Miracle-dz/UniFAR-FTUs huggingface dataset:
Hardware Architecture of a Decentralized Massive MIMO Equalizer based on Gauss-Seidel Detection

In massive multi-user multiple-input-multiple-output (MU-MIMO) communication systems, high interconnect data rates are a major problem when the number of antennas at the base station increases. For some years, decentralized approaches have been proposed which address this issue by distributing the receive samples of clusters of antennas to different dies. In this paper, we present a hardware implementation for a decentralized matrix-splitting signal estimator unit as Gauss-Seidel equalizer which lowers the requirements regarding interconnect data rates. We synthesize our full custom RTL-design for an Artix-7 FPGA-prototype and ASIC-implementation and present the results regarding performance parameters hardware complexity, power, latency and throughput.
Researches on Tunable BST Capacitors Under Electricity and Stress Fields

In the paper, tunable Ba0.5Sr0. 5TiO3 (BST) varactors on LiNbO3 substrates were investigated by changing electricity and stress fields applied to the BST varactors. The BST films were fabricated by RF Magnetron Sputtering on LiNbO3 substrates and the XRD of films showed high crystal quality on (110) surface. We demonstrate that the capacitance tunability can each 1.67 under applied DC voltage from 0 to 8V. Additionally, the tunability can be 1.46 by changing stress from 0 to 7. 2MPa. Interestingly, the tunability is 2.45 under the combinations of electricity and stress fields. This phenomenon implies that the applied dynamic stress can break restrictions in the limitation of the capacitance tunability of this thin film. It is of great significance to observe the direct relationship between the change of strain and dielectric tunability caused by stress fields, which can provide more ideas for the design of thin films varactors in future applications, such as tunable SAW filter, microwave delay line and acoustic stress sensor, etc.
Application of Process Analytical Technology-Based Feedback Control Strategies To Improve Purity and Size Distribution in Biopharmaceutical Crystallization

Purity is a critical quality attribute for both pharmaceutical and biopharmaceutical products. The presence of impurities (solvents, salts, or byproducts of the synthetic path) in drugs can cause a reduction of their effectiveness or can even be toxic for the patients. Biopharmaceuticals are produced by biological processes which are difficult to control. Therefore, the amount of impurities that has to be removed can be significantly higher than in the case of synthetic pharmaceuticals. The aim of this work is to exploit process analytical technology tools and different feedback control strategies (T-control, direct nucleation control, and supersaturation control) for the crystallization of a biopharmaceutical product. UV/vis spectroscopy and focused beam reflectance measurement combined with a Crystallization Process Informatics System (CryPRINS) were used to improve the crystal size distribution and purity of crystallized vitamin B12. The different feedback control strategies were compared to classical .
Joint Estimation of Hemodynamic Response and Stimulus Function in Functional Ultrasound Using Convolutive Mixtures

Functional ultrasound (fUS) is an exciting new neuroimaging technique that is able to record brain activity similar to functional magnetic resonance imaging, yet with higher spatiotemporal resolution and at lower cost. We consider the problem of jointly estimating the underlying neural sources and the hemodynamic response function (HRF) from fUS recordings. We propose to model the measured voxel time-series as a convolutive mixture of multiple source signals and solve the blind deconvolution problem via block-term decomposition. This allows us to estimate both the source time courses and a different HRF for each voxel and source combination, which accounts for the variability of HRF across different brain regions and events respectively. The proposed approach is proven to be robust against noise via simulations and further validated on real fUS data by performing a visual experiment on a mouse. The obtained results show that the proposed method is able to recover the timings of the visual paradigm.
Accurate Online Junction Temperature Estimation of IGBT Using Inflection Point Based Updated I–V Characteristics

The junction temperature (<inline-formula><tex-math notation="LaTeX">$T_j$</tex-math></inline-formula>) estimation of the insulated gate bipolar transistor (IGBT) is important for reliable operation of the power converters in various applications. For <inline-formula><tex-math notation="LaTeX">$T_j$</tex-math></inline-formula> estimation, <sc>on</sc>-state collector-emitter voltage (<inline-formula><tex-math notation="LaTeX">$v_{ce}$</tex-math></inline-formula>) at higher collector currents (<inline-formula><tex-math notation="LaTeX">$i_c$</tex-math></inline-formula>) is widely used temperature sensitive electrical parameter (TSEP). For real-time <inline-formula><tex-math notation="LaTeX">$T_j$</tex-math></inline-formula> estimation, this TSEP is calibrated using the I–V characteristics of the new IGBT. Due to bond-wire degradation, the original I–V characteristics of IGBT changes resulting in inaccurate <inline-formula><tex-math notation="LaTeX">$T_j$</tex-math></inline-formula> estimation. In this article, a technique is proposed to update the I–V characteristics of the degraded IGBT, without affecting the normal operation of the power converter. It is achieved by estimating the increment in bond-wire resistance (<inline-formula><tex-math notation="LaTeX">$\triangle R_{con}$</tex-math></inline-formula>) by using real-time samples of <inline-formula><tex-math notation="LaTeX">$v_{ce}$</tex-math></inline-formula> and inductor current. The mathematical analysis is also presented to find an error in estimated <inline-formula><tex-math notation="LaTeX">$\triangle R_{con}$</tex-math></inline-formula>. The major contributions of this article are as follows: a) it enables the accurate <inline-formula><tex-math notation="LaTeX">$T_j$</tex-math></inline-formula> estimation of the IGBT throughout its lifetime; and b) it also provides the parameter <inline-formula><tex-math notation="LaTeX">$\triangle R_{con}$</tex-math></inline-formula>, which could be utilized in condition monitoring of the IGBT. Further no additional circuitry is required. The proposed technique is validated on experimental setup, which is developed in the laboratory. The error in <inline-formula><tex-math notation="LaTeX">$T_j$</tex-math></inline-formula> estimation is observed within <inline-formula><tex-math notation="LaTeX">$1$</tex-math></inline-formula> °C the degraded IGBT, which shows the effectiveness of the proposed scheme.
A Review on Glucose Monitoring Using Enabling Technologies of Internet of Things

The applications of Internet of Things are one of the fastest developing sector in industry. In Health sector, the new technologies of patient monitoring using Internet of Things are increasing worldwide which is known as Internet of Health Things. This paper proposes idea of healthcare monitoring and ambient-assisted living based on Internet of Health Things. This technology based healthcare serve as a source of data for healthcare providers, researchers, technology specialists, and the general population. The numbers of people suffering from diabetes are increasing day by day. New technologies like continuous glucose monitoring and glucose flash monitoring are better in monitoring glucose levels and more efficient compared to the glucometer technology which is already established within the market. However due to the high cost it is not affordable to majority of the population. In order to solve this problem this paper proposes Freestyle Free sensor which allows continuous monitoring of glucose rate using technologies of Internet of Things. It is an affordable technology and provides doctors and caregivers information from remote location. The data collected from various sensors are processed by LibreMonitor and stored in cloud for future analysis. The glucose rates measured are analyzed with politician Freestyle Libre software during the same period. Here we propose an Internet of Health Things based system to monitor glucose levels of the diabetic patient continuously from remote locations by doctors which is accurate and of low cost.
Automated Fiber Quantification for Alzheimer’s Disease Diagnosis from Neuropsychological Biomarkers

Alzheimer’s disease is the gravest among the neurodegenerative diseases because of its high prevalence and large mortality rate. Its manual revelation has become clinically insignificant because of the in expertise, high rate of false positives (FP), and false-negative (FN). This paper frames a quick, affordable, and objective judgement of AD with a novel data mining method. The paper utilises the upgraded flavour of support vector machine-assisted with quantile hyper-spheres (QHSVM) for cataloguing, which frames quantile hyper-circle by utilising pinball loss in place of hinge loss, and thus, makes the model insensitive towards the noise. The trial results on the benchmark ADNI dataset displayed the QHSVM when equated with SVM, Pin-SVM, TWSVM, and THSVM through experimental investigations, which justified the better performance of the QHSVM, even on the addition of noise. The results revealed that SVM got an accuracy of 96.87%, which got dipped to 94.50% on the incorporation of noise. The QHSVM got 97.89% as the initial accuracy and 97.65% as the accuracy value after incorporation of noise, and thus, justifies the least effect of added noise on QHSVM.
Emotional Classification of EEG Signal using Image Encoding and Deep Learning

For humans, emotions are important and play a significant role in human insight. Emotions are commonly identified by speech, facial expression and gesture. Recently Electroencephalograph (EEG) based emotion recognition have accumulated solid interest in the research community and it provides cheap, portable and reliable techniques for emotion recognition. In this work, classification of seed database having three emotions like positive, neutral and negative was performed, which was publicly available. This paper has two parts, time-series to image conversion of EEG signal and classification of emotion. First part, the data is transformed to an image that is used to analyse the EEG signal and in second part, the transformed image passes through deep learning to understand the emotions encountered during the EEG signal generation. Experimental results indicate that the scalogram of image encoding provides the best classification accuracy of 98%, compared to spectrogram and Hilbert Huang Transform (HHT) 78% and 75% classification accuracy respectively.
Demystifying the Largest Live Game Streaming Platform via Black-Box Measurement

Empowered by today's fast development of e-sports (Electronic Sports) games, more and more people are watching live-game-streaming through various platforms aside from playing by themselves. Understanding the characteristics of these platforms can benefit both the game viewers and the platform. However, the live game streaming platforms in China, which has the largest game market in terms of both the revenue and the total number of gamers in the world, are still under the veil of mystery. In this paper, we present an initial investigation on Douyu, the largest live game streaming platform in China. We inferred the platform's architecture and workflow by analyzing the captured traffic and crawled data. Moreover, we investigated the statistics and patterns from both the broadcasters' and viewers' perspectives. Compared with the world-wide live game streaming platform Twitch, the regional platform Douyu has more regular daily changes in online channels, which can provide insights for resource provisioning in the future system optimization. Based on the measurement, it is found that the top 7.5 % of games have attracted 79.5 % viewer's attention. Moreover, the number of mobile viewers have exceeded the number of PC viewers, taking up about 64.8 % of the total viewer number. As the most popular mobile game in China, the game Arena of Valor (referred to as wzry in Douyu) has more than 4,500 simultaneous online broadcasters and accounting for 12.7% of the overall attention.
Comprehensive opinion analysis on recent QUAD formation in Indo Pacific region using Twitter corpus

India is known for its highly disciplined foreign policies, strategic location, vibrant and massive Diaspora. India envisages enhancing its scope of cooperation, trade and widens its sphere of relations with the Pacific. As a result, the world is witnessing the rise of Indo-Pacific ties. Before the 1980’s the keystone of the universe was called the Atlantic, but now a radical shift to the east is noticed by the term “Indo-Pacific‟. In this respect, a recent development occurred as a partnership, the Malabar exercise, in the waters of the Pacific and the Indian Oceanic region, which supports and proclaim free, open, and comprehensive Indo-Pacific and stays committed on a rule-based order. Considering the mass inclusion of people on social media platforms and contemplating their opinion has been of much interest in the research. Understanding and categorizing a large dataset of ideas into positive, negative, and neutral aspects is challenging. Our motive is to elucidate how the world, especially the citizens, is welcoming this strategic and influencing partnership. Also, Could social media influence the meaning of partnership? Our paper is divided into two aspects, one dealing with the comparison of various techniques and the other telling opinions of people regarding Indo-pacific relations. We used a geo-specific dataset obtained in various languages. Also, we applied a blend of various ML and DL techniques, feature extraction models, and opinion-based classification, which not only gives an analysis of opinions regarding “Indo-pacific” but also provides an in-depth insight to the comparison of various state-of-the-art sentiment analysis techniques, which is provided as a continuation of earlier review presented on sentiment analysis. The word-cloud visualization system assists people in understanding the changes of public sentiment reactions better.
LSTM Network-Assisted Belief Propagation Flip Polar Decoder

The belief propagation (BP) decoding has the advantage of providing soft information iteratively, which allows polar codes to be applicable in scenarios requiring high throughput. However, the unsatisfactory error-correction performance makes the BP polar decoding unable to compete with the successive cancellation list (SCL) decoding that is the baseline algorithm in 3GPP. The BP flip (BPF) decoding offers one option to address this issue through additional decoding attempts with a few bits flipped. However, current BPF decoders yield less efficient flipping accuracy. Considering the complex bit correlation and unanalyzable erroneous bit probability of BPF decoding, we propose a long short-term memory (LSTM) network-assisted BPF (LSTM-BPF) decoding algorithm. Given the soft information output by the original BP decoding, the trained LSTM network achieves more accurate erroneous bit prediction ability. The simulation results show that compared with the state-of-the-art BPF decoders, the LSTM-BPF decoder improves the hit rate of the error bits by 10%, thereby obtaining more than 0.1 dB gains at a frame error rate (FER) of 10−4 for polar codes with length 128 and code rate 0.5. The efficient hardware architecture of the proposed LSTM-BPF decoder is designed. By introducing a critical set to reduce the size of the network, the LSTM accelerator could significantly reduce the required computation and storage resources, thereby reducing the inference time.
Enhanced Postbond Test Architecture for Bridge Defects Between the TSVs

The use of through silicon vias (TSVs) is essential for vertically connecting the individual circuit layers of a 3-D IC. However, because the pitch between TSVs is being decreased, it becomes more vulnerable to bridge defects between the TSVs. In the previous architectures, the parallel topology between the power supply and the bridge defects reduces the resolution of the test results and makes them very sensitive to process variations. In this article, an enhanced postbond test architecture that improves the testability of bridge defects screening with a floating control circuit of supply voltage driver is proposed. The proposed structure allows the selective formation of a series circuit that includes the supply voltage driver and bridge defects between the TSVs; the structure can improve the output voltage resolution of the bridge defects by 27.2 times over that of the previous architectures. According to the experimental results, while exhibiting a proper test time, hardware overhead, and peak current consumption for mass production, the system guarantees testability for the detection of bridge defects. Moreover, the Monte Carlo simulation results show that the proposed architecture has a higher test reliability under process variations than the current test architectures.
A Neural Network Engine for Resource Constrained Embedded Systems

This paper introduces a dedicated neural network engine developed for resource constrained embedded devices such as hearing aids. It implements a novel dynamic two-step scaling technique for quantizing the activations in order to minimize word size and thereby memory traffic. This technique requires neither computing a scaling factor during training nor expensive hardware for on-the-fly quantization. Memory traffic is further reduced by using a 12-element vectorized multiply-accumulate datapath that supports data-reuse. Using a keyword spotting neural network as benchmark, performance of the neural network engine is compared with an implementation on a typical audio digital signal processor used by Demant in some of its hearing instruments. In general, the neural network engine offers small area as well as low power. It outperforms the digital signal processor and results in significant reduction of, among others, power (5×), memory accesses (5.5×), and memory requirements (3×). Furthermore, the two-step scaling ensures that the engine always executes in a deterministic number of clock cycles for a given neural network.
Lightweight Authentication Protocol Using Self-Certified Public Keys for Wireless Body Area Networks in Health-Care Applications

Health-care centers have been meeting challenges due to the increase in the aging population and chronic diseases that need continuous medical monitoring. Wireless body area network (WBAN) is a non-invasive technology consisting of diverse connected bio-medical sensors placed in the human body, which measure physiological parameters and make the information accessible to health-care professionals ubiquitously. However, a major problem in WBAN is the security and privacy of the patient’s medical information. An essential security method to protect the physiological data is authentication. Several authentication protocols have been proposed for WBANs; however, some require many computing resources, and some have security vulnerabilities. In this article, the Two-Party Lightweight Authentication Protocol (TLAP) for WBANs is proposed. It uses self-certified public keys based on Elliptic Curve Cryptography (ECC), scalar point multiplication, symmetric key encryption, and the lightweight operations xor and conventional hash function to reduce the computational cost of the protocol. Formal and informal analyses were made to demonstrate that TLAP provides mutual authentication and resists potential attacks in WBANs. The security and performance of TLAP and similar existing protocols were analyzed and compared. The analyzes showed TLAP supports more security features and has lower execution time and communication cost than the other protocols, which is significant to decrease the energy consumption in WBANs.
An FPGA based Real time Implementation of Nosé hoover Chaotic System using different numerical Techniques

In this paper, we have implemented the Nosé-Hoover chaotic generator by numerically coding the differential equations in Python and modeling them in Xilinx Kintex 7 FPGA development environment using Euler’s, Heun’s, and Runge Kutta’s 4th order-based algorithms in Verilog HDL. The performance of these algorithms has been analyzed by comparing Slice LUTs used, delay, DSPs, etc. The accuracy of the HDL implementation of each algorithm has been analyzed by calculating Root Mean Square Error(RMSE). In addition, the computation time of PC-based and FPGA-based implementation also have been compared for each algorithm.
NVMCache: Wear-Aware Load Balancing NVM-based Caching for Large-Scale Storage Systems

Load balancing is critical for storage clusters to scale out and improve performance under skewed workloads. Multi-layer caching mechanism is proposed to improve load balancing for large-scale storage systems. However, because dynamic random-access memory (DRAM)-based cache is limited by capacity, volatility and chip cost, non-volatile devices, such as flash-based SSD and 3D Xpoint, are gradually being deployed as caches in large-scale storage systems. The emerging non-volatile memories such as PCM and ReRAM are expected to replace DRAM as caches in the future. Experimental evaluations show that while multi-layer caching mechanism works well for DRAM-based caching, it leads to write imbalance under mixed read-write workloads for non-volatile memory (NVM)-based caching. Existing mechanisms for DRAM-based cache do not consider the limited endurance and read-write asymmetry issues that are associated with NVM-based caches. Thus, how to effectively utilize the physical characteristics of NVMs and extend their lifespans has become the main challenge of NVM-based caching systems. In this study, we present NVMCache, an NVM-based multilayer caching mechanism. NVMCache introduces a wear-balancing scheme to avoid local intensive write operations to balance both load and wear. NVMCache aims to 1) ensure I/O load balancing and avoid access bottlenecks for large-scale storage systems, 2) take advantage of NVM's read/write asymmetry and prioritize access to reduce write-request blocking and improve overall access performance, and 3) extend the overall service lifetime of the NVM-based cache. The experimental results show that under different workloads, compared with DistCache, the state-of-the-art multi-layer caching mechanism, NVMCache improves throughput by a maximum of 10% and reduces wear imbalance by a maximum of 80%.
Measuring the efficiency of highly funded scientists in China based on the data envelopment analysis

Since the competition in the scientific community is becoming increasingly fierce, measuring the research performance of highly funded scientists is growingly critical for relevant management authorities to make effective policies in scientific research and development. However, little quantitative analysis has focused on this issue. To facilitate the research in this area, we first construct a dataset containing both the funding inputs and the research paper outputs of highly funded scientists in China. Then, instead of directly summing quantitative indicators, in this paper, we measure the efficiency of highly funded scientists in China based on the data envelopment analysis models. We show that we can grade highly funded scientists into several grades and present their scores on attractiveness or progress to other grades. The gradual learning path for less efficient scientists is also provided. These patterns of highly funded scientists may be beneficial for management authorities to make effective policies.