Miracle-dz/UniFAR-FTUs huggingface dataset:
CO Gas Sensor Based on E-Beam Evaporated ZnO, MgZnO, and CdZnO Thin Films: A Comparative Study

This paper reports a comparative study of electron-beam evaporated ZnO, MgZnO, and CdZnO thin film based-gas sensor. At room temperature (RT), these semi-conductive thin films were deposited on Si/SiO2 substrate and an interdigitated pattern of chromium electrode deposited on these films. Device properties, such as structural, optical, and electrical, have been reported and analyzed. The sensors have been tested at different operating temperatures. At 250 °C, the sensor shows the best response for CdZnO thin films. We have obtained sensor response 4.86 with response time 15 sec for 100-PPM carbon mono oxide (CO) gas concentration for CdZnO thin film. Based on experimental results, Cd-doped ZnO has been found most suitable among these semiconducting metal oxides, when used as a CO gas sensor. A correlation between structural, optical, and electrical properties with these thin films has also been established.
Seam Manipulator: Leveraging Pixel Fusion for Depth-Adjustable Stereoscopic Image Retargeting

To enhance the depth perception for stereoscopic image retargeting, this paper presents a depth-adjustable stereoscopic image retargeting method via pixel fusion technique. The traditional methods enforce the depth-preserving constraint on the left and right images with poor depth adaption. To solve this issue, we try to address two critical properties that how to change the widths of unmatched region to control the depth perception, and how to change the alignment of left and right seams to satisfy visual comfort limits, and thus propose a seam manipulator tool to optimize the process for scaling factor determination of stereoscopic seams. The important contribution of our method is to take scaling preservation energy and disparity consistency energy into optimization. The experimental results demonstrate that our method can achieve promising performances on depth perception and visual comfort adjustment for better visual experience.
Combining Commercially Available Active and Passive Sensors Into a Millimeter-Wave Imager for Concealed Weapon Detection

Current imaging technology for concealed weapon detection in the millimeter-wave (mm-wave) and terahertz ranges is traditionally limited to a single type of sensor. In this paper, a system combining active and passive mm-wave sensors along with a depth-sensing camera is presented. These sensors are combined at a hardware stage (being integrated into a single imager) as well as at software level (via simultaneous processing and sensor fusion). The resulting images of the individual sensors are combined via image fusion techniques, using a consumer-grade optical sensor as an image noise-removing tool and highlighting the threat over the subject’s body. Each sensor is able to compensate some of the imaging limitations of the others, thus offering better overall detection capabilities. Furthermore, the final imager has been formed by combining commercially available devices, which provides a reduced-cost alternative to custom-made equipment.
Investigation of Influence of Pulse Electric Current on High-Speed Deformation of Copper M1 by Magnetic Pulse Loading

Experimental investigation of the high-speed deformation of the copper M1 samples was carried out by magnetic pulse loading with amplitude 0.1 GPa and microseconds duration. Fracture process of the sample after sequential accumulation of the residual opening of the groove ~60-70% is observed. The possibility of the realization of mechanical loading with simultaneously impulse current loading in the area of the deformation is shown for the investigation of electric current influence on the process of high-speed deformation.
A 300-GHz SPST Switch With a New Coupled-Line Topology in 65-nm CMOS Technology

A single-pole single-throw (SPST) switch operating around 300 GHz has been developed in this letter based on a 65-nm CMOS technology. The SPST switch adopts a novel coupled-line topology, in which MOSFETs are employed as a variable impedance component at the through- and coupled-ports to achieve a large isolation. Over the measured frequency band of 220–320 GHz, a minimum insertion loss of 3.9 dB (at 303 GHz) and a maximum isolation of 66 dB (at 250 GHz) were obtained. This achieved peak isolation is the largest value obtained so far beyond 100 GHz with a CMOS switch. An isolation larger than 39 dB was maintained for the entire frequency band measured. The measured return loss showed a maximum value of 29 dB at 312 GHz.
Structural and Schematic Methods of the Biological Neuron Networks Properties Projecting on the Architecture of Modern Integrated Neuron-Like Systems with Analog-Digital Information Processing

This paper substantiates the essential role of space-time dependence of the information representation form in the development of intelleсtual energy-efficient and noise-resistant data processing systems of large dimensions. The authors propose the artificial neuron networks design approach with local analog and global digital neural data processing (LAGDNDP). According to LAGDNDP approach, we interpret the role of different forms of neural networks plasticity as the basis of global and local network adaptation to external conditions of functioning, internal parameters variation, network elements failure and degradation. We show the primary role of structural and circuit design methods in the development of highly integrated neuron-like architectures of large dimensions with advanced scalability, local and global adaptation. LAGDNDP approach together with modern integrated analog and digital technologies supports structural self-organization while training on the input data flows, the formation of a hierarchical fractal structure, emulation of multi-level spatial-temporal dynamics, sub-threshold information processing.
Approximating Sample State Vectors Using the ESPT for Computationally Efficient Particle Filtering

The extrapolated single propagation technique (ESPT) based particle filter is proposed. Instead of propagating all the sample state vectors using multiple numerical integrations, only one sample state vector is propagated in the ESPT. Other sample states are computed at the next epoch with an accuracy of the second-order Taylor series expansion terms. It is theoretically shown that the particle filter algorithm, which uses the ESPT for state propagation, requires up to $\text{90}\%$ reduced processing time than the conventional particle filter. Using a benchmark non-linear estimation problem the performance of the modified algorithm is compared with the particle filter. The experimental results confirm the theoretical analysis and demonstrate the effectiveness of the proposed method for non-linear state estimation. The proposed algorithm significantly reduces the processing time of the particle filter with a minor degradation in the estimation accuracy.
Design of a MIMO Antenna With High Isolation for Smartwatch Applications Using the Theory of Characteristic Modes

This paper describes the design and testing of a multiple-in multiple-out (MIMO) antenna for metal-frame smartwatch applications. The antenna operates from 2.4 to 2.49 GHz, which can cover Bluetooth/WiFi band. The design procedure is based on the theory of characteristic modes and it is discussed thoroughly. By employing a pair of degenerated modes to design the MIMO antenna, a high port-to-port isolation can be achieved over the operating frequency band. Simulated and measured results are presented to verify the antenna design. Furthermore, the antenna has a low envelope correlation coefficient and a high effective diversity gain even when it is mounted on the human phantom. Hence, the proposed antenna proves to be a good candidate for smartwatch applications.
ANRIL, a long, noncoding RNA, is an unexpected major hotspot in GWAS

A large noncoding RNA called ANRIL (for antisense noncoding RNA in the INK4 locus) has been identified within the p15/CDKN2B‐p16/CDKN2A‐p14/ARF gene cluster. While the exact role of ANRIL awaited further elucidation, common disease genome‐wide association studies (GWAS) have surprisingly identified the ANRIL gene as a genetic susceptibility locus shared associated by coronary disease, intracranial aneurysm and also type 2 diabetes. Expression studies have confirmed the coregulation of p15/CDKN2B, p16/CDKN2A’, p14/ARF, and ANRIL. Among the cluster, ANRIL expression showed the strongest association with the multiple phenotypes linked to the 9p21.3 region. More recent GWAS also identified ANRIL as a risk locus for gliomas and basal cell carcinomas in accordance with the princeps observation. Moreover’ a mouse model has confirmed the pivotal role of ANRIL in regulation of CDKN2A/B expression through a cis‐acting mechanism and its implication in proliferation and senescence. The implication of ANRIL in cellular aging has provided an attractive unifying hypothesis to explain its association with various susceptibility risk factors. ANRIL identification emphasizes the underestimated role of long noncoding RNAs. Many GWAS have identified trait‐associated SNPs that felt in non‐coding genomic regions. It is conceivable to anticipate that long’ noncoding RNAs will map to many of these “gene deserts. ”—Pasmant, E. , Sabbagh, A. , Vidaud, M. , Bieche, I. ANRIL, a long, noncoding RNA, is an unexpected major hotspot in GWAS. FASEB J. 25, 444–448 (2011). www.fasebj. org
Carbon Nanotubes Deposited Optical Fibers for Continuous Refractive Index Sensing Applications

An optical fiber-based refractive index (RI) sensing probe fabricated through optical deposition of carbon nanotubes (CNTs) onto the cleaved end face of a multimode fiber (MMF) segment, fusion spliced at one end to a single mode fiber (SMF), is presented. The interference of the excited higher order modes in the MMF segment with the fundamental mode led to the formation of various spectral features in the resulting output spectrum which were then used as reference points for RI measurements. RI perturbations in the external environment of the sensing probe could then be characterized by variations in intensity of these spectral features with little wavelength dependence. The deposited CNTs extended the continuous measurement range of the sensor and the achievable sensitivity was calculated to be approximately 35.8 dB/RIU for the RI range of 1.33–1.40 and approximately 98.8 dB/RIU for the RI range of 1.40–1.46. The sensing probe also exhibited good stability with little fluctuations in intensity of the output spectrum for a constant RI.
Generalized Gaussian Mechanism for Differential Privacy

Assessment of disclosure risk is of paramount importance in data privacy research and applications. The concept of differential privacy (DP) formalizes privacy in probabilistic terms and provides a robust concept for privacy protection. Practical applications of DP involve development of DP mechanisms to release data at a pre-specified privacy budget. In this paper, we generalize the widely used Laplace mechanism to the family of generalized Gaussian (GG) mechanism based on the <inline-formula><tex-math notation="LaTeX">$l_p$</tex-math><alternatives><inline-graphic xlink:href="liu-ieq1-2845388.gif"/></alternatives></inline-formula> global sensitivity of statistical queries. We explore the theoretical requirement for the GG mechanism to reach DP at prespecified privacy parameters, and investigate the connections and differences between the GG mechanism and the Exponential mechanism based on the GG distribution. We also present a lower bound on the scale parameter of the Gaussian mechanism of <inline-formula><tex-math notation="LaTeX">$(\epsilon, \delta)$</tex-math><alternatives><inline-graphic xlink:href="liu-ieq2-2845388.gif"/></alternatives></inline-formula>-probabilistic DP as a special case of the GG mechanism, and compare the utility of sanitized results in the tail probability and dispersion between the Gaussian and Laplace mechanisms. Lastly, we apply the GG mechanism in three experiments and compare the accuracy of sanitized results in the <inline-formula><tex-math notation="LaTeX">$l_1$</tex-math><alternatives><inline-graphic xlink:href="liu-ieq3-2845388.gif"/></alternatives></inline-formula> distance and Kullback-Leibler divergence, and examine the prediction power of a SVM classifier constructed with the sanitized data relative to the original results.
Classification and Re-Identification of Fruit Fly Individuals Across Days With Convolutional Neural Networks

Fruit flies have commonly been used as model organisms to understand various biological processes from genetics and development to social behavior. When studying multiple flies in an experiment, maintaining the identities of individual flies is often essential. The field of "computational ethology" has provided several methods that help biologists in this regard. Unmarked fly identities can be maintained by tracking each fly throughout the experiment, either manually correcting errors or computing and storing the statistics at each frame, but this becomes infeasible for experiments spanning several days. A popular alternative is to mark insects (with tags, paint or wing-clipping), but this has shown to have adverse effects on many of the same behaviors under study. To solve the above problems, we developed a method that can augment existing tracking software with a deep learning-based approach to recognize and re-identify individual fruit flies across days. This method should prove invaluable to biologists as it promises to revolutionize our ability to perform long-term experiments. To the best of our knowledge, this is the first work to investigate and successfully re-identify unmarked fruit flies across multiple days. We acquired our own dataset, which has around 3M images of 60 different fruit fly individuals, acquired in independent sets of 20. A vast majority (95%) of the flies have greater than 90% accuracy on a per-frame basis, and a majority of those (74%) have over 98% accuracy. With our method, less than 1 fly in 20 (on average) should be difficult to identify without resorting to parsimonious methods (i.e. examining which ID has not yet been assigned). This high classification accuracy demonstrates that we are able to successfully re-identify unmarked flies across days.
The non‐ribosomal assembly and frequent occurrence of the protease inhibitors spumigins in the bloom‐forming cyanobacterium Nodularia spumigena

Nodularia spumigena is a filamentous nitrogen‐fixing cyanobacterium that forms toxic blooms in brackish water bodies worldwide. Spumigins are serine protease inhibitors reported from a single strain of N. spumigena isolated from the Baltic Sea. These linear tetrapeptides contain non‐proteinogenic amino acids including a C‐terminal alcohol derivative of arginine. However, very little is known about these compounds despite the ecological importance of N. spumigena. We show that spumigins are assembled by two non‐ribosomal peptide synthetases encoded in a 21 kb biosynthetic gene cluster. The compact non‐ribosomal peptide synthetase features a reductive loading and release mechanism. Our analyses demonstrate that the bulk of spumigins produced by N. spumigena are released as peptide aldehydes in contrast to earlier findings. The main spumigin E variant contains an argininal residue and is a potent trypsin inhibitor. Spumigins were present in all of the N. spumigena strains isolated from the Baltic Sea and comprised up to 1% of the dry weight of the cyanobacterium. Our results demonstrate that bloom‐forming N. spumigena strains produce a cocktail of enzyme inhibitors, which may explain in part the ecological success of this cyanobacterium in brackish water bodies worldwide.
Neuroligin 4 regulates synaptic growth via the bone morphogenetic protein (BMP) signaling pathway at the Drosophila neuromuscular junction

The neuroligin (Nlg) family of neural cell adhesion molecules is thought to be required for synapse formation and development and has been linked to the development of autism spectrum disorders in humans. In Drosophila melanogaster, mutations in the neuroligin 1–3 genes have been reported to induce synapse developmental defects at neuromuscular junctions (NMJs), but the role of neuroligin 4 (dnlg4) in synapse development has not been determined. Here, we report that the Drosophila neuroligin 4 (DNlg4) is different from DNlg1–3 in that it presynaptically regulates NMJ synapse development. Loss of dnlg4 results in reduced growth of NMJs with fewer synaptic boutons. The morphological defects caused by dnlg4 mutant are associated with a corresponding decrease in synaptic transmission efficacy. All of these defects could only be rescued when DNlg4 was expressed in the presynapse of NMJs. To understand the basis of DNlg4 function, we looked for genetic interactions and found connections with the components of the bone morphogenetic protein (BMP) signaling pathway. Immunostaining and Western blot analyses demonstrated that the regulation of NMJ growth by DNlg4 was due to the positive modulation of BMP signaling by DNlg4. Specifically, BMP type I receptor thickvein (Tkv) abundance was reduced in dnlg4 mutants, and immunoprecipitation assays showed that DNlg4 and Tkv physically interacted in vivo. Our study demonstrates that DNlg4 presynaptically regulates neuromuscular synaptic growth via the BMP signaling pathway by modulating Tkv.
Cluster-Based Cooperative Caching With Mobility Prediction in Vehicular Named Data Networking

Vehicular named data networking (VNDN) is a networking architecture candidate to support various kinds of content-oriented applications in high dynamic topology environment. Its inherent in-network caching facilitates content delivery and the communication efficiency in the vehicular network. However, during the process of data delivery, fast and varying vehicle mobility changes the position of relay routers, which makes data packet delivered through the reverse path difficult. Furthermore, frequent link disruption and packet retransmission lead to the increase of network load and the decrease of user QoE. To address these problems, we propose a cluster-based cooperative caching approach with mobility prediction (COMP) in VNDN. The main idea of COMP is to establish communication among vehicles with similar mobility pattern to mitigate the impact of vehicle mobility, as the link between nodes with a similar pattern is relatively stable and reliable. Specifically, we design a clustering algorithm to group vehicles with similar mobility pattern via mobility prediction and present a cooperative caching to construct intra-cluster and inter-cluster communication over the vehicle clusters. To increase the cache resource utilization and the diversity of the cached data, we classify the cached data into the most popular data and the less popular data based on request frequency, and furthermore, the corresponding cache placement and transmission schemes are proposed. The evaluation results show that most of the vehicles (>95%) can acquire feasible and efficient data delivery via COMP, and COMP significantly improves network performance and user QoE.
Design, Development and Control of a Three Flexible-Fingers Gripper Based On Hand Gesture

In this paper, the process of design, and construction of a flexible gripper with three fingers is presented. The fingers of the proposed gripper are fabricated using a 3D printing device. The gripper is controlled remotely by an autonomous glove which is designed to help people with disability. In order to ameliorate the movement accuracy of the gripper a Fuzzy-PID controller is implemented on the system. The result of experimental tests demonstrate a decrease of the gripper movement error to less than 2 degrees in average. An ultrasonic sensor is set on the gripper’s body to improve the grasping process by detecting the distance of the object to the gripper. The fabricated fingers are flexible and provides the ability of grasping the objects with irregular shapes. The experimental test approves the significant performance of the gripper in term of grasping complex shape objects.
Tensor Algebra Compilation with Workspaces

This paper shows how to extend sparse tensor algebra compilers to introduce temporary tensors called workspaces to avoid inefficient sparse data structures accesses. We develop an intermediate representation (IR) for tensor operations called concrete index notation that specifies when sub-computations occur and where they are stored. We then describe the workspace transformation in this IR, how to programmatically invoke it, and how the IR is compiled to sparse code. Finally, we show how the transformation can be used to optimize sparse tensor kernels, including sparse matrix multiplication, sparse tensor addition, and the matricized tensor times Khatri-Rao product (MTTKRP). Our results show that the workspace transformation brings the performance of these kernels on par with hand-optimized implementations. For example, we improve the performance of MTTKRP with dense output by up to 35%, and enable generating sparse matrix multiplication and MTTKRP with sparse output, neither of which were supported by prior tensor algebra compilers.
Classification of EEG Signals in the Improved Complete Ensemble EMD Domain

Epilepsy is one of the major neurological problems the today's world is facing. In this paper we have introduced a method for the detection of epileptic seizure in the improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) domain. Here, firstly we have applied improved CEEMDAN on the electroencephalogram (EEG) signals to get the modes (Ms) and on these modes we will apply fractal dimension, generalized Hurst exponent and higher order statistical moments. All the calculated features of the EEG dataset which is available online are feed to two classifiers namely support vector machine (SVM) and k-nearest neighbors algorithm (kNN). The classification result of both the classifiers show a good accuracy of 100% in order to classify the normal and ictal as well as interictal and ictal EEG signals.
Object Pre-Grasp Configuration of a Robot Articulated Hand in a Virtual Environment

The grasp of an object from a specific location, its handling and delivery in a well-defined working environment require knowledge concerning both manipulators and mobile robots. The positioning of a robot arm with the object involved in the mobile manipulation requires the calculation of the pre-grasp configurations as well as that of the selected final grasp that determines the final position of the robotized hand relatively to that of the object. The present research uses the 'gazebo-ros' simulator to locate the position of the hand relatively to that of the object. This is applied to the case of the ‘care-bot-3’ service robot where a global navigation software package is used to animate-simulate the initial functionality of the mobile robot in order to bring the object under consideration in the working environment of its arm. The robot begins its approach in local navigation form, the positioning in the horizontal plane of the object taking place on the basis of the pre-capture configurations related to the industrial manipulator '70DF'. The generation of the capture configurations is carried out through an inverse kinematic model developed under ‘ROS’ environment on its final articulation ‘arm_7_link’ in its basic referential ‘base_link’. The displacement of the object to its final position is carried out through a data set of trajectories of the object defined between the primitives of the hand 'schunk' mounted on the manipulator of ‘care_bot’. The efficiency of the approach developed is demonstrated through its application to the virtual environment 'gazebo' as well as on the display-visualizer ‘RVIZ’ via a kinect camera.