Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Selection of Tensor Decomposition for Compressing Convolutional Neural Networks A Case Study on VGG-type Networks

Tensor decomposition is one of the model reduction techniques for compressing deep neural networks. Existing methods use either Tucker decomposition (TD) or Canonical Polyadic decomposition (CPD) for model compression, but none of them tried to combine those two methods, owing to the complexity of choosing a proper decomposition method for each layer. In this paper, we adopted the automatic tuning technique to design an algorithm that can mix both tensor decomposition methods, called Mixed Tensor Decomposition (MTD). The goal is to achieve better compression ratio while keeping similar accuracy as the original models. We used VGG type networks for the case study since they are relatively heavy and computationally expensive. We first studied the relation of model accuracy and compression ratio for Tucker and CPD applying to convolution neural networks (CNN). Based on the studied results, we designed a strategy to select the most suitable decomposition method for each layer, and further fine-tunes the models to recover the accuracy. We have conducted experiments using VGG11 and VGG16 with CIFAR10 dataset, and compared MTD with other tensor decomposition algorithms. The results show that MTD can achieve compression ratio 32 × and 37 × for VGG11 and VGG16 respectively with less than 1% accuracy drops, which is much better than the state-of-the-art tensor decomposition algorithms for model compression.
Microservice-Based Performance Problem Detection in Cyber-Physical System Software Updates

Software embedded in Cyber-Physical Systems (CPSs) usually has a large life-cycle and is continuously evolving. The increasing expansion of IoT and CPSs has highlighted the need for additional mechanisms for remote deployment and updating of this software, to ensure its correct behaviour. Performance problems require special attention, as they may appear in operation due to limitations in lab testing and environmental conditions. In this context, we propose a microservice-based method to detect performance problems in CPSs. These microservices will be deployed in installation to detect performance problems in run-time when new software versions are deployed. The problem detection is based on Machine Learning algorithms, which predict the performance of a new software release based onknowledge from previous releases. This permits taking corrective actions so that system reliability is guaranteed.
A Programmable Analog Device Based Emulation Circuit Design for Excitatory and Inhibitory Neurons

In the real neuronal systems, the sum of the inhibitory and the excitatory effects makes the neuronal firing easier or more difficult in the post-synaptic neurons. The dynamics of interaction between simple excitatory and inhibitory neuron populations can be mimicked by the Wilson-Cowan (WC) neuronal model. On the other hand, recently, the hardware realization of the neuronal systems takes to attentions in the bio-inspired system based studies. The common outcomes of these studies are focused on the low power, the low area, the less element consumption, the sensitive parameter setting and the programmability issues and programmable analog/digital devices contains several of these features. In line of this motivation, the FPAA based realization of the W-C neuronal model, has been achieved, successfully in this study.
A Heavy-tailed Distribution Data Generation Method Based On Generative Adversarial Network

Heavy-tailed distribution widely exists in economic, financial, industrial and other data. The tail of heavy-tailed distribution is thicker than that of Gaussian distribution. Generative adversarial networks (GAN) is a data generation model, which is mainly used for image generation. The traditional GANs method usually assumes that the samples follow Gaussian distribution, so it is often not used directly in the heavy-tailed distribution data. In this paper, a generative adversarial network HTGAN for industrial heavy-tailed data is proposed. By improving the method of data preprocessing, the distribution of hidden variable Z is changed to T distribution. This method is applied to the fault data of TE process data. Compared with the traditional GAN method, the superiority of this method in industrial heavy tail data generation is proved.
Deep learning algorithms for gravitational waves core-collapse supernova detection

The detection of gravitational waves from core-collapse supernova (CCSN) explosions is a challenging task, yet to be achieved, in which it is key the connection between multiple messengers, including neutrinos and electromagnetic signals. In this work, we present a method for detecting these kind of signals based on machine learning techniques. We tested its robustness by injecting signals in the real noise data taken by the Advanced LIGO-Virgo network during the second observation run, O2. We trained three newly developed convolutional neural networks using time-frequency images corresponding to injections of simulated phenomenological signals, which mimic the waveforms obtained in 3D numerical simulations of CCSNe. With this algorithm we were able to identify signals from both our phenomenological template bank and from actual numerical 3D simulations of CCSNe. We computed the detection efficiency versus the source distance, obtaining that, for signal to noise ratio higher than 15, the detection efficiency is 70% at a false alarm rate lower than 5%. We notice also that, in the case of O2 run, it would have been possible to detect signals emitted at 1 kpc of distance, whilst lowering down the efficiency to 60%, the event distance reaches values up to 14 kpc.
Methodology for Specification and Verification of High-Level Requirements with MetAcsl

Specification and formal verification of high-level properties (such as security properties, like data integrity or confidentiality) over a large software product remains an important challenge for the industrial practice. Recent work introduced METACSL, a plugin of the FRAMA-C verification platform, that allows the user to specify high-level properties, called HIgh-Level ACSL REquirements or HILARE, for C programs and transform them into assertions that can then be verified by classic deductive verification. This paper presents a methodology of specification and verification of a wide range of high-level properties with METACSL and illustrates it on several examples. The goal is to provide verification practitioners with detailed methodological guidelines for common patterns of properties in order to facilitate their everyday work and to avoid some frequent pitfalls. The illustrating examples are inspired by very usual kinds of properties and illustrated on two use cases. One of them—on the real-life code of the bootloader module of the secure storage device Wookey—was fully verified using the described approach, demonstrating its capacity to scale to real-life code. The other one—on a microkernel of an OS—was added to illustrate other common properties, where the description of the system was intentionally left very generic.
Optimal controller design for state estimation of Boolean control networks

In this paper, a kind of optimal controller is proposed to estimate the state of Boolean control networks (BCNs). Different from the standard observer, the optimal state estimation is completed by designing the control input instead of directly using it, where the maximum-minimum method is employed such that the state of BCNs can be uniquely estimated in possible short time steps. A set observer is first proposed to estimate the state of BCNs at any time steps. Based on the set observer, an initial output-dependent reconstructible state tree is developed, where an algorithm is provided to generate the nodes of such tree and can be implemented offline. The optimal control sequence for uniquely determining the state of BCNs is derived from the reconstructible state tree by a breadth-first search algorithm, where the output of BCNs is dynamically employed. An example is given to illustrate the applicability and usefulness of the developed methods.
FPGA Implementation of Parallel Neurosynaptic Cores for Neuromorphic Architectures

Fully digital neuromorphic hardware architectures such as IBM’s TrueNorth [1] and Intel’s Loihi [2] have been developed to mimic biologically inspired spiking neural networks (SNNs). Besides being fabricated by CMOS technology, the neuromorphic architecture can be emulated in FPGA SoCs such as those proposed in the RANC framework [6]. In this paper, we present novel parallel neurosynaptic cores containing neurons working simultaneously. The proposed cores can be used in the TrueNorth-inspired neuromorphic architecture to improve performance as well as power consumption. To validate the functional behaviour of the proposed architecture, we construct a SNN containing five parallel neurosynaptic cores to handle the MNIST handwritten digit dataset. Each core consists of 256 neurons working simultaneously. The proposed SNN is implemented on Xilinx Kintex 7 SoC and is as accurate as the sequential SNN implemented in the RANC framework. The synthesised SNN utilises twice the number of LUTs and the same number of flip-flops, IOs and LUTRAM compared to the RANC based SNN. It is 86 times faster in term of images per second being processed. In addition, with the same performance of images per second, our SNN consumes only 36mW of dynamic power which is 22 times lower than the RANC based SNN.
Glancing Angle Deposition of Nanostructured ZnO Films for Ultrasonics

Ultrasonic sensors have demonstrated great potential for non-destructive testing (NDT) of materials, being widely applicable in health care/monitoring (e.g. biomedical, muscle recovery, cancer early detection), industry, and defence (e.g. proximity sensors used in unnamed aerial vehicles - UAV; detection of submarines). Most conventional ultrasonic sensors are based on monolithic piezoelectric ceramic materials (e.g. PZT, PbTiO3 or PMN-PT) which are too bulky and nonconforming to enable their integration on flexible substrates. To address these drawbacks, ZnO thin films have emerged as an alternative piezoelectric material for low profile and high-frequency ultrasonic transducers due to properties such as high piezoelectric coefficient, great tuneability of working frequency, large bandwidth, low-cost of materials and manufacturing, compatibility with flexible substrates, and biocompatibility. This work analyses glancing angle deposition (GLAD) of ZnO thin films at different reactive sputtering conditions optimised to meet dual requirements of highly crystalline c-axis orientation while controlling the inclined angle of resulting nanostructured films for their application as piezoelectric material in ultrasonic sensors. Characteristics of ZnO nanostructured films, including morphology, crystallinity, and composition, are analysed as a function of GLAD conditions (gas flux angle with respect the substrate surface (α) and plasma conditions (plasma power, substrate position, substrate temperature, total gas-flow, and processing/reactive gas ratio). The obtained piezoelectric values for β angles of α=88° present d33 values of 33.1±1.7 pm/V, surpassing the piezoelectric coefficient found in ZnO bulk 12.4 pm/V. The influence of film titled angle (β) on piezoelectric performance for ultrasound sensing applications will be studied.
An Request Offloading and Scheduling Approach Base on Particle Swarm Optimization Algorithm in IoT-Fog Networks

In Industrial Internet of Things environment, the requests from the terminal devices are usually processed by the fog servers. However, due to the limited hardware resources and software resources, the fog server may be in a high load state in some time. In this case, some real-time requests may not be processed in time, which will affect the performance of the network. Hence, we propose a request offloading and scheduling approach to improve the performance of the net in processing the requests. Considering the influence of the distance between the devices on wireless communication, and the different deadlines of requests, we sort the terminal devices, and add the deadline of the request and the communication distance between the devices into the offloading and scheduling algorithm. These methods not only consider the real-time nature of requests, but also minimizes the average processing delay of requests. Finally, the simulation results prove that the proposed method is verified with efficacy.
A Noise-Cancelling Harmonic Selection Receiver Using an N-Path Filter for 5G Applications

A wideband noise-cancelling harmonic rejection (NC-HR) RF receiver using two separate N-path filter-based down-conversion paths is presented to avoid amplification at harmonic blocker frequencies. The proposed harmonic blocker-tolerant architecture suppresses blockers placed at or around integer multiples of the local oscillator (LO) frequency. Moreover, the differential HR N-path switching system with resistive coefficients used in the main down-conversion path allows for the 3rd harmonic of the LO frequency to be selected, helping to reduce the dynamic power consumption of the multi-phase LO generator by a factor of three. Post-layout simulation results show that the 3.6 −7.2 GHz receiver implemented in a 65 nm CMOS process achieves a harmonic-rejection ratio (HRR) of 56 dB, a noise figure (NF) of less than 2.6 dB at a 130 MHz baseband frequency for a 7.2 GHz RF signal, with a power consumption of 12.6 mW including the LO current.
Adaptive Tracking Control of Flexible Joint Manipulator with Output Constraints

This paper proposes an adaptive tracking control scheme for the uncertain model flexible joint manipulators with output constraints to achieve satisfactory performance. A time-varying arctangent barrier Lyapunov function (TABLF) is first designed to ensure the system output constraints are not violated. By combining backstepping, the negative feedback controller is designed to guarantee the errors remain within the allowable range of the constraints. The unknown dynamic observers are employed to approximate the uncertainties in the system, and a tracking differentiator (TD) is applied to obtain the differentiation of the virtual control laws in the back stepping design. Finally, the comparative simulations are given to illustrate the effectiveness of the proposed scheme.
Estimation of the Optimal Radar Positioning for the Contactless Fall Detection

To the date, the elderly fall episodes pose a serious threat to their health and life, thus, the development of the effective and precision method for the fall detection remains one of the most relevant problem for the geriatry. Bioradiolocation-based methods have proven their effectiveness in automatic contactless detection of the elderly people falls in the recent studies, though, some aspects of the method are still to be researched. In particular, in the present paper we estimate the influence of the different radar placement height on the reliability of high-intense movement artifacts detection. A series of experiments with different types of movements were conducted and an experimental database of bioradar signals has been built. Then, a number of signal features have been calculated to discover the difference between bioradar signals with no movement and with the movement artifacts detected. The most informative features have been chosen for the non-parametric statistical tests to check the statistical significance of the results and to draw a conclusion about the preferable radar placement height. The results show that lower height appears to be more effective in the movement artifacts detection task.
Verifying Determinism in Sequential Programs

When a program is nondeterministic, it is difficult to test and debug. Nondeterminism occurs even in sequential programs iterating over the elements of a hash table. We have created a type system that expresses determinism specifications in a program. The key ideas in the type system are type qualifiers for nondeterminism, order-nondeterminism, and determinism; type well-formedness rules to restrict collection types; and enhancements to polymorphism that improve precision when analyzing collection operations. We implemented our type system for Java. Our type checker, the Determinism Checker, warns if a program is nondeterministic or verifies that the program is deterministic. In this paper, we explain how to obtain our tool and reproduce the results described inhttps://homes.cs. washington.edu/ mernst/pubs/determinismicse2021.pdf. Additionally, our tool and subject programs are available at https://zenodo.org/record/4536285#. YEaCxZNKiWw.
3D Shape Descriptor by Principal Component Analysis Embedding for Non-rigid 3D Shape Retrieval in A Learning Framework

In the paper, we propose a 3D shape descriptor which can be applied to areas such as non-rigid 3D shape analysis and retrieval. We start with the calculation of the Wave Kernel Signature (WKS) and the scale-invariant Heat Kernel Signature (siHKS) of surface points belong to a 3D shape. Then we combine them together and obtain their principle components by PCA (principle component analysis), which are employed as our own point signatures. We take a weighted average of all the point signatures over a 3D surface to obtain our own shape descriptor. Different from other approaches, we employ shape curvature as the element of weight in the construction of the shape descriptor. Moreover, our shape descriptor is also trained in a machine learning framework and then used to a non-rigid 3D shape retrieval application. The results of the experiments in the end of the paper show that our 3D shape descriptor is efficient and feasible for applications such as analysis of non-rigid 3D shape, non-rigid 3D shape matching and 3D shape retrieval, etc.
A 20 μW, 50 mV, Fully-Integrated Cross-Coupled Oscillator for On-Body IoT Applications

An ultra-low-power oscillator for on-body-devices wireless transmitters is designed and implemented in 130 nm CMOS process. This paper explores the reduction of the supply voltage to reduce the power consumption of LC oscillators used in transmitters, complying with imperative requirements of low power dissipation for connected on-body and implanted medical devices. The limits of the voltage supply reduction on the performance of the LC oscillators are researched and important parameters such as phase noise, power consumption and the typical figure of merit are inspected. Measurements results show that the oscillator achieves -88 dBc/Hz of phase noise for a 50-mV supply voltage and -111 dBc/Hz for 200 mV at Df = 200 kHz, while consuming only 20 mW and 118 mW of DC power, respectively.
A Thorax-Like Mesh Phantom for Testing Electrical Impedance Tomography System Performance

Electrical Impedance Tomography (EIT) is a non-invasive and radiation-free clinical imaging technology, which is considered to be an effective substitute for CT and MRI. Assessing the performance of EIT requires a phantom for validation, calibration, and comparison. In this paper, we present a thorax-like mesh phantom that can effectively simulate the distribution of human thorax conductivity and optimize the mesh by reducing the number of resistors and exploring the optimal electrode placement. The proposed optimization methods are verified by Simulation Program with Integrated Circuit Emphasis (SPICE) simulation and Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS) image reconstruction. Experimental results demonstrate that our proposed thorax-like mesh phantom gets a high image correlation coefficient (ICC) of 0.680 with only 313 resistors. Our proposed thorax-like mesh phantom can be used for EIT system validation and further promote the research of EIT system.
Parameter Adjustment for the Droop Control Operating a Discharge PEC in PMG-Based WECSs With Generator-Charged Battery Units

Permanent magnet generator (PMG)-based wind energy conversion systems (WECSs) with battery units, have become a popular class of distributed generation units. These distributed generation units are typically operated using various types of controllers, including droop controllers. Existing droop controllers are designed to operate grid-side dc-ac power electronic converters (PEC) to ensure stable and reliable power production by a PMG-based WECS. The employment of battery storage units (to mitigate fluctuations in the power produced by a PMG-based WECS) introduces additional considerations for the design of droop controllers. Such considerations are due to the power available from battery units that is dependent on the state-of-charge (SOC). This paper proposes adjustments in the parameters (droop constants) of the droop control (operate the the discharge PEC) based on the SOC of the battery units. These adjustments are made to further support stable and reliable power delivery of the PMG-based WECS into the point-of-common-coupling (PCC). The proposed adjustments of droop constants are evaluated using a 7.5 kW grid-connected PMG-based WECS with 3.52 kW generator-charged battery storage units. Performance tests are carried out for step changes in the active and reactive power demands, changes in the wind speed, and grid-side disturbances. Test results show that the proposed correction of the droop constants is critical for maintaining a stable, effective, and accurate power delivery by the battery units, thus supporting the voltage/frequency stability at the PCC under different operating conditions.
LSTM Classification of Functional Grasps Using sEMG Data from Low-Cost Wearable Sensor

Modelling human grasping and transferring this data to an anthropomorphic robotic hand to endow it with human like grasping capabilities is a complex task. In this paper the use of surface electromyography (sEMG) for classification of functional grasps associated with everyday life is carried out using a low-cost wearable sensor in conjunction with state-of-the-art recurrent neural networks. The results produced through these experiments demonstrate the potential for sEMG to be used as an effective medium for transferring human demonstration to a robotic system.