Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability Improvement of Static Random Access Memory Bit-Cells

The rapid growth in the semiconductor industry has paved the pathway for tremendous advancement in semiconductor memory technology. Internet of Things (IoT), wireless body sensor nodes and other high-end applications need power-efficient and reliable memories. Static Random Access Memory which is the heart of the chip occupies the major part of the area due to its importance. The demand for low power, deeply integrated and high-speed memory results in trade off between stability and area of the bit-cell, which are the two essential aspects of memory. As technology shrinks, the reliability of the memory gets affected. The stability involves soft error upset due to radiation in the working environment and the static noise margin is reduced due to process variation and operating conditions. In this paper, the SRAM bit-cell, which have both recovery from soft error upset and stability improvement by various read/write assist techniques, are discussed. The memory cells are designed using Cadence Virtuoso tool GPDK 180nm technology.
Switched Fractional Order Model Reference Adaptive Control for first order plants

This paper presents a model reference adaptive controller for first order plants with unknown parameters. The adaptive laws used to estimate the controller parameters are based on fractional differential equations, whose orders are switched among a fractional value in the interval (0,1) and 1 at a certain time instant T. Boundedness of the signals in the resulting controlled system is proved using recent results. A simulation study is provided to show how the proposed control strategy can improve the behavior of the controlled system and decrease the control energy used, compared to the classic model reference adaptive controllers and fractional order model reference adaptive controllers with non-switched adaptive laws.
Archivist: A Machine Learning Assisted Data Placement Mechanism for Hybrid Storage Systems

With the rapid growth of edge-cloud computing, emerging applications pose higher performance demand on the storage system for storing massive data that are generated from various sources. The multi-sourced data shows different properties in size, retention time, and read/write frequency. Hybrid storage system is promised to efficiently handle the data in edge-cloud computing environment satisfying different data demands. The key problem is how to place the data on the hybrid storage system according to the run-time status and the properties of both data and the storage systems. In this paper, we propose Archivist — a machine learning assisted data placement mechanism for hybrid storage systems to reduce file access latency. We first design a machine learning based approach for predicting the access patterns of the incoming data. Then, we present a data placement algorithm to optimize the data on the hybrid storage mediums by matching the properties of data and the features of storage mediums. Extensive experimental results show that Archivist can achieve up to 49% improvement of system performance for file accesses compared with baseline.
Congestion Free And Bandwidth Aware Multipath Protocol for MANET

A ‘mobile ad-hoc network’ (MANET) is formed by the cooperative effort of wireless nodes, without any fixed infrastructure. Communication among the nodes is either in single hop or multi-hop fashion. Owing to their dynamic topology and limited bandwidth, maintaining quality of service in such a network is a difficult task. This paper provides an enhancement to an existing routing protocol, namely AOMDV. It considers channel free time and available bandwidths while selecting path. Simulation of modified protocol shows a better throughput with lower end to end delay.
Adaptive Localization and Segmentation of Optic Disc using K-means and Active Contour

Glaucoma disease is the second world rank cause of blindness. Therefore, an accurate, reliable and fast computer-aided diagnosis (CAD) system is necessary for early detection and treatment to prevent a permanent vision loss. The proposed scheme in this study is pre-processing and segmentation of optic disc since both are the prior steps that determine the accuracy of CAD in detecting optic disc and optic cup. The pre-processing process contains the localization step and preparation step that facilitates the segmentation scheme. While segmentation process contains active contour and morphological closing operation. The proposed method successfully achieve accuracy of 100% for localization and accuracy of 97.28%, specificity of 99.21% and sensitivity of 92.47% for segmentation. These results indicate that the proposed schemes have the potential to be implemented in the CAD system for detecting optic disc.
Hardware-Assisted MMU Redirection for In-Guest Monitoring and API Profiling

With the advance of hardware, network, and virtualization technologies, cloud computing has prevailed and become the target of security threats such as the cross virtual machine (VM) side channel attack, with which malicious users exploit vulnerabilities to gain information or access to other guest virtual machines. Among the many virtualization technologies, the hypervisor manages the shared resource pool to ensure that the guest VMs can be properly served and isolated from each other. However, while managing the shared hardware resources, due to the presence of the virtualization layer and different CPU modes (root and non-root mode), when a CPU is switched to non-root mode and is occupied by a guest machine, a hypervisor cannot intervene with a guest at runtime. Thus, the execution status of a guest is like a black box to a hypervisor, and the hypervisor cannot mediate possible malicious behavior at runtime. To rectify this, we propose a hardware-assisted VMI (virtual machine introspection) based in-guest process monitoring mechanism which supports monitoring and management applications such as process profiling. The mechanism allows hooks placed within a target process (which the security expert selects to monitor and profile) of a guest virtual machine and handles hook invocations via the hypervisor. In order to facilitate the needed monitoring and/or management operations in the guest machine, the mechanism redirects access to in-guest memory space to a controlled, self-defined memory within the hypervisor by modifying the extended page table (EPT) to minimize guest and host machine switches. The advantages of the proposed mechanism include transparency, high performance, and comprehensive semantics. To demonstrate the capability of the proposed mechanism, we develop an API profiling system (APIf) to record the API invocations of the target process. The experimental results show an average performance degradation of about 2.32%, far better than existing similar systems.
Channel Ordering and Supermodularity

This work introduces a new preorder over channels that is monotonic with Shannon’s mutual information for all distributions over the input alphabet. Moreover, this monotonicity also holds when substituting mutual information for quantities relative to Arimoto-Rényi conditional entropies and guessing entropy. Several results connecting this new preorder with others from the literature are proven. This work also discusses an extension of Shannon ordering based on this new preorder, and establishes that channels ordered this way are also ordered with regards to both Shannon and min-capacity.
Learning Transformable and Plannable se(3) Features for Scene Imitation of a Mobile Service Robot

Deep neural networks facilitate visuosensory inputs for robotic systems. However, the features encoded in a network without specific constraints have little physical meaning. In this research, we add constraints on the network so that the trained features are forced to represent the actual twist coordinates of interactive objects in a scene. The trained coordinates describe 6d-pose of the objects, and <inline-formula><tex-math notation="LaTeX">$SE(3)$</tex-math></inline-formula> transformation is applied to change the coordinate system. This algorithm is developed for a mobile service robot that imitates an object-oriented task by watching human demonstrations. As the robot has mobility, the video demonstrations are collected from different viewpoints. Our feature trajectories of twist coordinates are synthesized in the global coordinate after <inline-formula><tex-math notation="LaTeX">$SE(3)$</tex-math></inline-formula> transformation is applied according to robot localization. Then, the trajectories are trained as probabilistic model and imitated by the robot with geometric dynamics of <inline-formula><tex-math notation="LaTeX">$se(3)$</tex-math></inline-formula>. Our main contribution is to develop a trainable robot with visually demonstrated human performances. Additionally, our algorithmic contribution is to design a scene interpretation network where <inline-formula><tex-math notation="LaTeX">$se(3)$</tex-math></inline-formula> constraints are incorporated to estimate 6d-pose of objects.
Spatially Coupled Sparse Regression Codes with Sliding Window AMP Decoding

We analyze the performance of spatially coupled sparse regression codes (SC-SPARCs) over the AWGN channel with a sliding window approximate message passing (AMP) decoder. In an SC-SPARC, the codewords are sparse linear combinations of columns of a Gaussian matrix that has a block-wise band-diagonal structure. In this paper we introduce and motivate the sliding-window AMP decoder for SC-SPARCs, and present the first steps towards a fully rigorous proof that the SC-SPARCs with sliding window AMP decoding are asymptotically capacity-achieving. We also provide numerical simulation results demonstrating the error performance of the sliding window AMP decoder at finite code lengths.
A Method to Empirically Set the Coefficients of an Analytical Model of Battery Aging

The increase of battery-powered devices and systems, from consumer electronics to electric vehicles, means that the reliability analysis of such systems concerns both energy cells and battery packs, as well as circuitry. For this reason, battery modeling has been one of most developed research areas during the last two decades. In this context, the Millner model, which is based on crack propagation theory, provides an accurate mathematical model for the analysis of the degradation (i.e., aging or capacity fading) of lithium-ion cells. In order to extend the application of the Millner model in a practical and simplified way, this work describes a method to empirically set the coefficients of this model by extracting these values from experimental data only published by the manufacturer and some researchers. When simulating an AMP20m1HD-A prismatic pouch cell at different working and operating conditions, the results show an accurate analysis of the capacity fading, with an error of generally less than 3%.
Heating up Antarctic Plants - Preliminary Results of a Thermal Actuator for Antarctic Bioindicators in a Context of the Climate Change

Colobanthus Quitensis and Deschampsia Antartica are the only native vascular species of plants in the Antarctic region and are a source of interest in the climate change studies. These plants have been studied for decades and recognized as bioindicators of the climate change. The article presents preliminary studies and results of a thermal actuator, which is going to be applied in a climate change studies. The elaborated, far infrared actuator together with a control system maintains the temperature of the Antarctic plant certain delta above the ambient temperature. Since the heat transfer is based on infrared radiation a thermal model is introduced, where the parameters of the model are determined empirically approximating it using a 1-st order dynamical system. Once determined, the sytem model paremeters help to design the control strategies: Control On-Off (1) and Control PID (proportional–integral–derivative) (2). As a result we obtain that the control PID maintains the objects temperature with major stability than On-Off, what makes it preferable in the final application. The future works involve modeling ambiental perturbation, which affects the system and determination thermal parameters of the Antarctic plants.
Design and Analysis of Piezoelectric Microcantilever Energy Harvesters

Piezoelectric energy harvesters are being increasingly explored due to their ease of energy trapping, higher energy density, broader output voltage range, independence from external voltage supply and compatibility with micromachining process for piezoelectric material deposition. We propose six piezoelectric microcantilever energy harvesters based on PZT-5H and Polyvinylidene fluoride. The structures were analyzed using COMSOL Multiphysics 5.3a. The resonant frequencies for the simulated structures were found to agree with the theoretical values. The von Mises stress analysis of the structures were also carried out. An Eigen frequency of 125.99 Hz and harvested voltage of 3.768 V was obtained for PZT-5H and Aluminium microcantilever with Titanium proof mass.
An Approach for Safety Assessment of Highly Automated Systems Applied to a Maritime Traffic Alert and Collision Avoidance System

Traffic density at sea has constantly grown over the last years due to rising shipping of goods. As a consequence it has become a more and more difficult task for the ship's operating crew to assess a traffic situation and anticipate its future development. Automated collision avoidance systems can assist the crew in this task to reduce the number and severity of accidents. Such a system is the MTCAS (Maritime Traffic Alert and Collision Avoidance System). Before safety-critical systems like this can go to market they have to undergo a verification and validation process to ensure safety. An important part of this process is the hazard and risk assessment. In this paper we review the state of the art of existing methods for hazard and risk assessment with regard to their applicability to highly automated systems. After identifying the limitations of existing methods we propose a new method especially tailored for highly automated systems and exemplary apply it to MTCAS.
CNJC: A cumulative similarity measure for Link Prediction in social networks

Link Prediction problem aims to predict a link in a social network that could surface in future. Considering the similarities between connections and predicting a future connection on that basis has been a reliable technique. This paper proposes a new similarity measure for comparing the connections. Well-known similarity measures are combined together to create new powerful measure. The new measure has been tested on real world datasets and found to perform better than the existing measures.
Formal Modeling and Verification of NAND Flash Memory Supporting Advanced Operations

NAND flash memory has become the de facto standard for several non-volatile storages used in commercial and mission-critical applications. The advanced operations supported by NAND flash memory contribute to device performance and also influence the design of some of the crucial mechanisms of the flash memory device controller. In this research, we consider a recent ONFI standard i.e. ONFI-3.2 and successfully model the NAND flash memory device with the advanced operations in addition to many basic operations in contrast to the existing research works. We encode all the properties obtained from the standard in LTL and proved those using symbolic model checking. Our modeling approach simplifies the state machines and reduces resource requirements while capturing the essential information needed to verify the requirement based properties.
Location Instruction-Based Motion Generation for Sequential Robotic Manipulation

Deep learning-based visuomotor control for manipulation are studied recently, which uses a neural network to learn the mapping from images to robotic actions directly. Most previous methods assume there is only one target object in the image. When multiple target objects are present, they are normally regarded as multi-task problems. One-hot vectors are often used to denote different tasks in the visuomotor framework. Nonetheless, these one-hot vector-based methods are easily disturbed and non-extendable. In this paper, a location instruction is used to guide the robot to generate different trajectories in the situation with multiple targets. The proposed framework mainly consists of three modules: the AutoEncoder (AE) network, the motion generation network and the location detection network. AE tries to process the perception information into small-scale feature maps. The location detection network gives the pixel coordinates of the center of the target object in the image. The motion generation network combines the location instruction and the preprocessed perception information and generates an entire motion trajectory to finish the specified manipulation task. For the object stacking tasks with distractors, the proposed method could obtain a success rate of 98%, while the one-hot vector-based method only has a success rate of 56%.
Intrusion detection system for SDN network using deep learning approach

Software Defined Network (SDN) is considered as the main component of the next generation network. Security, in this environment, has very challenges and risks. Attacking SDN controller or injecting false flow rules could affect the network and block the entire services. To enhance the SDN network security, we propose an anomaly-based intrusion detection system using deep learning approach. This solution aims to protect the communication channel between the SDN control layer and the SDN infrastructure layer against false data injection attack, and to detect any attempt of attack in SND southbound side. We analyze the flows that circulate in the SDN network, we use the logarithm function followed by the Min/Max scalar technique to normalize the flows features. For the flow classification, we exploit the Relu and Softmax functions. We test the proposed system with CICIDS2017 dataset on an experimental platform combining Mininet environment and ONOS controller. The evaluation results demonstrate the effectiveness and efficiency of the proposed security solution.
Linear Quadratic Regulator Controller (LQR) for AR. Drone's Safe Landing

Unmanned Aerial Vehicle (UAV) became one of the mobile robot technologies that are widely used by humans. It's used for commercial and development of quadcopter purpose. The reason quadcopter is widely used because of its ability to maneuver in all direction and flexibility. One of the interesting things to develop is safe landing problem of the quadcopter, due to a safe landing can reduce the risk of physical damage to the quadcopter. The purpose of this research was to implement Linear Quadratic Regulator (LQR) algorithm for the safe landing of an AR. Drone quadcopter. This method is an optimal control that produces feedback gains for a linear system by minimizing a quadratic cost function. LQR approach is used to control the attitudes and altitudes to perform landing of AR. Drone that is implemented in Robot Operating System (ROS). The results of this research have obtained a safe landing algorithm by knowing the value of settling time in landing of an AR. Drone quadcopter. Testing is done by observing settling time of landings drone at altitude which varies, i.e., 1.25 m, 2 m, and 3 m. Based on the results of the testing, the average of settling time for landing is 1.74 second for every one meter increment of altitude.
A fractional model of the friction-temperature behavior in robot joints

It is well-known that friction changes with the temperature in robotic joints, and the temperature depends on the action being performed. In this paper we present a fractional model that describes the relationship between power generation/loss and temperature in the joints of industrial robots. The proposed mathematical model is used to predict friction variation during the robot working cycle without using temperature sensors. Several experimental tests have been performed on a commercial 6 degree-of-freedom anthropomorphic manipulator. Results confirm that the proposed model is able to estimate the behavior of the friction in the joints during robot working cycles. This can be used to compensate for friction, thus improving the control performance, and to predict energy consumption.
Mixing Binary Face and Fingerprint based on Extended Feature Vector (EFV) Hashing

Multimodal biometric template protection (BTP) is gaining increasing attention as it overcomes the drawback of unimodal BTP. In this work, a token-less cancellable biometric scheme, namely Extended Feature Vector (EFV) Hashing, is applied to fuse the multimodal binary face and fingerprint template at a feature-level fusion. In advance to the unimodal EFV hashing, this work reorganizes the feature extension mechanism in EFV transformation and introduces the random sampling mechanism to increase the difficulty for reverse processing of the cancellable template. In addition, two fusion options are developed for producing the cancellable template. Experiments are conducted on the well-known Fingerprint FVC2004 and Face LFW datasets. The results demonstrate that the propose approach achieves a satisfactory verification rate with EER ~~ 0.1%.
Attendance System based on Deep Learning Face Recognition without Queue

Management of the presence of a lecturer and student in the classroom is one of the factors that determine the quality of learning in a university. Today, there are many researchers developing a system for managing student attendance in classrooms based on facial recognition technology, to replace the previous system. However, there is a new problem coming, when students come to the classroom simultaneously, then comes the queuing problem that often disrupts the learning process in the classroom. To overcome this problem in this article we will discuss the prototype system for managing student attendance in classroom based on deep learning facial recognition technology without queue. Feature extraction uses 128-d facial embeddings from FaceNet, and code implementation uses libraries originating from opencv and dlib. The installation of the device is done in such a way that it allows the process of identifying the faces of students present in the classroom can be done during the learning session in the class is still ongoing. The results show that this system is able to detect the presence of students in the classroom well.
A solution to the split & merge problem for blockchain-based applications in ad hoc networks

In recent years, studies have been conducted to evaluate the performance of blockchain-based technologies to solve various problems. In this paper, we present a proof of concept in which we evaluate the robustness of a blockchain-based application in an ad hoc network confronted with split merge problems that may be caused by node mobility. We highlight how a blockchain should behave according to the network state and we measure its cost in terms of network load. We show that the measurements depend greatly on the mining algorithm used to solve the consensus.