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John Archie Gillis Independent Researcher Halifax, Nova Scotia, Canada johnarchiegillis@gmail.com June 30, 2025 We present the Integer Wave Interference System, a fundamentally new paradigm for information representation that challenges core assumptions of information the-ory. Unlike traditional approaches that transmit data directly, our system transmits only instructions for reconstructing data through the superposition of implicitly-generated periodic waveforms. The method operates entirely within integer arith-metic, representing any n-bit binary pattern as a set of wave descriptors—compact tuples bits for n-bit patterns, thereby proving the non-existence of asymptotically incompressible strings. This scale-dependent view of information complexity reveals that patterns appearing random under traditional analysis often exhibit strong wave-based struc-ture. The system achieves perfect reconstruction through integer superposition and threshold extraction, avoiding floating-point errors and ensuring hardware-independent results. Beyond compression, the Integer Wave Interference System enables novel ap-proaches to cryptography (exploiting non-commutative wave operations), neural networks (storing weights as wave descriptors), genomic analysis (encoding DNA se-quences as interference patterns), and quantum simulation (modeling superposition classically). By reconceptualizing information as emerging from wave interference rather than sequential bit patterns, we establish a new computational model where pattern complexity is measured by minimal wave descriptors rather than entropy or algorithmic complexity. This paradigm shift from transmitting data to transmitting generative instructions opens unprecedented possibilities for information processing across all digital domains. Keywords Information theory, data compression, wave interference, integer arithmetic, scale-dependent complexity, asymptotic compressibility, pattern representation, modular arithmetic, im-plicit generation, theoretical computer science

FIELD: radio engineering.SUBSTANCE: result is achieved by connecting a detachable cable, which is different for each type of target missiles, of the target missile to the prelaunch automation unit and launch through the on-board equipment switching subunit, making a switching subunit for each type of target missiles separately, making a switching subunit for switching through a detachable cable of the control subunit and a computer subunit with the onboard equipment of the target missile, after switching on the power of the prelaunch automation unit and starting by checking the readiness of the computer subunit of each subunit for availability of required voltages, what type of target missile is installed on launching table by means of jumper on subunit of switching, and with readiness of all components of the pre-start automation unit and start by the presence of the required voltages and displaying information on the health of the components of the pre-start automation unit and launch, availability of required voltages and with what type of target missile this unit operates on remote control.EFFECT: possibility to unify and automate processes of prelaunch preparation of target missiles of various classes using one prelaunch preparation and launching unit.1 cl, 8 dwg

The present subject matter is a method of preparation of PHB from orange peel. The method steps comprise collection and preparation of orange peel; one step oxidation of orange peel waste; preparation of fermentation media, growth of bacterial stains like Lysinibacillus tabacifolii & Thioalbus denitrificans in the fermentation media producing PHB; and extraction and purification of PHB.

The present subject matter relates to a system and method for reinforcement learning based dynamic frequency scaling in a field programmable gate array based platform. The system 100 includes a custom hardware intellectual property (IP) core 101 implemented in programmable logic, whose operational performance scales with clock frequency. A programmable multi-output clock generation module 102 produces multiple discrete clock signals from a single stable reference clock, which are managed by a clock wrapper module 103. A PL-side clock controller 104 dynamically selects and applies a selected clock signal to the custom hardware IP core 101 during runtime. Real-time performance metrics are measured by a performance monitoring and feedback mechanism 105 and transmitted to a processing system 105 executing a reinforcement learning engine 107. Based on the received feedback, the reinforcement learning engine 107 determines an operating clock frequency that optimizes a power–performance tradeoff and generates frequency control commands. The reinforcement learning engine 107, performance monitoring and feedback mechanism 105, and PL-side clock controller 104 collectively form a closed-loop adaptive control system enabling real-time dynamic adjustment of programmable logic operating frequency. Figure 1

The present subject matter discloses a method for determining an optimal kernel size in a CNN model. The method includes defining a plurality of parameters of the CNN model. The method includes selecting a range of kernel sizes. The method includes creating a distinct variant for each kernel size. The method includes training the distinct variant for each kernel size on a target dataset to generate a unique set of trained weights for each distinct variant. The method includes injecting one or more random bit-flip errors in at least one trained weight across a range of BERs. The method includes evaluating a functional performance of the at least one trained weight corrupted with the one or more random bit-flip errors on a validation set to generate performance data. The method includes comparing a performance of the distinct variant of each kernel size at each BER to identify a high performing kernel size amongst the one or more kernel sizes with respect to a robustness and a stability. (FIG. 3)

The present subject matter comprises a nanoformulation for detecting the presence of mycotoxins in food samples has a composition of bentonite clay, ZnO nanoparticles and curcumin. The formulation exhibits a negative zeta potential of -21.311mV; a dense flaky, and compact surface morphology, indicating good interaction between the bentonite layers, ZnO nanoparticles and curcumin. A method of preparation comprises synthesizing Zinc Oxide nano particles; preparation of homogeneous aqueous mixture of bentonite clay, ZnO nanoparticles and curcumin; precipitating the bentonite clay bonded with ZnO nanoparticles and curcumin; and drying to obtain a fine powder. When added to a sample with aflatoxins a color change is observed to indicate the presence of aflatoxins.

The present subject matter is an onion dryer storage system for small scale onion storage comprises an onion storage unit, air heater unit, a water circulation unit, and a control unit. The onion storage unit comprises a plurality of racks to hold onions, tubes for circulation of warm air, and a plurality of vents for the hot air to flow through the onions in the plurality of racks. The air heater unit comprises a heat exchanger with inlet for ambient air and outlet for heated air which is sent to the tubes for circulation of warm air in the onion storage unit. The water circulation unit comprises a water pump to pump the water for heating air to the heat exchanger.

The present subject discloses a dinuclear copper(I) N-heterocyclic carbene (Cu–NHC) complex having the chemical structure formula 1, Formula 1 The present subject matter also relates to a method of preparing the same comprising the steps of mixing a ligand with potassium tert-butoxide (tBuOK) in acetonitrile to a form a mixture followed by addition of copper iodide under argon atmosphere, and stirring the mixture at a temperature range of 80 °C to 90 °C for 1-3 hours to form the Cu-NHC complex.

The present subject matter discloses a method (1800) for detecting suppressed sentiments of a subject. The method (1800) includes receiving a plurality of inputs associated with the subject. The method (1800) includes identifying one or more active action units from the plurality of facial videos by analyzing the plurality of facial videos. The method (1800) includes segmenting the plurality of fEMG signals into a plurality of segments based on the one or more active action units. The method (1800) includes determining that a duration associated with an active action unit from the one or more active action units associated with at least one segment from the plurality of segments is between a predetermined range of time duration. The method (1800) includes generating a feature set associated with at least one segment expressing one or more micro-expression variations of the subject. The method (1800) includes analyzing the feature set based on a plurality of factors to recognize one or more micro-expressions of the subject. The one or more micro-expressions associate with one or more suppressed sentiments of the subject. (FIG. 4)