Miracle-dz/UniFAR-FTUs huggingface dataset:
System Design for Computation-in-Memory: From Primitive to Complex Functions

In recent years, we are witnessing a trend moving away from conventional computer architectures towards Computation-In-Memory (CIM) based on emerging memristor devices. This is due to the fact that the performance and energy efficiency of traditional computer architectures can no longer be increased at the same pace as before. The main barriers which limit the performance and energy improvement are the memory and power walls. Thus far, the main effort from researchers is toward enabling CIM as an accelerator for specific applications. Consequently, this current application-specific nature/approach has put less emphasis on the potential general-purpose applicability of CIM, i.e., merging several accelerators into one that is less than the sum of the parts. In this paper, we demonstrate the CIM concept using a broader and generalized model. Considering this model, the state-of-the-art CIM-based logic and arithmetic primitive functions, which can be the building blocks for complex functions, are investigated. Besides, we present potential applications of CIM which provides insights into the challenges and opportunities of a generic CIM system design. Finally, we highlight the future directions regarding the construction of CIM-based systems.
Joint Air-Noise Pollution Monitoring System using IoT Elements

As of now, the total population is relied upon to be around 7.9B. A review from the recent study has shown that 18% of the populace on the planet possesses somewhere around one vehicle. Lately, the air and clamor have become one of the significant foundations for worry all over the planet. The degree of contamination is expanding quickly because of the variables, for example, enterprises, urbanization, expanding populace, vehicle utilize that are influencing human wellbeing. The continuous information of the air and commotion quality is gotten to through the brilliant gadgets and broke down to gauge the effect on city individuals. The shrewd gadgets are fit for estimating the Temperature, Humidity, Carbon Monoxide, LPG, Smoke, sound and other risky particulate matters like PM2.5 and PM10 levels in the climate. This paper discusses a novel methodology of monitoring the noise and air pollution on a joint basis using IoT elements
Intramonth oscillations of Atlantic ITCZ observed in SMAP satellite salinity

ABSTRACT The time variability of wind and rainfall in the Atlantic Intertropical Convergence Zone (ITCZ) has a wide oscillation spectrum that includes strong intra-month periodicities. These latter short-period oscillations span from individual convection lifecycle events of a few hours to longer pentad and multi-week convective cluster periods. This study reports on measured ITCZ rain variability at periods of a few weeks and its imprint on sea surface salinity (SSS) observed using satellite remote sensing. Despite small amplitude of variations of only psu, these changes are shown to be detectable in radiometer SSS estimates collected by the Soil Moisture Active Passive (SMAP) satellite. They are shown to develop in the mid-Atlantic ITCZ latitude band and to follow its seasonal meridional progression. These rain-correlated sea surface signatures appear quasi-synchronously across a wide range of longitude. Both the ITCZ-related seasonal migration and high zonal propagation speeds distinguish these intra-month SSS oscillations from that of tropical instability waves (TIW), with TIWs having periods greater than month and slower westward propagation. While detection of tropical Atlantic TIW salinity signatures using satellite data has received significant attention, it appears that atmospheric freshwater forcing impacts on the upper ocean at these shorter periods represents an additional process resolvable from space using SMAP. Correlation analyses of precipitation and winds indicate that low latitude convection clusters are accompanied by an upward Ekman pumping anomaly, which uplifts saltier water to surface layers diluted by enhanced precipitation. These vertical processes modify the salt balance and change the phase relationship between the oceanic SSS response and the atmospheric freshwater forcing.
Modification of safflower oil with triallyletheracrylate dienophile

Alkyd resins and reactive diluents which are among the main components of dye and coating industries, have properties such as binding and easy application. In order to improve the film properties of alkyd resins, a wide variety of modification studies are carried out. In this study, safflower oil was modified with triallyletheracrylate dienophile (TAEA) with Diels - Alder reactions and the product was called as reactive diluent (triallyletheracrylate safflower oil). TAEASO reactive diluent have been characterized by Nuclear Magnetic Resonance (NMR) and Fourier Transform Infrared Spectroscopy (FT-IR) methods. Then, the different dye formulations were prepared with a certain proportions of reactive diluent, wetting and drying agents and a soy oil (SO) based alkyd resin. Dye formulations were called as SO, TAEASO10-SO, TAEASO20-SO, TAEASO30-SO, TAEASO40-SO. The viscosity reductions of the dye formulations were determined, and then the dye formulations were applied to surface of Aliminium plates as film and the film properties such as drying time, film thickness, pencil hardness, brightness were evaluated after the curing process. The results show that the viscosity reduction is proportional to the ratio of the reactive diluent. As a result of the study, the drying time for the reactive diluents with alkyds were 1.5 times faster than alkyd alone.
Deep-Learning-Based Localization Approach with Pseudorange for Pseudolite Systems

The pseudolite system can broadcast navigation signals and can be used as a good supplement or substitute to the global navigation satellite systems. The pseudorange measurement is usually used to calculate the initial position. However, the pseudorange positioning is still faced with the multipath problem. To improve the positioning accuracy, a deep-learning localization method is proposed in this paper. The proposed method mainly includes the offline initial phase and the online localization phase. During the offline phase, the pseudorange data is collected and transformed into single-differenced pseudorange. The database is expanded by copying the pseudorange vectors and masking pseudorange values. Then a residual fully connected neural network (ResFCNN) is used to learn the mapping relationship between single-differenced pseudorange and location. A residual connection across fully connected layers is added in the ResFCNN to strengthen the learning ability of neural networks. During the online phase, the trained model and real-time pseudoranges are used to predict the location. Experimental results show that the root-mean-square error (RMSE) of the proposed method is 0.73 m under a multipath condition and 1.21 m with a signal absence, which are reductions in RMSE of 81 and 85%, respectively, compared to the conventional iterative least squares method.
Pretty simple bounds on quantum state discrimination

We show that the quantum measurement known as the pretty good measurement can be used to identify an unknown quantum state picked from any set of $n$ mixed states that have pairwise fidelities upper-bounded by a constant below 1, given $O(\log n)$ copies of the unknown state, with high success probability in the worst case. If the unknown state is promised to be pure, there is an explicit measurement strategy which solves this worst-case quantum state discrimination problem with $\widetilde{O}(\|G\|)$ copies, where $G$ is the Gram matrix of the states.
Development of a low-cost sEMG Acquisition and processing platform

The acquisition of surface electromyographic signal (sEMG) commonly requires the use of expensive devices or closed systems. In addition, a separate processing protocol is necessary in order to analyze the signal obtained, this requires time consuming techniques, and the results are not always accurate and easy to understand. Therefore, this platform aims to provide an integrated hardware and software system for the acquisition and processing of electromyography signals, automating most of the sEMG signal processing, yet providing its main features (i.e, activation intervals, maximum and average amplitude). The device sends the data to the program that implements different techniques used in sEMG analysis, such as filtering, spectral analysis and moving envelope while allowing different methods to be compared depending on the case. Tests were performed for different sEMG signals with different muscle targets, and movements. All the signals were easy to capture and the algorithm was able to identify muscle activation intervals, filter out most of the noise, and deliver its key indicators in a graphical interface.
First neutron data recorded at the V20 Test Instrument utilizing prototype chopper systems and beam monitors planned for ESS

The main objective of the European Spallation Source (ESS) is to perform consistently high impact neutron scattering science using the highest neutron flux of any facility in its class. This ambition is naturally accompanied by operational challenges related to the vertical integration of the instruments' hardware and software shared between collaborators from 13 European countries. The present work will detail integration tests performed at the V20 Test Instrument at Helmholtz-Zentrum Berlin in Germany. An ESS chopper prototype was successfully controlled through the ESS Chopper Integration Controller (CHIC) and Experimental Physics and Industrial Control System (EPICS). Neutron data were successfully collected and timestamped using neutron beam monitors, the ESS prototype detector readout electronics and the Data Management Software Center (DMSC) data acquisition (DAQ) software. Chopper rotation events were timestamped and the chopper disk showed excellent stability and neutron absorption characteristics. The test resulted in i) the expected time-of-flight (TOF) response, ii) collected data that compared well with instrument reference data and iii) revealing the diagnostic power of the performed integration tests. The results of this integration test validate the ESS chopper and detector DAQ architecture choices.
River Water Level Prediction for Flood Risk Assessment using NARX Neural Network

Flood is one of the primary natural disasters in Malaysia and becoming more frequent and on a large scale lately. Not excluded, Sabah encounters repeated floods caused by river overflow. Therefore, an efficient mechanism for flood risk assessment is needed until a more viable solution exists. This paper proposes using the Nonlinear Autoregressive with Exogenous Input (NARX) neural network to model the river water level as an approach for assessing flood risk. The NARX was trained, validated, and tested using the hydrological data obtained at the target areas: Wariu River (Sungai Wariu), Kota Belud, and Padas River (Sungai Padas), Beaufort. Inputs to the NARX are the current and previous water levels at the upstream and downstream rivers and rainfall at the target area. The output is the predicted water level at the downstream river that can be used to assess flood risk. Results show that NARX trained with the Levenberg-Marquardt training algorithm (trainlm) performs best compared to other training algorithms. Results also show that the NARX could predict up to thirty days ahead of water level prediction, with an R2 of 0.75 and above. However, it is more safe to conclude that a reliable prediction for up to five days ahead, with R2 above 0.85 can be obtained.
AGVS: A New Change Detection Dataset for Airport Ground Video Surveillance

Change detection is the foundation of intelligent video surveillance of the airport ground. However, experiments have shown that change detection algorithms with good performance on traditional datasets (e.g., CDnet2014) perform poorly in airport ground surveillance. The reason is that traditional datasets focus on the diversity of scenarios, while the practical application requires robustness against various changes in a single scene. We posit that the solution to this problem is to establish a unique dataset for airport ground surveillance and develop specific algorithms for this scenario. In this paper, we present an Airport Ground Video Surveillance benchmark (AGVS) for change detection of the airport ground. AGVS includes 25 long videos, amounting to about 100000 frames and accurate ground truth for all frames. Each video contains multiple challenges specific to the airport ground (e.g., haze, camouflage, strip shape, shadow and illumination change, simultaneous multi-scale objects) and various appearance changes of the aircraft). Change detection ground truth is generated by manual annotation. The AGVS benchmark can be downloaded from https://www.agvs-caac.com. Furthermore, we conduct a simple review of current change detection algorithms, both unsupervised or supervised, and then 21 state-of-the-art algorithms are tested and analyzed on the AGVS benchmark. Finally, we conclude with algorithm design principles of change detection for airport ground surveillance.
Low-Complexity Decoding and Construction of Space-Time Block Codes

Space-Time Block Coding is an efficient communication method used in multipleinput multiple-output wireless systems. The complexity with which a SpaceTime Block Code (STBC) can be decoded is important from an implementation point of view since it directly affects the receiver complexity and speed. In this thesis, we address the problem of designing low complexity decoding techniques for STBCs, and constructing STBCs that achieve high rate and full-diversity with these decoders. This thesis is divided into two parts, the first is concerned with the optimal decoder, viz. the maximum-likelihood (ML) decoder, and the second with non-ML decoders. An STBC is said to be multigroup ML decodable if the information symbols encoded by it can be partitioned into several groups such that each symbol group can be ML decoded independently of others, and thereby admitting low complexity ML decoding. In this thesis, we first give a new framework for constructing low ML decoding complexity STBCs using codes over the Klein group, and show that almost all known low ML decoding complexity STBCs can be obtained by this method. Using this framework we then construct new full-diversity STBCs that have the least known ML decoding complexity for a large set of choices of number of transmit antennas and rate. We then introduce the notion of AsymptoticallyGood (AG) multigroup ML decodable codes, which are families of multigroup ML decodable codes whose rate increases linearly with the number of transmit antennas. We give constructions for full-diversity AG multigroup ML decodable codes for each number of groups g > 1. For g > 2, these are the first instances of g-group ML decodable codes that are AG or have rate more than 1. For g = 2 and identical delay, the new codes match the known families of AG codes in terms of rate. In the final section of the first part we show that the upper triangular matrix i
Statistical Modeling of Tidal Weather in the Mesosphere and Lower Thermosphere

We study the statistical properties of tidal weather (variability period <30 days) of DW1 amplitude using the extended Canadian Middle Atmospheric Model (eCMAM) and Sounding of the Atmosphere using Broadband Emission Radiometry (SABER). A hierarchy of statistical models, for example, the autoregressive (AR), vector AR, and parsimonious vector AR models, are built to predict tidal weather. The quasi 23‐day oscillation found in the tidal weather is a key parameter in the statistical models. Comparing to the more complex vector AR and parsimonious vector AR models, which consider the spatial correlations of tidal weather, the simplest AR model can predict one‐day tidal weather with an accuracy of 89% (R2: correlation coefficient squared). In the AR model, 23 coefficients at each latitude and height are obtained from seven years of eCMAM data. Tidal weather is predicted via a linear combination of 23 days of tidal weather data prior to the prediction day. Different sensitivity tests are performed to prove the robustness of these coefficients. These coefficients obtained from eCMAM are in very good agreement with those from SABER. SABER tidal weather is predicted with an accuracy of 86% and 87% at one day by the AR models with coefficients from eCMAM and SABER, respectively. The five‐day forecast accuracy is between 60 and 65%.
Lattice-Reduction-Aided Equalization for V2V Communication Channel

World is moving towards the implementation of massive MIMO based communication system and that forces the researchers to design low complexity receiver architecture. MIMO system performance in Vehicular ad hoc networks (VANETs) is a popular research topic. And to support Vehicle-to-vehicle (V2V) communication in high speed mobility condition, it required to have reliable and secure of com-munication. This paper deals with the performance evaluation of the low complex LLR-MMSE receiver in terms of the channel capacity and bit error rate improvement. In this paper we have considered V2V Spatial Channel Model (SCM) and Nakagami-m chaneel model for the performance evaluation. The performance has been evaluated based on the mathematical calculation and simulated results. And also performance comparison between the conventional linear MIMO receivers with the lattics reduction aided MIMO receivers have been presented.
Effect of Pressmud, as an Organic Fertilizer, on Some Soil Properties, Growth of Tomato Plant and Infestation of Tuta absluta Under Saline Irrigation Water

A filed experiment was carried out to study the effect of addition pressmud (PM) as organic wastesunder saline irrigation water on some soilproperties, Tuta Absuluta Damage and plant growth. PM was applied at rates of 0, 5 and 10 ton/fed. The soil was cultivated with tomato (Genotype: Alissa) under salinity irrigation whichapplied having 0, 25 and 50 mM NaCl.Untreated soil was used as a control (CTRL). At the end of the experiment, soil samples and plants were taken from each treatment. The results showed that the application of PM significantly decreased the soil bulk density, soil pH, while, under saline irrigation water increased them compared with PM. On the other hand, PM increased each of ECe, cations such as Ca+2 + Mg+2, Na+ and K+ while these parameters were increased with increasing salinity levels compared with CTRL. In addition, data showed also that the parametersplant growth were significantly positively affected by the application of PM addition rates compared with control or salinity stress. Na+ was accumulated into plant tissues with increasing level NaCl. The Ca2++ Mg2+ and K+ contents reduced under saline irrigation water compared as control or PM addition rates. On the other hand, plants growing under 10 ton/fed of PM and freshwater had lower number of miners/leaf and infested fruits/plant compared with plants growing under 5 ton/fed of PM or control treatment. High level of salinity had a good effect on T. absluta damage than control treatment. These results support the hypothesis that plants growing under a good level of organic fertilizer and salt-stress are better defended againstT. absluta.
Simulation of Smart Traffic Light By Using Image Processing and Reinforcement Learning

Vehicle travel is on the rise across the world, particularly in metropolitan cities. As a result, simulating and optimizing traffic control algorithms is required to better handle this growing demand. In this research, we investigate the simulation and optimization of traffic light controllers in a city and provide a reinforcement learning-based approach. The algorithm that is used is deep Q-learning. There are four processes in this study. The first is data collection. Next, a simulation is built. Then, a reinforcement learning model is trained and tested in the model. Last, the results are compared between the traditional traffic lights and traffic lights that were applied with the reinforcement learning model. The results obtained in this study are queue length of vehicles in front of traffic light and delay time of vehicles after given green signal in two scenarios, which are an environment that uses traditional traffic light and an environment that uses traffic light that applied with reinforcement learning model. From the result, the environment that applied with reinforcement learning agent has shorter time delay and queue length of vehicles. Queue length of vehicles is reduced from 58 to 18 and time delay is reduced from 3900 seconds to 380 seconds.
Comprehensive Analysis of a Modular Dual Battery EV Charging System Architecture to Overcome Grid-Intermittency and Harmonics Distortion

This paper describes a dual battery EV charging system with variable battery voltage operations. A wide range of EVs is now available based upon their operational flexibility and power availability. However, frequent modification in charging architecture and related components based upon the battery voltage diverseness can't be assured. To fulfill the charging requirements for multiple low power EVs, a dual battery charging system with minimal circuit components is described in this paper. A non-linear sliding mode control architecture with direct power control also ensures smooth operation during the bi-directional charging operation. In addition, the renewable and battery energy storage support is also depicted in the charging architecture to make it self-sufficient to operate in standalone mode. The control configuration is designed with MATLAB and the Simulink platform to validate the bi-directional charging operation with a Level-I charging prototype.
3D Vertical Directional Coupler using Slope Waveguides on Si

3D multilayer photonics platform integrated with bulk Si or SOI can enable intra-chip optical connections and data processing without interfering with the on-chip electronics. Multi-layer integration has the potential to increase device density, offers more functionality in terms of novel materials and devices stacked in a multilayer assembly. It has been shown that interlayer coupling can be achieved by creating slopes between the two layers [1–3]. In this work, fabrication and preliminary characterisation of vertical directional coupler is demonstrated for optical signal distribution between two Si layers of the device. Lumerical was used to simulate waveguide geometry and coupling length. The bottom waveguide layer was fabricated on a 400nm SOI using e-beam lithography and inductively coupled plasma (ICP) etching. Fully etched waveguides of width 500nm were fabricated with input and output gratings for light coupling. This is followed by deposition of 1 μm thick SiO2 on the waveguide and the geometry was planarised using chemical mechanical polishing (CMP) leaving 100 nm oxide on top of the bottom waveguide layer. A 10 nm silicon nitride layer was deposited on it as etch-stop layer for buffered HF. Then another 1 μm thick layer of SiO2 is deposited and interlayer slope angle of 13.4° was formed using HF wet etch. Next, a 400 nm of low-loss amorphous Si (a-Si) was deposited by hot-wire chemical vapour deposition (HWCVD) [4] and waveguides with similar dimensions to that of bottom layer were fabricated and capped with another 1 pm of oxide. The gap between the bottom crystalline silicon and the top a-Si is 100nm. Fig. 1(a) shows SEM of the cross-section, (b) shows microscope image of the top view of the fabricated device and (c) the schematic of the vertical coupler. The waveguides were measured using tunable laser with wavelengths from 1540–1620 nm by coupling light into grating couplers using single mode fiber in TE polarisation and their transmission is plotted in Fig. 1 (d).