Abstract: Described is a decoder suitable for use with any communication or storage system. The described decoder has a modular decoder hardware architecture capable of implementing a noise guessing process and due to its dependency only on noise, the decoder design is independent of any encoder, thus making it a universal decoder. Hence, the decoder architecture described herein is agnostic to any coding scheme.

Abstract: Transient molecules in the gastrointestinal (GI) tract, such as nitric oxide and hydrogen sulfide, are important signals and mediators of inflammatory bowel disease (IBD). Because these molecules may be short-lived in the body, they are difficult to detect. To track these reactive molecules in the GI tract, a miniaturized device has been developed that integrates genetically engineered probiotic biosensors with a custom-designed photodetector and readout chip. Leveraging the molecular specificity of living sensors, bacteria were genetically encoded to respond to IBD-associated molecules by luminescing. Low-power electronic readout circuits (e.g., using nanowatt power) integrated into the device convert the light from just 1 ?L of bacterial culture into a wireless signal. Biosensor monitoring was demonstrated in the GI tract of small and large animal models and integration of all components into a sub-1.4 cm3 ingestible form factor capable of supporting wireless communication.

Abstract: Described is a decoder suitable for use with any communication or storage system. The described decoder has a modular decoder hardware architecture capable of implementing a noise guessing process and due to its dependency only on noise, the decoder design is independent of any encoder, thus making it a universal decoder. Hence, the decoder architecture described herein is agnostic to any coding scheme.

Abstract: Mechanisms for interferer detection can detect interferers by detecting elevated signal amplitudes in one or more of a plurality of bins (or bands) in a frequency range between a maximum frequency (fMAX) and a minimum frequency (fMIN). To perform rapid interferer detection, the mechanisms downconvert an input signal x(t) with a local oscillator (LO) to a complex baseband signal xI(t)+jxQ(t). xI(t) and xQ(t) are then multiplied by m unique pseudorandom noise (PN) sequences (e.g., Gold sequences) gm(t) to produce m branch signals for I and m branch signals for Q. The branch signals are then low pass filtered, converted from analog to digital form, and pairwise combined by a pairwise complex combiner. Finally, a support recovery function is used to identify interferers.

Abstract: Mechanisms for interferer detection can detect interferers by detecting elevated signal amplitudes in one or more of a plurality of bins (or bands) in a frequency range between a maximum frequency (fMAX) and a minimum frequency (fMIN). To perform rapid interferer detection, the mechanisms downconvert an input signal x(t) with a local oscillator (LO) to a complex baseband signal xI(t)+jxQ(t). xI(t) and xQ(t) are then multiplied by m unique pseudorandom noise (PN) sequences (e.g., Gold sequences) gm(t) to produce m branch signals for I and m branch signals for Q. The branch signals are then low pass filtered, converted from analog to digital form, and pairwise combined by a pairwise complex combiner. Finally, a support recovery function is used to identify interferers.

Abstract: Mechanisms for interferer detection can detect interferers by detecting elevated signal amplitudes in one or more of a plurality of bins (or bands) in a frequency range between a maximum frequency (fMAX) and a minimum frequency (fMIN). To perform rapid interferer detection, the mechanisms downconvert an input signal x(t) with a local oscillator (LO) to a complex baseband signal xI(t)+jxQ(t). xI(t) and xQ(t) are then multiplied by m unique pseudorandom noise (PN) sequences (e.g., Gold sequences) gm(t) to produce m branch signals for I and m branch signals for Q. The branch signals are then low pass filtered, converted from analog to digital form, and pairwise combined by a pairwise complex combiner. Finally, a support recovery function is used to identify interferers.

Abstract: Mechanisms for interferer detection can detect interferers by detecting elevated signal amplitudes in one or more of a plurality of bins (or bands) in a frequency range between a maximum frequency (fMAX) and a minimum frequency (fMIN). To perform rapid interferer detection, the mechanisms downconvert an input signal x(t) with a local oscillator (LO) to a complex baseband signal xI(t)+jxQ(t). xI(t) and xQ(t) are then multiplied by m unique pseudorandom noise (PN) sequences (e.g., Gold sequences) gm(t) to produce m branch signals for I and m branch signals for Q. The branch signals are then low pass filtered, converted from analog to digital form, and pairwise combined by a pairwise complex combiner. Finally, a support recovery function is used to identify interferers.

Abstract: Mechanisms for interferer detection can detect interferers by detecting elevated signal amplitudes in one or more of a plurality of bins (or bands) in a frequency range between a maximum frequency (fMAX) and a minimum frequency (fMIN). To perform rapid interferer detection, the mechanisms downconvert an input signal x(t) with a local oscillator (LO) to a complex baseband signal xI(t)+jxQ(t). xI(t) and xQ(t) are then multiplied by m unique pseudorandom noise (PN) sequences (e.g., Gold sequences) gm(t) to produce m branch signals for I and m branch signals for Q. The branch signals are then low pass filtered, converted from analog to digital form, and pairwise combined by a pairwise complex combiner. Finally, a support recovery function is used to identify interferers.

Abstract: Mechanisms for interferer detection can detect interferers by detecting elevated signal amplitudes in one or more of a plurality of bins (or bands) in a frequency range between a maximum frequency (fMAX) and a minimum frequency (fMIN). To perform rapid interferer detection, the mechanisms downconvert an input signal x(t) with a local oscillator (LO) to a complex baseband signal xI(t)+jxQ(t). xI(t) and xQ(t) are then multiplied by m unique pseudorandom noise (PN) sequences (e.g., Gold sequences) gm(t) to produce m branch signals for I and m branch signals for Q. The branch signals are then low pass filtered, converted from analog to digital form, and pairwise combined by a pairwise complex combiner. Finally, a support recovery function is used to identify interferers.