Filter-feature-based rotation-invariant joint Fourier transform correlator

Rotation-invariant target detection using a trained filter-feature-based joint Fourier transform (JFT) correlator is investigated. First, a composite reference image is obtained from a training set of targets. An optimum filter formulation is then applied on this composite image to come up with a new feature that we refer to as a filter feature. This feature is then used in a JFT correlator, which results in a simple and robust rotation-invariant target recognition system.     

Low voltage second-order alpha function synapse

This paper introduces a Transimpedance Amplifier (TIA) design capable of producing an incremental input resistance in the ohmic range, for input signals in the microampere range, such as are encountered in the design of instrumentation for electrochemical ampero-metric sensors, optical-sensing and current-mode circuits. This low input-resistance is achieved using an input stage incorporating negative feedback. In a Cadence simulation of an exemplary design using a 180 nm CMOS process and operating with?±?1.8 V supply rails, the input resistance is 1.05 ohms and the power dissipation is 93.6 µW. The bandwidth, for a gain of 100 dBohm, exceeded 9 MHz. For a 1µA, 1 MHz sinusoidal input signal the Total Harmonic Distortion, with this gain, is less than 1%. The input referred noise current with zero photodiode capacitance is 2.09 pA/√Hz and with a photodiode capacitance of 2pF is 8.52 pA/√Hz. Graphical data is presented to show the effect of a photodiode capacitance varying from 0.5 to 2 pF, when the TIA is used in optical sensing. In summary, the required very low input resistance, at a low input current level (µA) is achieved and furthermore a Table is included comparing the characteristics and a widely used Figure of Merit (FOM) for the proposed TIA and similar published low-power TIAs. It is apparent from the Table that the FOM of the proposed TIA is better than the FOMs of the other TIAs mentioned.

     

Knowledge Diffusion of the Internet of Things (IoT): A Main Path Analysis

Wireless Personal Communications - The Internet of Things (IoT) is a concept that has attracted significant attention since the emergence of wireless technology. The knowledge diffusion of IoT...     

Hybrid deep learning model for answering visual medical questions

Due to the increase in electronic documents containing medical information, the search for specific information is often complex and time-consuming. This has prompted the development of new tools designed to address this issue. Automated visual question/answer (VQA) systems are becoming more challenging to develop. These are computer programs that take images and questions as input and then combine all inputs to generate text-based answers. Due to the enormous amount of question and the limited number of specialists, many issues stay unanswered. It’s possible to solve this problem by using automatic question classifiers that guide queries to experts based on their subject preferences. For these purposes, we propose a VQA approach based on a hybrid deep learning model. The model consists of three steps: (1) the classification of medical questions based on a BERT model; (2) image and text feature extraction using a deep learning model, more specifically the extraction of medical image features by a hybrid deep learning model; and (3) text feature extraction using a Bi-LSTM model. Finally, to predict the appropriate answer, our approach uses a KNN model. Additionally, this study examines the influence of the Adam, AdaGrad, Stochastic gradient descent and RMS Prop optimization techniques on the performance of the network. As a consequence of the studies, it was shown that Adam and SGD optimization algorithms consistently produced higher outcomes. Experiments using the ImageCLEF 2019 dataset revealed that the suggested method increases BLEU and WBSS values considerably.

     

Near-Entropy Hotlink Assignments

Consider a rooted tree T of arbitrary maximum degree d representing a collection of n web pages connected via a set of links, all reachable from a source home page represented by the root of T. Each web page i carries a probability p _i representative of the frequency with which it is visited. By adding hotlinks—shortcuts from a node to one of its descendents—we wish to minimize the expected number of steps l needed to visit pages from the home page, expressed as a function of the entropy H(p) of the access probabilities p. This paper introduces several new strategies for effectively assigning hotlinks in a tree. For assigning exactly one hotlink per node, our method guarantees an upper bound on l of 1.141H(p)+1 if d>2 and 1.08H(p)+2/3 if d=2. We also present the first efficient general methods for assigning at most k hotlinks per node in trees of arbitrary maximum degree, achieving bounds on l of at most \frac2H(p)log(k+1)+1\frac{2H(p)}{\log(k+1)}+1 and \fracH(p)log(k+d)-logd+1\frac{H(p)}{\log(k+d)-\log d}+1 , respectively. All our methods are strong, i.e., they provide the same guarantees on all subtrees after the assignment. We also present an algorithm implementing these methods in O(nlog n) time, an improvement over the previous O(n ²) time algorithms. Finally we prove a Ω(nlog n) lower bound on the running time of any strong method that guarantee an average access time strictly better than 2H(p).     

MLP bilinear separation

In this paper, we present a thorough mathematical analysis of the use of neural networks to solve a specific classification problem consisting of a bilinear boundary. The network under consideration is a three-layered perceptron with two hidden neurons having the sigmoid serving as the activation function. The analysis of the hidden space created by the outputs of the hidden neurons will provide results on the network’s capacity to isolate two classes of data in a bilinear fashion, and the importance of the value of the sigmoid parameter is highlighted. We will obtain an explicit analytical function describing the boundary generated by the network, thus providing information on the effect each parameter has on the network’s behavior. Generalizations of the results are obtained with additional neurons, and a theorem concerned with analytical reproducibility of the boundary function is established.     

Characterization of renal tumours based on Raman spectra classification

In this study, we propose to evaluate the potential of Raman spectroscopy (RS) to assess renal tumours at surgery. Different classes of Raman renal spectra acquired during a clinical protocol are discriminated using support vector machines classifiers. The influence on the classification scores of various preprocessing steps generally involved in RS are also investigated and evaluated in the particular context of renal tumour characterization. Encouraging results show the interest of RS to evaluate kidney cancer and suggest the potential of this technique as a surgical assistance during partial nephrectomy.     

Exploring the relationship between Internet ethics in university students and the big five model of personality

The widespread use of the Internet and the convenient mechanism it provides, such as easy access, easy downloads, and easy copy and paste functions have made many types of unethical behaviors easier, particularly those involving students in academic settings. Among the issues in ethics within the academic environment that can be triggered by the Internet are fraudulence, plagiarism, falsification, delinquency, unauthorized help, and facility misuse. Given these issues, the study seeks to investigate the extent to which students at a public university in Malaysia engage in such unethical behavior and their relationship with the big five personality model. This study was conducted using a survey method of 252 students in three different academic faculties. The results of factor analyses confirm and refine the reliability of the scales for both big five personality variables and unethical Internet behaviors as conceptualized through Internet triggered academic dishonesty (ITADS). The findings indicate that personality traits such as (1) agreeableness, (2) conscientiousness and (3) emotional stability are significantly and negatively correlated with unethical Internet behavior in university students. Significant differences in facility misuse are also observed between the three academic faculties investigated. This research should provide significant contributions to educators in designing the computer ethics curriculum and in allowing for educational institutions as well as other organizations in developing relevant policies and guidelines on matters pertaining to academic conduct, utilization of computers and Internet, and recruitment exercises.     

Multi-contact vertical ladder climbing with an HRP-2 humanoid

We describe the research and the integration methods we developed to make the HRP-2 humanoid robot climb vertical industrial-norm ladders. We use our multi-contact planner and multi-objective closed-loop control formulated as a QP (quadratic program). First, a set of contacts to climb the ladder is planned off-line (automatically or by the user). These contacts are provided as an input for a finite state machine. The latter builds supplementary tasks that account for geometric uncertainties and specific grasps procedures to be added to the QP controller. The latter provides instant desired states in terms of joint accelerations and contact forces to be tracked by the embedded low-level motor controllers. Our trials revealed that hardware changes are necessary, and parts of software must be made more robust. Yet, we confirmed that HRP-2 has the kinematic and power capabilities to climb real industrial ladders, such as those found in nuclear power plants and large scale manufacturing factories (e.g. aircraft, shipyard) and construction sites.