Joseph Landa

Neural networks letter: Capturing significant events with neural networks

Smartphone video capture and transmission to the Web contributes to data pollution. In contrast, mammalian eyes sense all, capture only significant events, allowing us vividly recall the causalities. Likewise in our videos, we wish to skip redundancies and keep only significantly differences, as determined by real-time local medium filters. We construct a Picture Index (PI) of ones (center of gravity changes) among zeros (no changes) as Motion Organized Sparseness (MOS). Only non-overlapping time-ordered PI pair is admitted in the outer-product Associative Memory (AM). Another outer product between PI and its image builds Hetero-AM (HAM) for fault tolerant retrievals.

Smartphone household wireless electroencephalogram hat

Rudimentary brain machine interface has existed for the gaming industry. Here, we propose a wireless, real-time, and smartphonebased electroencephalogram (EEG) system for homecare applications. The systemuses high-density dry electrodes and compressive sensing strategies to overcome conflicting requirements between spatial electrode density, temporal resolution, and spatiotemporal throughput rate. Spatial sparseness is addressed by close proximity between active electrodes and desired source locations and using an adaptive selection of N active among 10N passive electrodes to form m-organized random linear combinations of readouts, m ≪ N ≪ 10N. Temporal sparseness is addressed via parallel frame differences in hardware. During the design phase, we took tethered laboratory EEG dataset and applied fuzzy logic to compute (a) spatiotemporal average of larger magnitude EEG data centers in 0.3 second intervals and (b) inside brainwave sources by Independent Component Analysis blind deconvolution without knowing the impulse response function. Our main contributions are the fidelity of quality wireless EEG data compared to original tethered data and the speed of compressive image recovery. We have compared our recovery of ill-posed inverse data against results using Block Sparse Code. Future work includes development of strategies to filter unwanted artifact from high-density EEGs (i.e., facial muscle-related events and wireless environmental electromagnetic interferences).

Image compression quality metrics

Battlefield reconnaissance through tactical surveillance video systems requires transmission of images through a limited bandwidth and capacity to achieve aided target recognition (ATR), of which some lossy compression is indispensable. Based on available resolution, ATR can have three functionality goals: (1) detection of a target, (2) recognition of target classes, and (3) identification of individual target membership. Thus, it is desirable to build an intelligent lookup table which maps a specific ATR goal into an appropriate image compression. Such a table may be built implicitly be employing the exemplar training procedure of artificial neutral networks. In order to illustrate this concept, we will introduce a computational metric called feature persistence measure, useful for x-ray luggage inspections, and further generalized here to capture human performance in a tactical imaging scenario.

Brain order disorder 2nd group report of f-EEG

Since the Brain Order Disorder (BOD) group reported on a high density Electroencephalogram (EEG) to capture the neuronal information using EEG to wirelessly interface with a Smartphone [1,2], a larger BOD group has been assembled, including the Obama BRAIN program, CUA Brain Computer Interface Lab and the UCSD Swartz Computational Neuroscience Center. We can implement the pair-electrodes correlation functions in order to operate in a real time daily environment, which is of the computation complexity of O(N3) for N=102~3 known as functional f-EEG. The daily monitoring requires two areas of focus. Area #(1) to quantify the neuronal information flow under arbitrary daily stimuli-response sources. Approach to #1: (i) We have asserted that the sources contained in the EEG signals may be discovered by an unsupervised learning neural network called blind sources separation (BSS) of independent entropy components, based on the irreversible Boltzmann cellular thermodynamics(ΔS < 0), where the entropy is a degree of uniformity. What is the entropy? Loosely speaking, sand on the beach is more uniform at a higher entropy value than the rocks composing a mountain – the internal binding energy tells the paleontologists the existence of information. To a politician, landside voting results has only the winning information but more entropy, while a non-uniform voting distribution record has more information. For the human’s effortless brain at constant temperature, we can solve the minimum of Helmholtz free energy (H = E − TS) by computing BSS, and then their pairwise-entropy source correlation function. (i) Although the entropy itself is not the information per se, but the concurrence of the entropy sources is the information flow as a functional-EEG, sketched in this 2nd BOD report. Area #(2) applying EEG bio-feedback will improve collective decision making (TBD). Approach to #2: We introduce a novel performance quality metrics, in terms of the throughput rate of faster (Δt) & more accurate (ΔA) decision making, which applies to individual, as well as team brain dynamics. Following Nobel Laureate Daniel Kahnmen’s novel “Thinking fast and slow”, through the brainwave biofeedback we can first identify an individual’s “anchored cognitive bias sources”. This is done in order to remove the biases by means of individually tailored pre-processing. Then the training effectiveness can be maximized by the collective product Δt * ΔA. For Area #1, we compute a spatiotemporally windowed EEG in vitro average using adaptive time-window sampling. The sampling rate depends on the type of neuronal responses, which is what we seek. The averaged traditional EEG measurements and are further improved by BSS decomposition into finer stimulus-response source mixing matrix [A] having finer & faster spatial grids with rapid temporal updates. Then, the functional EEG is the second order co-variance matrix defined as the electrode-pair fluctuation correlation function C(s~, s~’) of independent thermodynamic source components. (1) We define a 1-D Space filling curve as a spiral curve without origin. This pattern is historically known as the Peano-Hilbert arc length a. By taking the most significant bits of the Cartesian product a≡ O(x * y * z), it represents the arc length in the numerical size with values that map the 3-D neighborhood proximity into a 1-D neighborhood arc length representation. (2) 1-D Fourier coefficients spectrum have no spurious high frequency contents, which typically arise in lexicographical (zig-zag scanning) discontinuity [Hsu & Szu, “Peano-Hilbert curve,” SPIE 2014]. A simple Fourier spectrum histogram fits nicely with the Compressive Sensing CRDT Mathematics. (3) Stationary power spectral density is a reasonable approximation of EEG responses in striate layers in resonance feedback loops capable of producing a 100, 000 neuronal collective Impulse Response Function (IRF). The striate brain layer architecture represents an ensemble <IRF< e.g. at V1-V4 of Brodmann areas 17-19 of the Cortex, i.e. stationary Wiener-Kintchine-Einstein Theorem. Goal#1: functional-EEG: After taking the 1-D space-filling curve, we compute the ensemble averaged 1-D Power Spectral Density (PSD) and then make use of the inverse FFT to generate f-EEG. (ii) Goal#2 individual wellness baseline (IWB): We need novel change detection, so we derive the ubiquitous fat-tail distributions for healthy brains PSD in outdoor environments (Signal=310°C; Noise=27°C: SNR=310/300; 300°K=(1/40)eV). The departure from IWB might imply stress, fever, a sports injury, an unexpected fall, or numerous midnight excursions which may signal an onset of dementia in Home Alone Senior (HAS), discovered by telemedicine care-giver networks. Aging global villagers need mental healthcare devices that are affordable, harmless, administrable (AHA) and user-friendly, situated in a clothing article such as a baseball hat and able to interface with pervasive Smartphones in daily environment.

Smartphone Homecare Monitoring of Hearts

Homecare monitoring blood pressures and heartbeats are commercially available using dedicated devices, for example, wrist watch, pulse oximetry. With the advent of Smartphone and compressive sensing technology, we wish to monitor precisely the electrical waveforms of heartbeats called the electrocardiography (ECG) for an aging global villager biomedical wellness homecare system. Our design separates into 3 innovative modules within the size-weight and power-cost bandwidth (Swap-CB) limitation. We develop each separately but in concert with one another: (i) Smart Electrode (adopting a low-power-mixed signal embedded with modern compressive sensing firmware and applying the nanotechnology to improve the electrodes’ contact impedance as well as novel transduction mechanism, between ECG and electronics, e.g., a pressure mattress coupling, or fiber-optics coupling); (ii) Learnable Database (utilizing adaptive wavelets transforms for systolic and diastolic P-QRS-T-U features extraction Aided Target Recognition and adopting Sequential Query Language for a relational database allowing distant monitoring and retrievable); (iii) Smartphone (inheriting a large touch screen interface display with powerful computation capability and assisting caretaker reporting system with GPS and ID and two-way interaction with patient panic button for programmable emergence reporting procedure). While (i) is novel, (ii) and (iii) are mature. Together, they can eventually provide a supplementary home screening system for the post- or the prediagnosis care at home with a built-in database searchable with the time, the place, and the degree of urgency happened, using in situ screening.

Future enhancements to 3D printing and real time production

The cost and scope of additive printing machines range from several hundred to hundreds of thousands of dollars. For the extra money, one can get improvements in build size, selection of material properties, resolution, and consistency. However, temperature control during build and fusing predicts outcome and protects the IP by large high cost machines. Support material options determine geometries that can be accomplished which drives cost and complexity of printing heads. Historically, 3D printers have been used for design and prototyping efforts. Recent advances and cost reduction sparked new interest in developing printed products and consumables such as NASA who is printing food, printing consumer parts (e.g. cell phone cases, novelty toys), making tools and fixtures in manufacturing, and recursively print a self-similar printer (c.f. makerbot). There is a near term promise of the capability to print on demand products at the home or office... directly from the printer to use.

Adaptive compressive sensing camera

We have embedded Adaptive Compressive Sensing (ACS) algorithm on Charge-Coupled-Device (CCD) camera based on the simplest concept that each pixel is a charge bucket, and the charges comes from Einstein photoelectric conversion effect. Applying the manufactory design principle, we only allow altering each working component at a minimum one step. We then simulated what would be such a camera can do for real world persistent surveillance taking into account of diurnal, all weather, and seasonal variations. The data storage has saved immensely, and the order of magnitude of saving is inversely proportional to target angular speed. We did design two new components of CCD camera. Due to the matured CMOS (Complementary metal–oxide–semiconductor) technology, the on-chip Sample and Hold (SAH) circuitry can be designed for a dual Photon Detector (PD) analog circuitry for changedetection that predicts skipping or going forward at a sufficient sampling frame rate. For an admitted frame, there is a purely random sparse matrix [Φ] which is implemented at each bucket pixel level the charge transport bias voltage toward its neighborhood buckets or not, and if not, it goes to the ground drainage. Since the snapshot image is not a video, we could not apply the usual MPEG video compression and Hoffman entropy codec as well as powerful WaveNet Wrapper on sensor level. We shall compare (i) Pre-Processing FFT and a threshold of significant Fourier mode components and inverse FFT to check PSNR; (ii) Post-Processing image recovery will be selectively done by CDT&D adaptive version of linear programming at L1 minimization and L2 similarity. For (ii) we need to determine in new frames selection by SAH circuitry (i) the degree of information (d.o.i) K(t) dictates the purely random linear sparse combination of measurement data a la [Φ]M,N M(t) = K(t) Log N(t).

WaveNet compressed image restoration brassboard design

Recent advances in areas such as wavelet mathematics, and artificial neural networks have resulted in improved image compression, restoration, and filtering techniques. Although these techniques are capable of achieving excellent performance in terms of image quality, their computational complexity often requires expensive, and specialized hardware to run in near real time. Even general purpose parallel processing boards exceed the cost, size, and weight constraints of many applications including, remote sensors, security systems, commercial and home video teleconferencing. This paper describes a low cost board which supports a video compression, restoration, and filter system. The WaveNet board has been optimized for wavelet based compression techniques, and neural network based filters.

Selective-imaging camera

How can we design cameras that image selectively in Full Electro-Magnetic (FEM) spectra? Without selective imaging, we cannot use, for example, ordinary tourist cameras to see through fire, smoke, or other obscurants contributing to creating a Visually Degraded Environment (VDE). This paper addresses a possible new design of selective-imaging cameras at firmware level. The design is consistent with physics of the irreversible thermodynamics of Boltzmann’s molecular entropy. It enables imaging in appropriate FEM spectra for sensing through the VDE, and displaying in color spectra for Human Visual System (HVS). We sense within the spectra the largest entropy value of obscurants such as fire, smoke, etc. Then we apply a smart firmware implementation of Blind Sources Separation (BSS) to separate all entropy sources associated with specific Kelvin temperatures. Finally, we recompose the scene using specific RGB colors constrained by the HVS, by up/down shifting Planck spectra at each pixel and time.

Optical display for radar sensing

Boltzmann headstone S = kB Log W turns out to be the Rosette stone for Greek physics translation optical display of the microwave sensing hieroglyphics. The LHS is the molecular entropy S measuring the degree of uniformity scattering off the sensing cross sections. The RHS is the inverse relationship (equation) predicting the Planck radiation spectral distribution parameterized by the Kelvin temperature T. Use is made of the conservation energy law of the heat capacity of Reservoir (RV) change T Δ S = -ΔE equals to the internal energy change of black box (bb) subsystem. Moreover, an irreversible thermodynamics Δ S > 0 for collision mixing toward totally larger uniformity of heat death, asserted by Boltzmann, that derived the so-called Maxwell-Boltzmann canonical probability. Given the zero boundary condition black box, Planck solved a discrete standing wave eigenstates (equation). Together with the canonical partition function (equation) an average ensemble average of all possible internal energy yielded the celebrated Planck radiation spectral (equation) where the density of states (equation). In summary, given the multispectral sensing data (equation), we applied Lagrange Constraint Neural Network (LCNN) to solve the Blind Sources Separation (BSS) for a set of equivalent bb target temperatures. From the measurements of specific value, slopes and shapes we can fit a set of Kelvin temperatures T’s for each bb targets. As a result, we could apply the analytical continuation for each entropy sources along the temperature-unique Planck spectral curves always toward the RGB color temperature display for any sensing probing frequency.