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Wireless charging pads are common, yet their functionality is mainly restricted to charging. Existing gesture recognition techniques, such as those based on machine vision and WiFi, have drawbacks like high costs and poor precision. This paper presents a new human machine interaction solution using multicoil wireless charging pads. The proposed approach leverages the pads existing modules without additional wearable sensors. It determines gestures by monitoring current and power changes in different coils. The data processing includes noise removal, sorting, highpass filtering, and slicing. A Bayesian network and particle filtering are employed for motion tracking. Through experiments, this solution proves to have wide applications, high recognition accuracy, and low cost. It can effectively identify diverse gestures, increasing the value of wireless charging pads. It outperforms traditional methods, with a 0.73 improvement in recognition accuracy and better environmental adaptability.
Diffusion models have demonstrated impressive capabilities in modeling complex data distributions and are increasingly applied in various generative tasks. In this work, we propose Pose Analysis by Diffusion Synthesis PADS, a unified generative modeling framework for 3D human pose analysis. PADS first learns a task-agnostic 3D pose prior via unconditional diffusion synthesis and then performs training-free adaptation to a wide range of pose analysis tasks, including 3D pose estimation, denoising, completion, etc., through a posterior sampling scheme. By formulating each task as an inverse problem with a known forward operator, PADS injects task-specific constraints during inference while keeping the pose prior fixed. This plug-and-play framework removes the need for task-specific supervision or retraining, offering flexibility and scalability across diverse conditions. Extensive experiments on different benchmarks showcase the superior performance against both learning-based and optimization-based baselines, demonstrating the effectiveness and generalization capability of our method.
This paper introduces a pair of low-cost, light-weight and compliant force-sensing gripping pads used for manipulating box-like objects with smaller-sized humanoid robots. These pads measure normal gripping forces and center of pressure (CoP). A calibration method is developed to improve the CoP measurement accuracy. A hybrid force-alignment-position control framework is proposed to regulate the gripping forces and to ensure the surface alignment between the grippers and the object. Limit surface theory is incorporated as a contact friction modeling approach to determine the magnitude of gripping forces for slippage avoidance. The integrated hardware and software system is demonstrated with a NAO humanoid robot. Experiments show the effectiveness of the overall approach.
We have proposed and experimentally verified a tunable inter-qubit coupling scheme for large-scale integration of superconducting qubits. The key feature of the scheme is the insertion of connecting pads between qubit and tunable coupling element. In such a way, the distance between two qubits can be increased considerably to a few millimeters, leaving enough space for arranging control lines, readout resonators and other necessary structures. The increased inter-qubit distance provides more wiring space for flip-chip process and reduces crosstalk between qubits and from control lines to qubits. We use the term Tunable Coupler with Capacitively Connecting Pad (TCCP) to name the tunable coupling part that consists of a transmon coupler and capacitively connecting pads. With the different placement of connecting pads, different TCCP architectures can be realized. We have designed and fabricated a few multi-qubit devices in which TCCP is used for coupling. The measured results show that the performance of the qubits coupled by the TCCP, such as $T_1$ and $T_2$, was similar to that of the traditional transmon qubits without TCCP. Meanwhile, our TCCP also exhibited a wide tunable range
Bond pads are the electrical interconnections of a microelectronic device to the outside world. A polyimide layer (PI) on top of a microelectronic device protects the whole device against environmental impacts. The bond pads are accessible though openings in this electrically insulating layer. The oxide layer thickness and contaminations at the Al bond pad surface influence as well the quality of the mechanical and electrical joint between bond pad and bond wire as the durability of this interconnection. If a bond pad surface has to be analyzed with high spatial resolution Auger electron spectroscopy (AES) is the method of choice. AES utilizes an electron beam for excitation, which induces serious sample charging because of the PI layer. Sample charging can be avoided by metal grids, which are common in transmission electron microscopy (TEM) sample preparation. A TEM grid is pressed onto the sample while the bond pad of interest is centered in the square openings of the grid. Exemplarily analyses of bond pads demonstrate the applicability of this approach for AES measurements.
Landing is the most challenging and risky aspect of multirotor drone flight, and only simple landing methods exist for autonomous drones. We explore methods for autonomous drone landing in two scenarios. In the first scenario, we examine methods for landing on known landing pads using fiducial markers and a gimbal-mounted monocular camera. This method has potential in drone applications where a drone must land more accurately than GPS can provide (e.g.~package delivery in an urban canyon). We expand on previous methods by actuating the drone's camera to track the marker over time, and we address the complexities of pose estimation caused by fiducial marker orientation ambiguity. In the second scenario, and in collaboration with the RAVEN project, we explore methods for landing on solidified lava flows in Iceland, which serves as an analog environment for Mars and provides insight into the effectiveness of drone-rover exploration teams. Our drone uses a depth camera to visualize the terrain, and we are developing methods to analyze the terrain data for viable landing sites in real time with minimal sensors and external infrastructure requirements, so that the solution does not heavi
This paper analyzes the energy efficiency of a novel system model where unmanned aerial vehicles (UAVs) are used to provide coverage for user hotspots (user clusters) and are deployed on charging pads to enhance the flight time. We introduce a new notion of "cluster pairs" to capture the dynamic nature of the users' spatial distribution in order to exploit one of the top advantages of UAVs, which is the mobility and relocation flexibility. Using tools from stochastic geometry, we first derive a new distance distribution that is vital for energy efficiency analysis. Next, we compute the coverage probability under two deployment strategies: (i) one UAV per cluster pair, and (ii) one UAV per cluster. Finally, we compute the energy efficiency for both strategies. Our numerical results reveal which of the two strategies is better for different system parameters. Our work investigates some new aspects of the UAV-enabled communication system such as the dynamic density of users and the advantages or disadvantages of one- or two-UAV deployment strategies per cluster pair. By considering the relationships between the densities of user cluster pairs and the charging pads, it is shown that an
Remote attestation protocols are widely used to detect device configuration (e.g., software and/or data) compromise in Internet of Things (IoT) scenarios. Unfortunately, the performances of such protocols are unsatisfactory when dealing with thousands of smart devices. Recently, researchers are focusing on addressing this limitation. The approach is to run attestation in a collective way, with the goal of reducing computation and communication. Despite these advances, current solutions for attestation are still unsatisfactory because of their complex management and strict assumptions concerning the topology (e.g., being time invariant or maintaining a fixed topology). In this paper, we propose PADS, a secure, efficient, and practical protocol for attesting potentially large networks of smart devices with unstructured or dynamic topologies. PADS builds upon the recent concept of non-interactive attestation, by reducing the collective attestation problem into a minimum consensus one. We compare PADS with a state-of-the art collective attestation protocol and validate it by using realistic simulations that show practicality and efficiency. The results confirm the suitability of PADS f
We use computational simulations to compare the impact response of different football and U.S. Army helmet pad materials. We conduct experiments to characterize the material response of different helmet pads. We simulate experimental helmet impact tests performed by the U.S. Army to validate our methods. We then simulate a cylindrical impactor striking different pads. The acceleration history of the impactor is used to calculate the Head Injury Criterion for each pad. We conduct sensitivity studies exploring the effects of pad composition, geometry, and material stiffness. We find that: (1) The football pad materials do not outperform the currently used military pad material in militarily-relevant impact scenarios; (2) Optimal material properties for a pad depend on impact energy; and (3) Thicker pads perform better at all velocities. Our analysis suggests that by using larger helmet shells with correspondingly thicker pads, impact-induced traumatic brain injury may be significantly reduced. Keywords: helmet, pad, head injury, traumatic brain injury, head injury criterion, impact
This study estimates the cost of building lunar landing pads and examines whether any construction methods are economically superior to others. Some proposed methods require large amounts of mass transported from the Earth, others require high energy consumption on the lunar surface, and others have a long construction time. Each of these factors contributes direct and indirect costs to lunar activities. The most important economic variables turn out to be the transportation cost to the lunar surface and the magnitude of the program delay cost imposed by a construction method. The cost of a landing pad depends sensitively on the optimization of the mass and speed of the construction equipment, so a minimum-cost set of equipment exists for each construction method within a specified economic scenario. Several scenarios have been analyzed across a range of transportation costs with both high and low program delay costs. It is found that microwave sintering is currently the most favorable method to build the inner, high temperature zone of a lunar landing pad, although other methods are within the range of uncertainty. The most favorable method to build the outer, low temperature zone
I present a method for generating one-time pads from the digits of pi. Computer code is given to generate such pads from passphrases in a method having an extremely low probability (<10^-53) of a successful discovery of the one-time pads by a brute-force attack. The advantages and disadvantages of this method are discussed.
Soft adhesive pads are needed for many robotics applications, and one approach is based on electroadhesion. Here we present a general analytic model and numerical results for electroadhesion for soft solids with arbitrary time-dependent applied voltage, and arbitrary dielectric response of the solids, and including surface roughness. We consider the simplest coplanar-plate-capacitor model with a periodic array of conducting strips located close to the surface of the adhesive pad, and discuss the optimum geometrical arrangement to obtain the maximal electroadhesion force. For surfaces with roughness the (non-contact) gap between the solids will strongly influence the electroadhesion, and we show how the electroadhesion force can be calculated using a contact mechanics theory for elastic solids. The theory and models we present can be used to optimize the design of adhesive pads for robotics application.
When I was smaller, a five megabyte fixed disk cost $5,000, a 300 bps modem cost hundreds of dollars, and communication links were intercepted by attaching devices to the target subscriber's local loop. From then to now there have been three great implosions: the cost of storage, the cost of bandwidth, and the cost of surveillance. The wake of the first two implosions sheared away most obstacles to using one-time pads to encrypt data in flight, and the final imposition -- I mean implosion -- now makes consideration of one-time pads a practical necessity. So far as assurance of confidentiality is concerned, today's block ciphers and public key cryptosystems flunk the exam. I don't know how to recover an AES key or compute the discrete logarithm of an elliptic curve element, but there exists no proof that another cannot. Moreover, encrypted communications can be recorded and stored for later attack by algorithms and devices yet to be discovered. Equally concerning is that when a significant "break" is discovered for solving either puzzle, the safety of an entire planet's communication and data dissolves like instant pudding. The world is unready to ingest so much pudding. We need bal
Neuromorphic computing relies on low-power, high-reliability hardware, yet the integrity of input/output pads (IOPADs) remains an underexplored factor affecting system performance. This chapter examines the role of IOPAD integrity in neuromorphic VLSI design and connects algorithmic development with practical hardware implementation. While much attention has been given to spiking neural networks (SNNs) and ultra-low-power core logic, the electrical and functional robustness of the I/O interface is equally critical for ensuring signal fidelity and minimizing energy consumption. We review the structure and function of IOPADs, outline their influence on power, performance, and reliability, and discuss design trade-offs involving pad libraries, pad ring architectures, and bonding strategies. The chapter also introduces the fundamentals of SNNs and summarizes the digital hardware design flow from behavioral description to physical layout. Physical implementation considerations are highlighted using the SkyWater 130 nm CMOS process as a practical platform for neuromorphic prototyping. Real-world examples illustrate how early-stage I/O planning can prevent redesign, reduce yield loss, and
This article reports on the development and comprehensive evaluation of p-type silicon detector arrays fabricated at the Semi-Conductor Laboratory (SCL), Mohali, India. The detectors consist of an 8~$\times$~9 array of 1~$\times$~1~cm$^2$ pads fabricated on 6-inch wafers and read out using the High Granularity Calorimeter Readout Chip (HGCROC). Electrical characterization of the detector through current vs. voltage (IV) and capacitance vs. voltage (CV) measurements demonstrated consistent breakdown and full depletion voltages across all pads, in agreement with Technology Computer-Aided Design (TCAD) device simulations. Laboratory measurements with a $^{90}$Sr source and beam tests at PS, CERN with 10 GeV pions, showed a clear Minimum Ionizing Particle (MIP) signal, well separated from the pedestal and uniform response of the pads with an average signal-to-noise (S/N) ratio above 5.5. The measured shower profiles with 2-4 GeV positron beams for various thicknesses of a tungsten absorber placed in front of the detector are found to be in agreement with the corresponding Geant4 simulations. The performance test results for the detector show that it is a promising candidate for the fut
Three-dimensional (3D) integration continues to advance Moore's Law by facilitating dense interconnects and enabling multi-tier system architectures. Among the various integration approaches, Cu-Cu hybrid bonding has emerged as a leading solution for achieving high interconnect density in chiplet integration. In this work, we present YAP+, a yield modeling framework specifically tailored for wafer-to-wafer (W2W) and die-to-wafer (D2W) hybrid bonding processes. YAP+ incorporates a comprehensive set of yield-impacting failure mechanisms, including overlay misalignment, particle defects, Cu recess variations, surface roughness, and Cu pad density. Furthermore, YAP+ supports pad layout-aware yield analysis, considering critical, redundant, and dummy pads across arbitrary 2D physical layout patterns. To support practical evaluation, we developed an open-source yield simulator, demonstrating that our near-analytical model matches simulation accuracy while achieving over 1,000x speedup in runtime. This performance makes YAP+ a valuable tool for co-optimizing packaging technologies, assembly design rules, and system-level design strategies. Beyond W2W-D2W comparisons, we leverage YAP+ to i
Chain of thought is a natural inference-time method for increasing the computational power of transformer-based large language models (LLMs), but comes at the cost of sequential decoding. Are there more efficient alternatives to expand a transformer's expressive power without adding parameters? We consider transformers with padding tokens as a form of parallelizable test-time compute. We show that averaging-hard-attention, masked-pre-norm transformers with polynomial padding recognize precisely the class $\mathsf{FO}$-uniform $\mathsf{TC}^0$ of extremely parallelizable problems. While the $\mathsf{TC}^0$ upper bound was known, proving a matching lower bound had been elusive. Further, our novel analysis reveals the precise expanded power of padded transformers when coupled with another form of inference-time compute, namely dynamically increasing depth via looping. Our core technical contribution is to show how padding helps bring the notions of complete problems and reductions, which have been a cornerstone of classical complexity theory, to the formal study of transformers. Armed with this new tool, we prove that padded transformers with $O(\log^d n)$ looping on inputs of length $
Padding tokens are widely used in large language models (LLMs) to equalize sequence lengths during batched inference. While they should be fully masked, implementation errors can cause them to influence computation, and the extent of this influence is not well understood. We systematically study this effect across three open-source model families (Llama, Gemma, Qwen), inserting controlled amounts of padding and evaluating outcomes along four axes: activations, generation quality, bias, and safety. Even small amounts of padding shift hidden representations, degrade quality in smaller models, alter bias in unpredictable ways, and weaken safety guardrails. These findings demonstrate that padding is not a harmless detail but a robustness risk that must be carefully handled in deployment.
We present linear prediction as a differentiable padding method. For each channel, a stochastic autoregressive linear model is fitted to the padding input by minimizing its noise terms in the least-squares sense. The padding is formed from the expected values of the autoregressive model given the known pixels. We trained the convolutional RVSR super-resolution model from scratch on satellite image data, using different padding methods. Linear prediction padding slightly reduced the mean square super-resolution error compared to zero and replication padding, with a moderate increase in time cost. Linear prediction padding better approximated satellite image data and RVSR feature map data. With zero padding, RVSR appeared to use more of its capacity to compensate for the high approximation error. Cropping the network output by a few pixels reduced the super-resolution error and the effect of the choice of padding method on the error, favoring output cropping with the faster replication and zero padding methods, for the studied workload.
Bernoulli pads generate locally large wall shear stresses on workpieces, which can be used for cleaning, but may also damage delicate surfaces. This work presents direct measurements of the wall shear stress using constant temperature anemometry for the first time. A hot-film sensor was calibrated in the laminar and turbulent flow regimes using a purpose-built water flow channel. The calibrated sensor was then flush-mounted onto a smooth surface and a Bernoulli pad was traversed over the sensor and wall shear stress data were acquired. Numerical simulations of the flow field were also performed; they accurately predicted the maximum shear stress near the jet corner but over-predicted at large radii.