Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Analysis of the Tournament Size on Genetic Algorithms

We perform an experimental study about the effect of the tournament size parameter from the Tournament Selection operator. Tournament Selection is a classic operator for Genetic Algorithms and Genetic Programming. It is simple to implement and has only one control parameter, the tournament size. Even though it is commonly used, most practitioners still rely on rules of thumb when choosing the tournament size. For example, almost all works in the past 15 years use a value of 2 for the tournament size, with little reasoning behind that choice. To understand the role of the tournament size, we run a real-valued GA on 24 BBOB problems with 10, 20 and 40 dimensions. We also vary the crossover operator and the generational policy of the GA. For each combination of the above factors we observe how the quality of the final solution changes with the tournament size. Our findings do not support the indiscriminate use of tournament size 2, and recommend a more careful set up of this parameter.
Classification of Hand Motions within EEG Signals for Non-Invasive BCI-Based Robot Hand Control

The development of brain-computer interface (BCI) systems that are based on electroencephalography (EEG), and driven by spontaneous movement intentions, is useful for rehabilitation and external device control. In this study, we analyzed the decoding of five different hand executions and imageries from EEG signals, for a robot hand control. Five healthy subjects participated in this experiment. They executed and imagined five sustained hand motions. In this motor execution (ME) and motor imagery (MI) experiment, we proposed a subject-specific time interval selection method, and we used common spatial patterns (CSP) and the regularized linear discriminant analysis (RLDA) for the data analysis. As a result, we classified the five different hand motions offline and obtained average classification accuracies of 56.83% for ME, and 51.01% for MI, respectively. Both results were higher than the obtained accuracies from a comparison method that used a standard fixed time interval method. This result is encouraging, and the proposed method could potentially be used in future applications, such as a BCI-driven robot hand control.
Wearable Sensor-Based Human Behavior Understanding and Recognition in Daily Life for Smart Environments

Behavior recognition using motion sensors is getting prominence over other systems such as e-healthcare and life-log analysis systems especially in the healthcare domain for improving life expectancy and healthcare access. Accelerometers have been used in smart environments to recognize behavior since the last decade but heavy computation involved in recognizer model made them less acceptable. This paper proposed a computationally less expensive model with better recognition results for improved human behavior understanding system. Hierarchical features are used to ensure robustness as a performance attribute in the proposed system. These hierarchical features involve statistical features like signal magnitude, abrupt changes, and temporal variation among coordinates. Moreover, the extracted features are examined through the process of learning, training, and symbolization with the help of linear support vector machine. The examination of our recognition results based on feature extraction strategy show that our model excels others in terms of accuracy and computation time. The proposed system should be considered as a recommendation for systems involving human behavior recognition i.e. kindergarten, elderly at old-age houses and patients with Parkinson diseases.
High Voltage Generation Using Deep Trench Isolated Photodiodes in a Back Side Illuminated Process

We demonstrate passive high voltage generation using photodiodes biased in the photovoltaic region of operation. The photodiodes are integrated in a 90nm back side illuminated (BSI) deep trench isolation (DTI) capable imaging process technology. Four equal area, DTI separated arrays of photodiodes are implemented on a single die and connected using on-chip transmission gates (TG). The TGs control interconnects between the four arrays, connecting them in series or in parallel. A series configuration successfully generates an open-circuit voltage of 1.98V at 1klux. The full array generates 423nW/mm2 at 1klux of white LED illumination in series mode and 425nW/mm2 in parallel mode. Peak conversion efficiency is estimated at 16.1%, at 5.7klux white LED illumination.
Study of DRM During Closing Period of High Voltage Circuit Breaker

Dynamic resistance measurement (DRM) has been used to diagnose the contact condition of high voltage circuit breaker for many years. However, only the resistance curves during opening period are regarded as valid, partly because the curves during closing period are easy to be disturbed by many factors, such as vibration, noise and velocity. According to Holm’s theory, the contact resistance is inversely proportional to the contact force. In this paper, the contact resistance curves during closing period are studied, while a finite element model of the arc contact is developed and used to calculate the dynamic contact force. Based on the model, the inversely proportional relationship between the contact resistance and contact force during arc contact making process is verified. It shows that the resistance value becomes extreme high and will drop quickly when the arcing contact bounce off until the completion of arcing contact re-touching. In conclusion, the fluctuation of the contact resistance during closing process is attributed to the change of contact force and it is also meaningful and can be used to analyze the matching condition of arcing contact.
Beam Management for Millimeter-Wave Beamspace MU-MIMO Systems

Millimeter-wave (mm-wave) communication has attracted increasing attention as a promising technology for 5G networks. One of the key architectural features of mm-wave is the possibility of using large antenna arrays at both the transmitter and receiver sides. Therefore, by employing directional beamforming, both mm-wave base stations (MBSs) and mm-wave user equipments (MUEs) are capable of supporting multi-beam simultaneous transmissions. However, most of the existing research results have only considered a single beam. Thus, the potentials of mm-wave have not been fully exploited yet. In this context, in order to improve the performance of short-range indoor mm-wave networks with multiple reflections, we investigate the challenges and potential solutions of downlink multi-user multi-beam transmission, which can be described as a beamspace multi-user multiple-input multiple-output (MU-MIMO) technique. We first exploit the characteristic of MBS/MUEs supporting multiple beams simultaneously to improve the efficiency of multi-user BF training. Then, we analyze the inter-user interference to avoid beam selection conflicts. Furthermore, we propose blockage control strategies and multi-user multi-beam power allocation solutions for the beamspace MU-MIMO. The theoretical and numerical results demonstrate that the beamspace MU-MIMO compared with single beam transmission can largely improve the rate performance and robustness of mm-wave networks.
A Wideband 90-Nm CMOS Phase-Locked Loop with Current Mismatch Calibration for Spur Reduction

This paper presents a wideband low-spur phase-locked loop (PLL) in a standard 90-nm CMOS process. A simple but effective calibration technique is used to reduce the current mismatch of the charge-pump and achieve low reference spurs. The calibration is based on the successive-approximation- register (SAR) control in conjunction with the pulse-width scaling technique to enlarge the current mismatch for a better calibration resolution. The operation frequency range of the microwave PLL covers from 39.5 to 47.1 GHz. The measured phase noise and reference spurs are −92 dBc/Hz at 1-MHz offset and −57.63 dBc, respectively.
A Novel Heat-Aware Write Method with Optimized Heater Material and Structure in sub-20 nm PRAM for Low Energy Operation

For application of PRAM to IoT devices, it is necessary to reduce write energy consumption of PRAM. In a sub-20nm PRAM technology, the optimization of heater material and structure was performed with the Sentaurus TCAD simulator, which notices that the TiN is more preferred than the Poly Si or W and the wall structure has better write energy efficiency than the confined structure. In addition, a novel heat-aware write method (HAW) is proposed, which controls the write current and voltage considering with the heat generated by the heater. With the HSPICE simulation using $0.25-\boldsymbol{\mu} \mathbf{m}$ industry-compatible model, PRAM cell achieves the write energy improvement by 22%, 19%, and 36% in the case of the wall structure with TiN, the proposed HAW, and both, respectively.
A New Approach using Characteristic Video Signals to Improve the Stability of Manufacturing Processes

Observing production processes is a typical task for sensors in industrial environments. This paper deals with the use of camera systems as a sensor array to compare similar production processes with one another. The aim is to detect anomalies in production processes, such as the motion of robots or the flow of liquids. Since the comparison of high-resolution and long videos is very resource-intensive, we propose clustering the video into areas and shots. Therefore, we suggest interpreting each pixel of a video as a signal varying in time. In order to do that without any background knowledge and to be useful for any production environment with motion involved, we use an unsupervised clustering procedure. We show three different preprocessing approaches to avoid faulty clustering of static image areas and those relevant for the production and finally compare the results.
Integration of Speech/ Music Discrimination and Mood Classification with Audio Feature Extraction

In the area of multimedia indexing and processing, a big challenge is to classify the mood of the listener through music. It is also a prodigious challenge to separate the noisy signals into speech, music or silence. Mood of a person may depend upon music. According to the music psychologists, behavior of a person is defined by his mood. Thus, this research evaluates the features of audio that have substantial impact on mood of a person and focuses that how to distinguish the speech signals and music from audio file at the same time. Many audio extraction algorithms have been hashed out in this paper and proposes one MMCAD (music mood classification & Audio discrimination) which is the integration of mood detector and speech / music discriminator with improved efficiency and accuracy.
Multi-Class Recognition using Noisy Training Data with a Self-Learning Approach

Exploiting ConvNets for object classification systems requires extensive labor work, since these networks require to be trained by sufficiently large and accurately labeled datasets. We propose a novel self-learning approach, which is able to generate a reliable multi-class object classification model from a low-quality dataset that is disturbed with a high level of inter-class noise samples. This approach iteratively purifies the noisy training datasets for each class and updates the classification model. The iterations continue until the model and its parameters reach sufficient quality. The self-learning approach based on ConvNets is evaluated for a maritime surveillance use case, where vessels need to be classified into eight different types. The experimental results on the evaluation dataset show that the proposed approach improves the F1 score approximately by 5%, 8% and 25% at the end of the third iteration, while the initial training datasets contain 40%, 50% and 60% inter-class noise samples (erroneously classified labels of vessels), respectively. Additionally, the purification performance is highly dependent on inter- and inter-class similarities between training samples for higher noise levels. It was also found that the mean Average Precision (mAP) does not degrade so much, whereas other performance parameters show larger variation.
An 88.68% Tuning Range Enhancement and High Process Compatibility MM-Wave QVCO with SCHI Achieving −196.6 FOMT

A novel switched coupled hollow inductor (SCHI) technology is proposed to extend frequency tuning range and improve process compatibility for millimeter-wave (mm-wave) quadrature voltage-controlled oscillator (QVCO). Fabricated in Fujitsu 55nm CMOS process, the presented SCHI-QVCO achieves a tuning range from 23.98 to 36.10 GHz (40.34%), about 88.68% more than that of the reference NI-QVCO using the conventional inductor topology. The measured phase noise is - 120.16 to −130.15 dBc/Hz at 10MHz offset from carrier frequency and the corresponding FOMT is from −189.77 to −196.63 dBc/Hz over the entire tuning range.
Making the Most of Repetitive Mistakes: An Investigation into Heuristics for Selecting and Applying Feedback to Programming Coursework

In the acquisition of software-development skills, feedback that pinpoints errors and explains means of improvement is important in achieving a good student learning experience. However, it is not feasible to manually provide timely, consistent, and helpful feedback for large or complex coursework tasks, and/or to large cohorts of students. While tools exist to provide feedback to student submissions, their automation is typically limited to reporting either test pass or failure or generating feedback to very simple programming tasks. Anecdotal experience indicates that clusters of students tend to make similar mistakes and/or successes within their coursework. Do feedback comments applied to students' work support this claim and, if so, to what extent is this the case? How might this be exploited to improve the assessment process and the quality of feedback given to students? To help answer these questions, we have examined feedback given to coursework submissions to a UK level 5, university-level, data structures and algorithms course to determine heuristics used to trigger particular feedback comments that are common between submissions and cohorts. This paper reports our results and discusses how the identified heuristics may be used to promote timeliness and consistency of feedback without jeopardising the quality.
Learning Quasi-Kronecker Product Graphical Models

We consider the problem of learning graphical models where the support of the concentration matrix can be decomposed as a Kronecker product. We propose a method that uses the Bayesian hierarchical learning modeling approach. Thanks to the particular structure of the graph, we use a the number of hyperparameters which is small compared to the number of nodes in the graphical model. In this way, we avoid overfitting in the estimation of the hyperparameters. Finally, we test the effectiveness of the proposed method by a numerical example.
Real-Time Hybrid Control of Induction Motor Using Sliding Mode and PI Anti-Windup

This paper presents a combined hybrid command between the sliding mode and PI Anti-Windup applied for the speed pursuit of an induction motor, the nonlinearity of their mathematical model increases the conception complexity of a powerful controller, the conventional methods cannot guarantee good performances in the presence of structural or external disturbances. The advantage of using sliding mode control is the simplicity of the design and their insensitivity to parametric variations. But the chattering phenomenon appeared in the steady-state remains an obstacle limits the application of this technique. We proposed to use a PI Anti-Windup regulator for control in the steady-state, however, the sliding mode regulator will work during the transient regimes, where a classical supervisor can handle the transition from one controller to another via approximate information on the operating regime. Experimental results are presented and analyzed to justify the effectiveness of the proposed approach.
A 4-bit Architecture of SEED Block Cipher for IoT Applications

A compact architecture of the 128-bit SEED block cipher is presented in this paper. The proposed architecture uses a 4-bit datapath and therefore requires limited hardware resources. The target of this architecture is ultra-low area devices for IoT and wearable applications. The design was coded using the VERILOG language and the BASYS3 board (Artix 7 XC7A35T) was used for the hardware implementation. The proposed implementation utilizes only 425 FPGA LUTs, 382 FFs and $1024 \times 8$ bits Block RAM and reaches a data throughput of 45 Mbps at 204 MHz clock frequency for encryption or decryption.
Application of Biocompatible Organic/Inorganic Composite Film in Implantable Retinal Prosthesis

Although many groups worldwide have spent a lot of effort on developing devices for retinal prosthesis to restore blind eyesight, current technologies still lack of a reliable implantable packaging solution for high-density feedthroughs. Here, we propose the fabrication and latest testing results for a novel five-layer organic/inorganic film package sample, the organic parylene-C (PA) was deposited using chemical vapor deposition (CVD) and the inorganic aluminum oxide (Al2O3) film was deposited using atomic layer deposition (ALD) method. The final sample has passed through the He leakage, bio-compatible level test and the active soaking test, indicated its life time last for years.
Satellite Multi-Vehicle Tracking under Inconsistent Detection Conditions by Bilevel K-Shortest Paths Optimization

Tracking multiple vehicles in optical satellite videos can be achieved by detecting vehicles in individual frames and then link detections across frames. This is challenging since the low spatial resolution of satellite videos would lead to miss or partial detections. Failure in consistently detecting an identical vehicle would terminate a trajectory for loss of identity, or reinitialize a new trajectory for re-detection. Both would result in fragmented tracklets for an identical vehicle. In this paper, we propose a two-step global data association approach for multiple target tracking, which first generates local tracklets then merges them to global trajectories. In order to handle the frequent terminals of tracklets caused by the miss or partial detections, the spatial grid flow model has been extended to temporal neighboring grids to cover the possible connectivities in a wider temporal neighboring, so that the detection association could match temporal-unlinked detections. In the tracklet association model, we customize a tracklet transition probability based on Kalman Filter to link tracklets with large temporal intervals. A near-optimal solution to the proposed two-step association problem is found by Bilevel K-shortest Paths Optimization. Experiments on a satellite video show that our approach improves both detection and tracking performance of moving vehicles.
Fine-Grained Parallel Routing for FPGAs with Selective Expansion

FPGAs are reconfigurable architectures that can offer large performance and energy improvements over general purpose processors. However, compiling an application design onto the underlying FPGA device takes commonly too much time to allow efficient design turnaround times, significantly hindering designer productivity. Routing is always a very timing-consuming and critical process in FPGA compilation flow. To reduce FPGA routing time, parallel techniques have become more popular in recent years. In this paper, we propose a fine-grained GPU-based parallel routing approach that enables high concurrent single-net parallel routing for large-scale FPGAs. The proposed approach is novel in three ways. The first is Scheme-1. We route a single net only on its own bounding box rather than entire routing resource graph and then selectively expand its bounding box to make sure that single-net routing has a feasible solution. The second is Scheme-2. We impose a GPU thread to each node and perform path searching in dynamic programming algorithm on all the nodes simultaneously for single-net parallel routing on a single GPU. Note that these nodes are distributed in the bounding box of single net. The third is optimized Scheme-2. We attempt to partition single-net routing box into several sub-boxes, each of which is processed in a shared memory of GPU to enable high scalability and parallelism. Our evaluation with ten large designs from the academic VTR benchmark suite shows that our approach of combining the approximation of VPR 7.0 router (Scheme-1) and its parallelization (optimized Scheme-2) provides a 1.57x speedup improvement compared to the best parallelization-only approach of the original VPR 7.0 router, though at a small quality deterioration.
Feature Extraction from Online User Reviews

Online reviews have a huge commercial value if analyzed correctly. Most common way of analyzing reviews is subjecting them to sentimental analysis. However, this does not provide fine grain actionable information due to the lack of specificity. This study suggests a mechanism to extract product features that have been discussed within the reviews there by allowing a more meaningful opinion analysis. This can be challenging as different features are expressed in different ways in reviews. The proposed method intends to use double propagation and rule-based mining to overcome the extraction problem. This feature extraction module is developed as a part of a larger online review analysis platform for electronic products which is currently being implemented. Finally, this paper presents the results observed in the experiments carried out on multiple datasets.
Binarization of Color Character Strings in Scene Images using Deep Neural Network

This paper addresses the problem of binarizing multicolored character strings in scene images with complex backgrounds and heavy image degradations. The proposed method consists of three steps. The first step is combinatorial generation of binarized images via every dichotomization of K clusters obtained by K-means clustering of constituent pixels of an input image in the HSI color space. The second step is classification of each binarized image using deep neural network into two categories: character string and non-character string. The final step is selection of a single binarized image with the highest degree of character string as an optimal binarization result. Experimental results using ICDAR 2003 robust word recognition dataset show that the proposed method achieves a correct binarization rate of 87.4% that is highly competitive with the state of the art of binarization of scene character strings.
Teaching Key Machine Learning Principles Using Anti-learning Datasets

Much of the teaching of machine learning focuses on iterative hill-climbing approaches and the use of local knowledge to gain information leading to local or global maxima. In this paper we advocate the teaching of alternative methods of generalising to the best possible solution, including a method called anti-learning. By using simple teaching methods, students can achieve a deeper understanding of the importance of validation on data excluded from the training process and that each problem requires its own methods to solve. We also exemplify the requirement to train a model using sufficient data by showing that different granularities of cross-validation can yield very different results.
Applying Fitts' Law to a Brain-Computer Interface Controlling a 2D Pointing Device

We have developed an EEG based non-invasive, consumer-convenient dry electrode brain-computer interface (BCI) for two-dimensional cursor control. This BCI drives a robot operation system (ROS) implemented pointer on a computer screen in real-time. Within the chosen paradigm, test subjects achieved an average hit rate of 56.8% for a point-and-click task after a brief training phase. The measured average BCI throughput is 0.18 bit/s. The sensorimotor rhythm-based BCI's performance is then compared to that of a computer mouse and a joystick by evaluating parameters according to Fitts' law.