Miracle-dz/UniFAR-FTUs huggingface dataset:
A FPGA-based Hardware Accelerator for Bayesian Confidence Propagation Neural Network

The Bayesian Confidence Propagation Neural Network (BCPNN) has been applied in higher level of cognitive intelligence (e.g. working memory, associative memory). However, in the spike-based version of this learning rule the pre-, postsynaptic and coincident activity is traced in three low-passfiltering stages, the calculation processes of weight update are very computationally intensive. In this paper, a hardware architecture of the updating process for lazy update mode is proposed for updating 8 local synaptic state variables. The parallelism by decomposing the calculation steps of formulas based on the inherent data dependencies is optimized. The FPGA-based hardware accelerator of BCPNN is designed and implemented. The experimental results show the updating process on FPGA can be accomplished within 110 ns with a clock frequency of 200 MHz, the updating speed is greatly enhanced compared with the CPU test. The trade-off between performance, accuracy and resources on dedicated hardware is evaluated, and the impact of the module reuse on resource consumption and computing performance is evaluated.
Asymptomatic Hearing Impairment Frequently Occurs in Early-Onset Parkinson’s Disease

Objective Recent reports of hearing impairment in Parkinson’s disease (PD) have suggested that auditory dysfunction could be a non-motor manifestation of PD. These reports were based on observations of elderly patients for whom presbycusis may, to some extent, have contributed to hearing dysfunction. Therefore, we aimed to explore the auditory functions in younger patients with PD. Methods We conducted a case-control study in a relatively younger (< 55 years of age at study time) population of PD patients and healthy volunteers to test whether auditory dysfunction is a significant non-motor dysfunction in PD. Pure tone audiometry (PTA) and brainstem evoked response audiometry (BERA) were performed in all participants. Results None of the patients or controls reported hearing deficits. Fifty-one patients with PD and 50 healthy volunteers who were age- and gender-matched to the patients participated. PTA-detected hearing impairment was found in 64.7% of patients and 28% of controls (p < 0.001) for both low-mid and/or high frequencies. Hearing impairment was more frequent in the younger subgroups of patients than age-matched controls, while the frequency of hearing impairment was similar in older groups of subjects. BERA was not different between patients and controls. Conclusion Asymptomatic auditory dysfunction is a common non-motor manifestation of early-onset PD and more frequent in younger patients, indicating that it may be independent of aging. The mechanism underlying this dysfunction appears to be peripheral, although a central dysfunction cannot be ruled out based on the findings of this study.
Automatic-SSD: Full Hardware Automation over New Memory for High Performance and Energy Efficient PCIe Storage Cards

We propose Automatic-SSD that converts all storage management logic into hardware, which enable energy efficient, high performance fast memory based block storage. To achieve low operating power, Automatic-SSD directly reads or writes host-side data to underlying backend storage media without internal DRAM caches. To realize such DRAM-less approach with better performance and make it more energy efficient, Automatic-SSD also removes the internal processor(s) and firmware execution therein by fully automating the backend request management and data transfers over all pipelined hardware modules. We prototype Automatic-SSD on a middle-end FPGA custom board, employing massive numbers of phase change memories as representative of new memory technologies. Our evaluation results show that, compared to a conventional firmware-based approach, Automatic-SSD shows up 28.8× and 25.4× better bandwidth and latency behaviors, respectively, while consuming only 5% of the total energy, on overage.
Evolution, Mitigation, and Prevention of Ransomware

Tremendous growth of ransom malware demands valuable security methods to protect individuals and organizations. Ransomware or ransom malware is a type of malware that restricts users from accessing their files or system and demands a ransom payment to get back access to files. The hacked files are encrypted and asked for payment to decrypt and redeliver the files back to the user. To regain access back to hacked files one has to make digital payment. Ransomware of this type is dangerous as it hacks all the files and fails all the security methods available on the system also the possibility for retrieving your file is zero percent. Even if the payment is done still one cannot be sure that files will be delivered back to the user. The earliest ransomware came into existence in 1980, at that time one has to pay through snail mail. Ransomware is considered as the most widespread malware since 1989 and had caused global financial losses both to individuals and big organizations. Every year this loss is increasing. Therefore, protection of our data from ransomware is necessary. Today, originators of ransomware demand for payment via bitcoins or cryptocurrency. This paper provides the detailed overview about ransomware, its evolution, the reasons for paying or not paying a ransom, the existing approaches to avoid this problem, and the recovery techniques in case of infection.
Spatial Pattern of Electroencephalography (EEG) Extracted by Nonlinear Features during Working Memory Maintenance

When working on electroencephalography (EEG) of cognitive tasks, there is a problem of high channel dimension. In this work, a channel selection method is proposed to achieve efficient spatial pattern. First, it extracts the linear and nonlinear multi-features of the entire brain EEG signal and applies the Support Vector Machine (SVM), K-Nearest neighbors (KNN), Random Forest (RF) multiple classifiers for classification. It is found that the nonlinear feature has a better classification performance in the memory material classification task (images vs characters) based on scalp EEG signals of the working memory maintenance procedure. Then the SVM is applied for classification on each channel by using the nonlinear feature. The top-10 channels with better classification performance are proved as the specific spatial pattern of the working memory maintenance procedure based on working memory materials. The channel selection method proposed in this paper can be extended to other classification analysis of cognitive tasks to extract the efficient spatial patterns.
Comparison Between Independent Component Analysis and Wiener-Hopf Filter Techniques for Eye Blink Removal

Electroencephalogram (EEG) is useful in the clinical diagnosis of various brain diseases. However, the existence of artifacts corrupting the EEG reduces the ability to do such a proper diagnosis. One of these unwanted signals is the eye blink. Therefore, pre-processing of the EEG is of great importance for proper diagnosis. This work compares between using the Independent component Analysis (ICA) and the Wiener filter techniques for removing eye blink (EB) artifacts from EEG. EEG datasets contaminated with EBs are used for quantifying the performance of the EB rejection methods. Mean squared error (MSE) is calculated and compared between the two methods. The Results show that the Wiener-Hopf filter gives better results in rejecting EBs and producing artifact-free EEG signal.
MCell: Multi-Row Cell Layout Synthesis with Resource Constrained MAX-SAT Based Detailed Routing

Multi-row cell structure has become popular for modern designs, especially for the multi-bit flip-flop (MBFF) cells, but has not been under full investigation in previous cell library synthesis researches. In this work, we propose an entire placement and routing flow for synthesizing multi-row cell layouts. The proposed new A*-based multi-row transistor placement algorithm can optimize the intra-row and inter-row connections. We also present the first MAX-SAT based detailed router to optimize the crossrow connections that also conform to primitive design rules, but not only to obtain a legal routing result in previous SAT-based detailed router. Experimental results show that the quality of synthesized cells is similar to that of a state-of-the-art cell library in [6], and better aspect ratios of multi-row cells also offer more flexible capability in assembling block designs under some aspect ratio constraints as compared to single-row cell library.
A Study on the Impact of Domain Randomization for Monocular Deep 6DoF Pose Estimation

In this work, we apply domain randomization to synthetic images and train deep 6DoF monocular RGB pose estimation models to work on a real object. We compare 19 models trained with different combinations of synthetic and real data (fully synthetic, fully real, initially synthetic and supplemented with real, and a real-synthetic randomized mix). By gradually decreasing the amount of real data used, we show it is possible for deep 6DoF detection to obtain superior results while using less real data (which is harder to obtain) and suggest the best approach to train a model with synthetic data. Our method is validated using a textureless 3D printed object, as the textureless category is a challenging, common open problem in itself. A real and a synthetic dataset generated for this work, totalling over 24,800 annotated frames, are also made public. We also show that synthetic, randomized data can help generalize a model by training it to handle challenges such as illumination changes and fast motion. Finally, we also evaluate how a model trained for one camera sensor works with a different one, and show that synthetic simulations of real cameras can help overcoming this challenge.
A Study of Internet of Things Protocols and Communication

During the past couple of years, Internet of Things has been involved in most of the devices that we use. For instance, in mobile devices, healthcare, education, environment and so on. Internet of Things is interconnected devices that built within the sensors, network, software and hardware that collect, analyze and send information. Some of the Internet of Things protocols functionality are to let the devices communicate with each other. And to let the devices communicate safely with one another. In this paper, we provide a study of the Internet of Things architecture, and a review of Internet of Things protocols and their architectures. This comparison helps researchers to pick the protocols that meet their desire.
Concept for Safety-Related Development of Deep Neural Networks in the Automotive

The performance of Deep Neural Networks in field of computer vision as most attractive segment of current trends in automotive is not just remarkable, but also quite beneficial in comparison to the classic SW approaches, since many hundred thousand lines of codes can be easily replaced. On the other side, a completely different development process makes them not fully conform to the requirements of current standards for functional safety in automotive, ISO 26262 and SOTIF. Analyzing Deep Learning Networks from the perspective of classic SW approach was the first try to address the drawbacks and deviations from the current standards. Although worthwhile, such studies didn’t provide concepts, how to develop DNNs in a systematic, transparent and assessable way.This paper introduces a novel concept for the development of deep neural networks considering their characteristics, overcoming the drawback with tool evaluation process of wide established frameworks (such as TensorFlow, Keras etc.) and providing a systematic, traceable and assessable solution.
Fingerprint Recognition System based on Big Data and Multi-feature Fusion

This paper proposes a fingerprint recognition algorithm, which is based on deep learning and multi-feature fusion, is able to solve the shortcomings of traditional fingerprint recognition that purely relies on minutiae features for recognition, especially in the case of partially incomplete fingerprints or small-area fingerprints. For identification, while the user inserts a fingerprint in the terminal, the system will transmit raw data to the deep learning engine of the cloud in an anonymous and encrypted manner, which can quickly collect a large amount of fingerprint data in an aligned manner. Based on these data, parameters of the deep-learning model are adjusted incrementally, pursuing optimal accuracy of the system. Meanwhile, SaaS services are provided to other applications for fingerprint recognition.
Economic Model Predictive Control for a Microgrid with Depreciation LiFePO4 Battery with a Fixed Cycling Interval

This work demonstrates a modified control method of microgrid with LiFePO4 (LFP) battery using the economic model predictive control (EMPC). It shows how to calculate battery depreciation rates for calendar and cycling aging. Then EMPC for the microgrid is formulated with respect to the aging of the LFP battery. Finally, the simulation results are provided. From the results, it is seen that the developed method significantly decreases the costs of such systems by extending the lifetime of the LFP battery.
Comparative Evaluation of VVC, HEVC, H.264, AV1, and VP9 Encoders for Low-Delay Video Applications

The evolution of the Internet and wireless communications led real-time applications with high and ultra-high-definition video to flourish. However, due to the fact that video is a bandwidth-greedy media stream, numerous codecs such as AV1 and Versatile Video Coding (VVC) that implement different techniques, have emerged. Indeed, investigating the performance of different compression standards is crucial to select the appropriate codec for a specific application. This paper provides a comparative assessment of the compression efficiency for the above-mentioned standards according to their prominent predecessors VP9, HEVC and H.264. Seven encoder implementations were used for benchmarking AV1, VP9, VVC, HEVC and H.264. i.e. AV1, VP9, VTM, (HM and x265), as well as (JM and x264) respectively. The low-dealy configuration pattern was utilized as it provides the lowest delay in contrast to random-access and all-Intra configurations. Indeed, to address real-time mobile applications, class C video sequences that are very sought after in many applications such as video surveillance or road safety were used for the conducted experiments. The obtained results show that each newly born codec outperforms its predecessor in terms of coding efficiency with an increase in the computational complexity. VVC achieves the highest compression performance in comparison to its predecessors even with the optimized version (x265, x264) with a noticeable increase in encoding run time, while VP9 provides average bit-rate overhead according to its successor AV1 for the industrial standards.
Closed-Loop Control of Anesthesia: Survey on Actual Trends, Challenges and Perspectives

Automation empowers self-sustainable adaptive processes and personalized services in many industries. The implementation of the integrated healthcare paradigm built on Health 4.0 is expected to transform any area in medicine due to the lightning-speed advances in control, robotics, artificial intelligence, sensors etc. The two objectives of this article, as addressed to different entities, are: i) to raise awareness throughout the anesthesiologists about the usefulness of integrating automation and data exchange in their clinical practice for providing increased attention to alarming situations, ii) to provide the actualized insights of drug-delivery research in order to create an opening horizon towards precision medicine with significantly improved human outcomes. This article presents a concise overview on the recent evolution of closed-loop anesthesia delivery control systems by means of control strategies, depth of anesthesia monitors, patient modelling, safety systems, and validation in clinical trials. For decades, anesthesia control has been in the midst of transformative changes, going from simple controllers to integrative strategies of two or more components, but not achieving yet the breakthrough of an integrated system. However, the scientific advances that happen at high speed need a modern review to identify the current technological gaps, societal implications, and implementation barriers. This article provides a good basis for control research in clinical anesthesia to endorse new challenges for intelligent systems towards individualized patient care. At this connection point of clinical and engineering frameworks through (semi-) automation, the following can be granted: patient safety, economical efficiency, and clinicians’ efficacy.
A Temperature and Humidity Compensation Method for On-board NOx Sensors with LSTM Network

NOx is a pollutant component of vehicle exhaust. On-board NOx sensors usually operate under electro-chemical reactions. Temperature and humidity can influence these reactions, resulting in the inaccuracy of measurements. Thus, a method based on long short-term memory (LSTM) network for temperature and humidity compensation of on-board NOx sensors is proposed in this paper. The LSTM network is trained with the sensor-measured and the true NOx concentrations alongside with the temperature and relative humidity of tested gas. The testing results of the LSTM network shows that this method has good performance for the temperature and humidity compensation, and can efficiently improve the accuracy of on-board NOx sensors.
Analytical Estimation of Stress-Induced Birefringence in Panda-Type Polarization-Maintaining Fibers

An analytical model for estimating the stress -induced birefringence in true Panda-type polarization -maintaining fibers with imperfect geometry has been developed in this letter. The developed model is simpler and more accurate compared to conventional sophisticated and asymptotic formulas in reported works. Our model provides a clear and simple solution to demonstrate the periodic dependence of the birefringence on the misalignment angle between the two stress-applying parts, and the monotonic dependence on the geometric parameters of stress-applying parts. Our work also reveals the important role of the misalignment angle between the two stress-applying parts in practical Panda-type fibers.
Comparative Study of Single, Regular and Flip Well Subthreshold SRAMs in 22 nm FDSOI Technology

This study presents a comparative study of single, regular and flip well subthreshold SRAMs in 22 nm FDSOI technology. A 7T loadless SRAM cell with a decoupled read and write port has been used as a case study. Simulation results, based on the extracted netlist from layout, show that the speed of the flip well SRAM is significantly better than that of the single and regular well SRAMs. In terms of leakage current, single well is the best option. The regular well type has lower static noise margin (SNM) variability. Among all devices used (HVT, RVT, LVT and, SLVT) available in a commercially available 22 nm FDSOI technology, the best combination for minimizing energy per access is HVT devices as driver transistors and RVT for the rest of the transistors. This study may help designers to select an optimal architecture based on their application and performance requirements. The 22 nm FDSOI technology enables a wide range of back gate bias voltages to improve the read stability and write ability of the SRAMs and, hence, their minimum operating voltage and power consumption.
Effect of recombinant human nerve growth factor eye drops in patients with dry eye: a phase IIa, open label, multiple-dose study

Background Dry eye disease (DED) affects more than 14% of the elderly population causing decrease of quality of life, high costs and vision impairment. Current treatments for DED aim at lubricating and controlling inflammation of the ocular surface. Development of novel therapies targeting different pathogenic mechanisms is sought-after. The aim of this study is to evaluate safety and efficacy of recombinant human nerve growth factor (rhNGF) eye drops in patients with DED. Methods Forty consecutive patients with moderate to severe DED were included in a phase IIa, prospective, open label, multiple-dose, clinical trial to receive rhNGF eye drops at 20 µg/mL (Group 1: G1) or at 4 µg/mL (Group 2: G2) concentrations, two times a day in both eyes for 28 days (NCT02101281). The primary outcomes measures were treatment-emerged adverse events (AE), Symptoms Assessment in Dry Eye (SANDE) scale, ocular surface staining and Schirmer test. Results Of 40 included patients, 39 completed the trial. Both tested rhNGF eye drop concentrations were safe and well tolerated. Twenty-nine patients experienced at least one AE (14 in G1 and 15 in G2), of which 11 had at least 1 related AE (8 in G1 and 3 in G2). Both frequency and severity of DED symptoms and ocular surface damage showed significant improvement in both groups, while tear function improved only in G1. Conclusions The data of this study indicate that rhNGF eye drops in both doses is safe and effective in improving symptoms and signs of DED. Randomised clinical trials are ongoing to confirm the therapeutic benefit of rhNGF in DED. Trial registration number NCT02101281.
Design of Ku-Band Filters and Diplexer for a Heterodyne Transceiver Using Coupled Open-Loop Resonator Cells

This manuscript presents the design of two microstrip coupled open-loop resonator bandpass filters using a square loop-resonator, also know as splitring resonators, to be used in radio frequency Front-Ends. In addition, a microstrip diplexer using these cells is also detailed. The devices operate in ku band, using the frequencies 11.7–12.2GHz, to the receiver channel, and 14–14.5GHz, to the transmitter channel. All the design is made considering low-cost industrial manufacturing tolerances. A Monte Carlo analysis is performed to verify the sensibility of the device due to the manufacturing capabilities. The measurement and simulation curves agree with a good level of acceptance, diverging each other in just 1.7dB in the passband, in the worst case.
Routing-Free Crosstalk Prediction

Interconnect spacing is getting increasingly smaller in advanced technology nodes, which adversely increases the capacitive coupling of adjacent interconnect wires. It makes crosstalk a significant contributor to signal integrity and timing, and it is now imperative to prevent crosstalk-induced noise and delay issues in the earlier stages of VLSI design flow. Nonetheless, since the crosstalk effect depends primarily on the switching of neighboring nets, accurate crosstalk evaluation is only viable at the late stages of design flow with routing information available, e.g., after detailed routing. There have also been previous efforts in early-stage crosstalk prediction, but they mostly rely on time-expensive trial routing. In this work, we propose a machine learning-based routing-free crosstalk prediction framework. Given a placement, we identify routing and net topology-related features, along with electrical and logical features, which affect crosstalk-induced noise and delay. We then employ machine learning techniques to train the crosstalk prediction models, which can be used to identify crosstalk-critical nets in placement stages. Experimental results demonstrate that the proposed method can instantly classify more than 70% of crosstalk-critical nets after placement with a false-positive rate of less than 2%.