Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerating massive MIMO in 6G communications by analog in-memory computing circuits

Wireless communication systems are the backbone of today's digital society. To achieve unprecedented throughput and efficiency, 5G and 6G networks will leverage parallel communication over multiple spatial channels under the massive MIMO (multiple-input multiple-output) framework. One of the main limitations of MIMO is its heavy load of matrix operations, a task for which von Neumann-based computers are deeply unoptimized. This bottleneck can be solved by in-memory computing (IMC), where computation is performed directly within the memory, thus eliminating the constant data shuttling between the memory and the processing unit. Here, we provide an experimental validation of a closed-loop IMC (CL-IMC) system on a 90 nm CMOS integrated circuit. Zero-forcing and regularized-zero-forcing decoding are executed with $14\times 7$ MIMO link. The hardware demonstration shows 99.91% accuracy, which is close to a floating-point precision decoder. These results support CL-IMC as a promising candidate for data processing in massive MIMO for next-generation cellular networks.
A Threshold Voltage Generator Circuit with Automatic Refresh and Dynamic Updating for Ultra-Low-Power Continuous-Time Comparators

A Threshold Voltage Generator (TVG) circuit for continuous-time comparators is presented. It may be used in ULP IoT systems requiring nanoWatt power consumption, kbps bitrates and the reception of packets with lengths up to hundreds of bits. The circuit is based on a switched capacitor technique to generate the threshold voltage without requiring any large area resistors and with the system clock active only during the reception of data, thus minimizing energy consumption. Latency is also minimized as the threshold is generated within the first received bit of the packet. The reception of packets with no limits on their length is made possible by continuously updating the threshold, which also allows correct operation even in case of amplitude variations in the incoming signal during the data reception. It has been implemented and verified through post-layout simulations in an STMicroelectronics 90-nm CMOS technology with a 0.6-V supply, targeting a 1-kbps bitrate. It occupies an area lower than 0.001 mm2, which is less than 1% of the area of a standard RC-based TVG implemented in the same technology. A prototype of the proposed TVG is currently under fabrication.
Predictability assessment of northeast monsoon rainfall in India using sea surface temperature anomaly through statistical and machine learning techniques

The socio‐economic growth of India is adversely affected by the abnormal meteorological phenomena of floods and droughts. Thus, rainfall prediction is highly desirable for livelihood and sustainability. In this study, northeast monsoon rainfall (NEMR) is predicted over the Indian peninsular region for the months of October, November, and December using a global sea surface temperature (SST) anomaly as a predictor by linear regression (LR), artificial neural network (ANN), and extreme learning machine (ELM) techniques. The predictions are made by LR, ANN, and ELM models for the period 1990–2016 using the training input (1871–1989) of different time series samples of global SST anomaly data collected from Hadley Centre SST data set (HadSST3). Principal component analysis was used for dimensionality reduction of the data sets, and its application substantially improved the predictive ability of the machine learning techniques. The performance of the PC‐ELM technique with the ensemble method (ESM 8–9) training window is found to be more accurate than the LR and ANN techniques and provides minimal error scores as per the statistical analysis. This study concludes that the global SST anomaly has the potential to be used as a predictor for northeast monsoon rainfall and useful for long‐range climatic projections.
The Packing Landscapes of Quasi-One Dimensional Hard Sphere Systems

Packing Landscapes of Constrained Hard Sphere Fluids By Mahdi Zaeifi Yamchi When a liquid is cooled below its equilibrium freezing temperature, it becomes supercooled and the molecular motions slow down until the system becomes kinetically arrested, forming a glass, at the glass transition temperature. These amorphous materials have the mechanical properties of a solid while retaining the structural properties of a liquid, but do not exhibit the usual features of a thermodynamic phase transition. As such, they present a number of important challenges to our understanding of the dynamics and thermodynamics of condensed phases. For example, supercooled liquids are classified on the basis of the temperature dependence of their transport properties and structural relaxations times. Strong liquids display an Arrhenius behavior, with the logarithm of their viscosity growing linearly with inverse temperature. Fragile liquids behave in a super–Arrhenius manner, where the viscosity appears to diverge at temperatures above absolute zero, suggesting the possibility of an underlying thermodynamic origin to the glass transition. Some complex, network forming liquids, such as water and silica have also been shown to have a dynamical crossover from fragile to strong liquid behavior as the temperature is decreased. The potential energy landscape paradigm, combined with inherent structure formalism, provide a framework for connecting the way particles pack together with the thermodynamics and dynamics of the liquid and glassy phases. However, the complexity of this multi–dimensional surface makes it difficult to fully characterize, and find rigorous relationships between the nature of particle packings and glass forming properties have not been established. The goal of this thesis is to study some of the general features of the glass transition and find the connection between the dynamics and the thermodynamics of glass forming liquids. To this end, the packing landscapes of quasi–one–dimensional hard discs and hard spheres are studied. A two dimensional system of hard discs with diameter σ, confined between two hard walls (lines) of length L, separated by a distance 1 < Hd/σ < 1 + √ 3/4, is studied by using the
In-silico analysis of phytochemical profile of Capparis decidua evince substitutive approach against inhibition of acetycholinesterase in Alzhemier's

Alzheimer's disease (AD) is a persistent neurological condition that is identified by cognitive impairment and the deterioration of memory abilities. AD is supposedly linked to features indicative of the formation of lesions such as neuritic plaques, neurofibrillary tangles, cerebral amyloid angiopathy, neuronal loss, and cholinergic deficiency. Hence, making the associated proteins like amyloid precursor protein cleaving enzyme, butyrylcholinesterase (BuChE), and acetylcholinesterase (AChE) potent targets for curative intervention. As of yet, there is no medication that effectively treats AD. Therefore, the exploitation of bioactive compounds from natural sources can serve as a potential strategy to ameliorate the current treatment. Capparis decidua is a medicinal plant belonging to the family Capparacea has abundance of secondary metabolites like polyphenols, alkaloids, phytosterols, and terpenoids that have been associated with anti-diabetic, antioxidant, anthelmintic, and anti-inflammatory activities. The main goal of this study was to examine the potential of bioactive compounds present in Capparis decidua as an alternative medication for Alzheimer's disease (AD). To achieve this, we conducted an initial in-silico assessment of the therapeutic value of Capparis decidua in the treatment of AD. To achieve this, we analyzed the binding affinities of 55 phytochemicals towards the AChE target, using AutodockVina for molecular docking. Results revealed that Linolenic Acid and 2-hydroxy cinnamic Acid had the lowest binding energy scores of -7.8 (Kcal/mol) and -7.1 (Kcal/mol), respectively and hence the highest binding affinities, which were comparable to the binding energy of the standard control Donepezil (-7.0 Kcal/mol). In addition, we conducted various analyses to assess drug-likeness, bioactivity, permeation through the Blood-Brain Barrier (BBB), and bioavailability of selected phytochemicals, which may have potential as medicative agents against neurodegenerative diseases like AD.
A Single-Ended Digital Transmitter Based on I/Q-Sharing Switched-Capacitor Power Amplifier and Third-Order Harmonic-Rejection

This paper presents a design of 250MHz single-ended digital transmitter (DTX) with switched-capacitor power amplifier (SCPA) for communication between mobile phones and satellites. The implementation of SCPA enhances linearity and efficiency. For compact die area and high efficiency, 50%-duty-cycled clocks are used in the DTX eliminating second-order harmonic without differential architecture. Aiming at higher efficiency compared to conventional quadrature DTX, the technique of digital-domain in-phase (I) and quadrature phase (Q) cell sharing is applied in this work. Moreover, the combination of three signals with specific amplitude and phase eliminates third-order harmonic, improving the output spectrum significantly. The DTX is designed in 22nm standard CMOS process and reaches a peak output power of 8.87dBm at 250MHz and power-added efficiencies (PAE) of 57.4%. When modulating a 4 MHz 64-QAM signal, the error vector magnitude (EVM) is 0.61%, while the average output power and PAE are 1.93dBm and 32.9%, respectively.
Effects of Head and Discharge on Internal Flow Characteristics of a High-Quality Installed 1 GW Francis Turbine

In order to explore the flow characteristics of high-quality installed 1 GW Francis turbines, a full geometric model of the fluid domains, including spiral case, stay vane, guide vane, runner, labyrinth seals, balance tubes, and a draft tube, is established. Based on the Shear Stress Transport (SST) Turbulence model, Computational Fluid Dynamics (CFD) software is utilized to conduct numerical simulations of a 1 GW Francis turbine under rated operating condition, maximum head operating condition, and minimum head operating condition. According to simulation results, the flow state is the most stable under the rated operating condition. In addition, the pressure span in the guide vane, labyrinth seals, draft tube and other channels is uniform, and there is no flow shedding and blade passage vortex in the runner. Furthermore, the flow regime is relatively unstable in the other two operating conditions. The fluid flow impacts the blades, resulting in flow separation. There are many vortices in the draft tube, which may threaten the safe and stable operation of the turbine. Moreover, the pressure distribution trend in the fluid channel under the three operating conditions is almost the same. The results of this paper can provide a useful reference for the design and installation of giant Francis turbine units.
An Overview of IoT for Smart Healthcare Technologies

The Internet of Things (IoT) is critical to the healthcare industry's fast automation. Healthcare Internet of Things is the name given to the IoT branch dedicated to medical science (H-IoT). Data collection and processing are essential components of any H-IoT applications. The merging of machine learning (ML) procedures with H-IoT is expository due to the massive quantity of data that is included in healthcare and huge value contained by accurate predictions. This study seeks to be both a collection and review of different state-of-the-art implementations of machine learning algorithms that are combined with IoT in health sector. The extensively used machine learning algorithms are compactly introduced, hence their application in H-IoT has been estimated in terms of benefits, scope and potential improvements. In healthcare, a model's practical application necessitates it to be highly accurate and include numerous security safeguards. This paper offers an improved insight of existing trends of IoT in healthcare and determines the gaps for research in future. This study also updates the healthcare professionals regarding the recent advances and applications of IoT in healthcare sector.
A 3.84 GHz 32 fs RMS Jitter Over-Sampling PLL with High-Gain Cross-Switching Phase Detector

A 32 fs RMS jitter oversampling phase-locked loop (OSPLL) exploiting a high-gain cross-switching phase detector (CSPD) is proposed. The over-sampling PLL increases sam-pling frequency by 4x, reducing the in-band phase noise and overcoming the loop bandwidth limitation due to the reference frequency. Leveraging the increased loop bandwidth, the noise contribution of the voltage-controlled oscillator (VCO) is sig-nificantly suppressed. The high-gain CSPD adopts a common-mode sampling technique with time interleaving switches to ensure that the reference clock is sampled only at the maximum slew rate. The CSPD with a higher gain facilitates reducing the noise contribution from the phase detector (PD) and the transconductance cell. Additionally, an RC poly-phase filter (PPF) is employed to generate quadrature clocks, avoiding the deterioration of the PLL's low offset frequency phase noise. The PLL is implemented in a 40-nm CMOS process. Simulation results show that the PLL achieves a 32 fs RMS jitter integrated from 10 kHz to 100 MHz and a power consumption of 6.5 mW, resulting in an $FoM_{jitter}$ of -261 dB. At 3.84 GHz frequency, the in-band phase noise is -136.8 dBc/Hz at 100 kHz offset.
Clustering-Enabled Tracking Areas Design for Beyond-5G Networks: A Live Network Demo

One of the current challenges still facing artificial intelligence (AI) and machine learning (ML) based solutions is their performance in real life environments. In this work, we report and analyze the outcomes of the deployment of one of our previously proposed AI-based automatic tracking areas (TA) design algorithm in a real and live mobile network. The implemented automation is based on our solution in [1], and has been experimented in an area with accidental land morphology that often leads to aggressive overshooting, and other radio issues affecting many high traffic sites. The area under consideration includes one major city, surrounding suburbs and villages, and main road traffic sections. We first analyze the characteristics of the considered area of deployment, collect the required statistics in terms of handover attempts, mobility event measurement reports (MRs), total paging requests per source site, and the intersite distances ISD. We then build a real dataset and construct the needed similarity matrix to run our ML-based algorithm. Finally, we present and compare the results collected after the deployment of our proposed solution in the live network with the initially existing TA plans. The obtained reduction in overhead costs of both tracking area update and paging signaling exceeded the expectations and the MNO targets
Event-based Classification with Recurrent Spiking Neural Networks on Low-end Micro-Controller Units

Due to its intrinsic sparsity both in time and space, event-based data is optimally suited for edge-computing applications that require low power and low latency. Time varying signals encoded with this data representation are best processed with Spiking Neural Networks (SNN). In particular, recurrent SNNs (RSNNs) can solve temporal tasks using a relatively low number of parameters, and therefore support their hardware implementation in resource-constrained computing architectures. These premises propel the need of exploring the properties of these kinds of structures on low-power processing systems to test their limits both in terms of computational accuracy and resource consumption, without having to resort to full-custom implementations. In this work, we implemented an RSNN model on a low-end, resource-constrained ARM-Cortex-M4-based Micro Controller Unit (MCU). We trained it on a down-sampled version of the N-MNIST event-based dataset for digit recognition as an example to assess its performance in the inference phase. With an accuracy of 97.2%, the implementation has an average energy consumption as low as $4.1\ \mu\mathrm{J}$ and a worst-case computational time of $150.4\ \mu\mathrm{s}$ per time-step with an operating frequency of 180 MHz, so the deployment of RSNNs on MCU devices is a feasible option for small image vision real-time tasks.
Performance Analysis of Blended NIDS Model for Network Intrusion Detection System in WSN

Lighting up to bring far-off individuals closer is advantageous as network services expand. The absence of network security, however, has a greater negative effect on society. To provide dependable security to end users is a major global concern. In various applications to deliver services, a variety of electronic devices with network connections are essential. A preliminary duty is to secure these connections. Intrusion detection and prevention techniques are the most effective means of defending against attacks, securing the systems, and monitoring network traffic. The best method to find incursions is a new framework. The Blended model for Network Intrusion Detection, an unique strategy that makes use of machine learning, is presented in this research. It is intended to provide excellent accuracy when used with an ensemble learning algorithm. CIC-IDS2018, a dataset made available by the Canadian Institute of Cyber Security, was the one utilised in this study. This study intends to determine high detection accuracy of 95.49 and 95.39 F1 score by reducing the data dimension using SMOTE (Synthetic Minority Oversampling Technique) in conjunction with feature selection and Ensemble Random Forest algorithm.
A Self-Healing Pairwise Key Pre-Distribution Scheme in IoT-based WSNs

Wireless Sensor Networks (WSNs) are becoming integral to Internet of Things (IoT) applications. This emergence of the WSNs requires greater awareness of the security of IoT, which makes it inevitable to secure the communication medium of the WSNs. One way of securing the communication medium is to encrypt the data sent over the WSNs, requiring the usage of cryptographic materials to achieve the secrecy of the exchanged data between the sensor nodes. The main component of the cryptographic materials needed is the keys used to encrypt and decrypt the data. Symmetric pairwise keys are particularly convenient for resource-constrained networks like IoT-based WSNs as they ensure security while conserving resources. However, existing key management solutions based on symmetric cryptosystems are vulnerable to node compromising attacks. Another important aspect of WSNs is the addition of new sensor nodes (postdeployment) either periodically or depending on the use case, which means new symmetric pairwise keys must be provided to communicate with newly added sensor nodes. For this purpose, a new key pre-distribution scheme named POK (adaPtive and rObust Key pre-distribution) is presented in this paper. POK improves the way keys are generated and pre-loaded in the sensor nodes. The main idea of the POK is that newly added sensor nodes will be pre-loaded with pairwise keys computed by using a hash function and having knowledge of the number of future postdeployments. Comparison study with related works concludes that POK offers less communication overhead and doesn’t require time synchronization leading to an energy-efficient scheme. Also, POK decreases the impact of node compromising attacks and ensures the self-healing property where compromised nodes have a short time effect on the network and newly deployed nodes are not affected.
Reinforcement Learning-based Control for an Upper Limb Rehabilitation Robot

The concept and development of a robot-assisted rehabilitation program using an actor-critic-based deep reinforcement learning controller are detailed in this paper. This research aims to provide a therapy approach that employs a variety of technologies to decrease human participation to give intelligent robot-assisted therapy sessions. In this paper, a Reinforcement Learning (RL) agent utilizing the Deep Deterministic Policy Gradient (DDPG) algorithm was trained to tune a PID controller's parameters to precisely control the robot to track any set of therapeutic exercise trajectories. The reward function was formulated to obtain fast convergence as well as accurate tracking. The proposed approach was designed such that the robot's joint angle is always within an allowable range to ensure the safety of the patient and the effectiveness of the therapy. The RL-based control approach is simulated on a 2 degree-of-freedom (DOF) robot named iTbot, and the simulation results show efficient training and good tracking performance which highlight the effectiveness of the control strategy.
L-IDS: A Lifelong Learning Approach for Intrusion Detection

Intrusion detection systems (IDS) represent a class of defensive security tools with the purpose of protecting the network from intruders in the network administrator’s arsenal. Despite the high precision of traditional signature-based IDS, its effectiveness is still under question due to the growth of a number of encrypted attacks and the volume of network traffic. This is considered one of the main motivations for researchers to develop anomaly-based IDS, which usually suffer from a higher false positive rate. In this paper, we propose and implement a lifelong-learning anomaly detection IDS (L-IDS) with the capability of the network environment’s adaption to limit the false positive rate of anomaly detector in the range of signature-based IDS. We consider Snort as a baseline and UNSW-NB15 as the ground truth in the evaluation of our proposal. We demonstrate how L-IDS achieves a higher level of precision in comparison with the existing signature-based IDS.
Fusion of Feature Selection Techniques and Machine learning Algorithms for Attack Classification on 802.11 Wi-Fi AWID Dataset

Vulnerabilities pertaining to the Media Access Control (MAC) layer are the primary reason for the existence of attacks in 802.11 Wi-Fi networks. In this paper, we aim to explore the Aegean Wi-Fi intrusion dataset (AWID) and design a model for detection and classification of network intrusions. There are three classes of attacks present in the dataset: impersonation, flooding, and injection. In intrusion detection systems (IDS), machine learning classifiers are one of the most effective solutions for preventing unauthorised access to resources. The key aspect of building a model based on machine learning is feature selection. In this paper, we have used four machine learning classifiers, namely Decision Tree, Random Forest, Gaussian NB, and Bernoulli Naive Bayes, for the classification of data as normal or intrusive. We have further applied filter methods and wrapper methods for feature selection. To the best of our knowledge, machine learning along with feature selection has never been used on the AWID-CLS-R dataset. The proposed work achieved the highest accuracy of 95.2% using a random forest classifier in 104 seconds using filter methods as the feature selection technique. In addition, we have used a confusion matrix and model building time for evaluating other machine-learning classifiers.