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The efficient and reliable maintenance and repair of industrial machinery depend critically on circuit diagrams, which serve as essential references for troubleshooting and must be updated when machinery is modified. However, many circuit diagrams are not available in structured, machine-readable format; instead, they often exist as unstructured PDF files, rendered images, or even photographs. Existing digitization methods often address isolated tasks, such as symbol detection, but fail to provide a comprehensive solution. This paper presents a novel pipeline for extracting the underlying graph structures of circuit diagrams, integrating image preprocessing, pattern matching, and graph extraction. A U-net model is employed for noise removal, followed by gray-box pattern matching for device classification, line detection by morphological operations, and a final graph extraction step to reconstruct circuit connectivity. A detailed error analysis highlights the strengths and limitations of each pipeline component. On a skewed test diagram from a scan with slight rotation, the proposed pipeline achieved a device detection accuracy of 88.46% with no false positives and a line detection accuracy of 94.7%.

We infix the duality-symmetric and the mirror symmetry conversion processes into a dynamical system representing an electric circuit diagram with three input (or output) as shown in Figure 2. Hence, a new non-linear variable order initial value problem is obtained and solved using the Haar wavelet numerical method (HWNM). Error, stability and entropy analyzes show the reliability of the method. Numerical simulations are then implemented and show for the new system, existence of various attractors’ types (point attractors (PAs), limit cycles, strange attractors (SAs), double attractor (DA), coexisting attractors (CoAs)) with their mirror reflections. Both are in a symmetrical structure in which they face each other, separated by a changing symmetry line and exhibiting similar properties. The circuit implementation using a Field Programmable Gate Array (FPGA) is performed and concur with the expected results.

This paper presents an effective technique for segmentation and recognition of electronic components from hand-drawn circuit diagrams. Segmentation is carried out by using a series of morphological operations on the binarized images of circuits and discriminating between three categories of components (closed shape, components with connected lines, disconnected components). Each segmented component is characterized by computing the Histogram of Oriented Gradients (HOG) descriptor while classification is carried out using Support Vector Machine (SVM). The system is evaluated on 100 hand-drawn circuit diagrams with a total of 350 components. A segmentation accuracy of 87.7% while a classification rate of 92% is realized demonstrating the effectiveness of the proposed technique.

Unitary circuits subject to repeated projective measurements can undergo an entanglement phase transition (EPT) as a function of the measurement rate. This transition is generally understood in terms of a competition between the scrambling effects of unitary dynamics and the disentangling effects of measurements. We find that, surprisingly, EPTs are possible even in the absence of scrambling unitary dynamics, where they are best understood as arising from measurements alone. This finding motivates us to introduce measurement-only models, in which the “scrambling” and “unscrambling” effects driving the EPT are fundamentally intertwined and cannot be attributed to physically distinct processes. These models represent a novel form of an EPT, conceptually distinct from that in hybrid unitary-projective circuits. We explore the entanglement phase diagrams, critical points, and quantum code properties of some of these measurement-only models. We find that the principle driving the EPTs in these models is frustration, or mutual incompatibility, of the measurements. Surprisingly, an entangling (volume-law) phase is the generic outcome when measuring sufficiently long but still local (≳3-body) operators. We identify a class of exceptions to this behavior (“bipartite ensembles”) which cannot sustain an entangling phase but display dual area-law phases, possibly with different kinds of quantum order, separated by self-dual critical points. Finally, we introduce a measure of information spreading in dynamics with measurements and use it to demonstrate the emergence of a statistical light cone, despite the nonlocality inherent to quantum measurements.

In present study the total ionizing dose effects in a positive low-dropout linear voltage regulator IS-LS1-3.3V was investigated experimentally using the developed the X-ray research complex. It is established that the output voltage is changed slightly in all total ionizing dose intervals and voltage regulator preserves a functional state without failure. The analytical functional dependence of the output voltage on the total ionizing dose is determined. The circuit diagram and voltage regulator SPICE macromodel for circuit simulations taking into consideration total ionizing dose effects have been developed.

Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs.

This paper has outlined high-speed transmission circuits signaling in guided or unguided optical communication systems. An efficient prediction scheme to predict the signal distortion caused by intersymbol interference. As high-speed digital signals exceed many gigabits per second speeds, eye diagrams provide the means to quickly and accurately measure signal quality and system performance. Semiconductor understands the capacitance constraints that designers are faced with when using these high-speed interfaces and offers a wide line of ultra-low capacitance electrical spectral density protection devices that service these interfaces, and the noise can be reduced at the optimum circuit at both bandwidths of 167 MHz, and bandwidth of 200 MHz.

This paper investigates the chaos control problem of microelectromechanical system (MEMS) resonators by using the analog circuits. The dynamical analysis, based on bifurcation diagrams, phase diagrams and Lyapunov exponents (LEs), illustrates that transient chaotic behaviors and chaotic behaviors strongly depend on system parameters and the initial conditions of the MEMS resonator. Then, based on the energy flow theory, the circuit differential equation is consistent with its differential equation governing the dynamics, which could mimic the micro-resonator dynamic properties. Accordingly, an analog circuit is designed, and abundant experimental data reveal chaotic behaviors of the MEMS resonator at around 58.791 Hz (1.71 V) and 58.704 Hz (1.68 V). After that, to suppress harmful chaotic oscillation, an adaptive control scheme is proposed and verified by an analog circuit consisting of an error module, a parameter update module and a control input module. Finally, the experimental results of the circuit control system prove the effectiveness of the proposed control scheme.

In this letter, a structural analysis attack is explored as the first kind of attack to disclose the secret key of the circuit encrypted by the latest obfuscation technique JANUS‐HD. By extracting the finite state machine (FSM) diagram from the circuit and converting structural traces into constraints, the secret key can be deduced by the CP‐SAT solver within a few minutes or even seconds in most cases. Incorrect keys can be further pruned by sequential equivalence checking with several oracles in case more than one key is obtained. The entire attack procedure lasts no longer than a few hours even for an FSM with 4096 states. In this letter, the attack of JANUS‐HD obfuscated circuits is first modelled as a constraint programming (CP) problem. The previously unsolvable problem is made solvable by converting the structural traces of the finite state machine (FSM) into concise constraints. The attack efficiency is significantly improved by using compressed matrices and optimizing the way the CP‐SAT solver is called, especially for large FSMs.

Engineered biological systems that integrate multi-input sensing, sophisticated information processing, and precisely regulated actuation in living cells could be useful in a variety of applications. For example, anticancer therapies could be engineered to detect and respond to complex cellular conditions in individual cells with high specificity. Here, we show a scalable transcriptional/posttranscriptional synthetic regulatory circuit—a cell-type \"classifier\"—that senses expression levels of a customizable set of endogenous microRNAs and triggers a cellular response only if the expression levels match a predetermined profile of interest. We demonstrate that a HeLa cancer cell classifier selectively identifies HeLa cells and triggers apoptosis without affecting non-HeLa cell types. This approach also provides a general platform for programmed responses to other complex cell states.