Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate Computing With Stochastic Transistors’ Voltage Over-Scaling

Ubiquitous computing and the ever-rising need for energy efficiency pose challenges in terms of the processing requirements and the corresponding machine complexity. Nonetheless, the nature of the underlying applications, particularly dealing with real-world data, offers alternative paradigms toward the efficient utilization of the available design resources. In this paper, approximate computing is addressed as an accommodating technique that can benefit from the inherent resilience of the current applications to build low-power and low-complexity architectures. This paper proposes an alternative way of attaining approximation based on transistor dynamic variability. Furthermore, it presents a comprehensive study using voltage scaling scheme, starting from the impact of variation on the circuit-level output and investigating cascaded logic gates, storage elements, arithmetic building blocks, and on the application level, with an image compression outcome using 2-point discrete Fourier transform as a proof of concept. This paper addresses design analysis metrics and the efficiency of the proposed technique with respect to the technology node, operating frequency, energy and delay, process corner, and temperature. The configurable designs are shown to be possible with adaptive voltage scaling and energy-quality scalability. The proposed technique offers compromises in terms of the circuit design metrics with savings of up to 90% on energy for image compression application, in comparison with running at deterministic nominal value, while preserving the relative quality and accuracy of the output.
Patient-tailored classification for a NIRS triggered hand rehabilitation robot

Robotic neurorehabilitation that provides the support movement for the affected limb triggered by brain signal has a great potential to improve the recovery for post-stroke patients. We are studying a hand rehabilitation robotic system that a robotic hand orthosis is moved triggered by Near-Infrared Spectroscopy. In this paper, we propose a new method to classify the motion intention out of the NIRS signal. The classification accuracy that is an essential factor to extract the users’ motion intension, was significantly improved by parameterizing the individual hemodynamic response.
A Novel 0.6V MCML D-Latch Topology exploiting Dynamic Body Bias Threshold Lowering

This paper presents a novel topology to implement CML D-Latches in deeply scaled CMOS technologies under very low supply voltage requirements. The proposed modified Folded MCML D-Latch topology exploits a dynamic body bias approach to achieve a reduction of the threshold voltage of about 100mV thus allowing proper operation with a minimum supply voltage as low as 0.6V. Simulation results in a commercial 40nm CMOS process are provided to show the advantages of the proposed approach with respect to the state of the art. At the best of our knowledge, no other CML D-Latch topologies are able to operate at such a low supply voltage. The triple-tail D-Latch, also known as low voltage CML D-Latch allows a minimum supply voltage of 0.8V in the same reference 40nm CMOS process.
Scalable Multi-Queue Data Transfer Scheme for FPGA-Based Multi-Accelerators

In this paper, we present MQMAI, multi-queue command-based data transfer mechanism that enables efficient and scalable access to multiple accelerators on FPGA via PCIe. The proposed framework provides a user-friendly non-blocking API with enhanced I/O parallelism to access FPGA-based accelerators. MQMAI is composed of I/O software stack in host CPU and accelerator controller in FPGA. A round-robin policy is developed to allow efficient run-time access to shared accelerators requested through multiple threads/applications. Our experimental results show that our proposed framework reduces the total latency of multi accelerators access significantly compared to the current state-of-the-art framework, RIFFA, by 18x for 4 threads per accelerator access.
Spectral Super-resolution for RGB Images using Class-based BP Neural Networks

Hyperspectral images are of high spectral resolution and have been widely used in many applications, but the imaging process to achieve high spectral resolution is at the expense of spatial resolution. This paper aims to construct a high-spatial-resolution hyperspectral (HHS) image from a high-spatial-resolution RGB image, by proposing a novel class-based spectral super-resolution method. With the help of a set of RGB and HHS image-pairs, our proposed method learns nonlinear spectral mappings between RGB and HHS image-pairs using class-based back propagation neural networks (BPNNs). In the training stage, unsupervised clustering is used to divide an RGB image into several classes according to spectral correlation, and the spectrum-pairs from the classified RGB images and the corresponding HHS images are used to train the BPNNs, to establish the nonlinear spectral mapping for each class. In the spectral super-resolution stage, a supervised classification is used to classify the given RGB image into the classes determined during the training stage, and the final HHS image is reconstructed from the classified given RGB image using the trained BPNNs. Comparisons on three standard datasets, ICVL, CAVE and NUS, demonstrate that, our proposed method achieves a better spectral super-resolution quality than related state-of-the-art methods.
Optimal Real-Time Monitoring of an Information Source Under Communication Costs

In this work, we study the real-time monitoring of an information source over a communication link with transmission cost. As the most basic nontrivial setting, we consider a single source governed by a random walk process and design the scheduling policy that minimizes the cost sum of the transmission costs and the inaccuracy of the state information at the monitoring center. This problem closely relates to the emerging area of Age-of-Information (AoI) scheduling that focuses on the timely transfer of fresh information over resource-constrained networks. When the information updates are controlled by the scheduler, we can incorporate the internal dynamics of the information source into the transmission decisions. For a general cost function of estimation errors, we first establish the optimality of a threshold rule for minimizing a weighted sum of the transmission cost and the tracking inaccuracy for the information source. Then, we characterize the optimal threshold level as an explicit function of the transmission cost and the source behavior. Further, we provide preliminary results of a learning-based approach that finds the optimal threshold when the transmission cost is unknown a priori.
Mobile Big Data Based Network Intelligence

The mobile network is at a critical tipping point. On the one hand, it is heavily stressed by the explosive increase of traffic, and the steep transition from a smartphone-dominated market to one that is dazzling diverse, all subject to the same electromagnetic theory-ruled physical world. On the other hand, it is hopefully excited by the rapid development of data science, more powerful and versatile computing, and increasingly affordable hardware. It seems that we have come to the point at which in order to make the ends meet, one would need to put all the pieces together. In this paper, we aspire to establish a holistic framework for mobile big data (MBD) based network intelligence. Combining the top-down and bottom-up approaches, we put in context the recent development in the mobile network architecture, resource-based management theory, MBD orchestration, and data analytics, and establish a hierarchical network intelligence architecture fueled by MBD analytics. This architecture nurtures holistic understanding on how the multidimensional, multilateral, and multigranular MBD, together with its processing, can be plugged into the network architecture.
Video-Based Measurement of Physiological Parameters Using Peak-to-Valley Method for Minimization of Initial Dead Zone

In this paper, we propose a new method to improve the accuracy and real-time performance in iPPG (image photoplethysmography) approach which measures physiological parameters such as heart rate and oxygen saturation using PC camera. Most conventional iPPG methods extract physiological parameters by frequency or spectral analysis of signal traces in the moving temporal sliding window with fixed size. In this case, in order to minimize the influence of noise due to fluctuate in reflected light or face pose, the window size is made large enough in general, but it causes dead zone as much as the window size at the initial measurement time. In this study, we use size-increasing sliding window and modified peak-to-valley method instead of frequency analysis methods with fixed size window such as FFT or wavelet transform. Including these concepts, we have implemented a remote vital sign measurement system based on the real-time pipeline architecture which tracks the ROIs, extracts appropriate signal traces from the ROIs, pre-/post- processes to remove noise, and applies frequency/spectral analysis. Finally, the performance of the proposed method was verified by comparing measurement data from the proposed system and reference data from the contact pulse oximeter using RMSE and Bland-Altman plot evaluation methods.
A Framework for User Profile Enrichment in Content-based Recommender Systems by inferring the Semantic Couplings

Content-based recommender systems predict the inclination of users towards content-based items such as documents, news articles, web-pages, journals, etc. according to their interest area. In this work, a framework for the enrichment of user profiles in content-based recommender systems has been proposed. The enrichment of user profiles takes place from three different sources: the similar users existing in the system, the documents referred by the users and thirdly the semantic relationship between the features of the user profile. This framework also addresses the cold-start problem associated with new users in recommender systems. After Experimentation, improved recall and precision have been observed after the iterations.
Examining the Impact of Pedagogical Agents on Students Learning Experience in Virtual Worlds

Virtual worlds constitute an important part of the educational landscape that possesses unique learning capabilities and opens up new horizons in education. A vital part of 3D virtual worlds concerns the intelligent pedagogical agents that are integrated in the environments and aim to improve the interaction with the users and enhance their learning experience. The main purpose of this study is to assess the impact that the pedagogical agents have on students' engagement and learning experiences during training activities in virtual worlds. Specifically, we examine how students perceive the role of pedagogical agents as learning companions during specific exercises and activities and the impact that the guidance offered via pedagogical agents has on students' engagement and learning. In this regard, an experimental evaluation study was designed and performed in a 3D virtual world educational environment that is used to assist students in learning aspects of environmental engineering. The results of the study show that the assistance and the help offered to students via pedagogical agents have great impact on students' engagement and improved their learning experiences.
Application of the Preference Set-based Design Method to Cantilever for Electrical Contact

In the design of electrical contacts, it is required to pursue a solution which satisfies simultaneously multi-objective (electrical, mechanical, and thermal) performances including conflicting requirements. Preference Set-Based Design (PSD) has been proposed as practical procedure of the fuzzy set-based design method. This paper newly attempts to propose a concurrent design method by PSD to electrical contact, specifically a design of a cantilever for a preliminary study.In the PSD method, when all the performances meet with the required ones, the allowable ranges of design parameters are finally given with those in the most high level of their indexes of satisfaction and robustness. The main elements of the PSD process consists of phases of “set-representation”, “set propagation”, and “set narrowing”. The phase of “set representation” is to configure the region or range of the design parameters and the required performances. The “set propagation” is the phase of calculation searching sets of possible performances. At the phase of “set narrowing”, the sets are narrowed by removing non-available partial sets from the sets of the design parameters.The design parameters (width and thickness) of a cantilever for drive an electrical contact, which satisfy required performance (target deflection), are obtained in ranges successfully by PSD. The validity of the design parameters is demonstrated by numerical modeling. Further study that takes into account more complex conditions will be carried out.
Evaluating the Performance of QoI Algorithms in Realistic MANETs

Quality of Information (QoI) based solutions for multi-criteria decision-making problems have recently gained attention in tactical networks. These schemes combine application and network layer information to select the best source among multiple available options to meet the specific quality needs of the task. A large part of this research evaluates their performance using simple models of underlying network. In this paper, we study the performance of a QoI scheme in a more realistic network environment. For this purpose, we create a MANET environment with realistic radio propagation, MAC and routing protocols to study the performance of a state-of-the-art QoI scheme for source selection procedure. We cross compare this solution with a simple Quality of Service (QoS) algorithm that selects a source with highest available bandwidth. We evaluate the performance of both schemes in a 100 nodes network. In situations where a single network metric is of significance to the task, simple traffic engineering solution is a better choice with minimal overhead. In contrast, QoI scheme provides an efficient solution when multiple network criteria contribute equally to the decision making, at the cost of increased network overheads and long convergence time.
An Empirical Study on Using Agile Methods in Global Software Development

Agile software development methodologies have become very popular today in a world that demands quicker and more efficient ways to develop software. Many software development companies have adopted this methodology to deliver high quality software rapidly. Today global software development is the norm of developing software. When Agile methods are used in global software development, several challenges are introduced due to geographical distance, time zone differences, cultural differences and technology barriers. The focus of this research was to identify the impact of challenges on project success. A hypothetical model of challenges was developed based on the literature. A survey was conducted to collect data and the model was analysed using quantitative methods. According to study results, to ensure success of their projects, managers are urged to focus on choosing a technically competent team, ensure effective communication and continuous customer engagement throughout the project, encourage autonomy and manage a low staff turnover in teams.
Security, Performance and Energy Implications of Hardware-Assisted Memory Protection Mechanisms on Event-Based Streaming Systems

Major cloud providers such as Amazon [1], Google [2] and Microsoft [3] provide nowadays some form of infrastructure as a service (IaaS) which allows deploying services in the form of virtual machines [4], containers [5] or bare-metal [6] instances. Although software-based solutions like homomorphic encryption exit, privacy concerns [7] greatly hinder the deployment of such services over public clouds. It is particularly difficult for homomorphic encryption to match performance requirements of modern workloads [8]. Evaluating simple operations on basic data types with HElib [9], a homomorphic encryption library, against their unencrypted counter part reveals, that homomorphic encryption is still impractical under realistic workloads.
Forecasting Models for Self-Adaptive Cloud Applications: A Comparative Study

With the introduction of autoscaling, clouds have strengthened their position as self-adaptive systems. Nevertheless, the reactive nature of the existing autoscaling solutions provided by major Infrastructure-as-a-Service (IaaS) cloud services providers (CSP) heavily limits the ability of cloud applications for self-adaptation. The major reason of such limitations is the necessity for the manual configuration of the autoscaling rules. With the evolution of monitoring systems, it became possible to employ the data-driven approaches to derive the parameters of scaling rules in order to enable the autoscaling in advance, i.e. the predictive autoscaling. The change in the amount of requests to microservices could be considered as a reason to adapt the virtual infrastructure underlying the cloud application. By forecasting the amount of requests to cloud application, it is possible to estimate the upcoming demand to replicate the microservices in advance. Hence, anticipation of the demand on the cloud application helps to evolve its self-adaptive properties. In the scope of the paper, the authors have tested various extrapolation models on the real anonymized requests time series data for 261 microservices provided by the industry partner Instana. The tested models are: various seasonal ARIMA models with GARCH modifications and outliers detection, exponential smoothing models, singular spectrum analysis (SSA), support vector regression (SVR), and simple linear regression. In order to evaluate the accuracy of these models, an interval score was used. The time required to fit and use each model was also evaluated. Comparative results of this research and the classification of forecasting models based on the interval accuracy score and model fitting time are provided in the paper. The study provides an approach to evaluate the quality of forecasting models to be used for self-adapting cloud applications and virtual infrastructure.
Fast Adaptive Hinging Hyperplanes

This paper proposes a fast algorithm for the training of adaptive hinging hyperplanes (AHH), which is a popular and effective continuous piecewise affine (CPWA) model consisting of a linear combination of basis functions. The original AHH incrementally generates new basis functions by simply traversing all the existing basis functions in each dimension with the pre-given knots. Meanwhile, it also incorporates a backward procedure to delete redundant basis functions, which avoids over-fitting. In this paper, we accelerate the procedure of AHH in generating new basis functions, and the backward deletion is replaced with Lasso regularization, which is robust, requires less computation, and manages to prevent over-fitting. Besides, the selection of the splitting knots based on training data is also discussed. Numerical experiments show that the proposed algorithm significantly improves the efficiency of the existing AHH algorithm even with higher accuracy and it also enhances robustness in the given benchmark problems.
FPGA-based Implementation of Hand Gesture Recognition Using Convolutional Neural Network

Convolutional Neural Network (CNN) is a deep learning algorithm which is widely used in image processing and pattern recognition due to its robustness to feature invariance. However, it is also computation-intensive that results in a bad real-time performance. Field Programmable Gate Arrays (FPGA) has good performance in energy-efficiency, flexibility of parallel processing and pipelined operations. Thus it is expected to be used for accelerating deep learning algorithm. In this research, a FPGA based system is developed to realize the real-time Hand Gesture Recognition. We train a designed CNN model with caffe framework and obtain the model’s parameters on PC. Bilinear interpolation algorithm is used to adjust the size of the image captured by camera. Then we use FPGA to implement the inference process of Hand Gesture Recognition with obtained parameters by designing an accelerator using Xilinx SDx tools.
Design and Analysis of 66GHz Voltage Controlled Oscillators for FMCW Radar Applications with Phase Noise Impact Consideration

Accuracy in measurement is one of the most important characteristics of a radar. One of the main challenges in designing Frequency Modulated Continuous Wave (FMCW) radar is reducing phase noise and controlling leakage of transmitter phase noise into the receiver. Phase noise reduces the maximum transmission power, the ability of small targets detection and the measurable range. This paper presents a power-efficient and low-phase noise Voltage controlled oscillator (VCO) implemented in a 65nm TSMC CMOS technology. The VCO is designed for a 66 GHz phase-locked loop utilized in the frequency modulated continuous wave (FMCW) radar. The tuning range of the VCO is from 63.5 GHz to 68.6 GHz. Simulated phase noise is −94 dBc/Hz at IMHz offset. It dissipates 20 mW from 1.2 V supply.
Efficiency Calculation of Non-Periodic Metasurface Based on Modified Near Field to Far Field Transformation

We present a technique to characterize the efficiency of non-periodic metasurfaces, using near-field to far-field transformation. Integration of near-field plane located in the forward and backward regions of the metasurface is conducted to obtain their far-field scattering. Efficiency of the metasurface is then defined as the ratio of far-field intensities over the desired refraction angles, to the far-field intensities over the incident angles. Presenting the method for calculation of non-periodic metasurfaces efficiency, we observed a considerably accurate result which consistent with power conservation theorem.
Feasibility Study of EEG Super-Resolution Using Deep Convolutional Networks

The success of deep learning for super-resolution (SR) in image processing motivated us to investigate whether it is feasible and applicable to electroencephalography (EEG) data. We considered three questions: 1) How does noise type (white Gaussian or colored) and its signal-to-noise ratio (SNR) affect the EEG SR process? 2) How does SR work over various upscaling sizes? 3) Are there any approaches to improve signal quality when we perform SR? In this work, we proposed deep convolutional networks to enhance the spatial resolution of simulated EEG data. In the simulation of white Gaussian noise, we observed that the SR not only altered the signal from low-resolution (LR) to high-resolution (HR), but also improved signal quality. In the real (colored) noise, it recovered the signal to the level of its target data. Even when the upscaling ratio of SR increased, the signal quality obtained was acceptable. The limitation in reproducing real noisy EEG data may be overcome by applying whitening technique. It is expected that EEG SR can reduce experimental costs significantly, thus is quite promising.