In many natural and engineered systems, the circle and the spiral are the appropriate primitives. As example, rotating motion of fluid around a common centerline constitutes a vortex. In many of these situations, the associated fractal dimension is of analytical interest. The box counting approach to determining this dimension is not appropriate for such cases, because unlike squares and boxes that span 2- and 3-dimensional spaces, circles and spheres don’t. Since self-similarity or scale invariance is not an exact property, we propose the computation of the dimension is such cases based on the number of circles or spheres in the mapping process. This is illustrated by a few examples. How this may apply to spiral patterns that are repeated across scale is indicated.

Chronic obstructive pulmonary disease (COPD) is a major public health concern across the world. Since it is an incurable disease, early detection and accurate diagnosis are very crucial for preventing the progression of the disease. Lung sounds provide reliable and accurate prognoses for identifying respiratory diseases. Recently, Altan et al. recorded 12-channel real-time lung sound dataset, namely RespiratoryDatabase@TR, for five different severity levels of COPD at Antakya State Hospital Turkey, and proposed deep learning frameworks for two-class COPD classification and five-class classification using a deep belief network (DBN) classifier and extreme learning machine (ELM) classifier respectively. A classification accuracy of 95.84% and 94.31% were achieved for two-class and five-class respectively. In this paper, we have proposed a melspectrogram snippet representation learning framework for both two-class and five-class COPD classification. The proposed framework consists of the following stages: preprocessing, melspectrogram snippet representation generation from lung sound and fine tuning of a pretrained YAMNet. Experimental analysis on the RespiratoryDatabase@TR dataset demonstrates that the proposed framework achieves accuracies of 99.25% and 96.14% for binary and multi-class COPD severity classification respectively, which is superior to the only existing methods proposed by Altan et al. for severity analysis of COPD using lung sounds.

A broadband noise measurement system is newly developed and demonstrated at temperatures between 3 K and 300 K. Using the system, wideband noise spectroscopy (WBNS) from 20 kHz to 500 MHz is carried out for the first time, revealing that shot noise is the dominant white noise down to 3 K. The paper also suggests, by means of WBNS, the possibility of extracting the baseline noise characteristics, which do not include the noise component that varies a great deal from device to device.

Mobile user path prediction is a prerequisite for mobility-aware efficient task offloading in Multi-access Edge Computing (MEC), ultra-low latency services, and many other resource management operations. This manuscript addresses the mobile user path prediction by proposing two Deep Learning (DL) driven models that use Long Short-Term Memory (LSTM) to process time-series network data. The training and evaluation of the proposed models are done using our collected and preprocessed dataset which has been made a publicly learnable resource.

Forest inventory forms the foundation of forest management. Remote sensing (RS) is an efficient means of measuring forest parameters at scale. Remotely sensed species classification can be used to estimate species abundances, distributions, and to better approximate metrics such as above ground biomass.  State of the art methods of RS species classification rely on deep learning models such as convolutional neural networks (CNN). These models have 2 major drawbacks: they require large samples of each species to classify well and they lack explainablity. Therefore, rare species are poorly classified causing poor approximations of their associated parameters. We show that the classification of rare species can be improved by as much as 8 F1-points using a neuro-symbolic (NS) approach that combines CNNs with a NS framework. The framework allows for the incorporation of domain knowledge into the model through the use of mathematically represented rules, improving model explainability.

The main contribution of this paper is providing low complexity precoding design for RSMA with low complexity streams. We consider hierarchical streams and interference nulling to simplify choosing streams and precoding design, respectively. The paper mainly revolves around precoding design to maximize sum rate. Particularly, we formulate a convex problem for the upper bound of sum rate maximization problem and examine the cases where the upper bound problem has the same solution as the sum rate maximization problem. In addition, we propose a two-stage algorithm to design precoding based on interference nulling and signal-to-noise ratio maximization.

Diabetes mellitus (DB) is the most challenging and fastest-growing global public health challenge. An estimated 10.5% of the global adult population suffers from diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbated the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance (IGT) and impaired fasting glycemia (IFG), respectively. All the current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or a laboratory by trained professionals. At-risk subjects might remain undetected for years and miss the precious time window for early intervention in preventing or delaying the onset of diabetes and its complications. This study was conducted at KK Womenâ\euro™s and Childrenâ\euro™s Hospital of Singapore, and five hundred participants were recruited (mean age 38.73 ± 10.61 years; mean BMI 24.4 ± 5.1 kg/m2). The blood glucose levels, for most participants, were measured before and after 75g of sugary drink using both the conventional glucometer (Accu-Chek Performa) and the wrist-worn wearable. The results obtained from the glucometer were used as the ground truth measurements. We propose leveraging photoplethysmography (PPG) sensors and machine learning techniques to incorporate this into an affordable wrist-worn wearable device to detect elevated blood glucose levels (⩾ 7.8mmol/L) non-invasively. Multiple machine learning models were trained and assessed with 10-fold cross-validation using subject demographic data and critical features extracted from the PPG measurements as predictors. Support vector machine (SVM) with a radial basis function kernel has the best detection performance with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54% and F-score of 84.03%. Hence, PPG measurements can be utilized to identify subjects with elevated blood glucose measurements and assist in the screening of subjects for diabetes risk.

A code smell is a surface indication that usually corresponds to a deeper problem in the system. Detecting and removing code smells is crucial for sustainable software development. However, manual detection can be daunting and time-consuming. Machine learning (ML) is a promising approach towards the automation of code smell detection. The first ML-based methods were classifiers trained on feature vectors comprising software metrics extracted by off-the-shelf tools. Determining the optimal set of metrics is a complex problem that requires both ML and software engineering expertise. Recently source code embedding models emerged as a viable feature-inferring alternative. However, their potential is yet to be fully explored. To that aim, we compare state-of-the-art source code embedding models (CuBERT and CodeT5) with the models trained on metrics returned by the CK Tool and RepositoryMiner tools. We focus on detecting the Data Class and Feature Envy code smells within a large-scale, manually labeled, publicly available dataset. After extensive experiments (51 test/train splits), we found that source code embedding models have comparable performances with software metrics, a that they indeed can capture important characteristics of the source code. We discuss our findings in detail in the paper.

Although the concept of engineered molecular communication has been around for quite some time, practical approaches with truly biocompatible setups are still scarce. However, molecular communication has a large potential in future medical applications and may be a solution to size constraints of antenna based transmission systems. In this work we therefore present a testbed using biocompatible magnetic nanoparticles. Based on previous work, all testbed components have been improved regarding performance and size, making a large step forwards regarding miniaturisation and a data transmission approach. In addition, a setup for localised twodimensional sensing of magnetic nanoparticles is presented. All improvements are evaluated individually and combined to achieve a net data rate of more than 6 bit/s, significantly higher than any other comparable biocompatible setup.

In medium to high frequency power transformers, apart from minimizing the power loss in the core and copper, the design of its thermal circuit plays an important role in containing the temperature rise in different parts. It is extremely complex because there exist multiple loss centers with different heat removal characteristics. Moreover, the power loss is concentrated around a small core volume and the rest of it is available for convection and radiation. This article proposes to use a hybrid core configuration to improve the heat removal feature of the transformer. This article establishes through practical validation on suitability i.e., the magnetic compatibility of cores in the hybrid magnetic circuit. It further explores the application prospect of the proposed concept where the thermal performances have been compared using different core types in two different application domains. In the first case, the core could always remain energized to its rated value, and in the second design, both the windings could always draw their respective rated current.

Cryptocurrencies acquire user confidence by making the whole creation and transaction history transparent to the public. In exchange, the transaction history accurately captures the complete range of user activities related to cryptocurrencies. In this paper, the use of data mining methods in Bitcoin transactions is analyzed and summarized. Cryptocurrencies, similar to the well-known Bitcoin, were targeted to ensure transaction security and privacy and overcome the drawbacks of traditional banking systems as well as other centralized systems. We also conduct a thorough analysis of the literature on the challenges and applications of electronic currencies. We outline the evolution of digital currency from electronic cash to cryptocurrencies and put the spotlight on the methods used to increase user privacy. We also highlight security threats in the existing cryptocurrency systems that jeopardize the privacy of Bitcoin users. Finally, we identify several research gaps and trends that need to be looked at further.

In electric vehicles (EVs), the efficient energy consumption is very important. Based on demand, different electric motor technologies are employed by the manufacturers and are still evolving. This paper analyses the electric motors utilized in electric vehicles such as DC motor, Brushless DC (BLDC) motor, AC induction motor, permanent magnet synchronous motor (PMSM), switched reluctance motor (SRM), and permanent magnet synchronous reluctance motor (PMSynRM). The advantages and disadvantages correspond to the traction motors are pointed out. The main drawback is the power losses in the armature windings of the motors that reduce the overall efficiency and reduce the run per charge of EV due to additional power consumption from the battery pack. To reduce these losses, the authors have proposed a system that is more efficient as the windings from the rotor have been replaced by permanent magnets. Therefore, the power losses in armature windings have been eliminated, and reduced the power consumption by the motor from the battery pack, subsequently, the run per charge of EV has been increased. The overall efficiency of EVs has been increased to almost 100% by the proposed system. This claim is supported theoretically, graphically, as well as practically and presented in the results section of the paper. At last, the conclusions have been made on the traction motors used in the proposed EVs model.

Practical deployment of convolutional neural net?work (CNN) and cryptography algorithm on constrained devices are challenging due to the huge computation and memory requirement. Developing separate hardware accelerator for AI and cryptography incur large area consumption, which is not desirable in many applications. This paper proposes a viable solution to this issue by expressing the CNN and cryptography as Generic-Matrix-Multiplication (GEMM) operations and map them to the same accelerator for reduced hardware consumption. A novel systolic tensor array (STA) design was proposed to reduce the data movement, effectively reducing the operand registers by 2×. Two novel techniques, input layer extension and polynomial factorization, are proposed to mitigate the under-utilization issue found in existing STA architecture. Additionally, the Tensor Processing Element (TPE) is fused using DSP unit to reduce the Look-Up Table (LUT) and Flip-Flops (FF) consumption for implementing multipliers. On top of that, a novel memory efficient factorization technique is proposed to allow computation of polynomial convolution on the same STA. Experimental results show that Cryptensor achieved 22.3% better throughput for VGG-16 implementation on XC7Z020 FPGA; 95.0% lesser LUT when implementing on XC7Z045 compared to state-of-the-art result. Cryptensor can also flexibly support multiple security levels in NTRU scheme, with no additional hardware. The proposed hardware unifies the computation of two different domains that are critical for IoT applications, which greatly reduces the hardware consumption on edge nodes.

In order to enable molecular communication on macroscopic distances without a moving medium, the model of diffusion-based information transmission with a large number of relays suspended in the medium, hence at random positions, is proposed. With an increasing number of relays, the stochastic variations approach zero and the system exhibits a quasi-deterministic behavior. Furthermore, the transmission time becomes inversely proportional to the number of relays and the impulse response independent of the channel length. Thus, the model describes a wave-like propagation, which is fundamentally different from the established flow- and diffusion-based approaches in molecular communication.

Energy consumption minimization is of general interest in network design. This paper provides a quantum hyrid algorithm in this field. The results show that it outperforms those in literature.

Illumination change, image blur, and fast motion dramatically decrease the performance of visual-inertial navigation systems (VINS). This paper presents a new illumination-robust visual-inertial odometry (IR-VIO) based on adaptive weighting algorithm with two-layer confidence maximization. First, to prevent the VIO performance degradation caused by poor image quality in complex scenes and ignoring the confidence differences of feature points, we develop a novel adaptive weighting algorithm on the multi-sensor layer and visual feature layer to better fuse multi-sensor information and maximize the overall confidence of VIO. Second, to solve the problems of image feature tracking difficulty and excessive image noise in illumination-changing scenes, an image enhancement algorithm is introduced to enhance consecutive images to the same brightness level, while a block noise removal algorithm with constraint protection mechanism is proposed to dynamically remove feature points in noise areas. Finally, experimental results on the public dataset and real-world environments demonstrate that IR-VIO has superior performance in terms of accuracy and robustness compared with the state-of-the-art methods. Supplementary video is available at https://youtu.be/163Sk7twEMY.

We report a PET detector module (DM) designed for developing a preclinical positron emission tomography (PET)-electron paramagnetic resonance imaging (EPRI) hybrid system. The DM consists of a linear array of eight detector units, each of which is a 12×12 array of lutetium–yttrium oxyorthosilicate (LYSO) crystals read by a 4×4 silicon photomultiplier (SiPM) array. The crystal size is approximately 1.0×1.0×10 mm3. All surfaces of the crystal are polished; except for that coupled to SiPMs they are also covered with BaSO4 to reduce light loss. The pitches of the LYSO and SiPM arrays are about 1.05 mm and 3.2 mm, respectively. The front face of the resulting DM is about 1.28×10.24 cm2 in extext and its thickness is approximately 1.8 cm. A highly multiplexing readout is devised to produce only six outputs for a DM, including two outputs that are derived from the SiPM cathode signals for determining the event time and the active DU, and four outputs that are derived from the SiPM anode signals for determining the event energy and the active crystal within the active DU. At present, these outputs are acquired by waveform sampling and analyzed offline. We have successfully developed two DMs, both showing well discriminated DUs and crystals, and an average energy resolution of about 15%. Even though time-of-flight (ToF) is not needed for the proposed system, our data shows that the DM can potentially achieve a 300-400 ps ToF resolution.

We propose a novel reconstruction scheme for reconstructing charged particles in digital tracking calorimeters using model-free reinforcement learning aiming to benefit from the rapid progress and success of neural network architectures for tracking without the dependency on simulated or manually labeled data. Here we optimize by trial-and-error a behavior policy acting as a heuristic approximation to the full combinatorial optimization problem, maximizing the physical plausibility of sampled trajectories. In modern data processing pipelines used in high energy physics experiments and related high energy physics driven applications tracking plays an essential role allowing to identify and follow charged particle trajectories traversing particle detectors. Due to the usual high multiplicity of charged particles as well as the occurring physical interactions, randomly deflecting the particles from their initial path, the reconstruction is a challenging undertaking, requiring fast, accurate and robust algorithms. Our approach works on graph-structured data, capturing possible track hypotheses through edge connections between particles in the sensitive detector layers. We demonstrate in a comprehensive study on simulated data generated for a particle detector used for proton computed tomography, the overall high potential as well as the competitiveness of our approach compared to a heuristic search algorithm and a model trained on ground truth information. Finally, we point out limitations of our approach, guiding towards a robust foundation for further development of reinforcement learning based tracking algorithms in high energy physics.