Miracle-dz/UniFAR-FTUs huggingface dataset:
Preliminary Results for the Automated Assessment of Driving Simulation Results for Drivers with Cognitive Decline

Aging related changes and pathology affecting cognition and the ability to drive are significant issues for individuals, their families and the general population. Ensuring that unsafe drivers have their license suspended or get the additional training they need is important for the safety of the general population. On the other hand, allowing a person to continue to drive as long as they are safe is important for the social, emotional and cognitive wellbeing of the individual. This paper presents results of a preliminary study to see if an automated assessment based on trained machine learning models can correctly classify simulator drives as safe or unsafe in comparison to expert driver assessment opinion. The results show that the machine learning is able to achieve 85% accuracy in comparison to the experts for a combined group of 47 drivers that included 20 Healthy Controls, 9 diagnosed with Lewy Body Dementia and 18 diagnosed with mild Dementia of Alzheimer's Type. This work shows the potential for automated driver simulation assessment, which could reduce the burden on clinicians regarding driver safety evaluation.
Modification of Gold Substrate With Fe3O4-Graphene Nanocomposite to Increase Resolution of Surface Plasmon Resonance (SPR) Glucose Sensor

In this study, magnetite (Fe<sub>3</sub>O<sub>4</sub>) was composited with graphene (Gr) and used as a non-enzymatic glucose sensor material based of surface plasmon resonance (SPR). The Fe<sub>3</sub>O<sub>4</sub> nanoparticles were succesfully synthesized by ultrasonic co-precipitation method while variation composition of composite samples was successfully prepared at room temperature by varying the concentration ratio of graphene and Fe<sub>3</sub>O<sub>4</sub>. The resulting composites have good crystallinity and purity with Fe<sub>3</sub>O<sub>4</sub> nanoparticles attach on the graphene surface. The commercial gold (Au) chip with a thickness of 50 nm was modified with a composite by depositing the various compositions of the composite on the Au chip surface using a spin coater. The most optimal SPR response was shown by the Fe<sub>3</sub>O<sub>4</sub>-Gr (3:1) sample with a shift in resonance intensity of 18.2 r.u in detecting 5 mM of glucose. Moreover, the detection limit of the synthesized sensor reaches 0.1995 mM and has good stability at 20 measurement cycles.
Cat Face Recognition Based on MFCC and GMM

Biometrics are inherent in biology, and they are also widely used in human identity recognition because they are stable and not easy to forge. As the pet economy picks up, the pet industry continues to expand and improve, and the pet market shows a humanization trend. In the future, pet identification technology can be widely used in urban pet management, pet medical insurance, pet competitions and other public welfare scenarios. In this paper, based on Mel-Frequency Cepstrum Coefficients (MFCC) and GMM to design the cat face recognition system. Experiments show that this method is convenient and contactless, and has a high recognition rate.
Observing the Role of Social Media to Accelerate Creativity Development

To improve the quality of the repository of knowledge, it is felt that the importance of knowledge management in a scalable systematic manner is also a road-path to transform implicit knowledge into tacit knowledge of creativity to form a company's competitive advantage. Social media is a means of effective formulation of knowledge management and increases creativity because of the wide scope of its use. In line with this, the researcher deems it necessary to examine the relationship between the four levels of social media and the acceleration of creativity. Data collection is done through the distribution of online questionnaires to marketing employees who are believed to have a high level of use of social media, both for the purpose of social interaction and the implementation of their work. Data analysis used IBM Statistics Product and Service Solution (SPSS) 21 and multiple regression methods. The results showed that social media in the workplace influenced the acceleration of creativity (employees).
Dynamic Bayesian Network Modeling Based on Structure Prediction for Gene Regulatory Network

Gene regulatory network can intuitively reflect the interaction between genes, and an in-depth study of these relationships plays a significant role in the treatment and prevention of clinical diseases. Therefore, correct reconstruction of gene regulatory network has become the first critical step in the study of disease treatment and prevention at the genetic level. Among the methods for gene regulatory network reconstruction, the Bayesian network model has been widely concerned because of its advantages of expressing both the regulatory relationship and the degree of strength between genes. Nevertheless, the complexity of the Bayesian network model in structure learning is extremely high, making the efficiency of the reconstruction network is low and the scale is limited. Therefore, this paper proposed a dynamic Bayesian network modeling based on structure prediction (DBN-SP). The method combines the correlation model with the dynamic Bayesian network model. On the premise of making full use of the search strategy of dynamic Bayesian network model structure learning, the candidate parent node set is selected based on the structure prediction firstly. Based on this, some redundant information can be removed and the search space can be reduced in the DBN structure learning to improves the efficiency of the network reconstruction. After the network is reconstructed, structure optimization by using the conditional mutual information method can further remove redundant edges and make the network more accurate. The experimental results show that DBN-SP greatly improves the efficiency and scale of the gene regulatory network reconstruction, and the accuracy and other indexes are also improved. DBN-SP is freely accessible at https://github.com/quluxuan/DBN-SP.git.
Multi-objective Optimization of Planetary Reducer Based on an Improved Genetic Algorithm

This paper presents a multi-objective optimization of planetary reducer based on an improved multi-objective genetic algorithm (IMOGA). Minimization of volume, maximization of transmission ratio and efficiency are set as three objectives. However, owing to the difference of difficulty in solving objective functions, the optimization model of planetary reducer has the problem of uneven distribution of competitive pressure, the conventional evolutionary algorithm has poor convergence at the partial Pareto front. Thus, an improved multi-objective genetic algorithm using infeasible solution guidance and hybrid crossover operator of cytoplasm and chromosome is proposed. Experimental results of six test functions verify the effectiveness of the proposed algorithm and show that IMOGA has faster convergence speed and better convergence in comparison with NSGA-II. Ultimately, a planetary reducer optimization problem is solved by IMOGA and NSGA-II. Comparison results illustrate the competitiveness of IMOGA and prove that IMOGA can provide better solutions for designer. The Pareto set of the planetary reducer is distributed in stepped. The solutions on the same step have similar efficiency and different steps have different distribution ranges in transmission ratio and volume.
Content Matters: Clustering Web Pages for QoE Analysis With WebCLUST

The properties of a web page have a strong impact on its overall loading process, including the download of its contents and their progressive rendering at the browser. As a consequence, web page content has a strong impact on the experience of web users. In this paper, we present WebCLUST, a clustering-based classification approach for web pages, which groups pages into quality-meaningful content classes impacting the Quality of Experience (QoE) of the users. Groups are defined based on standard Multipurpose Internet Mail Extensions (MIME) content breakdown and external subdomain connections, obtained through in-browser, application level measurements. Using a large corpus of multi-device, heterogeneous web content and QoE-relevant measurements for the top-500 most popular websites in the Internet, we show how WebCLUST can automatically identify relevant web content classes showing significantly different performance in terms of Web QoE relevant metrics, such as Speed Index. We additionally evaluate the impact of content caching and device type on the identification performance of WebCLUST, showing how content classes might look significantly different, depending on the access device type (desktop vs mobile), as well as when considering browser caching. Our findings suggest that Web QoE assessment should explicitly consider page content and subdomain embedding within the analysis, especially when it comes to recent work on Web QoE inference through machine learning models. To the best of our knowledge, this is the first study showing the impact of web content on Web QoE metrics, opening the door to new Web QoE assessment strategies.
A Non-Intrusive Ultrasonic Sensor System for Water Flow Rate Measurement

Ultrasonic technologies have established themselves as optimal solutions for water flow rate measurement thanks to the high reliability and efficiency they offer. However, the existing applications often require ultrasonic sensors to be embedded in pipes, i.e., intrusive, which significantly increases initial deployment and maintenance costs. Considering the volume of employment, one to each house, the water meters have to be designed in a way that the consumers can deploy and maintain them without any skilled labourer. Hence, this paper proposes a delta time-of-flight $(\Delta ToF)$ -based non-intrusive sensor system for plug-and-play ultrasonic water flow rate metering. After introducing the measurement theory and our proposed design, we experimentally evaluated the performance of three different ΔToF calculation methods in terms of memory and computation requirements through a dedicated testbed consisting of a closed-loop multi-pipe layout. The results helped us to determine the optimal $\Delta ToF$ method for the employed platform, which is then used to select the best sensor and housing setting that is operable even under the worst deployment conditions (pipe material, diameter). Compared to its intrusive counterparts, the proposed system achieved a sufficient level of precision $(\pm 5.7\%)$ and hence provided the possibility of replacing the conventional methods of intrusive water metering at the household level.
A Triple-Modality Ultrasound Computed Tomography Based on Full-Waveform Data for Industrial Processes

Ultrasound computed tomography (USCT) is gaining interests in many application areas in industrial processes. The recent scientific research focuses on the possible uses of USCT for varied fields of industry such as flow monitoring in pipes, non-destructive inspection, and monitoring of stirred tanks chemical processes. Until now, most transmission tomography (UTT) and reflection tomography (URT) have been demonstrated individually for these applications. A full waveform USCT contain large amount of information on process under evaluation. The developed approach in this paper is focusing on demonstration of a triple modality USCT. First, an optimised transmission image is formed by fusion of time-of-flight (TOF) and acoustic attenuation (AA) images. Secondly, a reflection image is being optimised by using the information from the transmission image. This triple modality method enables integration of a shape-based approach obtained by URT mode with the quantitative image-based approach UTT mode. A delicate combination of the different information provided by various features of the full-wave signal offers optimal and increased spatial resolution and provides complementary information. Verification tests have been implemented using experimental phantoms of different combinations, sizes, and shapes, to investigate the qualitative imaging features. Moreover, experiments with different concentrations solutions further validate the quantitative traits to benefit from both reflection and transmission modes. This work displays the potential of the full-waveform USCT for industrial applications.
Indoor Fingerprinting Localization Based on Fine-grained CSI using Principal Component Analysis

With the development of Wi-Fi technology, the IEEE 802.11n series communication protocol and the subsequent wireless LAN protocols use multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) technologies. Channel state information (CSI) fingerprint positioning technology based on fine-grained channel state information is widely used in the field of WIFI indoor positioning. However, the propagation of CSI is still affected by indoor multipath, and we cannot obtain signals in some corner areas. Therefore, CSI needs a suitable calibration method to improve the accuracy of the position estimation system. This paper proposes a fine-grained CSI fingerprint location algorithm based on Principal Component Analysis (PCA). This novel algorithm uses a dimensionality reduction method on the basis of the Discrete Wavelet Transform (DWT) to optimize, eliminate the noise and redundancy of the original data and reduce the positioning error. Experimental results show that the proposed approach achieves significant localization accuracy improvement over using the RSSI fingerprint method and original CSI fingerprint method, while it incurs much less computational complexity. Meanwhile, the algorithm improves the influence of multiple paths in a complex indoor environment on location, and the method can obtain more accurate location results.
Lightweight Collaboration of Detecting and Tracking Algorithm in Low-Power Embedded Systems for Forward Collision Warning

The cause of the majority of vehicle accidents is a safety issue due to the driver's inattention, such as drowsy driving. A forward collision warning system (FCWS) can significantly reduce the number and severity of accidents by detecting the risk of collision with vehicles in front and providing an advanced warning signal to the driver. This paper describes a low power embedded system based FCWS for highway safety. The algorithm described in this paper computes time to collision (TTC) through detection, tracking, distance calculation for the vehicle ahead and current vehicle speed information with a single camera. Additionally, in order to operate in real time even in a low-performance embedded system, an optimization technique in the program with high and low levels will be introduced. The system has been tested through the driving video of the vehicle in the embedded system. As a result of using the optimization technique, the execution time was about 170 times faster than that when using the previous non-optimized process.
Deep Learning-Assisted Beamforming Design and BER Evaluation in Multi-User Downlink Systems

This paper studies deep learning-based beamforming design schemes for multi-user downlink systems. Two distinct objectives are considered: sum-rate maximization and min-rate maximization. Each of formulations is first tackled by classical majorization-minimization (MM) algorithms that find a locally optimum point iteratively. To reduce computational overheads of the MM algorithms, deep neural networks (DNNs) are introduced which yield optimized beamforming solutions from channel vector inputs. Performance of trained DNNs is evaluated in terms of bit-error rate (BER) measure. Numerical results show that deep learning approaches achieve the BER performance very close to MM algorithms with much reduced complexity. Also, it is desirable to adopt the minimum-rate criterion to achieve low BER performance rather than sum-rate.
Improving energy efficiency by transparently sharing SIMD Execution Units in Assymetric Multicores

Single-ISA Asymmetric multicore architectures (e.g., ARM big.LITTLE) combine high-performance and energy efficiency in the same chip by providing different microarchitectures so the applications can transparently migrate from one to another accordingly. However, in such architectures, the big core features resource-expensive Execution Units (EU) to support ISA extensions, such as SIMD and FP, which may rarely be used depending on the application at hand. These same extensions are supported by the little core but using power-efficient EUs. Given that, in this work, we propose a decoupled offloading mechanism to allow the big core to use such power-efficient EUs in the little core while its own can be power-gated, maintaining the original migration transparency of the architecture. Since applications may have different phases, thus having more or fewer extension instructions usage, we also propose an arbiter to decide when to activate the decoupled offloading at runtime. We evaluate our technique considering ARM NEON as the ISA extension and ARM A7 and A15 as the little and big cores, respectively. Our evaluation shows that, on average, our approach provides 15.9% in energy improvements at the cost of 2.2% in time overhead for mibench benchmarks, which represent embedded application workloads; and, on average, 6.4% in energy gains at the cost of 1.1% in time overhead for polybench benchmarks, which have high NEON usage.
Using Noise Pollution Data for Traffic Prediction in Smart Cities: Experiments Based on LSTM Recurrent Neural Networks

Traffic prediction is one of the most important use cases for smart cities. Accurate traffic information is key to managing traffic issues. Many approaches that use traffic time series data to predict traffic flow have been proposed. In addition to traffic- specific parameters, some other features (called signatures) may be associated with road traffic, i.e., air and noise pollution. In this paper, we show how noise pollution and traffic time-series data were used to train Long-Short Term Memory (LSTM) Recurrent Neural Networks (RNNs), which led to better traffic prediction on major roads in Madrid. This approach has already been used with pollution signatures. This work addresses a new potential investigation path closely related to the use of signature profiles and Artificial Intelligent techniques as a way to reduce the specialization of sensing infrastructure.
A 71dB DC Gain, 0.1% THD, 0.5-V Bulk-Driven Class-AB OTA Achieved by Novel CMFB Methods

In this paper, a 0.5 volt, two-stage, pseudo-differential, high gain, and rail-to-rail bulk-driven operational transconductance amplifier (OTA) is presented. At the input stage, a new common-mode feedback method based on the cross-coupled structure is adopted with an output conductance stabilization circuit to overcome the gain variation brought by this new solution. At the output stage, a novel class-AB output circuit is proposed which makes full use of bulk terminals. This OTA is designed in 180nm CMOS technology. The DC gain and unit gain bandwidth are 71 dB and 5.7 MHz, respectively with phase margin of 51° under a capacitive load of 20 pF at the normal case. The average slew rate is 3.55 V/µs. Other simulation results also presented in detail which validates the proposed solution.
High-energy, flexible micro-supercapacitors by one-step laser fabrication of a self-generated nanoporous metal/oxide electrode

A high-performance and flexible micro-supercapacitor based on a self-generated nanoporous silver layer was fabricated by a one-step laser-induced growth-sintering process of a particle-free organometallic solution. The porous structures self-generated on a polymer film and were freely adjustable by controlling the rate of laser input dose. By changing the patterning mode, the nanoporous electrodes with extremely high surface area and highly conductive current collectors were formed in a single processing domain. Electrodeposition of hetero metal oxides (manganese and iron oxides) as the active materials followed, and a flexible micro-supercapacitor with high volumetric energy density of 16.3 mW h cm−3 and power density of 3.54 W cm−3 was formed. This was achieved through the large surface area and high electrical conductivity of the nanoporous silver layer, and high operating voltage due to the asymmetrical electrode configuration. This method resulted in a faster and more cost-effective manufacturing process than conventional MSCs fabrication. It also achieved the highest volumetric energy density in metal/oxide-based MSCs as a state-of-the-art performance.
Stable Matching-based Mobility Agent Selection in Distributed Mobility Management

In partially distributed mobility management (P-DMM), location management functions are centralized whereas data forwarding functions are distributed to DMM gateways (DMM-GWs). Since all packets are processed by DMM-GWs, which one is selected as a serving DMM-GW has a significant impact on the performance of mobile nodes (MNs). In this paper, we propose a stable matching-based DMM-GW selection scheme considering the preferences of both DMM-GWs (w.r.t. load balancing) and MNs (w.r.t. location update cost). Evaluation results demonstrate that the proposed scheme generates about 50% location update cost while the load fairness increased to 0.75 in the most unbalanced mobile distributed state.
Application of Physiological Sensors for Personalization in Semi-Autonomous Driving: A Review

As automobiles become increasingly autonomous, there is growing interest in personalized automation based on human behavior. However, there is an increasing need for behavioral data from vehicle occupants that could be acquired through physiological sensors. Hence, the motivation of this review is to provide information to develop practical physiological sensors to assess behavior, while maintaining the acceptance, privacy, and safety of vehicle occupants. In order to improve driver and passenger experiences and build trust between autonomous vehicles and humans, the current review considers a number of studies addressing driving tasks based on sensors that monitor vehicle occupant behavior. By including applications to emergency and non-emergency driving scenarios, a survey of literature is conducted on physiological sensing for personalization of semi-autonomous driving. Given the limited availability of sensor technology that is specifically designed to monitor behavior in automobiles, the survey is provided as a resource to further physiological sensor development for automotive applications.
3D printed capacitive shear and normal force sensor using a highly flexible dielectric

We have investigated an entirely 3D printed capacitive sensor. Using a combination of 4 variable capacitors it allows to simultaneously measure shear and normal forces. To guide the design and analysis the behavior of the sensor has been modeled using both finite element method (FEM) simulations and an analytical model. The sensor was tested in a mechanical test setup by means of a linear actuator, loading the sensor with a force from various angles. The sensor showed it was able to measure both the normal and shear force components with a maximum noise floor of 1.5 N.