Guest editorial: Special issue on selected papers from IEEE BioCAS 2018

The papers in this special section were presented at the 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS 2018) that was held in in Cleveland, OH, from October 17–19, 2018

SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals

Recent advances in Wi-Fi sensing have ushered in a plethora of pervasive applications in home surveillance, remote healthcare, road safety, and home entertainment, among others. Most of the existing works are limited to the activity classification of a single human subject at a given time. Conversely, a more realistic scenario is to achieve simultaneous, multi-subject activity classification. The first key challenge in that context is that the number of classes grows exponentially with the number of subjects and activities. Moreover, it is known that Wi-Fi sensing systems struggle to adapt to new environments and subjects. To address both issues, we propose SiMWiSense, the first framework for simultaneous multi-subject activity classification based on Wi-Fi that generalizes to multiple environments and subjects. We address the scalability issue by using the Channel State Information (CSI) computed from the device positioned closest to the subject. We experimentally prove this intuition by confirming that the best accuracy is experienced when the CSI computed by the transceiver positioned closest to the subject is used for classification. To address the generalization issue, we develop a brand-new few-shot learning algorithm named Feature Reusable Embedding Learning (FREL). Through an extensive data collection campaign in 3 different environments and 3 subjects performing 20 different activities simultaneously, we demonstrate that SiMWiSense achieves classification accuracy of up to 97%, while FREL improves the accuracy by 85% in comparison to a traditional Convolutional Neural Network (CNN) and up to 20% when compared to the state-of-the-art few-shot embedding learning (FSEL), by using only 15 seconds of additional data for each class. For reproducibility purposes, we share our 1TB dataset and code repository.Comment: This work has been accepted for publication in IEEE WoWMoM 202

Preparation and Characteraction of New Magnetic Co–Al HTLc/Fe3O4Solid Base

Novel magnetic hydrotalcite-like compounds (HTLcs) were synthesized through introducing magnetic substrates (Fe3O4) into the Co–Al HTLcs materials by hydrothermal method. The magnetic Co–Al HTLcs with different Fe3O4contents were characterized in detail by XRD, FT-IR, SEM, TEM, DSC, and VSM techniques. It has been found that the magnetic substrates were incorporated with HTLcs successfully, although the addition of Fe3O4might hinder the growth rate of the crystal nucleus. The morphology of the samples showed the relatively uniform hexagonal platelet-like sheets. The grain boundaries were well defined with narrow size distribution. Moreover, the Co–Al HTLcs doped with magnetic substrates presented the paramagnetic property

Green innovation efficiency measurement of manufacturing industry in the Beijing-Tianjin-Hebei region of China based on Super-EBM model and Malmquist-Luenberger index

Promoting sustainable development in manufacturing is a paramount goal, with a focus on advancing green innovation. This study constructs a system for evaluating green innovation efficiency and employs the Super-EBM model, incorporating unexpected output, to assess the efficiency of green innovation in 13 cities across the Beijing-Tianjin-Hebei region from 2011 to 2020. The study further conducts dynamic analysis using the Malmquist-Luenberger index. Results reveal that, statically, the overall green innovation efficiency in the manufacturing industry of the Beijing-Tianjin-Hebei region is inefficient. There exists a considerable gap in green innovation efficiency among Beijing, Tianjin, and Hebei, with Beijing and Tianjin demonstrating superior performance compared to Hebei. Substantial variations exist in the green innovation efficiency of manufacturing across different cities in the Beijing-Tianjin-Hebei region. Only Beijing, Qinhuangdao, and Baoding achieve DEA-effective green innovation efficiency in the manufacturing industry, while the other cities do not. Dynamically, the green innovation efficiency of the manufacturing industry in the Beijing-Tianjin-Hebei region is on the rise. There is a varying degree of improvement in green innovation efficiency in Beijing, Tianjin, and Hebei, with Hebei showing the highest improvement, Tianjin ranking second, and Beijing having the least improvement. With the exception of Langfang and Hengshui, the green innovation efficiency in the manufacturing industry is improving in most cities in the Beijing-Tianjin-Hebei region, with Hebei witnessing the most significant improvement. This study aims to integrate “environmental pollution” into the evaluation index system for green innovation efficiency. It assesses green innovation efficiency in the manufacturing industry of the Beijing-Tianjin-Hebei region, considering both static and dynamic perspectives. This clarification offers insights into the level of green innovation, contributing valuable information for the advancement of high-quality development in the regional manufacturing industry

A New Approach for the Preparation of Variable Valence Rare Earth Alloys from Nano Rare Earth Oxides at a Low Temperature in Molten Salt

The solubility of RE2O3 (RE = Eu, Sm, and Yb) with variable valence in molten salts is extremely low. It is impossible to directly obtain variable valence metals or alloys from RE2O3 using electrolysis in molten salts. We describe a new approach for the preparation of variable valence rare earth alloys from nano rare earth oxide. The excellent dispersion of nano–Eu2O3 in LiCl–KCl melts was clearly observed using a luminescent feature of Eu3+ as a probe. The ratio of solubility of nano-Sm2O3/common Sm2O3 is 16.98. Electrochemical behavior of RE2O3 on a molybdenum and Al electrode in LiCl–KCl melts containing AlCl3 at 480 °C was investigated by different electrochemical techniques, such as cyclic voltammetry (CV), square wave voltammetry, and chronopotentiometry. Prior to the reduction peak of Al, the reduction peaks of Sm(III)/Sm(II), Yb(III)/Yb(II), and Eu(III)/Eu(II) were observed at about −0.85, −0.45, and 0.39 V insquare wave voltammetry, respectively. The underpotential deposition of RE on pre-deposited aluminum leads to the formation of Al–RE alloy. The structure, morphology, and energy dispersion analysis of the deposit obtained by potentiostatic electrolysis are analyzed. Al2Sm and Al3Sm alloys were successfully obtained from nano–Sm2O3

Kernel Flow:a high channel count scalable time-domain functional near-infrared spectroscopy system

Significance: Time-domain functional near-infrared spectroscopy (TD-fNIRS) has been considered as the gold standard of noninvasive optical brain imaging devices. However, due to the high cost, complexity, and large form factor, it has not been as widely adopted as continuous wave NIRS systems. Aim: Kernel Flow is a TD-fNIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-fNIRS system while integrating all of the components into a small modular device. Approach: The Kernel Flow modules are built around miniaturized laser drivers, custom integrated circuits, and specialized detectors. The modules can be assembled into a system with dense channel coverage over the entire head. Results: We show performance similar to benchtop systems with our miniaturized device as characterized by standardized tissue and optical phantom protocols for TD-fNIRS and human neuroscience results. Conclusions: The miniaturized design of the Kernel Flow system allows for broader applications of TD-fNIRS.</p

Micro-combs: a novel generation of optical sources

The quest towards the integration of ultra-fast, high-precision optical clocks is reflected in the large number of high-impact papers on the topic published in the last few years. This interest has been catalysed by the impact that high-precision optical frequency combs (OFCs) have had on metrology and spectroscopy in the last decade [1–5]. OFCs are often referred to as optical rulers: their spectra consist of a precise sequence of discrete and equally-spaced spectral lines that represent precise marks in frequency. Their importance was recognised worldwide with the 2005 Nobel Prize being awarded to T.W. Hänsch and J. Hall for their breakthrough in OFC science [5]. They demonstrated that a coherent OFC source with a large spectrum – covering at least one octave – can be stabilised with a self-referenced approach, where the frequency and the phase do not vary and are completely determined by the source physical parameters. These fully stabilised OFCs solved the challenge of directly measuring optical frequencies and are now exploited as the most accurate time references available, ready to replace the current standard for time. Very recent advancements in the fabrication technology of optical micro-cavities [6] are contributing to the development of OFC sources. These efforts may open up the way to realise ultra-fast and stable optical clocks and pulsed sources with extremely high repetition-rates, in the form of compact and integrated devices. Indeed, the fabrication of high-quality factor (high-Q) micro-resonators, capable of dramatically amplifying the optical field, can be considered a photonics breakthrough that has boosted not only the scientific investigation of OFC sources [7–13] but also of optical sensors and compact light modulators [6,14]

Compressive acquisition CMOS image sensor : from algorithm to hardware implementation

In recent years, image sensors have been widely used in various applications, such as biomedical micro-system applications, mobile imaging, internetvideo and video cameras. Since the image resolution and frame rate keep on increasing, extensive image processing capabilities, especially image compression, are becoming more and more significant for both still image and video devices. In addition, technology demand for high resolution, low power sensing devices is increasing particularly with the emergence of new generation of application. In late 1990s, Taking advantage of the development of CMOS technology, the pixel size of CMOS image sensor is now comparable to the pixel size of CCD image sensor. CMOS technology enables the integration of sensors and image precessing, making CMOS image sensor the optimum solution to improve the performance of the overall system. Research focusing on on-chip image compression integrated with CMOS image sensor can be traced back to the 1990s. Various compression algorithms have been integrated with CMOS image sensor. However, in most of the reported applications, the high computational complexity of the integrated compression algorithm, and the high on-chip storage requirement for both the raw data and the compressed data present a great challenge that need to be overcome. In this thesis, the concept of compressive acquisition sensor is proposed whereby the raw image is compressed during acquisition and prior to storage. The image sensor's design paradigm is therefore shifted from the traditional concept of capture → store → compress to a new design paradigm namely: capture → compress → store. The idea consists of compressing the data within each pixel before storage, making it possible to use fewer storage bits at the pixel level, and hence reducing the size of the memory required for digital pixel sensors (DPSs). The advantage of the proposed concept is three fold: (i) reduced on-chip storage requirement for on-chip image compression applications, (ii) improved performance of digital pixel sensor (DPS) (reduced silicon area and increased fill-factor) and (iii) compression processing integrated within the pixel array, enabling the concept of parallel processing. Different compression algorithms are proposed for the compressive acquisition CMOS image sensor. Mathematical models are derived in order to optimize the parameters of the proposed compression algorithm. Implementation of the proposed compressive acquisition processing is also proposed at the pixel-level/quadrant-level leading to improved DPS performance. Results illustrate that the proposed algorithms can result in more than 50% on-chip memory saving at an average PSNR level of around 25dB. The proposed compressive acquisition sensor not only reduces the storage memory requirement for on-chip image compression but also results in area saving (more than 60% reduced) and fill-factor improvements (about 40%) of DPS

Architecture of a low storage digital pixel sensor array with an on-line block-based compression

In this paper, a block-based architecture of digital pixel sensor (DPS) array integrated with an on-line compression algorithm is proposed. The proposed technique is based on a block divided storage and compression scheme of the original image. Image capture, storage, and reordering are completed simultaneously and performed on-line while storing pixel value into the on-chip memory array. More than 60% of memory saving is achieved using the proposed block-based design. Furthermore, block-based design greatly reduces the accumulation error inherent in DPCM type of processing. Simulation results show that the PSNR result can reach around 30dB with a compression ratio of less than 3BPP