Miracle-dz/UniFAR-FTUs huggingface dataset:
A 1Tb 3b/Cell 8th-Generation 3D-NAND Flash Memory with 164MB/s Write Throughput and a 2.4Gb/s Interface

As data sizes increase exponentially, the demand for higher-density NAND with a smaller cell size and a higher interface speed has also increased [1]–[4]. However, the increased number of WL-stack layers results in a smaller sensing circuit size and a smaller WL-to-WL spacing, which increases the intrinsic transistor variation and the inter-cell interference. One way to achieve good density while maintaining system performance is to support more multiple-plane operations with a circuit under cell array architecture, which leads to an increased noise power. Moreover, to achieve a 2.4Gb/s the I/O circuits need to support the faster speed while achieving lower power consumption. This paper presents the offset cancelling sensing latch (OCSL) scheme, the quad-group interference-free read (Q-IFR) scheme, and the common-source line (CSL) noise-tracking scheme to resolve the aforementioned challenges. In terms of the high-speed I/O bandwidth, a receiver circuit and an internal reference voltage generator are also proposed to increase the I/O speed, reduce the standby power consumption, and reduce the settling time when the chip is enabled.
Inverse Kinematics Model for a Ship Mounted Two-DoF Manipulator System

The model of inverse kinematics of a ship-mounted two-DoF manipulator for calculation of the joint angles is substantial due to the randomness of the ocean waves. This paper addresses such a task by investigating the effect of ocean random waves on the inverse kinematics process. Ship motions in terms of a surge, heave, sway, roll, pitch, and yaw motions are produced via Marine Systems Simulator (MSS) toolbox and considered as non-deterministic disturbances at the base of the manipulator. Given the specifications of the ship and manipulator, extensive numerical simulations and their validation are performed. The results reveal that the randomness of ocean waves seems to be appearing on the inverse results due to strong coupling among the ocean waves, manipulator system, and the ship. The latter slows down the iteration process where the computational time follows the increase of sea state code, and the increment has a non-linear trend. The results can be used as a baseline for developing visual servoing based adaptive control for manipulator system stabilization.
Production of open cell Nickel-based metal foam from polyurethane template

Metal foam is a novel class of metals that is inspired by naturally occurring, functionally graded, cellular structures like wood and bones. The potential applications of metal foam are diverse, especially in the field of filtration at high temperature and corrosive environments; hence the study of metal foams has become attractive to researchers and efforts are being made to optimize the methodology to develop the metal foam. Various methods of production of metal foam have surfaced over the past few years. Electro deposition technique is considered the easiest and economical ways to produce metal foam. Also, attributing to the outstanding corrosion resistant properties exhibited by Nickel, it has been selected as the base material in this manuscript. Step by step process know-how of electro deposition technique, characterization to investigate integrity parameters at each step, challenges encountered during production and their solution to optimize the process are presented in this paper. Quality foam with 10.22 MPa strength and 92% porosity after the sintering process was produced.
Analog Matrix Processor for Edge AI Real-Time Video Analytics

One of the salient hurdles for wide adoption of machine learning (ML) has been efficient and high-performance edge compute. ML developers use very large, expensive, and power-intensive systems to create new models, but the final implementation needs to be deployed in a cost-effective, small, and low-power solution. This has commonly led to situations where the models that can be deployed in an edge environment are significantly smaller than state-of-the-art models leading the benchmark scoreboards [1]. A flash-based Analog Matrix Processor (AMP) is well positioned to close this development/deployment gap by providing edge inference compute that is 10-to-100× more efficient than traditional digital inference. It accomplishes this by leveraging denser circuitry via smaller memory elements (multi-level flash instead of several SRAM cells) and denser communication (256+ level signaling instead of binary signaling). Deep Neural Networks (DNNs) are an ideal application for the AMP since they can use analog-aware training to overcome the inherent non-idealities present in analog signal chains. DNNs use large pattern matchers which naturally suppress noise and map well to the AMP, which prefers to amortize the fixed costs inside the analog system over larger calculations.
Development of model with energy balance using bioclimatic architecture for extreme warm climates

The paper demonstrates the research project modeled as BAITYKOOL prototype at The Sustainable City Dubai to test the bioclimatic principles for generating energy balance. This project designed and constructed as a competition entry for the Solar Decathlon Middle East (SDME 2018) is now working as a living lab testing the performance of the building designed with a biological ecosystem to suit the extreme warm climatic condition. The paper discusses the innovative systems and strategies developed to generate comfort conditions at the habitat and enhance the house performance with low energy demand. The principles of bioclimatic architecture to effectively reduce the cooling load and improve the ambient light quality are designed with a user-centric control mechanism suited to the activities. The house design is based on the ‘Compensation law’ of house functioning with reduced energy demand during the days and nights for the systems. The HVAC system takes advantage of the law of ‘Free Cooling’ tested at the prototype with the use of outdoor temperature to cool the house without the use of AHU during favorable outdoor climatic conditions. The radiative sky cooling system adds value to indoor cooling and reduced energy demand for a chiller with the use of the night sky as a heat sink. The house integrates the photovoltaic panels into the architectural design with the dynamic roof pergola and the vertical PV panes on the facade to meet the energy demand with renewable clean energy. Higher energy is generated with the biomimetic solar cells. The house with the smart monitoring system optimizes the use of electricity and waterworks towards maintaining the energy balance. The prototype designed to suit the extremely warm climate of the region has innovative systems, bioclimatic architecture, and strategies with an eco-responsive sustainable solution creating for a healthy living and working environment.
A novel neurophysiological feature based on quantifying EEG data for separating of patients in psychiatric disorders with comorbidities

Post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) share similar clinical symptoms, preventing the accurate diagnosis of two diseases. The aim of this study is to introduce a novel feature extraction method, thereby developing a more accurate computer-aided diagnosis (CAD) system for differentiating patients with PTSD and MDD. To this end, P300 event-related potential (ERP) data were recorded from 30 PTSD patients and 30 MDD patients. Diagnostic features were extracted by averaging P300 ERP waveforms over the time. The classification performance was evaluated using a support vector machine with leave-one out cross-validation. The best classification accuracy of 73.33 % was obtained when using one mean amplitude-based feature. Moreover, the used features would reflect the altered cognitive-related traits in PTSD patients compared to MDD patients.
A 10b Source-Driver IC with LSB-Stacked LV-to-HV-Amplify DAC Achieving 2688μm2/channel and 4.8mV DVO for Mobile OLED Displays

As the spatial resolution of mobile OLED displays increases, more than a thousand column channels must be integrated into a source-driver IC (SD-IC). Furthermore, the data resolution of the DAC occupying the majority area of the column channel must become higher for color-depth improvement. The top-left of Fig. 5.9. 1 shows a typical SD-IC architecture composed of R-DAC-based column channels sharing a global resistor-string. The switch-array size of the conventional R-DAC increases proportionally to a power of 2 with DAC resolution. Moreover, since the full-scale range ${\left(FSR\,=\,V_{H}\,-\,V_{L}\right)}$ of the R-string is directly correlated with the dynamic range in an OLED display, the R-DAC, including level-shifters (L/S), must be implemented with high-voltage MOSFETs (HV-MOS). Accordingly, even modern CMOS technology nodes are still unable to shrink the SD-IC size considerably. Thus far, many efforts to improve the DAC area efficiency employing a voltage-interpolative sub-DAC have been reported [1 – 3], as shown in the top-middle of Fig. 5.9. 1. However, the use of a 2-output HV R-dAc, which occupies 2× larger area, is mandatory for voltage interpolation. Mismatch between sub-DACs is also inevitable, and thus the inter-channel uniformity, one of the key performance metrics in a SD-IC, deteriorates significantly. This paper presents an ultra-compact-sized 10b SD-IC achieving an area of 2688μm2/nel even without adopting voltage-interpolation. As shown in the top-right of Fig. 5.9. 1, two key innovations of this work include: 1) a mismatch-insensitive switched-capacitor-based LV-to-HV-amplify DAC, which enables an 8b R-DAC to be realized with only low-voltage MOSFETs (LV-MOS) while obtaining the HV output, and 2) a deviation-free 2b LSB stack-up (LSU) technique enabling finer resolution consuming little area. Considering a 1.5V thin-gate MOS is 24×smaller than a 5V thick-gate MOS for the same ${R_{\text{ON}}}$ in 130nm CMOS, this work can achieve dramatic shrinkage of the chip size due to the all-LV-MOS-based R-DAC in conjunction with the elimination of L/S. In addition, both our innovations are highly robust to process variations and thus contribute to overcoming inter-channel mismatch, which is a drawback of prior voltage-interpolative schemes.
A neural circuit model for prospective control of interceptive reaching

Two prospective controllers of hand movements in catching - both based on required velocity control - were simulated. Under certain conditions, this required velocity control led to overshoots of the future interception point. These overshoots were absent in pertinent experiments. To remedy this shortcoming, the required velocity model was reformulated in terms of a neural network, the Vector Integration To Endpoint model, to create a Required Velocity Integration To Endpoint model. Addition of a parallel relative velocity channel, resulting in the Relative and Required Velocity Integration To Endpoint model, provided a better account for the experimentally observed kinematics than the existing, purely behavioral models. Simulations of reaching to intercept decelerating and accelerating objects in the presence of background motion were performed to make distinct predictions for future experiments.
Remote Monitoring Device for People Under Self-Quarantine Due to COVID-19

The COVID-19 pandemic has caused mass overcrowding in hospitals worldwide. Overcrowding leads to lower care standards and increase in the risk of infection on the hospital staff. The goal of this research paper is to design a low-cost remote monitoring device that will help to prevent overcrowding and lower the pressure on hospitals during the COVID-19 pandemic. The device will allow hospitals to monitor patients; while they are under quarantine outside the hospital. This will allow hospitals to focus on critical cases only and provide a better environment for the recovery of these patients. The proposed device is based on a Raspberry Pi Zero W Microprocessor and a collection of Heart Rate sensor (MAX30201), Blood Oxygen sensor (MAX30201), Body Temperature sensor (TMP117) and fall detection sensor (MPU6050). The device collects updated information about the health situation of the patient and sends it to the family of the patient and the healthcare center. It can also send a call for emergency in case of a critical health issue. The device was implemented and tested with very good results.
A IMax $\vert_{\max^{,}}$ Fully Integrated Multi-Phase Voltage Regulator with 91.5% Peak Efficiency at 1.8 to 1V, Operating at 50MHz and Featuring a Digitally Assisted Controller with Automatic Phase Shedding and Soft Switching in 4nm Class FinFET CMOS

Modern compute SoCs often consume 100s of amperes at voltages of 1V and below which poses huge challenges for delivering power to the socket. Fully integrated voltage regulators (FIVRs) can mitigate these challenges by providing local step-down conversion, hence reducing input current to the socket [1]–[4]. In addition to the large current draw, most digital compute domains employ dynamic power management techniques to optimize power consumption based on performance demand, that results in a large load current range in FIVRs used in granular domains. At the same time, transitions from low to high current can happen at nanosecond timescales. As a result, the FIVR must provide high efficiency over a wide load current range while retaining fast transient response to minimize performance impact from voltage droops. Past implementations used discontinuous conduction mode (DCM) to provide a good light load efficiency [2], however, DCM increases voltage ripple and can hurt transient response.
Data Analytics in Acute Kidney Injury Prediction: Opportunities and Challenges

Acute Kidney Injury (AKI) is a common medical condition with a high mortality rate. The incidence of AKI is exceptionally high in hospitalized patients, particularly those suffering from acute illness or postoperative patients. As AKI impacts both patient and financial outcomes, there has been a keen interest the disease. In recent years, AKI and big data synergies have been explored, particularly through electronic health records (EHR), ideal for AKI risk prediction. Due to the massive amount of data in EHR, machine learning (ML) models for data analytics are slowly rising. The application of ML is a promising approach due to its ability to collect EHR data and make predictions on AKI onset accordingly, instead of relying on independent health records. This systematic review aims to identify the opportunities and challenges that arise in integrating data analytics in AKI prediction.
Indentification of autism spectrum disorder based on an improved convolutional neural networks

In order to develop the application of deep learning for identification of autism with small sample size, an improved algorithm based convolutional neural networks (CNN) was proposed in this paper. Compared to the classical CNN, the 2-dimensional input was changed to a 1-dimensional input, PReLU was adopted as the activation function instead of ReLU, and the softmax was used as classifier at the time of pre-training and replaced with the parameter-adjustable SVM after pre-training. Furthermore, L2 regularization was used for cross entropy loss function in order to avoid overfitting. A total of 416 subjects with autism and 441 subjects with typical development across 14 sites were involved in the study. The original feature was extracted from the resting-state functional magnetic resonance imaging data. The improved CNN could identify the autism with an accuracy of 84.44% (sensitivity 85.39%, specificity 80.57%) by the 10-fold cross-validation. The results demonstrated that the proposed algorithm performs better than those in recent similar studies.
Reading Imagined Letter Shapes from the Mind’s Eye Using Real-time 7 Tesla fMRI

We present a 7 Tesla fMRI proof-of-concept study of the first letter speller BCI that decodes imagined letter shapes from activity patterns in early visual cortical areas. New tools are developed to enable real-time population receptive field retinotopic mapping for encoding and decoding. Using two different letter shapes (H and T), classification performance of generated activity patterns during imagery reaches 80% accuracy in each individual. Using a denoising autoencoder, recognizable letter shapes could be reconstructed and displayed as feedback to participants in the scanner.
A 300GHz 52mW CMOS Receiver with On-Chip LO Generation

Data communication in the 300GHz band has found renewed interest in the past few years. Two reasons can account for this trend. First, the IEEE 802.15. 3d standard was established in 2017 for radios in the unallocated frequency band from 252GHz to 322GHz [1]. Second, a number of researchers have demonstrated the viability of such radios in CMOS technology [2]–[5]. For 300GHz transceivers to serve as an attractive complement to WiFi and WiGig, they should perform extensive beam forming so as to overcome the path loss. This point underscores the importance of low power consumption per element. Moreover, high-order modulation schemes require a local oscillator (LO) signal with very low phase noise. Unfortunately, the generation and distribution of LO phases proves extremely power hungry, making the realization of a single-chip 300GHz CMOS data-communication radio a difficult challenge.
A 194nW Energy-Performance-Aware loT SoC Employing a 5.2nW 92.6% Peak Efficiency Power Management Unit for System Performance Scaling, Fast DVFS and Energy Minimization

A self-powered IoT system-on-chip (SoC) reduces power to sub-μw and employs multiple power-management techniques to trade-off ultra-low power (ULP), higher performance, smaller energy harvester footprint, and longer operating lifetime. Minimum Energy Point Tracking (MEPT) [1]–[4] keeps an SoC operating at the minimum energy point (MEP) to enhance system lifetime. Previous sample-and-hold MEPT schemes need frequent voltage comparisons and a high-frequency clock that increases power [2]. Current-ratio-based MEPT relies on specialized CMOS technology for body-bias tuning [3]. A switched-capacitor-based MEPT can achieve energy minimization at a targeted performance [4], but it uses a 30MHz clock $\text{wit}\mu \mathrm{W}$ power consumption and low power efficiency. For ULP IoT applications, SoCs need to have ultra-low quiescent power, high efficiency for energy delivery, performance scaling based on available energy, and energy minimization to increase system lifetime. In this work, we propose an ULP IoT SoC with a triple-mode power management unit (PMU) that integrates energy-performance scaling, event-driven fast DVFS, and MEPT features to improve the system energy efficiency, as shown in Fig. 13.8. 1. This work achieves a minimum 194nW power consumption for the SoC and 5.2nW quiescent power for the PMU with a 92.6% peak efficiency and >104 dynamic range. The timing waveform in Fig. 13.8. 1 (bottom), demonstrates the transition of the three modes including energy aware (EA), performance aware (PA), and MEPT based on event priority and input voltage level which reflects the energy availability. As such, the system energy consumption and performance could be well-balanced based on both the input and output conditions.
A 96.2-nJ/class Neural Signal Processor With Adaptable Intelligence for Seizure Prediction

This work presents the world’s first neural signal processor for seizure prediction, which includes a preprocessing unit, a feature extractor, a reconfigurable support vector machine (SVM) kernel, and a postprocessing unit. Seizure prediction performance is enhanced by on-chip training for model adaptation. Design optimization is applied across the layers of abstraction to minimize the area and energy. The area of the feature extractor is reduced by 28% with an approximated energy operator (AEO). The proposed scaling-based Newton–Raphson (NR) divider reduces the required number of iterations for division by 62.5%. For alternating direction method of multipliers (ADMM)-based SVM training, the computational complexity is reduced by up to 99.9% through pointer-based matrix multiplication. By leveraging the LDL decomposition, 80% multiplications for updating weights are saved. The chip achieves a seizure prediction performance with a 92.0% sensitivity and a 0.57/h false alarm rate (FAR). The training latency is 8.44 ms with a power dissipation of 2.31 mW at 6.05 MHz. Compared with an ARM Cortex-M3 microcontroller, this work achieves a $124\times $ higher area efficiency and a $299\times $ higher energy efficiency. The chip also supports seizure detection and achieves a sensitivity of 98.6% and an FAR of 0.18/h, exceeding the state-of-the-art designs.
Investigation of Atomic Layer Futuristic Memory Devices of Binary Chalcogenides WX2 (X = S and Se): First-Principles Study

We have investigated the spin-dependent structural, electronic and localized-induced magnetic moment in an atomic layer of binary chalcogenide semiconductors, Tungsten sulphide/selenide (WX2, where X= S, Se) using first-principle calculations. It was observed that the addition of fluorine to the WX2 monolayer lattice reduces the bandgap of the material and induced a magnetic moment of ~1 Bohr magneton. Moreover, the reasons behind this magnetic transition from non-magnetic semiconductors to magnetic semiconductors were investigated and discussed. The calculated binding energy reveals that the pristine monolayer is more stable than the fluorine doped WX2 sheet. Also, intermittent energy levels were created due to the fluorine atoms and resulted in p-type acceptor semiconductor behaviour in spin up and n-type donor behaviour in spin-down of WX2 monolayer. It was observed that the unparalleled behaviour of spin can be tuned to suitable applications such as memory devices and spintronics.
CovMix: Covariance Mixing Regularization for Motor Imagery Decoding

In this paper we study the problem of motor imagery (MI) decoding using electroencephalography (EEG) signals. The spatial covariance matrix of EEG signals is a feature with many applications on brain-computer interfaces. Several previous works use EEG covariances directly as inputs to Riemannian classifiers for MI decoding, restricting the potential models that can be used for classification, to Riemannian geometry frameworks. Other works use covariances either as inputs to optimization objectives that derive spatial filters, or to perform alignment with respect to reference states. Such methods discard temporal information that is contained in EEG signals. We take a different approach, and utilize covariances as a means to concurrently align EEG signals and regularize a Convolutional Neural Network (CNN) that is trained on MI classification. Specifically, we randomly mix session-level and trial-level co-variance matrices, traversing their geodesic on the Riemannian manifold, and perform EEG signal alignment using the mixed matrix. This is done during the training phase, effectively acting as regularization on the CNN model, as the signals are augmented using various transformation matrices to align them. We evaluate our method on the dataset of BCI Competition IV-2a, showing its superiority over traditional alignment.