in novel RAoD technique, energy efficiencies for optical distribution networks (ODN1), ODN2, ODN3, and ODN4 are 48.52 %, 46.4 %, 36.96 %, and 44.31 %. Further, DF option in channel selection improve 30 % more energy saving. In performance enhanced transmitter diversity employed system, at 60 km reduction in bit error rate (BER) are 25 % for ODN1, 5.88 % for ODN2, 20 % for ODN3, and 12.5 % for ODN3.

Machine learning experiment management tools: a mixed-methods empirical study

Machine Learning (ML) experiment management tools support ML practitioners and software engineers when building intelligent software systems. By managing large numbers of ML experiments comprising many different ML assets, they not only facilitate engineering ML models and ML-enabled systems, but also managing their evolution—for instance, tracing system behavior to concrete experiments when the model performance drifts. However, while ML experiment management tools have become increasingly popular, little is known about their effectiveness in practice, as well as their actual benefits and challenges. We present a mixed-methods empirical study of experiment management tools and the support they provide to users. First, our survey of 81 ML practitioners sought to determine the benefits and challenges of ML experiment management and of the existing tool landscape. Second, a controlled experiment with 15 student developers investigated the effectiveness of ML experiment management tools. We learned that 70% of our survey respondents perform ML experiments using specialized tools, while out of those who do not use such tools, 52% are unaware of experiment management tools or of their benefits. The controlled experiment showed that experiment management tools offer valuable support to users to systematically track and retrieve ML assets. Using ML experiment management tools reduced error rates and increased completion rates. By presenting a user’s perspective on experiment management tools, and the first controlled experiment in this area, we hope that our results foster the adoption of these tools in practice, as well as they direct tool builders and researchers to improve the tool landscape overall.

The Application of Internet Technology in Enhancing the Efficiency of the Gig Economy

As our country's overall strength continues to grow, internet technology has rapidly developed, giving rise to the gig economy as a new economic form. The gig economy is characterized by its strong flexibility and intelligence, becoming the choice of many, while providing businesses with a broader range of human resource options. Traditionally, the gig economy has been limited by geographical and informational constraints, making it difficult for supply and demand to match effectively. However, with the widespread adoption of internet technology, gig workers and demanders can easily post and search for information online, achieving precise matches. This efficient circulation of information not only reduces search costs but also improves matching efficiency, making the gig economy more flexible and efficient. Additionally, internet technology enhances the efficiency of the gig economy by optimizing organizational management and processes. With tools such as project management software, automation, and intelligent processes, teams within the gig economy can better coordinate work and improve work efficiency. These technologies reduce error rates, ensure the quality and effectiveness of work, and promote the sustainable development of the gig economy.

Demonstration of D-band 1×2 SIMO millimeter-wave wireless delivery over 1.2 km employing MRC technology

We experimentally demonstrate the transmission of 20-Gbaud QPSK millimeter-wave signals within a 1.2-km wireless range at 128-GHz using constant-modulus-algorithm (CMA) and the maximum ratio combining (MRC) technology with over 2.2-dB gain in a photonics-assisted millimeter wave (MMW) 1 × 2 Single Input Multi Output (SIMO) communication system. MRC is applied for SIMO signal merging which leads to Signal to Noise Ratio (SNR) enhancement and significant Bit Error Rate (BER) reduction. The comparison which is performed for Single Input Single Output (SISO) and SIMO system, demonstrates the BER improvement. To the best of our knowledge, this is the first experimental demonstration of high gain in a D-band MMW 1 × 2 SIMO system by using these technologies.