Electronics, Volume 9, Issue 7 (July 2020) – 124 articles

The scope of the project described in this paper is the development of a generalized underwater object detection solution based on Automated Machine Learning (AutoML) principles. Multiple scales, dual priorities, speed, limited data, and class imbalance make object detection a very challenging task. In underwater object detection, further complications come in to play due to acoustic image problems such as non-homogeneous resolution, non-uniform intensity, speckle noise, acoustic shadowing, acoustic reverberation, and multipath problems. Therefore, we focus on finding solutions to the problems along the underwater object detection pipeline. A pipeline for realizing a robust generic object detector will be described and demonstrated on a case study of detection of an underwater docking station in sonar images. The system shows an overall detection and classification performance average precision (AP) score of 0.98392 for a test set of 5000 underwater sonar frames. Full article

Data storage in the Industrial Internet-of-Things scenario presents critical aspects related to the necessity of bringing storage devices closer to the point where data are captured. Concerns on storage temperature are to be considered especially when Solid State Drives (SSD) based on 3D NAND Flash technology are part of edge gateway architectures. Indeed, self-heating effects caused by oppressive storage demands combined with harsh environmental conditions call for proper handling at multiple abstraction levels to minimize severe performance slow downs and reliability threats. In this work, with the help of a SSD co-simulation environment that is stimulated within a realistic Industrial Internet-of-Things (IIoT) workload, we explore a methodology orthogonal to performance throttling that can be applied in synergy with the operating system of the host. Results evidenced that by leveraging on the SSD micro-architectural parameters of the queuing system it is possible to reduce the Input/Output operations Per Second (IOPS) penalty due to temperature protection mechanisms with minimum effort by the system. The methodology presented in this work opens further optimization tasks and algorithmic refinements for SSD and system designers not only in the IIoT market segment, but generally in all areas where storage power consumption is a concern. Full article

Beat classification and denoising are two challenging and fundamental operations when processing digital electrocardiograms (ECG). This paper proposes the extended segmented beat modulation method (ESBMM) as a tool for automatic beat classification and ECG denoising. ESBMM includes four main steps: (1) beat identification and segmentation into PQRS and TU segments; (2) wavelet-based time-frequency feature extraction; (3) convolutional neural network-based classification to discriminate among normal (N), supraventricular (S), and ventricular (V) beats; and (4) a template-based denoising procedure. ESBMM was tested using the MIT–BIH arrhythmia database available at Physionet. Overall, the classification accuracy was 91.5% while the positive predictive values were 92.8%, 95.6%, and 83.6%, for N, S, and V classes, respectively. The signal-to-noise ratio improvement after filtering was between 0.15 dB and 2.66 dB, with a median value equal to 0.99 dB, which is significantly higher than 0 (p < 0.05). Thus, ESBMM proved to be a reliable tool to classify cardiac beats into N, S, and V classes and to denoise ECG tracings. Full article

The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks. Full article

This paper presents a rapid response system architecture for the distributed management of warehouses in logistics by applying the concept of tiered edge computing. A tiered edge node architecture is proposed for the system to process computing tasks of different complexity, and a corresponding rapid response algorithm is introduced. The paper emphasizes the classification of abstracted outlier sensing data which could better match different sensing types and transplant to various application fields. A software-defined simulation is used to evaluate the system performance on response time and response accuracy, from which it can be concluded that common predefined emergency cases can be detected and responded to, rapidly. Full article

Unmanned Aerial Vehicles (UAV) with on-board augmentation systems (UAS, Unmanned Aircraft System) have penetrated into civil and general-purpose applications, due to advances in battery technology, control components, avionics and rapidly falling prices. This paper describes the conceptual design and the validation campaigns performed for an embedded precision Positioning, field mapping, Obstacle Detection and Avoiding (PODA) platform, which uses commercial-off-the-shelf sensors, i.e., a 10-Degrees-of-Freedom Inertial Measurement Unit (10-DoF IMU) and a Light Detection and Ranging (LiDAR), managed by an Arduino Mega 2560 microcontroller with Wi-Fi capabilities. The PODA system, designed and tested for a commercial small quadcopter (Parrot Drones SAS Ar.Drone 2.0, Paris, France), estimates position, attitude and distance of the rotorcraft from an obstacle or a landing area, sending data to a PC-based ground station. The main design issues are presented, such as the necessary corrections of the IMU data (i.e., biases and measurement noise), and Kalman filtering techniques for attitude estimation, data fusion and position estimation from accelerometer data. The real-time multiple-sensor optimal state estimation algorithm, developed for the PODA platform and implemented on the Arduino, has been tested in typical aerospace application scenarios, such as General Visual Inspection (GVI), automatic landing and obstacle detection. Experimental results and simulations of various missions show the effectiveness of the approach. Full article

Recent advances in semiconductor technology provide us with the resources to explore alternative methods for fabricating transistors with the goal of further reducing their sizes to increase transistor density and enhance performance. Conventional transistors use semiconductor junctions; they are formed by doping atoms on the silicon substrate that makes p-type and n-type regions. Decreasing the size of such transistors means that the junctions will get closer, which becomes very challenging when the size is reduced to the lower end of the nanometer scale due to the requirement of extremely high gradients in doping concentration. One of the most promising solutions to overcome this issue is realizing junctionless transistors. The first junctionless device was fabricated in 2010 and, since then, many other transistors of this kind (such as FinFET, Gate-All-Around, Thin Film) have been proposed and investigated. All of these semiconductor devices are characterized by junctionless structures, but they differ from each other when considering the influence of technological parameters on their performance. The aim of this review paper is to provide a simple but complete analysis of junctionless transistors, which have been proposed in the last decade. In this work, junctionless transistors are classified based on their geometrical structures, analytical model, and electrical characteristics. Finally, we used figure of merits, such as $I_{on}/I_{off}$ , $DIBL$ , and $SS$ , to highlight the advantages and disadvantages of each junctionless transistor category. Full article

The home environment is a typical dynamic environment with moveable obstacles. The social robots working in home need to search for feasible paths in this complex dynamic environment. In this work, we propose an improved RRT algorithm to plan feasible path in home environment. The algorithm pre-builds a tree that covers the whole map and maintains the effectiveness of all nodes with branch pruning, reconnection, and regrowth process. The method forms a path by searching the nearest node in the tree and then quickly accessing the nodes near the destination. Due to the effectiveness-maintaining process, the proposed method can effectively deal with the complex dynamic environment where the destination and multiple moving obstacles change simultaneously. In addition, our method can be extended to the path-planning problem in 3D space. The simulation experiments verify the effectiveness of the algorithm. Full article

Collecting and preserving the smart environment logs connected to cloud storage is challenging due to the black-box nature and the multi-tenant cloud models which can pervade log secrecy and privacy. The existing work for log secrecy and confidentiality depends on cloud-assisted models, but these models are prone to multi-stakeholder collusion problems. This study proposes ’PLAF,’ a holistic and automated architecture for proactive forensics in the Internet of Things (IoT) that considers the security and privacy-aware distributed edge node log preservation by tackling the multi-stakeholder issue in a fog enabled cloud. We have developed a test-bed to implement the specification, as mentioned earlier, by incorporating many state-of-the-art technologies in one place. We used Holochain to preserve log integrity, provenance, log verifiability, trust admissibility, and ownership non-repudiation. We introduced the privacy preservation automation of log probing via non-malicious command and control botnets in the container environment. For continuous and robust integration of IoT microservices, we used docker containerization technology. For secure storage and session establishment for logs validation, Paillier Homomorphic Encryption, and SSL with Curve25519 is used respectively. We performed the security and performance analysis of the proposed PLAF architecture and showed that, in stress conditions, the automatic log harvesting running in containers gives a 95% confidence interval. Moreover, we show that log preservation via Holochain can be performed on ARM-Based architectures such as Raspberry Pi in a very less amount of time when compared with RSA and blockchain. Full article

Bipolar DC grids have become an adequate solution for high-power microgrids. This is mainly due to the fact that this configuration has a greater power transmission capacity. In bipolar DC grids, any distributed generation system can be connected through DC-DC converters, which must have a monopolar input and a bipolar output. In this paper, a DC-DC converter based on the combination of single-ended primary-inductor converter (SEPIC) and Ćuk converters is proposed, to connect a photovoltaic (PV) system to a bipolar DC grid. This topology has, as main advantages, a reduced number of components and a high efficiency. Furthermore, it can contribute to regulate/balance voltage in bipolar DC grids. To control the proposed converter, any of the techniques described in the literature and applied to converters of a single input and single output can be used. An experimental prototype of a DC-DC converter with bipolar output based on the combination of SEPIC and Ćuk converters was developed. On the other hand, a perturb and observe method (P and O) has been applied to control the converter and has allowed maximum power point tracking (MPPT). The combined converter was connected in island mode and in parallel with a bipolar DC microgrid. The obtained results have allowed to verify the behavior of the combined converter with the applied strategy. Full article

Backward compatibility is one of the key issues for radio equipment that supports IEEE 802.11, which is a typical communication protocol for wireless local area networks (WLANs). For achieving successful packet decoding with backward compatibility, frame format detection is the core precondition. In this study, we present a novel, deep-learning-based frame format detection method for IEEE 802.11 WLANs. Considering that the detection performance of conventional methods is mainly degraded because of poor performance in symbol synchronization and/or channel estimation in environments with a low signal-to-noise ratio, we propose a novel detection method based on a deep learning network to replace conventional detection procedures. The proposed deep-learning network method achieves robust detection directly from the received (Rx) data. Through extensive computer simulations performed in multipath fading channel environments (modeled by Project IEEE 802.11 Task Group ac), we confirmed that the proposed method exhibits significantly higher frame format detection performance than that of the conventional method. Full article

The development of computation technology and artificial intelligence (AI) field brings about AI to be applied to various system. In addition, the research on hardware-based AI processors leads to the minimization of AI devices. By adapting the AI device to the edge of internet of things (IoT), the system can perform AI operation promptly on the edge and reduce the workload of the system core. As the edge is influenced by the characteristics of the embedded system, implementing hardware which operates with low power in restricted resources on a processor is necessary. In this paper, we propose the intellino, a processor for embedded artificial intelligence. Intellino ensures low power operation based on optimized AI algorithms and reduces the workload of the system core through the hardware implementation of a neural network. In addition, intellino’s dedicated protocol helps the embedded system to enhance the performance. We measure intellino performance, achieving over 95% accuracy, and verify our proposal with an field programmable gate array (FPGA) prototyping. Full article

Radio Frequency Identification (RFID) technology is widely utilized by businesses, organizations and wireless communication systems. RFID technology is secured using different ways of data encryption, e.g., Advanced Encryption Standard (AES). The Substitution Box (S-Box) is the core of AES. In this paper, a new algorithm is proposed to generate a modified S-Box with new keys, specifically a key and plaintext-dependent S-Box using an improved RC4 encryption algorithm with Logistic Chaotic Maps (LCM). The strength of the proposed S-Box is tested throughout the paper, and compared against the state-of-the-art S-Box implementations, namely, the static S-Box, dynamic S-box, KSA and PRGA S-Box, and RC4 S-Boxes with Henon chaotic maps. The comparison between the state-of-the-art S-Boxes and the proposed S-Box demonstrates that the use of the Logistic Chaotic Map increases the security of the S-Box and makes the differential and linear cryptography more sturdy. In particular, using the strict avalanche test, we demonstrate that the proposed S-Box improves the security by achieving a cipher text bit-flip ratio of 0.4765, which is closer to 0.5 (where half the bits are flipped), while maintaining a minimum elapsed time of 19 milliseconds for encryption and decryption. Full article

The amassed growth in the size of data, caused by the advancement of technologies and the use of internet of things to collect and transmit data, resulted in the creation of large volumes of data and an increasing variety of data types that need to be processed at very high speeds so that we can extract meaningful information from these massive volumes of unstructured data. The process of mining this data is very challenging since a lot of the data suffers from the problem of high dimensionality. The quandary of high dimensionality represents a great challenge that can be controlled through the process of feature selection. Feature selection is a complex task with multiple layers of difficulty. To be able to grasp and realize the impediments associated with high dimensional data a more and in-depth understanding of feature selection is required. In this study, we examine the effect of appropriate feature selection during the classification process of anomaly network intrusion detection systems. We test its effect on the performance of Restricted Boltzmann Machines and compare its performance to conventional machine learning algorithms. We establish that when certain features that are representative of the model are to be selected the change in the accuracy was always less than 3% across all algorithms. This verifies that the accurate selection of the important features when building a model can have a significant impact on the accuracy level of the classifiers. We also confirmed in this study that the performance of the Restricted Boltzmann Machines can outperform or at least is comparable to other well-known machine learning algorithms. Extracting those important features can be very useful when trying to build a model with datasets with a lot of features. Full article

In this article a reconfigurable antenna for WLAN/WiMAX applications is presented. A super-shape radiator of an ellipsis shape is used to achieve wider intrinsic bandwidth compared to the classical rectangular patch antenna, while the dimensions remain comparable. The proposed antenna is fed at two points exciting both horizontal and vertical polarization but in different operating frequencies. To achieve wider bandwidth, as a whole but also for each polarization, the symmetrical feeding points for each excitation are also employed with a proper feeding network. PIN diodes are also used in the feeding network to provide the option of narrower bandwidth. The antenna substrate is Rogers RO4003C with dielectric constant ε_r = 3.55 and dissipation losses tanδ = 0.0027 with height h = 1.524 mm. The antenna operates in the range of 2.3 GHz to 2.55 GHz but, using the proposed procedure, it can be designed for different frequency ranges. Full article

Nowadays, the secure program execution of embedded processor has attracted considerable research attention, since more and more code tampering attacks and transient faults are seriously affecting the security of embedded processors. The program monitoring and fault recovery strategies are not only closely related to the security of embedded devices, but also directly affect the performance of the processor. This paper presents a security monitoring and fault recovery architecture for run-time program execution, which takes regular backup copies of the two-stage checkpoint. In this framework, the integrity check technology based on the basic block (BB) is utilized to monitor the program execution in real-time, while the rollback operation is taken once the integrity check is failed. In addition, a Monitoring Cache (M-Cache) is built to buffer the reference data for integrity checking. Moreover, a recovery strategy mainly for three tampered positions (registers in processor, instructions in Cache, and codes in memory) is provided to ensure the smooth running of the embedded system. Finally, the open RISC processor is adopted to implement and verify the presented security architecture, which has been proved to be effective for program detection in the execution of tamper attack and quick recovery of the running environment as well as code. Full article

Computing and networking systems traditionally record their activity in log files, which have been used for multiple purposes, such as troubleshooting, accounting, post-incident analysis of security breaches, capacity planning and anomaly detection. In earlier systems those log files were processed manually by system administrators, or with the support of basic applications for filtering, compiling and pre-processing the logs for specific purposes. However, as the volume of these log files continues to grow (more logs per system, more systems per domain), it is becoming increasingly difficult to process those logs using traditional tools, especially for less straightforward purposes such as anomaly detection. On the other hand, as systems continue to become more complex, the potential of using large datasets built of logs from heterogeneous sources for detecting anomalies without prior domain knowledge becomes higher. Anomaly detection tools for such scenarios face two challenges. First, devising appropriate data analysis solutions for effectively detecting anomalies from large data sources, possibly without prior domain knowledge. Second, adopting data processing platforms able to cope with the large datasets and complex data analysis algorithms required for such purposes. In this paper we address those challenges by proposing an integrated scalable framework that aims at efficiently detecting anomalous events on large amounts of unlabeled data logs. Detection is supported by clustering and classification methods that take advantage of parallel computing environments. We validate our approach using the the well known NASA Hypertext Transfer Protocol (HTTP) logs datasets. Fourteen features were extracted in order to train a k-means model for separating anomalous and normal events in highly coherent clusters. A second model, making use of the XGBoost system implementing a gradient tree boosting algorithm, uses the previous binary clustered data for producing a set of simple interpretable rules. These rules represent the rationale for generalizing its application over a massive number of unseen events in a distributed computing environment. The classified anomaly events produced by our framework can be used, for instance, as candidates for further forensic and compliance auditing analysis in security management. Full article

Finding changed and similar functions between a pair of binaries is an important problem in malware attribution and for the identification of new malware capabilities. This paper presents a new technique called Function Similarity using Family Context (FSFC) for this problem. FSFC trains a Support Vector Machine (SVM) model using pairs of similar functions from two program variants. This method improves upon previous research called Cross Version Contextual Function Similarity (CVCFS) e epresenting a function using features extracted not just from the function itself, but also, from other functions with which it has a caller and callee relationship. We present the results of an initial experiment that shows that the use of additional features from the context of a function significantly decreases the false positive rate, obviating the need for a separate pass for cleaning false positives. The more surprising and unexpected finding is that the SVM model produced by FSFC can abstract function similarity features from one pair of program variants to find similar functions in an unrelated pair of program variants. If validated by a larger study, this new property leads to the possibility of creating generic similar function classifiers that can be packaged and distributed in reverse engineering tools such as IDA Pro and Ghidra. Full article

Recent video captioning models aim at describing all events in a long video. However, their event descriptions do not fully exploit the contextual information included in a video because they lack the ability to remember information changes over time. To address this problem, we propose a novel context-aware video captioning model that generates natural language descriptions based on the improved video context understanding. We introduce an external memory, differential neural computer (DNC), to improve video context understanding. DNC naturally learns to use its internal memory for context understanding and also provides contents of its memory as an output for additional connection. By sequentially connecting DNC-based caption models (DNC augmented LSTM) through this memory information, our consecutively connected DNC architecture can understand the context in a video without explicitly searching for event-wise correlation. Our consecutive DNC is sequentially trained with its language model (LSTM) for each video clip to generate context-aware captions with superior quality. In experiments, we demonstrate that our model provides more natural and coherent captions which reflect previous contextual information. Our model also shows superior quantitative performance on video captioning in terms of BLEU (BLEU@4 4.37), METEOR (9.57), and CIDEr-D (28.08). Full article

In the context of constant growth in the utilization of the Li-ion batteries, there was a great surge in the quest for electrode materials and predominant usage that lead to the retiring of Li-ion batteries. This review focuses on the recent advances in the anode and cathode materials for the next-generation Li-ion batteries. To achieve higher power and energy demands of Li-ion batteries in future energy storage applications, the selection of the electrode materials plays a crucial role. The electrode materials, such as carbon-based, semiconductor/metal, metal oxides/nitrides/phosphides/sulfides, determine appreciable properties of Li-ion batteries such as greater specific surface area, a minimal distance of diffusion, and higher conductivity. Various classifications of the anode materials such as the intercalation/de- intercalation, alloy/de-alloy, and various conversion materials are illustrated lucidly. Further, the cathode materials, such as nickel-rich LiNi_xCo_yMn_zO₂ (NCM), were discussed. NCM members such as NCM 333, NCM 523 that enabled to advance for NCM622 and NCM81are reported. The nanostructured materials bridged the gap in the realization of next-generation Li-ion batteries. Li-ion batteries’ electrode nanostructure synthesis, performance, and reaction mechanisms were considered with great concern. The serious effects of Li-ion batteries disposal need to be cut significantly to reduce the detrimental effect on the environment. Hence, the recycling of spent Li-ion batteries has gained much attention in recent years. Various recycling techniques and their effect on the electroactive materials are illustrated. The key areas covered in this review are anode and cathode materials and recent advances along with their recycling techniques. In light of crucial points covered in this review, it constitutes a suitable reference for engineers, researchers, and designers in energy storage applications. Full article

Per-flow traffic measurement has emerged as a critical but challenging task in data centers in recent years in the face of massive network traffic. Many approximate methods have been proposed to resolve the existing resource-accuracy trade-off in per-flow traffic measurement, one of which is the sketch-based method. However, sketches are affected by their high computational cost and low throughput; moreover, their measurement accuracy is hard to guarantee under the conditions of changing network bandwidth or flow size distribution. Recently, FPGAplatforms have been widely deployed in data centers, as they demonstrate a good fit for high-speed network processing. In this work, we aim to address the problem of per-flow traffic measurement from a hardware architecture perspective. We thus design SAPTM, a pipelined systolic array-like architecture for high-throughput per-flow traffic measurement on FPGA. We adopt memory-friendly D-left hashing in the design of SAPTM, which guarantees high space utilization during flow insertion and eviction, successfully addressing the challenge of tracking a high-speed data stream under limited memory resources on FPGA. Evaluations on the Xilinx VCU118 platform with real-world benchmarks demonstrate that SAPTM possesses high space utilization. Comparisons with state-of-the-art sketch-based solutions show that SAPTM outperforms comparison methods in terms of throughput by a factor of 14.1x–70.5x without any accuracy loss. Full article

The zeta inverter has been used for single-phase grid-tied applications. For its use of energy storage systems, this paper proposes the bidirectional operation scheme of the grid-tied zeta inverter. A shoot-through switching state is introduced, providing reliable bidirectional operation modes. A shoot-through duty cycle is utilized for the bidirectional grid current control of the inverter. The grid current is bidirectionally controlled by the shoot-through duty cycle, which enables the inverter to operate with seamless change of operation modes. Over the state-of-the art techniques using flyback and Cuk inverter topologies, the grid-tied zeta inverter using the proposed operation scheme provides advantages of high efficiency, low cost, and high reliability. The operation principle is presented by describing the operation mode and control method for the grid-tied zeta inverter. A 500 W prototype has been built and tested to verify its operation principle. Full article

Chemical mechanical polishing (CMP) has become one of the most important process stages in the fabrication of advanced integrated circuits (IC). The CMP pattern effect strongly influences the planarization of the chip surface morphology after CMP, degrading the performance and the yield of the circuits. In this paper, we introduce a method to predict the post-CMP surface morphology with a convolutional neural network (CNN)-based CMP model. Then, CNN-based, density step height (DSH)-based, and common neural-network-based CMP models are built to compare the accuracy of the predictions. The test chips are designed and taped out and the predictions of the three models are compared with experimental results measured by an atomic force profiler (AFP) and scanning electron microscope (SEM). The results show that CNN-based CMP models have better accuracy by taking advantage of the CNN networks to extract features from images instead of the traditional equivalent pattern parameters. The effective planarization length (EPL) is introduced and defined to make better predictions with real-time CMP models and in dummy filling tasks. Experiments are designed to show a method to solve the EPL. Full article

Despite the progress of deep neural networks over the last decade, the state-of-the-art speech recognizers in noisy environment conditions are still far from reaching satisfactory performance. Methods to improve noise robustness usually include adding components to the recognition system that often need optimization. For this reason, data augmentation of the input features derived from the Short-Time Fourier Transform (STFT) has become a popular approach. However, for many speech processing tasks, there is an evidence that the combination of STFT-based and Hilbert–Huang transform (HHT)-based features improves the overall performance. The Hilbert spectrum can be obtained using adaptive mode decomposition (AMD) techniques, which are noise-robust and suitable for non-linear and non-stationary signal analysis. In this study, we developed a DeepSpeech2-based recognition system by adding a combination of STFT and HHT spectrum-based features. We propose several ways to combine those features at different levels of the neural network. All evaluations were performed using the WSJ and CHiME-4 databases. Experimental results show that combining STFT and HHT spectra leads to a 5–7% relative improvement in noisy speech recognition. Full article

We present biphasic stimulator electronics developed for a wearable functional electrical stimulation system. The reported stimulator electronics consist of a twenty four channel biphasic stimulator. The stimulator circuitry is physically smaller per channel and offers a greater degree of control over stimulation parameters than existing functional electrical stimulator systems. The design achieves this by using, off the shelf multichannel high voltage switch integrated circuits combined with discrete current limiting and dc blocking circuitry for the frontend, and field programmable gate array based logic to manage pulse timing. The system has been tested on both healthy adults and those with reduced upper limb function following a stroke. Initial testing on healthy users has shown the stimulator can reliably generate specific target gestures such as palm opening or pointing with an average accuracy of better than 4 degrees across all gestures. Tests on stroke survivors produced some movement but this was limited by the mechanical movement available in those users’ hands. Full article

Concerned about the noise pollution in urban environments, the European Commission (EC) has created an Environmental Noise Directive 2002/49/EC (END) requiring Member states to publish noise maps and noise management plans every five years for cities with a high density of inhabitants, major roads, railways and airports. The END also requires the noise pressure levels for these sources to be presented independently. Currently, data measurements and the representations of the noise pressure levels in such maps are performed semi-manually by experts. This process is time and cost consuming, as well as limited to presenting only a static picture of the noise levels. To overcome these issues, we propose the deployment of Wireless Acoustic Sensor Networks with several nodes in urban environments that can enable the generation of real-time noise level maps, as well as detect the source of the sound thanks to machine learning algorithms. In this paper, we briefly review the state of the art of the hardware used in wireless acoustic applications and propose a low-cost sensor based on an ARM cortex-A microprocessor. This node is able to process machine learning algorithms for sound source detection in-situ, allowing the deployment of highly scalable sound identification systems. Full article

Strokes are a constant concern for people and pose a major health concern. Tests that allow detection and the rehabilitation of patients have started to become more important and essential. There are several tests used by physiotherapists to speed up the recovery process of patients. This article presents a systematic review of existing studies using the Heel-Rise Test and sensors (i.e., accelerometers, gyroscopes, pressure and tilt sensors) to estimate the different levels and health statuses of individuals. It was found that the most measured parameter was related to the number of repetitions, and the maximum number of repetitions for a healthy adult is 25 repetitions. As for future work, the implementation of these methods with a simple mobile device will facilitate the different measurements on this subject. Full article

The Internet-of-Things (IoT) has gained significant importance in all aspects of daily life, and there are many areas of application for it. Despite the rate of expansion and the development of infrastructure, such systems also bring new concerns and challenges. Security and privacy are at the top of the list and must be carefully considered by designers and manufacturers. Not only do the devices need to be protected against software and network-based attacks, but proper attention must also be paid to recently emerging hardware-based attacks. However, low-cost unit software developers are not always sufficiently aware of existing vulnerabilities due to these kinds of attacks. To tackle the issue, various platforms are proposed to enable rapid and easy evaluation against physical attacks. Fault attacks are the noticeable type of physical attacks, in which the normal and secure behavior of the targeted devices is liable to be jeopardized. Indeed, such attacks can cause serious malfunctions in the underlying applications. Various studies have been conducted in other research works related to the different aspects of fault injection. Two of the primary means of fault attacks are clock and voltage fault injection. These attacks can be performed with a moderate level of knowledge, utilizing low-cost facilities to target IoT systems. In this paper, we explore the main parameters of the clock and voltage fault generators. This can help hardware security specialists to develop an open-source platform and to evaluate their design against such attacks. The principal concepts of both methods are studied for this purpose. Thereafter, we conclude our paper with the need for such an evaluation platform in the design and production cycle of embedded systems and IoT devices. Full article

The efficient multi-modal fusion of data streams from different sensors is a crucial ability that a robotic perception system should exhibit to ensure robustness against disturbances. However, as the volume and dimensionality of sensory-feedback increase it might be difficult to manually design a multimodal-data fusion system that can handle heterogeneous data. Nowadays, multi-modal machine learning is an emerging field with research focused mainly on analyzing vision and audio information. Although, from the robotics perspective, haptic sensations experienced from interaction with an environment are essential to successfully execute useful tasks. In our work, we compared four learning-based multi-modal fusion methods on three publicly available datasets containing haptic signals, images, and robots’ poses. During tests, we considered three tasks involving such data, namely grasp outcome classification, texture recognition, and—most challenging—multi-label classification of haptic adjectives based on haptic and visual data. Conducted experiments were focused not only on the verification of the performance of each method but mainly on their robustness against data degradation. We focused on this aspect of multi-modal fusion, as it was rarely considered in the research papers, and such degradation of sensory feedback might occur during robot interaction with its environment. Additionally, we verified the usefulness of data augmentation to increase the robustness of the aforementioned data fusion methods. Full article

The proliferation of various connected platforms, including Internet of things, industrial control systems (ICSs), connected cars, and in-vehicle networks, has resulted in the simultaneous use of multiple protocols and devices. Chaotic situations caused by the usage of different protocols and various types of devices, such as heterogeneous networks, implemented differently by vendors renders the adoption of a flexible security solution difficult, such as recent deep learning-based intrusion detection system (IDS) studies. These studies optimized the deep learning model for their environment to improve performance, but the basic principle of the deep learning model used was not changed, so this can be called a next-generation IDS with a model that has little or no requirements. Some studies proposed IDS based on unsupervised learning technology that does not require labeled data. However, not using available assets, such as network packet data, is a waste of resources. If the security solution considers the role and importance of the devices constituting the network and the security area of the protocol standard by experts, the assets can be well used, but it will no longer be flexible. Most deep learning model-based IDS studies used recurrent neural network (RNN), which is a supervised learning model, because the characteristics of the RNN model, especially when the long-short term memory (LSTM) is incorporated, are better configured to reflect the flow of the packet data stream over time, and thus perform better than other supervised learning models such as convolutional neural network (CNN). However, if the input data induce the CNN’s kernel to sufficiently reflect the network characteristics through proper preprocessing, it could perform better than other deep learning models in the network IDS. Hence, we propose the first preprocessing method, called “direct”, for network IDS that can use the characteristics of the kernel by using the minimum protocol information, field size, and offset. In addition to direct, we propose two more preprocessing techniques called “weighted” and “compressed”. Each requires additional network information; therefore, direct conversion was compared with related studies. Including direct, the proposed preprocessing methods are based on field-to-pixel philosophy, which can reflect the advantages of CNN by extracting the convolutional features of each pixel. Direct is the most intuitive method of applying field-to-pixel conversion to reflect an image’s convolutional characteristics in the CNN. Weighted and compressed are conversion methods used to evaluate the direct method. Consequently, the IDS constructed using a CNN with the proposed direct preprocessing method demonstrated meaningful performance in the NSL-KDD dataset. Full article