Miracle-dz/UniFAR-FTUs huggingface dataset:
Memory Access Driven Memory Layout and Block Replacement Techniques for Compressed Deep Neural Networks

Since deep neural networks (DNNs) are very intensive in terms of memory, compressing them with weight sharing is known to be an effective technique to deploy the networks on embedded systems with limited on-chip memory capacity, resulting in a considerable saving of energy consumption. However, the extreme compression sacrifices the accuracy of DNNs. In this context, this work introduces two unique optimization problems and proposes solutions for DNNs with “unfitted compression” to maintain the accuracy, in which all distinct weights of the compressed DNNs could not be entirely contained in on-chip memory. Precisely, given an access sequence of weights, (1) the first problem is to arrange the weights in off-chip memory, so that the number of memory accesses to the off-chip memory (equivalently the energy consumed by the accesses) be minimized, and (2) the second problem is to devise a strategy of selecting a weight block in on-chip memory for replacement when a block miss occurs, with the objective of minimizing the total energy consumed by the off-chip memory accesses and the overhead of scanning indexes for block replacement. Through experiments with the model of compressed AlexNet, it is shown that our solutions are able to reduce the total energy consumption of the off-chip memory accesses including the scanning overhead by 34.2% on average over the use of unoptimized memory layout and LRU replacement scheme.
A New Control Method For Elimination Of Current THD Under Extremely Polluted Grid Conditions Applied On A Three Phase PWM Rectifier

Depending on the reference frame, a proportional integral (PI) or proportional resonant (PR) controller is often utilized for current control in the three-phase PWM rectifiers. Due to the fact that the controller bandwidth is either smaller than the frequency of a specific order harmonic or the controller gain at that frequency is too low the harmonics cannot be effectively eliminated. Among all approaches introduced for harmonic elimination, selective harmonic compensation is believed to have a better performance. Selective harmonic compensation can successfully compensate for harmonics. It, however, forces a considerable computation burden on DSP. This paper proposes a new compensation technique that that can suppress the harmonics with less computation efforts.
Factors Influencing the Perception of Realism in Synthetic Facial Expressions

One way to synthesize facial expressions is to change an image to represent the desired emotion and it is useful in entertainment, diagnostic and psychiatric disorders therapy applications. Despite several existing approaches, there is little discussion about factors that contribute or hinder the perception of realism in synthetic facial expressions images. After presenting an approach for facial expressions synthesis through the deformation of facial features, this paper provides an evaluation by 155 volunteers regarding the realism of synthesized images. The proposed facial expression synthesis aims to generate new images using two source images (neutral and expressive face) and changing the expression in a target image (neutral face). The results suggest that assignment of realism depends on the type of image (real or synthetic). However, the synthesis presents images that can be considered realistic, especially for the expression of happiness. Finally, while factors such as color difference between subsequent regions and unnatural-sized facial features contribute to less realism, other factors such as the presence of wrinkles contribute to a greater assignment of realism to images.
Uncertainty Observer and Controller Based on the Takagi-Sugeno Fuzzy Model

A term of uncertainty in the T-S fuzzy model is considered to cover the discrepancy between the concerned system and T-S fuzzy model. Bearing designing a controller based on the T-S fuzzy model in mind, an observer of a partial uncertainty that influences the system through the control matrix the same as the control input is first proposed. The uncertainty observer is capable of estimating either the related uncertainty itself or its trajectory when it is acutely time-varying. Then, a fuzzy controller is designed, in which the estimated uncertainty is used directly to counteract the influence of the uncertainty on the system. In addition, the Nussbaum-type function is also involved in the controller in order to make the closed-loop system asymptotically stable.
Exploring the Impact of in Class Writing Exercises in an Engineering Course

Employers of engineers expect from their new graduates to be not only technically proficient but, also demonstrating good communication skills at the workplace. Despite these high expectations, no special measures are taken by most engineering faculties and engineering educators for fostering such attributes. Writing has long been seen as a tool to develop communication skills. The introduction of short writing exercises in class not only can improve writing skills of students but may also help in fostering 21st century skills. This introductory study explores the impact of in-class writing activities in a third-year engineering course with a view to improve written communication skills and promote critical thinking through real-world problem-based learning. The writing exercises were peer-reviewed by the students. The data was collected through a paper-based questionnaire containing closed as well as open-ended questions. The survey findings showed that these activities were perceived positively by students and improved their writing skills. It also stimulated students to learn, and apply their knowledge to solve real-world problems and provide constructive feedback on peers' work. The initial findings provide enough encouragement to further explore the implementation of teaching and learning activities based on writing as a strategy to foster written communication skills.
Long Term Causality Analyses of Industrial Pollutants and Meteorological Factors on PM2.5 Concentrations in Zhejiang Province

Recently with the accelerating process of urbanization, environmental degradation in air quality, especially the concentrations of PM2.5, has posed a heavy threat to the economic development and human life. To further clarify the cause of haze, in this paper we quantitatively analyzed different factors impacted on PM2.5 by establishing twelve joint regression models. Four industrial pollutants and eight meteorological factors were processed in the form of time series from a large number of monitoring data in Zhejiang province of China, and also the significant causality influence of each component which strongly correlates with PM2.5 was discussed. Results show that PM2.5 is sensitive to temperature and wind speed, and its formation is mainly due to NO2, followed by SO2 and CO, especially in the region of Hangzhou.
Access Privilege Elevation and Revocation in Collusion-Resistant Cloud Access Control

As a consequence of the growth in cloud services adoption, the majority of organisations and data owners now migrate their data to the cloud. This raises many security concerns due to remote untrusted storage and the loss of control over data. Cipher-text policy attribute based encryption (CP-ABE) can achieve cryptographic access control that tackles the majority of these concerns. However, the two key challenges of CP-ABE, which have not been efficiently addressed by the majority of the existing systems, are how to revoke any single shared attribute from any users without affecting others and build a collusion resistance system. In this paper, our proposed system extends the relevant existing techniques to resolve the inherent problems in CP-ABE, which is users' credential management according to access privilege customization. The novelty of our collusion-resistant system is to drive the access privileges in a specific way by updating the access policy as well as user revocation. Our theoretical and experimental findings indicate that our proposed system is more practical and secure compared to the current related systems.
Compact UWB Slot Antenna Utilizing Traveling-Wave Mode Based on Slotline Transitions

A compact ultra-wideband (UWB) slot antenna using narrow slots is proposed. Different from the traditional wideband slot antennas with multiple resonant modes, the proposed design of this paper utilizes the traveling-wave mode. By using a slotline–CPW transition as the convertor, which connects a slotline and four open slots, the forward wave in the slot has small reflection. The working mode keeps stable at different frequencies, i.e., operating in the traveling-wave mode. As a result, both wide bandwidth and stable radiation patterns are achieved with inherent characteristic of traveling-wave mode. The proposed antenna occupies a small area and the slot width is narrow, which make it suitable to be mounted on the edge of metallic surface for wireless devices. Given an instance for validation, a prototype is fabricated, with a slit width of 0.2 mm and a total size of <inline-formula> <tex-math notation="LaTeX">$25\times 30\times 0.762 $ </tex-math></inline-formula> mm<sup>3</sup> (<inline-formula> <tex-math notation="LaTeX">$0.24\times 0.29\times 0.007 \lambda _{L0}^{3} $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\lambda _{L0} $ </tex-math></inline-formula> is the wavelength in free space at the lowest operating frequency). Measured results show that a relative bandwidth (VSWR < 2) of about 121% is achieved, covering the entire UWB. Besides, stable gains and radiation patterns are obtained in the operating band. The time-domain characteristic is also investigated, and a small-signal distortion is achieved.
Plannable Demand Load in Size Optimisation of Hybrid Renewable Energy Systems

Size optimisation of hybrid renewable energy systems (HRES) is aimed at finding a solution which supplies a reliable power with minimum cost for a predefined demand load. In industrial applications, the demand load is mainly due to the operation of machines. In many cases there is some level of flexibility in the scheduling of the operation of machines. In other words, the demand load can have different forms. This is in contrary to the common practice in which the demand load is assumed to have a fixed profile. This paper presents the results of a preliminary study on the effect of including the flexibility in load scheduling in size optimization of HRES.
Joint Antenna Selection and Analog Precoder Design With Low-Resolution Phase Shifters

Analog precoder was introduced recently to millimeter wave (mmWave) systems with large-scale antenna arrays as a promising hardware-efficient transceiver architecture. In practice, low-resolution phase shifters (PSs) are generally adopted to achieve low hardware cost and low power consumption, which, however, makes fine beam tuning challenging. Recent achievements in antenna selection technology motivate us to exploit the augmented antenna diversity of a large-scale antenna array to compensate the accuracy loss caused by this low-resolution PS implementation. In this correspondence, we consider a narrow-band multiple-input single-output downlink mmWave system. Our goal is to jointly select a set of transmit antennas and design the analog precoder with constant amplitude and discrete phase constraints to maximize the spectral efficiency. In particular, we propose a novel joint antenna selection and analog beamformer design algorithm, which can provide satisfactory performance with low complexity. Simulation results demonstrate the advances of the proposed algorithm compared with the low-resolution analog precoding schemes without antenna selection.
A Visual Servoing with Collision Avoidance Mechanism for Redundant Manipulators

This paper presents a visual servoing method for a redundant manipulator to control quickly and accurately by image error in a complicated environment. The proposed approach combines two parts, one is position-based visual servoing, and another is avoiding obstacles by using virtual repulsive torque. The image features are found and mapped by Oriented FAST and Rotated BRIEF (ORB) and the image errors are calculated. Furthermore, the object pose is estimated in the world coordinate by the image errors and the current pose of the manipulator and the target EOAT pose compute will be obtained. After confirming the EOAT pose, this method introduces a virtual repulsive torque for solving generalized inverse kinematics problems by analytical and numerical methods. The proposed approach uses the property of the redundant manipulator to maintain the distance between links and obstacles. For the applicability, we transform the programs to the nodes and use Robot Operating System as the core to build the communication protocol for exchanging information. The validity and efficiency of the proposed method are measured in simulations, by which it verifies that the method is not only feasible to constrained motions but also has good performances in collision avoidance.
Balancing California's Grid Without Batteries

Demand dispatch refers to a collection of distributed control techniques to obtain grid services from flexible loads. A carefully designed control architecture can enable a collection of loads to behave in aggregate as a large virtual storage device. Grid-level ancillary services can be provided with minimal communication, while guaranteeing quality of service to the consumer. This work expands on prior work in several directions: • A natural notion of energy capacity is proposed for the special case of thermostatically controlled loads (TCLs). It is shown that this quantity is closely approximated by thermal energy capacity, which is a component of the “leaky battery model” introduced in prior work. • Simulation experiments in a distributed control setting show that these energy limits, and accompanying power capacity limits, are reliable indicators of online capacity, even for a heterogeneous population of loads. • A feedforward/feedback control scheme is proposed for a large collection of heterogeneous loads. At the local level, control loops are used to create cooperative responses from each load in a given class of homogeneous loads. This simplifies control of the aggregate based on two pieces of information: aggregate power consumption from each class of loads and the state of charge surrogate that is a part of the leaky battery model. This information is required at a slow time-scale (say, 5 minute sampling). • The paper concludes with economic implications. In particular, given that dispatchable loads are a form of virtual storage and not virtual generation, it is not surprising that the use of real time or time-of-use pricing has been problematic.
An Enhanced Simulation Framework for the Performance Evaluation of On-Chip Network Designs

Network-on-Chip (NoC) has emerged as an innovative solution to the communication bottlenecks of the System-on-Chip (SoC) designs. Being an emerging technology, this field requires an extensive research to cope up with the design challenges of the on-Chip networks. In this research work, we have developed and proposed a unified simulation framework named as ENoCTweak for the simulation and analysis of the important parameters such as latency, throughput. energy and power of the NoC system. This framework is mainly an enhanced and updated version of a well-known simulator NoCTweak with additional performance parameters and integration of different NoC components and simulation platforms. Important components from NOCMAP and ReliableNoC simulators are also integrated into the framework of the proposed ENoCTweak simulator. In this platform, we have embedded different mapping algorithms, like Branch & Bound (BB), Simulated Annealing (SA), Segmented Brute-Force Mapping (SBMAP), Branch & Bound based Exact Mapping (BEMAP), and Optimized Near-optimal Mapping (ONMAP) algorithms, in addition to the existing NMAP and Random algorithms. An External Mapping (EXMAP) setup is also developed to evaluate the mapping results of the existing NoC simulation platforms. In addition to the 2D mesh, other topologies like torus and folded torus are implanted to improve the simulation capabilities of the ENoCTweak simulator. Bit energy model along with the CMOS energy models are added to the simulation framework to enhance the characteristic performance of the simulator.
Exponential Stabilization of Takagi–Sugeno Fuzzy Systems With Aperiodic Sampling: An Aperiodic Adaptive Event-Triggered Method

In this paper, we study the exponential stabilization problem for continuous-time Takagi–Sugeno fuzzy systems subject to aperiodic sampling. By aiming to transmission reduction, an appropriate aperiodic event-triggered communication scheme with adaptive mechanism is put forward, which covers the existing periodic mechanisms as special cases. For the sake of reduction in design conservativeness, both the available information of sampling behavior and threshold error are fully acquired by constructing a novel time-dependent Lyapunov functional. Then, a new exponential stability criterion is presented to establish the quantitative relationship among the adaptive adjusted event threshold, the decay rate, the upper bound, and the lower bound of variable sampling period, simultaneously. By resorting to a matrix transformation, the corresponding stabilization criterion is further derived by which the sampled-data controller can be obtained. Finally, two illustrative examples are provided to demonstrate the virtue and applicability of proposed design method.
Optimization of Hybrid Optics with Multilevel Phase Mask for Improved Depth of Focus Broadband Imaging

This paper is devoted to designing of hybrid refractive/diffractive optics for high-quality imaging with improved depth of focus (DoF). The novelty of the concept is in sharing of the optical power of the refractive lens between the lens and a multilevel phase mask (MLM) as a diffractive optical element (DOE). The efficiency of the design is confirmed by numerical results. Broadband multiwavelength test-images are exploited for the design and testing of the system. It is shown that the obtained hybrid optical system in combination with computational inverse imaging provides both a better image quality (due to the robustness of the designed optics to chromatic aberrations) and an extended depth of focus as compared with the refractive lens and the diffractive lensless design with MLM.
A PSO-Based Algorithm Describes Ant Nest Move

Ants are believed to be workaholics, though biologists report that many ants are literally inactive in nest move. An open question on this ant's swarm behavior is whether such an active-to-population rate improves or deteriorates precise and swift nest move. In this study, we use an algorithm which is based on particle swarm optimization (PSO) and simulate ant nest move. The simulation of our PSO-based algorithm has revealed that the swarm which comprises 60% of active ants in the population obviously outperforms the swarm of full active population in both precise and swift nest move. Our simulation results are supported with the field researches carried by external ant experts.
Goal-Aware Team Affiliation in Collectives of Autonomous Robots

Collaboration in teams is essential in robot collectives. In order to achieve goals, individual robots would otherwise not be able to accomplish. In a such a distributed and highly dynamic system, a global coordination might not be possible. In this paper, we analyse static team affiliations, defined at deployment time, and compare its efficiency against dynamic team affiliations generated during runtime using random selection. Since operators might not be able to determine all dynamic aspects of the given environment at the time of deployment, we further propose a novel, goal-aware approach to affiliate each robot with a team. This approach brings together insights from biology, sociology, and psychology. In this novel approach, robots only operate on aggregated information from the network which is potentially changing during runtime. Finally, we also introduce an approach to select a team affiliation during runtime using machine learning techniques. Using 60,000 randomised scenarios, we analyse the efficiency and further discuss the different benefits and drawbacks of the proposed approaches.
Deep Convolution Neural Network Model to Predict Relapse in Breast Cancer

A mishap in anti-cancer drug distribution is critical in breast cancer patients due to poor prediction model to identify the treatment regime in ER+ve and ER-ve (Estrogen Receptor (ER)) patients. The traditional method for the prediction depends on the change in expression across the normal-disease pair. However, it certainly misses the multidimensional aspect and underlying cause of relapse, such as various mutations, drug dosage side effects, methylation, etc. In this paper, we have developed a multi-layer neural network model to classify multidimensional genomics data into their similar annotation group. Further, we used this multi-layer cancer genomics perceptron for annotating differentially expressed genes (DEGs) to predict relapse based on ER status in breast cancer. This approach provides multivariate identification of genes, not just by differential expression, but, cause-effect of disease status due to drug overdosage and genomics-driven drug balancing method. The multi-layered neural network model, where each layer defines the relationship of similar databases with multidimensional knowledge. We illustrate that the use of multilayer knowledge graph with gene expression data for training the deep convolution neural network stratify the patient relapse and drug dosage along with underlying molecular properties.
Motion Control of Mobile-Wheeled Inverted Pendulum Robot with Center-of-Mass Offset

The mobile-wheeled inverted pendulum (MWIP) robot with dual-arm manipulators is an unstable and nonlinear system with multi-variables and strong coupling. This paper proposes an intelligent motion control system to attain the desired position and direction while keeping the MWIP robot balanced. The control system is comprised of a position controller, a balance control, a direction controller and a COM compensator. A cascade structure, which combines the inner loop of balance control and the outer loop of position control, is illustrated its effectiveness. Since the dual-arm manipulator cannot execute the given motions of the end effector and move the dual-arm stable simultaneously, the COM compensator is designed to eliminate steady state error in the position controller. Finally, the real-time experimental results show that the COM compensator can improve the control performance of station-keeping testing scenario for the MWIP system.
Gain Improvement of Microstrip Patch Antenna using Octagonal-Loop Metasurface Superstrate and Octagonal-Shaped EBG Structure

This paper presents gain improvement of microstrip patch antenna using octagonal-loop metasurface superstrate and octagonal-shaped electromagnetic band gap (EBG) structure for 2.4 GHz band application. An FR-4 substrate with 150%150 mm2 with a dielectric constant of 4.3 was used in the antenna design. The metasurface worked as superstrate layer and the radiating patch was designed in fractal shaped. The EBG structure was mounted onto the antenna substrate around the radiating patch to reduce the antenna profile and suppress the surface wave. The superstrate suspends above the radiating patch at the distance of 7. 25mm, to improve the directivity. The measured results of the microstrip antenna with octagonal-loop metasurface superstrate and octagonal-shaped EBG structure have the reflection coefficient with less than -10 dB from 2.38 to 2.52 GHz. The antenna gain is 10.34 dBi at 2.45 GHz.