Miracle-dz/UniFAR-FTUs huggingface dataset:
An Hardware Recurrent Neural Network for Wearable Devices

Automatic classification of time series signals acquired by wearable or portable devices covers a central role in many critical healthcare applications, such as heart rate monitoring [1], sleep apnea study [2], gait analysis [3] and fall detection [4]. In recent years, many approaches have been adopted, including a wide range of methods ranging from threshold-based algorithms to Deep Learning techniques. The threshold-based methods have the advantage of being simple and not heavy from a computational point of view, but at the cost of low accuracy. Deep Learning approaches ensure a higher precision, but the computational complexity is increased. This is a critical issue for wearable devices because a high computational complexity strongly affects the processing time and the battery life. In this paper, we propose a hardware architecture for time series analysis using Recurrent Neural Networks (RNNs) exploiting FPGA technology. The architecture is validated with three-axial accelerometer data acquired by a wearable device used for automatic fall detection. The experimental results show that the proposed architecture outperforms state of the art solutions both in terms of processing time and power consumption.
Power Consumption Variation for a Single Technology Wireless Transceivers

In this paper, the power consumption model of a wireless transceiver is proposed. It is based on measurements of a number of off-the-shelf WiFi cards. The power consumption was divided into an idle, associated, and transmission states. Then, the eight-coefficients power consumption model depending on the rate and path-loss has been proposed and the values of coefficients have been determined. While there are significant differences in the power consumption among considered devices, the proposed model is well aligned with the measurements for each of them. Moreover, this paper shows that models based on a single device characterization can result in significant errors if employed to assess network power consumption.
A Review of Electron Emitters for High-Power and High-Frequency Vacuum Electron Devices

This work reviews the progress in the area of high current density electron emitters, specifically for use in high frequency and high-power microwave vacuum electron devices. The review is divided into two subsections: 1) thermionic cathodes and 2) field emission cathodes. The thermionic cathode section includes the discussion on the M-type, MM-type, Scandate and the CPD cathodes. CPD cathode promises better usability in a practical device as compared to the Scandate cathodes which are limited by emission nonuniformity, poor resistance to poisoning/ion bombardment and low life. With the increasing demand for miniaturized and high power VEDs in the near future, the development of high current density field emission cathodes is of immediate interest to the VED community. The Spindt, CNT, and graphene-based emitters are the potential candidates in the field emitters section. Graphene-based film cathodes, when compared to the Mo/Si FEAs and the CNTs, offer larger emission area as well as high current carrying capability. This review includes the evolution of the emission capabilities of the emitters, state-of-the-art performances, and possible future developments.
Computing Group Velocities and Group-Velocity Dispersions of Optical Fibers Through Automatic Differentiation of Explicit Forms of Propagation Constants

We present a method to compute differential coefficients of the first and second orders, for propagation constants of nondegenerate and degenerate modes, propagating in an optical fiber to calculate dispersion characteristics of group velocities (GV) and group-velocity dispersions (GVD). This method automatically differentiates explicit forms of eigenvalues extracted from determinants of a generalized eigenvalue problem. This problem is converted from a nonlinear eigenvalue problem derived from the Trefftz method (e.g., transfer matrix and multipole methods) by the Sakurai–Sugiura method. We compute the differential coefficients of the second order of degenerate modes without computing eigenvectors and their first order differential coefficients in the generalized eigenvalue problems. Therefore, in parametric optimization, we can compute the differential coefficients of all propagating modes without remodeling the waveguide cross-section, as analyzing the whole cross-section avoids the symmetric conditions derived from the numerical model. Computed results with the proposed method for step-index and holey fibers validate the method and its effectiveness. In particular, our results show that our method computes the GV and GVD more accurately than computation using numerical differentiation.
Toward data-driven modeling of material flow simulation: automatic parameter calibration of multiple agents from sparse production log

Modeling accurate material flow simulation is a time-consuming task and requires high expertise about both simulation techniques and production system. Recently, data-driven modeling approaches that build simulation models from production log are gathering attentions to automate the modeling process. However, in most practical cases, production log does not have enough resolution to specify the input and output of each agent in material flow simulation such as processing time agent and dispatching agent. For the issue, we proposed a novel approach and method that models multiple agents simultaneously from sparse production log. In our method, agents are described as machine learning models, then parameters in the models are calibrated to minimize simulation error. We confirmed the usefulness of the proposed method through experiments with virtual production system.
Design Considerations of Antennas and Adaptive Impedance Matching Networks for RF Energy Harvesting

This paper reviews recent developments in design of antennas and impedance matching networks for RF Energy Harvesting (RF-EH) at UHF. The antenna design is considered in conjunction with the requirements that it places on the impedance matching network (IMN), in order to match the load which is an RF to DC converter. It is shown that there may be advantages in using an interface impedance between the antenna and the IMN that differs from the conventional 50 Ω impedance. Various options are considered for the design of an IMN driving a rectifier load. A novel adaptive IMN architecture is proposed that can match two different load impedances, depending on the received power level. This can optimize Power Conversion Efficiency (PCE) over a range of different input powers that may be encountered in RF EH.
Lattice: An ADC/DAC-less ReRAM-based Processing-In-Memory Architecture for Accelerating Deep Convolution Neural Networks

Nonvolatile Processing-In-Memory (NVPIM) has demonstrated its great potential in accelerating Deep Convolution Neural Networks (DCNN). However, most of existing NVPIM designs require costly analog-digital conversions and often rely on excessive data copies or writes to achieve performance speedup. In this paper, we propose a new NVPIM architecture, namely, Lattice, which calculates the partial sum of the dot products between the feature map and weights of network layers in a CMOS peripheral circuit to eliminate the analog-digital conversions. Lattice also naturally offers an efficient data mapping scheme to align the data of the feature maps and the weights and hence, avoiding the excessive data copies or writes in the previous NVPIM designs. Finally, we develop a zero-flag encoding scheme to save the energy of processing zero-values in sparse DCNNs. Our experimental results show that Lattice improves the system energy efficiency by 4× ∼ 13.22× compared to three state-of-the-art NVPIM designs: ISAAC, PipeLayer, and FloatPIM.
Trading the Reliability of Approximate TMR in FPGAs with the Cost of Mitigation

A number of works have focused on relaxing circuit specification for building partial TMR circuits, but a framework for analysing the effect of circuit degradation on its dependability is missing in the literature. This paper aims to bridge this gap by developing a reliability model for approximate TMR circuits implemented in FPGAs and the parameter definitions for controlling design trade-offs with reliability in the approximation process. The framework is useful in the assignment of the design parameters such that the reliability constraints of the application at hand are satisfied. Reliability curves for different trade-offs show a sharp decline in reliability even at small error thresholds, requiring that for maintaining system operation in the high reliability region, a TMR approximation method must achieve the desired reduction in hardware overheads within tight error constraints. Furthermore, the effect of an unprotected voter on the overall system reliability is also quantified. To which end, it is shown that the simple unprotected voter results in significant degradation on the approximate TMR reliability and therefore using a fault-tolerant voting circuit is essential to a reliable system operation.
Hard and Soft Logic Trade-offs for Multipliers in VTR

This paper discusses improvements to the Verilog- To-Routing (VTR) Computer Aided Design (CAD) tool, that enables synthesis of Verilog circuits to a Field Programmable Gate Array (FPGA) architecture, previously impossible due to device size limitations imposed by device growth. The proposed solution allows reducing device sizes required for well known circuits, through exploring the space/performance trade-off question at a finer granularity at early CAD stages. Results of as much as 2.63 times increase in performance and a 48% reduction in device size have been achieved for some circuits.
A 3D Statistical Framework for the UK’s Mobile Network

A key challenge in deploying new wireless cellular technologies or architectures is accurate modelling of their impact on the wider network. In this contribution, we provide a new statistical framework and large scale analysis to characterise a real mobile network. Current industry guidelines relevant to modelling of mobile networks and their deployments are often over simplified and based on generalisation of the propagation environment and network topology. The following analysis details statistical deployment characteristics of mobile base station sites and their associated geographic environment using a highly detailed 3D topology model of large areas of the UK. High resolution LIDAR (Light Detecting and Ranging) data and cell mast properties are utilised to generate statistical descriptions of urban, suburban and rural cell site types as well as distributions for the clutter properties that can be found within each cell type’s coverage area. Our findings illustrate the important distinction between traditional cell classifications using a range of attributes whilst also highlighting the unique 3D properties of urban cell types.
Mice Hyperlipidemia Model Set up by Injection Lipide Mulsion

Objective:To tell a simple and convenient way to set up the hyperlipidemia model. Methods:Set up the mice hyperlipidemia model by ig lipid emulsion, then tested the total cholesterol (TC) and triglyceride (TG) in serum of two groups. Result: The hyperlipidemia model group was statistically different compared with the control group in the level of TC and TG.Conclusion: The way of set up hyperlipedemia is workable and convenient.
Highest performance requires an optimal effort: A MEG study on visual perception

Twelve subjects (6 males; 17-64 years) participated in magnetoencephalography (MEG) experiments designed to study visual response to flickering Necker cubes. Event-related fields in the lateral occipital complex and steady-state response in the visual region V1 and V2 were studied to characterise the early response time durations and attentional capacity during the trial time period of 5-s, respectively. We found that there was an optimal response time duration for which the attention capacity was maximum. The results also support the hypothesis that stimulus detection in brain is driven by coherence resonance.
Addition of a Secondary Functional Bloom Filter

Key–value data structures have been extensively used in various applications. When a large amount of data needs to be compactly stored in a fixed memory size, a functional Bloom filter is a space-efficient key–value structure. In this letter, we propose a 2-stage functional Bloom filter structure composed of a primary functional Bloom filter and a secondary functional Bloom filter to resolve the indeterminables produced from the primary functional Bloom filter. We analytically present the memory ratio allocated for each of the two Bloom filters to achieve the lowest search failure rate. The analytical result is validated through experiments, thereby demonstrating that the optimal performance is realized when the secondary functional Bloom filter uses 3% of the total memory.
3D Angular-Based Hybrid Precoding for Multi-Cell MU-Massive-MIMO Systems in C-RAN Architecture

This paper proposes a three-dimensional angular-based hybrid precoding (3D-AB-HP) for multi-cell multi-user massive multiple-input multiple-output (MU-mMIMO) systems. By employing the angle-of-departure (AoD) information, the RF precoder in the proposed 3D-AB-HP is designed to decrease the channel estimation overhead and the number of RF chains. Then, the baseband (BB) precoder in the 3D-AB-HP is constructed via regularized zero-forcing technique using the effective channel seen at the BB. To further mitigate the inter-cell interference for the cell-edge users, we propose three cooperation strategies for the multi-cell downlink transmission in the case of cloud radio access networks (C-RAN): (i) non-cooperation (NC), (ii) partial-cooperation (PC) and (iii) full-cooperation (FC). Illustrative results indicate that FC and PC improve remarkably the sum-rate performance via utilization of C-RAN in comparison to the traditional RAN. Moreover, by means of the directional beamforming, the proposed 3D-AB-HP with PC and NC can outperform the corresponding single-stage fully-digital precoding requiring higher hardware cost/complexity and larger channel estimation overhead.
Indoor Route and Location Inference using Smartphone IMU Sensors

In this paper, we devise a framework to infer a smartphone user’s location and walking routes in an indoor environment, using only the information from inertial measurement unit (IMU) sensors like gyroscope and accelerometer. To overcome the shortcoming of estimation drift over time in common PDR (pedestrian dead reckoning)-IMU systems, we propose a map-aided system which uses the map elements to help correct the user’s position. We generate a map based on the environment parameters and a map matching algorithm is applied to find the most likely location of the user. The reading from the IMU sensors contains amounts of noise when user is walking, therefore we propose an edge detection algorithm based on the pruned exact linear time (PELT) model to smooth the piece-wise signals and identify the time frame when the user is making a turn. We evaluate our system when the smartphone is held either in the user’s hand or in the backpack, and the system is able to give the correct walking path in both cases.
Performance of Networks with an Energy Buffer-Aided Source and a Data Buffer-Aided Relay

In this paper, we analyze the performance of a two-hop communication link between an energy-buffer equipped IoT-type self-sustaining source and a destination, aided by a data buffer equipped access point, which serves as a relay. The source harvests RF energy from the ambience and stores it in an energy buffer. Using a discrete-time continuous-state Markov chain model to model the energy buffer, and a discrete-state Markov chain to model the data buffer, we analyze performance of the link. We show how the continuous-state energy buffer and the discrete-state data buffer can both be balanced by suitable choice of the source transmit power, relay target data rate, and relay power. Since feedback of channel estimates and buffer status is not feasible in this scenario, link selection is based solely on energy-buffer status at the source. The derived analytical expressions are validated by Monte Carlo simulations.
Deep Learning Methods for Retinal Blood Vessel Segmentation: Evaluation on Images with Retinopathy of Prematurity

Automatic blood vessel segmentation from retinal images plays an important role in the diagnosis of many systemic and eye diseases, including retinopathy of prematurity. Current state-of-the-art research in blood vessel segmentation from retinal images is based on convolutional neural networks. The solutions proposed so far are trained and tested on images from few available retinal blood vessel segmentation datasets, which might limit their performance when given an image with retinopathy of prematurity signs. In this paper, the performance of three high-performing convolutional neural networks for retinal blood vessel segmentation is evaluated in the context of blood vessel segmentation on retinal images exhibiting retinopathy of prematurity. The main motive behind the study is to test if existing public datasets suffice to develop a high performing predictor that could assist an ophthalmologist in retinopathy of prematurity diagnosis. To do so, the authors created a dataset consisting solely of retinopathy of prematurity images. Retinal blood vessels are manually labeled in the images by two observers, including an ophthalmologist experienced in retinopathy of prematurity treatment. Experimental results show that all three solutions have difficulties in detecting the retinal blood vessels of infants due to a lower contrast compared to images from public datasets as demonstrated by significant drop in classification sensitivity. All three solutions segment alongside retinal also choroidal blood vessels which are not used to diagnose retinopathy of prematurity, but instead represent noise and are confused with retinal blood vessels. Through observations and numerical analysis of experimental data, it is concluded that existing solutions for retinal blood vessel segmentation need improvement oriented toward using more detailed datasets or deeper models in order to assist the ophthalmologist in retinopathy of prematurity diagnosis.
Evidence-driven Requirements Engineering for Uncertainty of Machine Learning-based Systems

Requirements engineering for machine learning (ML)-based systems involves unique difficulties. The core cause is the intrinsic uncertainty or unpredictability, not only in requirements and environments but also in implementation. In this paper, we discuss the impact of this type of uncertainty on requirements engineering methods such as goal-oriented requirements analysis (GORE). Many aspects in requirements analysis or prior decision making remain as hypotheses, which may be validated or invalidated with evidences from Proof-of Concept experiments, field tests, and operation. To deal with this point, we present principles of evidence-driven requirements engineering and instantiate them into a method that links GORE and ML operation (GORE-MLOps).
Fluoride as a chemical modifier for the determination of phosphorus by electrothermal atomic absorption spectrometry

The use of HF, NaF, KF, CsF and NH4F as chemical modifiers for the determination of phosphorus by electrothermal atomic absorption spectrometry is described. All the fluorides studied were found to stabilize the phosphorus atomic absorption signals, allowing for ashing temperatures higher than those with no modifier, without losses of the analyte. The best results using these modifiers were obtained with NaF. However, the sensitivity of the phosphorus signals was lower than when Pd was used as the modifier. The accuracy of the phosphorus determinations using the new NaF modifier was checked by analyses of NIST Standard Reference Materials (SRM 1570 Trace Elements in Spinach, SRM 1571 Orchard Leaves, SRM 1575 Pine Needles and SRM 1577a Bovine Liver)
An αβ-Frame Moving Average Filter to Improve the Dynamic Performance of Phase-Locked Loop

The moving average filter (MAF) is one of the most widely used methods to block harmonics in a phase-locked loop (PLL). This paper proposes a simple structure for MAF to work directly in the <inline-formula> <tex-math notation="LaTeX">$\alpha \beta $ </tex-math></inline-formula>-frame (called <inline-formula> <tex-math notation="LaTeX">$\alpha \beta $ </tex-math></inline-formula>MAF). Compared with the standard <italic>dq</italic>-frame MAF (<italic>dq</italic>MAF) used as an in-loop filter, <inline-formula> <tex-math notation="LaTeX">$\alpha \beta $ </tex-math></inline-formula>MAF acts as a prefilter and significantly improves the PLL dynamic response. Its derivation process and implementation is described in detail in both continuous and discrete time domains. In an adaptive prefilter-based PLL, a frequency feedback loop (FFL) is indispensable, but it is not easy to design. Therefore, nonadaptive <inline-formula> <tex-math notation="LaTeX">$\alpha \beta $ </tex-math></inline-formula>MAF is suggested, and a corrector is added to eliminate the phase error at off-nominal frequencies. In this paper, the small signal model of the proposed PLL is carried out through the perspective of frequency shift, which simplifies the derivation process. The effectiveness of the proposed technique is confirmed through simulation and experimental results.
A 1Mb/s 2.86% EVM GFSK Modulator Based on ΔΣ BB-DPLL without Background Digital Calibration

This paper presents a two-point modulation architecture based on the ΔΣ bang-bang digital PLL (BB-DPLL) that does not rely on high-resolution digital-to-time converter (DTC) to avoid long digital calibration time for wireless systems. Multiple techniques are integrated to improve in-band noise performance and overcome DCO nonlinearity. In the proposed two-point modulator, FIR-filtered 1b high-pass modulation overcomes the nonlinearity of a digitally-controlled oscillator (DCO), while low-pass modulation achieving good linearity with a 1b time-to-digital converter (TDC) having high reference frequency. To mitigate in-band noise degradation in the ΔΣ BB-DPLL, a third-order ΔΣ modulator with a 1b output, a 4b hybrid DTC using a 3b delay line and a 1b phase-interpolated frequency divider, and a ΔΣ BB-DPLL with high reference frequency are employed. A prototype 1.8GHz 1Mb/s GFSK modulator implemented in 65nm CMOS achieves the in-band phase noise of -94dBc/Hz and the EVM performance of 2.86%, while consuming 5.3mW from a 1V supply.